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Purpose: To identify the hub genes that associated with chemo-radiotherapy sensitivity for cervical cancer and to explore the relationship between hub genes and various cellular processes and potential mechanism of cervical cancer. Methods: The gene expression data of 21 patients with CESC and the mRNA expression profiles of 296 patients with CESC were obtained from the Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. The potential functions and regulatory mechanisms of differentially expressed genes (DEGs) were identified using GO and KEGG enrichment analyses. Hub genes were identified using random survival forest analysis. The relationship between hub genes and various cellular processes was comprehensively analyzed. The expression of hub genes was assessed using clinical data extracted from the Human Protein Atlas (HPA) database. Results: A total of 139 and 13 DEGs were found to be upregulated and downregulated, respectively, in CESC. The six hub genes, namely, SELP, PIM2, CCL19, SDS, NRP1, and SF3A2, were significantly correlated with immune cell infiltration, chemotherapy sensitivity, disease-related genes, and enriched signaling pathways (all p-value < 0.05). A nomogram and calibration curve were generated using the six hub genes to predict prognosis with high accuracy. A regulatory network comprising TFs (ZBTB3) and mRNAs (NRP1/PIM2/SELP) and several competitive endogenous RNA (ceRNA) networks comprising mRNAs, miRNAs, and lncRNAs were constructed. Data from HPA indicated that the protein expression of the six hub genes differed significantly between patients with CESC and healthy individuals. Conclusion: Upregulation of SELP, PIM2, CCL19, SDS, NRP1, and SF3A2 is associated with radiotherapy sensitivity and is involved in various cellular processes in CESC. These six genes may serve as biomarkers for predicting the radiotherapy response and prognosis in patients with CESC. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is a malignant neoplasm which is classified into various types defined by underlying molecular factors such as estrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor positive (HER2+) and triple negative (TNBC). Early detection of ER+ and TNBC is crucial in the choice of diagnosis and appropriate treatment strategy. Here we report the key genes associated to ER+ and TNBC using RNA-Seq analysis and machine learning models. Three ER+ and TNBC RNA seq datasets comprising 164 patients in-toto were selected for standard NGS hierarchical data processing and data analyses protocols. Enrichment pathway analysis and network analysis was done and finally top hub genes were identified. To come with a reliable classifier which could distinguish the distinct transcriptome patterns associated to ER+ and TNBC, ML models were built employing Naïve Bayes, SVM and kNN. 1730 common DEG's exhibiting significant logFC values with 0.05 p-value threshold were identified. A list of top ten hub genes were screened on the basis of maximal clique centrality (MCC) which included CDC20, CDK1, BUB1, AURKA, CDCA8, RRM2, TTK, CENPF, CEP55 and NDC80.These genes were found to be involved in crucial cell cycle pathways. k-Nearest Neighbor (kNN) model was observed to be best classifier with accuracy 84%, specificity 66% and sensitivity 95% to differentiate between ER+ and TNBC RNA-Seq transcriptomes. Our screened list of 10 hub genes can thus help unearth novel molecular signatures implicated in ER+ and TNBC onset, prognosis and design of novel protocols for breast cancer diagnostics and therapeutics. [ABSTRACT FROM AUTHOR]

Background: We aimed to identify hub genes in chronic obstructive pulmonary disease (COPD) plasma through the exploration of a putative miRNA–mRNA regulatory network. Methods: Three datasets (GSE24709, GSE102915, GSE136390) were utilized to discern differentially expressed miRNAs (DEMs) between COPD and normal plasma. miRNET was employed to predict the potential targets of DEMs. Subsequent GO and KEGG analyses were conducted using DAVID. For the construction of the protein-protein interaction (PPI) network and screening of hub genes, STRING and Cytoscape were employed. The expression validation was assessed through GSE56768. Results: The results revealed 395 genes targeted by up-regulated DEMs and 234 genes targeted by down-regulated DEMs. The target genes exhibited significant enrichment in the PI3K-Akt signaling pathway and the p53 signaling pathway. Through the validation of hub genes' expression, we proposed two potential miRNA-mRNA interactions: miR-126-5p/miR-495-3p/miR-193b-3p - YWHAZ and miR-937-5p/miR-183-5p/miR-34c-5p/miR-98-5p/miR-525-3p/miR-215-5p - ACTB. Conclusions: In conclusion, our study posits potential miRNA-mRNA interactions in COPD by analyzing datasets from public databases, contributing valuable insights into the understanding of COPD pathogenesis and potential therapeutic avenues. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the leading commonly diagnosed cancer in the world, with complex mechanisms underlying its development. There is an urgent need to enlighten key genes as potential therapeutic targets crucial to advancing BC treatment. This study sought to investigate the influence of doxorubicin (DOX) on identified key genes consistent across numerous BC datasets obtained through bioinformatic analysis. To date, a meta-analysis of publicly available coding datasets for expression profiling by array from the Gene Expression Omnibus (GEO) has been carried out. Differentially Expressed Genes (DEGs) identified using GEO2R revealed a total of 23 common DEGs, including nine upregulated genes and 14 downregulated genes among the datasets of three platforms (GPL570, GPL6244, and GPL17586), and the commonly upregulated DEGs, showed significant enrichment in the cell cycle in KEGG analysis. The top nine genes, NUSAP1, CENPF, TPX2, PRC1, ANLN, BUB1B, AURKA, CCNB2, and CDK-1, with higher degree values and MCODE scores in the cytoscape program, were regarded as hub genes. The hub genes were activated in disease states commonly across all the subclasses of BC and correlated with the unfavorable overall survival of BC patients, as verified by the GEPIA and UALCAN databases. qRT-PCR confirmed that DOX treatment resulted in reduced expression of these genes in BC cell lines, which reinforces the evidence that DOX remains an effective drug for BC and suggests that developing modified formulations of doxorubicin to reduce toxicity and resistance, could enhance its efficacy as an effective therapeutic option for BC. [ABSTRACT FROM AUTHOR]

Purpose: Osteosarcoma (OS) is a primary bone tumor with high malignancy and poor prognosis. Ferroptosis plays a crucial role in OS. This study aimed to evaluate the effects of Ankyrin 1 (ANK1) on OS and to investigate its specific mechanisms. Methods: Microarray datasets related to "osteosarcoma" were selected for this study. Relevant hub genes in OS were identified through bioinformatics analysis. Transfected U-2OS and MG-63 cells were used for in vitro experiments. The effects of ANK1 overexpression on cell viability, migration, and invasion were determined through CCK-8, wound healing, and transwell assays. An OS mouse model was established for the in vivo experiments. Hematoxylin-eosin staining and immunohistochemistry were conducted to observe the histological effects of ANK1 overexpression on mouse tumors. TUNEL staining was performed to evaluate apoptosis in mouse. Results: There were 159 common differentially expressed genes in the GSE16088 and GSE19276 datasets. The hub genes ANK1, AHSP, GYPB, GYPA, KEL, and ALAS2 were identified. Pan-cancer analysis verified that ANK1 was closely associated with cancer prognosis and immune infiltration. Furthermore, ANK1 overexpression inhibited the proliferation, migration, and invasion of OS cells and promoted ferroptosis, while ferroptosis inhibitor (fer-1) weakened these effects. Moreover, ANK1 overexpression suppressed tumor growth, promoted apoptosis, reduced the number of Ki67 positive cells, and elevated the number of caspase-3 positive cells in vivo. Conclusions: ANK1 is a prognosis biomarker of OS that can alleviate the progression of OS by promoting ferroptosis. [ABSTRACT FROM AUTHOR]

Neutrophil infiltration plays a key role in the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP). However, pertinent mechanisms remain poorly elucidated. Here, we obtained the data from gene expression omnibus (GEO) and gene set enrichment analysis (GSEA) to identify and validate neutrophil-associated hub genes in CRSwNP. We found that four neutrophil-associated hub genes, namely ICAM1, IL-1β, TYROBP, and BCL2A1, were markedly upregulated and positively correlated with neutrophil infiltration levels in patients with CRSwNP. Subsequently, this was confirmed by real-time quantitative PCR. In conclusion, we identified the role of neutrophil infiltration in the pathophysiology of CRSwNP, which may be the potential targets for the diagnosis and treatment of CRSwNP. [ABSTRACT FROM AUTHOR]

The diet breadth of generalist herbivores when compared to specialists tends to be associated with greater transcriptional plasticity. Here, we consider whether it may also contribute to variation in host range among two generalists with different levels of polyphagy. We examined two related polyphagous spider mites with different host ranges, Tetranychus urticae (1200 plants) and Tetranychus truncatus (90 plants). Data from multiple populations of both species domesticated on common beans and transferred to new plant hosts (cotton, cucumber, eggplant) were used to investigate transcriptional plasticity relative to population‐based variation in gene expression. Compared to T. truncatus, T. urticae exhibited much higher transcriptional plasticity. Populations of this species also showed much more variable expression regulation in response to a plant host, particularly for genes related to detoxification, transport, and transcriptional factors. In response to the different plant hosts, both polyphagous species showed enriched processes of drug/xenobiotics metabolism, with T. urticae orchestrating a relatively broader array of biological pathways. Through co‐expression network analysis, we identified gene modules associated with host plant response, revealing shared hub genes primarily involved in detoxification metabolism when both mites fed on the same plants. After silencing a shared hub CYP gene related to eggplant exposure, the performance of both species on the original bean host improved, but the fecundity of T. truncatus decreased when feeding on eggplant. The extensive transcriptomic variation shown by T. urticae might serve as a potential compensatory mechanism for a deficiency of hub genes in this species. This research points to nuanced differences in transcriptomic variability between generalist herbivores. [ABSTRACT FROM AUTHOR]

