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ORIGINAL RESEARCH article
Front. Nutr.
Sec. Nutrition and Metabolism
Volume 11 - 2024 |
doi: 10.3389/fnut.2024.1407265
Circadian gene signatures in the Progression of Obesity based on Machine learning and Mendelian randomization analysis
Provisionally accepted- 1 Department of Ophthalmology, The First Affiliated Hospital of Jinan University, Guangzhou Guangdong 510632, China, Guangzhou, China
- 2 Department of Ophthalmology, The Affiliated Shunde Hospital of Jinan University, Foshan Guangdong, China, Foshan Guangdong, China
Objective: Obesity, a global health concern, is associated with a spectrum of chronic diseases and cancers. Our research sheds light on the regulatory role of circadian genes in obesity progression, providing insight into the immune landscape of obese patients, and introducing new avenues for therapeutic interventions. Methods: Expression files of multiple datasets were retrieved from the GEO database. By 80 machine-learning algorithm combinations and Mendelian randomization analysis, we discovered the key circadian genes contributing to and protecting against obesity. Subsequently, an immune infiltration analysis was conducted to examine the alterations in immune cell types and their abundance in the body and to investigate the relationships between circadian genes and immune cells. Furthermore, we delved into the molecular mechanisms of key genes implicated in obesity. Results: Our study identified three key circadian genes (BHLHE40, PPP1CB, and CSNK1E) associated with obesity. BHLHE40 was found to promote obesity through various pathways, while PPP1CB and CSNK1E counteracted lipid metabolism disorders, and modulated cytokines, immune receptors, T cells, and monocytes. Conclusion: In conclusion, the key circadian genes (BHLHE40, CSNK1E, and PPP1CB) may serve as novel biomarkers for understanding obesity pathogenesis and have significant correlations with infiltrating immune cells, thus providing potential new targets for obese prevention and treatment.
Keywords: Obesity, circadian gene, immune, molecular mechanism, Mendelian randomization, machine learning
Received: 26 Mar 2024; Accepted: 03 Sep 2024.
Copyright: © 2024 Cheng and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiaoyong Liu, Department of Ophthalmology, The First Affiliated Hospital of Jinan University, Guangzhou Guangdong 510632, China, Guangzhou, China
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