Xuechao Yang’s research while affiliated with First Affiliated Hospital of China Medical University and other places

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Publications (1)


Flowchart of the study. The raw read count is pre-processed for differential and co-expression analysis. The co-expressed gene modules are subjected to hypergeometric test to identify pulpitis pain related modules. The GO enrichment analysis is performed for the up- and down-regulated genes in pulpitis related module (turquoise module). The correlation analysis was performed between lncRNAs and the turquoise module. Moreover, the expression of eight lncRNAs in the turquoise module was compared between normal and pulpitis samples. Then we constructed the LASSO classification model and identified two lncRNAs for pulpitis prediction
Screening and functional analysis of pulpitis genes. (A) Volcano map of differential analysis. Bar plots of differentially expressed genes enriched to the (B) BP, (C) CC, and (D) MF terms
Co-expression analysis of differential genes and identification of pain-related modules in pulpitis. (A) Co-expression analysis of the differentially expressed genes to obtain co-expressed gene modules. (B) The number of differentially expressed genes in each module. (C) Venn diagram of gene in the turquoise module and pulpitis pain genes. (D) The heat map of differential genes in the turquoise module demonstrated distinct expression patterns
Function and expression analysis of lncRNAs in the pain-related module. The enriched GO BP terms of (A) up- and (B) down-regulated genes in the pulpitis pain related module
The LASSO model of pulpitis based on eight core lncRNAs. (A) Expression correlation between lncRNAs and modules. (B) Expression comparison of eight core lncRNAs in normal and pulpitis samples. (C) The cross-validation error plot of LASSO classification model. (D) ROC curves plotted according to LINC00426, MIR181A2HG, and their combination

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Identification of pain-related long non-coding RNAs for pulpitis prediction
  • Article
  • Publisher preview available

January 2025

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5 Reads

Yongjie Tan

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Ying He

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Yuexuan Xu

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[...]

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Xuechao Yang

Objectives We investigated the recently generated RNA-sequencing dataset of pulpitis to identify the potential pain-related lncRNAs for pulpitis prediction. Materials and methods Differential 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. Results A 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. Conclusions PCED1B-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 relevance This 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.

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