Identification of biomarkers associated with proliferation and differentiation of mesenchymal stem cells in pulmonary adenocarcinoma and establishment of prognostic models.

Background: Mesenchymal stem cells (MSCs) hold potential as therapeutic agents in cancer, but their mechanisms in lung adenocarcinoma (LUAD) remain poorly understood. This study aimed to identify biomarkers associated with MSC proliferation and differentiation (MSCPD) and investigate their regulatory roles in LUAD.

Methods: Using the TCGA-LUAD and GSE72094 datasets, MSCPD-related gene (MSCPD-RG) scores were calculated, and samples were divided into high and low subgroups. Differentially expressed genes (DEGs1: between subgroups; DEGs2: tumor vs. normal) and module genes derived from weighted gene co-expression network analysis (WGCNA) were examined. Overlapping genes were subjected to Cox and LASSO regression to identify potential biomarkers. A prognostic risk model was developed and validated, followed by functional, immune, and drug sensitivity analyses.

Results: Four biomarkers (MS4A2, IGSF10, NTRK3, MFAP3L) were identified from 1,061 DEGs1, 6,604 DEGs2, and 610 module genes. The risk model based on these biomarkers accurately stratified prognosis. Both T stage and risk score were independent prognostic factors, and a nomogram integrating these factors demonstrated high predictive accuracy. These biomarkers were notably enriched in pathways related to ribosome function, cell cycle regulation, and oxidative phosphorylation. Immune cell analysis revealed significant differences in nine immune cell types (e.g., plasma cells, CD4 memory T cells) between LUAD and normal tissues.

Conclusion: In this study, four key biomarkers closely related to mesenchymal stem cell proliferation/differentiation (MSCPD) were identified in lung adenocarcinoma (LUAD), namely MS4A2, IGSF10, NTRK3, and MFAP3L. Through multi-omics integrated analysis and independent cohort validation, it was confirmed that these markers not only affect disease progression by regulating mesenchymal - epithelial transition (MET) and tumor microenvironment remodeling but can also effectively predict patient prognosis and response to immunotherapy.