Arsenic (As) contamination of agricultural soils poses a serious threat to crop productivity and food safety. Zinc oxide nanoparticles (ZnONPs) have emerged as a potential amendment for mitigating the adverse effects of As stress in plants. Soybean crop is mostly grown on marginalized land and is known for high accumulation of As in roots than others tissue. Therefore, this study aimed to elucidate the underlying mechanisms of ZnONPs in ameliorating arsenic toxicity in soybean. Our results demonstrated that ZnOB significantly improved the growth performance of soybean plants exposed to arsenic. This improvement was accompanied by a decrease (55%) in As accumulation and an increase in photosynthetic efficiency. ZnOB also modulated hormonal balance, with a significant increase in auxin (149%), abscisic acid (118%), gibberellin (160%) and jasmonic acid content (92%) under As(V) stress assuring that ZnONPs may enhance root growth and development by regulating hormonal signaling. We then conducted a transcriptomic analysis to understand further the molecular mechanisms underlying the NPs-induced As(V) tolerance. This analysis identified genes differentially expressed in response to ZnONPs supplementation, including those involved in auxin, abscisic acid, gibberellin, and jasmonic acid biosynthesis and signaling pathways. Weighted gene co-expression network analysis identified 37 potential hub genes encoding stress responders, transporters, and signal transducers across six modules potentially facilitated the efflux of arsenic from cells, reducing its toxicity. Our study provides valuable insights into the molecular mechanisms associated with metalloid tolerance in soybean and offers new avenues for improving As tolerance in contaminated soils. [ABSTRACT FROM AUTHOR]

Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes—FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5—were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study was to discern ferroptosis-related genes (FRGs) linked to non-obstructive azoospermia and investigate the associated molecular mechanisms. Method: A dataset related to azoospermia was retrieved from the Gene Expression Omnibus database, and FRGs were sourced from GeneCards. Ferroptosis-related differentially expressed genes (FRDEGs) were discerned. Subsequently, these genes underwent analyses encompassing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, as well as protein–protein interaction (PPI) networks and assessments of functional similarity. Following the identification of hub genes, an exploration of immune infiltration, single-cell expression, diagnostic utility, and interactions involving hub genes, RNA-binding proteins (RBPs), transcription factors (TFs), microRNAs (miRNAs), and drugs was conducted. Results: A total of 35 differentially expressed FRGs were discerned. These genes demonstrated enrichment in functions and pathways associated with ferroptosis. From the PPI network, eight hub genes were selected. Functional similarity analysis highlighted the potential pivotal roles of HMOX1 and GPX4 in azoospermia. Analysis of immune cell infiltration indicated a significant decrease in activated dendritic cells in the azoospermia group, with notable correlations between hub genes, particularly SAT1 and HMGCR, and immune cell infiltration. Unique expression patterns of hub genes across various cell types in the human testis were observed, with GPX4 prominently enriched in spermatid/sperm. Eight hub genes exhibited robust diagnostic value (AUC > 0.75). Lastly, a comprehensive hub gene-miRNA-TF-RBP-drug network was constructed. Conclusion: In summary, our investigation unveiled eight FRDEGs associated with azoospermia, which hold potential as biomarkers for the diagnosis and treatment of azoospermia. [ABSTRACT FROM AUTHOR]

Aims: Accumulating evidences indicate that abnormalities in tubular lipid metabolism play a crucial role in the development of diabetic kidney disease (DKD). We aim to identify novel lipid metabolism-related genes associated with tubular injury in DKD by utilizing bioinformatics approaches. Methods: Differentially expressed genes (DEGs) between control and DKD tubular tissue samples were screened from the Gene Expression Omnibus (GEO) database, and then were intersected with lipid metabolism-related genes. Hub genes were further determined by combined weighted gene correlation network analysis (WGCNA) and protein–protein interaction (PPI) network. We performed enrichment analysis, immune analysis, clustering analysis, and constructed networks between hub genes and miRNAs, transcription factors and small molecule drugs. Receiver operating characteristic (ROC) curves were employed to evaluate the diagnostic efficacy of hub genes. We validated the relationships between hub genes and DKD with external datasets and our own clinical samples. Results: There were 5 of 37 lipid metabolism-related DEGs identified as hub genes. Enrichment analysis demonstrated that lipid metabolism-related DEGs were enriched in pathways such as peroxisome proliferator-activated receptors (PPAR) signaling and pyruvate metabolism. Hub genes had potential regulatory relationships with a variety of miRNAs, transcription factors and small molecule drugs, and had high diagnostic efficacy. Immune infiltration analysis revealed that 13 immune cells were altered in DKD, and hub genes exhibited significant correlations with a variety of immune cells. Through clustering analysis, DKD patients could be classified into 3 immune subtypes and 2 lipid metabolism subtypes, respectively. The tubular expression of hub genes in DKD was further verified by other external datasets, and immunohistochemistry (IHC) staining showed that except ACACB, the other 4 hub genes (LPL, AHR, ME1 and ALOX5) exhibited the same results as the bioinformatics analysis. Conclusion: Our study identified several key lipid metabolism-related genes (LPL, AHR, ME1 and ALOX5) that might be involved in tubular injury in DKD, which provide new insights and perspectives for exploring the pathogenesis and potential therapeutic targets of DKD. [ABSTRACT FROM AUTHOR]

Glaucoma is a leading cause of permanent blindness, affecting 80 million people worldwide. Recent studies have emphasized the importance of neuroinflammation in the early stages of glaucoma, involving immune and glial cells. To investigate this further, we used the GSE27276 dataset from the GEO (Gene Expression Omnibus) database and neuroinflammation genes from the GeneCards database to identify differentially expressed neuroinflammation-related genes associated with primary open-angle glaucoma (POAG). Subsequently, these genes were submitted to Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analyses. Hub genes were picked out through protein-protein interaction networks and further validated using the external datasets (GSE13534 and GSE9944) and real-time PCR analysis. The gene–miRNA regulatory network, receiver operating characteristic (ROC) curve, genome-wide association study (GWAS), and regional expression analysis were performed to further validate the involvement of hub genes in glaucoma. A total of 179 differentially expressed genes were identified, comprising 60 upregulated and 119 downregulated genes. Among them, 18 differentially expressed neuroinflammation–related genes were found to overlap between the differentially expressed genes and neuroinflammation–related genes, with six genes (SERPINA3, LCN2, MMP3, S100A9, IL1RN, and HP) identified as potential hub genes. These genes were related to the IL-17 signaling pathway and tyrosine metabolism. The gene–miRNA regulatory network showed that these hub genes were regulated by 118 miRNAs. Notably, GWAS data analysis successfully identified significant single nucleotide polymorphisms (SNPs) corresponding to these six hub genes. ROC curve analysis indicated that our genes showed significant accuracy in POAG. The expression of these genes was further confirmed in microglia, Müller cells, astrocytes, and retinal ganglion cells in the Spectacle database. Moreover, three hub genes, SERPINA3, IL1R1, and LCN2, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the OGD/R-induced glaucoma model. This study suggests that the identified hub genes may influence the development of POAG by regulation of neuroinflammation, and it may offer novel insights into the management of POAG. [ABSTRACT FROM AUTHOR]

