Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.

The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis. Simultaneously, we created a machine learning-based approach for creating metabolism-related prognostic signature (MRPS). Gamma-glutamyltransferase 6 (GGT6) was further explored for deep biological insights through in vitro experiments. Compared to 51 published signatures and conventional clinical features, MRPS showed substantially higher accuracy. Meanwhile, high MRPS-risk samples demonstrated an immunosuppressive phenotype with more infiltrations of regulatory T cell (Treg) and tumour-associated macrophage (TAM). Following the administration of immune checkpoint inhibitors (ICIs), MRPS showed consistent and strong performance and was an independent risk factor for overall survival. GGT6, an essential metabolic indicator and component of MRPS, has been proven to support proliferation and invasion in ccRCC. MRPS has the potential to be a highly effective tool in improving the clinical results of patients with ccRCC.