RESUMO
BACKGROUND: The incidence of patients diagnosed with renal cell carcinoma (RCC) is increasing. There are no approved biofluid biomarkers for routine diagnosis of RCC patients. This retrospective study aims to identify cell-free microRNA (cfmiR) signatures in urine samples that can be utilized as biomarkers for early diagnosis of sporadic RCC patients. METHODS: Tissue, plasma, and urine samples (n = 221) from 56 sporadic RCC patients and respective normal healthy donors were profiled for 2083 microRNAs (miRs) using the next-generation sequencing-based HTG EdgeSeq miR Whole Transcriptome Assay. DESeq2 (FC |1.2|, false discovery rate <0.05) was performed to identify differentially expressed miRs. Data from RCC tissue samples of The Cancer Genome Atlas database were used for miR validation. RESULTS: We found a 10-miR signature that distinguished RCC tissues from remote normal kidney tissue or benign kidney lesion samples. Additionally, we identified subtype-specific miRs (miR-122-5p, miR-210-3p, and miR-21-3p) and miRs specific for all RCC subtypes (miR-106b-3p, miR-629-5p, and miR-885-5p). We observed that miR-155-5p was associated with tumor size. Using The Cancer Genome Atlas data sets, we validated the miRs found in RCC tissue samples. In plasma or urine analysis, we found cfmiRs that were consistently and significantly upregulated in RCC tissue samples. A 15-cfmiR signature was proposed in urine samples of RCC patients, of which miR-1275 was consistently upregulated in tissue, plasma, and urine samples. CONCLUSIONS: This integrative study found diagnostic miRs/cfmiRs for RCC patients, which were validated using The Cancer Genome Atlas data sets. Distinctive cfmiR signatures found in urine may have clinical utility for the diagnosis of RCC.