Integration of Multi-Scale Profiling and Machine Learning Reveals the Prognostic Role of Extracellular Matrix-Related Cancer-Associated Fibroblasts in Lung Adenocarcinoma
- Ziyi Chen 1,2, Mengyuan Chen 1,3, Changqing Yang 4, Jiajing Wang 1,2, Yuan Gao 1,2, Yuanying Feng 1,2, Dongqi Yuan 1,2, Peng Chen 1,2
- Ziyi Chen 1,2, Mengyuan Chen 1,3, Changqing Yang 4
- 1Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
- 2Tianjin's Clinical Research Center for Cancer, Department of Thoracic Oncology, Tianjin Lung Cancer Center, Tianjin Cancer Institute & Hospital, Tianjin Medical University, Tianjin, 300060, China.
- 3Department of Nutrition, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
- 4Respiratory Department, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- 0Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.
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View abstract on PubMed
Summary
This summary is machine-generated.Extracellular matrix cancer-associated fibroblasts (eCAFs) and SPP1+ macrophages drive lung adenocarcinoma (LUAD) progression. Targeting their interaction may offer new therapeutic strategies for LUAD patients.
Area Of Science
- Oncology
- Immunology
- Genomics
Background
- Lung adenocarcinoma (LUAD) is a major cause of cancer death, requiring new therapeutic targets.
- The tumor microenvironment, including cancer-associated fibroblasts (CAFs) and macrophages, significantly influences LUAD progression.
Purpose Of The Study
- To investigate the role of extracellular matrix cancer-associated fibroblasts (eCAFs) and SPP1+ macrophages in LUAD.
- To identify potential therapeutic targets and prognostic markers in LUAD based on stromal-immune interactions.
Main Methods
- Single-cell RNA sequencing of 15 LUAD tumors.
- Integration of multi-cohort transcriptomic data (TCGA, GSE31210, GSE72094).
- Pseudotime trajectory and cell-cell communication analyses, machine learning for prognostic model development.
Main Results
- eCAFs were identified as a dominant CAF subtype in advanced LUAD, associated with poor survival.
- SPP1+ macrophages were more abundant in advanced tumors and linked to adverse prognosis.
- eCAFs interact with SPP1+ macrophages via COL1A1-CD44 and COL1A2-CD44, potentially creating immune-excluded microenvironments.
- A 28-gene prognostic signature derived from eCAFs effectively stratified LUAD patients into risk groups.
Conclusions
- eCAFs are key drivers of LUAD progression, and their crosstalk with SPP1+ macrophages is a potential therapeutic target.
- The eCAFs-related prognostic signature provides clinical utility for LUAD risk stratification.
- ECM remodeling is a critical pathway in LUAD evolution, highlighting stromal-immune interactions.
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