Evidence map›Paper›PMID 41656416›Full record

ArticleScientific reports2026

Integrating multi-omics analysis identifies DNA damage-related gene CLSPN as a biomarker in gastric cancer.

Qiang Ma, Xingjie Yang, Naiying Sun, Limin Liu, Jin Bao, Li Liu

Abstract read
In one paragraph

Article in Scientific reports, 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

What it found

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2 · The registry

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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.

Qiang MaDepartment of Pathology, Sunshine Union Hospital, 9000 Yingqian Road, Weifang, 261000, Shandong Province, P.R. China.
Xingjie YangDepartment of Pathology, Sunshine Union Hospital, 9000 Yingqian Road, Weifang, 261000, Shandong Province, P.R. China.
Naiying SunDepartment of Pathology, Sunshine Union Hospital, 9000 Yingqian Road, Weifang, 261000, Shandong Province, P.R. China.
Limin LiuDepartment of Pathology, Sunshine Union Hospital, 9000 Yingqian Road, Weifang, 261000, Shandong Province, P.R. China.
Jin BaoDepartment of Pathology, Weifang Fangzi District People's Hospital, 3433 Longshan Road, Weifang, 261206, Shandong Province, P.R. China.
Li LiuDepartment of Pathology, Sunshine Union Hospital, 9000 Yingqian Road, Weifang, 261000, Shandong Province, P.R. China. liuli421405685@126.com.

Funding

Weifang Municipal Health Commission Research Project WFWSJK-2025-322
6 · The paper itself

Abstract

DNA damage exhibits a strong correlation with gastric cancer (GC). However, there is still a paucity of comprehensive, in-depth investigations into this relationship. We aimed to explore the association between DNA damage-related genes and GC to provide insights into its molecular mechanisms and potential biomarkers. For this study, Bulk RNA sequencing data of GC were obtained from The Cancer Genome Atlas (TCGA), single-cell RNA sequencing datasets were retrieved from the Gene Expression Omnibus (GEO), and a DNA damage-associated gene set was sourced from the GeneCards database. Through the application of survival analysis, differential expression gene analysis, and weighted gene co-expression network analysis, we identified DNA damage-related genes potentially linked to GC. Subsequently, multiple machine learning approaches were employed to screen out hub genes with considerable diagnostic potential. Analysis of bulk RNA sequencing data verified gene expression patterns in GC. Single-cell analysis further demonstrated cell-type-specific gene expression, and immunohistochemical experiments were conducted to validate the potential biomarker utility of key genes. Our findings revealed that thirteen DNA damage-related genes that may be linked to GC were identified. Subsequently, CLSPN and SALL4 were screened out as hub genes possessing considerable diagnostic potential. Analysis of bulk RNA sequencing data verified the upregulated expression of these two genes in GC, thereby underscoring their predictive significance. Across multiple machine learning methods, CLSPN was consistently ranked as the gene with the highest importance. Single-cell analysis further demonstrated that CLSPN is predominantly highly expressed in tumor cells, which emphasizes its cell-type-specific function in GC. To validate CLSPN's potential as a biomarker, we conducted immunohistochemical experiments; these experiments showed high CLSPN expression in GC tissues, and the expression levels were significantly correlated with age, tumor size, pT stage and lymph node metastasis. This study reinforces the link between DNA damage and GC and offers fresh perspectives on its underlying molecular mechanisms. Nonetheless, further validation in clinical evaluation is essential to confirm its practical value for GC management strategies.

Indexed as

Biomarkers, TumorDNA DamageStomach NeoplasmsTranscription FactorsGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMachine LearningMultiomicsBiomarkers, TumorTranscription FactorsCLSPNDNA damageGastric cancerPathologySingle-cell RNA sequencing

Identifiers

PMID41656416
PMCPMC12949068

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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.