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In recent years, burgeoning research has underscored the pivotal role of noncoding RNA in orchestrating the growth, development, and pathogenesis of various diseases across organisms. However, despite these advances, our understanding of the specific contributions of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) to lens development remains notably limited. Clarifying the intricate gene regulatory networks is imperative for unraveling the molecular underpinnings of lens-related disorders. In this study, we aimed to address this gap by conducting a comprehensive analysis of the expression profiles of messenger RNAs (mRNAs), lncRNAs, and circRNAs at critical developmental time points of the mouse lens, encompassing both embryonic (E10.5, E12.5, and E16.5) and postnatal stages (P0.5, P10.5, and P60). Leveraging RNA-sequencing technology, we identified key transcripts pivotal to lens development. Our analysis revealed differentially expressed (DE) mRNAs, lncRNAs, and circRNAs across various developmental stages. Particularly noteworthy, there were 1831 co-differentially expressed (CO-DE) mRNAs, 150 CO-DE lncRNAs, and 13 CO-DE circRNAs identified during embryonic stages. Gene Ontology (GO) enrichment analysis unveiled associations primarily related to lens development, DNA conformational changes, and angiogenesis among DE mRNAs and lncRNAs. Furthermore, employing protein-protein interaction networks, mRNA-lncRNA co-expression networks, and circRNA-microRNA-mRNA networks, we predicted candidate key molecules implicated in lens development. Our findings underscore the pivotal roles of lncRNAs and circRNAs in this process, offering fresh insights into the pathogenesis of lens-related disorders and paving the way for future exploration in this field. [ABSTRACT FROM AUTHOR]

Objective: Using bioinformatics analyses, this study aimed to identify lncRNAs related to the immune status of acute myeloid leukemia (AML) patients and ascertain the potential impact in immunity-related competing endogenous RNA (ceRNA) networks on AML prognosis. Methods: AML-related RNA-seq FPKM data, AML-related miRNA expression microarray data, and gene sets associated with immunity-related pathways were, respectively, obtained from the TCGA, GEO, and ImmReg databases. An immunity-related ceRNA network was then constructed according to the predicted interactions between AML-related mRNAs, lncRNAs, and miRNAs. After performing LASSO and multivariate Cox regression analyses, lncRNAs in the ceRNA network were used to establish an AML prognostic model. According to mutual regulatory relationships and consistent trends of expression among candidate ceRNAs, two ceRNA subnetworks related to the AML prognostic model were determined. Finally, the correlation between the expression levels of mRNAs, lncRNAs, and miRNAs in each ceRNA subnetwork and immune cell infiltration (assessed by combining the ESTIMATE and CIBERSORT methods and ssGSEA) was analyzed. Results: A total of 424 immunity-related differentially expressed (IR-DE) mRNAs (IR-DEmRNAs), 191 IR-DElncRNAs, and 69 IR-DEmiRNAs were obtained, and a ceRNA network of 20 IR-DElncRNAs, 6 IR-DEmRNAs, and 3 IR-DEmiRNAs was established. Univariate Cox regression analysis was conducted on 20 IRDElncRNAs, and 7 of these were identified to be significantly correlated with the overall survival (OS) time in AML patients. Then, two IR-DElncRNAs (MEG3 and HCP5) were screened as independent OS-related factors by LASSO and multivariable Cox regression analyses, and a prognostic model was constructed to evaluate the survival risk in AML patients. Survival analyses indicated that the OS of patients was often poor in the high-risk group. Additionally, from this model, two ceRNA regulatory pathways, namely, MEG3/miR-125a-5p/SEMA4C and HCP5/miR-125b-5p/IL6R, which were potentially involved in the immune regulation of AML prognosis were identified. Conclusion: lncRNAs HCP5 and MEG3 may act as key ceRNAs in the pathogenesis in AML by regulating immune cell representation as part of the regulatory lncRNAmiRNA-mRNA axes. The candidate mRNAs, lncRNAs, and miRNAs included in the ceRNA network identified here may serve as useful prognostic biomarkers and immunotherapeutic targets for AML. [ABSTRACT FROM AUTHOR]

Objective: Circular RNAs (circRNAs) have been demonstrated to be closely involved in colorectal cancer (CRC) pathogenesis and metastasis. More potential biomarkers are needed to be searched for colorectal cancer (CRC) diagnosis and treatment. The objective of this study is to seek differentially expressed circRNAs (DEcircRNAs), test their roles in CRC and construct a potential competing endogenous RNA (ceRNA) network. Methods: CircRNA microarrays were obtained from Gene Expression Omnibus, and differential expression was analyzed by R software. The relative expressions of DEcircRNAs were confirmed in CRC tissues and cell lines by qRT-PCR. MTs and Transwell experiments were performed for detecting the roles of circRNAs on CRC cell proliferation and migration, respectively. Targeted miRNAs of circRNAs and targeted mRNAs of miRNAs were predicted and screened by bioinformatics methods. A ceRNA network of DEcircRNAs was constructed by Cytoscape. To further verify the potential ceRNA network, the expressions of miRNAs and mRNAs in knockdown of DEcircRNAs CRC cells were detected by qRT-PCR. Results: Two DEcircRNAs (hsa_circ_0040809 and hsa_circ_0000467) were identified and validated in CRC tissues and cell lines. The results of MTs and Transwell experiments showed that hsa_circ_0040809 and hsa_circ_0000467 promoted CRC proliferation and migration. Bioinformatics analysis screened 3 miRNAs (miR-326, miR-330-5p, and miR-330-3p) and 2 mRNAs (FADS1 and RUNX1), and a ceRNA network was constructed. In knockdown of hsa_circ_0040809 HCT-116 cells, the expression of miR-330-3p was significantly upregulated, while RUNX1 was significantly downregulated. In knockdown of hsa_circ_0000467 HCT-116 cells, the expressions of miR-326 and miR-330-3p were upregulated, while FADS1was downregulated. Conclusion: We found that hsa_circ_0040809 and hsa_circ_0000467 were upregulated in CRC tissues and cell lines, and promoted CRC cell progression. A circRNA-miRNA-mRNA network based on hsa_circ_0040809 and hsa_circ_0000467 was constructed. [ABSTRACT FROM AUTHOR]

Slow-burning inflammation at the lesion rim is connected to the expansion of chronic multiple sclerosis (MS) lesions. However, the underlying processes causing expansion are not clearly realized. In this context, the current study used a bioinformatics approach to identify the expression profiles and related lncRNA-associated ceRNA regulatory axes in the periplaque region in MS patients. Expression data (GSE52139) from periplaque regions in the secondary progressive MS spinal cord and controls were downloaded from the Gene Expression Omnibus database (GEO), which has details on mRNAs and lncRNAs. Using the R software's limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also found using the DIANA-LncBase, miRTarBase, and HMDD databases. The Pearson correlation coefficient was used to determine whether there were any positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Finally, lncRNA-associated ceRNA axes were created based on co-expression and connections between DElncRNA, miRNA, and DEmRNA. We used the Enrichr tool to enrich the biological process, molecular function, and pathways for DEmRNAs and DElncRNAs. A network of DEmRNAs' protein-protein interactions was developed, and the top five hub genes were found using Cytoscape and STRING. The current study indicates that 15 DEmRNAs, including FOS, GJA1, NTRK2, CTNND1, and SP3, are connected to the MS ceRNA network. Additionally, four DElncRNAs (such as TUG1, ASB16-AS1, and LINC01094) that regulated the aforementioned mRNAs by sponging 14 MS-related miRNAs (e.g., hsa-miR-145-5p, hsa-miR-200a-3p, hsa-miR-20a-5p, hsa-miR-22-3p, hsa-miR-23a-3p, hsa-miR-27a-3p, hsamiR-29b-3p, hsa-miR-29c-3p, hsa-miR-34a-5p) were found. In addition, the analysis of pathway enrichment revealed that DEmRNAs were enriched in the pathways for the "MAPK signaling pathway", "Kaposi sarcoma-associated herpesvirus infection", "Human immunodeficiency virus one infection", "Lipid and atherosclerosis", and "Amphetamine addiction". Even though the function of these ceRNA axes needs to be investigated further, this study provides research targets for studying ceRNA-mediated molecular mechanisms related to periplaque demyelination in MS. [ABSTRACT FROM AUTHOR]

Background: Despite ample evidence demonstrating that anterior cruciate ligament (ACL) and meniscus tears are associated with posttraumatic osteoarthritis (PTOA) development, the contributing factors remain unknown. Synovial inflammation has recently been recognized as a pivotal factor in the pathogenesis of OA. However, there is a lack of data on synovial profiles after ACL or meniscus injuries, which may contribute to PTOA. Methods: Twelve patients with ACL tears and/or meniscus injuries were recruited. During surgery, synovial tissues were obtained from the injured knees. The inflammation status of the synovium was characterized according to macroscopic criteria and histological synovitis grades. Then the synovial tissues were classified as control group or inflamed group. Highthroughput RNA sequencing of the synovial samples (3 vs. 3) was conducted to identify differentially expressed (DE) RNAs. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) analyses were performed to investigate DE mRNAs. Next, competing endogenous RNA (ceRNA) networks were constructed based on bioinformatics analyses. Associations of the identified DE genes (DEGs) with infiltrating immune cells were explored using Pearson correlation analysis. Results: The results showed that 2793 mRNAs, 3392 lncRNAs and 211 miRNAs were significantly DE between two groups. The top 3 significantly upregulated GO terms and KEGG pathways were immune response, adaptive immune response and immune system process, systemic lupus erythematosus, haematopoietic cell lineage and cytokine-cytokine receptor interaction, respectively. In PPI networks, the top 10 hub genes were IL6, CCR7, C3, CCR5, CXCR3, CXCL8, IL2, CCR3, CCR2 and CXCL1. Seven mRNAs (EPHA5, GSN, ORC1, TLN2, SOX6, NKD2 and ADAMTS19), 4 lncRNAs (MIR4435-2HG, TNXA, CEROX1 and TMEM92-AS1) and 3 miRNAs (miR-486-5p, miR-199a-3p and miR-21-3p) were validated by quantitative real-time polymerase chain reaction and sub-networks were constructed. In correlation analysis, MMP9 correlated positively with M0 macrophages and plasma cells, NKD2 positively with CD8 T cells, and CCR7 and IL2RB positively with naive B cells. Conclusion: Our study provides foundational synovial inflammation profiles following knee trauma. The ceRNA and PPI networks provide new insight into the biological processes and underlying mechanisms of PTOA. The differential infiltration profiles of immune cells in synovium may contribute to PTOA development. This study also highlights immune-related DEGs as potential PTOA treatment biomarkers. [ABSTRACT FROM AUTHOR]

