Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.

backgroundLung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in patients with LUAD. The tumor microenvironment (TME) of LUAD BrM remains incompletely characterized, and there is an urgent need to identify biologically relevant biomarkers and potential therapeutic vulnerabilities.

methodsSingle-cell transcriptomic data from LUAD BrM samples were processed through dimensionality reduction, clustering, and cell annotation. BrM-associated core genes were identified from BrM scRNA-seq datasets using inferCNV and hdWGCNA. A prognostic model for LUAD was then constructed using 101 combinatorial approaches derived from 10 machine learning algorithms and validated in LUAD cohorts. We further performed supportive analyses including enrichment, pseudotime trajectory, cell-cell communication, immune infiltration, and single-cell-based drug sensitivity prediction to provide additional biological context. Key findings were validated by immunohistochemistry (IHC) in human tissues and by qRT-PCR and IHC in a mouse BrM model. Functional assays were performed in PC9-BrM cells to assess the roles of SEC61G and CFL1 in LUAD BrM.

resultsOur study unveiled a comprehensive single-cell atlas of the BrM TME and established a robust 12-gene BrM-informed prognostic signature for LUAD, validated across multiple LUAD survival cohorts. Functional analysis revealed dysregulated cell cycle control in BrM progression. Immune profiling demonstrated significantly reduced CD4+ and CD8+ T-cell infiltration in high-risk subgroups. Cell-cell interaction analysis highlighted the MIF-CD74/CXCR4 and MIF-CD74/CD44 signaling axes as key regulators of TME remodeling. IHC in human tissues and validation using a mouse BrM model supported the biological relevance of SEC61G and CFL1 in LUAD BrM. qRT-PCR further showed elevated expression of SEC61G and CFL1 mRNA in brain-metastatic cells. Knockdown of SEC61G or CFL1 significantly inhibited PC9-BrM cell viability, proliferation, and migration.

conclusionsThis study identified genes associated with LUAD BrM and established a machine-learning-based prognostic signature for LUAD. Further experimental validation supports SEC61G and CFL1 as candidate biomarkers with potential therapeutic relevance.