Evidence map›Paper›PMID 38639279›Full record

ArticleCurrent medicinal chemistry2025

CLDN18: Clinical, Pathological, and Genetic Signatures with Drug Screening in Gastric Adenocarcinoma.

Joon Young Hur, Kyueng-Whan Min, Yung-Kyun Noh, Young-Woong Won, Yoomi Yeo, Dong-Hoon Kim, Byoung Kwan Son, Mi Jung Kwon, Jung Soo Pyo

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

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Joon Young HurDepartment of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Gyeonggi-do, Republic of Korea.ORCID 0000-0003-0092-8370
Kyueng-Whan MinDepartment of Pathology, Uijeongbu Eulji Medical Center, School of Medicine, Eulji University, Uijeongbu, Gyeonggi-do, Republic of Korea.
Yung-Kyun NohDepartment of Computer Science, Hanyang University, Seoul, Republic of Korea.
Young-Woong WonDepartment of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Gyeonggi-do, Republic of Korea.
Yoomi YeoDepartment of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri, Gyeonggi-do, Republic of Korea.
Dong-Hoon KimDepartment of Pathology, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea.
Byoung Kwan SonDepartment of Internal Medicine, Uijeongbu Eulji Medical Center, School of Medicine, Eulji University, Uijeongbu, Gyeonggi-do, Republic of Korea.
Mi Jung KwonDepartment of Pathology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Gyeonggi-do, Republic of Korea.
Jung Soo PyoDepartment of Pathology, Uijeongbu Eulji Medical Center, School of Medicine, Eulji University, Uijeongbu, Gyeonggi-do, Republic of Korea.
Eulji University · KRHanyang University Guri Hospital · KRKorea Institute for Advanced Study · KRKangbuk Samsung Hospital · KR

Funding

research fund of Hanyang University HY-202200000001246
6 · The paper itself

Abstract

introductionThe CLDN18 gene, encoding claudin 18.1 and claudin 18.2, is a key component of tight junction strands in epithelial cells that form a paracellular barrier that is critical in Stomach Adenocarcinoma (STAD).

methodsOur study included 1,095 patients with proven STAD, 415 from The Cancer Genome Atlas (TCGA) cohort and 680 from the Gene Expression Omnibus database. We applied various analyses, including gene set enrichment analysis, pathway analysis, and in vitro drug screening to evaluate survival, immune cells, and genes and gene sets associated with cancer progression, based on CLDN18 expression levels. Gradient boosting machine learning (70% for training, 15% for validation, and 15% for testing) was used to evaluate the impact of CLDN18 on survival and develop a survival prediction model.

resultsHigh CLDN18 expression correlated with worse survival in lymphocyte-poor STAD, accompanied by decreased helper T cells, altered metabolic genes, low necrosis- related gene expression, and increased tumor proliferation. CLDN18 expression showed associations with gene sets associated with various stomach, breast, ovarian, and esophageal cancers, while pathway analysis linked CLDN18 to immunity. Incorporating CLDN18 expression improved survival prediction in a machine learning model. Notably, nutlin-3a and niraparib effectively inhibited high CLDN18-expressing gastric cancer cells in drug screening.

conclusionOur study provides a comprehensive understanding of the biological role of CLDN18-based bioinformatics and machine learning analysis in STAD, shedding light on its prognostic significance and potential therapeutic implications. To fully elucidate the molecular intricacies of CLDN18, further investigation is warranted, particularly through in vitro and in vivo studies.

Indexed as

AdenocarcinomaAntineoplastic AgentsClaudinsStomach NeoplasmsDrug Screening Assays, AntitumorFemaleHumansMachine LearningAntineoplastic AgentsClaudinsCLDN18 protein, humanCLDN18drugmachine learningniraparib.prognosisstomach cancer

Identifiers

PMID38639279
OpenAlexW4394955243

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.