Clear cell renal cell carcinoma (ccRCC) is a urinary disease with high incidence. The high incidence of metastasis is the leading cause of death in patients with ccRCC. This study was aimed to identify the gene signatures during the metastasis of ccRCC.
Two datasets, including one gene expression profile dataset and one microRNA (miRNA) expression profile dataset, were downloaded from Gene Expression Omnibus (GEO) database. The integrated bioinformatics analysis was performed using the (limma) R package, miRWalk, DAVID, STRING, Kaplan-Meier plotter databases. Quantitative real-time polymerase chain reaction (qPCR) was conducted to validate the expression of differentially expressed genes (DEGs) and DE-miRNAs.
In total, 84 DEGs (68 up-regulated and 16 down-regulated) and 41 DE-miRNAs (24 up-regulated and 17 down-regulated) were screened from GSE22541 and GSE37989 datasets, respectively. Furthermore, 11 hub genes and 3 key miRNAs were identified from the PPI network, including FBLN1, THBS2, SCGB1A1, NKX2-1, COL11A1, DCN, LUM, COL1A1, COL6A3, SFTPC, SFTPB, miR-328, miR-502, and miR-504. The qPCR data showed that most of the selected genes and miRNAs were consistent with that in our integrated analysis. A novel mRNA-miRNA network, SFTPB-miR-328-miR-502-miR-504-NKX2-1 was found in metastatic ccRCC after the combination of data from expression, survival analysis, and experiment validation.
In conclusion, key candidate genes and miRNAs were identified and a novel mRNA-miRNA network was constructed in ccRCC metastasis using integrated bioinformatics analysis and qPCR validation, which might be utilized as diagnostic biomarkers and molecular targets of metastatic ccRCC.