Computational Elucidation of Hub Genes and Pathways Correlated with the Development of 5-Fluorouracil Resistance in HCT 116 Colorectal Carcinoma Cell Line
- PMID: 39883358
- DOI: 10.1007/s10528-025-11041-2
Computational Elucidation of Hub Genes and Pathways Correlated with the Development of 5-Fluorouracil Resistance in HCT 116 Colorectal Carcinoma Cell Line
Abstract
Colorectal cancer (CRC) is the third most deadly cancer diagnosed in both men and women. 5-Fluorouracil (5-FU) treatment frequently causes the CRC cells to become chemoresistance, which has a negative impact on prognosis. Using bioinformatic techniques, this work describes important genes and biological pathways linked to 5-FU resistance in CRC cells. In our studies, a 5-FU-resistant HCT 116 cell line exhibiting elevated TYMS was created and validated using various tests. Bioinformatic studies were conducted to determine which differentially expressed genes (DEGs) were responsible for the establishment of 5-FU resistance in the same cell line. After screening 3949 DEGs from the two public datasets (GSE196900 and GSE153412), 471 overlapping DEGs in 5-FU-resistant HCT 116 cells were chosen. These overlapping DEGs were used to build the PPI network, and a major cluster module containing 21 genes was found. Subsequently, using three topological analysis algorithms, 10 hub genes were identified, which included HLA-DRA, HLA-DRB1, CXCR4, MMP9, CDH1, SMAD3, VIM, SYK, ZEB1, and SELL. Their roles were ascertained by utilizing Gene Ontology keywords and pathway enrichment studies. Our results also demonstrated that the miRNA and transcription factors (TFs) that had the strongest connection with the hub genes were hsa-mir-26a-5p, hsa-mir-30a-5p, RELA, and NFKB1. Ultimately, 84 FDA-approved drugs that target those hub genes were found to potentially treat 5-FU resistance CRC. Our research's findings increase our understanding of the fundamental factors that contribute to the prevalence of 5-FU resistance CRC, which could ultimately assist in the identification of valuable malignancy biomarkers and targeted treatment approaches based on key regulatory pathways.
Keywords: Bioinformatic analysis; Differentially expressed genes; Functional enrichment analysis; MicroRNA; Transcription factor.
© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Conflict of interest statement
Declarations. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical Approval: This article does not contain any studies with human participants or animals performed by any of the authors.
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