Identifying the hub gene in gastric cancer by bioinformatics analysis and in vitro experiments.
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ABSTRACT:
SUBMITTER: Wang F
PROVIDER: S-EPMC7469508 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Gastric cancer (GC) is a prevalent malignant cancer of digestive system. To identify key genes in GC, mRNA microarray GSE27342, GSE29272, and GSE33335 were downloaded from GEO database. METHODS:Differentially expressed genes (DEGs) were obtained using GEO2R. DAVID database was used to analyze function and pathways enrichment of DEGs. Protein-protein interaction (PPI) network was established by STRING and visualized by Cytoscape software. Then, the influence of hub genes on overall survival (OS) was performed by the Kaplan-Meier plotter online tool. Module analysis of the PPI network was performed using MCODE. Additionally, potential stem loop miRNAs of hub genes were predicted by miRecords and screened by TCGA dataset. Transcription factors (TFs) of hub genes were detected by NetworkAnalyst. RESULTS:In total, 67 DEGs were identified; upregulated DEGs were mainly enriched in biological process (BP) related to angiogenesis and extracellular matrix organization and the downregulated DEGs were mainly enriched in BP related to ion transport and response to bacterium. KEGG pathways analysis showed that the upregulated DEGs were enriched in ECM-receptor interaction and the downregulated DEGs were enriched in gastric acid secretion. A PPI network of DEGs was constructed, consisting of 43 nodes and 87 edges. Twelve genes were considered as hub genes owing to high degrees in the network. Hsa-miR-29c, hsa-miR-30c, hsa-miR-335, hsa-miR-33b, and hsa-miR-101 might play a crucial role in hub genes regulation. In addition, the transcription factors-hub genes pairs were displayed with 182 edges and 102 nodes. The high expression of 7 out of 12 hub genes was associated with worse OS, including COL4A1, VCAN, THBS2, TIMP1, COL1A2, SERPINH1, and COL6A3. CONCLUSIONS:The miRNA and TFs regulation network of hub genes in GC may promote understanding of the molecular mechanisms underlying the development of gastric cancer and provide potential targets for GC diagnosis and treatment.
| S-EPMC6009060 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.MethodsGene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytohubba plugin. Their prognostic values were assessed by Kaplan-Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs.ResultsTotal of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.ConclusionsWe have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.
| S-EPMC8201699 | biostudies-literature
Project description:PurposeThe current treatment methods available for advanced gastric cancer are not very promising. Hence, it is important to explore novel biomarkers and potential therapeutic agents to treat gastric cancer (GC). This study aimed to identify hub genes associated with GC prognosis and explore potential drugs for its treatment.Materials and methodsThree gene expression data of GC and normal tissues were downloaded from the Gene Expression Omnibus (GEO) and processed to identify the differentially expressed genes (DEGs). We conducted a comprehensive analysis of DEGs, including functional enrichment analysis, construction of protein-protein interaction (PPI) network, identification of hub genes, survival analysis and expression verification of hub genes. Finally, we constructed the network of miRNA-mRNA, and predicted the drugs that might be effective for GC treatment.ResultsA total of 340 DEGs, including 94 up-regulated and 246 down-regulated genes, were identified. Among the up-regulated DEGs, the enrichment terms were primarily related to tumorigenesis and tumor progression, extracellular matrix organization, and collagen catabolic process. Additionally, 10 hub genes (FN1, COL3A1, COL1A2, BGN, THBS2, COL5A2, THBS1, COL5A1, SPARC, and COL4A1) were identified, out of which 7 genes were significantly associated with poor overall survival (OS) in GC. The expression levels of these 7 hub genes were verified using real-time PCR, immunohistochemistry, and the GEPIA2 (Gene Expression Profiling Interactive Analysis) server. A regulatory network of miRNA-mRNA was also constructed, and the top 4 interactive miRNAs (hsa-miR-29b-3p, hsa-miR-140-3p, hsa-miR-29a-3p, and hsa-miR-29c-3p) that targeted the most hub genes were identified. Finally, fourteen small molecules were predicted to be effective in treating GC.ConclusionThe identification of the hub genes, miRNA-mRNA network, and potential candidate drugs associated with GC provides new insights into the molecular mechanisms and treatment of GC.
| S-EPMC8643151 | biostudies-literature
Project description:BackgroundGastric cancer is one of the most common malignant cancers worldwide. Despite substantial developments in therapeutic strategies, the five-year survival rate remains low. Therefore, novel biomarkers and therapeutic targets involved in the progression of gastric tumors need to be identified.MethodsWe obtained the mRNA microarray datasets GSE65801, GSE54129 and GSE79973 from the Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). We used the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to analyze DEG pathways and functions, and the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape to obtain the protein-protein interaction (PPI) network. Next, we validated the hub gene expression levels using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA), and conducted stage expression and survival analysis.ResultsFrom the three microarray datasets, we identified nine major hub genes: COL1A1, COL1A2, COL3A1, COL5A2, COL4A1, FN1, COL5A1, COL4A2, and COL6A3.ConclusionOur study identified COL1A1 and COL1A2 as potential gastric cancer prognostic biomarkers.
