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Abstract Background Recombination signal-binding protein for immunoglobulin kappa J region (RBPJ) is a transcriptional regulator that plays an important role in maintaining immune homeostasis. This study aimed to estimate the expression of RBPJ in rheumatoid arthritis (RA) patients and investigate its relationship with RA. Methods A total of 83 newly diagnosed RA patients and 70 healthy controls were included. mRNA was extracted from peripheral blood mononuclear cells (PBMCs), and the expression of RBPJ was detected using quantitative real-time PCR (qRT‒PCR). An RA dataset (GSE89408) was obtained from the Gene Expression Omnibus (GEO) database, and RA synovial tissues were divided into two groups. The differentially expressed genes (DEGs) were selected with the “DESeq2” R package. Results RBPJ expression was lower in RA patients than in health controls and was negatively correlated with the DAS28 score, C-reactive protein (CRP) level and erythrocyte sedimentation rate (ESR). RA synovial tissues from GSE89408 were classified into RBPJ-low (≤ 25%) and RBPJ-high (≥ 75%) groups according to RBPJ expression, and 562 DEGs were identified. Gene Ontology (GO) enrichment analyses revealed that the DEGs significantly affected the regulation of T cell activation and lymphocyte/mononuclear cell differentiation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the most enriched pathways of DEGs were the T cell receptor signaling pathway, Th1/2 and Th17 cell differentiation, the PI3K − Akt signaling pathway and cytokine‒cytokine receptor interaction. CytoHubba Plugin revealed that most of the top 10 genes were involved in osteoclast differentiation, the T cell receptor signaling pathway and cytokine‒cytokine receptor interaction. Conclusions RBPJ expression was significantly lower in RA patients and negatively correlated with disease activity. GEO dataset analysis demonstrated that RBPJ may be involved in osteoclast differentiation, T cell activation and differentiation, and the T cell receptor signaling pathway. Our research may contribute to understanding the potential mechanisms by which RBPJ regulates T cell differentiation and cytokine‒cytokine receptor interaction in RA patients.

Abstract Background Pulmonary arterial hypertension (PAH) is a progressive disease of vascular remodeling characterized by persistent pulmonary arterial pressure elevation, which can lead to right heart failure and premature death. Given the complex pathogenesis and poor prognosis of PAH, the identification and investigation of biomarkers become increasingly critical for advancing further understanding of the disease. Methods PAH-related datasets, GSE49114, GSE180169 and GSE154959, were downloaded from the publicly available GEO database. By performing WGCNA on the GSE49114 dataset, a total of 906 PAH-related key module genes were screened out. By carrying out differential analysis on the GSE180169 dataset, a total of 576 differentially expressed genes were identified. Additionally, the GSE154959 single-cell sequencing dataset was also subjected to differential analysis, leading to the identification of 34 DEGs within endothelial cells. By taking intersection of the above three groups of DEGs, five PAH-related hub genes were screened out, namely Plvap, Cyp4b1, Foxf1, H2-Ab1, and H2-Eb1, among which H2-Ab1 was selected for subsequent experiments. Results A SuHx mouse model was prepared using the SU5416/hypoxia method, and the successful construction of the model was evaluated through Hematoxylin-Eosin staining, hemodynamic detection, fulton index, and Western Blot (WB). The results of WB and qRT-PCR demonstrated a significant upregulation of H2-Ab1 expression in SuHx mice. Consistent with the results of bioinformatics analysis, a time-dependent increase was observed in H2-Ab1 expression in hypoxia-treated mouse pulmonary artery endothelial cells (PAECs). To investigate whether H2-Ab1 affects the development and progression of PAH, we knocked down H2-Ab1 expression in PAECs, and found that its knockdown inhibited the viability, adhesion, migration, and angiogenesis, while concurrently promoted the apoptosis of PAECs. Conclusion H2-Ab1 could regulate the proliferation, apoptosis, adhesion, migration, and angiogenesis of PAECs.

Hypopharyngeal cancer is a disease that is associated with EGFR‐mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Background: Osteoarthritis (OA) can lead to chronic joint pain, and currently there are no methods available for complete cure. Utilizing the Gene Expression Omnibus (GEO) database for bioinformatics analysis combined with Mendelian randomization (MR) has been widely employed for drug repurposing and discovery of novel therapeutic targets. Therefore, our research focus is to identify new diagnostic markers and improved drug target sites. Methods: Gene expression data from different tissues of synovial membrane, cartilage and subchondral bone were collected through GEO data to screen out differential genes. Two-sample MR Analysis was used to estimate the causal effect of expression quantitative trait loci (eQTL) on OA. Through the intersection of the two, core genes were obtained, which were further screened by bioinformatics analysis for in vitro and in vivo molecular experimental verification. Finally, drug prediction and molecular docking further verified the medicinal value of drug targets. Results: In the joint analysis utilizing the GEO database and MR approach, five genes exhibited significance across both analytical methods. These genes were subjected to bioinformatics analysis, revealing their close association with immunological functions. Further refinement identified two core genes (ARL4C and GAPDH), whose expression levels were found to decrease in OA pathology and exhibited a protective effect in the MR analysis, thus demonstrating consistent trends. Support from in vitro and in vivo molecular experiments was also obtained, while molecular docking revealed favorable interactions between the drugs and proteins, in line with existing structural data. Conclusion: This study identified potential diagnostic biomarkers and drug targets for OA through the utilization of the GEO database and MR analysis. The findings suggest that the ARL4C and GAPDH genes may serve as therapeutic targets, offering promise for personalized treatment of OA. [ABSTRACT FROM AUTHOR]

Aim: Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characterized by the lacking estrogen receptors, progesterone receptors, and HER2 expression, making it challenging to treat with targeted therapies. Olaparib, a PARP inhibitor, has shown promise in treating TNBC, particularly in patients with BRCA1 or BRCA2 mutations. This study aims to elucidate the metabolic pathways affected by olaparib in TNBC using bioinformatics analysis. Material and Method: For bioinformatics analysis, mRNA microarray data of control MDA-MB-468 cells (non-treated) and OlaR MDAMB- 468 (3µM olaparib-treated MDA-MB-468 cells) with the study numbered GSE165914 were downloaded from Gene Expression Omnibus (GEO) database. GEO2R was used to analyze and identify differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto gene and genome encyclopedia (KEGG) analysis were carried out for DEGs to determine significant genes and the biological pathways influenced by olaparib treatment. Protein-protein interaction (PPI) network analysis further identified key proteins and interactions within these pathways. Results: For GEO2R analysis adjusted P-value<0.05 and |log2FC|>1.0 were selected. The results revealed the upregulation of 2277 genes and downregulation of 2298 genes in olaparib-treated cells compared to the controls. It was reported that DEGs enriched in pathways including, metabolic pathways, pathways in cancer, chemical carcinogenesis - reactive oxygen species, cell cycle, autophagy - animal, Efferocytosis and TNF signaling pathway. Both upregulated and downregulated DEGs were associated with metabolic pathways. Moreover, NDUFA5, NDUFA6, NDUFS6, NDUFB3, NDUFB10, NDUFB7, NDUFA7, NDUFA9, H2AC8, H2AC13, H2AC17, H4C11, H4C12, H2BC12, H2BC21 and H2BC4 were identified as the most significant candidate genes. Conclusion: This comprehensive bioinformatics approach provides insights into the molecular mechanisms of olaparib's action and identifies potential targets for combination therapies to enhance treatment efficacy in breast cancer. [ABSTRACT FROM AUTHOR]

