Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis

Objective Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC. Methods The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein–protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan–Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples. Results A total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan–Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted. Conclusion In conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.

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In silico analyses for potential key genes associated with gastric cancer

PeerJ ◽

10.7717/peerj.6092 ◽

2018 ◽

Vol 6 ◽

pp. e6092 ◽

Cited By ~ 14

Author(s):

Ping Yan ◽

Yingchun He ◽

Kexin Xie ◽

Shan Kong ◽

Weidong Zhao

Keyword(s):

Gastric Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Therapeutic Targets ◽

Receptor Interaction ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Background Understanding hub genes involved in gastric cancer (GC) metastasis could lead to effective approaches to diagnose and treat cancer. In this study, we aim to identify the hub genes and investigate the underlying molecular mechanisms of GC. Methods To explore potential therapeutic targets for GC,three expression profiles (GSE54129, GSE33651, GSE81948) of the genes were extracted from the Gene Expression Omnibus (GEO) database. The GEO2R online tool was applied to screen out differentially expressed genes (DEGs) between GC and normal gastric samples. Database for Annotation, Visualization and Integrated Discovery was applied to perform Gene Ontology (GO) and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network of these DEGs was constructed using a STRING online software. The hub genes were identified by the CytoHubba plugin of Cytoscape software. Then, the prognostic value of these identified genes was verified by gastric cancer database derived from Kaplan-Meier plotter platform. Results A total of 85 overlapped upregulated genes and 44 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, endodermal cell differentiation, and endoderm formation. Moreover, five KEGG pathways were significantly enriched, including ECM-receptor interaction, amoebiasis, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, protein digestion and absorption. By combining the results of PPI network and CytoHubba, a total of nine hub genes including COL1A1, THBS1, MMP2, CXCL8, FN1, TIMP1, SPARC, COL4A1, and ITGA5 were selected. The Kaplan-Meier plotter database confirmed that overexpression levels of these genes were associated with reduced overall survival, except for THBS1 and CXCL8. Conclusions Our study suggests that COL1A1, MMP2, FN1, TIMP1, SPARC, COL4A1, and ITGA5 may be potential biomarkers and therapeutic targets for GC. Further study is needed to assess the effect of THBS1 and CXCL8 on GC.

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SMC4, CCNB1 and CKS1B as potential targets and new critical biomarkers for the prognosis of human bladder cancer

10.21203/rs.3.rs-595148/v1 ◽

2021 ◽

Author(s):

Hongpeng Fang ◽

Zhansen Huang ◽

Xianzi Zeng ◽

Jiaming Wan ◽

Jieying Wu ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Transcriptional Level ◽

Hub Genes ◽

Kaplan Meier ◽

High Form

Abstract Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

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Integration of Gene Expression Profile Data to Screen and Verify Hub Genes Involved in Osteoarthritis

BioMed Research International ◽

10.1155/2018/9482726 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Zhaoyan Li ◽

Qingyu Wang ◽

Gaoyang Chen ◽

Xin Li ◽

Qiwei Yang ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Osteoclast Differentiation ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Hub Genes ◽

Ppi Networks ◽

Pathogenic Genes ◽

Synovial Membranes

Osteoarthritis (OA) is one of the most common diseases worldwide, but the pathogenic genes and pathways are largely unclear. The aim of this study was to screen and verify hub genes involved in OA and explore potential molecular mechanisms. The expression profiles of GSE12021 and GSE55235 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 39 samples, including 20 osteoarthritis synovial membranes and 19 matched normal synovial membranes. The raw data were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The Gene Ontology (GO) and pathway enrichment of DEGs were performed by DAVID and Kyoto Encyclopedia of Genes and Genomes (KEGG) online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The top 10 hub genes VEGFA, IL6, JUN, IL1β, MYC, IL4, PTGS2, ATF3, EGR1, and DUSP1 were identified from the PPI network. Module analysis revealed that OA was associated with significant pathways including TNF signaling pathway, cytokine-cytokine receptor interaction, and osteoclast differentiation. The qRT-PCR result showed that the expression level of IL6, VEGFA, JUN, IL-1β, and ATF3 was significantly increased in OA samples (p < 0.05), and these candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of OA.