Objective: miR-497/195, located at 17p13.1, is a highly conserved miRNA cluster whose abnormal expression is a key regulator of carcinogenesis. We performed a comprehensive analysis of the miR-497/195 cluster to determine its prognostic utility and role in cervical cancer (CC) using publicly available datasets. Results: In silico analysis and validation revealed that this cluster is downregulated in CC. A total of 60 target genes of miR-497/195 cluster were identified as differentially expressed between normal and CC samples. ShinyGO, STRING, CytoHubba, Timer 2.0, HPA, and HCMBD were used for functional enrichment, PPIN network construction, hub gene identification, immune infiltration correlation, histopathological expression, and determination of the metastatic potential of miR-497/195 cluster and their target genes. PPIN analysis identified CCNE1, CCNE2, ANLN, RACGAP1, KIF23, CHEK1, CDC25A, E2F7, CDK1, and CEP55 as the top 10 hub genes (HGs). Furthermore, the upregulation of RECK, ATD5, and BCL2, downregulation of OSBPL3, RCAN3, and HIST1H3H effected overall survival of CC patients. We identified 6 targets (TFAP2A, CLSPN, RASEF, HIST1H3H, AKT3, and ITPR1) of miR-497/195 cluster to influence metastasis. In addition, 8 druggable genes and 38 potential drugs were also identified. Our study identified miR-497/195 cluster target genes and pathways that could be used for prognostic and therapeutic applications in CC. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. Its accurate pathogenic mechanisms are incompletely clarified, and effective therapeutic treatments are still inadequate. Autophagy is closely associated with AD and plays multiple roles in eliminating harmful aggregated proteins and maintaining cell homeostasis. This study identified 1191 differentially expressed genes (DEGs) based on the GSE5281 dataset from the GEO database, intersected them with 325 autophagy-related genes from GeneCards, and screened 26 differentially expressed autophagy-related genes (DEAGs). Subsequently, GO and KEGG enrichment analysis was performed and indicated that these DEAGs were primarily involved in autophagy–lysosomal biological process. Further, eight hub genes were determined by PPI construction, and experimental validation was performed by qRT-PCR on a SH-SY5Y cell model. Finally, three hub genes (TFEB, TOMM20, GABARAPL1) were confirmed to have potential application for biomarkers. A multigenic prediction model with good predictability (AUC = 0.871) was constructed in GSE5281 and validated in the GSE132903 dataset. Hub gene-targeted miRNAs closely associated with AD were also retrieved through the miRDB and HDMM database, predicting potential therapeutic agents for AD. This study provides new insights into autophagy-related genes in brain tissues of AD patients and offers more candidate biomarkers for AD mechanistic research as well as clinical diagnosis. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to integrate bioinformatics technology to explore shared hub genes and related mechanisms between diabetes and tuberculosis and to provide a theoretical basis for revealing the disease mechanisms in patients with both diabetes and tuberculosis. Methods: Differentially expressed genes and Venn analysis were used to identify shared genes between diabetes and tuberculosis. PPI network analysis was used to screen key hub genes. GO and KEGG analyses were used to analyze the potential biological functions of these key hub genes. Immune infiltration analysis was performed using the ssGSEA algorithm. EnrichR online analysis website was used to explore potential therapeutic drugs. Results: The dataset analysis showed that PSMB9, ISG15, RTP4, CXCL10, GBP2, and GBP3 were six hub genes shared by diabetes and tuberculosis, which not only could distinguish between the two disease samples but also had a high diagnostic rate. GO and KEGG analyses showed that these six genes mainly mediate immune-related biological processes such as interferon, interleukin, and chemokine receptor binding, as well as signaling pathways such as RIG-I-like receptor, NOD-like receptor, and proteasome. Immune infiltration analysis showed that high expression of TIL may mediate the development of both diabetes and tuberculosis. In addition, suloctidil HL60 UP, thioridazine HL60 UP, mefloquine HL60 UP, 1-NITROPYRENE CTD 00001569, and chlorophyllin CTD 00000324 were the candidate drugs predicted by this study that were most likely to target hub genes. Conclusion: Six differentially expressed genes shared by both diseases (PSMB9, ISG15, RTP4, CXCL10, GBP2, and GBP3) may play a key role in the disease progression of patients with both diabetes and tuberculosis. Candidate drugs targeting these hub genes have therapeutic potential and are worthy of further research. In summary, this study reveals potential shared pathogenic mechanisms between tuberculosis and diabetes. [ABSTRACT FROM AUTHOR]

The aetiology of bone metastasis in prostate cancer (PCa) remains unclear. This study aims to identify hub genes involved in this process. We utilized machine learning, GO, KEGG, GSEA, Single‐cell analysis, ROC methods to identify hub genes for bone metastasis in PCa using the TCGA and GEO databases. Potential drugs targeting these genes were identified. We validated these results using 16 specimens from patients with PCa and analysed the relationship between the hub genes and clinical features. The impact of APOC1 on PCa was assessed through in vitro experiments. Seven hub genes with AUC values of 0.727–0.926 were identified. APOC1, CFH, NUSAP1 and LGALS1 were highly expressed in bone metastasis tissues, while NR4A2, ADRB2 and ZNF331 exhibited an opposite trend. Immunohistochemistry further confirmed these results. The oxidative phosphorylation pathway was significantly enriched by the identified genes. Aflatoxin B1, benzo(a)pyrene, cyclosporine were identified as potential drugs. APOC1 expression was correlated with clinical features of PCa metastasis. Silencing APOC1 significantly inhibited PCa cell proliferation, clonality, and migration in vitro. This study identified 7 hub genes that potentially facilitate bone metastasis in PCa through mitochondrial metabolic reprogramming. APOC1 emerged as a promising therapeutic target and prognostic marker for PCa with bone metastasis. [ABSTRACT FROM AUTHOR]

Purpose: Age-related macular degeneration (AMD) is a chronic and progressive macular degenerative disease that culminates in a gradual deterioration of central vision. Despite its prevalence, the key biomarkers for AMD have not yet been fully elucidated. In this study, we aimed to efficiently identify biomarkers crucial for diagnosing AMD. Methods: Three datasets pertaining to retinal pigment epithelium(RPE)/choroid tissues associated withAMDwere selected from theGEOdatabase. The GSE50195 datasetwas utilized to conduct weighted gene co-expression network analysis (WGCNA) for identifying module genes linked to AMD. KEGG and GO enrichment analyses were subsequently conducted on these module genes. GSE29801 and GSE135092 datasets were subjected to differential expression analysis to pinpoint the DEGs intersecting with the module genes. Subsequently, wet AMD (wAMD) and dry AMD (dAMD) mouse models were developed, from which RPE/choroid tissues were harvested to validate the hub genes via RT-qPCR and Western blot. Results: Using theWGCNA, we selected the "antiquewhite4" module (r=0.91 andp = 7e-07), which contains a total of 325 genes. Through the intersection ofmodule genes with DEGs, nine hub genes were identified. Pathways involved in complement and coagulation cascades, ECM-receptor interactions, unsaturated fatty acid biosynthesis, and fatty acid elongation play important roles in AMD. Notably, CDH18 demonstrated notable variance across all three datasets. Post validation using RT-qPCR experiments revealed a significant downregulation of CDH18 in both dAMDand wAMD. EGLN3was expressed at low levels in wAMD. In dAMD, EYA2, LTB, and PODXL were significantly downregulated, whereas APOC1 was notably upregulated. Western blot confirmed that CDH18 was lowly expressed in dAMD and wAMD mouse models. Conclusion: CDH18 was identified as the key gene involved in the pathogenesis of AMD. An imbalance of the complement and coagulation cascades is a potential mechanism of AMD. This study provides a novel idea for diagnosing and treating AMD in the future. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer has extremely high morbidity and mortality, and its pathogenesis is still in the exploratory stage. This study aimed to screen and identify differentially expressed genes (DEGs) related to cervical cancer through bioinformatics analysis. Methods: GSE63514 and GSE67522 were selected from the GEO database to screen DEGs. Then GO and KEGG analysis were performed on DEGs. PPI network of DEGs was constructed through STRING website, and the hub genes were found through 12 algorithms of Cytoscape software. Meanwhile, GSE30656 was selected from the GEO database to screen DEMs. Target genes of DEMs were screened through TagetScan, miRTarBase and miRDB. Next, the hub genes screened from DEGs were merged with the target genes screened from DEMs. Finally, ROC curve and nomogram analysis were performed to assess the predictive capabilities of the hub genes. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry. Results: Six hub genes, TOP2A, AURKA, CCNA2, IVL, KRT1, and IGFBP5, were mined through the protein-protein interaction network. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry, and it was found that TOP2A, AURKA as well as CCNA2 were overexpressed and IGFBP5 was low expression in cervical cancer. Conclusions: This study showed that TOP2A, AURKA, CCNA2 and IGFBP5 screened through bioinformatics analysis were significantly differentially expressed in cervical cancer samples compared with normal samples, which might be biomarkers of cervical cancer. [ABSTRACT FROM AUTHOR]