| S-EPMC7255341 | biostudies-literature
Project description:Introduction: Crohn's disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and miRNAs with diagnostic and therapeutic potential in CD. Materials and methods: Three CD datasets (GSE179285, GSE102133, GSE75214) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and CD tissues were identified using the GEO2R online tool. The Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were conducted using the clusterProfiler function in the R package. Protein-protein interaction network (PPI) analysis and visualization were performed with STRING and Cytoscape. Ten hub genes were identified using cytoHubba's MCC algorithm and validated with datasets GSE6731 and GSE52746. Finally, the miRNA gene regulatory network was constructed by Cytoscape and NetworkAnalyst to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 97 DEGs were identified, consisting of 88 downregulated genes and 9 upregulated genes. The enriched functions and pathways of the DEGs include immune system process, response to stress, response to cytokine and extracellular region. KEGG pathway analysis indicates that the genes were significantly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway, Rheumatoid arthritis and TNF signaling pathway. In combination with the results of the protein-protein interaction (PPI) network and CytoHubba, 10 hub genes including IL1B, CXCL8, CXCL10, CXCL1, CXCL2, CXCL5, ICAM1, IL1RN, TIMP1 and MMP3 were selected. Based on the DEG-miRNAs network construction, 5 miRNAs including hsa-mir-21-5p, hsa-mir-93-5p, hsa-mir-98-5p, hsa-mir-1-3p and hsa-mir-335-5p were identified as potential critical miRNAs. Conclusion: In conclusion, a total of 97 DEGs, 10 hub genes and 5 miRNAs that may be involved in the progression or occurrence of CD were identified in this study, which could be regarded as biomarkers of CD.
| S-EPMC9471261 | biostudies-literature
Project description: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.
| S-EPMC6033081 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most commonly diagnosed cancers and the fourth leading cause of cancer-related deaths in the world. Although the treatment of HCC has made great progress in recent years, the therapeutic effects on HCC are still unsatisfactory due to difficulty in early diagnosis, chemoresistance and high recurrence rate post-surgery.MethodsIn this study, we identified differentially expressed genes (DEGs) based on four Gene Expression Omnibus (GEO) datasets (GSE45267, GSE98383, GSE101685 and GSE112790) between HCC and normal hepatic tissues. A protein-protein interaction (PPI) network was established to identify the central nodes associated with HCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the central nodes were conducted to find the hub genes. The expression levels of the hub genes were validated based on the ONCOMINE and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Additionally, the genetic alterations of the hub genes were evaluated by cBioPortal. The role of the hub genes on the overall survival (OS) and relapse survival (RFS) of HCC patients was evaluated by Kaplan-Meier plotter. At last, the mechanistic role of the hub genes was illustrated by in vitro experiments.ResultsWe found the following seven hub genes: BUB1B, CCNB1, CCNB2, CDC20, CDK1, MAD2L1 and RRM2 using integrated bioinformatics analysis. All of the hub genes were significantly upregulated in HCC tissues. And the seven hub genes were associated with the OS and RFS of HCC patients. Finally, in vitro experiments indicated that BUB1B played roles in HCC cell proliferation, migration, invasion, apoptosis and cell cycle by partially affecting mitochondrial functions.ConclusionsIn summary, we identified seven hub genes that were associated with the expression and prognosis of HCC. The mechanistic oncogenic role of BUB1B in HCC was first illustrated. BUB1B might play an important role in HCC and could be potential therapeutic targets for HCC.
