Evaluation of diagnostic and prognostic relevance of genes related to trastuzumab resistance ingastric cancer based on machine learning(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2025年第4期
- Page:
- 525-533
- Research Field:
- 医学生物信息
- Publishing date:
Info
- Title:
- Evaluation of diagnostic and prognostic relevance of genes related to trastuzumab resistance ingastric cancer based on machine learning
- Author(s):
- LIU Tao1; LI Tongtong1; YU Chunyan1; HUANG Yichu1; JIANG Lei2
- 1. The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, China; 2. Department of General Surgery, the FirstHospital of Lanzhou University, Lanzhou 730000, China
- Keywords:
- gastric cancer; drug resistance; trastuzumab; machine learning; bioinformatics
- PACS:
- R318；R735.2
- DOI:
- 10.3969/j.issn.1005-202X.2025.04.015
- Abstract:
- Objective To explore the diagnostic and prognostic relevance of genes associated with trastuzumab resistance andsensitivity in gastric cancer using machine learning algorithms. Methods The data on resistant and sensitive genes weredownloaded from the GEO database and subjected to functional enrichment analysis. Intersection analysis was performedusing TCGA and GEO data to identify feature genes related to gastric cancer drug-resistance. LASSO and SVM-RFEmethods were used for feature gene selection. The expressions of these feature genes were detected in both test and validationgroups, and their diagnostic value was analyzed using receiver operating characteristic curves. The prognostic value ofSH3GL2 was assessed using online databases, and its role in patient survival was further explored. CIBERSORT algorithmwas used to evaluate the relationship between SH3GL2 and immune cell infiltration in gastric cancer, and analyze its effecton immune microenvironment. Results Fifteen resistance-related genes were identified, and 12 diagnostic biomarkers relatedto 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, andits high expression was associated with poorer prognosis in gastric cancer (P<0.01). SH3GL2 expression level was related tovarious immune cells (activated CD8+ T cells, activated DC cells) and showed positive correlations with immune suppressivefactors (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 lowexpression in gastric cancer, and its high expression might shorten patient survival by inhibiting anti-tumor immunity.
Last Update: 2025-04-30