Evidence map›Paper›PMID 42904604›Full record

ArticleChemical biology & drug design2026

Single-Cell Dissection of Inflammatory Suppression and Stemness Plasticity Reveals STMN1-Associated Malignant Epithelial Cells in Severe Lung Cancer.

Yanmei Chen, Sisi Chen, Biao Cheng, Yuzhu Tang, Yiyan Huang, Miao Chen

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Article in Chemical biology & drug design, 2026. 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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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yanmei ChenDepartment of Endocrinology, The Affiliated Yongchuan Hospital of Chongqing Medical University, Chongqing, China.
Sisi ChenChongqing Medical and Pharmaceutical College, Chongqing, China.ORCID https://orcid.org/0000-0002-0812-7730
Biao ChengDepartment of Oncology, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Yuzhu TangDepartment of Oncology, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Yiyan HuangSir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Miao ChenInstitute of Health Sciences, China Medical University, Shenyang, Liaoning, China.ORCID https://orcid.org/0009-0005-4447-1386

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The disease course in patients with advanced lung cancer with malignant serous effusions (ALC-MSE) is complex, and treatment outcomes are difficult to predict; systematic studies on the tumor stem cell-like characteristics and regulatory mechanisms of this condition are still lacking. Based on single-cell transcriptomic data and the TCGA-LUAD cohort, this study systematically analyzed the functional characteristics and key regulatory networks of malignant epithelial cells in severe lung cancer. Key findings include: epithelial cells in severe lung cancer exhibit significantly attenuated inflammation- and immunity-related signaling pathways (including NF-κB, TNF, and JAK-STAT signaling pathways), enhanced stem cell-like properties, and metabolic reprogramming; we identified a highly stem-like malignant epithelial cell subpopulation, represented by Cluster 3, which is enriched in stem-like pathways such as Notch, Hippo, TGF-β, and FoxO, and is accompanied by enhanced mitochondrial metabolism and stress adaptability; STMN1 is a key molecule regulating this stem-like state, showing a significant positive correlation with stemness scores and mRNAi; cells with high STMN1 expression gain a survival advantage and may maintain their stemness by attenuating CAF/ECM signaling with other cells (COL1A1-CD44), inhibiting complement activation and antigen presentation signaling (such as C3, ANXA1, and MHC-II-related molecules), and enhancing stemness niche signaling (MIF-CD74/CD44). Furthermore, NELFE, MYBL2, FOXN3, POLR2A, and RAD21 were identified as potential upstream regulators of STMN1. In vitro experiments confirmed that, following induction of stem cell-like properties, STMN1 was significantly overexpressed in lung adenocarcinoma cells, accompanied by upregulation of related stem cell markers (SOX2, NANOG, OCT4, and CD44). In summary, this study reveals a significantly enhanced stemness state in malignant epithelial cells in advanced lung cancer, accompanied by suppressed inflammatory signaling and enhanced metabolic adaptation, and identifies STMN1 as a key molecule driving stemness maintenance and disease progression.

Indexed as

Lung NeoplasmsNeoplastic Stem CellsCell Line, TumorEpithelial CellsHumansInflammationMetabolic ReprogrammingSignal TransductionSingle-Cell AnalysisStathminStathminSTMN1 protein, humanadvanced lung cancer with malignant serous effusions (ALC‐MSE)cell‐to‐cell communicationinflammatory signalingsingle‐cell transcriptomicsSTMN1tumor stemness

Identifiers

PMID42904604
PMCPMC13685055

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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.