Identification of a three-miRNA signature as a novel prognostic model for papillary renal cell carcinoma

Abstract

Abstract Background Papillary renal cell carcinoma (pRCC) accounting for near 20% of renal cell carcinoma is the second most common histological subtype. MiRNAs have been demonstrated to played significant roles on predicting prognosis of patients with tumors. An appropriate and comprehensive miRNAs analysis based on a great deal of pRCC samples from The Cancer Genome Atlas (TCGA) will provide perspective in this field. Methods We integrated the expression of mRNAs, miRNAs and the relevant clinical data of 321 pRCC patients recorded in the TCGA database. The survival-related differential expressed miRNAs (sDEmiRs) were estimated by COX regression analysis. The high-risk group and the low-risk group were separated by the median risk score of the risk score model (RSM) based on three screened sDEmiRs. The target genes, underlying molecular mechanisms of these sDEmiRs were explored by computational biology. The expression levels of the three sDEmiRs and their correlations with clinicopathological parameters were further validated by qPCR. Results Based on univariate COX analysis (P < 0.001), eighteen differential expressed miRNAs (DEmiRs) were remarkably related with the overall survival (OS) of pRCC patients. Three sDEmiRs with the most significant prognostic values (miR-34a-5p, miR-410-3p and miR-6720-3p) were employed to establish the RSM which was certified as an independent prognosis factor and closely correlated with OS. In the verification of clinical samples, the overexpression of miR-410-3p and miR-6720-3p were detected to be associated with the advanced T-stages, while miR-34a-5p showed the reversed results. Conclusion The study developed a RSM based on the identified sDEmiRs with significant prognosis prediction values for pRCC patients. The results pave the avenue for establishing and optimizing a reliable and referable risk assessing model and provide novel insight into the researches of biomarkers and clinical treatment strategies.