A mechanical stimulation-related gene signature predicts prognosis, reflects immune microenvironment remodeling, and identifies

Background: Mechanical stimulation is a key biomechanical characteristic of the tumor microenvironment and is involved in tumor progression, immune microenvironment remodeling, and therapy resistance. This study aimed to develop a mechanical stimulation-related gene (MSRG)-based prognostic signature for survival prediction and to explore its association with the tumor immune microenvironment in lung adenocarcinoma (LUAD). Methods: We used a multifaceted approach to analyze MSRGs in LUAD. The AUCell algorithm was used to calculate mechanical stimulation scores at the single-cell level, while CellChat and NicheNet analyses were applied to reveal cell-cell communication and ligand-target regulatory patterns under high mechanical stimulation. Mechanical stimulation activity in TCGA-LUAD samples was quantified by ssGSEA, and WGCNA was then performed to identify key gene modules associated with this trait. An integrated machine learning framework was then used to develop a prognostic risk model. Immune infiltration, stromal and immune scores, immune checkpoint-related features, and immune evasion potential were further evaluated. Finally, the selected key gene, Results: Analysis of scRNA-seq data demonstrated that mechanical stimulation (MS) scores varied considerably across different cell types. The high-MS subgroup showed enhanced intercellular communication, suggesting active tumor microenvironment remodeling. Furthermore, a prognostic model for LUAD with robust predictive performance was constructed using WGCNA and machine learning algorithms. Notably, the high-risk group showed reduced immune infiltration, higher tumor purity, elevated Conclusion: We established an MSRG-based prognostic signature that not only predicts survival in LUAD but also reflects tumor immune microenvironment remodeling and potential immune escape. Our preliminary findings also suggest that