Machine learning identifies INHBA DPT ADH7 FBP2 and GPR155 as diagnostic biomarkers for gastric cancer

Sep 30, 2026

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Gastric cancer poses a severe health threat with poor prognosis, creating an urgent need for reliable biomarkers for early detection and targeted therapy. The study applies comprehensive bioinformatics and machine learning to uncover diagnostic protein biomarkers for gastric cancer. Using NCBI Gene Expression Omnibus datasets, differential expression analysis finds 171 genes distinguishing tumor from control samples. LASSO, SVM-RFE, and RF identify five candidate biomarkers, while logistic regression achieves the best performance among ten models. SHAP supports gene contributions, and enrichment analyses assess gene functions, including immune infiltration and patient diagnosis implications.

LanguageEnglish

CategoryResearch & Report

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Pages19

Reading Time48 min

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Abstract

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1 Introduction

Zhao et al. Discover Oncology (2026) 17:21 [https://doi.org/10.1007/s12672-025-04208-1](https://doi.org/10.1007/s12672-025-04208-1) Discover Oncology ANALYSIS Open Access Machine learning identifies INHBA DPT ADH7 FBP2 and GPR155 as diagnostic biomarkers for gastric cancer Jianbo Zhao 1†, Damu Agu2†, Xiongfeng Li2, Youge Su 1, Haidong Cheng2* and Mingxing Hou2* †Jianbo Zhao and Damu Agu contributed equally to this work. *Correspondence: Haidong Cheng [chd2476@163.com](chd2476@163.com)[ ](chd2476@163.com)Mingxing Hou [hmx6412@163.com](hmx6412@163.com) 1Inner Mongolia Medical University, Hohhot 010110, Inner Mongolia, China 2Department of Gastrointestinal Surgery, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, Inner Mongolia, China Abstract Gastric cancer stil l is a severe threat to human health, often presenting with a poor prognosis, effective biomarkers for early detection and targeted treatment are urgently needed. This study performed a comprehensive bioinformatics and machine learning approach to identify key protein biomarkers for gastric cancer and elucidate their potential functions. Gastric cancer-related datasets were obtained from the NCBI Gene Expression Omnibus database. Differential expression analysis identified 171 genes with noticeable differences between control and tumor samples. Utilizing LASSO, SVM-RFE, and RF algorithms, five genes—INHBA, DPT, ADH7, FBP2 and GPR155—were identified as potential biomarkers. A logistic regression model demonstrated the highest performance among ten machine learning models constructed using these five genes. Shapley additive explanations (SHAP) were employed to illustrate the detailed contribution of the pivotal genes to the logstics model. Gene set enrichment analysis and gene set variation analysis were then used to find out the functional roles of these genes in gastric cancer cells. At length, we revealed the distinctive effects of signature genes on immune cell infiltration and patient diagnosis. In conclusion, the identified proteins have the potential to serve as diagnostic biomarkers and provide treatment value for gastric cancer. This study offers a comprehensive, data-driven approach to uncover critical molecular targets for improved detection and management of this deadly disease. Keywords Gastric cancer, INHBA, DPT, ADH7, FBP2, GPR155, Biomarkers, Machine learning, SHapley additive exPlanations 1 Introduction The based on GLOBOCAN 2022 data, gastric cancer (GC) recorded approximately 968,000 new cases and 659,000 deaths in 2022, ranking fifth in both incidence and mortality rates [1]. Despite it has decline in incidence and mortality, the incidence of GC among young individuals is steadily increasing annually, presenting a persistent global health challenge [2]. Adenocarcinoma accounts for over 95% of GC cases. High-risk factors for GC include consumption of nitrite-rich foods and moldy foods, Helicobacter pylori infection [3], excessive alcohol intake, smoking and other unhealthy behaviors [4, © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly fr

FAQs

What datasets and methods are used to discover gastric cancer biomarkers?

Gastric cancer datasets are obtained from the NCBI Gene Expression Omnibus database. The workflow uses differential expression analysis followed by machine learning algorithms to identify candidate biomarkers.

Which five genes are identified as potential diagnostic biomarkers?

The five genes reported as potential biomarkers are INHBA, DPT, ADH7, FBP2, and GPR155.

How is the logistic regression model evaluated and interpreted?

The study compares performance across ten machine learning models and states that logistic regression performs the highest. Shapley additive explanations (SHAP) are used to illustrate each key gene’s contribution to the model output.

Sep 30, 2026

Upload by James