Premature ovarian insufficiency, characterized by ovarian infertility and low fertility, has become a significant problem in developed countries due to its propensity for late delivery. It has been described that the vital role of lncRNA in the development and progression of POI. The aim of this work was to create a POI-based lncRNA-mRNA network (POILMN) to recognize key lncRNAs. Overall, differently expressed mRNAs (DEGs) and differently expressed lncRNAs (DELs) were achieved by using the AnnoProbe and limma R packages. POI-based lncRNA-mRNA network (POILMN) construction was carried out using the tinyarray R package and hypergeometric distribution. To identify key lncRNAs, we used CentiScaPe plug-in Cytoscape as a screening tool. In total, 244 differentially expressed lncRNAs (DELs) and 288 differentially expressed mRNAs (DEGs) were obtained in this study. Also, 177 lncRNA/mRNA pairs (including 39 lncRNAs and 86 mRNAs) were selected using the hypergeometric test. Finally, we identified four lncRNA (HCP5, NUTM2A-AS1, GABPB1-IT1, and SMIM25) intersections by topological analysis between two centralities (degree and betweenness), and we explored their subnetwork GO and KEGG pathway enrichment analysis. Here, we have provided strong evidence for a relationship with apoptosis, DNA repair damage, and energy metabolism terms and pathways in the key lncRNAs in our POI-based lncRNA-mRNA network. In addition, we evaluated the localization information of genes related to POI and found that genes were more distributed on chromosomes 15, 16, 17, and 19. However, more experiments are needed to confirm the functional significance of such predicted lncRNA/mRNA. In conclusion, our study identified four long non-coding RNA molecules that may be relevant to the progress of premature ovarian insufficiency. [ABSTRACT FROM AUTHOR]

Objective: Pelvic organ prolapse (POP) affects a large proportion of adult women, but the pathogenesis of POP remains unclear. The increase in global population aging will impose a substantial medical burden. Herein, we aimed to explore the related RNAs regulating the occurrence of POP and provide potential therapeutic targets. Method: Tissue biopsies were collected from the anterior vaginal wall of six women with POP and six matched subjects without POP. The profiles of mRNAs, circRNAs, lncRNAs, and miRNAs were obtained by whole transcriptome RNA sequencing. Result: The findings revealed that 71 circRNAs, 76 known lncRNAs, 84 miRNAs, and 931 mRNAs were significantly altered (p < 0.05 and |log2FC| > 1). GO and KEGG enrichment analyses indicated that the differentially expressed genes (DEGs) were mainly enriched in the focal adhesion signaling pathway. FLT, ITGA9, VEGFD, PPP1R12B, and ROCK2 were identified as focal adhesion signaling pathway-related hub genes by protein-protein interaction network analysis. Based on the relationships between the DEGs and miRNA, lncRNA and circRNA targets, we constructed a focal adhesion signaling pathway-related ceRNA network. The ceRNA network includes hsa_circ_0002190/hsa_circ_0046843/lnc-CARMN -miR-23a-3p - ROCK2 and hsa_circ_0001326/hsa_circ_0007733/lnc-AC107959/lnc-TPM1-AS - miR-205-5p - ROCK2/PPP1R12B/VEGFD. Moreover, abnormalities in the cytoskeleton in fibroblasts from individuals with POP were observed. Conclusion: In this study, a focal adhesion signaling pathway-related ceRNA network was constructed, and this network may serve as a target for finding suitable drugs for the treatment of POP. [ABSTRACT FROM AUTHOR]

Growing evidence implicates that miRNAs can interact with long non-coding RNAs (lncRNAs) to regulate target mRNAs through competitive interactions. However, this mechanism that regulate tumorigenesis and cancer progression remains largely unexplored. Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs),which play a significant role in regulating gene expression. The purpose of our study was to determine potential lncRNA biomarkers to predict the prognosis of HCC by comprehensive analysis of a ceRNA network. The edgeR package was used to obtain the differentially expressed RNA datasets by analyzing 370HCCtissues and 50 adjacent non-HCC tissues from The Cancer Genome Atlas (TCGA). Through investigating the differentially expressed between HCC tissues and adjacent non-HCC tissues, a total of 947 lncRNAs, 52 miRNAs, and 1,650 mRNAs were obtained. The novel constructed ceRNA network incorporated 99 HCC-specific lncRNAs, four miRNAs, and 55 mRNAs. Survival analysis identified 22 differentially expressed mRNAs, four miRNAs, and nine lncRNAs which were associated with overall survival (OS) time in HCC (p < 0.05), and further exploration was performed to assess the correlation of these differentially expressed genes with tumor stage. The Interpretation of the potential functions of these differentially expressed genes in HCC was realized by Gene ontology (GO) and KEGG pathway enrichment analyses. Seven lncRNAs were confirmed based on univariate Cox regression analysis, lasso COX regression analysis and multivariate Cox regression analysis to construct a predictive model in HCC patients which were related to the prognosis of OS. In summary, ceRNAs contributed to explore the mechanism of tumorigenesis and development, and a model with seven lncRNAs might be potential biomarker to predict the prognosis of HCC. These findings supported the need to studies on the mechanisms involved in the regulation of HCC by ceRNAs. [ABSTRACT FROM AUTHOR]

Slow-burning inflammation at the lesion rim is connected to the expansion of chronic multiple sclerosis (MS) lesions. However, the underlying processes causing expansion are not clearly realized. In this context, the current study used a bioinformatics approach to identify the expression profiles and related lncRNA-associated ceRNA regulatory axes in the periplaque region in MS patients. Expression data (GSE52139) from periplaque regions in the secondary progressive MS spinal cord and controls were downloaded from the Gene Expression Omnibus database (GEO), which has details on mRNAs and lncRNAs. Using the R software’s limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also found using the DIANA-LncBase, miRTarBase, and HMDD databases. The Pearson correlation coefficient was used to determine whether there were any positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Finally, lncRNA-associated ceRNA axes were created based on co-expression and connections between DElncRNA, miRNA, and DEmRNA. We used the Enrichr tool to enrich the biological process, molecular function, and pathways for DEmRNAs and DElncRNAs. A network of DEmRNAs’ protein-protein interactions was developed, and the top five hub genes were found using Cytoscape and STRING. The current study indicates that 15 DEmRNAs, including FOS, GJA1, NTRK2, CTNND1, and SP3, are connected to the MS ceRNA network. Additionally, four DElncRNAs (such as TUG1, ASB16-AS1, and LINC01094) that regulated the aforementioned mRNAs by sponging 14 MS-related miRNAs (e.g., hsa-miR-145-5p, hsa-miR-200a-3p, hsa-miR-20a-5p, hsa-miR-22-3p, hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p, hsa-miR-34a-5p) were found. In addition, the analysis of pathway enrichment revealed that DEmRNAs were enriched in the pathways for the “MAPK signaling pathway”, “Kaposi sarcoma-associated herpesvirus infection”, “Human immunodeficiency virus one infection”, “Lipid and atherosclerosis”, and “Amphetamine addiction”. Even though the function of these ceRNA axes needs to be investigated further, this study provides research targets for studying ceRNA-mediated molecular mechanisms related to periplaque demyelination in MS.

Tetralogy of Fallot (TOF) is one of the most common cyanotic congenital heart diseases (CHD) worldwide; however, its pathogenesis remains unclear. Recent studies have shown that circular RNAs (circRNAs) act as "sponges" for microRNAs (miRNAs) to compete for endogenous RNA (ceRNA) and play important roles in regulating gene transcription and biological processes. However, the mechanism of ceRNA in TOF remains unclear. To explore the crucial regulatory connections and pathways of TOF, we obtained the human TOF gene, miRNA, and circRNA expression profiling datasets from the Gene Expression Omnibus (GEO) database. After data pretreatment, differentially expressed mRNAs (DEmRNAs), microRNAs (DEmiRNAs), and circRNAs (DEcircRNAs) were identified between the TOF and healthy groups, and a global triple ceRNA regulatory network, including circRNAs, miRNAs, and mRNAs based on the integrated data, was constructed. A functional enrichment analysis was performed on the Metascape website to explore the biological functions of the selected genes. Then, we constructed a protein-protein interaction (PPI) network and identified seven hub genes using the cytoHubba and MCODE plug-ins in the Cytoscape software, including BCL2L11, PIK3R1, SOCS3, OSMR, STAT3, RUNX3, and IL6R. Additionally, a circRNA--miRNA--hub gene subnetwork was established, and its enrichment analysis results indicated that the extrinsic apoptotic signaling pathway, JAK-STAT signaling pathway and PI3K-Akt signaling pathway may be involved in the pathogenesis of TOF. We further identified the hsa_circ_000601/hsa-miR-148a/BCL2L11 axis as a crucial signaling pathway axis from the subnetwork. This study provides a novel regulatory network for the pathogenesis of TOF, revealing the possible molecular mechanisms and crucial regulatory pathways that may provide new strategies for candidate diagnostic biomarkers or potential therapeutic targets for TOF. [ABSTRACT FROM AUTHOR]

Objective: To explore the possible biological functions of the differentially expressed genes in patients with benign tracheal stenosis, and to provide a valuable molecular basis for investigating the pathogenesis of benign tracheal stenosis. Method: Whole transcriptome sequencing was performed on blood samples collected from patients with benign tracheal stenosis and normal controls. Differentially expressed mRNA, lncRNA, and circRNA were analyzed using the DESeq2 package. The protein interaction networks for differentially expressed mRNAs were constructed by STRING. The results of gene co-expression network analysis, Starbase database prediction, and differential gene expression were combined to construct a competing endogenous RNA network. The transcription factors of key genes were predicted using the Network Analyst database and a transcription factor-mRNA regulatory network was constructed. The classical pathways, intermolecular interaction networks, and upstream regulatory components of key genes were analyzed using Ingenuity Pathway Analysis (IPA). Finally, the DGIDB database was used to predict the potential therapeutic drugs to target the identified key genes. Result: Based on mRNA, lncRNA and circRNA expression data, we found that differentially expressed mRNAs were enriched in oxygen transport, neutrophil activation, immune response, and oxygen binding. Then the pearson correlation between mRNAs of 46 key genes and lncRNAs and cricRNAs were calculated, and the correlation greater than 0.9 were selected to construct the co-expression network of "mRNA-lncRA" and "mRNA-cricRNA." Moreover, a "lncRNA-miRNA-mRNA" network and a "circRNA-miRNA-mRNA" network were constructed. IPA analysis showed that the 46 key genes were significantly associated with inflammatory activation and acute respiratory distress syndrome. The constructed TF-mRNA regulatory network was composed of 274 nodes and 573 interacting pairs. 251 potential therapeutic drugs were identified from the DGIDB database. Conclusion: This study analyzed the differential genes associated with benign tracheal stenosis and explored the potential regulatory mechanisms, providing a scientific reference for further studies on the pathogenesis of benign tracheal stenosis. [ABSTRACT FROM AUTHOR]

Objective: To explore the possible biological functions of the differentially expressed genes in patients with benign tracheal stenosis, and to provide a valuable molecular basis for investigating the pathogenesis of benign tracheal stenosis.Method: Whole transcriptome sequencing was performed on blood samples collected from patients with benign tracheal stenosis and normal controls. Differentially expressed mRNA, lncRNA, and circRNA were analyzed using the DESeq2 package. The protein interaction networks for differentially expressed mRNAs were constructed by STRING. The results of gene co-expression network analysis, Starbase database prediction, and differential gene expression were combined to construct a competing endogenous RNA network. The transcription factors of key genes were predicted using the Network Analyst database and a transcription factor-mRNA regulatory network was constructed. The classical pathways, intermolecular interaction networks, and upstream regulatory components of key genes were analyzed using Ingenuity Pathway Analysis (IPA). Finally, the DGIDB database was used to predict the potential therapeutic drugs to target the identified key genes.Result: Based on mRNA, lncRNA and circRNA expression data, we found that differentially expressed mRNAs were enriched in oxygen transport, neutrophil activation, immune response, and oxygen binding. Then the pearson correlation between mRNAs of 46 key genes and lncRNAs and cricRNAs were calculated, and the correlation greater than 0.9 were selected to construct the co-expression network of “mRNA-lncRA” and “mRNA-cricRNA.” Moreover, a “lncRNA-miRNA-mRNA” network and a “circRNA-miRNA-mRNA” network were constructed. IPA analysis showed that the 46 key genes were significantly associated with inflammatory activation and acute respiratory distress syndrome. The constructed TF-mRNA regulatory network was composed of 274 nodes and 573 interacting pairs. 251 potential therapeutic drugs were identified from the DGIDB database.Conclusion: This study analyzed the differential genes associated with benign tracheal stenosis and explored the potential regulatory mechanisms, providing a scientific reference for further studies on the pathogenesis of benign tracheal stenosis.

