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

Autor: Li J; Qilu Hospital of Shandong University, 107 Wenhua West Road, Lixia District, Jinan, Shandong Province, China., Zhao W; Qilu Hospital of Shandong University, 107 Wenhua West Road, Lixia District, Jinan, Shandong Province, China., Yang J; Liaocheng People's Hospital, Liaocheng, China., Lu P; Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China., Sun H; Department of Computer and Information Science and Engineering, University of Florida, Gainesville, USA., Zhang Z; Medical Imaging Center, Qilu Hospital of Shandong University Dezhou Hospital, Jinan, China., Gu J; Qilu Hospital of Shandong University, 107 Wenhua West Road, Lixia District, Jinan, Shandong Province, China. jianhuagu@yeah.net.; Office of National Central Cancer Registry, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. jianhuagu@yeah.net.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 25; Vol. 14 (1), pp. 25309. Date of Electronic Publication: 2024 Oct 25.
DOI: 10.1038/s41598-024-75912-1
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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