Integrated gene expression analysis identifies shared inflammatory and metabolic pathways in polycystic ovarian syndrome, rheumatoid arthritis, and osteoarthritis

The present study aimed to compare the molecular mechanisms of rheumatoid arthritis (RA) and osteoarthritis (OA). The microarray dataset no. GSE29746 was downloaded from Gene Expression Omnibus. After data pre-processing, differential expression analysis between the RA group and the control, as well as between the OA group and the control was performed using the LIMMA package in R and differentially expressed transcripts (DETs) with |log2fold change (FC)|>1 and P<0.01 were identified. DETs screened from each disease group were then subjected to functional annotation using DAVID. Next, DETs from each group were used to construct individual interaction networks using the BIND database, followed by sub-network mining using clusterONE. Significant functions of nodes in each sub-network were also investigated. In total, 19 and 281 DETs were screened from the RA and OA groups, respectively, with only six common DETs. DETs from the RA and OA groups were enriched in 8 and 130 gene ontology (GO) terms, respectively, with four common GO terms, of which to were associated with phospholipase C (PLC) activity. In addition, DETs screened from the OA group were enriched in immune response-associated GO terms, and those screened from the RA group were largely associated with biological processes linked with the cell cycle and chromosomes. Genes involved in PLC activity and its regulation were indicated to be altered in RA as well as in OA. Alterations in the expression of cell cycle-associated genes were indicated to be linked with the occurrence of OA, while genes participating in the immune response were involved in the occurrence of RA.

RNA seq study of synovial biopsies from early osteoarthritis and early rheumatoid arthritis patients

Osteoarthritis (OA) and rheumatoid arthritis (RA) are two common diseases that result in limb disability and a decrease in quality of life. The major symptoms of OA and RA are pain, swelling, stiffness, and malformation of joints, and each disease also has unique characteristics. To compare the pathological mechanisms of OA and RA via weighted correlation network analysis (WGCNA) and immune infiltration analysis and find potential diagnostic and pharmaceutical targets for the treatment of OA and RA. The gene expression profiles of ten OA and ten RA synovial tissue samples were downloaded from the Gene Expression Omnibus (GEO) database (GSE55235). After obtaining differentially expressed genes (DEGs) via GEO2R, WGCNA was conducted using an R package, and modules and genes that were highly correlated with OA and RA were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network analyses were also conducted. Hub genes were identified using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Immune infiltration analysis was conducted using the Perl program and CIBERSORT software. Two hundred ninety-nine DEGs, 24 modules, 16 GO enrichment terms, 6 KEGG pathway enrichment terms, 10 hub genes (CXCL9, CXCL10, CXCR4, CD27, CD69, CD3D, IL7R, STAT1, RGS1, and ISG20), and 8 kinds of different infiltrating immune cells (plasma cells, CD8 T cells, activated memory CD4 T cells, T helper follicular cells, M1 macrophages, Tregs, resting mast cells, and neutrophils) were found to be involved in the different pathological mechanisms of OA and RA. Inflammation-associated genes were the top differentially expressed hub genes between OA and RA, and their expression was downregulated in OA. Genes associated with lipid metabolism may have upregulated expression in OA. In addition, immune cells that participate in the adaptive immune response play an important role in RA. OA mainly involves immune cells that are associated with the innate immune response.

BackgroundAtrial fibrillation (AF) is at least partially heritable, affecting 2–3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets.MethodsMicroarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was used to explore the interaction between potential crucial genes and AF.ResultWe identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 or ≤ 2/3 (|log2 FC| ≥ 0.58) and 5 with FC ≥ 2 or ≤ 0.5 (|log2 FC| ≥ 1) of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. CTD showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF.ConclusionsThe 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2, may be associated with risk of AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