Background: Precise classification has been reported as a central challenge in the clinical research on diagnosis and prediction of treatment efficacy in asthma. In this study, the aim was to investigate the underlying competing endogenous RNA network mechanism of asthma, especially T2 asthma, as well as to find more diagnostic biomarkers and effective therapeutic targets. Methods: Multiple sets of T2 asthma airway biopsy transcription profiles were collected, which involved long non-coding RNA (lncRNA), mRNA, and microRNA (miRNA). DIANA-LncBase, targetscan, mirwalk, and miRDB databases were employed to predict interactions between lncRNAs, miRNAs and target mRNAs. To identify mRNAs correlated with T2 asthma, differential expression and network analyses were conducted through weighted gene co-expression network analysis (WGCNA). Subsequently, the expressions of potential biomarkers were examined through qRT-PCR analysis in the T2 asthma coreinteracting cellular factor (IL-13/IL-33) induced experimental model. Lastly, the ceRNA network was confirmed by plasmid transfection and RNAi experiments in a 16HBE cell line. Results: 30 lncRNAs, 22 miRNAs and 202 mRNAs were differentially expressed in airway biopsies from T2 asthma patients. As indicated by the ROC analysis, the lncRNA, PCAT19, had high diagnostic accuracy (AUC >0.9) in distinguishing T2 asthma patients from non-T2 asthma patients and healthy controls. Furthermore, a competing ceRNA network was established, consisting of 13 lncRNAs, 12 miRNAs, as well as eight mRNAs. The reliability of this network was verified by testing several representative interactions in the network. Conclusion: To the best of our knowledge, this study has been the first to establish an lncRNA-mediated ceRNA regulatory network for studying T2 asthma. The findings of this study may elucidate the pathogenesis and help find potential therapeutic targets for T2 asthma. In T2 asthma, PCAT19 -dominated ceRNA regulation networks may play a critical role, and PCAT19 may serve as a potential immune-related biomarker for asthma and other respiratory diseases correlated with eosinophilic inflammation. [ABSTRACT FROM AUTHOR]

Background: Expression of the long noncoding RNA (lncRNA) HOXA11-AS significantly increased in keloids by unclarified molecular regulation mechanisms. Methods: Using successfully primary cultured keloid-derived fibroblasts from central region of chronic keloid tissues (sample 0), small interfering RNAs were designed and transfected into two keloid fibroblast samples (samples 1 and 2) to knockdown HOXA11-AS. One nonspecific transfection control (sample 3) and one blank control (sample 4) were used to remove nonspecific overlap from the studied group. The lncRNAs, messenger RNAs (mRNAs), and microRNAs (miRNAs) of five samples were sequenced to identify differentially expressed (DE) profiles in HOXA11-AS-knockdown keloid fibroblasts in samples 1 and 2 (by intersection), which facilitated removal of overlap with the nonspecific controls (samples 3 and 4, by union). Using stepwise bioinformatic analysis, a HOXA11-AS-interacted competing endogenous network (ceRNA) was screened based on three DE profiles. Results: Keloid fibroblasts with or without HOXA11-AS as well as with or without nonspecific interferences were successfully constructed respectively. A total of 1,396 mRNAs and 39 lncRNAs were significantly changed in keloid fibroblast with HOXA11-AS knockdown. Simultaneously, 1,626 mRNAs and 99 lncRNAs were significantly changed in keloid fibroblast with nonspecific interference. With removal of nonspecific overlap, a lncRNA–mRNA interactive network characterized by close natural/intronic antisense relationship was initially constructed in keloid fibroblast with HOXA11-AS knockdown. Based on this network, a lncRNA–mRNA–protein interaction network was extended by integration of the human protein–protein interaction network. Significant functional genes were screened using PageRank algorithm in the extended network. Three genes, including SNED1, NIPAL3, and VTN, were validated by real-time PCR in HOXA11-AS-knockdown keloid fibroblasts. Only NIPAL3 was predicted to be a target gene for HOXA11-AS via three competing endogenous miRNAs (hsa-miRNA-19a-3p, hsa-miR-141-3p, and hsa-miR-140-5p). Conclusion: An interactive network of HOXA11-AS–three miRNAs–NIPAL3 was predicted in keloid fibroblasts by integrative bioinformatic analysis and in vitro validation. [ABSTRACT FROM AUTHOR]

Although long non coding RNAs (lncRNAs) and circular RNAs (circRNAs) play important roles in the pathogenesis of diseases, endometriosis related lncRNAs and circRNAs are still rarely reported. This study focused on the potential molecular mechanism of endometriosis related competitive endogenous RNA (ceRNA) composed of lncRNAs and circRNAs. We performed high-throughout sequencing of six normal endometria, six eutopic endometria and six ectopic endometria for the first time to describe and analyze the expression profile of lncRNA, circRNA and mRNA. Our results showed that 140 lncRNAs, 107 circRNAs and 1,206 mRNAs were differentially expressed in the ectopic group, compared with the normal and eutopic groups. We established an lncRNA/circRNA-mRNA co-expression network using pearson correlation test. Meanwhile, the results of Gene set enrichment analysis analysis showed that the 569 up-regulated differentially expressed mRNA (DEmRNA) were mainly related to the epithelial-mesenchymal transition, regulation of immune system process and immune effector process. Subsequently, we established a DElncRNA-miRNA and DEcircRNA-miRNA network using the starbase database, identified the common miRNAs and constructed DElncRNA/DEcircRNA-miRNA pairs. miRDB, Targetscan, miRwalk and circRNA/lncRNA-mRNA pairs jointly determined the miRNA-mRNA portion of the circRNA/lncRNA-miRNA-mRNA co-expression network. RT-qPCR results of 15 control samples and 25 ectopic samples confirmed that circGLIS2, circFN1, LINC02381, IGFL2-AS1, CD84, LYPD1 and FAM163A were significantly overexpressed in ectopic tissues. In conclusion, this is the first study to illustrate ceRNA composed of differentially expressed circRNA, lncRNA and mRNA in endometriosis. We also found that lncRNA and circRNA exerted a pivotal function on the pathogenesis of endometriosis, which can provide new insights for further exploring the pathogenesis of endometriosis and identifying new targets. [ABSTRACT FROM AUTHOR]

Background: Rheumatoid arthritis (RA) is a common autoimmune disease characterized by progressive, destructive polyarthritis. However, the cause and underlying molecular events of RA are not clear. Here, we applied integrated bioinformatics to identify tissue-specific expressed hub genes involved in RA and reveal potential targeted drugs. Methods: Three expression profiles of human microarray datasets involving fibroblast-like synoviocytes (FLS) were downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed mRNAs (DEGs), miRNAs (DEMs), and lncRNAs (DELs) between normal and RA synovial samples were screened using GEO2R tool. BioGPS was used to identified tissue-specific expressed genes. Functional and pathway enrichment analyses were performed for common DEGs using the DAVID database, and the protein-protein interaction (PPI) network of common DEGs was constructed to recognize hub genes by the STRING database. Based on receiver operating characteristic (ROC) curve, we further investigated the prognostic values of tissue-specific expressed hub genes in RA patients. Connectivity Map (CMap) was run to identify novel anti-RA potential drugs. The DEM–DEG pairs and ceRNA network containing key DEMs were established by Cytoscape. Results: We obtain a total of 418 DEGs, 23 DEMs and 49 DELs. 64 DEGs were verified as tissue-specific expressed genes, most derive from the hematologic/immune system (20/64, 31.25%). GO term and KEGG pathway enrichment analysis showed that DEGs focused primarily on immune-related biological process and NF-κB pathway. 10 hub genes were generated via using MCODE plugin. Among them, SPAG5, CUX2, and THEMIS2 were identified as tissue-specific expressed hub genes, these 3 tissue-specific expressed hub genes have superior diagnostic value in the RA samples compared with osteoarthritis (OA) samples. 5 compounds (troleandomycin, levodopa, trichostatin A, LY-294002, and levamisole) rank among the top five in connectivity score. In addition, 5 miRNAs were identified to be key DEMs, the lncRNA–miRNA–mRNA network with five key DEMs was formed. The networks containing tissue-specific expressed hub genes are as follows: ARAP1-AS2/miR-20b-3p/TRIM3, ARAP1-AS2/miR-30c-3p/FRZB. Conclusion: This study indicates that screening for identify tissue-specific expressed hub genes and ceRNA network in RA using integrated bioinformatics analyses could help us understand the mechanism of development of RA. Besides, SPAG5 and THEMIS2 might be candidate biomarkers for diagnosis of RA. LY-294002, trichostatin A, and troleandomycin may be potential drugs for RA. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Currently, treatment methods generally cause poor prognosis. Therefore, in order to seek new treatment options, we explored the internal mechanism of NSCLC. Firstly, the SOX2-OT/miR-30d-5p/PDK1 axis regulated by lncRNA SOX2-OT was predicted by bioinformatics methods, and the expression of SOX2-OT, miR-30d-5p, and PDK1 mRNA in cells were detected by qRT-PCR while PDK1 protein expression was detected by western blot. The results expressed that in NSCLC, SOX2-OT, and PDK1 were notably overexpressed while miR-30d-5p was markedly under-expressed. The interaction between them was verified by dual-luciferase reporter and RNA binding protein immunoprecipitation assays. Subsequently, through CCK8, scratch healing, cell invasion and flow cytometry assays, we revealed that inhibiting the expression of SOX2-OT could inhibit the proliferation, migration and invasion of NSCLC cells and promote cell apoptosis; while simultaneous overexpression of PDK1 or inhibition of miR-30d-5p expression could reverse the inhibitory effect of SOX2-OT silence-mediated malignant progression of NSCLC cells. Then, the combined application of overexpressed PDK1 and rapamycin verified that PDK1 could regulate the expression of PD-L1 in NSCLC cells through the mTOR signaling pathway. Co-culture of CD8+ T cells verified that silencing SOX2-OT could inhibit the apoptosis of CD8+ T cells through miR-30d-5p/PDK1. Finally, tumor formation assay in animals confirmed that overexpression of SOX2-OT could promote the growth of NSCLC tumor in vivo. In this study, assays in vitro and in vivo were conducted to elucidate the mechanism by which SOX2-OT/miR-30d-5p/PDK1 drives PD-L1 through the mTOR signaling pathway to promote the malignant progression and immune escape of NSCLC. [ABSTRACT FROM AUTHOR]

