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Publications

Journal Article Epublish

PubMed: 40657391 DOI: 10.7150/ijms.113580

Integration of Multi-Scale Profiling and Machine Learning Reveals the Prognostic Role of Extracellular Matrix-Related Cancer-Associated Fibroblasts in Lung Adenocarcinoma.

Int J Med Sci 22(12):2956-2972

Authors: Ziyi Chen, Mengyuan Chen, Changqing Yang, Jiajing Wang, Yuan Gao, Yuanying Feng, Dongqi Yuan, Peng Chen

Affiliations

  • Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China. Tianjin's Clinical Research Center for Cancer, Department of Thoracic Oncology, Tianjin Lung Cancer Center, Tianjin Cancer Institute & Hospital, Tianjin Medical University, Tianjin, 300060, China.
  • Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China. Department of Nutrition, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
  • Respiratory Department, Tianjin Medical University General Hospital, Tianjin, 300052, China.
  • Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
  • Tianjin's Clinical Research Center for Cancer, Department of Thoracic Oncology, Tianjin Lung Cancer Center, Tianjin Cancer Institute & Hospital, Tianjin Medical University, Tianjin, 300060, China.

Abstract

Lung adenocarcinoma (LUAD) remains a leading cause of cancer mortality, necessitating novel therapeutic targets and prognostic strategies. This study investigates the role of extracellular matrix cancer-associated fibroblasts (eCAFs) and their interaction with SPP1+ macrophages in LUAD progression and prognosis. Utilizing single-cell RNA sequencing from 15 LUAD tumors and integrating multi-cohort transcriptomic data (TCGA, GSE31210, GSE72094), we identified eCAFs as a dominant CAF subtype in advanced-stage tumors and high-grade pathological subtypes, correlating with poor patient survival. Similarly, SPP1+ macrophages exhibited increased abundance in advanced tumors and adverse prognosis. Pseudotime trajectory analysis revealed eCAFs as an evolutionary endpoint in CAF differentiation, associated with extracellular matrix remodeling pathways (COLLAGEN, FN1). Cell-cell communication analysis highlighted eCAFs-SPP1+ macrophage interactions via COL1A1-CD44 and COL1A2-CD44 ligand-receptor pairs, suggesting a mechanism for immune-excluded microenvironments. A prognostic model incorporating 28 eCAFs-related genes, validated through 101-machine learning algorithms, effectively stratified patients into high- and low-risk groups across cohorts. This study underscores eCAFs as key drivers of LUAD progression and proposes their interplay with SPP1+ macrophages as a therapeutic target. The developed prognostic signature offers clinical utility for risk stratification, though further experimental validation is warranted. These findings advance understanding of stromal-immune crosstalk in LUAD and highlight ECM remodeling as a critical pathway in tumor evolution.

MeSH

Cells, Lung, Macrophages, Neoplasms, Adenocarcinoma, Multiple Chronic Conditions, Adenocarcinoma Of Lung, Machine Learning, Cancer-Associated Fibroblasts, Fibroblasts, Therapeutics, Learning, Extracellular Matrix, Tumor Microenvironment, Prognosis