AbstractAlzheimer’s disease (AD) is the most prevalent neurodegenerative disease. There are currently no effective interventions to slow down or prevent the occurrence and progression of AD. Neutrophil extracellular traps (NETs) have been proven to be tightly linked to AD. This project attempted to identify hub genes for AD based on NETs. Gene expression profiles of the training set and validation set were downloaded from the Gene Expression Omnibus (GEO) database, including non-demented (ND) controls and AD samples. NET-related genes (NETRGs) were collected from the literature. Differential analysis identified 21 AD differentially expressed NETRGs (AD-DE-NETRGs) majorly linked to functions such as defense response to bacterium as well as pathways including IL-17 signaling pathway, as evidenced by enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein–protein interaction (PPI) network, Minutia Cylinder-Code (MCC) algorithm, and molecular complex detection (MCODE) algorithm in the CytoHubba plug-in were employed to identify five hub genes (NFKBIA, SOCS3, CCL2, TIMP1, ACTB). Their diagnostic ability was validated in the validation set using receiver operating characteristic (ROC) curves and gene differential expression analysis. A total of 16 miRNAs and 132 lncRNAs were predicted through the mirDIP and ENCORI databases, and a lncRNA–miRNA–mRNA regulatory network was constructed using Cytoscape software. Small molecular compounds such as Benzo(a)pyrene and Copper Sulfate were predicted to target hub genes using the CTD database. This project successfully identified five hub genes, which may serve as potential biomarkers for AD, proffering clues for new therapeutic targets. [ABSTRACT FROM AUTHOR]

Crohn's disease (CD) presents with diverse clinical phenotypes due to persistent inflammation of the gastrointestinal tract. Its global incidence is on the rise. Neutrophil extracellular traps (NETs) are networks released by neutrophils that capture microbicidal proteins and oxidases targeting pathogens. Research has shown that NETs are implicated in the pathogenesis of several immune‐mediated diseases such as rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease. The goal of this study was to identify a panel of NET‐related genes to construct a diagnostic and therapeutic model for CD. Through analysis of the GEO database, we identified 1950 differentially expressed genes (DEGs) associated with CD. Gene enrichment and immune cell infiltration analyses indicate that neutrophil infiltrates and chemokine‐related pathways are predominantly involved in CD, with other immune cells such as CD4 and M1 macrophages also playing a role in disease progression. Utilizing weighted gene co‐expression network analysis (WGCNA) and protein–protein interaction (PPI) networks, we identified six hub genes (SPP1, SOCS3, TIMP1, IRF1, CXCL2 and CD274). To validate the accuracy of our model, we performed external validation with statistical differences(p < 0.05). Additionally, immunohistochemical experiments demonstrated higher protein expression of the hub genes in colonic tissues from CD patients compared to healthy subjects (p < 0.05). In summary, we identified six effective hub genes associated with NETs as potential diagnostic markers for CD. These markers not only offer targets for future research but also hold promise for the development of novel therapeutic interventions for CD. [ABSTRACT FROM AUTHOR]

Background: Pulmonary arterial hypertension (PAH) is a progressive disease of vascular remodeling characterized by persistent pulmonary arterial pressure elevation, which can lead to right heart failure and premature death. Given the complex pathogenesis and poor prognosis of PAH, the identification and investigation of biomarkers become increasingly critical for advancing further understanding of the disease. Methods: PAH-related datasets, GSE49114, GSE180169 and GSE154959, were downloaded from the publicly available GEO database. By performing WGCNA on the GSE49114 dataset, a total of 906 PAH-related key module genes were screened out. By carrying out differential analysis on the GSE180169 dataset, a total of 576 differentially expressed genes were identified. Additionally, the GSE154959 single-cell sequencing dataset was also subjected to differential analysis, leading to the identification of 34 DEGs within endothelial cells. By taking intersection of the above three groups of DEGs, five PAH-related hub genes were screened out, namely Plvap, Cyp4b1, Foxf1, H2-Ab1, and H2-Eb1, among which H2-Ab1 was selected for subsequent experiments. Results: A SuHx mouse model was prepared using the SU5416/hypoxia method, and the successful construction of the model was evaluated through Hematoxylin-Eosin staining, hemodynamic detection, fulton index, and Western Blot (WB). The results of WB and qRT-PCR demonstrated a significant upregulation of H2-Ab1 expression in SuHx mice. Consistent with the results of bioinformatics analysis, a time-dependent increase was observed in H2-Ab1 expression in hypoxia-treated mouse pulmonary artery endothelial cells (PAECs). To investigate whether H2-Ab1 affects the development and progression of PAH, we knocked down H2-Ab1 expression in PAECs, and found that its knockdown inhibited the viability, adhesion, migration, and angiogenesis, while concurrently promoted the apoptosis of PAECs. Conclusion: H2-Ab1 could regulate the proliferation, apoptosis, adhesion, migration, and angiogenesis of PAECs. [ABSTRACT FROM AUTHOR]

Hypopharyngeal cancer is a disease that is associated with EGFR‐mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is the most prevalent breast cancer subtype. Its prognosis is poor because there are no effective treatment targets. Despite several attempts, the molecular pathways of TNBC remain unknown, posing a significant clinical barrier in the search for viable targets. Two microarray datasets were used to identify possible targets for TNBC, GSE38959 and GSE45827, retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in TNBC samples compared with normal samples were identified using the GEO2R program. KEGG pathway enrichment and Gene Ontology functions were assessed for DEG pathways and functional annotation using ShinyGO 0.77. The STRING database and Cytoscape program were used for protein-protein interaction (PPI) analysis. Furthermore, we evaluated the predictive significance of hub gene expression in TNBC patients using the GEPIA2 online tool. We developed a comprehensive technique to assess whether intrinsically disordered regions (IDRs) are present in the TNBC hub genes. There were 48 DEGs were identified, all of which were upregulated. A putative protein complex containing these four core genes was selected for further analysis. Breast cancer patients with TTK, TOP2A, CENPF, and CCNA2 upregulation had a poor prognosis; TTK and CCNA2 were partially disordered, whereas TOP2A and CENPF were primarily disordered, according to IDR analysis. According to our study, TOP2A and CENPF may be useful therapeutic targets for disruption of the TNBC PPI network.

Objective To investigate the target of indirubin in psoriasis, and to elucidate the possible effect of indirubin on psoriasis through MAPK14. Methods The GSE30999 and GSE78097 psoriasis datasets downloaded from the Gene Expression Omnibus (GEO) database were used as training and validation sets, respectively. The differentially expressed genes were screened, and their biological functions and pathways were analyzed. Intersections with indirubin target genes were used to obtain differentially expressed indirubin target genes. Meanwhile gene correlation was analyzed and differential gene expression was verified. LASSO and Cox regression model were used to screen the best target genes for indirubin. Molecular docking of indirubin was performed to analyze the structural relationship. Results A total of 8 indirubin target genes were found in the GSE30999 dataset, which were differentially expressed compared with the control group. Seven genes had high diagnostic value (AUC>0.8), and one gene had low diagnostic value (AUC>0.6). GO and KEGG enrichment analyses showed that the eight indirubin target genes were mainly related to cell aging, chemical carcinogenic receptor activation, lipid and atherosclerosis, acute myeloid leukemia, and AGE-RAGE signaling pathways in diabetic complications. The best target gene of indirubin, MAPK14 gene, was screened by LASSO and Cox regression model. Molecular docking suggested that indirubin was tightly bound to MAPK14. Conclusion Indirubin may be involved in the regulation of the pathogenesis and treatment of psoriasis through MAPK14.

