Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in severe acute respiratory syndrome corona virus 2 infection/COVID 19

Abstract Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying SARS-CoV-2 infection remain unclear. Here we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of SARS-CoV-2 infection. Expression profiling by high throughput sequencing (GSE149273) was downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in remdesivir traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.3 were first identified by limma in R software package. Next, Pathway and Gene Ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the Network Analyzer plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, target gene - miRNA regulatory network, and target gene - TF regulatory network construction was also performed. Finally, receiver‐operating characteristic (ROC) analyses were for diagnostic values associated with hub genes. A total of 909 DEGs were identified, including 453 up regulated genes and 457 down regulated genes. As for the pathway and GO enrichment analysis, the up regulated genes were mainly linked with influenza A and defense response, whereas down regulated genes were mainly linked with Drug metabolism - cytochrome P450 and reproductive process. Additionally, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1 and APBB1) were identified. ROC analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5 and PDE1A) had good diagnostic values. In summary, the data may produce new insights regarding pathogenesis of SARS-CoV-2 infection and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for SARS-CoV-2 infection in future.

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Transcriptome Signatures Reveal Candidate Key Genes in the Peripheral Blood Mononuclear Cells of Patients With Coronary Artery Disease

10.21203/rs.3.rs-122558/v1 ◽

2020 ◽

Author(s):

Vijayakrishna Kolur ◽

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti ◽

Anandkumar Tengli

Keyword(s):

Coronary Artery Disease ◽

Gene Ontology ◽

Coronary Artery ◽

Regulatory Network ◽

Target Gene ◽

Enrichment Analysis ◽

Hub Genes ◽

Go Enrichment ◽

Artery Disease ◽

Go Enrichment Analysis

Abstract BackgroundCoronary artery disease (CAD) is one of the most common disorders in the cardiovascular system. This study aims to explore potential signaling pathways and important biomarkers that drive CAD development. MethodsThe CAD GEO Dataset GSE113079 was featured to screen differentially expressed genes (DEGs). The pathway and Gene Ontology (GO) enrichment analysis of DEGs were analyzed using the ToppGene. We screened hub and target genes from protein-protein interaction (PPI) networks, target gene - miRNA regulatory network and target gene - TF regulatory network, and Cytoscape software. Validations of hub genes were performed to evaluate their potential prognostic and diagnostic value for CAD. Results1,036 DEGs were captured according to screening criteria (525upregulated genes and 511downregulated genes). Pathway and Gene Ontology (GO) enrichment analysis of DEGs revealed that these up and down regulated genes are mainly enriched in thyronamine and iodothyronamine metabolism, cytokine-cytokine receptor interaction, nervous system process, cell cycle and nuclear membrane. Hub genes were validated to find out potential prognostic biomarkers, diagnostic biomarkers and novel therapeutic target for CAD. ConclusionsIn summary, our findings discovered pivotal gene expression signatures and signaling pathways in the progression of CAD. CAPN13, ACTBL2, ERBB3, GATA4, GNB4, NOTCH2, EXOSC10, RNF2, PSMA1 and PRKAA1 might contribute to the progression of CAD, which could have potential as biomarkers or therapeutic targets for CAD.

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Transcriptome signatures reveal candidate key genes in the peripheral blood mononuclear cells of patients with coronary artery disease and prediction of small drug molecules

10.21203/rs.3.rs-122558/v2 ◽

2020 ◽

Author(s):

Vijayakrishna Kolur ◽

Basavaraj Vastrad ◽

Anandkumar Tengli ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Coronary Artery Disease ◽

Gene Ontology ◽

Coronary Artery ◽

Regulatory Network ◽

Target Gene ◽

Enrichment Analysis ◽

Hub Genes ◽

Go Enrichment ◽

Artery Disease ◽

Go Enrichment Analysis

Abstract Coronary artery disease (CAD) is one of the most common disorders in the cardiovascular system. This study aims to explore potential signaling pathways and important biomarkers that drive CAD development. The CAD GEO Dataset GSE113079 was featured to screen differentially expressed genes (DEGs). The pathway and Gene Ontology (GO) enrichment analysis of DEGs were analyzed using the ToppGene. We screened hub and target genes from protein-protein interaction (PPI) networks, target gene - miRNA regulatory network and target gene - TF regulatory network, and Cytoscape software. Validations of hub genes were performed to evaluate their potential prognostic and diagnostic value for CAD. A final, molecular docking study was performed. 1,036 DEGs were captured according to screening criteria (525upregulated genes and 511downregulated genes). Pathway and Gene Ontology (GO) enrichment analysis of DEGs revealed that these up and down regulated genes are mainly enriched in thyronamine and iodothyronamine metabolism, cytokine-cytokine receptor interaction, nervous system process, cell cycle and nuclear membrane. Hub genes were validated to find out potential prognostic biomarkers, diagnostic biomarkers and novel therapeutic target for CAD. A small drug molecule was predicted. In summary, our findings discovered pivotal gene expression signatures and signaling pathways in the progression of CAD. CAPN13, ACTBL2, ERBB3, GATA4, GNB4, NOTCH2, EXOSC10, RNF2, PSMA1 and PRKAA1 might contribute to the progression of CAD, which could have potential as biomarkers or therapeutic targets for CAD.

