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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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A prognostic model for gastric cancer constructed by multiple machine learning algorithms.

Xueli Yang^1,2,3,4, Xu Huang^1,2,3,4, Wang Ying^1,2,3,4

¹Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China.

Journal of Molecular Histology

|October 14, 2025

Summary

This study developed a machine learning risk model using 7 hub genes to predict gastric cancer (GC) prognosis. The model accurately identifies high-risk patients, aiding personalized treatment strategies for this heterogeneous disease.

Keywords:

Biomarkers Gastric cancer (GC)Machine learning Prognosis

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Gastric cancer (GC) is a heterogeneous disease with variable prognoses.
Accurate prognostic models are crucial for effective patient management.
Machine learning offers powerful tools for biomarker discovery and prognostic model development.

Purpose of the Study:

To integrate bioinformatics and machine learning to construct a predictive risk model for GC prognosis.
To identify novel prognostic biomarkers for gastric cancer.
To validate the model's predictive accuracy and its potential as an independent prognostic factor.

Main Methods:

Utilized TCGA and GEO databases for transcriptome and clinical data.
Employed univariate Cox regression and machine learning (RSF, GBM) to screen prognostic hub genes.
Validated the risk model using Kaplan-Meier curves, ROC analysis, and Cox regression.
Assessed protein expression of hub genes via immunohistochemistry.

Main Results:

Identified 7 hub genes (CGB5, FEM1A, MATN3, ZNF101, MARCKS, BRI3BP, APOD) significantly correlated with GC prognosis.
Developed a high-precision risk model demonstrating good predictive ability for GC patient outcomes.
Found elevated protein expression of CGB5, MATN3, MARCKS, and APOD in GC tissues, correlating with pathological characteristics.
The risk score derived from the model served as an independent prognostic factor.

Conclusions:

The 7-hub-gene risk model accurately predicts gastric cancer prognosis.
This model can serve as an independent prognostic indicator for GC patients.
Findings support the potential for precise and personalized treatment strategies in gastric cancer.