Comprehensive Transcriptomic Analysis of Critical RNA Regulation Associated With Metabolism and Prognosis in Clear Cell Renal Carcinoma.

Si Liu,Honglan Zhou,Xin Lian,Gang Wang

doi:10.3389/fcell.2021.709490

Abstract

This study focuses on investigating the metabolism-related gene profile and prognosis of clear cell renal cell carcinoma (ccRCC) patients. The research data from the Gene Expression Omnibus database, including GSE40435, GSE53757, and GSE53000, were used to analyze the consistently differentially expressed RNAs (cDERs) by the MetaDE limma package. Gene expression profiling associated with metabolism was downloaded from the GSEA database. The cancer genome atlas (TCGA) dataset of ccRCC (the training set) and RNA sequencing data of E-MTAB-3267 from EBI ArrayExpress database (the validation set) were obtained to construct a prognostic model. A series of bioinformatics analysis, including functional enrichment analysis, Cox regression analysis, and constructing a prognostic score (PS) model, was performed. Further in vitro experiments including cell proliferation assay and flow cytometry were performed to validate our results. We constructed a metabolism-related prognostic model based on 27 DElncRNAs and 126 DEGs. Gene Set Enrichment Analysis revealed that 19 GO terms and 9 KEGG signaling pathways were significantly associated with lipid metabolic pathways. Furthermore, we generated a nomogram illustrating the association between the identified DERs and the tumor recurrence risk in ccRCC. The results from experimental validation showed that lncRNA SNHG20 was significantly upregulated in tumor tissues compared with adjacent tissues. Knockdown of SNHG20 suppressed the proliferation and induced cell cycle G0/G1 arrest, and apoptosis in ccRCC cells. Our study might contribute to a better understanding of metabolic pathways and to the further development of novel therapeutic approaches for ccRCC.

Highlights

Clear cell renal cell carcinoma, accounting for approximately 80–90% of renal cell carcinoma cases, is characterized as high metastasis and relapse rate compared with other subtypes (Ljungberg et al, 2019)
A total of 528 Clear cell renal cell carcinoma (ccRCC) samples that had both mRNA and lncRNA expression profiling were obtained by mapping the clinical prognosis for each sample from the the cancer genome atlas (TCGA) data portal,2 which were used as training dataset
Based on the complete clinical information provided by the TCGA ccRCC dataset (n = 528), we performed univariate and multivariate Cox regression analyses to identify the prognostic factors of overall survival for ccRCC

Summary

Introduction

Clear cell renal cell carcinoma (ccRCC), accounting for approximately 80–90% of renal cell carcinoma cases, is characterized as high metastasis and relapse rate compared with other subtypes (Ljungberg et al, 2019). There is an urgent need to identify effective prognostic biomarkers/therapeutic targets associated with metabolism for the prediction and treatment of ccRCC (Verbiest et al, 2018). With the development of gene chips and high-throughput second-generation sequencing technologies, bioinformatics has been widely applied to analyze, and identify genes associated with the progression of renal cell carcinoma. Wan et al (2020) used the cancer genome atlas (TCGA) database bioinformatics platform to identify DEGs that eliminated patients with high immune and stromal scores in the ccRCC microenvironment. Xu et al (2019) used available RNA-sequence data from TCGA and Fudan University Shanghai Cancer Center (FUSCC) to reveal that AQP may act as an oncogene and a promising prognostic marker in ccRCC. The role of the metabolism-related gene set in ccRCC remains largely unclear

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Conclusion