Chronic intermittent hypoxia (CIH) is the main feature of obstructive sleep apnea (OSA) and is known to exaggerate cardiac remodeling after myocardial infarction (MI). However, the specific contribution of CIH to overall OSA-induced pathological complications and the transcriptomic mechanisms underlying CIH-exaggerated post-MI remodeling remains unclear. In this study, we used RNA-sequencing to construct the expression profiles of cardiac mRNAs, microRNAs, and long non-coding RNAs (lncRNA) in four groups of C57BL/6J mice (Sham, CIH, MI, MI + CIH) to evaluate how CIH regulates cardiac remodeling after MI. Compared with the other three groups, the MI + CIH group exhibited 345 lncRNAs, 35 microRNAs, and 5,220 differentially expressed mRNAs. Further analysis showed that CIH led to significant changes in Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the differentially expressed mRNAs. Co-expression network analysis identified two core lncRNAs (Mirt1 and AC125351.1) and two core microRNAs (miR-466i-5p and miR-574-5p) during the development of CIH-exaggerated post-MI remodeling, and they were verified by quantitative real-time PCR (qRT-PCR). LncRNA-mRNA correlation analysis further showed that lncRNA Mirt1 was positively correlated with Apbb1ip and Lcp2. In addition, microRNA-mRNA correlation analysis showed that microRNA miR-466i-5p was positively correlated with Snai2, Cdc27, and Ngfr. Furthermore, combining with lncRNA-mRNA and miRNA-mRNA networks, 44 RNAs were identified in the competitive endogenous RNA (ceRNA) network. Mirt1 acts as a ceRNA to bind to miR-466i-5p to further regulate the expression levels of the target gene, thereby aggravating cardiac remodeling after MI. In conclusion, our study provides a systematic perspective on the potential functions of mRNAs, microRNAs, and lncRNAs in CIH-exaggerated post-MI cardiac remodeling. Our data suggest that lncRNA Mirt1 may be the most critical regulator of MI aggravated by CIH. [ABSTRACT FROM AUTHOR]

Background: Precise classification has been reported as a central challenge in the clinical research on diagnosis and prediction of treatment efficacy in asthma. In this study, the aim was to investigate the underlying competing endogenous RNA network mechanism of asthma, especially T2 asthma, as well as to find more diagnostic biomarkers and effective therapeutic targets.Methods: Multiple sets of T2 asthma airway biopsy transcription profiles were collected, which involved long non-coding RNA (lncRNA), mRNA, and microRNA (miRNA). DIANA-LncBase, targetscan, mirwalk, and miRDB databases were employed to predict interactions between lncRNAs, miRNAs and target mRNAs. To identify mRNAs correlated with T2 asthma, differential expression and network analyses were conducted through weighted gene co-expression network analysis (WGCNA). Subsequently, the expressions of potential biomarkers were examined through qRT-PCR analysis in the T2 asthma coreinteracting cellular factor (IL-13/IL-33) induced experimental model. Lastly, the ceRNA network was confirmed by plasmid transfection and RNAi experiments in a 16HBE cell line.Results: 30 lncRNAs, 22 miRNAs and 202 mRNAs were differentially expressed in airway biopsies from T2 asthma patients. As indicated by the ROC analysis, the lncRNA, PCAT19, had high diagnostic accuracy (AUC >0.9) in distinguishing T2 asthma patients from non-T2 asthma patients and healthy controls. Furthermore, a competing ceRNA network was established, consisting of 13 lncRNAs, 12 miRNAs, as well as eight mRNAs. The reliability of this network was verified by testing several representative interactions in the network.Conclusion: To the best of our knowledge, this study has been the first to establish an lncRNA-mediated ceRNA regulatory network for studying T2 asthma. The findings of this study may elucidate the pathogenesis and help find potential therapeutic targets for T2 asthma. In T2 asthma, PCAT19-dominated ceRNA regulation networks may play a critical role, and PCAT19 may serve as a potential immune-related biomarker for asthma and other respiratory diseases correlated with eosinophilic inflammation.

Background: Rheumatoid arthritis (RA) is a common autoimmune disease characterized by progressive, destructive polyarthritis. However, the cause and underlying molecular events of RA are not clear. Here, we applied integrated bioinformatics to identify tissue-specific expressed hub genes involved in RA and reveal potential targeted drugs.Methods: Three expression profiles of human microarray datasets involving fibroblast-like synoviocytes (FLS) were downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed mRNAs (DEGs), miRNAs (DEMs), and lncRNAs (DELs) between normal and RA synovial samples were screened using GEO2R tool. BioGPS was used to identified tissue-specific expressed genes. Functional and pathway enrichment analyses were performed for common DEGs using the DAVID database, and the protein-protein interaction (PPI) network of common DEGs was constructed to recognize hub genes by the STRING database. Based on receiver operating characteristic (ROC) curve, we further investigated the prognostic values of tissue-specific expressed hub genes in RA patients. Connectivity Map (CMap) was run to identify novel anti-RA potential drugs. The DEM–DEG pairs and ceRNA network containing key DEMs were established by Cytoscape.Results: We obtain a total of 418 DEGs, 23 DEMs and 49 DELs. 64 DEGs were verified as tissue-specific expressed genes, most derive from the hematologic/immune system (20/64, 31.25%). GO term and KEGG pathway enrichment analysis showed that DEGs focused primarily on immune-related biological process and NF-κB pathway. 10 hub genes were generated via using MCODE plugin. Among them, SPAG5, CUX2, and THEMIS2 were identified as tissue-specific expressed hub genes, these 3 tissue-specific expressed hub genes have superior diagnostic value in the RA samples compared with osteoarthritis (OA) samples. 5 compounds (troleandomycin, levodopa, trichostatin A, LY-294002, and levamisole) rank among the top five in connectivity score. In addition, 5 miRNAs were identified to be key DEMs, the lncRNA–miRNA–mRNA network with five key DEMs was formed. The networks containing tissue-specific expressed hub genes are as follows: ARAP1-AS2/miR-20b-3p/TRIM3, ARAP1-AS2/miR-30c-3p/FRZB.Conclusion: This study indicates that screening for identify tissue-specific expressed hub genes and ceRNA network in RA using integrated bioinformatics analyses could help us understand the mechanism of development of RA. Besides, SPAG5 and THEMIS2 might be candidate biomarkers for diagnosis of RA. LY-294002, trichostatin A, and troleandomycin may be potential drugs for RA.

Background: Expression of the long noncoding RNA (lncRNA) HOXA11-AS significantly increased in keloids by unclarified molecular regulation mechanisms.Methods: Using successfully primary cultured keloid-derived fibroblasts from central region of chronic keloid tissues (sample 0), small interfering RNAs were designed and transfected into two keloid fibroblast samples (samples 1 and 2) to knockdown HOXA11-AS. One nonspecific transfection control (sample 3) and one blank control (sample 4) were used to remove nonspecific overlap from the studied group. The lncRNAs, messenger RNAs (mRNAs), and microRNAs (miRNAs) of five samples were sequenced to identify differentially expressed (DE) profiles in HOXA11-AS-knockdown keloid fibroblasts in samples 1 and 2 (by intersection), which facilitated removal of overlap with the nonspecific controls (samples 3 and 4, by union). Using stepwise bioinformatic analysis, a HOXA11-AS-interacted competing endogenous network (ceRNA) was screened based on three DE profiles.Results: Keloid fibroblasts with or without HOXA11-AS as well as with or without nonspecific interferences were successfully constructed respectively. A total of 1,396 mRNAs and 39 lncRNAs were significantly changed in keloid fibroblast with HOXA11-AS knockdown. Simultaneously, 1,626 mRNAs and 99 lncRNAs were significantly changed in keloid fibroblast with nonspecific interference. With removal of nonspecific overlap, a lncRNA–mRNA interactive network characterized by close natural/intronic antisense relationship was initially constructed in keloid fibroblast with HOXA11-AS knockdown. Based on this network, a lncRNA–mRNA–protein interaction network was extended by integration of the human protein–protein interaction network. Significant functional genes were screened using PageRank algorithm in the extended network. Three genes, including SNED1, NIPAL3, and VTN, were validated by real-time PCR in HOXA11-AS-knockdown keloid fibroblasts. Only NIPAL3 was predicted to be a target gene for HOXA11-AS via three competing endogenous miRNAs (hsa-miRNA-19a-3p, hsa-miR-141-3p, and hsa-miR-140-5p).Conclusion: An interactive network of HOXA11-AS–three miRNAs–NIPAL3 was predicted in keloid fibroblasts by integrative bioinformatic analysis and in vitro validation.

Although long non coding RNAs (lncRNAs) and circular RNAs (circRNAs) play important roles in the pathogenesis of diseases, endometriosis related lncRNAs and circRNAs are still rarely reported. This study focused on the potential molecular mechanism of endometriosis related competitive endogenous RNA (ceRNA) composed of lncRNAs and circRNAs. We performed high-throughout sequencing of six normal endometria, six eutopic endometria and six ectopic endometria for the first time to describe and analyze the expression profile of lncRNA, circRNA and mRNA. Our results showed that 140 lncRNAs, 107 circRNAs and 1,206 mRNAs were differentially expressed in the ectopic group, compared with the normal and eutopic groups. We established an lncRNA/circRNA-mRNA co-expression network using pearson correlation test. Meanwhile, the results of Gene set enrichment analysis analysis showed that the 569 up-regulated differentially expressed mRNA (DEmRNA) were mainly related to the epithelial-mesenchymal transition, regulation of immune system process and immune effector process. Subsequently, we established a DElncRNA-miRNA and DEcircRNA-miRNA network using the starbase database, identified the common miRNAs and constructed DElncRNA/DEcircRNA-miRNA pairs. miRDB, Targetscan, miRwalk and circRNA/lncRNA-mRNA pairs jointly determined the miRNA-mRNA portion of the circRNA/lncRNA-miRNA-mRNA co-expression network. RT-qPCR results of 15 control samples and 25 ectopic samples confirmed that circGLIS2, circFN1, LINC02381, IGFL2-AS1, CD84, LYPD1 and FAM163A were significantly overexpressed in ectopic tissues. In conclusion, this is the first study to illustrate ceRNA composed of differentially expressed circRNA, lncRNA and mRNA in endometriosis. We also found that lncRNA and circRNA exerted a pivotal function on the pathogenesis of endometriosis, which can provide new insights for further exploring the pathogenesis of endometriosis and identifying new targets.

Chronic intermittent hypoxia (CIH) is the main feature of obstructive sleep apnea (OSA) and is known to exaggerate cardiac remodeling after myocardial infarction (MI). However, the specific contribution of CIH to overall OSA-induced pathological complications and the transcriptomic mechanisms underlying CIH-exaggerated post-MI remodeling remains unclear. In this study, we used RNA-sequencing to construct the expression profiles of cardiac mRNAs, microRNAs, and long non-coding RNAs (lncRNA) in four groups of C57BL/6J mice (Sham, CIH, MI, MI + CIH) to evaluate how CIH regulates cardiac remodeling after MI. Compared with the other three groups, the MI + CIH group exhibited 345 lncRNAs, 35 microRNAs, and 5,220 differentially expressed mRNAs. Further analysis showed that CIH led to significant changes in Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the differentially expressed mRNAs. Co-expression network analysis identified two core lncRNAs (Mirt1 and AC125351.1) and two core microRNAs (miR-466i-5p and miR-574-5p) during the development of CIH-exaggerated post-MI remodeling, and they were verified by quantitative real-time PCR (qRT-PCR). LncRNA-mRNA correlation analysis further showed that lncRNA Mirt1 was positively correlated with Apbb1ip and Lcp2. In addition, microRNA-mRNA correlation analysis showed that microRNA miR-466i-5p was positively correlated with Snai2, Cdc27, and Ngfr. Furthermore, combining with lncRNA-mRNA and miRNA-mRNA networks, 44 RNAs were identified in the competitive endogenous RNA (ceRNA) network. Mirt1 acts as a ceRNA to bind to miR-466i-5p to further regulate the expression levels of the target gene, thereby aggravating cardiac remodeling after MI. In conclusion, our study provides a systematic perspective on the potential functions of mRNAs, microRNAs, and lncRNAs in CIH-exaggerated post-MI cardiac remodeling. Our data suggest that lncRNA Mirt1 may be the most critical regulator of MI aggravated by CIH.

