Identification of Significant Genes And Pathways In ARDS Via Bioinformatical Analysis

Enrichment Analysis ◽

Receptor Interaction ◽

Integrated Analysis ◽

Signal Pathways ◽

Ppi Network ◽

Hub Genes

Abstract Background and Aims: Acute respiratory distress syndrome (ARDS) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS.Methods: Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL 339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis, protein-protein interaction (PPI) network construction, Transcription Factors (TFs) forecasting, and their expression in varied organs.Results: A total of 39 differential expressed genes were screened from the datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, cellular response to interferon-beta and also involved in some signal pathways, including cytokine-cytokine receptor interaction, salmonella infection, legionellosis, chemokine, and Toll-like receptor signal pathway. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. Conclusions: This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.

Identification of significant alteration genes, pathways and TFs induced by LPS in ARDS via bioinformatical analysis

BMC Infectious Diseases ◽

10.1186/s12879-021-06578-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Weina Lu ◽

Ran Ji

Keyword(s):

Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Integrated Analysis ◽

Signal Pathways ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction

Abstract Background and aims Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS. Methods Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis in DAVID database, protein–protein interaction (PPI) network construction evaluated by the online database STRING, Transcription Factors (TFs) forecasting based on the Cytoscape plugin iRegulon, and their expression in varied organs in The Human Protein Atlas. Results A total of 39 differential expressed genes were screened from the two datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, and also involved in some signal pathways, including cytokine–cytokine receptor interaction, Salmonella infection, Legionellosis, Chemokine, and Toll-like receptor signal pathway with an integrated analysis. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. Conclusions This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.

Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Analyzing gene expression profiles with preliminary validations in cardiac hypertrophy induced by pressure overload

Canadian Journal of Physiology and Pharmacology ◽

10.1139/cjpp-2017-0585 ◽

2018 ◽

Vol 96 (8) ◽

pp. 701-709 ◽

Cited By ~ 1

Author(s):

Jing Gao ◽

Yuhong Li ◽

Tongmei Wang ◽

Zhuo Shi ◽

Yiqi Zhang ◽

...

Keyword(s):

Gene Expression ◽

Cardiac Hypertrophy ◽

Expression Profiles ◽

Pressure Overload ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ontology Term ◽

The Common

The aim of this study was to identify the key genes involved in the cardiac hypertrophy (CH) induced by pressure overload. mRNA microarray data sets GSE5500 and GSE18801 were downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were screened using the Limma package; then, functional and pathway enrichment analysis were performed for common DEGs using the Database for Annotation, Visualization and Integrated Discovery database. Furthermore, the top DEGs were further validated using quantitative PCR in the hypertrophic heart tissue induced by isoprenaline. A total of 113 common DEGs with absolute fold change > 0.5, including 60 significantly upregulated DEGs and 53 downregulated DEGs, were obtained. Gene ontology term enrichment analysis suggested that common upregulated DEG were mainly enriched in neutrophil chemotaxis, extracellular fibril organization, and cell proliferation; and the common downregulated genes were significantly enriched in ion transport, endoplasmic reticulum, and dendritic spine. Kyoto Encyclopedia of Genes and Genomes pathway analysis found that the common DEGs were mainly enriched in extracellular matrix receptor interaction, phagosome, and focal adhesion. Additionally, the expression of Mfap4, Ltbp2, Aspn, Serpina3n, and Cnksr1 were upregulated in the model of CH, while the expression of Anp32a was downregulated. The current study identified the key deregulated genes and pathways involved in the CH, which could shed new light to understand the mechanism of CH.

Identification of Prognostic Biomarkers of Potential Hub Genes in Urothelial Carcinoma and Function in Microenvironment

10.21203/rs.3.rs-502631/v2 ◽

2021 ◽

Author(s):

Wei Chu ◽

Bing Zhang ◽

Haifeng Gong ◽

Qianqian Zhao ◽

Jun Chen ◽

...

Keyword(s):

