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Identification of PDE7B as a Potential Core Gene Involved in the Metastasis of Clear Cell Renal Cell Carcinoma.

ABSTRACT: Background:Metastasis is the main cause of treatment failure in various cancer, including ccRCC. However, the key genes involved in ccRCC metastasis remain largely unknown. Purpose:The identification of the aberrant gene expression patterns associated with metastatic traits is of great clinical significance. The aim of this study was to investigate the clinical significance and function of PDE7B in ccRCC. Materials and Methods:Expression profiling data for patient-matched primary and metastatic ccRCC tumors were obtained from GEO Dataset. Limma package was used to identify differentially expressed genes (DEGs) between the metastatic and the primary groups. Gene Ontology, Kyoto Encyclopedia of Genes Genomes (KEGG), and PPI network analysis were used to study the interacting activities and the interconnection of the DEGs. CCK-8 assays and Transwell assays were performed to detect the proliferation and migration of renal cancer cells. Results:We obtained 163 DEGs, including 132 that were upregulated and 31 that were downregulated in metastatic ccRCC tissues. Both Gene Ontology function and KEGG pathway analysis showed that DEGs were involved in extracellular matrix (ECM) organization and cell adhesion. After utilizing PPI network to explore the interconnection among the DEGs, 22 genes were selected as the hub genes. Subsequently, survival analysis revealed that seven hub genes (SFN, NKX2-1, HP, MAPT, EPHA4, KCNAB1, and PDE7B) were significantly associated with overall survival disease-specific survival, and progression-free interval in ccRCC. Moreover, the low expression of PDE7B was found in clinical ccRCC samples and correlated with TNM stage and histologic grade. We further showed that knockdown of PDE7B increased cell growth and migration of renal cancer cells. Conclusion:Our results implicated that PDE7B may play a key role in the development of metastatic RCC.

SUBMITTER: Sun Y

PROVIDER: S-EPMC7367933 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of <i>PDE7B</i> as a Potential Core Gene Involved in the Metastasis of Clear Cell Renal Cell Carcinoma.

Sun Yi Y Zou Junxia J Ouyang Wei W Chen Ke K

Cancer management and research 20200712

<h4>Background</h4>Metastasis is the main cause of treatment failure in various cancer, including ccRCC. However, the key genes involved in ccRCC metastasis remain largely unknown.<h4>Purpose</h4>The identification of the aberrant gene expression patterns associated with metastatic traits is of great clinical significance. The aim of this study was to investigate the clinical significance and function of PDE7B in ccRCC.<h4>Materials and methods</h4>Expression profiling data for patient-matched p ...[more]

PMID: 32765073

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Project description:The purpose of the present study was to screen potential pathogenic biomarkers of clear cell renal cell carcinoma (ccRCC) via microarray analysis. The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441. A total of 5 ccRCC tissue samples and 5 normal kidney tissue samples were contained in each profile of GSE96574 and GSE71302, and 46 ccRCC tissue samples and 46 normal kidney tissue samples were involved in GSE61441. The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues. The Two Sample t-test and the Beta distribution test were used to identify the differentially methylated sites (DMSs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs. The targets of the DEMs were screened with the miRWalk database, and the further combination analyses of DEGs, DEMs and DMSs were conducted. Additionally, reverse transcription PCR (RT-PCR) and methylation-specific PCR (MS-PCR) were performed to detect the mRNA level and methylation status of HAPLN1. The mRNA levels of hsa-miR-204 and hsa-miR-218 were tested by RT-PCR. A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples. The DEGs were enriched in 1,015 GO terms and 69 KEGG pathways. A total of 10,601 miRNA-gene pairs were identified in at least 5 algorithms of the miRWalk database. A total of 143 overlaps were identified between the DEGs and the differentially methylated genes. Furthermore, the DEGs were involved in 851 miRNA-gene pairs, including 127 pairs in which the target genes were negatively associated with their corresponding DEMs and DMSs. HAPLN1 was lowly expressed and highly methylated in ccRCC tissues, while hsa-miR-204 and hsa-miR-218 were highly expressed. The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC.

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.

Dataset Information

Identification of PDE7B as a Potential Core Gene Involved in the Metastasis of Clear Cell Renal Cell Carcinoma.

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Identification of <i>PDE7B</i> as a Potential Core Gene Involved in the Metastasis of Clear Cell Renal Cell Carcinoma.

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