Dataset Information

A cluster of metabolism-related genes predict prognosis and progression of clear cell renal cell carcinoma.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) has long been considered as a metabolic disease characterized by metabolic reprogramming due to the abnormal accumulation of lipid droplets in the cytoplasm. However, the prognostic value of metabolism-related genes in ccRCC remains unclear. In our study, we investigated the associations between metabolism-related gene profile and prognosis of ccRCC patients in the Cancer Genome Atlas (TCGA) database. Importantly, we first constructed a metabolism-related prognostic model based on ten genes (ALDH6A1, FBP1, HAO2, TYMP, PSAT1, IL4I1, P4HA3, HK3, CPT1B, and CYP26A1) using Lasso cox regression analysis. The Kaplan-Meier analysis revealed that our model efficiently predicts prognosis in TCGA_KIRC Cohort and the clinical proteomic tumor analysis consortium (CPTAC_ccRCC) Cohort. Using time-dependent ROC analysis, we showed the model has optimal performance in predicting long-term survival. Besides, the multivariate Cox regression analysis demonstrated our model is an independent prognostic factor. The risk score calculated for each patient was significantly associated with various clinicopathological parameters. Notably, the gene set enrichment analysis indicated that fatty acid metabolism was enriched considerably in low-risk patients. In contrast, the high-risk patients were more associated with non-metabolic pathways. In summary, our study provides novel insight into metabolism-related genes' roles in ccRCC.

SUBMITTER: Liu M

PROVIDER: S-EPMC7395775 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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A cluster of metabolism-related genes predict prognosis and progression of clear cell renal cell carcinoma.

Liu Mei M Pan Qiufeng Q Xiao Ruihai R Yu Yi Y Lu Wenbao W Wang Longwang L

Scientific reports 20200731 1

Clear cell renal cell carcinoma (ccRCC) has long been considered as a metabolic disease characterized by metabolic reprogramming due to the abnormal accumulation of lipid droplets in the cytoplasm. However, the prognostic value of metabolism-related genes in ccRCC remains unclear. In our study, we investigated the associations between metabolism-related gene profile and prognosis of ccRCC patients in the Cancer Genome Atlas (TCGA) database. Importantly, we first constructed a metabolism-related ...[more]

PMID: 32737333

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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.

Project description:Numerous studies have shown that microRNA (miRNA) serves as key regulatory factors in the origin and development of cancers. However, the biological mechanisms of miRNAs in kidney renal clear cell carcinoma (KIRC) are still unknown. It is necessary to construct an effective miRNA-clinical model to predict the prognosis of KIRC. In this study, 94 differentially expressed miRNAs were found between para-tumor and tumor tissues based on the Cancer Genome Atlas (TCGA) database. Seven miRNAs (hsa-miR-21-5p, hsa-miR-3613-5p, hsa-miR-144-5p, hsa-miR-376a-5p, hsa-miR-5588-3p, hsa-miR-1269a, and hsa-miR-137-3p) were selected as prognostic indicators. According to their cox coefficient, a risk score formula was constructed. Patients with risk scores were divided into high- and low-risk groups based on the median score. Kaplan-Meier curves analysis showed that the low-risk group had a better survival probability compared to the high-risk group. The area under the ROC curve (AUC) value of the miRNA model was 0.744. In comparison with clinical features, the miRNA model risk score was considered as an independent prognosis factor in multivariate Cox regression analysis. In addition, we built a nomogram including age, metastasis, and miRNA prognostic model based on the results of multivariate Cox regression analysis. The decision curve analysis (DCA) revealed the clinical net benefit of the prognostic model. Gene set enrichment analysis (GSEA) results suggested that several important pathways may be the potential pathways for KIRC. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the target genes of 7 miRNAs revealed that miRNAs may participate in KIRC progression via many specific pathways. Additionally, the levels of seven prognostic miRNAs showed a significant difference between KIRC tissues and adjacent non-tumorous tissues. In conclusion, the miRNA-clinical model provides an effective and accurate way to predict the prognosis of KIRC.

Dataset Information

A cluster of metabolism-related genes predict prognosis and progression of clear cell renal cell carcinoma.

Publications

A cluster of metabolism-related genes predict prognosis and progression of clear cell renal cell carcinoma.

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