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鉴定与疼痛相关的长非编码RNA,用于预测脉

Yongjie Tan¹, Ying He², Yuexuan Xu¹

¹School of Automation, Guangdong University of Technology, Guangzhou Higher Education Mega Center, No. 100 Waihuan Xi Road Panyu District, Guangzhou, 510006, China.

Clinical oral investigations

|January 22, 2025

概括

这项研究在脉中确定了与疼痛相关的长非编码RNA (lncRNAs),开发了用于准确预测脉和早期分子诊断的两种lncRNA签名.

科学领域:

分子生物学分子生物学
基因组学就是基因组学.
牙科研究牙科研究

背景情况:

牙髓炎 (pulpitis) 是牙髓的炎症状况,需要准确的诊断工具.
长非编码RNAs (lncRNAs) 越来越多地被认为是它们在各种生物过程中的作用,包括炎症和疾病的发病.

研究的目的:

通过使用RNA测序数据在脉中识别与疼痛相关的lncRNAs.
开发一种基于已识别的 lncRNAs 的脉炎预测模型.
为了探索背后的分子机制脉疼痛.

主要方法:

差异基因表达分析和加权基因同表达网络分析 (WGCNA) 在皮和正常样本上进行.
使用LASSO回归模型构建基于lncRNA的分类模型.
共识聚类和基因组变异分析 (GSVA) 用于脉炎亚型.
预测和分析了miRNA-目标相互作用.

主要成果:

在pulpitis样本中发现了1830种差异性RNA.
确定了一种与疼痛相关的模块,其中包含与炎症相关的8个lncRNAs.
在LASSO模型中,MIR181A2HG和LINC00426显示出优异的预测性能 (AUC=1).
皮炎样本被分为两种亚型,其中MIR181A2HG被确定为渐进性标志物.

结论:

一些lncRNAs,包括PCED1B-AS1,MIAT,MIR181A2HG,LINC00926,LINC00861,LINC00528,LINC00426和ITGB2-AS1,是脉疼痛的潜在标记物.
一个两种lncRNA签名 (LINC00426和MIR181A2HG) 准确地预测肺炎,有助于分子诊断.
GATA3可能调节这些lncRNA和下游NF-kappa B信号通路.

关键词:

生物信息学分析疼痛疼痛疼痛疼痛预测预测预测传道台 (pulpitis) 是一个传道台.在cnRNA中.

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相关概念视频

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...