This study was designed to identify differently expressed circular RNAs (circRNAs) and investigate their potential roles in lacrimal sacs from patients with chronic dacryocystitis. The lacrimal sac samples of three chronic dacryocystitis patients and three control subjects were collected for RNA sequencing after ribosomal RNA was depleted. Differently expressed circRNAs and messenger RNAs (mRNAs) were used for co-expression analysis. CircRNA-microRNA (miRNA)-mRNA interaction network were also established by miRanda software. Meanwhile, pathway and functional enrichment analysis were conducted for the down- and up-regulated mRNAs in the circRNA-mRNA co-expression network. The expression levels of circRNAs and mRNAs in chronic dacryocystitis and control samples were validated by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). In all the 3,909 circRNAs predicted through RNA sequencing, 25 circRNAs (20 up-regulated and 5 down-regulated) expressed differently in chronic dacryocystitis samples. Besides, there identified 1,486 differentially expressed mRNAs. Of these differently expressed circRNAs and mRNAs, eight were validated by qRT-PCR, including MYH2, DSP, CD27, CCL5, FN1, has_circ_0004792, has_circ_0001062, and has_circ_0115476. Gene Ontology (GO) analysis indicated that the majority of altered mRNAs in this co-expression network were involved in immune system processes and meanwhile Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that these altered expressed mRNAs were also amplified in bacterial invasion of epithelial cells, both of which were thought to be involved in the pathogenesis of chronic dacryocystitis. In the circRNA-miRNA-mRNA interaction network, six circRNAs were found to be related to Th1 and Th2 cell differentiation, which was closely associated with the development of chronic dacryocystitis. This study identified statistically significant differences between circRNAs and mRNAs of lacrimal sac samples of chronic dacryocystitis patients and control individuals and provides novel insight into the regulatory mechanism of circRNAs, miRNAs, and mRNAs in the pathogenesis of chronic dacryocystitis.

Non-coding RNAs have remarkable roles in acute lung injury (ALI) initiation. Nevertheless, the significance of long non-coding RNAs (lncRNAs) in ALI is still unknown. Herein, we purposed to identify potential key genes in ALI and create a competitive endogenous RNA (ceRNA) modulatory network to uncover possible molecular mechanisms that affect lung injury. We generated a lipopolysaccharide-triggered ALI mouse model, whose lung tissue was subjected to RNA sequencing, and then we conducted bioinformatics analysis to select genes showing differential expression (DE) and to build a lncRNA-miRNA (microRNA)- mRNA (messenger RNA) modulatory network. Besides, GO along with KEGG assessments were conducted to identify major biological processes and pathways, respectively, involved in ALI. Then, RT-qPCR assay was employed to verify levels of major RNAs. A protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and the hub genes were obtained with the Molecular Complex Detection plugin. Finally, a key ceRNA subnetwork was built from major genes and their docking sites. Overall, a total of 8,610 lncRNAs were identified in the normal and LPS groups. Based on the 308 DE lncRNAs [ p -value < 0.05, |log2 (fold change) | > 1] and 3,357 DE mRNAs [ p -value < 0.05, |log2 (fold change) | > 1], lncRNA-miRNA and miRNA-mRNA pairs were predicted using miRanda. The lncRNA-miRNA-mRNA network was created from 175 lncRNAs, 22 miRNAs, and 209 mRNAs in ALI. The RT-qPCR data keep in step with the RNA sequencing data. GO along with KEGG analyses illustrated that DE mRNAs in this network were mainly bound up with the inflammatory response, developmental process, cell differentiation, cell proliferation, apoptosis, and the NF-kappa B, PI3K-Akt, HIF-1, MAPK, Jak-STAT, and Notch signaling pathways. A PPI network on the basis of the 209 genes was established, and three hub genes (Nkx2-1, Tbx2, and Atf5) were obtained from the network. Additionally, a lncRNA-miRNA-hub gene subnetwork was built from 15 lncRNAs, 3 miRNAs, and 3 mRNAs. Herein, novel ideas are presented to expand our knowledge on the regulation mechanisms of lncRNA-related ceRNAs in the pathogenesis of ALI. [ABSTRACT FROM AUTHOR]

Coronary heart disease (CHD) is a global health concern with high morbidity and mortality rates. This study aimed to identify the possible long non-coding RNA (lncRNA) biomarkers of CHD. The lncRNA- and mRNA-related data of patients with CHD were downloaded from the Gene Expression Omnibus database (GSE113079). The limma package was used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs, respectively). Then, miRcode, TargetScan, miRDB, and miRTarBase databases were used to form the competing endogenous RNA (ceRNA) network. Furthermore, SPSS Modeler 18.0 was used to construct a logistic stepwise regression prediction model for CHD diagnosis based on DElncRNAs. Of the microarray data, 70% was used as a training set and 30% as a test set. Moreover, a validation cohort including 30 patients with CHD and 30 healthy controls was used to verify the hub lncRNA expression through real-time reverse transcription-quantitative PCR (RT-qPCR). A total of 185 DElncRNAs (114 upregulated and 71 downregulated) and 382 DEmRNAs (162 upregulated and 220 downregulated) between CHD and healthy controls were identified from the microarray data. Furthermore, through bioinformatics prediction, a 38 lncRNA-21miRNA-40 mRNA ceRNA network was constructed. Next, by constructing a logistic stepwise regression prediction model for 38 DElncRNAs, we screened two hub lncRNAs AC010082.1 and AC011443.1 (p < 0.05). The sensitivity, specificity, and area under the curve were 98.41%, 100%, and 0.995, respectively, for the training set and 93.33%, 91.67%, and 0.983, respectively, for the test set. We further verified the significant upregulation of AC010082.1 (p < 0.01) and AC011443.1 (p < 0.05) in patients with CHD using RT-qPCR in the validation cohort. Our results suggest that lncRNA AC010082.1 and AC011443.1 are potential biomarkers of CHD. Their pathological mechanism in CHD requires further validation. [ABSTRACT FROM AUTHOR]

Globally, esophageal cancer (ECA) is the seventh most common cancer and sixth most common cause of cancer-associated mortality. However, there are no reliable prognostic and predictive molecular markers for ECA; in addition, the pathogenesis of ECA is not fully elucidated. The expressions of circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) of ECA and control groups were obtained from the RNA-sequencing (RNA-seq) data of our hospital, the Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) datasets. Analyses of differentially expressed genes, the circRNA–miRNA–mRNA–competing endogenous RNA (ceRNA) network, and functional/pathway enrichment were conducted. The key targets in the ceRNA network that showed significant results in survival Cox regression analyses were selected. Furthermore, analyses of immune infiltration and autophagy genes related to the key targets were performed. Seven circRNAs, 22 miRNAs, and 34 mRNAs were identified as vital genes in ECA; the nuclear factor-κ-gene binding (NF-κB) and phosphatidylinositol-3 kinase/protein kinase B (PI3K-Akt) signaling were identified as the most enriched pathways. In addition, the LIM domain containing 2 (LIMD2) was an independent predictor of prognosis in ECA patients and closely associated with immunity and autophagy. Moreover, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) revealed significant upregulation of LIMD2 expression in ECA tissues. ECA may be closely correlated with NF-κB and PI3K/Akt signaling. In addition, LIMD2 could be a potential prognostic and predictive marker of ECA. [ABSTRACT FROM AUTHOR]

Globally, esophageal cancer (ECA) is the seventh most common cancer and sixth most common cause of cancer-associated mortality. However, there are no reliable prognostic and predictive molecular markers for ECA; in addition, the pathogenesis of ECA is not fully elucidated. The expressions of circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) of ECA and control groups were obtained from the RNA-sequencing (RNA-seq) data of our hospital, the Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) datasets. Analyses of differentially expressed genes, the circRNA–miRNA–mRNA–competing endogenous RNA (ceRNA) network, and functional/pathway enrichment were conducted. The key targets in the ceRNA network that showed significant results in survival Cox regression analyses were selected. Furthermore, analyses of immune infiltration and autophagy genes related to the key targets were performed. Seven circRNAs, 22 miRNAs, and 34 mRNAs were identified as vital genes in ECA; the nuclear factor-κ-gene binding (NF-κB) and phosphatidylinositol-3 kinase/protein kinase B (PI3K-Akt) signaling were identified as the most enriched pathways. In addition, the LIM domain containing 2 (LIMD2) was an independent predictor of prognosis in ECA patients and closely associated with immunity and autophagy. Moreover, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) revealed significant upregulation of LIMD2 expression in ECA tissues. ECA may be closely correlated with NF-κB and PI3K/Akt signaling. In addition, LIMD2 could be a potential prognostic and predictive marker of ECA.

Coronary heart disease (CHD) is a global health concern with high morbidity and mortality rates. This study aimed to identify the possible long non-coding RNA (lncRNA) biomarkers of CHD. The lncRNA- and mRNA-related data of patients with CHD were downloaded from the Gene Expression Omnibus database (GSE113079). The limma package was used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs, respectively). Then, miRcode, TargetScan, miRDB, and miRTarBase databases were used to form the competing endogenous RNA (ceRNA) network. Furthermore, SPSS Modeler 18.0 was used to construct a logistic stepwise regression prediction model for CHD diagnosis based on DElncRNAs. Of the microarray data, 70% was used as a training set and 30% as a test set. Moreover, a validation cohort including 30 patients with CHD and 30 healthy controls was used to verify the hub lncRNA expression through real-time reverse transcription-quantitative PCR (RT-qPCR). A total of 185 DElncRNAs (114 upregulated and 71 downregulated) and 382 DEmRNAs (162 upregulated and 220 downregulated) between CHD and healthy controls were identified from the microarray data. Furthermore, through bioinformatics prediction, a 38 lncRNA-21miRNA-40 mRNA ceRNA network was constructed. Next, by constructing a logistic stepwise regression prediction model for 38 DElncRNAs, we screened two hub lncRNAs AC010082.1 and AC011443.1 (p < 0.05). The sensitivity, specificity, and area under the curve were 98.41%, 100%, and 0.995, respectively, for the training set and 93.33%, 91.67%, and 0.983, respectively, for the test set. We further verified the significant upregulation of AC010082.1 (p < 0.01) and AC011443.1 (p < 0.05) in patients with CHD using RT-qPCR in the validation cohort. Our results suggest that lncRNA AC010082.1 and AC011443.1 are potential biomarkers of CHD. Their pathological mechanism in CHD requires further validation.

