Exploratory experiment designed to discover new patterns targeting PRMT1 in computational analysis using human datasets. Primary outcome: identification of core biomarker genes
A comprehensive computational analysis combining network pharmacology, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms to identify biomarkers and molecular mechanisms of ginger's anti-gastric cancer effects. The study screened active ingredients and targets of ginger through public databases, identified gastric cancer genes using disease databases and GEO datasets, and applied WGCNA to find co-expression modules. Machine learning algorithms were used to identify core genes from the intersection of ginger targets and gastric cancer genes. The analysis included clinical relevance assessment, gene mutation analysis, epigenetic regulation analysis, and immune infiltration analysis.
Database screening for ginger active compounds and targets, GEO database and WGCNA analysis for gastric cancer genes, intersection analysis to identify potential core genes, machine learning algorithm screening, PPI network analysis, clinical relevance analysis, gene mutation relationship analysis, epigenetic regulation analysis, immune infiltration analysis
identification of key genes mediating ginger's anti-gastric cancer effects and elucidation of molecular mechanisms
successful identification of core genes with strong computational evidence and biological relevance
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