Identification and external validation of a prognostic signature based on N6-methyladenosine- and tertiary lymphoid structures-related genes to evaluate survival prognosis and treatment efficacy in lung adenocarcinoma

The N6-Methyladenosine (m6A) RNA modification critically regulates cancer biology, and tertiary lymphoid structures (TLSs) shape antitumor immunity. However, their combined prognostic roles in lung adenocarcinoma (LUAD) remain unclear. This study applies an integrative multi-omics approach to construct an m6A- and TLS-related prognostic model and uncover underlying molecular mechanisms. We identified m6A and TLS-related genes (MTGs) associated with LUAD and constructed a prognostic model using machine learning, which was validated with nomograms. Subsequent analyses included immune microenvironment profiling, tumor mutational burden (TMB), enrichment assays, drug sensitivity testing, and single-cell RNA sequencing (scRNA-seq). The expression of MTGs was detected using quantitative reverse transcription polymerase chain reaction (RT-qPCR). The risk model we developed demonstrated strong prognostic value, with areas under the curve (AUCs) exceeding 0.8 at 1, 3, and 5 years. The prognosis of the high-risk cohort (HRC) was significantly worse (P < 0.001). A nomogram incorporating this risk model (AUC = 0.825) outperformed one without it. TMB analysis revealed a higher TMB in the HRC, which is likely associated with a poorer prognosis. Drugs targeting the microtubule dynamics and apoptosis pathways showed increased efficacy in the HRC. Enrichment analysis indicated that the MTGs are primarily involved in cell adhesion, immune response, hematopoietic cell lineage, and cell cycle regulation. The scRNA-seq analysis further revealed that these 8 MTGs are predominantly expressed in fibroblasts and T/NK cell clusters, indicating their possible involvement in regulating local immune responses. RT-qPCR analysis confirmed the differential expression of MTGs. This study demonstrates that the integrative multi-omics model reveals not only potential therapeutic targets but also new perspectives on LUAD immunogenetics.