T Cells ◽

Urothelial Carcinoma ◽

Checkpoint Inhibitors ◽

Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Hub Genes ◽

Database Analysis ◽

Normal Tissues

Abstract Background: Urothelial carcinoma (UC) is the most common histological type of urinary system. In the past decades, despite the advances in UC diagnosis and therapy, there are still challenges to improve the overall survival (OS) of UC patients. PD-L1 inhibitor and PD-1 inhibitor have been approved for treating invasive UC, however, only about 20% of patients with metastatic UC show clinical benefits from immune checkpoint inhibitors. Therefor, bioinformatics tools were utilized to screen prognostic-related biomarkers, and analyze their relationship with immunocyte in UC, hoping to provide new ideas for the clinical treatment of UC patients.Methods: Three gene expression profiles (i.e. GSE32548, GSE32894 and GSE48075) were selected from GEO, and divide them into invasive and superficial UC group for study. NetworkAnalyst tool was used to construct gene regulatory network of DEGs, while DAVID and Metascape were utilized to perform GO/KEGG enrichment analysis of DEGs. The hub genes were screened by STRING and cytoscape, and the ONCOMINE, GEPIA, UALCAN, cBioPortal and HPA databases were used to analyze the expression differences at the DNA, RNA, protein levels and prognostic of UC. TIMER was used to analyze the relationship between hub genes and immunocyte infiltration.Results: In total, 63 DEGs were identified from the GEO database of UC, of which 31 and 32 were up-and down-regulated. GO/KEGG pathway analysis identified DEGs were mainly enriched in the collagen catabolic process, extracellular matrix (ECM) organization, ECM structural constituent and ECM-receptor interaction. Nine hub genes (i.e. COL1A1, COL1A2, COL3A1, COL5A2, MMP9, POSTN, SPP1, VCAN and THBS2) upregulated in invasive UC compared with superficial UC were identified. cBioportal database analysis showed that 35% of UC patients presented genetic variants in the hub genes, of which amplification and deletion mutations were the most common. ONCOMINE and UALCAN database analysis showed that the mRNA expression of all hub genes in invasive UC was significantly higher than that in superficial UC and normal tissues. HPA database analysis showed that there was up-regulation of COL3A1, SPP1, POSTN and VCAN protein in UC tissues than in normal tissues. GEPIA showed that COL1A2, COL3A1, THBS2, and VCAN were positively correlated with the OS rate among patients with UC (P < 0.05). UALCAN showed that UC patients with high expression of COL1A1, COL1A2, COL5A2 and POSTN had a poorer prognosis (P < 0.05). TRRUST database analysis indicated that there was a significant correlation between the expression of the hub genes and the infiltration of CD4+T cells, CD8+T cells, macrophages, neutrophils and dendritic cells. Conclusion: Hub genes played important roles in pathogenesis and treatment prognosis of UC and they can provides new biomolecular predictions for immunotherapy and prognosis judgment of UC.

Identification of Key Genes and Pathways in SARS-CoV-2 Infection using Bioinformatics Analysis

10.21203/rs.3.rs-72821/v1 ◽

2020 ◽

Author(s):

Zhe Wang ◽

Chenhao Jiang ◽

Xuxuan Zhang ◽

Yingna Zhang ◽

Yan Ren ◽

...

Keyword(s):

High Throughput Sequencing ◽

Expression Profiles ◽

Severe Pneumonia ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Airway Epithelial ◽

Hub Genes ◽

Bioinformatics Analyses ◽

Human Airway

Abstract Background: Coronavirus disease 2019 (COVID-19) is a disease that causes fatal disorders including severe pneumonia. Our study aimed to utilize bioinformatics method to analyze the expression profiling by high throughput sequencing in human bronchial organoids/primary human airway epithelial infected with SARS-CoV-2 to identify the potentially crucial genes and pathways associated with COVID-19.Methods: We analyzed microarray datasets GSE153970 and GSE150819 derived from the GEO database. Firstly, the Differentially expressed genes （DEGs） in human bronchial organoids/primary human airway epithelial infected with SARS-CoV-2. Next, the DEGs were used for GO and KEGG pathway enrichment analysis. Then, the PPI network was constructed and Cytoscape was used to find the key genes.Results: Gene expression profiles of GSE153970 and GSE150819, in all 12 samples were analyzed. A total of 145 DEGs and 5 hub genes were identified in SARS-CoV-2. Meanwhile, we found that the 145 genes are associated with immune responses and the top 5 hub genes including CXCL8, CXCL1, CXCL2, CCL20, and CSF2 were mainly related to leukocyte migration, endoplasmic reticulum lumen, receptor ligand activity. In addition, the results also showed that the hub genes were associated with Cytokine−cytokine receptor interaction, IL−17 signaling pathway, and Rheumatoid arthritis in SARS-CoV-2 infection.Conclusion: The five crucial genes consisting of CXCL8, CXCL1, CXCL2, CCL20, and CSF2 were considered as hub genes of SARS-CoV-2, which may be used as diagnostic biomarkers or molecular targets for the treatment of SARS-CoV-2. It is evidenced that bioinformatics analyses in SARS-CoV-2 can be useful for understanding the underlying molecular mechanism and exploring effective therapeutic targets.

