Integrating machine learning and bioinformatics has proven to be a transformative approach in developing a gene-based prognostic model for gastric cancer. This study utilized transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and validation datasets from the Gene Expression Omnibus (GEO) to identify genes associated with patient survival outcomes.
Integrating machine learning and bioinformatics has proven to be a transformative approach in developing a gene-based prognostic model for gastric cancer. This study utilized transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and validation datasets from the Gene Expression Omnibus (GEO) to identify genes associated with patient survival outcomes. Advanced machine learning techniques, such as random survival forest and generalized boosted regression modeling, were applied to analyze the data and pinpoint seven key genes—CGB5, FEM1A, MATN3, ZNF101, MARCKS, BRI3BP, and APOD—that are closely linked to prognosis in gastric cancer patients.
The identified genes were used to construct a high-precision risk score model capable of predicting patient survival outcomes with accuracy. Validation methods, including Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and Cox regression analysis, confirmed the model’s robustness and demonstrated that the risk score is an independent prognostic factor for gastric cancer. Immunohistochemical analysis further revealed that several hub genes, such as CGB5, MATN3, MARCKS, and APOD, were more highly expressed in cancerous tissues compared to normal tissues, highlighting their correlation with disease pathology.
This integration of machine learning with gene expression profiling enables precise risk stratification, paving the way for personalized and targeted treatment strategies in gastric cancer management. The study underscores the potential of combining computational tools and molecular insights to advance cancer prognosis and therapy.
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