CPADS enables comprehensive analysis of drug resistance across 44 cancer types

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CPADS integrates data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Genomics of Drug Sensitivity in Cancer (GDSC) databases, encompassing over 29,000 samples across 44 cancer types and involving 288 drugs. It provides five main analysis modules: differential expression analysis, correlation analysis, pathway analysis, drug analysis, and gene perturbation analysis. These modules enable users to explore gene expression changes, correlations between genes or drugs, pathway enrichment, drug sensitivity, and the impact of genetic perturbations on drug resistance.

The differential expression analysis module allows users to compare gene expression levels between control and drug-treated groups or between drug-sensitive and -resistant groups. The correlation analysis module supports both single-gene and multigene correlation studies, revealing how gene expression correlates with drug IC50 values. Pathway analysis is facilitated through Gene Set Enrichment Analysis (GSEA), Single Sample Gene Set Enrichment Analysis (ssGSEA), and Pathview, enabling users to explore the enrichment of specific pathways in drug-treated samples. The drug analysis module examines the relationship between gene expression and drug IC50 values, helping to identify potential drug resistance markers. Lastly, the gene perturbation analysis module leverages data from GPSAdb and CGP to screen for genes associated with drug resistance.

The article highlights the case study of L1CAM as a potential drug resistance target in non-small cell lung cancer (NSCLC). Through GSEA enrichment analysis, the study identified the upregulation of L1CAM in cisplatin-treated samples, suggesting its role in drug resistance. Further analysis using CPADS confirmed that L1CAM expression was significantly associated with cisplatin resistance and potentially with resistance to other drugs like bosutinib and rapamycin.

CPADS stands out from other web-based tools due to its extensive dataset, large sample size, and versatile analytical capabilities. It offers customizable visualizations and detailed guidelines, making it accessible to users without programming expertise. The tool is designed to evolve with the integration of new datasets and advanced analytical methods, aiming to become a comprehensive resource for pancancer pharmacogenomic research.

#ResearchChemistry, #ChemicalInnovation, #Science, #ScienceResearch, #ScientificResearch, #ResearchAndDevelopment, #ChemistryEducation, #ChemistryExperiments, #ChemistryLab, #ChemistryStudents, #ChemistryStudy, #OrganicChemistry, #InorganicChemistry, #PhysicalChemistry, #AnalyticalChemistry, #Biochemistry, #MaterialsChemistry, #TheoreticalChemistry, #AppliedChemistry, #MedicinalChemistry

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