Evidence map›Paper›PMID 40899470›Full record

ArticleCancer medicine2025

Serum Pepsinogen as a Biomarker of Gastrointestinal Stromal Tumors (GIST) in Stomach.

Zhiying Gao, Laizhi Luo, Yueting Han, Yan Sun, Yonghong Huang, Shixia Li, Xingyun Chen, Huimin Yang, Zhijuan Peng, Xinyi Wang and 7 more

Abstract read
In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Zhiying GaoPeking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Laizhi LuoGuangzhou Medical University, Guangzhou, China.ORCID https://orcid.org/0009-0002-2441-6556
Yueting HanKey Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Yan SunKey Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Yonghong HuangInstitute of Pathology, Qiqihar Medical University, Qiqihar, China.
Shixia LiKey Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Xingyun ChenKey Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Huimin YangKey Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Zhijuan PengKey Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Xinyi WangKey Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Wei ZhaoGastrointestinal Department of Tianjin Medical University General Hospital, Tianjin, China.
Xi WuNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Huan WuUltrasound Department of Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Jing BaiBeijing Friendship Hospital, Capital Medical University, Beijing, China.
Wu SunThe Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University and Clinical Cancer Institute of Nanjing University, Nanjing, China.
Likun ZhouKey Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Yi BaPeking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0003-0255-4376

Funding

China Postdoctoral Science Foundation 2022M710450National Natural Science Foundation of China 82373158Tianjin Municipal Health Commission 2023037
6 · The paper itself

Abstract

backgroundGastric GISTs (GG) are significant mesenchymal tumors. No biomarker has been identified for GG detection. We first observed mucosal atrophy surrounding GG tumors, leading to the hypothesis that localized atrophy may alter serum pepsinogen (PG) levels. Therefore, we developed a machine learning (ML) model incorporating serum PG levels and clinical features to predict GG and differentiate it from gastric cancer (GC).

methodsWe retrospectively analyzed GG and GC patients with tested PG levels before medical intervention. Seven ML algorithms were assessed, and feature importance was determined using SHapley Additive exPlanations (SHAP). Gastric atrophy was assessed histologically using the updated Sydney System.

resultsAfter screening 562 GG and 1090 GC patients, 100 GG and 174 GC samples were included. The multilayer perceptron (MLP) model achieved the highest AUC. The final MLP model, which included 4 features-gender, PGI levels, PGI/PGII ratio, and CEA-predicted GG with an AUC of 0.854. Considering clinical practice and the feature importance identified by the final MLP model, we established a Positive-Gastric-GIST-PG-CEA criterion (PGI < 70 ng/mL, PGI/PGII ratio ≥ 3.0, and CEA ≤ 5 μg/L) referring to the cutoff values revealed by the ROC curve. The Positive-Gastric-GIST-PG-CEA displayed exceptional performance in predicting GG (AUC = 0.772, accuracy = 0.748, specificity = 0.787, sensitivity = 0.680), with performance comparable to the final MLP model (ΔAUC = 0.082, p > 0.05). The contributions of PGI levels, PGI/PGII ratio, and CEA in the Positive-Gastric-GIST-PG-CEA model performance were 0.33, 0.15, and 0.13 based on SHAP analysis. Histopathological evaluation of gastric mucosal atrophy in 50 GG patients revealed peri-tumoral glandular atrophy in 29 cases (58%).

conclusionsThe Positive-Gastric-GIST-PG-CEA criterion is valuable for detecting GG and distinguishing it from GC. Integrating our criteria into existing PG tests could help in GG detection without additional economic expense.

Indexed as

Biomarkers, TumorGastrointestinal Stromal TumorsPepsinogen AStomach NeoplasmsAdultAgedAged, 80 and overFemaleGastric MucosaHumansMachine LearningMaleMiddle AgedRetrospective StudiesROC CurveBiomarkers, TumorPepsinogen Abiomarkergastric atrophyGISTserum pepsinogenstomach

Identifiers

PMID40899470
PMCPMC12406084

What OpenQuestion holds

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Registered trials

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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.