Evidence map›Paper›PMID 40223964›Full record

ArticleJournal of thoracic disease2025

Asthma-derived genetic signature predicts lung cancer prognosis: a multi-scale omics investigation.

Youpeng Chen, Enzhong Li, Yifei Xie, Zhenglin Chang, Xiaoyi Chen, Haojie Wu, Weilin Feng, Zhangkai J Cheng, Baoqing Sun

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PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Youpeng Chen *Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.ORCID https://orcid.org/0000-0002-9677-2333
Enzhong Li *Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Yifei Xie *KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Zhenglin ChangDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Xiaoyi ChenKingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China.
Haojie WuDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Weilin FengDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Zhangkai J ChengDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
Baoqing SunDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Asthma and lung cancer are prevalent respiratory diseases with unclear interrelationships. This study aimed to investigate their association using multi-omics data and develop an asthma-related prognostic model for lung cancer. Methods: Global incidence trends and correlations were analyzed using Global Burden of Disease data from 1990 to 2019. Two-sample Mendelian randomization (MR) was performed using genome-wide association study summary statistics to assess the causal relationship. Bulk RNA sequencing data from public databases were analyzed to identify differentially expressed genes (DEGs) common to both diseases. A prognostic model was constructed using asthma-related DEGs and validated through immune cell correlation, checkpoint gene analysis, and single-cell RNA sequencing (scRNA-seq). Results: Epidemiological data showed a positive correlation between asthma and lung cancer, while MR supported a potential causal relationship. A prognostic model incorporating four asthma-related genes ( Conclusions: This multi-omics study provides insights into the asthma-lung cancer link and presents a promising prognostic model for lung cancer based on asthma-related genes. The model's association with immune features suggests its potential utility in immunotherapy patient selection.

Indexed as

Asthmalung cancermulti-omics analysisprognostic model

Identifiers

PMID40223964
PMCPMC11986785

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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.