Abstract
Lung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer and continues to contribute substantially to global cancer mortality. Within the tumor ecosystem, cancer-associated fibroblasts (CAFs) are key stromal components that significantly influence LUAD progression. However, their phenotypic diversity and clinical implications remain incompletely elucidated. We integrated two single-cell RNA sequencing datasets (GSE171145 and GSE189357) to delineate the transcriptional landscape and developmental trajectory of fibroblasts in LUAD. A fibroblast-related signature (FRS) was developed by intersecting fibroblast-specific markers with differentially expressed genes from the TCGA-LUAD cohort, followed by univariate Cox analysis and machine learning modeling. A total of 101 combinations of ten machine learning algorithms were evaluated. The prognostic value of the FRS was validated across multiple GEO datasets. We further investigated its associations with immune infiltration, genomic alterations, and therapeutic response. The core gene TIMP1 was subjected to in vitro and clinical validation. We identified pronounced fibroblast heterogeneity in LUAD, with distinct differentiation trajectories revealed by pseudotime analysis. The constructed FRS exhibited robust prognostic performance across cohorts and was significantly correlated with immunosuppressive features, tumor mutation burden, and predicted immunotherapy outcomes. Clinically, the FRS served as an independent prognostic indicator and showed favorable calibration when combined with TNM stage in a nomogram. TIMP1, one of the top-ranked risk genes in univariate Cox analysis, was confirmed to be upregulated in tumor samples and to promote cell invasion and proliferation in vitro, supporting its functional role in LUAD progression. This study developed a fibroblast-based prognostic signature through integrative single-cell and bulk transcriptomic analyses. The FRS effectively stratifies LUAD patients and highlights the dynamic roles of fibroblasts in shaping tumor progression, providing potential biomarkers and therapeutic targets.
Data availability
The data for this study were sourced from online databases, the details of which are stated in the article. For further inquiries, please contact the corresponding author.
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Acknowledgements
The authors of this article would like to thank the online databases such as TCGA and GEO for providing the data. We are grateful to the Institute of Cardiovascular Research of Tianjin Chest Hospital and the Cardiothoracic Surgery Research Group of Tianjin Chest Hospital. We also appreciate the sincere and constructive comments from the editors and peer reviewers.
Funding
This study was supported by the Natural Funding Project of Tianjin Science and Technology Bureau (No. 20JCYBJC01350), Tianjin Municipal Health and Family Planning Science and Technology Project Key Discipline Special Fund (TJWJXK016),Tianjin Health Research Project(TJWJ2024QN063),Tianjin Health Science and Technology Project Key Projects (ZD20023) and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-018 A).
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Shizhao Cheng, Han Zhang, and Qiuqiao Mu jointly conceptualized the study, performed data analysis, and drafted the manuscript. Hao Zhang contributed to data analysis and experimental validation. Lin Tan provided critical input on the study design and interpretation of the results. Daqiang Sun supervised the study, critically reviewed the manuscript, and approved the final version for submission. All authors reviewed and approved the final manuscript and agreed to its publication.
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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The authors declare no competing interests.
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All participants in this study signed informed consent forms. The study was conducted in accordance with the Declaration of Helsinki and received approval from the Ethics Committee of Tianjin Chest Hospital. Participants were prospectively recruited between May 2024 and March 2025.
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Cheng, S., Zhang, H., Mu, Q. et al. Integrative single-cell and machine learning framework reveals prognostic fibroblast subtypes and constructs a fibroblast-related risk signature in lung adenocarcinoma. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35830-w
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DOI: https://doi.org/10.1038/s41598-026-35830-w
