Development and Validation of a Prognostic Model for Lung Cancer Based on Machine Learning and Immune Microenvironment Analysis

Lung cancer prognosis varies significantly among patients, highlighting the need for accurate prediction tools. Emerging evidence suggests that the immune microenvironment plays a crucial role in lung cancer progression and treatment response. We collected RNA expression profiles and clinical data of lung cancer patients from TCGA and GEO databases. Differential expression analysis identified 276 lung cancer-associated genes using strict statistical criteria (logFC > 1, FDR < 0.05). Unsupervised consensus clustering divided patients into 'lung cancer-related' and 'non-lung cancer-related' subgroups. We evaluated 10 machine learning algorithms and 101 algorithmic combinations for prognostic model development. Single-cell RNA sequencing data were analysed using Seurat and CellChat to investigate immune cell interactions within the lung cancer microenvironment. Our prognostic model demonstrated excellent predictive performance with AUC values of 0.874, 0.891 and 0.925 at 1, 2 and 3 years, respectively (C-index = 0.874). Six key immune genes (TLR2, TLR4, CCR7, IL18, TIRAP and FOXP3) showed cell-type specific expression patterns in the lung cancer microenvironment. Intercellular communication analysis revealed complex signalling networks between B cells, T cells, NK cells and dendritic cells. CIBERSORT and ESTIMATE analyses confirmed significant differences in immune infiltration between high-risk and low-risk patients, with distinct patterns of T cell subsets, macrophages and dendritic cells. This study provides a reliable prognostic tool for lung cancer and offers insights into the critical role of the immune microenvironment in lung cancer pathogenesis. Our findings may guide the development of personalised immunotherapy strategies for lung cancer patients.