Developing a Metabolic-Associated Prognostic Index for Risk Stratification and Therapeutic Guidance in Stage I Lung Adenocarcinoma via Multiomics Analysis.

PURPOSEEarly-stage lung adenocarcinoma (LUAD) exhibits substantial clinical heterogeneity that is not fully explained by TNM staging, highlighting the need for biology-driven prognostic tools. Although metabolic reprogramming is an established cancer hallmark, its systematic prognostic significance in stage I LUAD remains unexplored.

MATERIALS AND METHODSWe analyzed stage I LUAD samples from The Cancer Genome Atlas, Gene Expression Omnibus, and European Genome-phenome Archive databases. Weighted gene coexpression network analysis and differential expression analysis were conducted to identify metabolic genes associated with LUAD malignancy and prognosis. A metabolic-associated prognostic index (MAPI) was subsequently developed using machine-learning combinations. The performance of MAPI was evaluated from multiple biologic perspectives and at the single-cell level. Key gene functions were experimentally verified in vitro.

RESULTSMAPI robustly stratified patients into high- and low-risk groups with significantly divergent survival outcomes, outperformed conventional clinicopathologic features and previously published signatures, and emerged as an independent prognostic factor across all validation cohorts. The high-risk group was characterized by enhanced cancer stemness, genetic heterogeneity, metabolic reprogramming, immune exclusion, and reduced responsiveness to immunotherapy. We also pinpointed three potential therapeutic agents (paclitaxel, bortezomib, and vincristine) for high-risk patients. The single-cell RNA sequencing further validated the association between MAPI and malignant progression. Functional analyses demonstrated that knockdown of DEGS1 or PLOD1 significantly suppressed LUAD cell proliferation and migration.

CONCLUSIONOur results establish MAPI as a biologically interpretable and clinically applicable tool for risk stratification and precision treatment decision making in patients with stage I LUAD.