Evidence mapPaperPMID 40281381Full record

ArticleDiscover oncology2025

ATP6AP1 drives pyroptosis-mediated immune evasion in hepatocellular carcinoma: a machine learning-guided therapeutic target.

Lei Tang, Xiyue Wang, Zhengzheng Xia, Jiayu Yan, Shanshan Lin

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Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers 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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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lei Tang *Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.
Xiyue Wang *The First Clinical College, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China.
Zhengzheng XiaThe First Clinical College, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China.
Jiayu YanThe First Clinical College, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China. yjiayu@gdmu.edu.cn.
Shanshan LinThe First Clinical College, Guangdong Medical University, Zhanjiang, 524023, Guangdong, China. LinShanShan@gdmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) remains a major therapeutic challenge due to its immunosuppressive tumor microenvironment (TME) and resistance to immune checkpoint inhibitors (ICIs). Pyroptosis is a form of cell death with complex dual functions in tumor immunity. However, the precise regulatory mechanisms and interactions between pyroptosis and immune evasion in HCC remain poorly understood. This study aimed to elucidate the role of ATP6AP1 in pyroptosis-mediated TME remodeling and its potential as a therapeutic target.

methodsWe integrated large-scale datasets from TCGA and GEO databases to identify core modules by weighted gene co-expression network analysis (WGCNA), while mutation profiling and survival analysis verified clinical relevance. Multiple machine learning techniques, including GBM (gradient boosting machine), XGBoost (extreme gradient boosting machine), SVM (support vector machine), LASSO (least absolute shrinkage and selection operator) and random forest, as well as functional analysis, were used to systematically investigate the role of ATP6AP1 in HCC. Finally, CIBERSORT was used to analyze the immune infiltration pattern to gain insight into the mechanism.

resultsThrough a rigorous multi-algorithm screening process, ATP6AP1 was found to be a highly reliable biomarker with an area under the curve (AUC) of 0.979. We found that it has a recurrent C > T mutation with an incidence of 68%. Notably, its expression level was associated with stage (P < 0.001). We also found that regions with high ATP6AP1 expression were enriched in resting DCS (P < 0.05) and regulatory T cells (P < 0.05), which further promoted immunosuppressed TME.

conclusionsIn our study, the machine learning-trained diagnostic model (AUC = 0.998) and the identified pyroptosis-related core gene ATP6AP1 provided an actionable strategy to overcome immune resistance in HCC. Mechanistically, ATP6AP1 stabilizes V-ATPase, which acidifies lysosomes, impairs antigen presentation, and drives pyroptotic inflammasome activation. This study highlights that ATP6AP1 plays a key role in promoting the lysosomal acidisis-pyroptosis-immunosuppression axis, and targeting ATP6AP1 can reshape the TME and enhance the efficacy of immunotherapy in HCC patients.

Indexed as

ATP6AP1Hepatocellular carcinoma (HCC)PyroptosisRegulatory T cellsResting dendritic cellsTumor microenvironment (TME)

Identifiers

PMID40281381
PMCPMC12032091

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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.