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Int J Mol Sci ; 25(8)2024 Apr 11.
Artículo en Inglés | MEDLINE | ID: mdl-38673800

RESUMEN

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.


Asunto(s)
Carcinoma de Células Renales , Regulación Neoplásica de la Expresión Génica , Redes Reguladoras de Genes , Neoplasias Renales , Aprendizaje Automático , Humanos , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/patología , Neoplasias Renales/genética , Neoplasias Renales/patología , Biomarcadores de Tumor/genética , Metástasis de la Neoplasia/genética , MicroARNs/genética , Perfilación de la Expresión Génica/métodos , Transcriptoma , ARN Endógeno Competitivo
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