Proteomic and serological markers for diagnosing cardia gastric cancer and precursor lesions in a Chinese population.
Humans
Stomach Neoplasms
/ diagnosis
Male
Female
Middle Aged
Proteomics
/ methods
Biomarkers, Tumor
/ blood
Precancerous Conditions
/ blood
Cardia
/ pathology
Case-Control Studies
China
/ epidemiology
Helicobacter pylori
/ isolation & purification
Gastrins
/ blood
Helicobacter Infections
/ diagnosis
Pepsinogen A
/ blood
Aged
Adult
East Asian People
Cardia gastric cancer
Machine learning
Precancerous lesions
Proteomics
Serological markers
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
25 Oct 2024
25 Oct 2024
Historique:
received:
03
07
2024
accepted:
09
10
2024
medline:
26
10
2024
pubmed:
26
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
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 < 0.05). The eXtreme Gradient Boosting (XGBoost) model performed best in discriminative ability among the 9 ML models. Following feature reduction based on predictive performance, a final explainable XGBoost model was developed, incorporating five protein biomarkers (CDHR2, ICAM4, PTPRM, CDC27, and FLT1). This model exhibited excellent performance in distinguishing individuals with CGC and precancerous lesions from healthy controls (AUC = 0.931 for CGC, 0.867 for CHGD, and 0.763 for CLGD), surpassing the traditional serological marker-based model. This study underscores the diagnostic potential of serological markers and proteomic profiling in the detection of CGC. Further validation and exploration of combined biomarker approaches are warranted to enhance early diagnosis and improve outcomes in high-risk populations.
Identifiants
pubmed: 39455684
doi: 10.1038/s41598-024-75912-1
pii: 10.1038/s41598-024-75912-1
doi:
Substances chimiques
Biomarkers, Tumor
0
gastrin 17
60748-06-3
Gastrins
0
Pepsinogen A
9001-10-9
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25309Subventions
Organisme : Natural Science Foundation of Shandong Province
ID : ZR2023QH381
Organisme : Taishan Scholar Foundation of Shandong Province
ID : NO.tsqn202306356
Informations de copyright
© 2024. The Author(s).
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