Integrative Multi-Omics Reveals Energy Metabolism–Related Prognostic Signatures and Immunogenetic Landscapes in Lung Adenocarcinoma

Lei XieYajie ZhouZijian HuWenxiong Zhang^*Xiaoqiang Zhang^*

• Nanchang University, Nanchang, China

Background: Energy metabolism (EM) is critically involved in driving tumor development, therapeutic resistance, and modulation of the immune response. However, its genetic basis and prognostic value in lung adenocarcinoma (LUAD) remain unclear. This study integrates multi-omics approaches to develop an EM-related prognostic model for assessing LUAD prognosis and uncovering relevant immunogenetic pathways. Methods: Differential analysis combined with Mendelian randomization was used to identify EM-related genes (EMRGs) with a causal link to LUAD, which were then used to build a prognostic model via machine learning. Nomogram integrating clinical features and risk model was developed to enhance prognostic accuracy. Subsequent analyses, including immune invasion, enrichment analysis, and tumor mutational burden (TMB), were conducted to explore biological associations. The heterogeneity and cell-specific expression of critical EMRGs were explored through single-cell RNA sequencing (scRNA-seq). The transcriptional levels of the chosen EMRGs were experimentally validated using reverse transcription quantitative PCR (RT-qPCR). Results: A prognostic model was established in our study using Random Survival Forest (RSF) machine learning (ML) algorithm. Survival outcomes were substantially lower in the high-risk group (HRG) than in the low-risk group (LRG), as reflected by an AUC value of 0.73. A nomogram incorporating this risk model outperformed one without it. Gene Ontology (GO)/ Kyoto Encyclopedia of Genes and Genomes (KEGG)-based analyses showed a significant enrichment of these genes in pathways linked to immune regulation and extracellular matrix (ECM) dynamics. An elevated TMB in HRG may predict a worse prognosis. Evaluation of pharmacologic susceptibility revealed enhanced drug sensitivity in the HRG, such as Cytotoxic Chemotherapy and Apoptosis-inducing small molecule inhibitors, etc. ScRNA-seq revealed that prognostic EMRGs were mainly enriched in T and NK cells, myeloid cells, and fibroblasts, suggesting their involvement in immune regulation and remodeling of the tumor microenvironment (TME). RT-qPCR confirmed their differential expression in LUAD and normal cell lines. Conclusions: This integrative model reveals the prognostic and therapeutic relevance of EMRGs in LUAD, presenting a novel structure for immunogenetic risk assessment and personalized treatment strategies.