POS0743 GENE EXPRESSION MICROARRAY IN LUPUS NEPHRITIS BY BIOINFORMATIC ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.2062 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 623.1-623

Author(s):

C. Wang ◽

S. X. Zhang ◽

S. Song ◽

J. Qiao ◽

R. Zhao ◽

...

Keyword(s):

Lupus Nephritis ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Deep Understanding ◽

Enrichment Analysis ◽

Shanxi Province ◽

Ppi Network ◽

Hub Genes

Background:Nephritis is one of the predominant causes of morbidity and mortality in patients with lupus1 2.The lack of understanding regarding the molecular mechanisms of lupus nephritis(LN) hinders the development of specific targeted therapy for this progressive disease3.Objectives:In this study, we use bioinformatics method to analyze the genes involved in regulating the potential pathogenesis of LN.Methods:The expression profile of LN(GSE104948 and GSE32591) was obtained from the GEO database.GSE104948 was a memory chip, which included 32 LN glomerular biopsy tissues and 3 glomerular tissues from living donors.GSE32591 dataset included 32 LN glomerular biopsy tissues and 15 glomerular tissues from living donors. The Oligo package was used to process the data to obtain the expression matrix files of all the related genes.P<0.05 and |log2(FC)|>2 were setted as cut-off criteria for the DEGs.Ggplot2, heatmap packages were used to DEGs visualization. Metascape online tool was used to annotating DEGs for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis performed.We used STRING online database to construct protein-protein interaction (PPI) network. Hub genes were identified by Cytoscape.Results:In differential expression analysis,357 DEGs were identified,including 248 up-regulated genes and 109 down-regulated genes (Figure 1A,B).GO enrichment showed that these DEGs were primarily enriched in biological pathways, cell localization and molecular function and revealed that LN-related genes mainly involved in immune response.KEGG pathway annotation enrichment analysis revealed these DEGs were closely associated with Staphylococcus aureus infection,Complement and coagulation cascades (Figure 1D). Fourteen hub genes(IFT3,IRF7,OAS3,GBP2,RSAD2,MX1,IFIT2,IFI6,MX2,ISF15,IFIT1,QAS2,OASL,OAS1) were identified from PPI network (Figure 1C,E).Conclusion:Illuminating the molecular mechanisms of LN was help for deep understanding of LN.References:[1]Song J, Zhao L, Li Y. Comprehensive bioinformatics analysis of mRNA expression profiles and identification of a miRNA-mRNA network associated with lupus nephritis. Lupus 2020;29(8):854-61. doi: 10.1177/0961203320925155 [published Online First: 2020/05/22].[2]Yao F, Sun L, Fang W, et al. HsamiR3715p inhibits human mesangial cell proliferation and promotes apoptosis in lupus nephritis by directly targeting hypoxiainducible factor 1alpha. Mol Med Rep 2016;14(6):5693-98. doi: 10.3892/mmr.2016.5939 [published Online First: 2016/11/24].[3]Dall’Era M. Treatment of lupus nephritis: current paradigms and emerging strategies. Curr Opin Rheumatol 2017;29(3):241-47. doi: 10.1097/BOR.0000000000000381 [published Online First: 2017/02/17].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

Identification of candidate genes and proteins in aging skeletal muscle (sarcopenia) using gene expression and structural analysis

PeerJ ◽

10.7717/peerj.5239 ◽

2018 ◽

Vol 6 ◽

pp. e5239 ◽

Cited By ~ 11

Author(s):

Gita Shafiee ◽

Yazdan Asgari ◽

Akbar Soltani ◽

Bagher Larijani ◽

Ramin Heshmat

Keyword(s):

