Bioinformatics Identification of Key Genes and Pathways Associated with Fluorouracil Folinate and Irinotecan Resistance in Colorectal Cancer Liver Metastases

Abstract

Background: The fluorouracil folinate and irinotecan (FOLFIRI) regimen, comprising fluorouracil, calcium folinate, and irinotecan, is the primary chemotherapy drug for managing colorectal cancer (CRC) liver metastasis. However, the emergence of chemotherapy resistance limits the therapeutic efficacy of this regimen. This study aimed to use bioinformatics analysis to elucidate factors linked to FOLFIRI regimen resistance in CRC liver metastasis, focusing on genes and pathways, and providing a theoretical framework for enhancing chemotherapy resistance in affected patients. Methods: Initially, the gene expression profiles of GSE3964 were investigated using the Gene Expression Omnibus (GEO) database, revealing differentially expressed genes (DEGs) in FOLFIRI regimen-sensitive and resistant tissues. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway evaluation. The STRING tool facilitated the construction of a protein-protein interactions (PPI) network, and Molecular Complex Detection (MCODE) plugins identified pivotal genes. Survival analysis was conducted using the Cancer Genome Atlas (TCGA) database to generate Kaplan-Meier (K-M) survival curves. Additionally, miRNAs associated with resistance were predicted using the miRWalk database. Results: Data analysis unveiled 135 DEGs comprising 87 upregulated and 48 downregulated genes. GO analysis highlighted the association of these genes with structural components of the extracellular matrix, imparting stress resistance and receptor activator activity. The PPI network identified 17 key genes and two high-scoring clusters. KEGG pathways analysis revealed DEGs predominantly linked to cell division, DNA replication, progesterone-mediated maturation of the oocyte, hypoxia inducible factor-1 (HIF-1) signaling pathway, and FoxO signaling pathway. K-M survival curves demonstrated a more favorable prognosis in individuals with elevated ASF1 anti-silencing function 1 homolog B (ASF1B), GINS complex subunit 2 (GINS2), minichromosome maintenance complex component 2 (MCM2), polo-like kinase 1 (PLK1), and TTK Protein Kinase (TTK), while elevated TIMP metallopeptidase inhibitor 1 (TIMP1) expression indicated a poorer prognosis. Four miRNAs related to resistance were identified using miRWalk prediction: hsa-miR-4284, hsa-miR-6795-5p, hsa-miR-3945, and hsa-miR-4433a-3p. Conclusion: Bioinformatics analysis has elucidated crucial genes and signaling pathways in FOLFIRI regimen resistance. This study enhances our understanding of the underlying mechanisms contributing to resistance in CRC liver metastases and establishes a robust foundation for future clinical research on diagnostic and treatment strategies.