Background: The heightened risk of cardiovascular and cerebrovascular events is associated with the increased instability of atherosclerotic plaques. However, the lack of effective diagnostic biomarkers has impeded the assessment of plaque instability currently. This study was aimed to investigate and identify hub genes associated with unstable plaques through the integration of various bioinformatics tools, providing novel insights into the detection and treatment of this condition. Methods: Weighted Gene Co-expression Network Analysis (WGCNA) combined with two machine learning methods were used to identify hub genes strongly associated with plaque instability. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was utilized to assess immune cell infiltration patterns in atherosclerosis patients. Additionally, Gene Set Variation Analysis (GSVA) was conducted to investigate the potential biological functions, pathways, and mechanisms of hub genes associated with unstable plaques. To further validate the diagnostic efficiency and expression of the hub genes, immunohistochemistry (IHC), quantitative real-time polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assay (ELISA) were performed on collected human carotid plaque and blood samples. Immunofluorescence co-staining was also utilized to confirm the association between hub genes and immune cells, as well as their colocalization with mitochondria. Results: The CIBERSORT analysis demonstrated a significant decrease in the infiltration of CD8 T cells and an obvious increase in the infiltration of M0 macrophages in patients with atherosclerosis. Subsequently, two highly relevant modules (blue and green) strongly associated with atherosclerotic plaque instability were identified. Through intersection with mitochondria-related genes, 50 crucial genes were identified. Further analysis employing least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithms revealed six hub genes significantly associated with plaque instability. Among them, NT5DC3, ACADL, SLC25A4, ALDH1B1, and MAOB exhibited positive correlations with CD8 T cells and negative correlations with M0 macrophages, while kynurenine 3-monooxygenas (KMO) demonstrated a positive correlation with M0 macrophages and a negative correlation with CD8 T cells. IHC and RT-qPCR analyses of human carotid plaque samples, as well as ELISA analyses of blood samples, revealed significant upregulation of KMO and MAOB expression, along with decreased ALDH1B1 expression, in both stable and unstable samples compared to the control samples. However, among the three key genes mentioned above, only KMO showed a significant increase in expression in unstable plaque samples compared to stable plaque samples. Furthermore, the expression patterns of KMO in human carotid unstable plaque tissues and cultured mouse macrophage cell lines were assessed using immunofluorescence co-staining techniques. Finally, lentivirus-mediated KMO silencing was successfully transduced into the aortas of high-fat-fed ApoE-/- mice, with results indicating that KMO silencing attenuated plaque formation and promoted plaque stability in ApoE-/- mice. Conclusions: The results suggest that KMO, a mitochondria-targeted gene associated with macrophage cells, holds promise as a valuable diagnostic biomarker for assessing the instability of atherosclerotic plaques. [ABSTRACT FROM AUTHOR]

Background: Obesity and gastric cancer (GC) are prevalent diseases worldwide. In particular, the number of patients with obesity is increasing annually, while the incidence and mortality rates of GC are ranked high. Consequently, these conditions seriously affect the quality of life of individuals. While evidence suggests a strong association between these two conditions, the underlying mechanisms of this comorbidity remain unclear. Methods: We obtained the gene expression profiles of GSE94752 and GSE54129 from the Gene Expression Omnibus database. To investigate the associated biological processes, pathway enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes for the shared differentially expressed genes in obesity and GC. A protein-protein interaction (PPI) network was subsequently established based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, followed by the screening of the core modules and central genes in this network using Cytoscape plug-in MCODE. Furthermore, we scrutinized the co-expression network and the interplay network of transcription factors (TFs), miRNAs, and mRNAs linked to these central genes. Finally, we conducted further analyses using different datasets to validate the significance of the hub genes. Results: A total of 246 shared differentially expressed genes (209 upregulated and 37 downregulated) were selected for ensuing analyses. Functional analysis emphasized the pivotal role of inflammation and immune-associated pathways in these two diseases. Using the Cytoscape plug-in CytoHubba, nine hub genes were identified, namely, CXCR4, CXCL8, CXCL10, IL6, TNF, CCL4, CXCL2, CD4, and CCL2. IL6 and CCL4 were confirmed as the final hub genes through validation using different datasets. The TF-miRNA-mRNA regulatory network showed that the TFs primarily associated with the hub genes included RELA and NFKB1, while the predominantly associated miRNAs included has-miR-195-5p and has-miR-106a-5p. Conclusion: Using bioinformatics methods, we identified two hub genes from the Gene Expression Omnibus datasets for obesity and GC. In addition, we constructed a network of hub genes, TFs, and miRNAs, and identified the major related TFs and miRNAs. These factors may be involved in the common molecular mechanisms of obesity and GC. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a condition that affects the nervous system and that requires considerably more in-depth study. Abnormal Nicotinamide Adenine Dinucleotide (NAD+) metabolism and disulfide levels have been demonstrated in AD. This study investigated novel hub genes for disulfide levels and NAD+ metabolism in relation to the diagnosis and therapy of AD. Methods: Data from the gene expression omnibus (GEO) database were analyzed. Hub genes related to disulfide levels, NAD+ metabolism, and AD were identified from overlapping genes for differentially expressed genes (DEGs), genes in the NAD+ metabolism or disulfide gene sets, and module genes obtained by weighted gene co-expression network analysis (WGCNA). Pathway analysis of these hub genes was performed by Gene Set Enrichment Analysis (GSEA). A diagnostic model for AD was constructed based on the expression level of hub genes in brain samples. CIBERSORT was used to evaluate immune cell infiltration and immune factors correlating with hub gene expression. The DrugBank database was also used to identify drugs that target the hub genes. Results: We identified 3 hub genes related to disulfide levels in AD and 9 related to NAD+ metabolism in AD. Pathway analysis indicated these 12 genes were correlated with AD. Stepwise regression analysis revealed the area under the curve (AUC) for the predictive model based on the expression of these 12 hub genes in brain tissue was 0.935, indicating good diagnostic performance. Additionally, analysis of immune cell infiltration showed the hub genes played an important role in AD immunity. Finally, 33 drugs targeting 10 hub genes were identified using the DrugBank database. Some of these have been clinically approved and may be useful for AD therapy. Conclusion: Hub genes related to disulfide levels and NAD+ metabolism are promising biomarkers for the diagnosis of AD. These genes may contribute to a better understanding of the pathogenesis of AD, as well as to improved drug therapy. [ABSTRACT FROM AUTHOR]

Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main methods used to control mosquito populations for malaria prevention. The efficacy of these strategies is threatened by the spread of insecticide resistance (IR), limiting the success of malaria control. Studies of the genetic evolution leading to insecticide resistance could enable the identification of molecular markers that can be used for IR surveillance and an improved understanding of the molecular mechanisms associated with IR. This study used a weighted gene co-expression network analysis (WGCNA) algorithm, a systems biology approach, to identify genes with similar co-expression patterns (modules) and hub genes that are potential molecular markers for insecticide resistance surveillance in Kenya and Benin. A total of 20 and 26 gene co-expression modules were identified via average linkage hierarchical clustering from Anopheles arabiensis and An. gambiae, respectively, and hub genes (highly connected genes) were identified within each module. Three specific genes stood out: serine protease, E3 ubiquitin-protein ligase, and cuticular proteins, which were top hub genes in both species and could serve as potential markers and targets for monitoring IR in these malaria vectors. In addition to the identified markers, we explored molecular mechanisms using enrichment maps that revealed a complex process involving multiple steps, from odorant binding and neuronal signaling to cellular responses, immune modulation, cellular metabolism, and gene regulation. Incorporation of these dynamics into the development of new insecticides and the tracking of insecticide resistance could improve the sustainable and cost-effective deployment of interventions. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese medicine is promising for managing challenging and complex disorders, including cancer, and in particular, saffron is applied in treating various cancer types. However, its potential therapeutic efficacy and active components in managing squamous cell carcinoma of the head and neck (HNSCC) remain unclear yet. Methods: Using network pharmacology approaches, active ingredients of saffron, their target genes, and HNSCC-related genes were identified. Enrichment analyses were conducted for determining molecular functions and pathways enriched by genes that overlapped between the saffron target gene set and the HNSCC gene set. Among the four known active ingredients of saffron, crocetin was found to have the strongest inhibitory impact on HNSCC, based on the findings of cell viability and migration assays. Therefore, the potential target genes of crocetin in HNSCC cells were examined using molecular docking experiments and were confirmed by qPCR. Results: Four active ingredients of saffron and 184 of their target genes were identified. Further, a total of 34 overlapping saffron-/HNSCC-associated targets related to the four active ingredients were screened, and crocetin was chosen for further investigation because it had the strongest inhibitory effect on HNSCC cells. Molecular docking experiments indicated that ESR1 and CCND1 were the target genes of crocetin. These results were confirmed through qPCR analysis, in which crocetin was found to lower the expression of the ESR1 and CCND1 genes in AMC-HN-8 and FaDu cells. Conclusion: According to our results, crocetin is a primary active anti-cancer component of saffron that may have potential in the development of novel HNSCC-treating medications. However, more thorough molecular research is necessary for confirming these results and elucidating the anti-cancer mechanism underlying saffron. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a common degenerative joint disorder that adversely affects the quality of life of patients. Identification of novel diagnostic biomarkers is pivotal for the early detection and prevention of OA. Dataset GSE185059 was selected from Gene Expression Omnibus database to obtain differentially expressed lncRNAs (DE-lncRNAs), mRNAs (DE-mRNAs), and circRNAs (DE-circRNAs) between OA and normal samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses as well as protein–protein interaction (PPI) network construction of DE-mRNAs were conducted. Hub genes were identified from PPI networks and validated by RT-qPCR. starBase database was utilized for predicting miRNAs binding with hub genes, selected DE-lncRNAs and DE-circRNAs, respectively. The competing endogenous RNA (ceRNA) networks were constructed. A total of 818 DE-mRNAs, 191 DE-lncRNAs, and 2053 DE-circRNAs were identified. The DE-mRNAs were significantly enriched in several inflammation-related GO terms and KEGG pathways such as positive regulation of cell–cell adhesion, TNF-alpha signaling pathway and NF-kappa B signaling pathway. Thirteen hub genes were identified, which were CFTR, GART, SMAD2, NCK1, TJP1, UBE2D1, EFTUD2, PRKACB, IL10, SNRPG, CHD4, RPS24, and SRSF6. OA-related DE-lncRNA/circRNA-miRNA-hub gene networks were constructed. We identified 13 hub genes and constructed the ceRNA networks related to OA, providing a theoretical basis for further research. [ABSTRACT FROM AUTHOR]