Abstract Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan–Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p

Xiaodan Yu,1 Meizhu Pan,2 Lili Jiang,1,&ast; Kuiran Liu1,&ast; 1Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China; 2Department of Gynecology, the First Affiliated Hospital of China Medical University, Shenyang, Liaoning, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Kuiran Liu; Lili Jiang, Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Shenyang, Liaoning Province, 110004, People’s Republic of China, Email liukr0412@163.com; jiangll311@163.comPurpose: This study focuses on evaluating the prognostic value of the NDC80 kinetochore complex in ovarian cancer (OC) using the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database and reveals the relationship between the NDC80 complex and immune infiltrates in OC.Methods: We collected data on NDC80 complex expression levels in both OC tissues and non-OC ovarian tissues from the University of California Santa Cruz Xena and GEO databases. The clinicopathological characteristics correlated with overall survival were analyzed using Cox regression and the Kaplan–Meier method. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis and CIBERSORT were performed using data from TCGA database. Immunohistochemical staining was used to verify the higher expression level of NUF2 protein in OC in vitro. Meanwhile, we utilized the Tumor Immune Estimation Resource to analyze the correlation between the NDC80 complex and immunocyte infiltration.Results: The NDC80 complex expression level was prominently higher in OC tissues than in non-OC ovarian tissues and correlated with advanced histologic grade characteristics. Gene expression profiling interactive analysis and the Kaplan–Meier survival curve uncovered a close relationship between high expression of the NDC80 complex and poor overall survival in OC patients. The univariate Cox regression hazard model produced age, pathologic stage, tumor status, primary therapy outcome, SPC24 expression level, and Karnofsky performance score as prognostic factors for OC patients. NDC80 complex expression levels were highly associated with immune cell infiltration, showing NK CD56 bright cells and NK cells with a negative correlation and T helper 2 cells with a positive correlation (P< 0.05).Conclusion: These findings provide evidence that an increased expression level of the NDC80 complex is closely associated with the progression of OC and could also serve as a novel target of immunotherapy in OC.Keywords: bioinformatics analysis, gene expression omnibus, NDC80 kinetochore complex, epithelial ovarian cancer, immunoinfiltration

Abstract This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 $$^{-/-}$$ - / - p73 $$^{+/+}$$ + / + and p53 $$^{-/-}$$ - / - p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan–Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan–Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients’ prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan–Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p < 0.001). The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups. Further research identified variations in tumor mutational burden (TMB) and differential responses to immunotherapies and chemotherapies. Our validation, using three GEO datasets (GSE31210, GSE30219, and GSE50081), revealed that the C-index exceeded 0.67 for GSE31210 and GSE30219. Significant differences in disease-free survival (DFS) and OS were observed across all validation cohorts among different risk groups. The prognostic model offers potential as a molecular biomarker for LUAD prognosis. [ABSTRACT FROM AUTHOR]

Objective To investigate the target of indirubin in psoriasis, and to elucidate the possible effect of indirubin on psoriasis through MAPK14. Methods The GSE30999 and GSE78097 psoriasis datasets downloaded from the Gene Expression Omnibus (GEO) database were used as training and validation sets, respectively・ The differentially expressed genes were screened, and their biological functions and pathways were analyzed・ Intersections with indirubin target genes were used to obtain differentially expressed indirubin target genes・ Meanwhile gene correlation was analyzed and differential gene expression was verified・ LASSO and Cox regression model were used to screen the best target genes for indirubin. Molecular docking of indirubin was performed to analyze the structural relationship・ Results A total of 8 indirubin target genes were found in the GSE30999 dataset, which were differentially expressed compared w让h the control group・ Seven genes had high diagnostic value (AUC >0. 8), and one gene had low diagnostic value (AUC >0. 6). GO and KEGG enrichment analyses showed that the eight indirubin target genes were mainly related to cell aging, chemical carcinogenic receptor activation, lipid and atherosclerosis, acute myeloid leukemia, and AGE-RAGE signaling pathways in diabetic complications. The best target gene of indirubin, MAPK14 gene, was screened by LASSO and Cox regression modeL Molecular docking suggested that indirubin was tightly bound to MAPK14 ・ Conclusion Indirubin may be involved in the regulation of the pathogenesis and treatment of psoriasis through MAPK14. [ABSTRACT FROM AUTHOR]

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 - / - p73 + / + and p53 - / - p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan–Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan–Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to pinpoint immune-related genes that show heightened activity in cancerous tissue and explore their correlation with cell infiltration in the tumor microenvironment using bioinformatics analysis. By examining gene expression from both stomach cancer and adjacent healthy tissues, we aimed to uncover their significance in cancer development and their impact on the body's immune response. We identified several genes, including FN1, COL1A2, THBS2, COL3A1, COL5A1, and BGN, which appear to be associated with poorer outcomes for stomach cancer patients. These genes also demonstrate connections to specific immune cells within cancerous tissue. Understanding the role of these genes in the immune response to cancer could facilitate the development of novel treatments and enhance prognostic capabilities for individuals with stomach cancer. Objective: Gastric carcinoma (GC) is the fifth most commonly diagnosed cancer and the third leading cause of cancer-related deaths globally. The tumor microenvironment plays a significant role in the pathogenesis, prognosis, and response to immunotherapy. However, the immune-related molecular mechanisms underlying GC remain elusive. Bioinformatics analysis of the gene expression of GC and paracancerous healthy tissues from the same patient was performed to identify the key genes and signaling pathways, as well as their correlation to the infiltration of the tumor microenvironment (TME) by various immune cells related to GC development. Methods: We employed GSE19826, a gene expression profile from the Gene Expression Omnibus (GEO), for our analysis. Functional enrichment analysis of Differentially Expressed Genes (DEGs) was conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database. Results: Cytoscape software facilitated the identification of nine hub DEGs, namely, FN1, COL1A1, COL1A2, THBS2, COL3A1, COL5A1, APOE, SPP1, and BGN. Various network analysis algorithms were applied to determine their high connectivity. Among these hub genes, FN1, COL1A2, THBS2, COL3A1, COL5A1, and BGN were found to be associated with a poor prognosis for GC patients. Subsequent analysis using the TIMER database revealed the infiltration status of the TME concerning the overexpression of these six genes. Specifically, the abovementioned genes demonstrated direct correlations with cancer-associated fibroblasts, M1 and M2 macrophages, myeloid-derived suppressor cells, and activated dendritic cells. Conclusion: Our findings suggest that the identified hub genes, particularly BGN, FN1, COL1A2, THBS2, COL3A1, and COL5A1, play crucial roles in GC prognosis and TME cell infiltration. This comprehensive analysis enhances our understanding of the molecular mechanisms underlying GC development and may contribute to the identification of potential therapeutic targets and prognostic markers for GC patients. [ABSTRACT FROM AUTHOR]

Mitochondria play a critical role in nerve regeneration, yet the impact of gene expression changes related to mitochondria in facial nerve regeneration remains unknown. To address this knowledge gap, we analyzed the expression profile of the facial motor nucleus (FMN) using data obtained from the Gene Expression Omnibus (GEO) database (GSE162977). By comparing different time points in the data, we identified differentially expressed genes (DEGs). Additionally, we collected mitochondria-related genes from the Gene Ontology (GO) database and intersected them with the DEGs, resulting in the identification of mitochondria-related DEGs (MIT-DEGs). To gain further insights, we performed functional enrichment and pathway analysis of the MIT-DEGs. To explore the interactions among these MIT-DEGs, we constructed a protein–protein interaction (PPI) network using the STRING database and identified hub genes using the Degree algorithm of Cytoscape software. To validate the relevance of these genes to nerve regeneration, we established a rat facial nerve injury (FNI) model and conducted a series of experiments. Through these experiments, we confirmed three MIT-DEGs (Myc, Lyn, and Cdk1) associated with facial nerve regeneration. Our findings provide valuable insights into the transcriptional changes of mitochondria-related genes in the FMN following FNI, which can contribute to the development of new treatment strategies for FNI. [ABSTRACT FROM AUTHOR]

The study investigated the potential association of the low-density lipoprotein (LDL) genome with endometrial cancer progression based on the Gene Expression Omnibus data set and The Cancer Genome Atlas data set. Differential and weighted gene coexpression network analysis was performed on endometrial cancer transcriptome datasets GSE9750 and GSE106191. The protein-protein interaction network was built using LDL-receptor proteins and the top 50 tumor-associated genes. Low-density lipoprotein-related receptors 5/6 (LRP5/6) in endometrial cancer tissues were correlated with oncogenes, cell cycle-related genes, and immunological checkpoints using Spearman correlation. MethPrimer predicted the LRP5/6 promoter CpG island. LRP2, LRP6, LRP8, LRP12, low-density lipoprotein receptor-related protein-associated protein, and LRP5 were major LDL-receptor-related genes associated with endometrial cancer. LRP5/6 was enriched in various cancer-related pathways and may be a key LDL-receptor-related gene in cancer progression. LRP5/6 may be involved in the proliferation process of endometrial cancer cells by promoting the expression of cell cycle-related genes. LRP5/6 may be involved in the proliferation of endometrial cancer cells by promoting the expression of cell cycle-related genes. LRP5/6 may promote the immune escape of cancer cells by promoting the expression of immune checkpoints, promoting endometrial cancer progression. The MethPrimer database predicted that the LRP5/6 promoter region contained many CpG islands, suggesting that DNA methylation can occur in the LRP5/6 promoter region. LRP5/6 may aggravate endometrial cancer by activating the phosphoinositide 3-kinase/protein kinase B pathway. [ABSTRACT FROM AUTHOR]