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Identification of potential core genes in hepatoblastoma via bioinformatics analysis

10.1101/2020.12.22.20248756 ◽

2020 ◽

Author(s):

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Gene Ontology ◽

Regulatory Network ◽

Target Gene ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Metabolic Process ◽

Chromosome Organization ◽

Hub Genes ◽

Go Enrichment ◽

Go Enrichment Analysis

AbstractHepatoblastoma is the childhood liver cancer. Profound efforts have been made to illuminate the pathology, but the molecular mechanisms of hepatoblastoma are still not well understood. To identify the candidate genes in the carcinogenesis and progression of hepatoblastoma, microarray dataset GSE131329 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and pathway and Gene Ontology (GO) enrichment analysis were performed. The protein-protein interaction network (PPI), module analysis, target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed. A total of 996 DEGs were identified, consisting of 499 up regulated genes and 497 down regulated genes. The pathway and Gene Ontology (GO) enrichment analysis of the DEGs include proline biosynthesis, superpathway of tryptophan utilization, chromosome organization and organic acid metabolic process. Twenty-four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell cycle, chromosome organization, lipid metabolic process and oxidation-reduction process. Validation of hub genes showed that TP53, PLK1, AURKA, CDK1, ANLN, ESR1, FGB, ACAT1, GOT1 and ALAS1 may be involved in the carcinogenesis, invasion or recurrence of hepatoblastoma. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of hepatoblastoma, and provide candidate targets for diagnosis and treatment of hepatoblastoma.

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Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis

Bioinformatics and Biology Insights ◽

10.1177/11779322211067365 ◽

2021 ◽

Vol 15 ◽

pp. 117793222110673

Author(s):

G Prashanth ◽

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Shivakumar Kotrashetti

Keyword(s):

Target Gene ◽

Operating Characteristic ◽

Receiver Operating Characteristic Analysis ◽

Enrichment Analysis ◽

Characteristic Analysis ◽

Hub Genes ◽

Go Enrichment ◽

Diagnostic Values ◽

Upregulated Genes ◽

Go Enrichment Analysis

Introduction: Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected patients. Methods: Expression profiling by high-throughput sequencing dataset (GSE149273) was downloaded from the Gene Expression Omnibus, and the differentially expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples with an adjusted P value of <.05 and a |log fold change| > 1.3 were first identified by limma in R software package. Next, pathway and gene ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the NetworkAnalyzer plugin and the other bioinformatics approaches including protein-protein interaction network analysis, module analysis, target gene—miRNA regulatory network, and target gene—TF regulatory network. Finally, a receiver-operating characteristic analysis was performed for diagnostic values associated with hub genes. Results: A total of 909 DEGs were identified, including 453 upregulated genes and 457 downregulated genes. As for the pathway and GO enrichment analysis, the upregulated genes were mainly linked with influenza A and defense response, whereas downregulated genes were mainly linked with drug metabolism—cytochrome P450 and reproductive process. In addition, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and APBB1) were identified. Receiver-operating characteristic analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A) had good diagnostic values. Conclusion: This study provided insights into the molecular mechanism of remdesivir-treated SARS-CoV-2 infection that might be useful in further investigations.

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Mining Featured Biomarkers Linked with Epithelial Ovarian Cancer Based on Bioinformatics

Diagnostics ◽

10.3390/diagnostics9020039 ◽

2019 ◽

Vol 9 (2) ◽

pp. 39

Author(s):

◽

Chanabasayya Vastrad ◽

◽

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Target Gene ◽

Enrichment Analysis ◽

Microrna Target ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Go Enrichment ◽

Gene Regulatory ◽

Go Enrichment Analysis

: Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors ) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C) , VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.