Background: Ulcerative colitis (UC) is a long-lasting idiopathic condition, but its precise mechanisms remain unclear. Meanwhile, evidence has demonstrated that ferroptosis seems to interlock with the progress of UC. This research sought to identify hub genes of UC related to ferroptosis. Methods: First, the relevant profiles for this article were obtained from GEO database. From the FerrDb, 479 genes linked to ferroptosis were retrieved. Using analysis of the difference and WGCNA on colonic samples from GSE73661, the remaining six hub genes linked to ferroptosis and UC were discovered. Through logistic regression analyses, the diagnostic model was constructed and was then evaluated by external validation using dataset GSE92415. Afterwards, the correlation between immune cell filtration in UC and hub genes was examined. Finally, a mice model of colitis was established, and the results were verified using qRT-PCR. Results: We acquired six hub genes linked to ferroptosis and UC. In order to create a diagnostic model for UC, we used logistic regression analysis to screen three of the six ferroptosis related genes (HIF1A, SLC7A11, and LPIN1). The ROC curve showed that the three hub genes had outstanding potential for disease diagnosis (AUC = 0.976), which was subsequently validated in samples from GSE92415 (AUC = 0.962) and blood samples from GSE3365 (AUC = 0.847) and GSE94648 (AUC = 0.769). These genes might be crucial for UC immunity based upon the results on the immune system. Furthermore, mouse samples examined using qRT-PCR also verified our findings. Conclusion: In conclusion, the findings have important implications for ferroptosis and UC, and these hub genes may also offer fresh perspectives on the aetiology and therapeutic approaches of UC. [ABSTRACT FROM AUTHOR]

Abstract Purpose To identify the hub genes that associated with chemo-radiotherapy sensitivity for cervical cancer and to explore the relationship between hub genes and various cellular processes and potential mechanism of cervical cancer. Methods The gene expression data of 21 patients with CESC and the mRNA expression profiles of 296 patients with CESC were obtained from the Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. The potential functions and regulatory mechanisms of differentially expressed genes (DEGs) were identified using GO and KEGG enrichment analyses. Hub genes were identified using random survival forest analysis. The relationship between hub genes and various cellular processes was comprehensively analyzed. The expression of hub genes was assessed using clinical data extracted from the Human Protein Atlas (HPA) database. Results A total of 139 and 13 DEGs were found to be upregulated and downregulated, respectively, in CESC. The six hub genes, namely, SELP, PIM2, CCL19, SDS, NRP1, and SF3A2, were significantly correlated with immune cell infiltration, chemotherapy sensitivity, disease-related genes, and enriched signaling pathways (all p-value

Abstract Breast cancer (BC) is a malignant neoplasm which is classified into various types defined by underlying molecular factors such as estrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor positive (HER2+) and triple negative (TNBC). Early detection of ER+ and TNBC is crucial in the choice of diagnosis and appropriate treatment strategy. Here we report the key genes associated to ER+ and TNBC using RNA-Seq analysis and machine learning models. Three ER+ and TNBC RNA seq datasets comprising 164 patients in-toto were selected for standard NGS hierarchical data processing and data analyses protocols. Enrichment pathway analysis and network analysis was done and finally top hub genes were identified. To come with a reliable classifier which could distinguish the distinct transcriptome patterns associated to ER+ and TNBC, ML models were built employing Naïve Bayes, SVM and kNN. 1730 common DEG’s exhibiting significant logFC values with 0.05 p-value threshold were identified. A list of top ten hub genes were screened on the basis of maximal clique centrality (MCC) which included CDC20, CDK1, BUB1, AURKA, CDCA8, RRM2, TTK, CENPF, CEP55 and NDC80.These genes were found to be involved in crucial cell cycle pathways. k-Nearest Neighbor (kNN) model was observed to be best classifier with accuracy 84%, specificity 66% and sensitivity 95% to differentiate between ER+ and TNBC RNA-Seq transcriptomes. Our screened list of 10 hub genes can thus help unearth novel molecular signatures implicated in ER+ and TNBC onset, prognosis and design of novel protocols for breast cancer diagnostics and therapeutics.

Abstract Neutrophil infiltration plays a key role in the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP). However, pertinent mechanisms remain poorly elucidated. Here, we obtained the data from gene expression omnibus (GEO) and gene set enrichment analysis (GSEA) to identify and validate neutrophil-associated hub genes in CRSwNP. We found that four neutrophil-associated hub genes, namely ICAM1, IL-1β, TYROBP, and BCL2A1, were markedly upregulated and positively correlated with neutrophil infiltration levels in patients with CRSwNP. Subsequently, this was confirmed by real-time quantitative PCR. In conclusion, we identified the role of neutrophil infiltration in the pathophysiology of CRSwNP, which may be the potential targets for the diagnosis and treatment of CRSwNP.

Abstract Purpose Osteosarcoma (OS) is a primary bone tumor with high malignancy and poor prognosis. Ferroptosis plays a crucial role in OS. This study aimed to evaluate the effects of Ankyrin 1 (ANK1) on OS and to investigate its specific mechanisms. Methods Microarray datasets related to “osteosarcoma” were selected for this study. Relevant hub genes in OS were identified through bioinformatics analysis. Transfected U-2OS and MG-63 cells were used for in vitro experiments. The effects of ANK1 overexpression on cell viability, migration, and invasion were determined through CCK-8, wound healing, and transwell assays. An OS mouse model was established for the in vivo experiments. Hematoxylin-eosin staining and immunohistochemistry were conducted to observe the histological effects of ANK1 overexpression on mouse tumors. TUNEL staining was performed to evaluate apoptosis in mouse. Results There were 159 common differentially expressed genes in the GSE16088 and GSE19276 datasets. The hub genes ANK1, AHSP, GYPB, GYPA, KEL, and ALAS2 were identified. Pan-cancer analysis verified that ANK1 was closely associated with cancer prognosis and immune infiltration. Furthermore, ANK1 overexpression inhibited the proliferation, migration, and invasion of OS cells and promoted ferroptosis, while ferroptosis inhibitor (fer-1) weakened these effects. Moreover, ANK1 overexpression suppressed tumor growth, promoted apoptosis, reduced the number of Ki67 positive cells, and elevated the number of caspase-3 positive cells in vivo. Conclusions ANK1 is a prognosis biomarker of OS that can alleviate the progression of OS by promoting ferroptosis.

Abstract Chikungunya virus (CHIKV) is a single-stranded RNA virus belonging to the genus Alphavirus and is responsible for causing Chikungunya fever, a type of arboviral fever. Despite extensive research, the pathogenic mechanism of CHIKV within host cells remains unclear. In this study, an in-silico approach was used to predict that CHIKV produces micro-RNAs that target host-specific genes associated with host cellular regulatory pathways. Putative micro-RNAs of CHIKV were predicted using the miRNAFold and Vmir RNA structure web servers, and secondary structure prediction was performed using RNAfold. Host-specific target genes were then predicted, and hub genes were identified using CytoHubba and module selection through MCODE. Functional annotations of hub genes revealed their association with various pathways, including osteoclast differentiation, neuroactive ligand-receptor interaction, and mRNA surveillance. We used the freely available dataset GSE49985 to determine the level of expression of host-specific target genes and found that two genes, F-box and leucine-rich repeat protein 16 (FBXL16) and retinoic acid receptor alpha (RARA), were down-regulated, while four genes, RNA binding protein with serine-rich domain 1 (RNPS1), RNA helicase and ATPase (UPF1), neuropeptide S receptor 1 (NPSR1), and vasoactive intestinal peptide receptor 1 (VIPR1), were up-regulated. These findings provide insight into novel miRNAs and hub genes associated with CHIKV infection and suggest potential targets for therapeutic intervention. Further experimental validation of these targets could lead to the development of effective treatments for CHIKV-mediated diseases.

Abstract Objective miR-497/195, located at 17p13.1, is a highly conserved miRNA cluster whose abnormal expression is a key regulator of carcinogenesis. We performed a comprehensive analysis of the miR-497/195 cluster to determine its prognostic utility and role in cervical cancer (CC) using publicly available datasets. Results In silico analysis and validation revealed that this cluster is downregulated in CC. A total of 60 target genes of miR-497/195 cluster were identified as differentially expressed between normal and CC samples. ShinyGO, STRING, CytoHubba, Timer 2.0, HPA, and HCMBD were used for functional enrichment, PPIN network construction, hub gene identification, immune infiltration correlation, histopathological expression, and determination of the metastatic potential of miR-497/195 cluster and their target genes. PPIN analysis identified CCNE1, CCNE2, ANLN, RACGAP1, KIF23, CHEK1, CDC25A, E2F7, CDK1, and CEP55 as the top 10 hub genes (HGs). Furthermore, the upregulation of RECK, ATD5, and BCL2, downregulation of OSBPL3, RCAN3, and HIST1H3H effected overall survival of CC patients. We identified 6 targets (TFAP2A, CLSPN, RASEF, HIST1H3H, AKT3, and ITPR1) of miR-497/195 cluster to influence metastasis. In addition, 8 druggable genes and 38 potential drugs were also identified. Our study identified miR-497/195 cluster target genes and pathways that could be used for prognostic and therapeutic applications in CC.