Kuihan Tang,1 Li Sun,2 Long Chen,2 Xiaobo Feng,1 Jiarui Wu,2 Hao Guo,2 Yong Zheng1 1Department of Orthopedics, Beijing Jishuitan Hospital Guizhou Hospital, Guiyang, 550014, People’s Republic of China; 2Department of Orthopedics, Guizhou Provincial People’s Hospital, Guiyang, 550000, People’s Republic of ChinaCorrespondence: Yong Zheng, Email 1210244258@qq.comBackground: Mitochondrial autophagy is closely related to the pathogenesis of osteoarthritis, In order to explore the role of mitochondrial autophagy related genes in knee osteoarthritis (KOA) and its molecular mechanism.Methods: KOA-related transcriptome data were extracted from the Gene Expression Omnibus (GEO) database. Differentially expressed mitochondrial autophagy gene (DEMGs) were screened in patients with KOA by differential expression analysis. The STRING website was used to construct a protein-protein interaction (PPI) network among DEMGs. Molecular complex detection (MCODE) method in Cytoscape software was performed to identify hub DEMGs. Support vector machine recursive feature elimination (SVM-RFE) method was used to construct the hub DEMG diagnosis model. Genes with diagnostic value were identified as biomarkers by plotting receiver operating characteristic (ROC) curves and Expression validation. CIBERSORT algorithm was used to calculate the proportion of 22 immune cells in each sample in the GSE114007 dataset. Finally, biomarker expression was verified by qPCR.Results: A total of 15 DEMGs were obtained and enrichment analyses showed that these DEMG strains were mainly enriched in the mitophagy-animal, shigellosis, autophagy-animal and FoxO signal pathways. The PPI network unveiled 13 DEMGs with interactions. In addition, 8 hub DEMGs (ULK1, CALCOCO2, MAP1LC3B, BNIP3L, GABARAPL1, BNIP3, FKBP8 and FOXO3) were obtained for KOA. And 5 model DEMGs (BNIP3L, BNIP3, MAP1LC3B, ULK1 and FOXO3) were screened. The ROC curves revealed that BNIP3 and FOXO3 has strong diagnostic value in these models of DEMG. Immune-infiltration and correlation analysis showed that BNIP3 and FOXO3 were significantly correlated with three different immune cells, including primary B cells, M0 macrophage and M2 macrophage. The cartilage tissue samples qPCR verification results show that FOXO3 and BNIP3 were all down-regulated in KOA (p < 0.01), and the validation results are consistent with the above analysis.Conclusion: BNIP3 and FOXO3 have been identified as biomarkers for the diagnosis of KOA, which might supply a new insight for the pathogenesis and treatment of KOA.Keywords: Gene Expression Omnibus, diagnostic, bioinformatics analysis, immune infiltration, biomarkers

Glioma stem cells (GSCs) contribute to the pathogenesis of glioblastoma (GBM), which is the most malignant form of glioma. The implications and underlying mechanisms of protein glycosylation in GSC phenotypes and GBM malignancy are not fully understood. The implication of protein glycosylation and the corresponding candidate genes on the stem cell properties of GSCs and poor clinical outcomes in GBM were investigated, using datasets from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Chinese Glioma Genome Atlas, accompanied by biological validation in vitro. N-linked glycosylation was significantly associated with GSC properties and the prognosis of GBM in the integrated bioinformatics analyses of clinical specimens. N-linked glycosylation was associated with the glioma grade, molecular biomarkers, and molecular subtypes. The expression levels of the asparagine-linked glycosylation (ALG) enzyme family, which is essential for the early steps in the biosynthesis of N-glycans, were prominently associated with GSC properties and poor survival in patients with GBM with high stem-cell properties. Finally, the oxidative phosphorylation pathway was primarily enriched in GSCs with a high expression of the ALG enzyme family. These findings suggest the role of N-linked glycosylation in the regulation of GSC phenotypes and GBM malignancy.

Glioma stem cells (GSCs) contribute to the pathogenesis of glioblastoma (GBM), which is the most malignant form of glioma. The implications and underlying mechanisms of protein glycosylation in GSC phenotypes and GBM malignancy are not fully understood. The implication of protein glycosylation and the corresponding candidate genes on the stem cell properties of GSCs and poor clinical outcomes in GBM were investigated, using datasets from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Chinese Glioma Genome Atlas, accompanied by biological validation in vitro. N-linked glycosylation was significantly associated with GSC properties and the prognosis of GBM in the integrated bioinformatics analyses of clinical specimens. N-linked glycosylation was associated with the glioma grade, molecular biomarkers, and molecular subtypes. The expression levels of the asparagine-linked glycosylation (ALG) enzyme family, which is essential for the early steps in the biosynthesis of N-glycans, were prominently associated with GSC properties and poor survival in patients with GBM with high stem-cell properties. Finally, the oxidative phosphorylation pathway was primarily enriched in GSCs with a high expression of the ALG enzyme family. These findings suggest the role of N-linked glycosylation in the regulation of GSC phenotypes and GBM malignancy. [ABSTRACT FROM AUTHOR]

Objective: The present study aimed to elucidate mechanisms of liothyronine on the treatment of ischemic stroke (IS). Methods: Differential analysis based on R limma package was used to identify differentially expressed genes, which were then mapped into the connectivity map database for identification of liothyronine associated with IS. Tumor necrosis factor (TNF) signaling pathway was verified through pathway enrichment analysis via Enrichr online. Ischemia stroke mouse model was built up for further analysis. Infarct area and regional cerebral blood flow (rCBF) were measured by 2, 3, 5‐triphenyltetrazolium chloride and laser Doppler flowmetry, respectively. Light microscope was used for the evaluation of body weight and dark neurons. Serum TXB2, 6‐Keto‐PGF1a, TNF‐α, and interleukin‐6 (IL‐6) levels in mice were measured using enzyme‐linked immuno sorbent assay. In addition, relative protein expression levels of brain‐derived neurotrophic factor, nestin, and Sox2 were detected by Western blot analysis. Results: Liothyronine with a negative connectivity was identified as one promising treatment for IS through TNF signaling pathway. The experimental results showed that liothyronine treatment significantly meliorated infarct area and the number of dark neurons in IS mice. Liothyronine greatly ameliorated the expression levels of TXB2 and 6‐Keto‐PGF1a. Besides, rCBF and body weight change of IS mice were increased gradually with increase of drug concentration. Based on pathway enrichment analysis, anti‐inflammatory response (TNF‐α and IL‐6) relevant to TNF signaling pathway was identified, which was further validated in vitro. Furthermore, proteins as neural stem cell markers made a difference with liothyronine treatment. Conclusion: Liothyronine may be a novel therapeutic component to exploit an effective medicine for the treatment of IS. [ABSTRACT FROM AUTHOR]