BackgroundHepatic fibrosis is a widespread disease worldwide. Millions of people lose their lives due to hepatic fibrosis every year. The main causes of hepatic fibrosis include viral infection, alcoholism, and obesity. Many studies have been conducted on the single factors that cause hepatic fibrosis; however, no studies have examined whether hepatic fibrosis caused by multiple factors has concomitant expression molecules and signaling pathways. In this study, we sought to analyze the common differentially expressed messenger ribonucleic acids (mRNAs) of hepatic fibrosis caused by different factors, including hepatitis B virus (HBV) hepatic fibrosis, alcoholic hepatic fibrosis, and non-alcoholic hepatic fibrosis, and identify potential preventive and therapeutic targets.MethodsThe GSE171294, GSE142530, and GSE126848 datasets from the Gene Expression Omnibus (GEO) public database were used in this study. A |log fold change| >0.5 and a P value <0.05 were defined as differentially expressed mRNAs via R software screening. To further screen the target mRNAs, the differential mRNAs were subjected to a functional enrichment analysis based on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Finally, the relationships between differentially expressed mRNA-encoded proteins were analyzed by a protein-protein interaction (PPI) analysis.ResultsA total of 54 differentially expressed mRNAs were identified. The KEGG analysis showed that the functions of different mRNAs mainly focused on Gonadotropin Releasing Hormone (GnRH) secretion, bile secretion and insulin secretion. The GO enrichment analysis showed that the differential mRNAs were mainly present in the cytoplasmic membrane region and exerted biological functions, such as activating channels and binding proteins by regulating biological processes (BPs), such as cells, cytoskeleton and heparin. The PPI network analysis revealed 16 nodes with 12 pairs of interactions. The 16 critical nodes included BCL6, CD4, CD24, IL32, CALD1, TRAF3, SOX9, KANSL3, MRGBP, PKD2, PKHD1, SYT1, ANXA4, KCNMA1, KCNN2, and CACNA1H.ConclusionsKCNN2, CD4, CD24, BCL6, KCNMA1, and other molecules obtained by the bioinformatics analysis of the RNA-sequencing data can be used as new research targets for hepatic fibrosis induced by different causes. Our findings could provide novel ideas for the treatment of hepatic fibrosis.

BackgroundLiver fibrosis is a transitional stage from hepatitis to cirrhosis, and hepatitis B virus (HBV) is the most common cause of liver disease. Transcriptome sequencing technology and bioinformatics analysis are increasingly being used to screen diagnostic targets for liver fibrosis.MethodsThe GSE171294 dataset of HBV‐induced liver fibrosis tissue and normal tissue was obtained from the Gene Expression Omnibus (GEO) public database and used to screen for differentially expressed mRNAs using R software. mRNAs with |log fold change| &gt;1 and p &lt; 0.05 were considered to be differentially expressed. A heat map was drawn to visualize the expression patterns of the differentially expressed mRNAs. To screen for candidate target mRNAs, the differentially expressed mRNAs were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis. Finally, a protein–protein interaction (PPI) network was constructed to analyze the relationships between the differentially expressed mRNAs.ResultsA total of 243 differentially expressed mRNAs were identified (p &lt; 0.05); 129 were up‐regulated and 114 were down‐regulated. The up‐regulated and down‐regulated mRNAs were significantly enriched in 16 and 8 KEGG pathways, respectively. The enriched KEGG pathways included Salmonella infection, Protein processing in the endoplasmic reticulum, IL‐17 signaling pathway, and Aldosterone synthesis and secretion. The enriched GO terms were related mainly to cell proliferation, apoptosis, endoplasmic reticulum complex assembly, and myosin synthesis. The PPI network contained 161 nodes and 120 pairs of interactions. The top 10 key nodes were CAV1, CD4, NR3C1, PDIA3, EZR, IRF4, SOX9, HSP90AB1, CD40, and SEC13.ConclusionsBioinformatics analysis of the transcriptome sequencing data in the GSE171294 dataset identified CD4, NR3C1, and EZR and other genes at key nodes as new targets for the treatment of liver fibrosis caused by HBV. These results provide new insights for HBV‐induced liver fibrosis research and clinical treatment.