Non-coding RNAs have remarkable roles in acute lung injury (ALI) initiation. Nevertheless, the significance of long non-coding RNAs (lncRNAs) in ALI is still unknown. Herein, we purposed to identify potential key genes in ALI and create a competitive endogenous RNA (ceRNA) modulatory network to uncover possible molecular mechanisms that affect lung injury. We generated a lipopolysaccharide-triggered ALI mouse model, whose lung tissue was subjected to RNA sequencing, and then we conducted bioinformatics analysis to select genes showing differential expression (DE) and to build a lncRNA-miRNA (microRNA)- mRNA (messenger RNA) modulatory network. Besides, GO along with KEGG assessments were conducted to identify major biological processes and pathways, respectively, involved in ALI. Then, RT-qPCR assay was employed to verify levels of major RNAs. A protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and the hub genes were obtained with the Molecular Complex Detection plugin. Finally, a key ceRNA subnetwork was built from major genes and their docking sites. Overall, a total of 8,610 lncRNAs were identified in the normal and LPS groups. Based on the 308 DE lncRNAs [p-value < 0.05, |log2 (fold change) | > 1] and 3,357 DE mRNAs [p-value < 0.05, |log2 (fold change) | > 1], lncRNA-miRNA and miRNA-mRNA pairs were predicted using miRanda. The lncRNA-miRNA-mRNA network was created from 175 lncRNAs, 22 miRNAs, and 209 mRNAs in ALI. The RT-qPCR data keep in step with the RNA sequencing data. GO along with KEGG analyses illustrated that DE mRNAs in this network were mainly bound up with the inflammatory response, developmental process, cell differentiation, cell proliferation, apoptosis, and the NF-kappa B, PI3K-Akt, HIF-1, MAPK, Jak-STAT, and Notch signaling pathways. A PPI network on the basis of the 209 genes was established, and three hub genes (Nkx2-1, Tbx2, and Atf5) were obtained from the network. Additionally, a lncRNA-miRNA-hub gene subnetwork was built from 15 lncRNAs, 3 miRNAs, and 3 mRNAs. Herein, novel ideas are presented to expand our knowledge on the regulation mechanisms of lncRNA-related ceRNAs in the pathogenesis of ALI.

In recent years, burgeoning research has underscored the pivotal role of non-coding RNA in orchestrating the growth, development, and pathogenesis of various diseases across organisms. However, despite these advances, our understanding of the specific contributions of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) to lens development remains notably limited. Clarifying the intricate gene regulatory networks is imperative for unraveling the molecular underpinnings of lens-related disorders. In this study, we aimed to address this gap by conducting a comprehensive analysis of the expression profiles of messenger RNAs (mRNAs), lncRNAs, and circRNAs at critical developmental time points of the mouse lens, encompassing both embryonic (E10.5, E12.5, and E16.5) and postnatal stages (P0.5, P10.5, and P60). Leveraging RNA-sequencing technology, we identified key transcripts pivotal to lens development. Our analysis revealed differentially expressed (DE) mRNAs, lncRNAs, and circRNAs across various developmental stages. Particularly noteworthy, there were 1831 co-differentially expressed (CO-DE) mRNAs, 150 CO-DE lncRNAs, and 13 CO-DE circRNAs identified during embryonic stages. Gene Ontology (GO) enrichment analysis unveiled associations primarily related to lens development, DNA conformational changes, and angiogenesis among DE mRNAs and lncRNAs. Furthermore, employing protein-protein interaction networks, mRNA-lncRNA co-expression networks, and circRNA-microRNA-mRNA networks, we predicted candidate key molecules implicated in lens development. Our findings underscore the pivotal roles of lncRNAs and circRNAs in this process, offering fresh insights into the pathogenesis of lens-related disorders and paving the way for future exploration in this field., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Liu, Li, Liu, Zhang, Liu and Wang.)

Background: Acute Myeloid Leukemia (AML) is a complex and heterogeneous hematologic malignancy. However, the function of prognosis-related signature genes in AML remains unclear. Methods: In the current study, transcriptome sequencing was performed on 15 clinical samples, differentially expressed RNAs were identified using R software. The potential interactions network was constructed by using the common genes between target genes of differentially expressed miRNAs with transcriptome sequencing results. Functional and pathway enrichment analysis was performed to identify candidate gene-mediated aberrant signaling pathways. Hub genes were identified by the cytohubba plugin in Cytoscape software, which then expanded the potential interactions regulatory module for hub genes. TCGA-LAML clinical data were used for the prognostic analysis of the hub genes in the regulatory network, and GVSA analysis was used to identify the immune signature of prognosis-related hub genes. qRT-PCR was used to verify the expression of hub genes in independent clinical samples. Results: We obtained 1,610 differentially expressed lncRNAs, 233 differentially expressed miRNAs, and 2,217 differentially expressed mRNAs from transcriptome sequencing. The potential interactions network is constructed by 12 lncRNAs, 25 miRNAs, and 692 mRNAs. Subsequently, a sub-network including 15 miRNAs as well as 12 lncRNAs was created based on the expanded regulatory modules of 25 key genes. The prognostic analysis results show that CCL5 and lncRNA UCA1 was a significant impact on the prognosis of AML. Besides, we found three potential interactions networks such as lncRNA UCA1 /hsa-miR-16-5p/ COL4A5 , lncRNA UCA1 /hsa-miR-16-5p/ SPARC , and lncRNA SNORA27 /hsa-miR-17-5p/ CCL5 may play an important role in AML. Furthermore, the evaluation of the immune infiltration shows that CCL5 is positively correlated with various immune signatures, and lncRNA UCA1 is negatively correlated with the immune signatures. Finally, the result of qRT-PCR showed that CCL5 is down-regulated and lncRNA UCA1 is up-regulated in AML samples separately. Conclusions: In conclusion, we propose that CCL5 and lncRNA UCA1 could be recognized biomarkers for predicting survival prognosis based on constructing competing endogenous RNAs in AML, which will provide us novel insight into developing novel prognostic, diagnostic, and therapeutic for AML. [ABSTRACT FROM AUTHOR]

Lung cancer (LC) is one of the most frequently diagnosed cancers and the leading cause of cancer death worldwide, and most LCs are non-small cell lung cancer (NSCLC). Radiotherapy is one of the most effective treatments for patients with lung cancer, either alone or in combination with other treatment methods. However, radiotherapy responses vary considerably among NSCLC patients. The efficacy of radiotherapy is influenced by several factors, among which autophagy is of importance. Autophagy is induced by radiotherapy and also influences cell responses to radiation. We explored the clinical significance of autophagy-related genes (ARGs) and gene sets (ARGSs) and the underlying mechanism in NSCLC patients treated with radiotherapy. First, differentially expressed ARGs (SNCA, SESN3, DAPL1, and ELAPOR1) and miRNAs (miR-205-5p, miR-26a-1-3p, miR-6510-3p, miR-194-3p, miR-215-5p, and miR-375-3p) were identified between radiotherapy-resistant and radiotherapy-sensitive groups. An autophagy-related radiosensitivity risk signature (ARRS) by nine ARmRNAs/miRNAs and an autophagy-related overall survival risk signature (AROS) by three ARmRNAs were then constructed with estimated AUCs of 0.8854 (95% CI: 0.8131–0.9576) and 0.7901 (95% CI: 0.7168–0.8685), respectively. The correlations between ARGSs or prognostic signatures and clinicopathological factors, short-term radiotherapy responses (radiotherapy sensitivity), long-term radiotherapy responses (overall survival), and immune characteristics were analyzed. Both ARGSs and prognostic signatures were related to immune checkpoint inhibitors (ICIs), infiltration of tumor-infiltrating immune cells (TIICs), and the activity of the cancer immune cycle. Finally, after target prediction and correlation analysis, circRNA (hsa_circ_0019709, hsa_circ_0081983, hsa_circ_0112354, hsa_circ_0040569, hsa_circ_0135500, and hsa_circ_0098966)-regulated miRNA/ARmRNA axes (miR-194-3p/SESN3, miR-205-5p/ELAPOR1, and miR-26a-1-3p/SNCA) were considered potential modulatory mechanisms by influencing the regulation of autophagy, macroautophagy, and chaperone-mediated autophagy. [ABSTRACT FROM AUTHOR]

Background: Acute Myeloid Leukemia (AML) is a complex and heterogeneous hematologic malignancy. However, the function of prognosis-related signature genes in AML remains unclear.Methods: In the current study, transcriptome sequencing was performed on 15 clinical samples, differentially expressed RNAs were identified using R software. The potential interactions network was constructed by using the common genes between target genes of differentially expressed miRNAs with transcriptome sequencing results. Functional and pathway enrichment analysis was performed to identify candidate gene-mediated aberrant signaling pathways. Hub genes were identified by the cytohubba plugin in Cytoscape software, which then expanded the potential interactions regulatory module for hub genes. TCGA-LAML clinical data were used for the prognostic analysis of the hub genes in the regulatory network, and GVSA analysis was used to identify the immune signature of prognosis-related hub genes. qRT-PCR was used to verify the expression of hub genes in independent clinical samples.Results: We obtained 1,610 differentially expressed lncRNAs, 233 differentially expressed miRNAs, and 2,217 differentially expressed mRNAs from transcriptome sequencing. The potential interactions network is constructed by 12 lncRNAs, 25 miRNAs, and 692 mRNAs. Subsequently, a sub-network including 15 miRNAs as well as 12 lncRNAs was created based on the expanded regulatory modules of 25 key genes. The prognostic analysis results show that CCL5 and lncRNA UCA1 was a significant impact on the prognosis of AML. Besides, we found three potential interactions networks such as lncRNA UCA1/hsa-miR-16-5p/COL4A5, lncRNA UCA1/hsa-miR-16-5p/SPARC, and lncRNA SNORA27/hsa-miR-17-5p/CCL5 may play an important role in AML. Furthermore, the evaluation of the immune infiltration shows that CCL5 is positively correlated with various immune signatures, and lncRNA UCA1 is negatively correlated with the immune signatures. Finally, the result of qRT-PCR showed that CCL5 is down-regulated and lncRNA UCA1 is up-regulated in AML samples separately.Conclusions: In conclusion, we propose that CCL5 and lncRNA UCA1 could be recognized biomarkers for predicting survival prognosis based on constructing competing endogenous RNAs in AML, which will provide us novel insight into developing novel prognostic, diagnostic, and therapeutic for AML.