Objectives: Synovial neovascularization is an early and remarkable event that promotes the development of rheumatoid arthritis (RA) synovial hyperplasia. This study aimed to find potential diagnostic markers and molecular therapeutic targets for RA at the mRNA molecular level. Method: We download the expression profile dataset GSE46687 from the gene expression ontology (GEO) microarray, and used R software to screen out the differentially expressed genes between the normal group and the disease group. Then we performed functional enrichment analysis, used the STRING database to construct a protein-protein interaction (PPI) network, and identify candidate crucial genes, infiltration of the immune cells and targeted molecular drug. Results: Rheumatoid arthritis datasets included 113 differentially expressed genes (DEGs) including 104 upregulated and 9 downregulated DEGs. The enrichment analysis of genes shows that the differential genes are mainly enriched in condensed chromosomes, ribosomal subunits, and oxidative phosphorylation. Through PPI network analysis, seven crucial genes were identified: RPS13, RPL34, RPS29, RPL35, SEC61G, RPL39L, and RPL37A. Finally, we find the potential compound drug for RA. Conclusion: Through this method, the pathogenesis of RA endothelial cells was further explained. It provided new therapeutic targets, but the relationship between these genes and RA needs further research to be validated in the future. [ABSTRACT FROM AUTHOR]

Background: Mounting evidence suggest that there are an association between psoriasis and ulcerative colitis (UC), although the common pathogeneses are not fully understood. Our study aimed to find potential crucial genes in psoriasis and UC through machine learning and integrated bioinformatics. Methods: The overlapping differentially expressed genes (DEGs) of the datasets GSE13355 and GSE87466 were identified. Then the functional enrichment analysis was performed. The overlapping genes in LASSO, SVM‐RFE and key module in WGCNA were considered as potential crucial genes. The receiver operator characteristic (ROC) curve was used to estimate their diagnostic confidence. The CIBERSORT was conducted to evaluate immune cell infiltration. Finally, the datasets GSE30999 and GSE107499 were retrieved to validate. Results: 112 overlapping DEGs were identified in psoriasis and UC and the functional enrichment analysis revealed they were closely related to the inflammatory and immune response. Eight genes, including S100A9, PI3, KYNU, WNT5A, SERPINB3, CHI3L2, ARNTL2, and SLAMF7, were ultimately identified as potential crucial genes. ROC curves showed they all had high confidence in the test and validation datasets. CIBERSORT analysis indicated there was a correlation between infiltrating immune cells and potential crucial genes. Conclusion: In our study, we focused on the comprehensive understanding of pathogeneses in psoriasis and UC. The identification of eight potential crucial genes may contribute to not only understanding the common mechanism, but also identifying occult UC in psoriasis patients, even serving as therapeutic targets in the future. [ABSTRACT FROM AUTHOR]

A sudden rise in intra-abdominal pressure that causes the pressure in the bladder to rise during physical movement and/or activity, such as coughing, sneezing, laughing, running, or weightlifting, is known as stress urinary incontinence. This condition causes an uncontrollable overflow of urine. The study's goal was to determine whether effector molecules, specifically ADP ribosylation factor GTPase activated protein 3, might play a part in the female pelvic floor muscle's ability to heal after suffering damage during vaginal delivery. Pelvic floor muscle samples were taken from women who had at least one vaginal delivery and were enrolled in either the IU group (n = 45; issue of stress urinary incontinence) or the NL group (n = 85; no issue of stress urinary incontinence) depending on whether they had a problem with stress urinary incontinence. Vesicle transport-related genes in female pelvic floor muscle injury repair were discovered using Gene Expression Omnibus. For gene analysis and screening, RT-qPCR was employed. On the first day following injury, the expression level of ARFGAP3 mRNA increased by 2.8 times (p 0.05) and by 5 times (p 0.01) on the third day. On the first day following damage, STMN1 mRNA expression rose by 0.3 times (p 0.05). On the first day following injury, the expression level of THBS2 mRNA increased by 1.6 times (p 0.01). On the third day following the injury, the expression level of PLXNB2 mRNA increased by 1.2 times (p 0. 01), and on the fifth day following the injury, it increased by 2.5 times (p 0. 01). After pelvic floor muscle damage, the mRNA expression levels of the CSF1R, ANXA4, and EMR1 genes dropped. Between those with and without pelvic floor muscle damage, there was no statistically significant difference in the expression levels of LGARLS3, KDELR3, and KIF20A mRNA (p > 0. 05 for all). The differential expression of genes after pelvic floor muscle injury can identify the target in the process of pelvic floor muscle injury repair and regeneration. [ABSTRACT FROM AUTHOR]

The current research attempted to identify possible hub genes and pathways of coronary artery disease (CAD) and to detect the possible mechanisms. Array data from GSE90074 were downloaded from the Gene Expression Omnibus (GEO) database. Integrated weighted gene co-expression network analysis (WGCNA) was performed to analyze the gene module and clinical characteristics. Gene Ontology annotation (GO), Disease Ontology (DO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by clusterProfiler and the DOSE package in R. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection (MCODE) to identify hub genes. Then, further functional validation of hub genes in other microarrays and population samples was performed, and survival analysis was performed to investigate the prognosis. A total of 660 genes were located in three modules and associated with CAD. GO functions identified 484 biological processes, 39 cellular components, and 22 molecular functions with an adjusted P < 0.05. In total, 38 pathways were enriched in KEGG pathway analysis, and 147 DO items were identified with an adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). There was a total of four modules with a score > 10 after PPI network analysis using the MCODE app, and two hub genes (TLR2 and CD14) were identified. Then, we validated the information from the GSE60993 dataset using the GSE59867 dataset and population samples, and we found that these two genes were associated with plaque vulnerability. These two genes varied at different time points after myocardial infarction, and both of them had the lowest prognosis of heart failure when they were expressed at low levels. We performed an integrated WGCNA and validated that TLR2 and CD14 were closely associated with the severity of coronary artery disease, plaque instability and the prognosis of heart failure after myocardial infarction. [ABSTRACT FROM AUTHOR]

Background: The induction of cardiomyocyte (CM) proliferation is a promising approach for cardiac regeneration following myocardial injury. MicroRNAs (miRNAs) have been reported to regulate CM proliferation. In particular, miR‐431 expression decreases during cardiac development, according to Gene Expression Omnibus (GEO) microarray data. However, whether miR‐431 regulates CM proliferation has not been thoroughly investigated. Methods: We used integrated bioinformatics analysis of GEO datasets to identify the most significantly differentially expressed miRNAs. Real‐time quantitative PCR and fluorescence in situ hybridization were performed to determine the miRNA expression patterns in hearts. Gain‐ and loss‐of‐function assays were conducted to detect the role of miRNA in CM proliferation. Additionally, we detected whether miR‐431 affected CM proliferation in a myocardial infarction model. The TargetScan, miRDB and miRWalk online databases were used to predict the potential target genes of miRNAs. Luciferase reporter assays were used to study miRNA interactions with the targeting mRNA. Results: First, we found a significant reduction in miR‐431 levels during cardiac development. Then, by overexpression and inhibition of miR‐431, we demonstrated that miR‐431 promotes CM proliferation in vitro and in vivo, as determined by immunofluorescence assays of 5‐ethynyl‐2'‐deoxyuridine (EdU), pH3, Aurora B and CM count, whereas miR‐431 inhibition suppresses CM proliferation. Then, we found that miR‐431 improved cardiac function post‐myocardial infarction. In addition, we identified FBXO32 as a direct target gene of miR‐431, with FBXO32 mRNA and protein expression being suppressed by miR‐431. FBXO32 inhibited CM proliferation. Overexpression of FBXO32 blocks the enhanced effect of miR‐431 on CM proliferation, suggesting that FBXO32 is a functional target of miR‐431 during CM proliferation. Conclusion: In summary, miR‐431 promotes CM proliferation by targeting FBXO32, providing a potential molecular target for preventing myocardial injury. [ABSTRACT FROM AUTHOR]

Myelomeningocele is a common congenital anomaly associated with polygenic disorders worldwide. However, the intricate molecular mechanisms underlying myelomeningocele remain elusive. To investigate whether ferroptosis and ferritinophagy contribute to the pathomechanism of myelomeningocele, differentially expressed genes (DEGs) were identified as novel biomarker and potential treatment agents. The GSE101141 dataset from Gene Expression Omnibus (GEO) was analyzed using GEO2R web tool to obtain DEGs based on |log2 fold change (FC)|≥1.5 and p