Gene Expression ◽

Skeletal Muscle ◽

Ribosome Biogenesis ◽

Expression Profiles ◽

Enrichment Analysis ◽

Lipid Storage ◽

Integrated Analysis ◽

Ppi Network ◽

Men And Women

Sarcopenia is an age-related disease characterized by the loss of muscle mass and muscle function. A proper understanding of its pathogenesis and mechanisms may lead to new strategies for diagnosis and treatment of the disease. This study aims to discover the underlying genes, proteins, and pathways associated with sarcopenia in both genders. Integrated analysis of microarray datasets has been performed to identify differentially expressed genes (DEGs) between old and young skeletal muscles. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed to uncover the functions of the DEGs. Moreover, a protein–protein interaction (PPI) network was constructed based on the DEGs. We have identified 41,715 DEGs, including 19 downregulated and 41,696 upregulated ones, in men. Among women, 3,015 DEGs have been found, with 2,874 of them being upregulated and 141 downregulated genes. Among the top up-regulated and downregulated genes, the ribosome biogenesis genes and genes involved in lipid storage may be closely related to aging muscles in men and women respectively. Also, the DEGs were enriched in the pathways including those of ribosome and Peroxisome proliferator-activated receptor (PPAR) in men and women, respectively. In the PPI network, Neurotrophic Receptor Tyrosine Kinase 1 (NTRK1), Cullin 3 (CUL3) and P53 have been identified as significant hub proteins in both genders. Using the integrated analysis of multiple gene expression profiles, we propose that the ribosome biogenesis genes and those involved in lipid storage would be promising markers for sarcopenia in men and women, respectively. In the reconstructed PPI network, neurotrophic factors expressed in skeletal muscle are essential for motoneuron survival and muscle fiber innervation during development. Cullin E3 ubiquitin ligase (Cul3) is an important component of the ubiquitin–proteasome system—it regulates the proteolysis. P53 is recognized as a central regulator of the cell cycle and apoptosis. These proteins, which have been identified as the most significant hubs, may be involved in aging muscle and sarcopenia.

Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/2085173 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Yiran Li ◽

Hongyan Zhang ◽

Jinyan Shao ◽

Jindong Chen ◽

Tiancheng Zhang ◽

...

Keyword(s):

Prion Diseases ◽

Expression Profiles ◽

Enrichment Analysis ◽

Signal Pathways ◽

Ppi Network ◽

Protein Protein Interaction ◽

Search Tool ◽

Rising Incidence ◽

Medical Burden

Purpose. Sepsis becomes the main death reason in hospitals with rising incidence, causing a growing economic and medical burden. However, the genes related to the pathogenesis and prognosis of sepsis are still unclear, which is a problem that needs to be solved urgently. Materials and Methods. Gene expression profiles of GSE69528 were obtained from the National Center for Biotechnology Information. Limma software package got employed to search for differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for enrichment analysis. Protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes (STRING) database. Results. We screened 101 DEGs, containing 81 upregulated DEGs and 20 downregulated DEGs. GO analysis demonstrated that the upregulated DEGs were chiefly concentrated in negative regulation of response to interferon-gamma and regulation of granulocyte differentiation. KEGG analysis revealed that the pathways of upregulated DEGs were concentrated in prion diseases, complement and coagulation cascades, and Staphylococcus aureus infection. The PPI network constructed by upregulated DEGs contained 67 nodes (proteins) and 110 edges (interactions). Analysis of bioinformatics results showed that CEACAM8, MPO, and RETN were hub genes of sepsis. Conclusion. Our analysis reveals a series of signal pathways and key genes related to the mechanism of sepsis, which are promising biotargets and biomarkers of sepsis.

Prognostic values and prospective pathway signaling of MicroRNA-182 in ovarian cancer: a study based on gene expression omnibus (GEO) and bioinformatics analysis

Journal of Ovarian Research ◽

10.1186/s13048-019-0580-7 ◽

2019 ◽

Vol 12 (1) ◽

Author(s):

Yaowei Li ◽

Li Li

Keyword(s):

Gene Expression ◽

Target Genes ◽

Gynecological Cancer ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction

Abstract Background Ovarian carcinoma (OC) is a common cause of death among women with gynecological cancer. MicroRNAs (miRNAs) are believed to have vital roles in tumorigenesis of OC. Although miRNAs are broadly recognized in OC, the role of has-miR-182-5p (miR-182) in OC is still not fully elucidated. Methods We evaluated the significance of miR-182 expression in OC by using analysis of a public dataset from the Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we downloaded three mRNA datasets of OC and normal ovarian tissues (NOTs), GSE14407, GSE18520 and GSE36668, from GEO to identify differentially expressed genes (DEGs). Then the targeted genes of hsa-miR-182-5p (TG_miRNA-182-5p) were predicted using miRWALK3.0. Subsequently, we analyzed the gene overlaps integrated between DEGs in OC and predicted target genes of miR-182 by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and the prognostic effects of the hub genes were analyzed. Results A common pattern of up-regulation for miR-182 in OC was found in our review of the literature. A total of 268 DEGs, both OC-related and miR-182-related, were identified, of which 133 genes were discovered from the PPI network. A number of DEGs were enriched in extracellular matrix organization, pathways in cancer, focal adhesion, and ECM-receptor interaction. Two hub genes, MCM3 and GINS2, were significantly associated with worse overall survival of patients with OC. Furthermore, we identified covert miR-182-related genes that might participate in OC by network analysis, such as DCN, AKT3, and TIMP2. The expressions of these genes were all down-regulated and negatively correlated with miR-182 in OC. Conclusions Our study suggests that miR-182 is essential for the biological progression of OC.