Purpose: Clear cell renal cell carcinoma (ccRCC) is one of the most common cancers with high mortality worldwide. However, biomarkers for predicting prognosis in ccRCC are limited. In this study, we attempted to identify potential prognostic biomarkers of ccRCC.
Methods: Clinical information and the preprocessed ccRCC mature miRNA expression profiles in The Cancer Genome Atlas database were downloaded from UCSC Xena. The miRNAs differentially expressed between ccRCCs and matched normal tissues were analyzed using the “limma” package. A miRNA-based signature was constructed using the multivariate Cox regression model with prognosis index (PI) formula. Patients with ccRCC were divided into low-risk and high-risk subgroups according to median PI. The survival times were compared between the two groups using Kaplan–Meier analysis with log-rank test. The training set was used to construct a miRNA-based signature for predicting prognosis. The test set was used to verify the signature. Target gene prediction and functional enrichment analysis of the four miRNAs were performed using miRNet.
Results: We identified four miRNAs, miRNA-21-5p, miRNA-9-5p, miR-149-5p, and miRNA-30b-5p, as independent prognostic indicators. Next, we used these four miRNAs to construct a four-miRNA PI for each patient. Results revealed that patients in the high-risk group (n=119) had significantly shorter survival time than those in the low-risk group (n=118) (high-risk/low-risk group log-rank P =0.000). This four-miRNA signature is an independent prognostic factor compared with routine clinicopathological features in the test set. These miRNAs targeted 1,634 genes, and a miRNA-target gene network was constructed using miRNet. The target genes of these four miRNAs were involved in various pathways related to cancer.
Conclusion: Our observations suggest that the four-miRNA signature correlated with the survival of patients with ccRCC and can be used as a prognostic biomarker of ccRCC.
Keywords: ccRCC, miRNA signature, overall survival, prognostic biomarkers