Pan-cancer analysis implicates novel insights of cholesterol biosynthesis into immunotherapy response prediction and...

Cancer therapy has been transformed by immune checkpoint inhibitors (ICIs); however, their limited response rates and significant heterogeneity remain major challenges, necessitating continued focus. Targeting cholesterol biosynthesis (CB) in tumor cells has emerged as a promising strategy to enhance anti-tumor immune responses, representing a novel therapeutic approach. To elucidate the relationship between CB and ICI responsiveness, we analyzed publicly accessible single-cell RNA sequencing (scRNA-seq) data from patients treated with ICIs. Our analysis integrated a comprehensive pan-cancer dataset comprising 40 scRNA-seq datasets, 30 Cancer Genome Atlas (TCGA) pan-cancer cohorts, and 9 ICI transcriptomics cohorts. We formulated and validated a novel CB-associated signature (CB.SIG). The therapeutic potential of CB. SIG genes were further explored using 17 CRISPR datasets to identify potential immunotherapy targets. In two scRNA-seq immunotherapy cohorts, the CB score significantly correlated with resistance to immunotherapy. Across the pan-cancer dataset, CB. SIG exhibited a significant negative correlation with the imbalance of anti-tumor immunity and immune cell infiltration, while positively correlated with tumor-promoting pathways. Using CB. SIG, machine learning models predicted immunotherapy response and patient prognosis with the AUC values of 0.73 on both validation and test sets. Notably, CB. SIG outperformed previously reported biomarkers across multiple cancer types. Through CRISPR screening, TNFRSF10B was identified as a pan-cancer biomarker predictive of immunotherapy responsiveness and survival, a finding further supported by spatial transcriptomics and immunohistochemistry datasets. Functional assays indicated that silencing TNFRSF10B reduced proliferation, migration, and invasion while increasing apoptosis in non-small cell lung cancer and breast cancer cells. Overall, CB. SIG performed better in predicting ICI responses than alternative signatures and offers a potent tool for identifying patients suitable for immunotherapy.