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Lipid Metabolism Related Gene ACSL3 as a Biomarker for Predicting Immunotherapy Outcomes in Lung Adenocarcinoma
Taiping He1, Jinhan Hu2, Haoyue Guo1
1Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Purpose:
Investigate the role of lipid metabolism in the tumor immune microenvironment (TIME) of lung adenocarcinoma (LUAD) and identify vital lipid metabolism-related genes (LMRGs) that contribute to immunotherapy outcomes.
Materials And Methods:
One thousand one hundred thirty LUAD patients were acquired utilizing public databases. Multiple algorithms were used to analyze the contribution of lipid metabolism in TIME. Importantly, cell lines, clinical samples (52 patients in surgery cohort and 36 in immunotherapy cohort), animal models, RNA sequencing (RNA-seq), experiments in protein and mRNA levels were conducted for identifying and validating key biomarker in LUAD immunotherapy.
Results:
A prognostic signature comprising 33 LMRGs was developed and validated as an effective predictor of prognosis and TIME, with a C-index of 0.766 (95% confidence interval, 0.729 to 0.804). Additionally, we identified acyl-CoA synthetase long-chain family member 3 (ACSL3) as a potential biomarker for immunotherapy prognosis. The expression of ACSL3 was verified in 88 clinical tissues from LUAD patients, which indicated that elevated ACSL3 expression was correlated with worse progression-free survival (p < 0.001) and overall survival (p=0.008). Subsequent experiments revealed that knockdown of ACSL3 in vivo enhanced the efficacy of immunotherapy, potentially through increasing interferon-α secretion, as indicated by bulk RNA-seq and enzyme-linked immunosorbent assay analysis, thereby promoting the infiltration of antitumor immune cells.
Conclusion:
The study established a model that accurately predicts immunotherapy response, prognosis, and TIME dynamics in LUAD patients. Notably, the pivotal role of ACSL3 in driving tumor progression and immune evasion was uncovered, offering novel insights into the optimization of immunotherapy strategies for LUAD.
