ArticleWorld journal of gastrointestinal oncology2025
Blood-based machine learning classifiers for early diagnosis of gastric cancer
Article in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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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.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Identifying regulatory driver motifs in non-small cell lung carcinoma via a systematic approach.PloS one · 2026Pooled it
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly screening methods for gastric cancer (GC) are lacking; therefore, the disease often progresses to an advanced stage when patients first start to exhibit typical symptoms. Endoscopy and pathological biopsy remain the primary diagnostic approaches, but they are invasive and not yet widely applicable for early population screening. miRNA is a highly conserved type of RNA that exists stably in plasma. Dysfunction of miRNA is linked to tumorigenesis and progression, indicating that individual miRNAs or combinations of multiple miRNAs may serve as potential biomarkers.
aimTo identify effective plasma miRNA biomarkers and investigate the clinical value of combining multiple miRNAs for early detection of GC.
methodsPlasma samples from multiple centres were collected. Differentially expressed genes among healthy controls, early-stage GC patients, and advanced-stage GC patients were identified through small RNA sequencing (sRNA-seq) and validated
resultsPlasma samples of 107 normal, 71 early GC and 97 advanced GC patients were obtained from three centres, and serum samples of 8443 normal and 1583 GC patients were obtained from the GEO database. The sRNA-seq and RT-qPCR experiments revealed that miR-452-5p, miR-5010-5p, miR-27b-5p, miR-5189-5p, miR-552-5p and miR-199b-5p were significantly increased in early GC patients compared with healthy controls and in advanced GC patients compared with early GC patients (
conclusionPotential miRNA biomarkers, including miR-452-5p, miR-5010-5p, miR-27b-5p, miR-5189-5p, miR-552-5p and miR-199b-5p, were identified. A GC classifier based on these miRNAs was developed, benefiting early detection and population screening.
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