Evidence map›Paper›PMID 41776080›Full record

ArticleCellular and molecular life sciences : CMLS2026

Senescent epithelial cells remodel the tumor microenvironment and drive early LUAD progression: a multiomics risk model and single-cell analysis.

Yujia Zhou, Chen Chen, Fengyi Zuo, Siqi Ding, Hui Wang, Xinyu Xu, Bin Zhu, Bangyu Wu, Chen Liu, Tianhao Yuan and 2 more

Abstract read
In one paragraph

Article in Cellular and molecular life sciences : CMLS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Yujia ZhouDepartment of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, China.
Chen ChenDepartment of Oncology, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, China. chenchen881021@njmu.edu.cn.
Fengyi ZuoThe Second Clinical Medical School of Nanjing Medical University, Nanjing, China.
Siqi DingDepartment of Medical Imaging, Nanjing Medical University, Nanjing, Jiangsu, China.
Hui WangDepartment of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, China.
Xinyu XuDepartment of Pathology, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, China.
Bin ZhuHospital Development Management Office, Nanjing Medical University, Nanjing, China.
Bangyu WuThe Second Clinical Medical School of Nanjing Medical University, Nanjing, China.
Chen LiuThe First Clinical Medical College of Nanjing Medical University, Nanjing Medical University, Nanjing, China.
Tianhao YuanThe Second Clinical Medical School of Nanjing Medical University, Nanjing, China.
Dawei MaDepartment of Pathology, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, China. madawei2016@njmu.edu.cn.
Xing HuangDepartment of Pathology, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, China. polofly2012@njmu.edu.cn.ORCID http://orcid.org/0000-0001-9982-8622

Funding

the Medical Research Project of the Jiangsu Provincial Health Commission Grant No. H2023007the National Natural Science Foundation of China Grant No. 81902334
6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD) remains a major cause of cancer-related mortality, and there are currently few reliable biomarkers available for accurate prognosis and effective targeted therapy. Accumulating evidence demonstrates that senescent cells play an important role in tumor progression and immune evasion; nevertheless, their specific contribution to LUAD pathogenesis has not yet been fully elucidated. Accordingly, a five-gene senescence-related risk model comprising FGF2, GAPDH, CCNA2, ENO1, and DKK1 was established using the Least Absolute Shrinkage and Selection Operator regression applied to bulk RNA sequencing data obtained from The Cancer Genome Atlas (TCGA). Patients stratified as high risk by this model exhibited significantly poorer overall survival and progression-free survival, accompanied by marked activation of pathways associated with immune infiltration, epithelial–mesenchymal transition, and extracellular matrix remodeling. Integrative single-cell RNA sequencing analysis further revealed a distinct epithelial subpopulation (E9) defined by preferential activation of the senescence-associated gene DKK1. This subpopulation, which emerged from integrative single-cell transcriptomic analysis, exhibited pronounced senescence-associated characteristics, significantly increased cellular stemness, and extensive intercellular communication capacity, and uniquely expressed COL17A1 together with transcriptional programs that were strongly associated with epidermal development. Pseudotime analyses consistently positioned E9 cells at an early stage of tumor evolution. At the functional level, DKK1 contributed to senescence-associated phenotypic features, thereby promoting tumor cell migration and metastatic potential. From a clinical perspective, patients with concurrent high levels of COL17A1 and DKK1 expression experienced significantly worse clinical outcomes. Collectively, these findings identify a senescence-driven epithelial subpopulation that contributes to LUAD progression via DKK1-mediated activation of aging-related pathways and highlight DKK1 as a potential therapeutic target.

Indexed as

Adenocarcinoma of LungCellular SenescenceEpithelial CellsLung NeoplasmsTumor MicroenvironmentDisease ProgressionEpithelial-Mesenchymal TransitionGene Expression Regulation, NeoplasticHumansIntercellular Signaling Peptides and ProteinsMultiomicsSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisIntercellular Signaling Peptides and ProteinsCellular senescenceLung adenocarcinoma (LUAD)Multi-omics prognostic risk modelSingle-cell RNA sequencing analysisTumor microenvironment remodeling

Identifiers

PMID41776080
PMCPMC12979735

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.