Applied end-to-end downstream metabolomic analysis to identify coordinated metabolic changes linked to colorectal cancer. The workflow included data preprocessing, PQN normalization, differential metabolite analysis, weighted network analysis, PCA, and PLS-DA to prioritize discriminative features. This integrated approach helped identify CRC-related modules, high-influence hub metabolites, and network patterns that suggest promising biomarker candidates and mechanistic pathways.
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