Prediction tools for assessing functional impacts of gene mutations in Clear Cell Renal Cell Carcinoma: A comparative study

Abstract

Renal cell carcinoma (RCC) is a well-known malignancy characterized by specific gene mutations that elevate its occurrence. It can be categorized into clear cell RCC (ccRCC), papillary RCC, and chromophobe RCC, which account for approximately 85 % of all primary RCC cases. The mutations typically involve single-nucleotide variants (SNVs) that lead to amino acid substitutions, which influence various biological functions, including gene expression. Therefore, predicting the functional consequences of RCC-related single-nucleotide polymorphisms (SNPs), including substitutions, insertions, deletions, and duplications, is crucial for the effective clinical management of RCC. In recent years, the accessibility and popularity of tools for predicting SNP functional variations have grown, especially in research concerning the potential risks associated with key gene mutations in cancer. Accordingly, this study focused on commonly mutated genes in ccRCC, namely VHL, BAP1, PBRM1, and SETD2. Public data from sources such as The Cancer Genome Atlas and the National Center for Biotechnology Information were used to identify 61 gene mutation positions. Nine nonsynonymous mutation prediction tools were used for analysis, namely SIFT, Polyphen-2, Mutation Assessor, Fathmm, MutPred, CHASM, Revel, Provean, and SNP&GO. The tools were evaluated by their statistical performance, prediction distribution, and receiver operating characteristic curves. The three tools with the highest accuracy for predicting the functional consequences of mutations in the four frequently mutated genes associated with ccRCC were SIFT (accuracy of 0.75), Provean (0.7), and Polyphen-2 (0.69). These findings offer valuable insights into predicting ccRCC-related gene mutation effects, but further research and validation are essential to support their clinical applications.