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Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis

10.1101/2020.12.21.423732 ◽

2020 ◽

Author(s):

Vikrant Ghatnatti ◽

Basavaraj Vastrad ◽

Swetha Patil ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Gene Ontology ◽

Signaling Pathways ◽

Target Gene ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Condensed Chromosome ◽

Go Enrichment ◽

Go Enrichment Analysis

AbstractPituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was acquired from Gene Expression Omnibus datasets, which included 5 pituitary prolactinoma samples and 4 normal pituitaries samples. We screened differentially expressed genes (DEGs) with limma and investigated their biological function by pathway and Gene Ontology (GO) enrichment analysis. A protein-protein interaction (PPI) network of the up and down DEGs were constructed and analyzed by HIPPIE and Cytoscape software. Module analyses were performed. In addition, a target gene - miRNA network and target gene - TF network of the up and down DEGs were constructed by NetworkAnalyst and Cytoscape software. The set of DEGs exhibited an intersection consisting of 989 genes (461 up-regulated and 528 down-regulated), which may be associated with pituitary prolactinoma. Pathway enrichment analysis showed that the 989 DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis also showed that sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene miRNA network and target gene - TF network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which may improve our understanding of the mechanisms of the pathogenesis and integration; genes may be therapeutic targets and prognostic markers for pituitary prolactinoma.

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Identification of Key Genes Potentially Related to Triple Receptor Negative Breast Cancer by Microarray Analysis

10.1101/2020.12.21.423796 ◽

2020 ◽

Author(s):

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cell Cycle ◽

Immune Response ◽

Target Gene ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Go Enrichment ◽

Go Enrichment Analysis

AbstractTriple receptor negative breast cancer (TNBC) is the type of gynecological cancer in the elderly women. This study is aimed to explore molecular mechanism of TNBC via bioinformatics analysis. The gene expression profiles of GSE88715 (including 38 TNBC and 38 normal control) was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the limma package in R software. Pathway and gene ontology (GO) enrichment analysis were performed based on various pathway dabases and GO database. Then, InnateDb interactome database, Cytoscape and PEWCC1 were applied to construct the protein-protein interaction (PPI) network and screen hub genes. Similarly, miRNet database, NetworkAnalyst database and Cytoscape were applied to construct the target gene - miRNA network and target gene - TF network, and screen targate genes. Pathway and GO enrichment analysis was further performed for hub genes, gene clusters identified via module analysis and targate genes. The expression of hub genes with prognostic values was validated on the UALCAN, cBio Portal, The Human Protein Atlas, receiver operator characteristic (ROC) curve analysis, RT-PCR analysis and immune infiltration analysis. A total of 949 DEGs were identified in TNBC (469 up regulated genes, and 480 down regulated genes), and they were mainly enriched in the terms of phospholipases, toxoplasmosis, immune response, cell surface, glycolysis, biosynthesis of amino acids, carboxylic acid metabolic process and organic substance catabolic process extracellular space. Hub genes including UBD, HLA-B, MYC and HSP90AB1 were identified via PPI network and modules, which were mainly enriched in immune response, antigen processing and presentation, cell cycle and pathways in cancer. Targate genes including CCDC80, PEG10, HOPX and CCNA2 were identified via target gene - miRNA network and target gene - TF network, which were mainly enriched in extracellular structure organization, validated targets of C-MYC transcriptional activation, ensemble of genes encoding core extracellular matrix including ECM glycoproteins and cell cycle. The top five significantly overexpressed mRNA (ADAM15, BATF, NOTCH3, ITGAX and SDC1) and the top five significantly underexpressed mRNA (RPL4, EEF1G, RPL3, RBMX and ABCC2) were selected for further validation in TNBCpatients and healthy controls. Analysis of the expression of genes in the various databases showed that ADAM15, BATF, NOTCH3, ITGAX, SDC1, RPL4, EEF1G, RPL3, RBMX and ABCC2 expressions have a cancer specific pattern in TNBC. Collectively, ADAM15, BATF, NOTCH3, ITGAX, SDC1, RPL4, EEF1G, RPL3, RBMX and ABCC2 may be useful candidate biomarkers for TNBC diagnosis, prognosis and theraputic targates.

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Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses

10.1101/2020.12.21.20248688 ◽

2020 ◽

Author(s):

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Regulatory Network ◽

Molecular Mechanisms ◽

Target Genes ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Bioinformatics Analyses ◽

Go Enrichment ◽

Jakob Disease

AbstractSporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. Pathway and GO enrichment analyses of DEGs were performed. Furthermore, the protein-protein interaction (PPI) network was predicted using the IntAct Molecular Interaction Database and visualized with Cytoscape software. In addition, hub genes and important modules were selected based on the network. Finally, we constructed target genes - miRNA regulatory network and target genes - TF regulatory network. Hub genes were validated. A total of 891 DEGs 448 of these DEGs presented significant up regulated, and the remaining 443 down regulated were obtained. Pathway enrichment analysis indicated that up regulated genes were mainly linked with glutamine degradation/glutamate biosynthesis, while the down regulated genes were involved in melatonin degradation. GO enrichment analyses indicated that up regulated genes were mainly linked with chemical synaptic transmission, while the down regulated genes were involved in regulation of immune system process. hub and target genes were selected from the PPI network, modules, and target genes - miRNA regulatory network and target genes - TF regulatory network namely YWHAZ, GABARAPL1, EZR, CEBPA, HSPB8, TUBB2A and CDK14. The current study sheds light on the molecular mechanisms of sCJD and may provide molecular targets and diagnostic biomarkers for sCJD.

