Identification of the molecular subtype and prognostic characteristics of lung adenocarcinoma based on CD8 ⁺ T cell-related gene signature

BackgroundLung adenocarcinoma (LUAD) stands as a major respiratory malignancy with high mortality. With the advent of immunotherapy, new therapeutic avenues have emerged in cancer treatment.ObjectiveOur focus aimed at developing a CD8⁺ T cell-based immune gene prognostic model (CDIGPM) for LUAD, shedding light on the immunological aspects and the potential advantages of immunotherapy in distinct CDIGPM-defined LUAD categories.MethodsData from LUAD patients were extracted from the TCGA and GEO databases (GSE11969). The differentially expressed genes (DEGs) were intersected with immune genes from ImmPort and InnateDB, yielding 89 significant immune genes related to CD8⁺ T cells (CDIGs). Univariate Cox regression and LASSO regression analyses were performed on 10 hub CDIGs (ADM, CAV1, CTSL, HLA-DMB, HLA-DQA1, IGHM, PLSCR1, PTGDS, S100A16, and WFDC2). Furthermore, the immunological attributes and the immunotherapy efficacy in CDIGPM-defined categories were explored. Moreover, to support the findings of the bioinformatics analysis, fifteen LUAD patients' tumor and adjacent tissues were collected for qRT-PCR detection of CDIGPM-related genes.ResultsKaplan-Meier analysis revealed that the high-CDIGPM group exhibited significantly poorer overall survival (OS) trajectories, whereas the low-CDIGPM group showed more favorable OS trajectories, indicating a better prognosis. Age, tumor stage, and CDIGPM score were identified as independent prognostic factors. The high-CDIGPM group was enriched in pathways related to the cell cycle, focal adhesion, and cancer, while the low-CDIGPM group was associated with immune response-related pathways. The CDIGPM model effectively differentiated clinical subtypes in patients with LUAD. QRT-PCR detection of Clinical LUAD samples also validated the differentially expression of CDIGPM model related genes.ConclusionsThe study highlights the prognostic importance of CDIGs in LUAD using the CDIGPM model, linking age, stage and CDIGPM score to poor outcomes. The identified genes and pathways provide potential therapeutic targets, deepening our understanding of LUAD's molecular landscape.