Dataset Information

Identification of Core Genes Involved in the Metastasis of Clear Cell Renal Cell Carcinoma.

ABSTRACT:

Introduction

Renal cell carcinoma (RCC) is one of the most common malignancies globally, among which clear cell carcinoma (ccRCC) accounts for 85-90% of all pathological types. This study aims to screen out potential genes in metastatic ccRCC so as to provide novel insights for ccRCC treatment.

Methods

GSE53757 and GSE84546 datasets in the Gene Expression Omnibus (GEO) were profiled to identify differentially expressed genes (DEGs) from ccRCC samples with or without metastasis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and the gene ontology (GO) analysis were performed to analyze pathway enrichment and functional annotation of DEGs. Protein-protein interaction (PPI) network was constructed, and survival analysis was conducted to evaluate the clinical values of the identified hub genes. In vitro loss-of-function assays were performed to explore the biological roles of these genes.

Results

The bioinformatic analysis indicated that 312 DEGs were identified, including 148 upregulated genes and 164 downregulated ones. Using PPI and Cytoscape, 10 hub genes were selected (C3, CXCR4, CCl4, ACKR3, KIF20A, CCNB2, CDCA8, CCL28, S1PR5, and CCL20) from DEGs which might be closely related with ccRCC metastasis. In Kaplan-Meier analysis, three potential prognostic biomarkers (KIF20A, CCNB2 and CDCA8) were identified. Finally, cell proliferative and invasive assays further verified that KIF20A, CCNB2 and CDCA8 were associated with the proliferation and invasion of ccRCC cells.

Conclusion

Our results demonstrated that metastatic ccRCC was partially attributed to the aberrant expression of KIF20A, CCNB2 and CDCA8, and more personalized therapeutic approaches should be explored targeting these hub genes.

SUBMITTER: Peng R

PROVIDER: S-EPMC7779301 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Identification of Core Genes Involved in the Metastasis of Clear Cell Renal Cell Carcinoma.

Peng Rui R Wang Yahui Y Mao Likai L Fang Fang F Guan Han H

Cancer management and research 20201230

<h4>Introduction</h4>Renal cell carcinoma (RCC) is one of the most common malignancies globally, among which clear cell carcinoma (ccRCC) accounts for 85-90% of all pathological types. This study aims to screen out potential genes in metastatic ccRCC so as to provide novel insights for ccRCC treatment.<h4>Methods</h4>GSE53757 and GSE84546 datasets in the Gene Expression Omnibus (GEO) were profiled to identify differentially expressed genes (DEGs) from ccRCC samples with or without metastasis. Th ...[more]

PMID: 33408516

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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 tumor that frequently shows the hematogenous pathway and tends to be resistant to radiotherapy and chemotherapy. However, the exact mechanism of ccRCC metastasis remains unknown.MethodsDifferentially expressed genes (DEGs) of three gene expression profiles (GSE85258, GSE105288 and GSE22541) downloaded from the Gene Expression Omnibus (GEO) database were analyzed by GEO2R analysis, and co-expressed DEGs among the datasets were identified using a Venn drawing tool. The co-expressed DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and hub genes were determined based on the protein-protein interaction network established by STRING. After survival analysis performed on UALCAN website, possible key genes were selected and verified in ccRCC cell lines and ccRCC tissues (n = 44). Statistical analysis was conducted using GraphPad Prism (Version 8.1.1).ResultsA total of 104 co-expressed DEGs were identified in the three datasets. Pathway analysis revealed that these genes were enriched in the extracellular matrix (ECM)-receptor interaction, protein digestion and absorption and focal adhesion. Survival analysis on 17 hub genes revealed that four key genes with a significant impact on survival: procollagen C-endopeptidase enhancer (PCOLCE), prolyl 4-hydroxylase subunit beta (P4HB), collagen type VI alpha 2 (COL6A2) and collagen type VI alpha 3 (COL6A3). Patients with higher expression of these key genes had worse survival than those with lower expression. In vitro experiments revealed that the mRNA expression levels of PCOLCE, P4HB and COL6A2 were three times higher and that of COL6A3 mRNA was 16 times higher in the metastatic ccRCC cell line Caki-1 than the corresponding primary cell line Caki-2. Immunohistochemistry revealed higher expression of the proteins encoded by these four genes in metastatic ccRCC compared with tumors from the corresponding primary sites, with statistical significance.ConclusionPCOLCE, P4HB, COL6A2 and COL6A3 are upregulated in metastatic ccRCC and might be related to poor prognosis and distant metastases.

Dataset Information

Identification of Core Genes Involved in the Metastasis of Clear Cell Renal Cell Carcinoma.

Introduction

Methods

Results

Conclusion

Publications

Identification of Core Genes Involved in the Metastasis of Clear Cell Renal Cell Carcinoma.

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