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Systematic identification of key genes and pathways in clear cell renal cell carcinoma on bioinformatics analysis.

ABSTRACT: Background:Clear cell renal cell carcinoma (ccRCC) is the most common subtype of adult renal neoplasm and has a poor prognosis owing to a limited understanding of the disease mechanisms. The aim of this study was to explore and identify the key genes and signaling pathways in ccRCC. Methods:The GSE36895 gene expression profiles were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were then screened using software packages in R. After Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, a protein-protein interaction (PPI) network of DEGs was constructed with Cytoscape software, and submodules were subsequently analyzed using the MCODE plug-in. Results:Twenty-nine ccRCC samples and 23 normal samples were incorporated into this study, and a total of 468 DEGs were filtered, consisting of 180 upregulated genes and 288 downregulated genes. The upregulated DEGs were significantly enriched in the immune response, response to wounding, inflammatory response, and response to hypoxia, whereas downregulated genes were mainly enriched in ion transport, anion transport, and monovalent inorganic cation transport biological processes (BPs). According to Molecular Complex Detection analysis in PPI, C1QA, C1QB, C1QC, CCND1 and EGF had higher degrees of connectivity and could participate in the majority of important pathways, such as cytokine-cytokine receptor interactions, the chemokine signaling pathway, and the complement and coagulation cascade pathways. Conclusions:Our study suggests that C1QA, C1QB, C1QC, CCND1 and EGF may play key roles in the progression of ccRCC, which will be useful for future studies on the underlying mechanisms of ccRCC.

SUBMITTER: Tian ZH

PROVIDER: S-EPMC6462636 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Systematic identification of key genes and pathways in clear cell renal cell carcinoma on bioinformatics analysis.

Tian Zhao-Hui ZH Yuan Cheng C Yang Kang K Gao Xing-Liang XL

Annals of translational medicine 20190301 5

<h4>Background</h4>Clear cell renal cell carcinoma (ccRCC) is the most common subtype of adult renal neoplasm and has a poor prognosis owing to a limited understanding of the disease mechanisms. The aim of this study was to explore and identify the key genes and signaling pathways in ccRCC.<h4>Methods</h4>The GSE36895 gene expression profiles were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were then screened using software packages in R. After Gen ...[more]

PMID: 31019939

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Project description:Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development.Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets.Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.MethodsTranscriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.ResultsIn the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.ConclusionsThe present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.

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Project description:The aim of the present study was to identify the differentially expressed genes (DEGs) in esophageal squamous-cellcarcinoma (ESCC) and provide potential therapeutic targets. The microarray dataset GSE20347 was downloaded from the Gene Expression Omnibus (GEO) database, and included 17 tissue samples and 13 normal adjacent tissue samples from patients with ESCC. A total of 22,277 DEGs were identified. A heat map for the DEGs was constructed with the Morpheus online tool and the top 200 genes (100 upregulated and 100 downregulated) were selected for further bioinformatics analysis, including analysis of gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, protein-protein interaction networks and Spearman's correlation tests. The results of the GO analysis indicated that the upregulated DEGs were most significantly enriched in membrane-bounded vesicles in the cellular component (CC) category, but were not significantly enriched in any GO terms of the categories biological process (BP) or molecular function (MF); furthermore, the downregulated DEGs were most significantly enriched in regulation of DNA metabolic processes, nucleotide binding and chromosomes in the categories BP, MF and CC, respectively. The KEGG analysis indicated that the downregulated DEGs were enriched in the regulation of cell cycle pathways. The top 10 hub proteins in the protein-protein interaction network were cyclin-dependent kinase 4, budding uninhibited by benzimidazoles 1, cyclin B2, heat shock protein 90AA1, aurora kinase A, H2A histone family member Z, replication factor C subunit 4, and minichromosome maintenance complex component 2, -4 and -7. These proteins are mainly involved in regulating tumor progression. The genes in the four top modules were mainly implicated in regulating cell cycle pathways. Secreted Ly-6/uPAR-related protein (SLURP) was the hub gene, and SLURP and its interacting genes were most enriched in the chromosomal part in the CC category, organelle organization in the BP category and protein binding in the MF category, and were involved in pathways including DNA replication, cell cycle and P53 signaling. The expression of SLURP-1 in fifteen patients with esophageal carcinoma was detected using quantitative polymerase chain reaction analysis, and the results indicated that SLURP-1 expression was significantly decreased in the tumor samples relative to that in normal adjacent tissues. These results suggest that several hub proteins and the hub gene SLURP-1 may serve as potential therapeutic targets, and that gene dysfunction may be involved in the tumorigenesis of ESCC.

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Systematic identification of key genes and pathways in clear cell renal cell carcinoma on bioinformatics analysis.

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Systematic identification of key genes and pathways in clear cell renal cell carcinoma on bioinformatics analysis.

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