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Identification of the Core MicroRNAs and Potential Molecular Mechanismsin Sarcoidosis Using Bioinformatics Analysis

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.644232 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yuan Cao ◽

Hua Zhang ◽

Lulu Zheng ◽

Qiao Li

Keyword(s):

Regulatory Networks ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Hub Genes ◽

The Core ◽

Ppi Networks ◽

Go Enrichment ◽

Mirnas Expression ◽

Go Enrichment Analysis ◽

Insight Into

Sarcoidosis is a systemic heterogeneous inflammatory disease; however, the etiology and pathogenesis of sarcoidosis are still unknown. Herein, we investigated the core microRNAs and potential molecular mechanisms in sarcoidosis. The DE-miRNAs were diagnosed using the LIMMA software package. DIANA-mirPath was employed to perform pathway and GO enrichment analysis of the DE-miRNAs. PPI networks and miRNA-target gene regulatory networks were used to obtain insight into the actions of DE-miRNAs. Expression of the hub genes along with miRNAs was validated in clinical specimens. Overall, 266 DE-miRNAs were screened. Among these DE-miRNAs, hsa-miR-144, hsa-miR-126, as well as hsa-miR-106a were the upmost upregulated miRNAs; hsa-miR-151-3p, hsa-miR-320d, and hsa-miR-324-3p were the top downregulated miRNAs. NR3C1, ZBTB7A, NUFIP2, BZW1, ERGIC2, and VEGFA were mapped as the most targeted hub genes in the upregulation of miRNAs, and MCL1 and SAE1 were the most targeted hub genes in the downregulation of miRNA. VEGFA and NR3C1 were selected and potentially modulated by hsa-miR-20b, hsa-miR-126, and hsa-miR-106a. In sarcoidosis pathological tissue, hsa-miR-126 was highly expressed, and VEGFA and NR3C1 were overexpressed. In conclusion, our results revealed the dysregulation of hsa-miR-126 and a potential regulatory mechanism for pathogenesis in sarcoidosis.

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Identification of Hub Genes Associated With Progression and Prognosis of Bladder Cancer by Integrated Bioinformatics Analysis.

10.21203/rs.3.rs-139588/v1 ◽

2021 ◽

Author(s):

Daoquan Liu ◽

Jianhong Ma ◽

Sheng Wei ◽

Jianmin Liu ◽

Mingzhou Li ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Muscle Contraction ◽

Focal Adhesion ◽

Enrichment Analysis ◽

Hub Genes ◽

Expression Levels ◽

Qrt Pcr ◽

Go Enrichment ◽

Go Enrichment Analysis

Abstract Background: Bladder cancer (BLCA) is the most popular malignant carcinomas in genitourinary system which has a high incidence and is prone to relapse. However, the molecular mechanism of BLCA remains to be unclear. Moreover, there is still a shortage of effective biomarkers that can predict progression and prognosis of BLCA. The objective of current study is to screen significant genes as biomarkers to forecast the progression and prognosis of BLCA patients.Methods: Gene expression profile downloaded from TCGA database and GEO database was used. Differential gene expression analysis and WGCNA were conducted to identify differential co-expression genes. In addition, GO enrichment analysis and KEGG pathway analysis were used to explore the functions of these genes. Moreover, PPI network, OS and DFS were used to identify survival-related hub genes. Finally, the expression levels of these genes were validated by qRT-PCR and HPA database.Results: About 124 differential co-expression genes were identified. And these genes were mainly enriched in muscle system process and muscle contraction (BP), contractile fiber, myofibril, sarcomere, focal adhesion and cell-substrate junction (CC) and actin binding (MF) in GO enrichment analysis, while enriched in vascular smooth muscle contraction, focal adhesion, cardiac muscle contraction, hypertrophic cardiomyopathy, dilated cardiomyopathy and regulation of actin cytoskeleton in KEGG analysis. Furthermore, five survival-related hub genes (MYH11, ACTA2, CALD1, TPM1, MYLK) were identified via overall OS and DFS. In addition, the expression levels of the five survival-related genes were upregulated with the procession of BLCA, such as grade, stage and TNM stage. Finally, all survival-related hub genes were found to be down-regulated in BLCA via qRT-PCR and HPA database.Conclusions: Our current study verified five new key genes in BLCA, which could help us better understand the pathogenesis of BLCA. And these five hub genes may be involved in the development and progression of BLCA and served as potential biomarkers.

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