Ji Yang , Yikun Zhou , Jiarui Zhang , Yongqin Zheng , Jundong He
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引用次数: 0
Abstract
Aim
Fatty acid metabolism is pivotal for lipid synthesis, cellular signaling, and maintaining cell membrane integrity. However, its diagnostic significance in type 2 diabetes mellitus (T2DM) remains unclear.
Materials and methods
Three datasets and fatty acid metabolism-related genes were retrieved. Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. Functional analysis, ceRNA network construction, immune microenvironment assessment, and drug prediction were conducted to explore the underlying molecular mechanisms.
Results
Six feature genes were identified with strong diagnostic performance and were involved in processes such as ribosome function and fatty acid metabolism. Immune cells, including dendritic cells, eosinophils, and neutrophils, may play a role in the progression of T2DM. ceRNA and drug-target network analysis revealed potential interactions, such as RP11-miR-29a-YTHDF3 and BPA-MSANTD1. The expression patterns of the feature genes, except for YTHDF3, were consistently upregulated in T2DM, aligning with trends observed in the training set.
Conclusion
This study investigated the potential molecular mechanisms of six fatty acid metabolism-related genes in T2DM, offering valuable insights that may guide future research and therapeutic development.
期刊介绍:
Open access, online only, peer-reviewed international journal in the Life Sciences, established in 2014 Biochemistry and Biophysics Reports (BB Reports) publishes original research in all aspects of Biochemistry, Biophysics and related areas like Molecular and Cell Biology. BB Reports welcomes solid though more preliminary, descriptive and small scale results if they have the potential to stimulate and/or contribute to future research, leading to new insights or hypothesis. Primary criteria for acceptance is that the work is original, scientifically and technically sound and provides valuable knowledge to life sciences research. We strongly believe all results deserve to be published and documented for the advancement of science. BB Reports specifically appreciates receiving reports on: Negative results, Replication studies, Reanalysis of previous datasets.