Since its discovery, clear cell renal cell carcinoma (ccRCC) has been the most prevalent and lethal kidney malignancy. Our research aims to identify possible prognostic genes of ccRCC and to develop efficient prognostic models for ccRCC patients based on multi-omics investigations to shed light on the treatment and prognosis of ccRCC.
To determine a risk score for each patient, we screened out differentially expressed genes using data from tumor samples, and control samples mined from The Cancer Genome Atlas (TCGA) and GTEx datasets. Somatic mutation and copy number variation profiles were analyzed to look for specific genomic changes connected to risk scores. To investigate potential functional relationships of prognostic genes, gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were carried out. We created a prognostic model by fusing risk ratings with other clinical variables. For validation, the 786-O cell line was used to carry out the dual-gRNA approach to knock down CAPN12 and MSC. This was followed by qRT-PCR to verify the knockdown of CAPN12 and MSC.
For ccRCC, seven predictive genes were discovered: PVT1, MSC, ALDH6A1, TRIB3, QRFPR, CYS1, and CAPN12. The most enriched pathways in the GSVA study and GSEA analysis promote tumorigenesis and immune system modulation. The risk score derived from prognostic genes corresponds with immune infiltration cells and helps predict how well a medicine will work. The mutation of numerous oncogenes was also linked to a high-risk score. A prognostic model with a high ROC value was created for the risk score. An
A thorough prognostic model with good performance has been developed for ccRCC patients using seven prognostic genes that were discovered to be related to ccRCC prognosis. In ccRCC, CAPN12 and MSC were significant indicators and would make good therapeutic targets.