Proteomic and serological markers for diagnosing cardia gastric cancer and precursor lesions in a Chinese population

Cardia gastric cancer (CGC) is prevalent in East Asia, and noninvasive, cost-effective screening methods are needed. This study investigated the diagnostic value of serum pepsinogen (PG), gastrin-17 (G-17), Helicobacter pylori (H. pylori) antibodies, and proteomic profiling for CGC and precancerous...

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Bibliographic Details
Published in:Scientific reports 2024-10, Vol.14 (1), p.25309-11, Article 25309
Main Authors: Li, Jiqing, Zhao, Wei, Yang, Jia, Lu, Peipei, Sun, Heming, Zhang, Zhenhong, Gu, Jianhua
Format: Article
Language:English
Subjects:
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631/67/1504
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Adult
Aged
Algorithms
Biomarkers
Biomarkers, Tumor - blood
Biopsy
Cardia - pathology
Cardia gastric cancer
Case-Control Studies
China - epidemiology
East Asian People
Female
Gastric cancer
Gastrin
Gastrins - blood
Helicobacter Infections - blood
Helicobacter Infections - diagnosis
Helicobacter pylori
Helicobacter pylori - isolation & purification
Humanities and Social Sciences
Humans
Lesions
Machine learning
Male
Middle Aged
multidisciplinary
Pepsinogen A - blood
Population studies
Precancerous Conditions - blood
Precancerous Conditions - diagnosis
Precancerous lesions
Prediction models
Proteomics
Proteomics - methods
Science
Science (multidisciplinary)
Serological markers
Serology
Stomach Neoplasms - blood
Stomach Neoplasms - diagnosis
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Summary:Cardia gastric cancer (CGC) is prevalent in East Asia, and noninvasive, cost-effective screening methods are needed. This study investigated the diagnostic value of serum pepsinogen (PG), gastrin-17 (G-17), Helicobacter pylori (H. pylori) antibodies, and proteomic profiling for CGC and precancerous lesions. We conducted a case-control study involving biopsy-confirmed patients with CGC (n = 60), low-grade intraepithelial neoplasia (CLGD, n = 60), high-grade intraepithelial neoplasia (CHGD, n = 64), and healthy controls (n = 120) matched for age and sex from high-incidence areas in China. Serological markers including PGI, PGII, G-17, and H. pylori were measured using ELISA and Western blot, while plasma protein markers were assessed using Olink® technology. The VSOLassoBag algorithm and nine machine learning (ML) algorithms were employed to identify crucial features and construct predictive models. Various evaluation metrics, including the area under the receiver-operating-characteristic curve (AUC), were utilized to compare predictive performance. Elevated PGII levels, decreased PGR, and H. pylori infection were significantly associated with an increased risk of CGC and precancerous lesions (P for trend 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-75912-1