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Metabolic reprogramming-driven stratification and therapeutic targeting in lung adenocarcinoma: implications for
Jiabao Jia1, Shiyun Feng1, Rui Wang1
1Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China.
Background:
Metabolic reprogramming is a hallmark of lung adenocarcinoma (LUAD) progression and therapy resistance, yet its clinical integration for prognosis and treatment remains limited. This study aimed to develop a metabolism-related prognostic score (MRPs) and investigate its biological, immunological, and therapeutic implications in LUAD.
Methods:
Transcriptomic and clinical data from TCGA and GEO were used to construct a prognostic model based on metabolism-associated genes via LASSO-Cox regression. The model was validated in internal and external cohorts. Functional enrichment, immune infiltration analysis, drug sensitivity prediction (TIDE), and single-cell RNA sequencing were performed. WARS2 was identified as a key gene and validated through qPCR, Western blot, immunohistochemistry, and functional assays in vitro and in vivo.
Results:
The MRPs model stratified LUAD patients into two subgroups with distinct prognoses. The high-risk MRP I subgroup showed enhanced oxidative phosphorylation, cell cycle activity, and protein synthesis, along with suppressed immune infiltration and lower immune scores. WARS2 was highly expressed in malignant epithelial cells and associated with proliferative and metabolic programs at both bulk and single-cell levels. WARS2-positive cells exhibited increased intercellular communication via adhesion-related pathways. Functional assays confirmed that WARS2 silencing impaired LUAD cell proliferation, invasion, and tumor growth.
Conclusion:
This study presents a novel MRPs system that captures LUAD metabolic and immune heterogeneity and surpasses traditional clinicopathologic parameters in prognostic performance. WARS2 emerges as a functional driver and therapeutic target. These findings offer a comprehensive framework for metabolic-state-based stratification and personalized treatment strategies in LUAD.
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