Background
ccRCC is the prevailing form of RCC, accounting for the majority of cases. The formation of cancer and the body's ability to fight against tumors are strongly connected to Gamma delta (γδ) T cells.
Conclusions
In summary, we have created a precise predictive biomarker using a risk model centered on γδ T cells, which can anticipate clinical results and provide direction for the advancement of innovative targeted therapies.
Methods
We examined and analyzed the gene expression patterns of 535 individuals diagnosed with ccRCC and 72 individuals serving as controls, all sourced from the TCGA-KIRC dataset, which were subsequently validated through molecular biology experiments.
Results
In ccRCC, we discovered 304 module genes (DEGRGs) that were ex-pressed differentially and linked to γδ T cells. A risk model for ccRCC was constructed using 13 differentially DEGRGs identified through univariate Cox and LASSO regression analyses, which were found to be associated with prognosis. The risk model exhibited outstanding performance in both the training and validation datasets. The comparison of immune checkpoint inhibitors and the tumor immune microenvironment between the high- and low-risk groups indicates that immunotherapy could lead to positive results for low-risk patients. Moreover, the inhibition of ccRCC cell proliferation, migration, and invasion was observed in cell culture upon knocking down TMSB10, a gene associated with different types of cancers. Conclusions: In summary, we have created a precise predictive biomarker using a risk model centered on γδ T cells, which can anticipate clinical results and provide direction for the advancement of innovative targeted therapies.