Machine Learning Gene Signature to Metastatic ccRCC based on ceRNA Network

Abstract

Renal carcinoma is a pathology of silent and multifactorial development characterized by a high rate of metastases in patients. After several studies have elucidated the activity of coding genes in the metastatic progression of renal carcinoma, new studies seek to evaluate the association of non-coding genes, such as competitive endogenous RNA (ceRNA). Thus, this study aims to build a gene signature for clear cell renal cell carcinoma (ccRCC) associated with metastatic development from a ceRNA network and to analyze the probable biological functions performed by the participants of the signature. Using ccRCC data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with the differentially expressed genes, assembled nine gene signatures from eight feature selection techniques, and analyzed the evaluation metrics of the classification models in the benchmarking process. With the signature, we performed somatic and copy number alteration analysis, survival and metastatic progression risk analysis, and functional annotation analysis. In this study, we present an 11-gene signature (SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, RASD1). Validation using the external dataset of the International Cancer Genome Consortium (ICGC-RECA) made it possible to assess the generalization of the signature, which showed an Area Under Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions (G-index > 2). The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 had a significant relationship between expression and patient survival, and the first two had a significant association with metastatic development. In addition, functional annotation resulted in relevant pathways for tumor development, such as PI3K/AKT, TNF, FoxO, RNA polymerase two transcription regulation, and cell control. Finally, by analyzing the connections of the signature genes within the ceRNA network in conjunction with studies in the literature, it was possible to obtain an overview of their activities within the ccRCC. Therefore, this gene signature identified new coding and non-coding genes and could act as potential biomarkers for a better understanding of renal carcinoma and in the development of future treatments in the clinical area.