Tertiary lymphoid structures (TLSs) are key components of the tumor immune microenvironment and show prognostic relevance in many cancers. However, their genetic association with lung adenocarcinoma (LUAD) is still lacking. This study aims to construct a TLS-related prognostic model through an integrated multi-omics strategy and to elucidate relevant immunogenetic mechanisms. TLS-related genes (TRGs) showing genetically supported associations with LUAD were identified using Mendelian randomization (MR). A TRG-based model was established using machine learning (ML), with its accuracy assessed through a nomogram. Downstream analyses were performed, including immune microenvironment, tumor mutational burden (TMB), pathway enrichment, drug sensitivity profiling, and single-cell RNA sequencing (scRNA-seq). The expression of TRGs was confirmed using reverse transcription quantitative polymerase chain reaction (RT-qPCR). The prognostic model we built using the best algorithm showed strong prognostic value (1-, 3-, and 5-year AUCs > 0.75). Individuals classified in the high-risk (H-R) cohort exhibited markedly poorer outcomes (p < 0.001). Incorporation of the risk model into the nomogram improved its predictive accuracy compared with the model without this variable (AUC = 0.769 for risk score). TMB analysis suggested a higher TMB in the H-R group, which may predict a worse prognosis. Drugs targeting the PI3K-AKT-mTOR and cell cycle pathways showed higher efficacy in the H-R group. According to enrichment results, TRGs were mainly involved in immune activation and cell cycle regulation, suggesting that these genes may regulate LUAD prognosis through PI3K-AKT-mTOR and cell cycle pathways. The scRNA-seq analysis showed that the 10 TRGs were predominantly localized within T/NK and myeloid cell clusters, indicating their potential involvement in modulating local immune responses. The differential expression patterns of these genes across multiple cell lines were validated using RT-qPCR. In summary, this comprehensive model highlights the significance of TRGs in LUAD, providing a new paradigm for immunogenetic risk evaluation and personalized therapy.
Zhou et al. (Sun,) studied this question.