| S-EPMC7908873 | biostudies-literature
Project description:Purpose:To explore the role of FKBP prolyl isomerase 10 (FKBP10) protein in the progression of gastric cancer. Methods:Four independent gastric cancer databases (GSE27342, GSE29272, GSE54129 and TCGA-STAD) were used to identify differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was used to identify the abnormally active pathways in patients with gastric cancer. Univariate Cox regression analysis was used to identify genes with stable prognostic value in gastric cancer patients based on three independent gastric cancer databases (GSE15459, GSE62254, TCGA-STAD). Gene set enrichment analysis (GSEA) was used to explore the possible pathways related to FKBP10. The reverse transcription-polymerase chain reaction (RT-PCR) was employed to determine the expression of FKBP10 mRNA in the HGC-27 and MKN-7 cell lines. Adhesion assay was used to detect changes in cell adhesion ability. FKBP10, ITGA1, ITGA2, ITGA5, ITGAV, ITGA6, P- AKT 473, P- AKT 308, AKT, and ?-actin were evaluated by Western blot (WB). Results:We first performed differential expression genes (DEGs) screening of four independent GC databases (GSE27342, GSE29272, GSE54129 and TCGA-STAD). Eighty-nine genes showed consistent up-regulation in GC, the results of pathway analysis showed that they were related to "Focal adhesion". The prognostic value of these 89 genes was tested in three independent GC databases GSE15459, GSE62254 and TCGA-STAD cohort. Finally, 12 genes, in which the expression of FKBP10 was prominently increased in patients with lymph node metastasis (LNM), showed stable prognostic value. The following gene set enrichment analysis (GSEA) also showed that FKBP10 is mainly involved in cell adhesion process, while adhesion experiments confirmed that cell adhesion was down-regulated after silencing FKBP10 in GC cells, and adhesion-related molecules integrin ?V and ?6 were down-regulated. Conclusion:FKBP10 may be used as a marker for lymph node metastasis of GC and could be used as a potential target for future treatment of GC.
| S-EPMC7395699 | biostudies-literature
Project description:Background:Colon cancer is a heterogeneous disease, differing in clinical symptoms, epigenetics, and prognosis for each individual patient. Identifying the core genes is important for early diagnoses and it provides a more precise method for treating colon cancer. Materials and Methods:In this study, we wanted to pinpoint these core genes so we obtained GSE101502 microRNA profiles from the GEO database, which resulted in 17 differential expressed microRNAs that were identified by GEO2R analysis. Then, 875 upregulated and 2920 downregulated target genes were predicted by FunRich. GO and KEGG pathway were used to do enrich analysis. Results:GO analysis indicated that upregulated genes were significantly enriched in the regulation of cell communication and signaling and in nervous system development, while the downregulated genes were significantly enriched in nervous system development and regulation of transcription from the RNA polymerase II promoter. KEGG pathway analysis suggested that the upregulated genes were enriched in axon guidance, MAPK signaling pathway, and endocytosis, while the downregulated genes existed in pathways in cancer, focal adhesion, and PI3K-Akt signaling pathway. The top four molecules including 82 hub genes were identified from the PPI network and involved in endocytosis, spliceosome, TGF-beta signaling pathway, and lysosome. Finally, NUDT21, GNB1, CLINT1, and COL1A2 core gene were selected due to their correlation with the prognosis of IIA stage colon cancer. Conclusion:this study suggested that NUDT21, GNB1, CLINT1, and COL1A2 might be the core genes for colon cancer that play an important role in the development and prognosis of IIA stage colon cancer.
| S-EPMC6383401 | biostudies-literature
Project description:Lung cancer is one of the most common types of cancer worldwide. Understanding the molecular mechanisms underlying the development and progression of lung cancer may improve early diagnosis, treatment and prognosis. The aim of the present study was to examine the pathogenesis of lung cancer and to identify potentially novel biomarkers. Gene expression datasets of patients with lung cancer were obtained from the Gene Expression Omnibus. Genes which were most closely associated with lung cancer (core genes) were screened by weighted gene co‑expression network analysis. In vitro cell based experiments were further utilized to verify the effects of the core genes on the proliferation of lung cancer cells, adhesion between cells and the matrix, and the associated metabolic pathways. Based on WGCNA screening, two gene modules and five core genes closely associated with lung cancer, including immunoglobulin superfamily member 10 (IGSF10) from the turquoise module, and ribonucleotide reductase regulatory subunit M2, protein regulator of cytokinesis 1, kinesin family member (KIF)14 and KIF2C from the brown module were identified as relevant. Survival analysis and differential gene expression analysis showed that there were significant differences in IGSF10 expression levels between the healthy controls and patients with lung cancer. In patients with lung cancer, IGSF10 expression was decreased, and the overall survival time of patients with lung cancer was significantly shortened. An MTT and colony formation assay showed that IGSF10‑knockout significantly increased proliferation of lung cancer cells, and Transwell assays and adhesion experiments further suggested that the adhesion between cells and the matrix was significantly increased in IGSF10‑knockout cells. Gene Set Enrichment Analysis showed that the expression level of IGSF10 was significantly associated with the activation of the integrin‑β1/focal adhesion kinase (FAK) pathway. Western blotting revealed that knockout of IGSF10 resulted in the activation of the integrin‑β1/FAK pathway, as the protein expression levels of integrin‑β1, phosphorylated (p)‑FAK and p‑AKT were significantly upregulated. Activation of the integrin‑β1/FAK pathway, following knockout of IGSF10, affected the proliferation and adhesion of lung cancer cells. Therefore, IGSF10 my serve as a potential prognostic marker of lung cancer.
| S-EPMC6910184 | biostudies-literature