In silico Study of Clinical Prognosis Associated MicroRNAs for Patients with Metastasis in Clear Cell Renal Carcinoma

Background: Metastasis involves multiple stages and various genetic and epigenetic alterations. MicroRNA has been investigated as a biomarker and prognostic tool in various cancer types and stages. Nevertheless, exploring the role of miRNA in kidney cancer remains a significant challenge, given the ability of a single miRNA to target multiple genes within biological networks and pathways. Objective: This study aims to propose a computational research framework that hypothesizes that a set of miRNAs functions as key regulators in modulating gene expression networks of kidney cancer survival. Methods: We retrieved the NGS data from the TCGA-KIRC extracted from UCSC Xena. A set of prognostic miRNAs was acquired through multiple Cox regression analyses. We adopted machine learning approaches to evaluate miRNA prognosis's classification performance between normal, primary (M0), and metastasis (M1) samples. The molecular mechanism between primary cancer and metastasis was investigated by identifying the regulatory networks of miRNA's target genes. Results: A total of 14 miRNAs were identified as potential prognostic indicators. A combination of high-expression miRNAs was associated with survival probability. Machine learning achieved an average accuracy of 95% in distinguishing primary cancer from normal tissue and 79% in predicting the metastasis from primary tissue. Correlation analysis of miRNA prognostics with target genes unveiled regulatory network disparities between metastatic and primary tissues. Conclusion: This study has identified 14 miRNAs that could potentially serve as vital biomarkers for diagnosing and prognosing ccRCC. Differential regulatory networks between metastatic and primary tissues in this study provide the molecular basis for assessment and therapeutic treatment for ccRCC patients.