Lung cancer (LC) is one of the most frequently diagnosed cancers and the leading cause of cancer death worldwide, and most LCs are non-small cell lung cancer (NSCLC). Radiotherapy is one of the most effective treatments for patients with lung cancer, either alone or in combination with other treatment methods. However, radiotherapy responses vary considerably among NSCLC patients. The efficacy of radiotherapy is influenced by several factors, among which autophagy is of importance. Autophagy is induced by radiotherapy and also influences cell responses to radiation. We explored the clinical significance of autophagy-related genes (ARGs) and gene sets (ARGSs) and the underlying mechanism in NSCLC patients treated with radiotherapy. First, differentially expressed ARGs (SNCA, SESN3, DAPL1, and ELAPOR1) and miRNAs (miR-205-5p, miR-26a-1-3p, miR-6510-3p, miR-194-3p, miR-215-5p, and miR-375-3p) were identified between radiotherapy-resistant and radiotherapy-sensitive groups. An autophagy-related radiosensitivity risk signature (ARRS) by nine ARmRNAs/miRNAs and an autophagy-related overall survival risk signature (AROS) by three ARmRNAs were then constructed with estimated AUCs of 0.8854 (95% CI: 0.8131–0.9576) and 0.7901 (95% CI: 0.7168–0.8685), respectively. The correlations between ARGSs or prognostic signatures and clinicopathological factors, short-term radiotherapy responses (radiotherapy sensitivity), long-term radiotherapy responses (overall survival), and immune characteristics were analyzed. Both ARGSs and prognostic signatures were related to immune checkpoint inhibitors (ICIs), infiltration of tumor-infiltrating immune cells (TIICs), and the activity of the cancer immune cycle. Finally, after target prediction and correlation analysis, circRNA (hsa_circ_0019709, hsa_circ_0081983, hsa_circ_0112354, hsa_circ_0040569, hsa_circ_0135500, and hsa_circ_0098966)-regulated miRNA/ARmRNA axes (miR-194-3p/SESN3, miR-205-5p/ELAPOR1, and miR-26a-1-3p/SNCA) were considered potential modulatory mechanisms by influencing the regulation of autophagy, macroautophagy, and chaperone-mediated autophagy.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Currently, treatment methods generally cause poor prognosis. Therefore, in order to seek new treatment options, we explored the internal mechanism of NSCLC. Firstly, the SOX2-OT/miR-30d-5p/PDK1 axis regulated by lncRNA SOX2-OT was predicted by bioinformatics methods, and the expression of SOX2-OT, miR-30d-5p, and PDK1 mRNA in cells were detected by qRT-PCR while PDK1 protein expression was detected by western blot. The results expressed that in NSCLC, SOX2-OT, and PDK1 were notably overexpressed while miR-30d-5p was markedly under-expressed. The interaction between them was verified by dual-luciferase reporter and RNA binding protein immunoprecipitation assays. Subsequently, through CCK8, scratch healing, cell invasion and flow cytometry assays, we revealed that inhibiting the expression of SOX2-OT could inhibit the proliferation, migration and invasion of NSCLC cells and promote cell apoptosis; while simultaneous overexpression of PDK1 or inhibition of miR-30d-5p expression could reverse the inhibitory effect of SOX2-OT silence-mediated malignant progression of NSCLC cells. Then, the combined application of overexpressed PDK1 and rapamycin verified that PDK1 could regulate the expression of PD-L1 in NSCLC cells through the mTOR signaling pathway. Co-culture of CD8+ T cells verified that silencing SOX2-OT could inhibit the apoptosis of CD8+ T cells through miR-30d-5p/PDK1. Finally, tumor formation assay in animals confirmed that overexpression of SOX2-OT could promote the growth of NSCLC tumor in vivo. In this study, assays in vitro and in vivo were conducted to elucidate the mechanism by which SOX2-OT/miR-30d-5p/PDK1 drives PD-L1 through the mTOR signaling pathway to promote the malignant progression and immune escape of NSCLC.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Currently, treatment methods generally cause poor prognosis. Therefore, in order to seek new treatment options, we explored the internal mechanism of NSCLC. Firstly, the SOX2-OT/miR-30d-5p/PDK1 axis regulated by lncRNA SOX2-OT was predicted by bioinformatics methods, and the expression of SOX2-OT, miR-30d-5p, and PDK1 mRNA in cells were detected by qRT-PCR while PDK1 protein expression was detected by western blot. The results expressed that in NSCLC, SOX2-OT, and PDK1 were notably overexpressed while miR-30d-5p was markedly under-expressed. The interaction between them was verified by dual-luciferase reporter and RNA binding protein immunoprecipitation assays. Subsequently, through CCK8, scratch healing, cell invasion and flow cytometry assays, we revealed that inhibiting the expression of SOX2-OT could inhibit the proliferation, migration and invasion of NSCLC cells and promote cell apoptosis; while simultaneous overexpression of PDK1 or inhibition of miR-30d-5p expression could reverse the inhibitory effect of SOX2-OT silence-mediated malignant progression of NSCLC cells. Then, the combined application of overexpressed PDK1 and rapamycin verified that PDK1 could regulate the expression of PD-L1 in NSCLC cells through the mTOR signaling pathway. Co-culture of CD8+ T cells verified that silencing SOX2-OT could inhibit the apoptosis of CD8+ T cells through miR-30d-5p/PDK1. Finally, tumor formation assay in animals confirmed that overexpression of SOX2-OT could promote the growth of NSCLC tumor in vivo. In this study, assays in vitro and in vivo were conducted to elucidate the mechanism by which SOX2-OT/miR-30d-5p/PDK1 drives PD-L1 through the mTOR signaling pathway to promote the malignant progression and immune escape of NSCLC. [ABSTRACT FROM AUTHOR]

Anorectal malformations (ARMs) are among the most common congenital terminal digestive tract malformations. Circular RNAs (circRNAs), a novel type of endogenous non-coding RNAs, play roles in the development of the digestive system; however, their contributions to the pathogenesis of ARMs are not well-established. In this study, we explored the mechanism underlying ethylenethiourea (ETU)-induced ARMs by profiling circRNA expression via RNA-seq and constructing a regulatory circRNA-miRNA-mRNA network. Nine pregnant rats were gavage-fed a single dose of 125 mg/kg 1% ETU (ARM group) on gestational day 10 (GD10), and another 9 pregnant rats received a similar dose of saline (normal group) as a control. Embryos were obtained by cesarean section on the key time-points of anorectal development (GD14, GD15, and GD16). Hindgut samples isolated from the fetuses were evaluated by high-throughput sequencing and differentially expressed circRNAs were validated by reverse transcription-quantitative polymerase chain reaction, agarose gel electrophoresis, and Sanger cloning and sequencing. A total of 18295 circRNAs were identified in the normal and ARM groups. Based on the 425 differentially expressed circRNAs (|Fc| > 2, p < 0.05), circRNA-miRNA and miRNA-mRNA pairs were predicted using miREAP, miRanda, and TargetScan. A total of 55 circRNAs (14 up- and 41 downregulated in the ARM group compared to the normal group) were predicted to bind to 195 miRNAs and 947 mRNAs. Competing endogenous RNA networks and a Kyoto Encyclopedia of Genes and Genomes analysis revealed that novel_circ_001042 had the greatest connectivity and was closely related to ARM-associated signaling pathways, such as the Wingless Type MMTV integration site family, mitogen-activated protein kinase, and transforming growth factor-β pathways. These results provide original insight into the roles of circRNAs in ARMs and provide a valuable resource for further analyses of molecular mechanisms and signaling networks.

Glioblastoma accounts for 45.2% of central nervous system tumors. Despite the availability of multiple treatments (e.g., surgery, radiotherapy, chemotherapy, biological therapy, immunotherapy, and electric field therapy), glioblastoma has a poor prognosis, with a 5-year survival rate of approximately 5%. The pathogenesis and prognostic markers of this cancer are currently unclear. To this end, this study aimed to explore the pathogenesis of glioblastoma and identify potential prognostic markers. We used data from the GEO and TCGA databases and identified five genes (ITGA5, MMP9, PTPRN, PTX3, and STX1A) that could affect the survival rate of glioblastoma patients and that were differentially expressed between glioblastoma patients and non-tumors groups. Based on a variety of bioinformatics tools for reverse prediction of target genes associated with the prognosis of GBM, a ceRNA network of messenger RNA (STX1A, PTX3, MMP9)-microRNA (miR-9-5p)-long non-coding RNA (CRNDE) was constructed. Finally, we identified five potential therapeutic drugs (bacitracin, hecogenin, clemizole, chrysin, and gibberellic acid) that may be effective treatments for glioblastoma.

Anorectal malformations (ARMs) are among the most common congenital terminal digestive tract malformations. Circular RNAs (circRNAs), a novel type of endogenous non-coding RNAs, play roles in the development of the digestive system; however, their contributions to the pathogenesis of ARMs are not well-established. In this study, we explored the mechanism underlying ethylenethiourea (ETU)-induced ARMs by profiling circRNA expression via RNA-seq and constructing a regulatory circRNA-miRNA-mRNA network. Nine pregnant rats were gavage-fed a single dose of 125 mg/kg 1% ETU (ARM group) on gestational day 10 (GD10), and another 9 pregnant rats received a similar dose of saline (normal group) as a control. Embryos were obtained by cesarean section on the key time-points of anorectal development (GD14, GD15, and GD16). Hindgut samples isolated from the fetuses were evaluated by high-throughput sequencing and differentially expressed circRNAs were validated by reverse transcription-quantitative polymerase chain reaction, agarose gel electrophoresis, and Sanger cloning and sequencing. A total of 18295 circRNAs were identified in the normal and ARM groups. Based on the 425 differentially expressed circRNAs (|Fc| > 2, p < 0.05), circRNA-miRNA and miRNA-mRNA pairs were predicted using miREAP, miRanda, and TargetScan. A total of 55 circRNAs (14 up- and 41 downregulated in the ARM group compared to the normal group) were predicted to bind to 195 miRNAs and 947 mRNAs. Competing endogenous RNA networks and a Kyoto Encyclopedia of Genes and Genomes analysis revealed that novel_circ_001042 had the greatest connectivity and was closely related to ARM-associated signaling pathways, such as the Wingless Type MMTV integration site family, mitogen-activated protein kinase, and transforming growth factor-β pathways. These results provide original insight into the roles of circRNAs in ARMs and provide a valuable resource for further analyses of molecular mechanisms and signaling networks. [ABSTRACT FROM AUTHOR]

Glioblastoma accounts for 45.2% of central nervous system tumors. Despite the availability of multiple treatments (e.g., surgery, radiotherapy, chemotherapy, biological therapy, immunotherapy, and electric field therapy), glioblastoma has a poor prognosis, with a 5-year survival rate of approximately 5%. The pathogenesis and prognostic markers of this cancer are currently unclear. To this end, this study aimed to explore the pathogenesis of glioblastoma and identify potential prognostic markers. We used data from the GEO and TCGA databases and identified five genes (ITGA5 , MMP9 , PTPRN , PTX3 , and STX1A) that could affect the survival rate of glioblastoma patients and that were differentially expressed between glioblastoma patients and non-tumors groups. Based on a variety of bioinformatics tools for reverse prediction of target genes associated with the prognosis of GBM, a ceRNA network of messenger RNA (STX1A, PTX3, MMP9)-microRNA (miR-9-5p)-long non-coding RNA (CRNDE) was constructed. Finally, we identified five potential therapeutic drugs (bacitracin, hecogenin, clemizole, chrysin, and gibberellic acid) that may be effective treatments for glioblastoma. [ABSTRACT FROM AUTHOR]

AimsActivation of cardiac fibroblasts (CF) is crucial to cardiac fibrosis. We constructed a cardiac fibroblast-related competing endogenous RNA (ceRNA) network. Potential functions related to fibrosis of “hub genes” in this ceRNA network were explored.Materials and MethodsThe Gene Expression Omnibus database was searched for eligible datasets. Differentially expressed messenger (m)RNA (DE-mRNA) and long non-coding (lnc)RNA (DE-lncRNA) were identified. microRNA was predicted and validated. A predicted ceRNA network was constructed and visualized by Cytoscape, and ceRNA crosstalk was validated. A Single Gene Set Enrichment Analysis (SGSEA) was done, and the Comparative Toxicogenomics Database (CTD) was employed to analyze the most closely associated pathways and diseases of DE-mRNA in the ceRNA network. The functions of DE-mRNA and DE-lncRNA in the ceRNA network were validated by small interfering (si)RNA depletion.ResultsThe GSE97358 and GSE116250 datasets (which described differentially expressed genes in human cardiac fibroblasts and failing ventricles, respectively) were used for analyses. Four-hundred-and-twenty DE-mRNA and 39 DE-lncRNA, and 369 DE-mRNA and 93 DE-lncRNA were identified, respectively, in the GSE97358 and GSE116250 datasets. Most of the genes were related to signal transduction, cytokine activity, and cell proliferation. Thirteen DE-mRNA with the same expression tendency were overlapped in the two datasets. Twenty-three candidate microRNAs were predicted and the expression of 11 were different. Only two DE-lncRNA were paired to any one of 11 microRNA. Finally, two mRNA [ADAM metallopeptidase domain 19, (ADAM19) and transforming growth factor beta induced, (TGFBI)], three microRNA (miR-9-5p, miR-124-3p, and miR-153-3p) and two lncRNA (LINC00511 and SNHG15) constituted our ceRNA network. siRNA against LINC00511 increased miR-124-3p and miR-9-5p expression, and decreased ADAM19 and TGFBI expression, whereas siRNA against SNHG15 increased miR-153-3p and decreased ADAM19 expression. ADAM19 and TGFBI were closely related to the TGF-β1 pathway and cardiac fibrosis, as shown by SGSEA and CTD, respectively. Depletion of two mRNA or two lncRNA could alleviate CF activation.ConclusionsThe CF-specific ceRNA network, including two lncRNA, three miRNA, and two mRNA, played a crucial role during cardiac fibrosis, which provided potential target genes in this field.