AIM: To prevent neovascularization in diabetic retinopathy (DR) patients and partially control disease progression. METHODS: Hypoxia-related differentially expressed genes (DEGs) were identified from the GSE60436 and GSE102485 datasets, followed by gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Potential candidate drugs were screened using the CMap database. Subsequently, a protein-protein interaction (PPI) network was constructed to identify hypoxia-related hub genes. A nomogram was generated using the rms R package, and the correlation of hub genes was analyzed using the Hmisc R package. The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve (ROC) curves. Finally, a hypoxia-related miRNA-transcription factor (TF)-Hub gene network was constructed using the NetworkAnalyst online tool. RESULTS: Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified, such as ruxolitinib, meprylcaine, and deferiprone. In addition, 8 hub genes were also identified: glycogen phosphorylase muscle associated (PYGM), glyceraldehyde-3-phosphate dehydrogenase spermatogenic (GAPDHS), enolase 3 (ENO3), aldolase fructose-bisphosphate C (ALDOC), phosphoglucomutase 2 (PGM2), enolase 2 (ENO2), phosphoglycerate mutase 2 (PGAM2), and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3). Based on hub gene predictions, the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs, 77 TFs, and hub genes. The results of ROC showed that the except for GAPDHS, the area under curve (AUC) values of the other 7 hub genes were greater than 0.758, indicating their favorable diagnostic performance. CONCLUSION: PYGM, GAPDHS, ENO3, ALDOC, PGM2, ENO2, PGAM2, and PFKFB3 are hub genes in DR, and hypoxia-related hub genes exhibited favorable diagnostic performance.

Abstract Background Cervical cancer has extremely high morbidity and mortality, and its pathogenesis is still in the exploratory stage. This study aimed to screen and identify differentially expressed genes (DEGs) related to cervical cancer through bioinformatics analysis. Methods GSE63514 and GSE67522 were selected from the GEO database to screen DEGs. Then GO and KEGG analysis were performed on DEGs. PPI network of DEGs was constructed through STRING website, and the hub genes were found through 12 algorithms of Cytoscape software. Meanwhile, GSE30656 was selected from the GEO database to screen DEMs. Target genes of DEMs were screened through TagetScan, miRTarBase and miRDB. Next, the hub genes screened from DEGs were merged with the target genes screened from DEMs. Finally, ROC curve and nomogram analysis were performed to assess the predictive capabilities of the hub genes. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry. Results Six hub genes, TOP2A, AURKA, CCNA2, IVL, KRT1, and IGFBP5, were mined through the protein-protein interaction network. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry, and it was found that TOP2A, AURKA as well as CCNA2 were overexpressed and IGFBP5 was low expression in cervical cancer. Conclusions This study showed that TOP2A, AURKA, CCNA2 and IGFBP5 screened through bioinformatics analysis were significantly differentially expressed in cervical cancer samples compared with normal samples, which might be biomarkers of cervical cancer.

Abstract YEATS domain containing 2 (YEATS2), it may function as a proto-oncogene. This study aims to investigate if YEATS2 correlates with prognosis in hepatocellular carcinoma. The prognostic landscape of YEATS2 and its relationship with expression in hepatocellular carcinoma were deciphered with public databases, RT-qPCR and western-blot in tissue samples. The expression profiling and prognostic value of YEATS2 were explored using UALCAN, TIMER, OncoLnc database. Transcription and survival analyses of YEATS2 in hepatocellular carcinoma were investigated with cBioPortal database. The STRING database was explored to identify molecular functions and signaling pathways downstream of YEATS2. YEATS2 expression was significantly higher in hepatocellular carcinoma compared with adjacent non-malignant tissues. Promoter methylation of YEATS2 exhibited different patterns in hepatocellular carcinoma. High expression of YEATS2 was associated with poorer survival. Mechanistically, YEATS2 was involved in mediating multiple biological processes including morphogenesis and migration.

Abstract Background The heightened risk of cardiovascular and cerebrovascular events is associated with the increased instability of atherosclerotic plaques. However, the lack of effective diagnostic biomarkers has impeded the assessment of plaque instability currently. This study was aimed to investigate and identify hub genes associated with unstable plaques through the integration of various bioinformatics tools, providing novel insights into the detection and treatment of this condition. Methods Weighted Gene Co-expression Network Analysis (WGCNA) combined with two machine learning methods were used to identify hub genes strongly associated with plaque instability. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was utilized to assess immune cell infiltration patterns in atherosclerosis patients. Additionally, Gene Set Variation Analysis (GSVA) was conducted to investigate the potential biological functions, pathways, and mechanisms of hub genes associated with unstable plaques. To further validate the diagnostic efficiency and expression of the hub genes, immunohistochemistry (IHC), quantitative real-time polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assay (ELISA) were performed on collected human carotid plaque and blood samples. Immunofluorescence co-staining was also utilized to confirm the association between hub genes and immune cells, as well as their colocalization with mitochondria. Results The CIBERSORT analysis demonstrated a significant decrease in the infiltration of CD8 T cells and an obvious increase in the infiltration of M0 macrophages in patients with atherosclerosis. Subsequently, two highly relevant modules (blue and green) strongly associated with atherosclerotic plaque instability were identified. Through intersection with mitochondria-related genes, 50 crucial genes were identified. Further analysis employing least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithms revealed six hub genes significantly associated with plaque instability. Among them, NT5DC3, ACADL, SLC25A4, ALDH1B1, and MAOB exhibited positive correlations with CD8 T cells and negative correlations with M0 macrophages, while kynurenine 3-monooxygenas (KMO) demonstrated a positive correlation with M0 macrophages and a negative correlation with CD8 T cells. IHC and RT-qPCR analyses of human carotid plaque samples, as well as ELISA analyses of blood samples, revealed significant upregulation of KMO and MAOB expression, along with decreased ALDH1B1 expression, in both stable and unstable samples compared to the control samples. However, among the three key genes mentioned above, only KMO showed a significant increase in expression in unstable plaque samples compared to stable plaque samples. Furthermore, the expression patterns of KMO in human carotid unstable plaque tissues and cultured mouse macrophage cell lines were assessed using immunofluorescence co-staining techniques. Finally, lentivirus-mediated KMO silencing was successfully transduced into the aortas of high-fat-fed ApoE-/- mice, with results indicating that KMO silencing attenuated plaque formation and promoted plaque stability in ApoE-/- mice. Conclusions The results suggest that KMO, a mitochondria-targeted gene associated with macrophage cells, holds promise as a valuable diagnostic biomarker for assessing the instability of atherosclerotic plaques.

Abstract Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main methods used to control mosquito populations for malaria prevention. The efficacy of these strategies is threatened by the spread of insecticide resistance (IR), limiting the success of malaria control. Studies of the genetic evolution leading to insecticide resistance could enable the identification of molecular markers that can be used for IR surveillance and an improved understanding of the molecular mechanisms associated with IR. This study used a weighted gene co-expression network analysis (WGCNA) algorithm, a systems biology approach, to identify genes with similar co-expression patterns (modules) and hub genes that are potential molecular markers for insecticide resistance surveillance in Kenya and Benin. A total of 20 and 26 gene co-expression modules were identified via average linkage hierarchical clustering from Anopheles arabiensis and An. gambiae, respectively, and hub genes (highly connected genes) were identified within each module. Three specific genes stood out: serine protease, E3 ubiquitin-protein ligase, and cuticular proteins, which were top hub genes in both species and could serve as potential markers and targets for monitoring IR in these malaria vectors. In addition to the identified markers, we explored molecular mechanisms using enrichment maps that revealed a complex process involving multiple steps, from odorant binding and neuronal signaling to cellular responses, immune modulation, cellular metabolism, and gene regulation. Incorporation of these dynamics into the development of new insecticides and the tracking of insecticide resistance could improve the sustainable and cost-effective deployment of interventions.

