Identification of Candidate Diagnostic gene CDC25C for Trametinib-induced Cardiotoxicity by Integrating Bioinformatics and Machine Learning

Abstract

Trametinib is a MEK inhibitor that has been shown to have considerable efficacy in retarding melanoma progression. However, the exact mechanism of cardiotoxicity induced by trametinib remains unclear. Our study was designed to investigate the underlying mechanisms by which trametinib-induced cardiotoxicity (TIC) might exist, offering novel perspectives and guidance for potential prediction, diagnosis, and treatment of TIC. The GSE217421 dataset indicated 574 up-regulated and 705 down-regulated DEGs. According to the KEGG analysis, these genes were implicated in several pathways and functions, including Cell Cycle, Axon Guidance, Cellular Senescence, and Dilated Cardiomyopathy. The GO analysis suggested their association with Mitotic Cell Cycle, Microtubule Cytoskeleton and Adenyl Nucleotide Binding. The hub genes (CDC25C) for TIC were screened through Multiple machine learning algorithms. Next, The expression level of CDC25C was verified using the GSE217423 validation set. Nomogram model based on CDC25C demonstrated excellent diagnostic capability according to three different evaluation measures. To further explore the regulatory mechanism of CDC25C in TIC, we constructed a multi-regulatory network using miRNAs-lncRNAs-TFs-CDC25C and conducted an immunoinfiltration analysis. Our study suggests that CDC25C may be a candidate diagnostic gene and a potential therapeutic target for the early occurrence and development of TIC. This provides new ideas for the prediction, diagnosis and treatment strategies for TIC.