An integrative analysis framework for identifying the prognostic markers from multidimensional RNA data of clear cell renal cell carcinoma

In this study, we employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value with 9 of them novel. We then established a 16-RNA-based score for prognosis prediction of ccRCC with significance (p-value < 0.0001). The AUC for the score model is 0.868-0.870 in the training cohort and 0.714-0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. Our study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data and the findings will facilitate potential clinical applications of the biomarkers.PMID:35063405 | DOI:10.1016/j.ajpath.2021.12.009
Source: The American Journal of Pathology - Category: Pathology Authors: Source Type: research