Evaluation of diagnostic and prognostic relevance of genes related to trastuzumab resistance in gastric cancer based on machine learning
10.3969/j.issn.1005-202X.2025.04.015
- VernacularTitle:基于机器学习评估曲妥珠单抗耐药相关基因在胃癌中的诊断和预后效能
- Author:
Tao LIU
1
;
Tongtong LI
;
Chunyan YU
;
Yichu HUANG
;
Lei JIANG
Author Information
1. 兰州大学第一临床医学院,甘肃 兰州 730000
- Publication Type:Journal Article
- Keywords:
gastric cancer;
drug resistance;
trastuzumab;
machine learning;
bioinformatics
- From:
Chinese Journal of Medical Physics
2025;42(4):525-533
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the diagnostic and prognostic relevance of genes associated with trastuzumab resistance and sensitivity in gastric cancer using machine learning algorithms.Methods The data on resistant and sensitive genes were downloaded from the GEO database and subjected to functional enrichment analysis.Intersection analysis was performed using TCGA and GEO data to identify feature genes related to gastric cancer drug-resistance.LASSO and SVM-RFE methods were used for feature gene selection.The expressions of these feature genes were detected in both test and validation groups,and their diagnostic value was analyzed using receiver operating characteristic curves.The prognostic value of SH3GL2 was assessed using online databases,and its role in patient survival was further explored.CIBERSORT algorithm was used to evaluate the relationship between SH3GL2 and immune cell infiltration in gastric cancer,and analyze its effect on immune microenvironment.Results Fifteen resistance-related genes were identified,and 12 diagnostic biomarkers related to gastric cancer were selected through machine learning,including MMP7,COCH,VCAN,SH3GL2,SYNM,KLK6,STC2,PPP1R1B,CDH3,WNT11,PMEPA1,and BCAT1.SH3GL2 showed low expression in both test and validation groups,and its high expression was associated with poorer prognosis in gastric cancer(P<0.01).SH3GL2 expression level was related to various immune cells(activated CD8+T cells,activated DC cells)and showed positive correlations with immune suppressive factors(such as TGFB1,VTCN1)and negative correlations with immune stimulatory factors(such as CD70,CD80).Conclusion The 12 selected feature genes can serve as potential diagnostic biomarkers for gastric cancer.SH3GL2 has a low expression in gastric cancer,and its high expression might shorten patient survival by inhibiting anti-tumor immunity.