Abstract Purpose Tenosynovial giant cell tumour (TGCT) is a benign hyperplastic and inflammatory disease of the joint synovium or tendon sheaths, which may be misdiagnosed due to its atypical symptoms and imaging features. We aimed to identify biomarkers with high sensitivity and specificity to aid in diagnosing TGCT. Methods Two scRNA-seq datasets (GSE210750 and GSE152805) and two microarray datasets (GSE3698 and GSE175626) were downloaded from the Gene Expression Omnibus (GEO) database. By integrating the scRNA-seq datasets, we discovered that the osteoclasts are abundant in TGCT in contrast to the control. The single-sample gene set enrichment analysis (ssGSEA) further validated this discovery. Differentially expressed genes (DEGs) of the GSE3698 dataset were screened and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were conducted. Osteoclast-specific up-regulated genes (OCSURGs) were identified by intersecting the osteoclast marker genes in the scRNA-seq and the up-regulated DEGs in the microarray and by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The expression levels of OCSURGs were validated by an external dataset GSE175626. Then, single gene GSEA, protein–protein interaction (PPI) network, and gene-drug network of OCSURGs were performed. Result 22 seurat clusters were acquired and annotated into 10 cell types based on the scRNA-seq data. TGCT had a larger population of osteoclasts compared to the control. A total of 159 osteoclast marker genes and 104 DEGs (including 61 up-regulated genes and 43 down-regulated genes) were screened from the scRNA-seq analysis and the microarray analysis. Three OCSURGs (MMP9, SPP1, and TYROBP) were finally identified. The AUC of the ROC curve in the training and testing datasets suggested a favourable diagnostic capability. The PPI network results illustrated the protein–protein interaction of each OCSURG. Drugs that potentially target the OCSURGs were predicted by the DGIdb database. Conclusion MMP9, SPP1, and TYROBP were identified as osteoclast-specific up-regulated genes of the tenosynovial giant cell tumour via bioinformatic analysis, which had a reasonable diagnostic efficiency and served as potential drug targets.

Jingwei Wang,1,2,&ast; Bowen Xia,3,&ast; Ruimin Ma,1 Qiao Ye1,2 1Beijing Institute of Respiratory Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Occupational Medicine and Toxicology, Clinical Center for Interstitial Lung Diseases, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People’s Republic of China; 3Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qiao Ye, Department of Occupational Medicine and Toxicology, Clinical Center for Interstitial Lung Diseases, Beijing Chaoyang Hospital, Capital Medical University, No. 8 Worker’s Stadium, Chaoyang District, Beijing, 100020, People’s Republic of China, Email yeqiao_chaoyang@sina.comPurpose: Chronic obstructive pulmonary disease (COPD) is the main cause of mortality world widely. Non-coding RNAs (lncRNAs) and associated competitive endogenous RNAs (ceRNAs) networks were recently proved to lead to mRNA gene expression downregulation but were still unclear in COPD. This study aims to investigate and elucidate the mechanisms underlying the involvement of ceRNA co-expression networks in COPD pathogenesis.Methods: Obtained expression signature of data from the Gene Expression Omnibus database and compared the differentially expression of mRNAs and miRNAs between COPD patients and healthy smokers. Predicted the miRNA–lncRNA and miRNA–mRNA interaction using online library and employed CIBERSORT to measure the proportions of the 22 immune cells in the COPD and control groups.Results: Established a ceRNA-network comprising 11 lncRNAs, 5 miRNAs, and 16 mRNAs. Using the weighted correlation network analysis method, we identified hub genes and hub miRNAs and obtained one core sub-network, XIST, FGD5-AS1, KCNQ1OT1, HOXA11-AS, LINC00667, H19, PRKCQ-AS1, NUTM2A-AS1/has-mir-454-3p/ZNF678, PRRG4. COPD patients had different proportions of immune cells than controls, and these variations were associated with the magnitude of pulmonary function parameters.Conclusion: The ceRNA-network, particularly the core sub-network, may be a putative goal for COPD, in which specific immune cells were involved.Keywords: chronic obstructive pulmonary disease, competing endogenous RNA network, microRNAs, miRNAs, CIBERSORT algorithm, gene expression omnibus

Abstract Background High-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis. Methods The RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets. Results A total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p

Abstract Background Intracranial aneurysm (IA) is the most common cerebrovascular disease, and subarachnoid hemorrhage caused by its rupture can seriously impede nerve function. Pyroptosis is an inflammatory mode of cell death whose underlying mechanisms involving the occurrence and rupture of IAs remain unclear. In this study, using bioinformatics analysis, we identified the potential pyroptosis-related genes (PRGs) and performed their inflammatory response mechanisms in IAs. Methods The mRNA expression matrix of the IA tissue was obtained from the Gene Expression Omnibus database, and 51 PRGs were obtained from previous articles collected from PubMed. The differentially expressed PRGs (DEPRGs) were performed using R software. Subsequently, we performed enrichment analysis, constructed a protein–protein interaction network, performed weighted gene coexpression network analysis (WGCNA) and external validation using another dataset, and identified a correlation between hub genes and immune cell infiltration. Finally, the expression and tissue distribution of these hub genes in IA tissues were detected using Western blotting and immunohistochemical (IHC) staining. Results In total, 12 DEPRGs associated with IA were identified in our analysis, which included 11 up-regulated and one down-regulated genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that the DEPRGs were mostly enriched in the NOD-like receptor signaling pathway, interleukin-1 beta production, and the inflammasome complex. Three hub genes, NLRP3, IL1B and IL18, were identified using Cytoscape software and the WGCNA correlation module, and external validation revealed statistically significant differences between the expression of these hub genes in the ruptured and unruptured aneurysm groups (p 0.75. Immune cell infiltration analysis suggested that the hub genes are related to CD8 T cell, macrophages and mast cells. Finally, IHC staining revealed that the protein levels of these hub genes were higher in ruptured and unruptured IA tissues than in normal tissues (p

Abstract Purpose Steroid-induced necrosis of the femoral head (SONFH) is a refractory orthopedic hip disease occurring in young and middle-aged people, with glucocorticoids being the most common cause. Previous experimental studies have shown that cell pyroptosis may be involved in the pathological process of SONFH, but its pathogenesis in SONFH is still unclear. This study aims to screen and validate potential pyroptosis-related genes in SONFH diagnosis by bioinformatics analysis to further elucidate the mechanism of pyroptosis in SONFH. Methods There were 33 pyroptosis-related genes obtained from the prior reviews. The mRNA expression was downloaded from GSE123568 dataset in the Gene Expression Omnibus (GEO) database, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples. The pyroptosis-related differentially expressed genes involved in SONFH were identified with “affy” and “limma” R package by intersecting the GSE123568 dataset with pyroptosis genes. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the pyroptosis-related differentially expressed genes involved in SONFH were conducted by “clusterProfiler” R package and visualized by “GOplot” R package. Then, the correlations between the expression levels of the pyroptosis-related differentially expressed genes involved in SONFH were confirmed with “corrplot” R package. Moreover, the protein–protein interaction (PPI) network was analysed by using GeneMANIA database. Next, The ROC curve of pyroptosis-related differentially expressed genes were analyzed by “pROC” R package. Results A total of 10 pyroptosis-related differentially expressed genes were identified between the peripheral blood samples of SONFH patients and non-SONFH patients based on the defined criteria, including 20 upregulated genes and 10 downregulated genes. The GO and KEGG pathway enrichment analyses revealed that these 10 pyroptosis-related differentially expressed genes involved in SONFH were particularly enriched in cysteine-type endopeptidase activity involved in apoptotic process, positive regulation of interleukin-1 beta secretion and NOD-like receptor signaling pathway. Correlation analysis revealed significant correlations among the 10 differentially expressed pyroptosis-related genes involved in SONFH. The PPI results demonstrated that the 10 pyroptosis-related differentially expressed genes interacted with each other. Compared to non-SONFH samples, these pyroptosis-related differentially expressed genes had good predictive diagnostic efficacy (AUC = 1.000, CI = 1.000–1.000) in the SONFH samples, and NLRP1 had the highest diagnostic value (AUC: 0.953) in the SONFH samples. Conclusions There were 10 potential pyroptosis-related differentially expressed genes involved in SONFH were identified via bioinformatics analysis, which might serve as potential diagnostic biomarkers because they regulated pyroptosis. These results expand the understanding of SONFH associated with pyroptosis and provide new insights to further explore the mechanism of action and diagnosis of pyroptosis associated in SONFH.

