A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients

Abstract

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

The cuproptosis-associated 13 gene signature as a robust predictor for outcome and response to immune- and targeted-therapies in clear cell renal cell carcinoma

“…Gene expression patterns have been shown to improve cancer classification and prediction of patient outcomes, and several groups have developed expression profiling-based molecular tools for ccRCC prognostication ( 5 – 10 , 40 ). For instance, Rini et al.…”

“…Our analyses of the TCGA cohort of ccRCC showed that higher FDX1 expression is significantly associated with longer OS and PFS, and moreover, its downregulation occurs in ccRCC, which collectively indicates that cuproptosis may act as tumor suppressor in this cancer type. Moreover, according to correlation with cuproptosis factors, we identified a panel of cuproptosis-associated genes and developed the CuAGS-13 score model that could predict patient OS/PFS and recurrence risk with a high accuracy.Gene expression patterns have been shown to improve cancer classification and prediction of patient outcomes, and several groups have developed expression profiling-based molecular tools for ccRCC prognostication(5)(6)(7)(8)(9)(10)40).For instance, Rini et al introduced a 16-gene score for recurrence risk stratification in ccRCC patients at stage I -III (10), and Buttner et al set up the S-3 score (the 97 gene signature based on gene expression in the terminal part of proximal tubules) for survival assessment…”

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network

“…The signature construction used the ceRNA network genes following the methodology of Terrematte and colleagues [94]. The gene signatures were produced using the feature selection techniques in Table S1 and the OmicSelector package (v1.0.0) [95].…”

APOA1 mRNA and protein in kidney renal clear cell carcinoma correlate with the disease outcome

“…KIRC exhibits significant molecular heterogeneity 4 that involves multiple changes in gene expression. With the help of bioinformatics, a series of key genes associated with renal cancer have been identified as potential biomarkers for diagnosis or survival prediction and as therapeutic targets 5 – 7 . However, since most of these biomarkers are not measured routinely in clinical practice, they are not clinically useful.…”