Anticancer research · 2026
Integrative Analysis Combining Machine Learning and Functional Experiments Uncovers ISG15 As a Key Determinant of Cisplatin Resistance in Gastric Cancer.
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Wang Weiwei, Ling Haiquan, Hu Songhao, Chen Zehuan, Zhou Jun, Feng Aiwen, Wang Cunchuan, Ma Minghui
Аннотация
BACKGROUND/AIM: Cisplatin resistance remains a major obstacle in advanced gastric cancer (GC). This study aimed to identify key molecular determinants of cisplatin resistance, with a focus on interferon-stimulated genes (ISGs), and to systematically investigate the functional role of interferon-stimulated gene 15 (ISG15) in mediating chemoresistance.
MATERIALS AND METHODS: Candidate genes associated with cisplatin resistance were first identified through transcriptomic profiling of gastric cancer tissues. An integrative machine learning framework was applied to prioritize these genes: least absolute shrinkage and selection operator (LASSO) regression extracted genes with the highest predictive potential, support vector machine recursive feature elimination (SVM-RFE) ranked genes by their influence on classification precision, and random forest (RF) analysis evaluated the relative importance of each gene across multiple decision trees. Top candidates from these complementary approaches were further validated both bioinformatically and experimentally in normal gastric epithelial cells (GES-1), cisplatin-sensitive (AGS), and cisplatin-resistant (AGS/DDP) cells. Functional validation was conducted using siRNA-mediated knockdown to assess effects on cell viability, colony formation, migration, and cisplatin sensitivity.
RESULTS: ISG15, OAS2, IFI44, and IFIT3 were identified as central hub genes. Experimental validation confirmed marked upregulation of these genes at both mRNA and protein levels in AGS/DDP cells, with ISG15 showing the most prominent and dose-dependent induction upon cisplatin exposure. Functional assays demonstrated that ISG15 knockdown significantly reduced cell viability, colony formation, and migration, while enhancing cisplatin sensitivity.
CONCLUSION: ISG15 functions as a central regulator of cisplatin resistance and tumor progression in GC. The study highlights the effectiveness of integrating machine learning-driven candidate prioritization with functional validation to identify clinically relevant therapeutic targets.
MATERIALS AND METHODS: Candidate genes associated with cisplatin resistance were first identified through transcriptomic profiling of gastric cancer tissues. An integrative machine learning framework was applied to prioritize these genes: least absolute shrinkage and selection operator (LASSO) regression extracted genes with the highest predictive potential, support vector machine recursive feature elimination (SVM-RFE) ranked genes by their influence on classification precision, and random forest (RF) analysis evaluated the relative importance of each gene across multiple decision trees. Top candidates from these complementary approaches were further validated both bioinformatically and experimentally in normal gastric epithelial cells (GES-1), cisplatin-sensitive (AGS), and cisplatin-resistant (AGS/DDP) cells. Functional validation was conducted using siRNA-mediated knockdown to assess effects on cell viability, colony formation, migration, and cisplatin sensitivity.
RESULTS: ISG15, OAS2, IFI44, and IFIT3 were identified as central hub genes. Experimental validation confirmed marked upregulation of these genes at both mRNA and protein levels in AGS/DDP cells, with ISG15 showing the most prominent and dose-dependent induction upon cisplatin exposure. Functional assays demonstrated that ISG15 knockdown significantly reduced cell viability, colony formation, and migration, while enhancing cisplatin sensitivity.
CONCLUSION: ISG15 functions as a central regulator of cisplatin resistance and tumor progression in GC. The study highlights the effectiveness of integrating machine learning-driven candidate prioritization with functional validation to identify clinically relevant therapeutic targets.