Evidence map›Paper›PMID 40594761›Full record

ArticleScientific reports2025

Development and validation of a basement membrane inflammatory response gene signature in lung adenocarcinoma.

Shilei Liu, Fengchu Liao, Qingming Xie, Sai Zhang, Dehong Tan, Xuesheng Xu, Jianlan Luo, Xujun Liang, Pengfei Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Shilei LiuNHC Key Laboratory of Cancer Proteomics, Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Fengchu LiaoHunan Provincial Key Laboratory of Geochemical Processes and Resource Environmental Effects, Geophysical and Geochemical Survey Institute of Hunan, Changsha, 410114, Hunan, People's Republic of China.
Qingming XieNHC Key Laboratory of Cancer Proteomics, Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Sai ZhangNHC Key Laboratory of Cancer Proteomics, Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Dehong TanNHC Key Laboratory of Cancer Proteomics, Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Xuesheng XuHunan Provincial Key Laboratory of Geochemical Processes and Resource Environmental Effects, Geophysical and Geochemical Survey Institute of Hunan, Changsha, 410114, Hunan, People's Republic of China.
Jianlan LuoHunan Provincial Key Laboratory of Geochemical Processes and Resource Environmental Effects, Geophysical and Geochemical Survey Institute of Hunan, Changsha, 410114, Hunan, People's Republic of China.
Xujun LiangNHC Key Laboratory of Cancer Proteomics, Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China. liangxujun@csu.edu.cn.
Pengfei ZhangNHC Key Laboratory of Cancer Proteomics, Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China. zhangpf690421@csu.edu.cn.

Funding

Key Research and Development Project of Hunan Province No. 2020SK2071National Natural Science Foundation of China No. 82073008Natural Science Foundation of Hunan Province No. 2024JJ9131, 2020JJ4924Open Project Program in Hunan Provincial Key Laboratory of Geochemical Processes and Resource Enviromnental Effects, Geophysical and Geochemical Survey institute of Hunan No. GRE02304K
6 · The paper itself

Abstract

Inflammatory cell infiltration within the tumor microenvironment compromises the basement membrane' integrity, contributing to chemotherapy resistance and distant metastasis. This study investigates the interplay between inflammatory responses and basement membrane-related genes (BMIRGs) in lung adenocarcinoma (LUAD). With datasets from the TCGA and GEO databases, the BMIRG risk-score model was developed based on univariate Cox regression and a LASSO-Cox regression model. Nine genes were identified as comprising the BMIRG signature. Using this signature, we developed the predictive models, including BMIRG risk scores and nomogram and Cox regression models. Stratifying patients into high-and low-risk groups based on BMIRG risk scores revealed a more favorable prognosis in the latter group. Additionally, the signature exhibited correlations with drug sensitivity, immunotherapy response, infiltration of multiple immune cells and inflammatory factors in microenvironment. The role of one BMIRG signature, CCL20, was further validated using in intro experiments and tissue samples from Chinese patients with LUAD. Functional experiments revealed that inhibiting CCL20 attenuated cell migration in both A549 and H1299 cells. Immunohistochemistry and Western blot analyses demonstrated that CCL20 protein levels were significantly elevated in LUAD tissues compared to adjacent non-tumor tissues and also associated with tumor the TNM stage. High CCL20 expression was also linked to shorter overall survival. In conclusion, the BMIRG signature is a robust predictor of survival and offers a valuable clinical tool for LUAD management. Moreover, CCL20 emerges as a potential diagnostic and therapeutic target, given its high expression in LUAD patients and association poorer prognosis.

Indexed as

Adenocarcinoma of LungBasement MembraneChemokine CCL20InflammationLung NeoplasmsA549 CellsBiomarkers, TumorCell Line, TumorCell MovementFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisBiomarkers, TumorCCL20 protein, humanChemokine CCL20Basement membraneImmune microenvironmentInflammatory responseLung adenocarcinomaPrognosis

Identifiers

PMID40594761
PMCPMC12219087

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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.