Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data.

Banlai Ruan,Wenzhen Shi,Zhiwei Dong,Ziyin Chen,Xueyi Chen,Lu Qian,Jinping Tian,Qianshan Ding,Jie Liu,Man Wang,Kai Xu,Xianzhen Feng,Qi Wang,Jian Ma

doi:10.1155/2020/8824717

Abstract

Background With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC). Methods Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis. Results A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes (P < 0.01). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC. Conclusion The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.

Highlights

Kidney cancer is one of the most prevalent types of cancer worldwide [1, 2]
We found that a panel of four genes, including cyclin-dependent kinase like 2 (CDKL2), leucine-rich repeat and fibronectin type III domaincontaining 1 (LRFN1), signal transducer and activator of transcription 2 (STAT2), and sosondowah ankyrin repeat domain family member B (SOWAHB), was a promising module for predicting the survival of patients with kidney renal clear cell carcinoma (KIRC)
Consistent with the thresholding power, these differentially expressed genes (DEGs) were divided into eight effective gene modules, and the grey module was considered an ineffective module for preserving nonmodular genes (Figure 2(c))

Summary

Introduction

Kidney renal clear cell carcinoma (KIRC) is characterized by high recurrence and metastasis rates, challenging the health and quality of life of patients [3, 4]. High recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC). Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC

Objectives

Methods

Results

Conclusion