Abstract
INTRODUCTION: Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare, aggressive malignancy with limited therapeutic options and poor prognosis. Despite immune checkpoint inhibitors (ICIs) showing efficacy in other cancers, responses in FH-deficient RCC remain suboptimal. Metabolic remodeling, particularly the Warburg effect-driven glycolysis, is implicated in immune evasion and tumor progression, highlighting the need for predictive biomarkers and combinatorial strategies. METHODS: We integrated 41 single-cell RNA sequencing (scRNA-Seq) datasets (19 malignancies, 405 patients, 1,220,365 cells) to develop a glycolytic signature (Glyc.Sig). Validation included pan-cancer transcriptomic analysis (30 cancer types, n=10,154), CRISPR screening data (4 cancers), and clinical immunotherapy cohorts (5 cancers, n=921). LDHA was identified as a top-ranked immune-resistant candidate through CRISPR screening analysis, validated via immunoblotting and immunohistochemistry in Renji Hospital cohorts. RESULTS: Glyc.Sig exhibited a robust inverse correlation between glycolytic activity and ICI efficacy across malignancies. It outperformed conventional biomarkers in predicting immunotherapy outcomes. CRISPR screening prioritized LDHA, a key glycolytic enzyme, as a target to enhance ICI response. Clinical validation confirmed elevated LDHA expression in FH-deficient RCC tumor tissues, which may correlate with immunosuppressive microenvironments and resistance to ICIs. Combinatorial LDHA inhibition and ICI treatment may demonstrate synergistic antitumor effects. DISCUSSION: This study establishes Glyc.Sig as a dual diagnostic-predictive biomarker system, linking glycolytic reprogramming to immune evasion. Comparative validation revealed its enhanced predictive capacity for ICI responsiveness relative to existing molecular signatures. LDHA inhibition emerges as a promising strategy to overcome ICI resistance in FH-deficient RCC and other glycolytic tumors. These findings underscore the therapeutic potential of targeting cancer metabolism to optimize immunotherapy efficacy.