Background: The mechanism by which chondrocyte senescence aggravate OA progression has not yet been well elucidated. The aim of this study was to investigate the chondrocyte senescence related gene biosignatures in OA, and to analyze on the underlying mechanisms of senescence in OA. Materials and methods: We intersected osteoarthritis dataset GSE82107 from GEO database and senescence dataset from CellAge database of human senescence-associated genes based on genetic manipulations experiments plus gene expression profilin, and screened out 4 overlapping genes. The hub genes were verified in vitro and in human OA cartilage tissues by qRT-PCR. We further confirmed the function of mitogen-activated protein kinase 12 (MAPK12) and Fos proto-oncogene (FOS) in OA in vitro and in vivo by qRT-PCR, western blotting, Edu staining, immunofluorescence, SA-β-gal staining, HE, IHC, von frey test, and hot plate. Results: 1458 downregulated and 218 upregulated DEGs were determined from GSE82107, and 279 human senescence-associated genes were downloaded from CellAge database. After intersection assay, we screened out 4 overlapping genes, of which FOS, CYR61 and TNFSF15 were upregulated, MAPK12 was downregulated. The expression of MAPK12 was obviously downregulated, whereas the expression profiles of FOS, CYR61 and TNFSF15 were remarkedly upregulated in H2O2- or IL-1β-stimulated C28/I2 cells, human OA cartilage tissues, and knee cartilage of aging mice. Furthermore, both MAPK12 over-expression and FOS knock-down can promote cell proliferation and cartilage anabolism, inhibit cell senescence and cartilage catabolism, relieve joint pain in H2O2- or IL-1β-stimulated C28/I2 cells and mouse primary chondrocytes, destabilization of the medial meniscus (DMM) mice. Conclusion: This study explored that MAPK12 and FOS are involved in the occurrence and development of OA through modulating chondrocyte senescence. They might be biomarkers of OA chondrocyte senescence, and provides some evidence as subsequent possible therapeutic targets for OA. The translational potential of this article: The translation potential of this article is that we revealed MAPK12 and FOS can effectively alleviate OA by regulating chondrocyte senescence, and thus provided potential therapeutic targets for prevention or treatment of OA in the future.

Objective Increasing breast meat production is one of the primary goals of the broiler industry. Over the past few decades, tremendous progress has been made in genetic selection and the identification of candidate genes for improving the breast muscle mass. However, the molecular network contributing to muscle production traits in chickens still needs to be further illuminated. Methods A total of 150 1-day-old male 817 broilers were reared in a floor litter system. At the market age of 50 d, eighteen healthy 817 broilers were slaughtered and the left pectoralis major muscle sample from each bird was collected for RNA-seq sequencing. The birds were then plucked and eviscerated and the whole breast muscle was removed and weighed. Breast muscle yield was calculated as the ratio of the breast muscle weight to the eviscerated weight. To identify the co-expression networks and hub genes contributing to breast muscle yield in chickens, we performed weighted gene co-expression network analysis (WGCNA) based on the 18 transcriptome datasets of pectoralis major muscle from eighteen 817 broilers. Results The WGCNA analysis classified all co-expressed genes in the pectoral muscle of 817 broilers into 44 modules. Among these modules, the turquoise and skyblue3 modules were found to be most significantly positively (r = 0.78, p = 1e–04) and negatively (r = −0.57, p = 0.01) associated with breast meat yield, respectively. Further analysis identified several hub genes (e.g., DLX3, SH3RF2, TPM1, CAV3, MYF6, and CFL2) that involved in muscle structure and muscle development were identified as potential regulators of breast meat production. Conclusion The present study has advanced our understanding of the molecular regulatory networks contributing to muscle growth and breast muscle production and will contribute to the molecular breeding of chickens in the future.

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the "limma" package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. Cytohubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. subsequently, a series of bioinformatics analyses were employed, including protein--protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, AND PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MaPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is the predominant manifestation of inflammatory arthritis, distinguished by an increasing burden of morbidity and mortality. The intricate interplay of genes and signalling pathways involved in synovial inflammation in patients with RA remains inadequately comprehended. This study aimed to ascertain the role of necroptosis in RA, as along with their associations with immune cell infiltration. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify central genes for RA. In this study, identified total of 28 differentially expressed genes (DEGs) were identified in RA. Utilising WGCNA, two co-expression modules were generated, with one module demonstrating the strongest correlation with RA. Through the integration of differential gene expression analysis, a total of 5 intersecting genes were discovered. these 5 hub genes, namely fused in sarcoma (FUS), transformer 2 beta homolog (TRA2B), eukaryotic translation elongation factor 2 (EEF2), cleavage and polyadenylation specific factor 6 (CPSF6) and signal transducer and activator of transcription 3 (STAT3) were found to possess significant diagnostic value as determined by receiver operating characteristic (ROC) curve analysis. the close association between the concentrations of various immune cells is anticipated to contribute to the diagnosis and treatment of RA. Furthermore, the infiltration of immune cells mentioned earlier is likely to exert a substantial influence on the initiation of this disease. [ABSTRACT FROM AUTHOR]

Abstract Background Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis. Methods To investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients’ tumor tissues compared to controls. Results A total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients’ tumor tissues compared to controls. Conclusions To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.

Abstract Background Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia. Methods Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein–protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated. Results The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93). Conclusion We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.

The purpose of this research was to examine the interaction between metabolites of Lactobacillus plantarum YW11, characterized through GC–mass spectra (MS) analysis, and the FN1 protein in cases of familial pulmonary fibrosis, found from hub genes analysis. GC–MS analysis was performed to identify metabolites in L. plantarum. Then, gene expression analysis and functional annotations were conducted to investigate the hub genes. A network of hub genes and transcription factors (TFs) was constructed, highlighting the significance of FN1 in the disease’s etiology. Molecular docking was employed to explore the interaction between the characterized metabolites and the FN1 protein. Toxicity analysis was also carried out. Thirty-two active compounds of L. plantarum YW11 were characterized by GC–MS. The gene expression analysis identified 295 differentially expressed genes, including 10 hub genes and 6 TFs, providing further support for the involvement of FN1 protein in the disease. The results of the molecular docking studies suggest the therapeutic potential of targeting FN1, with the best docking result observed for the interaction between FN1 and the 2,4-di-tert-butylphenol metabolite (energy of −6.9 kcal/mol). The toxicity analysis and molecular dynamic simulations support the suitability of 2,4-di-tert-butylphenol as a candidate for targeting FN1.

Abstract Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. However, soil salinization becomes one of the main limiting factors of peanut production. Therefore, developing salt-tolerant varieties and understanding the molecular mechanisms of salt tolerance is important to protect peanut yield in saline areas. In this study, we selected four peanut varieties with contrasting response to salt challenges with T1 and T2 being tolerance and S1 and S2 being susceptible. High-throughput RNA sequencing resulted in more than 314.63 Gb of clean data from 48 samples. We identified 12,057 new genes, 7,971of which have functional annotations. KEGG pathway enrichment analysis of uniquely expressed genes in salt-tolerant peanut revealed that upregulated genes in the root are involved in the MAPK signaling pathway, fatty acid degradation, glycolysis/gluconeogenesis, and upregulated genes in the shoot were involved in plant hormone signal transduction and the MAPK signaling pathway. Na+ content, K+ content, K+/ Na+, and dry mass were measured in root and shoot tissues, and two gene co-expression networks were constructed based on weighted gene co-expression network analysis (WGCNA) in root and shoot. In this study, four key modules that are highly related to peanut salt tolerance in root and shoot were identified, plant hormone signal transduction, phenylpropanoid biosynthesis, starch and sucrose metabolism, flavonoid biosynthesis, carbon metabolism were identified as the key biological processes and metabolic pathways for improving peanut salt tolerance. The hub genes include genes encoding ion transport (such as HAK8, CNGCs, NHX, NCL1) protein, aquaporin protein, CIPK11 (CBL-interacting serine/threonine-protein kinase 11), LEA5 (late embryogenesis abundant protein), POD3 (peroxidase 3), transcription factor, and MAPKKK3. There were some new salt-tolerant genes identified in peanut, including cytochrome P450, vinorine synthase, sugar transport protein 13, NPF 4.5, IAA14, zinc finger CCCH domain-containing protein 62, beta-amylase, fatty acyl-CoA reductase 3, MLO-like protein 6, G-type lectin S-receptor-like serine/threonine-protein kinase, and kinesin-like protein KIN-7B. The identification of key modules, biological pathways, and hub genes in this study enhances our understanding of the molecular mechanisms underlying salt tolerance in peanuts. This knowledge lays a theoretical foundation for improving and innovating salt-tolerant peanut germplasm.

Abstract Allergic rhinitis is a prevalent inflammatory condition that impacts individuals of all age groups. Despite reports indicating the potential of berberine in alleviating allergic rhinitis symptoms, the specific molecular mechanisms and therapeutic targets of berberine remain unclear. This research aims to explore the pharmacological mechanism of berberine in the treatment of allergic rhinitis through bioinformatic analyses and experimental validation. The research utilized public databases to identify potential targets of berberine. Furthermore, differentially expressed genes (DEGs) related to allergic rhinitis were pinpointed from the GSE52804 dataset. Through bioinformatics techniques, the primary targets were discovered and key KEGG and GO-BP pathways were established. To confirm the therapeutic mechanisms of berberine on allergic rhinitis, an OVA-induced allergic rhinitis model was developed using guinea pigs. We identified 32 key genes responsible for the effectiveness of berberine in treating allergic rhinitis. In addition, five central genes (Alb, Il6, Tlr4, Ptas2, and Il1b) were pinpointed. Further examination using KEGG and GO-BP pathways revealed that the main targets were primarily involved in pathways such as NF-kappa B, IL-17, TNF, and inflammatory response. Molecular docking analysis demonstrated that berberine exhibited strong affinity towards these five key targets. Furthermore, the expression levels of IL-6, TLR4, PTGS2, and IL-1β were significantly upregulated in the model group but downregulated following berberine treatment. This research has revealed the mechanism through which berberine combats allergic rhinitis and has identified its potential to regulate pathways linked to inflammation. These discoveries provide valuable insights for the development of novel medications for the treatment of allergic rhinitis.