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Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

BioMed Research International ◽

10.1155/2022/1776082 ◽

2022 ◽

Vol 2022 ◽

pp. 1-17

Author(s):

Md. Rakibul Islam ◽

Lway Faisal Abdulrazak ◽

Mohammad Khursheed Alam ◽

Bikash Kumar Paul ◽

Kawsar Ahmed ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Clinical Information ◽

Gene Expression Omnibus ◽

Hub Genes ◽

New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log fold change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

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Screening and Identification of Key Biomarkers in Breast Cancer with Brain Metastasis: Evidence from Bioinformatic Analysis

10.21203/rs.3.rs-38542/v1 ◽

2020 ◽

Author(s):

tao ming Shao ◽

zhi yang Hu ◽

wen wei Li ◽

long yun Pan

Keyword(s):

Breast Cancer ◽

Protein Interactions ◽

Poor Prognosis ◽

Target Genes ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Hub Genes ◽

Clinical Prognosis ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Abstract Purpose. Breast cancer (BC) has a poor prognosis when brain metastases (BM) occur, and the treatment effect is limited. In this study, we aim to identify representative candidate biomarkers for clinical prognosis of patients with BM and explore the mechanisms underlying the progression of BC.Methods. Herein, we examined the Microarray datasets (GSE125989) obtained from the Gene Expression Omnibus database to find the target genes in BC patients with BM. We employed the GEO2R tool to filter the differentially expressed genes (DEGs) that participate in primary BC and BC with BM. Subsequently, using the DAVID tool, we conducted an enrichment analysis with the screened DEGs based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional annotation. The STRING database was employed to analyze the protein-protein interactions of the DEGs and visualized using Cytoscape software. Lastly, the Kaplan-Meier plotter database was employed to determine the prognostic potential of hub genes in BC.Results. We screened out 311 upregulated DEGs and 104 downregulated DEGs. The enrichment analyses revealed that all the DEGs were` enriched in the biological process of extracellular matrix organization, cell adhesion, proteolysis, collagen catabolic process and immune response. The significant enrichment pathways were focal adhesion, protein absorption and digestion, ECM-receptor interaction, PI3K-Akt signalling pathway, and Pathways in cancer. The top ten hub nodes screened out included FN1, VEGFA, COL1A1, MMP2, COL3A1, COL1A2, POSTN, DCN, BGN and LOX. The Kaplan-Meier plotter results showed that the three hub genes (FN1, VEGFA and DCN) are candidate biomarkers for clinical prognosis of patients with BM.Conclusion. we identified seven genes related to poor prognosis in BCBM. FN1, VEGFA and DCN can be considered as potential prognostic markers for BCBM. Meantime, COL1A1, POSTN, BGN and LOX may be linked to the distant transformation of BC.

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CDK1 and CCNB1 as potential diagnostic markers of rhabdomyosarcoma: validation following bioinformatics analysis

BMC Medical Genomics ◽

10.1186/s12920-019-0645-x ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

Qianru Li ◽

Liang Zhang ◽

Jinfang Jiang ◽

Yangyang Zhang ◽

Xiaomeng Wang ◽

...

Keyword(s):

Molecular Mechanisms ◽

Striated Muscle ◽

Fusion Gene ◽

Prognostic Significance ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Data Sets ◽

Rt Pcr ◽

Hub Genes ◽

New Perspective

Abstract Background Rhabdomyosarcoma (RMS), a common soft-tissue malignancy in pediatrics, presents high invasiveness and mortality. However, besides known changes in the PAX3/7-FOXO1 fusion gene in alveolar RMS, the molecular mechanisms of the disease remain incompletely understood. The purpose of the study is to recognize potential biomarkers related with RMS and analyse their molecular mechanism, diagnosis and prognostic significance. Methods The Gene Expression Omnibus was used to search the RMS and normal striated muscle data sets. Differentially expressed genes (DEGs) were filtered using R software. The DAVID has become accustomed to performing functional annotations and pathway analysis on DEGs. The protein interaction was constructed and further processed by the STRING tool and Cytoscape software. Kaplan–Meier was used to estimate the effect of hub genes on the ending of sarcoma sufferers, and the expression of these genes in RMS was proved by real-time polymerase chain reaction (RT-PCR). Finally, the expression of CDK1 and CCNB1 in RMS was validated by immunohistochemistry (IHC). Results A total of 1932 DEGs were obtained, amongst which 1505 were up-regulated and 427were down-regulated. Up-regulated genes were largely enriched in the cell cycle, ECM-receptor interaction, PI3K/Akt and p53 pathways, whilst down-regulated genes were primarily enriched in the muscle contraction process. CDK1, CCNB1, CDC20, CCNB2, AURKB, MAD2L1, HIST2H2BE, CENPE, KIF2C and PCNA were identified as hub genes by Cytoscape analyses. Survival analysis showed that, except for HIST2H2BE, the other hub genes were highly expressed and related to poor prognosis in sarcoma. RT-PCR validation showed that CDK1, CCNB1, CDC20, CENPE and HIST2H2BE were significantly differential expression in RMS compared to the normal control. IHC revealed that the expression of CDK1 (28/32, 87.5%) and CCNB1 (26/32, 81.25%) were notably higher in RMS than normal controls (1/9, 11.1%; 0/9, 0%). Moreover, the CCNB1 was associated with the age and location of the patient’s onset. Conclusions These results show that these hub genes, especially CDK1 and CCNB1, may be potential diagnostic biomarkers for RMS and provide a new perspective for the pathogenesis of RMS.