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with poor prognosis. The disease originates from the cortex of adrenal gland and lacks effective treatment. Efforts have been made to elucidate the pathogenesis of ACC, but the molecular mechanisms remain elusive. To identify key genes and pathways in ACC, the expression profiles of GSE12368, GSE90713 and GSE143383 were downloaded from the Gene Expression Omnibus (GEO) database. After screening differentially expressed genes (DEGs) in each microarray dataset on the basis of cut-off, we identified 206 DEGs, consisting of 72 up-regulated and 134 downregulated genes in three datasets. Function enrichment analyses of DEGs were performed by DAVID online database and the results revealed that the DEGs were mainly enriched in cell cycle, cell cycle process, mitotic cell cycle, response to oxygen-containing compound, progesterone-mediated oocyte maturation, p53 signaling pathway. The STRING database was used to construct the protein– protein interaction (PPI) network, and modules analysis was performed using Cytoscape. Finally, we filtered out eight hub genes, including CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1 and AURKA. Biological process analysis showed that these hub genes were significantly enriched in nuclear division, mitosis, M phase of mitotic cell cycle and cell cycle process. Violin plot, KaplanMeier curve and stage plot of these hub genes confirmed the reliability of the results. In conclusion, the results in this study provided reliable key genes and pathways for ACC, which will be useful for ACC mechanisms, diagnosis and candidate targeted treatment. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) tumors have increasing incidence worldwide with their underlying mechanisms still not being fully understood. The use of tumor-educated platelets (TEPs) in liquid biopsy is a newly-emerged blood-based cancer diagnostic method. Herein, we aimed to investigate the genomic changes of TEPs in GI tumor development and their potential functions using network-based meta-analysis combined with bioinformatic methods. We used a total of three eligible RNA-seq datasets, which were integrated using multiple meta-analysis methods on the NetworkAnalyst website, and identified 775 DEGs (differentially expressed genes; 51 upregulated and 724 down-regulated genes) in GI tumor relative to healthy control (HC) samples. These TEP DEGs were mostly enriched in bone marrow-derived cell types and associated with gene ontology (GO) of "carcinoma" and could affect pathways of "Integrated Cancer Pathway" and "Generic transcription pathway" respectively for highly and lowly expressed DEGs. Combined network-based meta-analysis and protein-protein interaction (PPI) analysis identified cyclin dependent kinase 1 (CDK1) and heat shock protein family A (Hsp70) member 5 (HSPA5) to be the hub genes with the highest degree centrality (DC), being up-regulated and down-regulated in TEPs, respectively. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) results showed that the hub genes were primarily related to cell cycle and division, nucleobase-containing compound and carbohydrate transport, and endoplasmic reticulum unfolded protein response. Additionally, the nomogram model suggested that the two-gene signature owns extraordinary predictive power for GI tumor diagnosis. Further, the two-gene signature was demonstrated to have potential value for metastatic GI tumor diagnosis. The expression levels of CDK1 and HSPA5 in clinical platelet samples were verified to be consistent with the results from bioinformatic analysis. This study identified a two-gene signature encompassing CDK1 and HSPA5 that can be used as a biomarker for GI tumor diagnosis and maybe even cancer-associated thrombosis (CAT)-related prognosis. [ABSTRACT FROM AUTHOR]

Although vascular dementia (VD) and systemic lupus erythematosus (SLE) may share immune-mediated pathophysiologic processes, the underlying mechanisms are unclear. This study investigated shared gene signatures in SLE versus VD, as well as their potential molecular mechanisms. Bulk RNA sequencing (RNAseq) and single-cell or single-nucleus RNAseq (sc/snRNAseq) datasets from SLE blood samples and VD brain samples were obtained from Gene Expression Omnibus. The identification of genes associated with both SLE and VD was performed using the weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. For the sc/snRNAseq data, an unbiased clustering pipeline based on Seurat and CellChat was used to determine the cellular landscape profile and examine intracellular communication, respectively. The results were subsequently validated using a mice model of SLE with cognitive dysfunction (female MRL/lpr mice). WGCNA and machine learning identified C1QA, LY96, CD163, and MS4A4A as key genes for SLE and VD. sc/snRNAseq analyses revealed that CD163 and MS4A4A were upregulated in mononuclear phagocytes (MPs) from SLE and VD samples and were associated with monocyte-macrophage differentiation. Intriguingly, LGALS9-associated molecular pathway, as the only signaling pathway common between SLE and VD via CellChat analysis, exhibited significant upregulation in cortical microglia of MRL/lpr mice. Our analyses identified C1QA, LY96, CD163, and MS4A4A as potential biomarkers for SLE and VD. Moreover, the upregulation of CD163/MS4A4A and activation of LGALS9 signaling in MPs may contribute to the pathogenesis of VD with SLE. These findings offer novel insight into the mechanisms underlying VD in SLE patients. [ABSTRACT FROM AUTHOR]

This study aimed to elucidate the differences in vulval squamous cell carcinomas (VSCC) based on the HPV infection status. The sequencing data GSE183454 which contains 23 VSCC samples based on its HPV infection status was obtained from the Gene Expression Omnibus (GEO) database. We comprehensively dissected the differences of genomic and tumour microenvironment (TME) immune cell infiltration landscapes between HPV + and HPV- VSCC. The potential molecular mechanisms of prognostic genes were explored by functional enrichment analysis. Five novel key molecules (SYCP2, SMC1B, RNF212, MAJIN and C14orf39) with significantly up-regulated expression in HPV + VSCC were identified while protein-protein interaction (PPI) networks were created upon Cytoscape software. Additionally, VSCC with up-regulated expression of these key molecules exhibited a significantly decreased TME immune cell infiltration. SYCP2 is overexpressed in HPV + VSCC and could be a candidate therapy target for further research. What is already known on this subject? VSCC are characterised by two aetiological pathways. The former occurs in the background of lichen sclerosus, while the latter is related to HPV infection. VSCC most commonly arises from the non-HPV related pathway portends worse prognosis than VSCC derived from HPV infection. What do the results of this study add? Five key molecules are identified and significantly up-regulated in HPV + VSCC. In which, SYCP2 is overexpressed in HPV + VSCC and exhibited a significantly decreased TME immune cell infiltration. SYCP2 constant expression could be a potential biomarker of neoplasms associated with HPV and could be a candidate therapy target in VSCC especially HPV + VSCC for further research. What are the implications of these findings for clinical practice and/or further research? SYCP2 could be a candidate therapy target in VSCC especially with HPV + for further research. [ABSTRACT FROM AUTHOR]

Background: High-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis. Methods: The RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets. Results: A total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples. Conclusion: This study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC. [ABSTRACT FROM AUTHOR]

