Evidence map›Paper›PMID 42365218›Full record

ArticleDiscover oncology2026

Multi-cohort integration and machine learning identify CPVL as a novel oncogenic driver in gastric cancer.

Xin Li, Jiahan Chen, Hongpen Tian, Guangjun Zhang

Abstract read
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Article in Discover oncology, 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

4 authors.

Xin LiThe Affiliated Hospital of North Sichuan Medical College CN, Nanchong, 637000, Sichuan Province, China.
Jiahan ChenThe Affiliated Hospital of North Sichuan Medical College CN, Nanchong, 637000, Sichuan Province, China.
Hongpen TianThe Affiliated Hospital of North Sichuan Medical College CN, Nanchong, 637000, Sichuan Province, China.
Guangjun ZhangThe Affiliated Hospital of North Sichuan Medical College CN, Nanchong, 637000, Sichuan Province, China. zhanggj1977@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, and the prognosis of advanced GC remains poor. Systematic identification of robust biomarkers through multi-cohort integration and computational prioritization may facilitate the discovery of novel therapeutic targets.

aimTo identify key genes associated with gastric cancer progression through integrative multi-omics analysis and to elucidate the biological functions and molecular mechanisms of the top-prioritized candidate gene.

methodsComprehensive bioinformatics analyses integrating The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) datasets were performed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), Cox regression, and eight machine-learning algorithms to systematically identify and prioritize GC-associated hub genes. Among the identified candidates, CPVL was selected for further validation based on its diagnostic and prognostic performance. CPVL expression and clinical relevance were validated by independent datasets and immunohistochemistry. Lentiviral constructs were used to overexpress or silence CPVL in GC cell lines. Functional assays were performed, including CCK-8, colony formation, EdU incorporation, and flow cytometry, to assess cell proliferation and cell-cycle distribution. Western blotting and JAK2 inhibitor (AZD1480) rescue experiments were performed to elucidate the underlying mechanisms, and a nude mouse xenograft model was used to evaluate tumorigenicity in vivo.

resultsMulti-cohort screening identified five hub genes (CPVL, AADAC, BCAT1, CPXM1, and FBN1). Among them, CPVL exhibited the highest diagnostic accuracy (AUC = 0.895) and the strongest correlation with poor overall survival, and was therefore selected for mechanistic investigation. CPVL expression was markedly upregulated in GC tissues and cell lines. Functional assays demonstrated that CPVL promotes GC cell proliferation and accelerates G1/S-phase transition. Mechanistically, CPVL activated the JAK2/STAT3 signaling pathway, upregulating Cyclin D1 and CDK4 while downregulating p27. Treatment with the JAK2 inhibitor AZD1480 partially reversed these effects. In vivo, CPVL knockdown significantly inhibited tumor growth.

conclusionThrough systematic multi-cohort integration and machine-learning prioritization, CPVL was identified as a novel oncogenic driver in gastric cancer. CPVL promotes tumor growth via activation of the JAK2/STAT3 pathway and regulation of the Cyclin D1/CDK4/p27 axis, highlighting its potential as a diagnostic biomarker and therapeutic target.

Indexed as

BiomarkerCarboxypeptidase vitellogenic-like (CPVL)Cell cycleCell proliferationGastric cancerJAK2/STAT3 signaling pathway

Identifiers

PMID42365218
PMCPMC13572353

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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.