Statistical Analysis of Microarray Data to Identify Key Gene Expression Patterns in Primary Hyperoxaluria
DOI:
https://doi.org/10.6000/1929-6029.2024.13.38Keywords:
Primary Hyperoxaluria, Gene Expression Analysis, Molecular Mechanisms, Differential Gene Expression, Bioinformatics, Statistical Analysis, Data VisualizationAbstract
This study aims to utilize microarray data deposited by Romero et al. and conduct bioinformatic analysis for identifying differentially expressed genes (DEGs) associated with a novel method involving gene correction at the Alanine–Glyoxylate Aminotransferase (AGXT) locus and direct conversion of fibroblasts from primary hyperoxaluria type 1 (PH1) patients into healthy induced hepatocytes (iHeps) using Clustered Regularly Interspaced Short Palindromic Repeats - CRISPR-associated protein 9 (CRISPR-Cas9) technology. Additionally, the study aims to elucidate the molecular mechanisms underlying hyperoxaluria compared to oxalate crystal formation. Romero et al.'s GSE226019 microarray data was retrieved from Gene Expression Omnibus. Statistical analysis was done in R and Bioconductor, utilizing rigorous methods to ensure robust and reproducible results. The limma program compared gene expression levels across groups. Pathway analysis, protein-protein interaction (PPI) network creation, and miRNA-target interaction network analysis were constructed. The top ten DEGs included ANGPTL3, SLC38A3, KNG1, BDH1, GC, ADH1C, ARG1, CYP3A4, AMBP, and CYP2C9. Enrichment analysis revealed significant associations with various biological pathways, including Linoleic acid metabolism and Retinol metabolism. Volcano plots and mean difference plots highlighted significant gene expression changes between different sample groups. Protein-protein interaction networks and miRNA-target interaction networks provided insights into molecular interactions and regulatory mechanisms. The top ten differentially expressed genes include ANGPTL3, SLC38A3, KNG1, BDH1, GC, ADH1C, ARG1, CYP3A4, AMBP, and CYP2C9—emerge as key players with strong associations to critical biological pathways like Linoleic acid metabolism and drug metabolism-cytochrome P450. Understanding the regulatory role of specific miRNAs (hsa-miR-4501, hsa-miR-5692c, hsa-miR-6731-3p, hsa-miR-6867-5p, hsa-miR-616-3p, hsa-miR-4468, hsa-miR-3692-3p, hsa-miR-4277, hsa-miR-4763-5p, hsa-miR-4797-5p) in gene expression could provide further insights into disease mechanisms and potential therapeutic avenues. The statistical findings provide a foundation for predictive modeling, hypothesis testing, and exploring personalized therapeutic strategies.
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