The aim of the present study was to identify potential key candidate genes and mechanisms associated with rheumatoid arthritis (RA). Gene expression data from GSE55235, GSE55457 and GSE1919 datasets were downloaded from the Gene Expression Omnibus database. These datasets comprised 78 tissue samples collectively, including 25 healthy synovial membrane samples and 28 RA synovial membrane samples, whilst the 25 osteoarthritis (OA) samples were not included in the analysis. The differentially expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package in R. Gene Ontology (GO) functional term and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses were also performed. In addition, Protein-Protein Interaction (PPI) network and module analyses were visualized using Cytoscape, and subsequent hub gene identification as well as GO and KEGG enrichment analyses of the modules was performed. Finally, reverse transcription-quantitative PCR (RT-qPCR) was used to validate the expression of the DEGs identified by GO and KEGG analysis in vitro. The analysis identified 491 DEGs, including 289 upregulated and 202 downregulated genes, which were mainly enriched in the following pathways: ‘Cytokine-cytokine receptor interaction’, ‘Rheumatoid arthritis’, ‘Chemokine signaling pathway’, ‘Intestinal immune network for IgA production’ and ‘Primary immunodeficiency’. The top 10 hub genes identified from the PPI network were IL-6, protein tyrosine phosphatase receptor type C, VEGFA, CD86, EGFR, C-X-C chemokine receptor type 4, matrix metalloproteinase 9, CC-chemokine receptor type (CCR)7, CCR5 and selectin L. KEGG signaling pathway enrichment analysis of the top two modules identified from the PPI network revealed that the genes in Module 1 were mainly enriched in the ‘Cytokine-cytokine receptor interaction’ and ‘Chemokine signaling pathway’, whereas analysis of Module 2 revealed that the genes were mainly enriched in ‘Primary immunodeficiency’ and ‘Cytokine-cytokine receptor interaction’. Finally, the results of the RT-qPCR and western blot analysis demonstrated that the expression levels of inflammation and NF-κB signaling pathway-related mRNAs were significantly upregulated following lipopolysaccharide stimulation. In conclusion, the findings of the present study identified key genes and signaling pathways associated with RA, which may improve the current understanding of the molecular mechanisms underlying its development and progression. The identified hub genes may also be used as potential targets for RA diagnosis and treatment.

Rheumatoid arthritis (RA) is a prevalent autoimmune disease with synovial inflammation and hyperplasia, which can potentially cause degradation of articular cartilage, ultimately causing joint deformity, and impaired function. However, exact mechanisms underlying RA remain incompletely understood. This study seeks to uncover genomic signatures and potential biomarkers of RA, along with exploring the biological processes involved. Six microarray datasets from RA patients, osteoarthritis (OA) and healthy controls (HC) of synovial tissue were obtained from the Gene Expression Omnibus (GEO) database for integrated analysis. Differentially expressed genes (DEGs) between groups were identified by "limma" package. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out. Protein-protein interaction (PPI) network was analyzed by STRING and presented by Cytoscape. Weighted gene co-expression network analysis (WGCNA) was conducted to discover and construct the co-expression gene modules correlated with clinical phenotype. CytoHubba and MCODE were utilized for screening hub genes. Additionally, immune cell infiltration analysis was conducted utilizing CIBERSORT algorithm. The correlation of hub genes with immune cells were examined through Pearson Correlation Analysis. The overlapped 92 up-regulated genes were determined between RA versus normal controls and RA versus OA, which were primarily enriched in immune response, lymphocyte activation, and chemokine signaling pathway. By integrating WGCNA, Cytohubba and MCODE algorithms, 16 hub genes were identified including CXCL13, ITK, CXCL9, CCR5, CCR7, NKG7, CCR7, and CD52. We validated the diagnostic significance of these markers in RA by qRT-PCR. Moreover, the analysis of immune cell infiltration demonstrated a positive association between these hub genes with B cell naïve, plasma cell, T cells follicular helper, and macrophages M1. The abundance of these cells was markedly greater in RA compared to OA and normal controls. This research ultimately identified 5 potential diagnostic biomarkers of RA in the synovial tissue, namely NKG7, CD52, ITK, CXCL9, and GZMA. These findings have enhanced our comprehension of RA pathogenesis and identified promising diagnostic and therapeutic targets of RA.

Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory infections in children, yet biomarkers for assessing disease severity remain limited. Herein, we investigated the differential expression biomarkers between RSV infected hospitalized patients, healthy groups and RSV infected outpatients. Two publicly available transcriptomic datasets (GSE77087 and GSE188427) were retrieved from the Gene Expression Omnibus (GEO) database. The GSE77087 dataset comprised peripheral blood samples from 81 children with confirmed RSV infection (61 hospitalized and 20 outpatient) and 23 healthy controls. The GSE188427 dataset included 147 RSV-infected children (113 hospitalized and 34 outpatient) and 51 healthy controls. Genes with |log2 fold change (logFC)| > 0 and false discovery rate (FDR) < 0.05 were considered differentially expressed. Overlapping DEGs between the two datasets were identified using the VennDiagram package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the intersecting DEGs via the clusterProfiler package, with terms deemed significant at FDR < 0.05.The CIBERSORT algorithm was applied to estimate the relative proportions of 22 immune cell types in 228 RSV-infected samples. Potential drug interactions for hug genes were predicted using the Drug-Gene Interaction Database (DGIdb). Competing endogenous RNA (ceRNA) networks were constructed using the SpongeScan database to identify lncRNAs interacting with the target miRNAs. Networks were visualized using Cytoscape (v3.10.1).Finally, Machine Learning-Based Biomarker Selection and hub gene identification and validation. Differential gene expression analysis revealed 81 overlapping genes between hospitalized and outpatient RSV-infected children. Machine learning models, particularly SVM (area under the curve, AUC = 0.950), prioritized CD79A and GADD45A as key predictors of hospitalization. CD79A was significantly downregulated in severe cases, correlating with impaired B-cell responses and cytotoxic immunity, while GADD45A, upregulated in severe infections, linked to oxidative stress and neutrophil-driven inflammation. Immune cell profiling highlighted distinct infiltration patterns, with severe cases showing elevated naïve B cells and M0 macrophages versus activated NK cells and M1 macrophages in mild cases. Clinical validation in 92 children confirmed CD79A suppression and GADD45A elevation in severe RSV (p < 0.001), aligning with younger age, lower weight, and respiratory distress. Functional enrichment implicated endoplasmic reticulum stress and neutrophil extracellular traps in disease progression. Drug-target predictions and ceRNA networks further revealed therapeutic potential. These findings establish CD79A and GADD45A as clinically actionable biomarkers for RSV severity, offering insights into immune dysregulation and guiding personalized management strategies.

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two common joint diseases with similar clinical manifestations. Our study aimed to identify differential gene biomarkers in the synovial tissue between RA and OA using bioinformatics analysis and validation. GSE36700, GSE1919, GSE12021, GSE55235, GSE55584, and GSE55457 datasets were downloaded from the Gene Expression Omnibus database. A total of 57 RA samples and 46 OA samples were included. The differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed. Protein-protein interaction (PPI) network of DEGs and the hub genes were constructed and visualized via Search Tool for the Retrieval of Interacting Genes/Proteins, Cytoscape, and R. Selected hub genes were validated via reverse transcription-polymerase chain reaction. A total of 41 DEGs were identified. GO functional enrichment analysis showed that DEGs were enriched in immune response, signal transduction, regulation of immune response for biological process, in plasma membrane and extracellular region for cell component, and antigen binding and serine-type endopeptidase activity for molecular function. KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. PPI network analysis established 70 nodes and 120 edges and 15 hub genes were identified. The expression of CXCL13, CXCL10, and ADIPOQ was statistically different between RA and OA synovial tissue. Differential expression of CXCL13, CXCL10, and ADIPOQ between RA and OA synovial tissue may provide new insights for understanding the RA development and difference between RA and OA. Key Points • Bioinformatics analysis was used to identify the differentially expressed genes in the synovial tissue between rheumatoid arthritis and osteoarthritis. • CXCL13, CXCL10, and ADIPOQ might provide new insight for understanding the differences between RA and OA.

