Identification of pain-related long non-coding RNAs for pulpitis prediction

被引：0

作者：

Tan, Yongjie ^{[1
]}

He, Ying ^{[2
]}

Xu, Yuexuan ^{[1
]}

Qiu, Xilin ^{[1
]}

Liu, Guanru ^{[1
]}

Liu, Lingxian ^{[1
]}

Jiang, Ye ^{[2
]}

Li, Mingyue ^{[1
]}

Sun, Weijun ^{[1
,3
]}

Xie, Ziqiang ^{[4
]}

Huang, Yonghui ^{[1
]}

Chen, Xin ^{[1
]}

Yang, Xuechao ^{[2
,5
]}

机构：

[1] Guangdong Univ Technol, Guangzhou Higher Educ Mega Ctr, Sch Automat, 100 Waihuan Xi Rd, Guangzhou 510006, Peoples R China

[2] Guangzhou Med Univ, Guangdong Engn Res Ctr Oral Restorat & Reconstruct, Dept Endodont,Affiliated Stomatol Hosp, Guangzhou Key Lab Basic & Appl Res Oral Regenerat, Guangzhou, Peoples R China

[3] Guangdong Univ Technol, Guangdong Key Lab IoT Informat Technol, Guangzhou, Peoples R China

[4] Nanchang Univ Coll Sci & Technol, Dept Sci & Technol, Jiujiang, Peoples R China

[5] Guangzhou Med Univ, Dept Endodont, Affiliated Stomatol Hosp, 195 Dongfeng West Rd, Guangzhou 510182, Peoples R China

来源：

CLINICAL ORAL INVESTIGATIONS | 2025年 / 29卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Pulpitis; Pain; lncRNA; Prediction; Bioinformatic analysis; EXPRESSION;

D O I：

10.1007/s00784-025-06164-0

中图分类号：

R78 [口腔科学];

学科分类号：

1003 ;

摘要：

ObjectivesWe investigated the recently generated RNA-sequencing dataset of pulpitis to identify the potential pain-related lncRNAs for pulpitis prediction.Materials and methodsDifferential analysis was performed on the gene expression profile between normal and pulpitis samples to obtain pulpitis-related genes. The co-expressed gene modules were identified by weighted gene coexpression network analysis (WGCNA). Then the hypergeometric test was utilized to screen pain-related core modules. The functional enrichment analysis was performed on the up- and down-regulated genes in the core module of pulpitis pain to explore the underlying mechanisms. A pain-related lncRNA-based classification model was constructed using LASSO. Consensus clustering and gene set variation analysis (GSVA) on the infiltrating immunocytes was used for pulpitis subtyping. miRanda predicts miRNA-target relationship, which was filtered by expression correlation. Hallmark pathway and enrichment analysis was performed to investigate the candidate target pathways of the lncRNAs.ResultsA total of 1830 differential RNAs were identified in pulpitis. WGCNA explored seven co-expressed modules, among which the turquoise module is pain-related with hypergeometric test. The up-regulated genes were significantly enriched in immune response related pathways. Down-regulated genes were significantly enriched in differentiation pathways. Eight lncRNAs in the pain-related module were related to inflammation. Among them, MIR181A2HG was downregulated while other seven lncRNAs were upregulated in pulpitis. The LASSO classification model revealed that MIR181A2HG and LINC00426 achieved outstanding predictive performances with perfect ROC-AUC score (AUC = 1). We differentiated the pulpitis samples into two progression subtypes and MIR181A2HG is a progressive marker for pulpitis. The miRNA-mRNA-lncRNA regulatory network of pulpitis pain was constructed, with GATA3 as a key transcription factor. NF-kappa B signaling pathway is a candidate pathway impacted by these lncRNAs.ConclusionsPCED1B-AS1, MIAT, MIR181A2HG, LINC00926, LINC00861, LINC00528, LINC00426 and ITGB2-AS1 may be potential markers of pulpitis pain. A two-lncRNA signature of LINC00426 and MIR181A2HG can accurately predict pulpitis, which could facilitate the molecular diagnosis of pulpitis. GATA3 might regulate these lncRNAs and downstream NF-kappa B signaling pathway.Clinical relevanceThis study identified potential pain-related lncRNAs with underlying molecular mechanism analysis for the prediction of pulpitis. The classification model based on lncRNAs will facilitate the early diagnosis of pulpitis.

引用

页数：14