Evidence map›Paper›PMID 41836290›Full record

ArticleFrontiers in cell and developmental biology2026

Characterization of ANXA1 in chemotherapy resistance of head and neck squamous cell carcinoma: insights from artificial intelligence and integrative bioinformatics analysis.

Ling Zheng, Haiyan Yang, Yan Cheng, Binxue Xia, Honghui Liu, Hong Xiong

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In one paragraph

Article in Frontiers in cell and developmental biology, 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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1 · What the graph read from it

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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

6 authors.

Ling ZhengSchool of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Haiyan YangSchool of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Yan ChengEmergency Medicine Department, Southwest Medical University Affiliated Hospital, Luzhou, Sichuan, China.
Binxue XiaSchool of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Honghui LiuSchool of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Hong XiongDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Head and neck squamous cell carcinoma (HNSCC) exhibits intensive chemoresistance (CR), leading to frequent recurrence and poor prognosis; however, actionable biomarkers and therapeutic options remain limited. Methods: By utilizing bulk profiles of HNSCC patients (TCGA-HNSCC cohort and GSE6631) from TCGA and GEO databases, we identified CR-associated DEGs via Limma and WGCNA frameworks. Importantly, LASSO-Cox regression was utilized for the construction of a predictive model and identification of the CR-associated hub gene in TCGA-HNSCC cohort. In addition, predictive model performance was validated in the HNSCC patient bulk profile (GSE65858). Furthermore, the molecular and immune characteristics of the hub gene were estimated at HNSCC patient bulk (TCGA-HNSCC cohort) and single-cell (GSE163872) levels, especially in artificial intelligence (AI)-empowered virtual cells. Specifically, AI-driven therapeutic framework (RefLector) and molecular docking were performed for the recognition of an optimal therapeutic framework for the treatment of HNSCC by targeting the hub gene. Finally, the cariogenic role of the hub gene was evaluated in an Results: CR-associated DEGs can guide the risk stratification of HNSCC patients. ANXA1 was identified as a downregulated, malignancy-distributed, prognostic, and druggable biomarker for HNSCC patients, which was also associated with HNSCC progression. BRD-K10482608 can be considered a potential therapeutic agent for the treatment of HNSCC. Conclusion: Our study highlighted the CR in risk stratification for HNSCC patients and ANXA1 in the pathogenesis of HNSCC, which can guide personalized and precision medicine for HNSCC patients.

Indexed as

ANXA1artificial intelligencechemoresistancehead and neck squamous cell carcinomamulti-omics

Identifiers

PMID41836290
PMCPMC12979454

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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 Socratic graph.