Identification of key candidate genes and biological pathways in neuropathic pain

Systemic lupus erythematosus (SLE) is an autoimmune disorder characterized by immune dysregulation and chronic inflammation across various organ systems. While anti-dsDNA and anti-Sm antibodies are commonly associated with SLE, the presence of anti-RNP antibodies is often linked to unique gene expression profiles and immune responses. This study aims to investigate the gene expression profiles in ribonucleoprotein (RNP) autoantibody-positive SLE patients by analyzing publicly available transcriptomic data. This study analyzed transcriptomic data from the GEO dataset GSE61635, which includes gene expression profiles from 79 anti-RNP-positive SLE patients and 30 healthy controls. Differentially expressed genes (DEGs) were identified using the GEO2R tool with a p-value < 0.05 and |log2fold change| > 1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Tissue-specific and cell-type enrichment analyses highlighted the involvement of immune tissues. A total of 1891 DEGs were identified between anti-RNP-positive SLE patients and healthy controls. Among the identified DEGs, SLC4A1 and EPB42 were notably downregulated, while PIP4K2A was highly upregulated. Enrichment analyses revealed significant dysregulation in antiviral response and immune regulation pathways. PPI network analysis highlighted key hub genes, suggesting a heightened antiviral state in these patients. Tissue-specific enrichment and cell-type enrichment identified the bone marrow and immune tissues as being highly affected by the altered gene expression. Additionally, gene frequency analysis highlighted RASD2 as being recurrently significant across multiple studies. The findings suggest that anti-RNP-positive SLE patients exhibit distinct gene expression and immune dysregulation profiles, particularly in antiviral and immune regulation pathways. These results provide insights into the molecular mechanisms driving SLE in this patient subset and may guide future therapeutic interventions.

Identification of the etiology, molecular mechanisms, and carcinogenic pathways of tongue squamous cell carcinoma (TSCC) is crucial for developing new diagnostic and therapeutic strategies. This study used bioinformatics methods to identify key genes in TSCC and explored the potential functions and pathway mechanisms related to the malignant biological behavior of TSCC. Gene chip data sets (i.e., GSE13601 and GSE34106) containing the data of both TSCC patients and normal control subjects were selected from the Gene Expression Omnibus (GEO) database. Using a gene expression analysis tool (GEO2R) of the GEO database, the differentially expressed genes (DEGs) were identified using the following criteria: |log fold change| >1, and P<0.05. The GEO2R tool was also used to select the upregulated DEGs in the chip candidates based on a P value <0.05. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO) function analysis, and a protein-protein interaction (PPI) network analysis were then conducted. The results were displayed using R language packages, including volcano plots, Venn diagrams, heatmaps, and enriched pathway bubble charts. Genes from the MalaCards database were compared with the candidate genes, and a thorough review of the literature was conducted to determine the clinical significance of these genes. Finally, feature gene-directed chemical drugs or targeted drugs were predicted using the Comparative Toxicogenomics Database (CTD). In total, 767 upregulated DEGs were identified from GSE13601 and 695 from GSE34106. By intersecting the upregulated DEGs from both data sets using a Venn diagram, 100 DEGs related to TSCC were identified. The enrichment analysis of the KEGG signaling pathways identified the majority of the pathways associated with the upregulated DEGs, including the Toll-like receptor signaling pathway, the extracellular matrix-receptor interaction, the tumor necrosis factor (TNF) signaling pathway, cytokine-cytokine receptor interaction, the chemokine signaling pathway, the interlukin-17 signaling pathway, and natural killer cell-mediated cytotoxicity. The PPI network and module analyses of the shared DEGs ultimately resulted in five clusters and 55 candidate genes. A further intersection analysis of the TSCC-related genes in the MalaCards database via a Venn diagram identified three important shared DEGs; that is, matrix metalloproteinase-1 (MMP1), MMP9, and MMP13. In the CTD, seven drugs related to MMP13 were identified for treating tongue tumors. This study identified key genes and signaling pathways involved in TSCC and thus extended understandings of the molecular mechanisms that underlie the development and progression of TSCC. Additionally, this study showed that MMP13 may influence the malignant biological behavior of TSCC through the TNF signaling pathway. This finding could provide a theoretical basis for research into early differential diagnosis and targeted treatment.