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

ObjectiveIntracerebral hemorrhage (ICH) is a subtype of stroke with high mortality and morbidity rates. Our aim was to comprehensively analyze transcriptome and proteome in an experimental ICH model.MethodsAll mice were divided into ICH model (n = 3) and sham groups (n = 3). ICH was induced by collagenase VII. The ipsilateral hemisphere was used for whole transcriptome and proteomics resequencing. After preprocessing, differentially expressed lncRNAs (DElncRNAs), mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and DEproteins between ICH and sham groups were identified. Functional enrichment analysis was performed using the clusterProfiler package, followed by protein–protein interaction (PPI) analysis. After that, the Pearson correlation coefficient between DEmRNAs and DElncRNAs or between DEmRNAs and DEproteins was calculated. DElncRNAs with similar functions were analyzed by the GOSemSim package. After prediction of DEmiRNA–DEmRNA and DElncRNA–DEmiRNA relationships, a competing endogenous RNA (ceRNA) network was constructed. Several DEmRNAs and DElncRNAs were validated in ipsilateral hemisphere tissues of the ICH model and control groups using RT-qPCR and western blot.ResultsBetween the ICH and sham groups, 31 DElncRNAs, 367 DEmRNAs, 35 DEmiRNAs, and 96 DEproteins were identified. DEmRNAs were mainly enriched in inflammation, such as cytokine–cytokine receptor interaction, IL-17, and TNF signaling pathways. A PPI network of DEmRNAs was constructed and hub genes were identified, such as IL6 (degree = 59), TNF (degree = 44), and CXCR2 (degree = 39). 24 DElncRNAs with similar functions were identified, including 15 up- and 9 down-regulated lncRNAs. After integration of DEmiRNA–DEmRNA and DElncRNA–DEmiRNA relationships, we constructed a ceRNA network, composed of 71 DEmRNAs, 17 DEmiRNAs, and 12 DElncRNAs. RT-qPCR and western blot results confirmed that C3, Fga, and Slc4a1 proteins were more lowly expressed and Penk was more highly expressed in ICH than control groups, which could become potential markers for ICH.ConclusionOur findings identified ICH-related DE-RNAs and proteins and potential molecular mechanisms of ICH by transcriptome resequencing and quantitative proteomic analyses.

Objective: Intracerebral hemorrhage (ICH) is a subtype of stroke with high mortality and morbidity rates. Our aim was to comprehensively analyze transcriptome and proteome in an experimental ICH model. Methods: All mice were divided into ICH model (n = 3) and sham groups (n = 3). ICH was induced by collagenase VII. The ipsilateral hemisphere was used for whole transcriptome and proteomics resequencing. After preprocessing, differentially expressed lncRNAs (DElncRNAs), mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and DEproteins between ICH and sham groups were identified. Functional enrichment analysis was performed using the clusterProfiler package, followed by protein–protein interaction (PPI) analysis. After that, the Pearson correlation coefficient between DEmRNAs and DElncRNAs or between DEmRNAs and DEproteins was calculated. DElncRNAs with similar functions were analyzed by the GOSemSim package. After prediction of DEmiRNA–DEmRNA and DElncRNA–DEmiRNA relationships, a competing endogenous RNA (ceRNA) network was constructed. Several DEmRNAs and DElncRNAs were validated in ipsilateral hemisphere tissues of the ICH model and control groups using RT-qPCR and western blot. Results: Between the ICH and sham groups, 31 DElncRNAs, 367 DEmRNAs, 35 DEmiRNAs, and 96 DEproteins were identified. DEmRNAs were mainly enriched in inflammation, such as cytokine–cytokine receptor interaction, IL-17, and TNF signaling pathways. A PPI network of DEmRNAs was constructed and hub genes were identified, such as IL6 (degree = 59), TNF (degree = 44), and CXCR2 (degree = 39). 24 DElncRNAs with similar functions were identified, including 15 up- and 9 down-regulated lncRNAs. After integration of DEmiRNA–DEmRNA and DElncRNA–DEmiRNA relationships, we constructed a ceRNA network, composed of 71 DEmRNAs, 17 DEmiRNAs, and 12 DElncRNAs. RT-qPCR and western blot results confirmed that C3, Fga, and Slc4a1 proteins were more lowly expressed and Penk was more highly expressed in ICH than control groups, which could become potential markers for ICH. Conclusion: Our findings identified ICH-related DE-RNAs and proteins and potential molecular mechanisms of ICH by transcriptome resequencing and quantitative proteomic analyses. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications. [ABSTRACT FROM AUTHOR]

Lymphoma is a systemic malignancy, originating from the lymphatic system, which accounts for 3 to 4% of all tumors. There are two major subtypes of lymphoma, namely, diffuse large B-cell lymphoma (DLBCL) and Hodgkin’s lymphoma (HL). Elucidation of the pathogenesis of these two lymphoma types is crucial for the identification of potential therapeutic targets. Compared with the corresponding knowledge of other diseases, the understanding of the regulatory networks involved in DLBCL and HL is relatively deficient. To address this, we comprehensively analyzed the mRNAs, lncRNAs, and miRNAs that were differentially expressed between normal and tumor samples of DLBCL and HL. In addition, functional enrichment analysis of the differentially expressed mRNAs was performed. We constructed two specific ceRNA networks of DLBCL and HL. The pathways enriched by dysregulated mRNAs in DLBCL and HL were mainly involved in immune responses, transcription process, and metabolism process. The ceRNA network analysis revealed that 45 ceRNAs were shared between the two ceRNA networks, including five pivotal lncRNAs (MALAT1, CTBP1-AS, THUMPD3-AS, PSMA3-AS1, and NUTM2A-AS1). In addition, we proposed a DLBCL survival risk model based on a DLBCL-specific network constructed by Lasso regression analysis. The model, which is based on eight mRNAs, exhibited excellent performance in regard to predicting outcomes in DLBCL patients, with a p value of 0.0017 and AUC of 0.9783. In summary, although the molecular mechanisms underlying tumorigenesis in DLBCL and HL were quite different, the same pivotal lncRNAs acted as key regulators. Our findings identify novel potential prognostic and therapeutic targets for DLBCL and HL.

Prostate cancer remains a significant cause of cancer-related deaths in male population. More recently, accumulating evidence continues to implicate long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in various types of cancers, including prostate cancer. The current study aimed to elucidate the role of lncRNA AGAP2-AS1/miR-195-5p/PDZ and LIM domain 5 (PDLIM5) in prostate cancer progression. Initially, microarray expression profiles were applied to screen differentially expressed lncRNAs/miRNAs/genes associated with prostate cancer. Dual-luciferase reporter and RNA pull-down/RIP assays were subsequently performed to explore the interactions among lncRNA AGAP2-AS1, miR-195-5p, and PDLIM5, after which their expression was detected in cancer tissues and cells. Next, gain- and loss-of-function approaches were employed to elucidate the mechanism of lncRNA AGAP2-AS1/miR-195-5p/PDLIM5 in the processes of cell proliferation, migration and invasion as well as tumor growth. LncRNA AGAP2-AS1 was found to be highly expressed in prostate cancer. Silencing of lncRNA AGAP2-AS1 contributed to the suppression of proliferation, migration and invasion of cancer cells in vitro. Besides, lncRNA AGAP2-AS1 could bind to miR-195-5p which targeted PDLIM5 and subsequently downregulated its expression, ultimately impeding the progression of prostate cancer. Additionally, lncRNA AGAP2-AS1 inhibition led to an up-regulated expression of miR-195-5p and down-regulated PDLIM5 expression, resulting in delayed tumor growth in vivo. Taken together, the key findings of our study demonstrated that lncRNA AGAP2-AS1 silencing exerted suppressive effects on the development of prostate cancer via the miR-195-5p-dependent downregulation of PDLIM5. Our findings highlighted the potential of lncRNA AGAP2-AS1 as a promising novel molecular target for prostate cancer therapy.

BackgroundLong non-coding RNAs (lncRNAs) play pivotal roles in various kinds of human diseases, especially in cancer. However, regulatory role, clinical significance and underlying mechanisms of lncRNAs in colorectal cancer (CRC) liver metastasis still remain largely unknown. This study aimed to report a novel lncRNA, lnc-HSD17B-11:1, and its functional role in CRC progression.Materials and methodsDifferentially expressed lnc-HSD17B11-1:1 was screened and identified from a lncRNA profile microarray. Quantitative real-time PCR was used to determine the expression levels and prognostic values of lncRNA in CRC cohorts. In vitro and in vivo functional experiments were performed to investigate the effects of lnc-HSD17B11-1:1 on tumor growth and metastasis in CRC. Mechanistically, Base Scope, bioinformatics analyses, dual luciferase reporter assay and RNA immunoprecipitation experiments were performed to confirm the association of lnc-HSD17B11-1:1 and miR-338-3p. Dual luciferase reporter assay, qRT-PCR and western blot analysis were performed to assess the relationships among lnc-HSD17B11-1:1, miR-338-3p, and MACC1.ResultsEvidently up-regulation of lnc-HSD17B11-1:1 in CRC primary tissues was correlated with the depth of invasion (p = 0.043), clinical stage (p = 0.027), distant metastasis (p = 0.003) and poor prognosis of patients with CRC. lnc-HSD17B11-1:1 promoted CRC cell proliferation, mobility and invasion in vitro and in vivo. Mechanistic analysis revealed that lnc-HSD17B11-1:1 may act as a competing endogenous RNA (ceRNA) by acting as a sponge for miR-338-3p to upregulate the expression of MACC1.ConclusionThese findings suggest that lnc-HSD17B11-1:1 promotes CRC progression through lnc-HSD17B11-1:1/miR-338-3p/MACC1 axis and this might serve as a new diagnostic marker or target for treatment of CRC.