Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient’s survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as “sponges” upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

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.

Metastasis is not a de novo functional module innovation but rather the pathological redeployment of deeply conserved biological programs. Here, we reconstruct the evolutionary landscape of biological functions involved in the early stages of the metastatic cascade, including cell adhesion, extracellular matrix organization (ECM), regulation of metallopeptidase activity, cell junction organization, epithelial-mesenchymal transition (EMT), and cellular extravasation alongside the physiological constraints that suppress them (Metastasis Suppressor Genes). Using phyletic pattern reconstruction of 787 orthologs across 473 Clusters of Orthologous Groups with the Bridge algorithm, we identified a divergence between the evolutionary timelines of metastasis-enabling programs and metastasis suppressors. Our results indicate that the molecular systems associated with the structural capacity for tumor dissemination are evolutionary ancient. ECM organization traces to the Human-Discoba last common ancestor, EMT to Ctenophora, cell adhesion to Sauropsida, and cellular extravasation to Actinopterygii. In contrast, mechanisms responsible for suppressing genomically unstable cells during continuous tissue renewal emerged later, peaking in the vertebrate lineage. These findings support the Serial Atavism Model and suggest that metastasis arises from the progressive erosion of recently evolved regulatory constraints, allowing the reactivation of ancestral cellular programs that predate complex multicellularity.