IntroductionClear cell renal cell carcinoma (ccRCC) is the most prevalent histological subtype of renal carcinoma. To diagnose ccRCC and assess its prognosis more accurately, it is essential to screen novel prognostic biomarkers and construct prognostic signatures.MethodsImmune infiltration analysis of the TCGA cohort was performed via single-sample gene set enrichment analysis (ssGSEA). The ccRCC cohort from the TCGA database was used to identify MDSC/Treg-related genes. Hub genes were selected from the common genes in the MDSC/Treg-related gene list via machine learning approaches. These hub genes were then employed to construct the risk signature through multivariate analysis.The prognostic performance, immune performance, and functional analysis of the signature were comprehensively assessed. Two independent GEO datasets were used to verify the major findings above. Potential drugs were screened to promote clinical transformation via the CellMiner platform. Finally, the expression levels of six markers were validated through RT-qPCR analysis of clinical tissue samples.ResultsSix MDSC/Treg-related DEGs were identified via machine learning approaches based on the Cancer Genome Atlas cohort. A novel signature (risk score = -0.5579*wdfy4-0.2198*il16 + 0.8014*fcgr1b + 0.3344*nod2 + 0.4111*relt + 0.1131*mki67) was subsequently constructed. More advanced clinical subgroups had higher scores. In addition, the signature was an independent prognostic indicator (HR = 2.0, 95% CI: 1.6-2.4, p value 0.05).ConclusionA signature related to MDSC/Treg DEGs was constructed. This signature can differentiate between immune and clinical features, enabling the prediction of both clinical and immunotherapy prognoses. However, some PCR experiments did not fully validate the bioinformatics results.
Xu et al. (Mon,) studied this question.