Abstract Background Pulmonary arterial hypertension (PAH) is a complex and progressive illness that has a multifaceted origin, significant fatality rates, and profound effects on health. The pathogenesis of PAH is poorly defined due to the insufficient understanding of the combined impact of endoplasmic reticulum (ER) stress and immune infiltration, both of which play vital roles in PAH development. This study aims to identify potential ER stress-related biomarkers in PAH and investigate their involvement in immune infiltration. Methods The GEO database was used to download gene expression profiles. Genes associated with ER stress were obtained from the MSigDB database. Weighted gene co-expression network analysis (WGCNA), GO, KEGG, and protein-protein interaction (PPI) were utilized to conduct screening of hub genes and explore potential molecular mechanisms. Furthermore, the investigation also delved into the presence of immune cells in PAH tissues and the correlation between hub genes and the immune system. Finally, we validated the diagnostic value and expression levels of the hub genes in PAH using subject-workup characterization curves and real-time quantitative PCR. Results In the PAH and control groups, a total of 31 genes related to ER stress were found to be differentially expressed. The enrichment analysis revealed that these genes were primarily enriched in reacting to stress in the endoplasmic reticulum, dealing with unfolded proteins, transporting proteins, and processing proteins within the endoplasmic reticulum. EIF2S1, NPLOC4, SEC61B, SYVN1, and DERL1 were identified as the top 5 hub genes in the PPI network. Immune infiltration analysis revealed that these hub genes were closely related to immune cells. The receiver operating characteristic (ROC) curves revealed that the hub genes exhibited excellent diagnostic efficacy for PAH. The levels of SEC61B, NPLOC4, and EIF2S1 expression were in agreement with the findings of bioinformatics analysis in the PAH group. Conclusions Potential biomarkers that could be utilized are SEC61B, NPLOC4, and EIF2S1, as identified in this study. The infiltration of immune cells was crucial to the development and advancement of PAH. This study provided new potential therapeutic targets for PAH.

Abstract Background Acute myocardial infarction (AMI) and diabetic nephropathy (DN) are common clinical co-morbidities, but they are challenging to manage and have poor prognoses. There is no research on the bioinformatics mechanisms of comorbidity, and this study aims to investigate such mechanisms. Methods We downloaded the AMI data (GSE66360) and DN datasets (GSE30528 and GSE30529) from the Gene Expression Omnibus (GEO) platform. The GSE66360 dataset was divided into two parts: the training set and the validation set, and GSE30529 was used as the training set and GSE30528 as the validation set. After identifying the common differentially expressed genes (DEGs) in AMI and DN in the training set, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and protein–protein interaction (PPI) network construction were performed. A sub-network graph was constructed by MCODE, and 15 hub genes were screened by the Cytohubba plugin. The screened hub genes were validated, and the 15 screened hub genes were subjected to GO, KEGG, Gene MANIA analysis, and transcription factor (TF) prediction. Finally, we performed TF differential analysis, enrichment analysis, and TF and gene regulatory network construction. Results A total of 46 genes (43 up-regulated and 3 down-regulated) were identified for subsequent analysis. GO functional analysis emphasized the presence of genes mainly in the vesicle membrane and secretory granule membrane involved in antigen processing and presentation, lipopeptide binding, NAD + nucleosidase activity, and Toll-like receptor binding. The KEGG pathways analyzed were mainly in the phagosome, neutrophil extracellular trap formation, natural killer cell-mediated cytotoxicity, apoptosis, Fc gamma R-mediated phagocytosis, and Toll-like receptor signaling pathways. Eight co-expressed hub genes were identified and validated, namely TLR2, FCER1G, CD163, CTSS, CLEC4A, IGSF6, NCF2, and MS4A6A. Three transcription factors were identified and validated in AMI, namely NFKB1, HIF1A, and SPI1. Conclusions Our study reveals the common pathogenesis of AMI and DN. These common pathways and hub genes may provide new ideas for further mechanistic studies.

Abstract Objective Duchenne muscular dystrophy (DMD) is a devastating X-linked neuromuscular disorder caused by various defects in the dystrophin gene and still no universal therapy. This study aims to identify the hub genes unrelated to excessive immune response but responsible for DMD progression and explore therapeutic siRNAs, thereby providing a novel treatment. Methods Top ten hub genes for DMD were identified from GSE38417 dataset by using GEO2R and PPI networks based on Cytoscape analysis. The hub genes unrelated to excessive immune response were identified by GeneCards, and their expression was further verified in mdx and C57 mice at 2 and 4 months (M) by (RT-q) PCR and western blotting. Therapeutic siRNAs were deemed as those that could normalize the expression of the validated hub genes in transfected C2C12 cells. Results 855 up-regulated and 324 down-regulated DEGs were screened from GSE38417 dataset. Five of the top 10 hub genes were considered as the candidate genes unrelated to excessive immune response, and three of these candidates were consistently and significantly up-regulated in mdx mice at 2 M and 4 M when compared with age-matched C57 mice, including Col1a2, Fbn1 and Fn1. Furthermore, the three validated up-regulated candidate genes can be significantly down-regulated by three rational designed siRNA (p

Abstract Background Parkinson’s disease (PD) is a progressive neurodegenerative disease with increasing prevalence. Effective diagnostic markers and therapeutic methods are still lacking. Exploring key molecular markers and mechanisms for PD can help with early diagnosis and treatment improvement. Methods Three datasets GSE174052, GSE77668, and GSE168496 were obtained from the GEO database to search differentially expressed circRNA (DECs), miRNAs (DEMis), and mRNAs (DEMs). GO and KEGG enrichment analyses, and protein–protein interaction (PPI) network construction were implemented to explore possible actions of DEMs. Hub genes were selected to establish circRNA-related competing endogenous RNA (ceRNA) networks. Results There were 1005 downregulated DECs, 21 upregulated and 21 downregulated DEMis, and 266 upregulated and 234 downregulated DEMs identified. The DEMs were significantly enriched in various PD-associated functions and pathways such as extracellular matrix organization, dopamine synthesis, PI3K-Akt, and calcium signaling pathways. Twenty-one hub genes were screened out, and a PD-related ceRNA regulatory network was constructed containing 31 circRNAs, one miRNA (miR-371a-3p), and one hub gene (KCNJ6). Conclusion We identified PD-related molecular markers and ceRNA regulatory networks, providing new directions for PD diagnosis and treatment.

Abstract Background Deciphering the genetic architecture of drought tolerance could allow the candidate genes identification responding to water stress. In the Andean Patagonian forest, the genus Nothofagus represents an ecologically relevant species to be included in different genomic studies. These studies are scarce in South American ecosystems however represent an important source of genomic data in order to interpret future climate-change environment scenarios of these emblematic forests. Here, we achieved the assemblage of the transcriptome of N. alpina while searching for key genes of activated or suppressed metabolic pathways in response to drought stress. Results De novo transcriptome assembly resulted in 104,030 transcripts. Following confirmation of drought conditions, based on reduction of leaf water potential and stomatal conductance, a differential gene expression analysis resulted in 2720 significantly expressed genes (1601 up-regulated and 1119 down-regulated). Enrichment analysis (over-representation analysis and gene set enrichment analysis) resulted in more than one hundred stress-responsive term ontologies (i.e. biological processes) and pathways. Terms such as response to abscisic acid and pathways such as plant hormone signal transduction or starch and sucrose metabolism were over-represented. Protein–protein interaction assessment resulted in networks with significantly expressed top common hub gene clusters (e.g. plant-type cell wall biogenesis among down-regulated or ABA-signalling among up-regulated). These networks evidenced important regulators at gene expression such as transcriptional factors. Conclusions Responses of N. alpina seedlings to drought stress were evidenced by the activation of several genes linked to GO biological processes and KEGG pathways, which were mainly based on over-expression of specific protein kinases, phosphatases, synthases and transcription factors. This suggests an up-regulation of signalling pathways, triggered through plant hormones such as abscisic acid or auxin, which could counteract the osmotic stress created as a probable immediate response to drought. On the other hand, groups of carbon fixation genes related to the galactose metabolism, photosynthesis, secondary wall biogenesis, and fatty acid biosynthesis degradation were down-regulated under drought. Overall, our results provide new genomic data for understanding how non-model long-lived trees of Patagonian forests would acclimate to environmental changes.