Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus

IF 2.3 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Biochemistry and Biophysics Reports Pub Date : 2024-10-22 DOI:10.1016/j.bbrep.2024.101849
{"title":"Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus","authors":"","doi":"10.1016/j.bbrep.2024.101849","DOIUrl":null,"url":null,"abstract":"<div><h3>Aim</h3><div>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.</div></div><div><h3>Materials and methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":8771,"journal":{"name":"Biochemistry and Biophysics Reports","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochemistry and Biophysics Reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405580824002139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
鉴定与 2 型糖尿病脂肪酸代谢有关的基因
目的脂肪酸代谢对脂质合成、细胞信号传导和维持细胞膜完整性至关重要。材料与方法检索了三个数据集和脂肪酸代谢相关基因。采用差异表达分析、WGCNA、机器学习算法、诊断分析和验证来确定关键特征基因。结果发现了六个具有较强诊断能力的特征基因,它们参与了核糖体功能和脂肪酸代谢等过程。ceRNA和药物靶点网络分析发现了潜在的相互作用,如RP11-miR-29a-YTHDF3和BPA-MSANTD1。除 YTHDF3 外,其他特征基因的表达模式在 T2DM 中持续上调,与训练集中观察到的趋势一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Biochemistry and Biophysics Reports
Biochemistry and Biophysics Reports Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
4.60
自引率
0.00%
发文量
191
审稿时长
59 days
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.
期刊最新文献
Development and optimization of an efficient RNA isolation protocol from bovine (Bos indicus) spermatozoa Identification of hub genes and pathways in Uterine corpus endometrial carcinoma (UCEC): A comprehensive in silico study Investigation of CST7 and hsa-miR-4793-5p gene expression in breast cancer CCN5/WISP2 serum levels in patients with coronary artery disease and type 2 diabetes and its correlation with inflammation and insulin resistance; a machine learning approach LINC00261 triggers DNA damage via the miR-23a-3p/CELF2 axis to mitigate the malignant characteristics of 131I-resistant papillary thyroid carcinoma cells
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1