Evidence map›Paper›PMID 42352335›Full record

ArticleBiomolecules2026

Serum microRNA Profiles Reflect Differentiation Status and Age in Early Gastric Cancer.

Marwa Shekfeh, Mariam M Konaté, Hari Sankaran, Ming-Chung Li, Yingdong Zhao

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Article in Biomolecules, 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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5 · Who and what money

Authors and funding

5 authors.

Marwa ShekfehBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.
Mariam M KonatéBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.ORCID 0000-0003-1708-1348
Hari SankaranBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.
Ming-Chung LiBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.ORCID 0009-0002-7053-5652
Yingdong ZhaoBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAge at diagnosis and histologic differentiation are clinically relevant in early gastric cancer (GC), as poorly differentiated tumors and those diagnosed in younger patients often demonstrate more aggressive characteristics. Serum microRNAs (miRNAs) may provide insights into the molecular basis of these features.

methodsWe compared expression profiles between undifferentiated and differentiated early GC cases to identify differentially expressed miRNAs (DEmiRNAs) and associated enriched pathways. Using Lasso regression, we developed and cross-validated a histologic differentiation classifier based on miRNA profiles from 1399 early GC serum samples. Finally, cancer-specific miRNA differences between adolescent and young adult (AYA) and non-AYA patients were evaluated using samples from cancer cases and normal controls.

resultsWe identified 75 differentiation-associated DEmiRNAs targeting genes enriched in cancer hallmark pathways such as TP53 and PI3K/AKT/mTOR signaling. In the validation set, the combined Lasso model predicted differentiation status with a sensitivity of 69.2%, specificity of 75.3%, positive predictive value (PPV) of 66.9%, negative predictive value (NPV) of 77.2%, an overall accuracy of 73.1%, and an area under the curve (AUC) of 79.7%. Comparison of AYA and non-AYA groups identified 52 cancer-specific and age-related miRNAs. Notably, three components of a previously reported four-miRNA GC diagnostic signature were significantly associated with age.

conclusionsAge-related variation in miRNA expression suggests that patient age may influence the performance of the existing four-miRNA diagnostic signature in early GC. Overall, our findings demonstrate the utility of miRNA profiling for predicting differentiation status in early GC and reveal age-associated variation in cancer-specific miRNAs.

Indexed as

MicroRNAsStomach NeoplasmsAdolescentAdultAgedAge FactorsBiomarkers, TumorCell DifferentiationFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedYoung AdultBiomarkers, TumorMicroRNAsAYAclassificationdifferentiation statusgastric cancerLasso regressionliquid biopsymiRNA

Identifiers

PMID42352335
PMCPMC13296932

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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.