This study analyzed potential key genes involved in the mechanism of acute liver injury induced by sepsis through bioinformatics techniques, aiming to provide novel insights for the identification of early-stage sepsis-induced acute liver injury and its diagnosis. Gene chip data sets containing samples from acute liver injury induced by sepsis and control groups (GSE22009 and GSE60088) were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) with |log fold change| >1 and p<0.05 were screened with the GEO2R tool, which was also used for the selection of upregulated DEGs in the chips with p<0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene Ontology, and protein-protein interaction (PPI) analyses were then conducted. Results were visualized using R language packages, including volcano plots, Venn diagrams, and boxplots. The intersection of candidate genes with relevant genes in the Comparative Toxicogenomics Database (CTD) was performed, and the clinical significance of these genes was explored through a literature review. A rat model of acute liver injury was developed by inducing sepsis with the cecum ligation and puncture method. Real-time PCR was performed to determine the gene expression in rat liver tissues. A total of 646 upregulated DEGs were determined in GSE22009 and 146 in GSE60088. A Venn diagram was used to find the intersection of the upregulated DEGs between the two data sets, and 67 DEGs associated with sepsis-mediated acute liver damage were obtained. Enrichment analysis from the KEGG pathway showed that DEG upregulation was primarily associated with various pathways: TNF, NF-κB, IL-17, ferroptosis, mTOR, and JAK-STAT signaling pathways. DEGs resulted in three clusters and 15 candidate genes, as revealed by the PPI network and module analyses. Intersection with sepsis-induced acute liver injury-related genes in the CTD resulted in the identification of three significant differentially co-expressed genes: CXCL1, ICAM1, and TNFRSF1A. Sepsis-induced liver tissue indicated the overexpression of CXCL1, ICAM1, and TNFRSF1A mRNA, as compared with the control group (p<0.05). The key genes identified and related signaling pathways provided insights into the molecular mechanisms of sepsis-induced acute liver injury. In vivo studies revealed the overexpression of CXCL1, ICAM1, and TNFRSF1A mRNA in sepsis-mediated injured liver tissues, providing a theoretical basis for early diagnosis and targeted treatment research.

BackgroundOvarian cancer (OC) ranks fifth as a cause of gynecological cancer-associated death globally. Until now, the molecular mechanisms underlying the tumorigenesis and prognosis of OC have not been fully understood. This study aims to identify hub genes and therapeutic drugs involved in OC.MethodsFour gene expression profiles (GSE54388, GSE69428, GSE36668, and GSE40595) were downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in OC tissues and normal tissues with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.0 were first identified by GEO2R and FunRich software. Next, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed for functional enrichment analysis of these DEGs. Then, the hub genes were identified by the cytoHubba plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, survival analysis, and miRNA-hub gene network construction was also performed. Finally, the GEPIA2 and DGIdb databases were utilized to verify the expression levels of hub genes and to select the candidate drugs for OC, respectively.ResultsA total of 171 DEGs were identified, including 114 upregulated and 57 downregulated DEGs. The results of the GO analysis indicated that the upregulated DEGs were mainly involved in cell division, nucleus, and protein binding, whereas the biological functions showing enrichment in the downregulated DEGs were mainly negative regulation of transcription from RNA polymerase II promoter, protein complex and apicolateral plasma membrane, and glycosaminoglycan binding. As for the KEGG-pathway, the upregulated DEGs were mainly associated with metabolic pathways, biosynthesis of antibiotics, biosynthesis of amino acids, cell cycle, and HTLV-I infection. Additionally, 10 hub genes (KIF4A, CDC20, CCNB2, TOP2A, RRM2, TYMS, KIF11, BIRC5, BUB1B, and FOXM1) were identified and survival analysis of these hub genes showed that OC patients with the high-expression of CCNB2, TYMS, KIF11, KIF4A, BIRC5, BUB1B, FOXM1, and CDC20 were statistically more likely to have poorer progression free survival. Meanwhile, the expression levels of the hub genes based on GEPIA2 were in accordance with those based on GEO. Finally, DGIdb database was used to identify 62 small molecules as the potentially targeted drugs for OC treatment.ConclusionsIn summary, the data may produce new insights regarding OC pathogenesis and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for OC in future.