Objective: Oral squamous cell carcinoma (OSCC) is one of the most common neoplasms worldwide. The current study aimed to identify potential biomarkers associated with OSCC survival. Materials and Methods: Differentially expressed genes (DEGs) in atypical OSCC cases were identified using two public datasets: The Cancer Genome Atlas and the Gene Expression Omnibus database. Receiver operating characteristic (ROC) analysis was performed to identify the cutoff, and the candidate DEGs related to survival. Kaplan–Meier and Cox regression analysis using the categorized genes were employed to identify genes that impact the overall survival in OSCC. Results: A total of 263 OSCC samples and 105 healthy tissues were used to identify 295 upregulated and 131 downregulated genes expressed only in non‐smokers. ROC analyses identified 25 candidate genes associated with death. Survival analyses demonstrated that the following DEGs, namely CSTA, FGFR2, MMP19, OLR1, PCSK1, RAMP2, and CGB5, are potential OSCC prognostic factors. Conclusion: We found that CSTA, FGFR2, MMP19, OLR1, PCSK1, RAMP2, and CGB5 are associated with a low survival rate in OSCC. However, further studies are needed to validate our findings and facilitate the development of these factors as potential biomarkers for OSCC survival. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus (T2DM), which has a high incidence and several harmful consequences, poses a severe danger to human health. Research on the function of ferroptosis in T2DM is increasing. This study uses bioinformatics techniques identify new diagnostic T2DM biomarkers associated with ferroptosis. Methods: To identify ferroptosis‐related genes (FRGs) that are differentially expressed between T2DM patients and healthy individuals, we first obtained T2DM sequencing data and FRGs from the Gene Expression Omnibus (GEO) database and FerrDb database. Then, drug‐gene interaction networks and competitive endogenous RNA (ceRNA) networks linked to the marker genes were built after marker genes were filtered by two machine learning algorithms (LASSO and SVM‐RFE algorithms). Finally, to confirm the expression of marker genes, the GSE76895 dataset was utilized. The protein and RNA expression of some marker genes in T2DM and nondiabetic tissues was also examined by Western blotting, immunohistochemistry (IHC), immunofluorescence (IF) and quantitative real‐time PCR (qRT‐PCR). Results: We obtained 58 differentially expressed genes (DEGs) associated with ferroptosis. GO and KEGG enrichment analyses showed that these DEGs were significantly enriched in hypoxia and ferroptosis. Subsequently, eight marker genes (SCD, CD44, HIF1A, BCAT2, MTF1, HILPDA, NR1D2, and MYCN) were screened by LASSO and SVM‐RFE machine learning algorithms, and a model was constructed based on these eight genes. This model also has high diagnostic power. In addition, based on these eight genes, we obtained 48 drugs and constructed a complex ceRNA network map. Finally, Western blotting, IHC, IF, and qRT‐PCR results of clinical samples further confirmed the results of public databases. Conclusions: The diagnosis and aetiology of T2DM can be greatly aided by eight FRGs, providing novel therapeutic avenues. [ABSTRACT FROM AUTHOR]

Abstract Background Rosenroot (Rhodiola rosea) is a traditional Chinese herbal medicine. It has been used to treat patients with coronary artery disease (CAD). Salidroside is the main active constituent of rosenroot. This study was designed to explore the mechanism of salidroside in treating CAD and its role in angiogenesis in CAD systematically. Methods In this study, potential targets related to salidroside and CAD were obtained from public databases. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) and CellMarker enrichment analyses were performed. The binding of salidroside to angiogenesis-related targets was assessed by PyMOL and Ligplot. Furthermore, the effects of salidroside on collateral circulation were evaluated by correlation analysis of these angiogenesis-related targets with the coronary flow index (CFI), and the influence of salidroside on human umbilical vein endothelial cell (HUVEC) proliferation and migration was assessed. Results Eighty-three targets intersected between targets of salidroside and CAD. GO and KEGG analyses indicated that salidroside mainly treated CAD through angiogenesis and anti-inflammatory action. There were 12 angiogenesis-related targets of salidroside in coronary heart disease, among which FGF1 (r = 0.237, P = 2.597E-3), KDR (r = 0.172, P = 3.007E-2) and HIF1A (r = -0.211, P = 7.437E-3) were correlated with the coronary flow index (CFI), and salidroside docked well with them. Finally, cell experiments confirmed that salidroside promoted the proliferation and migration of HUVECs. Conclusions This study revealed the potential molecular mechanism of salidroside on angiogenesis in CAD and provided new ideas for the clinical application of salidroside in the treatment of CAD.

Abstract Objective To characterize alpha‐fetoprotein (AFP)‐producing gastric cancer (AFPGC) at the single‐cell level and to identify regulatory factors for AFP expression and malignancy. Methods ScRNA‐seq was performed on two tumors collected from patients with AFPGC. InferCNV and sub‐clustering were applied to identify typical AFPGC cells, followed by AddModuleScore, pathway enrichment, Pseudo‐time, and Scenic analyses. Data from a gastric cancer (GC) cohort were collected for conjoint analysis. The analytical results were verified by cell experiments and immunohistochemistry. Results AFPGC cells are similar to hepatocytes in transcriptome and transcriptional regulation, with kinetic malignancy‐related pathways, compared to the common malignant epithelium. In addition, compared to common GC cells, malignancy‐related pathways, such as epithelial‐mesenchymal transition (EMT) and angiogenesis, were upregulated in AFPGC. Mechanistically, Dickkopf‐1 (DKK1) was found to be associated with AFP expression and malignant phenotype upon combining our scRNA‐seq data with a public database, which was further verified by a series of in vitro experiments and immunohistochemistry. Conclusion We demonstrated the single‐cell characteristics of AFPGC and that DKK1 facilitates AFP expression and malignancy.

This study aimed to elucidate the differences in vulval squamous cell carcinomas (VSCC) based on the HPV infection status. The sequencing data GSE183454 which contains 23 VSCC samples based on its HPV infection status was obtained from the Gene Expression Omnibus (GEO) database. We comprehensively dissected the differences of genomic and tumour microenvironment (TME) immune cell infiltration landscapes between HPV + and HPV- VSCC. The potential molecular mechanisms of prognostic genes were explored by functional enrichment analysis. Five novel key molecules (SYCP2, SMC1B, RNF212, MAJIN and C14orf39) with significantly up-regulated expression in HPV + VSCC were identified while protein-protein interaction (PPI) networks were created upon Cytoscape software. Additionally, VSCC with up-regulated expression of these key molecules exhibited a significantly decreased TME immune cell infiltration. SYCP2 is overexpressed in HPV + VSCC and could be a candidate therapy target for further research.IMPACT STATEMENT What is already known on this subject? VSCC are characterised by two aetiological pathways. The former occurs in the background of lichen sclerosus, while the latter is related to HPV infection. VSCC most commonly arises from the non-HPV related pathway portends worse prognosis than VSCC derived from HPV infection. What do the results of this study add? Five key molecules are identified and significantly up-regulated in HPV + VSCC. In which, SYCP2 is overexpressed in HPV + VSCC and exhibited a significantly decreased TME immune cell infiltration. SYCP2 constant expression could be a potential biomarker of neoplasms associated with HPV and could be a candidate therapy target in VSCC especially HPV + VSCC for further research. What are the implications of these findings for clinical practice and/or further research? SYCP2 could be a candidate therapy target in VSCC especially with HPV + for further research.

Objective: Ferroptosis is of vital importance in the development of Rheumatoid arthritis (RA). The purpose of this project is to clarify the potential ferroptosis-related genes, pathways, and immune infiltration in RA by bioinformatics analysis. Methods: We acquired ferroptosis-related genes (FRGs) from Ferroptosis database (FerrDb). We obtained the Gene dataset of RA (GSE55235) from the Gene Expression Omnibus (GEO) Database, screened the differentially expressed genes (DEGs) in RA and control samples, and then took the intersection of it and FRGs. Aiming to construct the protein-protein interaction (PPI) networks of the FRGs-DEGs, STRING database and Cytoscape software 3.7.0 would be used. Furthermore, hub genes were identified by CytoNCA, a Cytoscape plug-in. The gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of FRGs-DEGs were performed. Results: We identified 34 FRGs-DEGs, including 7 upregulated and 27 downregulated genes by taking the intersection of the FRGs and DEGs. PPI analysis identified a total of 3 hub genes(VEGFA, PTGS2, and JUN). GO enrichment analyses and KEGG Pathway enrichment displayed that the FRGs-DEGs are involved in the response to oxidative stress and corticosteroid, heme binding, FoxO-signal pathway. Results of immune infiltration displayed that increased infiltration of T cells, while Macrophages M2 less may be related to the occurrence of RA. Conclusion: The hub genes involved in ferroptosis in RA may be VEGFA, PTGS2, and JUN, which are mainly involved in FoxO-signal pathway. T cell, Mac, and plasma cells may be involved in the regulation of RA-joints-synovial-inflammation.