Dataset Information

Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy.

ABSTRACT: Background:Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC. Methods:mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, and miRNA-gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC. Results:Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC. Conclusions:In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future.

SUBMITTER: Yang W

PROVIDER: S-EPMC6530124 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy.

Yang Wanli W Zhao Xinhui X Han Yu Y Duan Lili L Lu Xin X Wang Xiaoqian X Zhang Yujie Y Zhou Wei W Liu Jinqiang J Zhang Hongwei H Zhao Qingchuan Q Hong Liu L Fan Daiming D

Cancer cell international 20190522

<h4>Background</h4>Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC.<h4>Methods</h4>mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics ap ...[more]

PMID: 31139019

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

Project description:ObjectiveThrough bioinformatics analysis methods, the public databases GEO and TCGA were used to research mRNA and squamous cell carcinoma of the esophagus, construct a lncRNA-mRNA network, and screen hub genes and lncRNAs related to prognosis.MethodDownload esophageal squamous cell carcinoma-related mRNA and lncRNA datasets GEO and TCGA public datasets, as well as clinical data, use bioinformatic tools to perform gene differential expression analysis on the datasets to obtain differentially expressing mRNA (DEmRNA) and lncRNA (DElncRNA), and plot volcano plots and cluster heatmaps. The differential intersection of differentially expressed DEmRNA and DElncRNA was extracted by Venn diagram and imported into CytoScape software, a regulatory network visualization software, to construct a lncRNA-mRNA network and use cytoHubba and MCODE plug-ins to screen hub genes and key lncRNAs. The DEmRNA in the network was imported into the Gene and Protein Interaction Retrieval Database (STRING), gene-encoded protein-protein interactions (PPI) network maps were created, and the genes in the PPI network maps were submitted to GO functional annotation and pathway enrichment analysis using Kyoto Encyclopedia of Gene Genomes (KEGG) (KEGG). The link between hub gene and prognosis was studied using the clinical data collected by TCGA.ResultRetrieve the datasets GSE23400 and GSE38129 from the GEO database and the esophageal squamous cell carcinoma-related mRNAs from TCGA databases and then obtain intersection. Differentially regulated genes revealed a correlation of 326 (up) with 191 (down) in terms of the differential intersection; for this study, we need to collect the GSE130078 dataset from GEO, as well as the lncRNAs from TCGA databases that are connected to esophageal squamous cell cancer. There were 184 differentially up- and downregulated genes in the differential intersection. A differential intersection network of the differential intersection lncRNA-mRNA network allowed us to identify the hub genes, including COL5A2 (COL3A1), COL1A1 (COL1A1), CTD-2171N6.1 (CTD-2171N6.1), and RP11-863P13.3 (RP11-863P13.3). The extracellular matrix, which is important in protein digestion and absorption, was shown to be the primary site of functional enrichment, as shown by GO/KEGG analysis. Squamous cell carcinoma of the mouth and throat is associated with a poor prognosis because of a change in the extracellular matrix structure caused by specific long noncoding RNA (lncRNA) regulatory upregulation.ConclusionFor the purpose of predicting the prognosis of cancer of the esophagus, researchers studied the esophageal squamous cell carcinoma-related hub genes and important noncoding RNAs (ncRNAs).

Project description:BackgroundHepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis.MethodsA bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003).ResultsFirstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05).ConclusionMCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

Dataset Information

Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy.

Publications

Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy.

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