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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 ◽

Gene Expression Profiles ◽

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.

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Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

Iranian Journal of Public Health ◽

10.18502/ijph.v50i11.7578 ◽

2021 ◽

Author(s):

Congcong Wang ◽

Jianping Guo ◽

Xiaoyang Zhao ◽

Jia Jia ◽

Wenting Xu ◽

...

Keyword(s):

Bioinformatics Analysis ◽

Expression Profiles ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Analysis Tool ◽

Hub Genes ◽

Protein Protein Interaction ◽

Kegg Analysis ◽

Prognostic Assessment ◽

Scheme Selection

Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis. Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA. Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1. Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.

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Comprehensive Bioinformatics Analysis Identifies POLR2I as a Key Gene in the Pathogenesis of Hypertensive Nephropathy

Frontiers in Genetics ◽

10.3389/fgene.2021.698570 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shilong You ◽

Jiaqi Xu ◽

Boquan Wu ◽

Shaojun Wu ◽

Ying Zhang ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Chronic Hypertension ◽

Hub Genes ◽

Hypertensive Nephropathy ◽

Protein Levels

Hypertensive nephropathy (HN), mainly caused by chronic hypertension, is one of the major causes of end-stage renal disease. However, the pathogenesis of HN remains unclarified, and there is an urgent need for improved treatments. Gene expression profiles for HN and normal tissue were obtained from the Gene Expression Omnibus database. A total of 229 differentially co-expressed genes were identified by weighted gene co-expression network analysis and differential gene expression analysis. These genes were used to construct protein–protein interaction networks to search for hub genes. Following validation in an independent external dataset and in a clinical database, POLR2I, one of the hub genes, was identified as a key gene related to the pathogenesis of HN. The expression level of POLR2I is upregulated in HN, and the up-regulation of POLR2I is positively correlated with renal function in HN. Finally, we verified the protein levels of POLR2I in vivo to confirm the accuracy of our analysis. In conclusion, our study identified POLR2I as a key gene related to the pathogenesis of HN, providing new insights into the molecular mechanisms underlying HN.

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Integration of Gene Expression Profile Data to Verify Hub Genes of Patients with Stanford A Aortic Dissection

BioMed Research International ◽

10.1155/2019/3629751 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Weitie Wang ◽

Tiance Wang ◽

Yong Wang ◽

Hulin Piao ◽

Bo Li ◽

...

Keyword(s):

Gene Expression ◽

Aortic Dissection ◽

Gene Expression Profile ◽

Expression Profile ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Smooth Muscle Contraction ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Pathogenic Genes

Thoracic aortic dissection (TAD) is a catastrophic disease worldwide, but the pathogenic genes and pathways are largely unclear. This study aims at integrating two gene expression profile datasets and verifying hub genes and pathways involved in TAD as well as exploring potential molecular mechanisms. We will combine our mRNAs expression profile (6 TAD tissues versus 6 non-TAD tissues) and GSE52093 downloaded from the Gene Expression Omnibus (GEO) database. The two mRNAs expression profiles contained 13 TAD aortic tissues and 11 non-TAD tissues. The two expression profile datasets were integrated and we found out coexpression of differentially expressed genes (DEGs) using bioinformatics methods. The gene ontology and pathway enrichment of DEGs were performed by DAVID and Kyoto Encyclopedia of Genes and Genomes online analyses, respectively. The protein-protein interaction networks of the DEGs were constructed according to the data from the STRING database. Cytohubber calculating result shows the top 10 hub genes with CDC20, AURKA, RFC4, MCM4, TYMS, MCM2, DLGAP5, FANCI, BIRC5, and POLE2. Module analysis revealed that TAD was associated with significant pathways including cell cycle, vascular smooth muscle contraction, and adrenergic signaling in cardiomyocytes. The qRT-PCR result showed that the expression levels of all the hub genes were significantly increased in OA samples (p < 0.05), and these candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of TAD.

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