A novel brown adipocytes-related gene signature predicts and validates prognosis and immune infiltration of clear cell renal cell carcinoma.

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer. The crosstalk between tumor tissue and adjacent adipose tissue has been appreciated recently. This study examines the predictive usefulness of brown adipocyte-related genes (BARGs) in ccRCC.

Methods: The transcriptome and clinical data of ccRCC patients were obtained from TCGA-KIRC and USA-ccRCC cohorts (848 tumor samples; 72 normal samples). Lasso-Cox methods were used to construct the risk prognostic signature model. We used Kaplan-Meier survival analysis to evaluate the prognostic significance of the risk model with ROC curves ascertaining prediction accuracy. The differences in immune cell infiltrates and signature risk scores between different risk categories were analyzed. Finally, biological experiments were performed to explore the functions of candidate genes.

Results: TCGA-KIRC patients were classified into two clusters that differed significantly regarding overall survival (OS) and tumor microenvironment. After screening BARGs candidates, a signature consisting of PPP1R1A, DPYSL3, and PTPRM was created to calculate risk score. Patients were assigned to the high or low-risk group, and the high-risk group had a significantly worse prognosis. Consistent trend was validated in external USA-ccRCC cohort. Meanwhile, the signature risk score affected immune cell infiltrates within the ccRCC microenvironment, positively correlated with the infiltration of CD4⁺ T cells, CD8⁺ T cells, CD56^dim, CD56^bright NK cells, MDSCs, and macrophage cells, while negatively correlated with neutrophil, iDCs, mast cells, and eosinophil. Finally, knockdown of PPP1R1A and DPYSL3 in renal cancer cells showed impairment in tumor proliferation ability of ccRCC in vitro and in vivo. Conversely, knockdown of PTPRM exhibited a promotive effect.

Conclusion: We developed a predictive BARGs-related risk signature for early diagnosis and classifying ccRCC patients, which offers potential targets for individualized treatment of ccRCC.