Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection.

This study presents an innovative methodology for predicting chemotherapy responses in colorectal cancer patients by integrating gene expression data from matched colorectal tumor and organoid samples. Employing Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across multiple datasets, we identified key gene modules and hub genes linked to patient response to chemotherapy, focusing on 5-fluorouracil (5-FU). This integrative approach marks a significant advancement in precision medicine, enhancing the specificity and accuracy of chemotherapy regimen selection based on individual tumor profiles. Our predictive model, validated by independent datasets demonstrated improved accuracy over traditional methods. This strategy shows promise in overcoming typical challenges in high-dimensional genomic data analysis for cancer biomarker research.