Evidence map›Paper›PMID 41691850›Full record

ArticleTranslational oncology2026

Integrative bioinformatics identifies NSCLC biomarkers associated with LPS metabolism and circadian disruption.

Yu Chen, Yifan Zhao, Qinghua Kong, Yabin Li, Ping Jiang, Lichao Fan

Abstract read
In one paragraph

Article in Translational oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yu ChenDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Yifan ZhaoDepartment of Pharmacy, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou, China.
Qinghua KongDepartment of Respiratory and Critical Care Medicine, Dahua Hospital, Shanghai, China.
Yabin LiSchool of Life Science and Technology, Tongji University, Shanghai, China.
Ping JiangDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Lichao FanDepartment of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: 1801041@tongji.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeNon-small cell lung carcinoma (NSCLC) is a leading cause of cancer-related mortality worldwide, highlighting the urgent need for early detection and targeted therapies. While lipopolysaccharide (LPS) metabolism and circadian rhythm disruption are emerging as important factors in cancer progression, their specific roles in NSCLC remain poorly understood.

methodsWe integrated multiple GEO datasets to identify NSCLC-associated differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) identified key gene modules, followed by functional enrichment analysis. A hybrid machine learning approach combining Lasso regression and random forest was used to identify hub genes. Immune infiltration analysis evaluated associations with the tumor microenvironment, and diagnostic performance was validated in an independent cohort. Functional roles of the candidate gene CACNA2D2 were assessed through gain- and loss-of-function experiments in A549 cells, evaluating viability, proliferation, migration, and invasion.

resultsWe identified 889 significant DEGs enriched in inflammatory and immune-related pathways. WGCNA revealed the magenta module as highly associated with NSCLC, involved in angiogenesis and extracellular matrix organization. Machine learning identified nine hub genes (CACNA2D2, ASPA, LRRN3, ABCA6, TNFSF12, AHNAK, TACC1, ID4, TSLP) showing excellent diagnostic performance (AUC: 0.832-0.906). These genes correlated significantly with immune cell infiltration patterns. Functional validation established CACNA2D2 as a tumor suppressor, where its depletion enhanced malignant phenotypes while overexpression suppressed them.

conclusionThis study identifies a novel gene signature linked to LPS metabolism and circadian disruption in NSCLC, with validated diagnostic utility and implications for tumor immune regulation. CACNA2D2 emerges as a key tumor suppressor, offering insights for early detection and targeted therapy development.

Indexed as

Circadian rhythm disruptionLipopolysaccharide (LPS) metabolismNon-small cell lung cancer (NSCLC)Targeted therapyTumor immune microenvironment

Identifiers

PMID41691850
PMCPMC12925077

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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.