Identification of DYRK2 and TRIM32 as keloids programmed cell death-related biomarkers: insights from bioinformatics and machine learning in multiple cohorts.
IF 1.7 4区 医学Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xi Yang, Yao Yang, Mingjian Zhao, He Bai, Chongyang Fu
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引用次数: 0
Abstract
This study aims to explore the expression patterns and mechanisms of programmed cell death-related genes in keloids and identify molecular targets for early diagnosis and treatment. We first explored the expression, immune, and biological function profiles of keloids. Using various machine learning methods, two key genes, DYRK2 and TRIM32, were identified, with ROC curves demonstrating their diagnostic potential. Further analyses, including GSEA, immune cell profiling, competing endogenous RNA network, and single-cell analysis, revealed their mechanism of action and regulatory network. Finally, SB-431542 was identified as a potential therapeutic agent for keloids through CMap and molecular docking.
期刊介绍:
The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.