Scholarlink

Background <p>Age-related meniscus injury (MI) involves inflammaging, but biomarkers and mechanisms remain unclear.</p> Methods <p>Ribonucleic acid (RNA) sequencing of meniscal tissues from 15 young (&lt; 45 years) and 15 aging (≥ 45 years) MI patients (training dataset) identified differentially expressed inflammation-related genes (DE-IRGs). Biomarkers were screened via least absolute shrinkage and selection operator (LASSO) and support vector machines-recursive feature elimination (SVM-RFE) in the training dataset and validated through GSE191157 (testing dataset) and immunohistochemistry (IHC) staining in additional clinical specimens. Support vector machines (SVM) model and artificial neural network (ANN) model were constructed to predict the diagnostic performance of biomarkers for aging MI. Moreover, biomarkers enrichment analysis, and correlation among biomarkers, function-related genes, immune factors and immune cells were completed. Finally, transcription factor (TF)-biomarker-microRNAs (miRNAs) and competing endogenous RNA (ceRNA) regulatory networks analysis, and drug prediction were performed.</p> Results <p>IRF7 and SPHK1 were significantly downregulated in aging MI and predicted aging MI with high accuracy (area under the curve (AUC) &gt; 0.7). Both biomarkers correlated with chemokine/cytokine pathways, immune senescence (positively with γδT cells/Th17/monocytes; negatively with LAG3/IL6R/TNFSF13), and sphingolipid metabolism (PLPP2 synergy) (|r| = 0.49 ~ 0.86, <i>p</i> &lt; 0.05). Regulatory networks implicated <i>NORAD</i> lncRNA (upregulated) negatively regulated the expression of <i>SPHK1</i> and IRF7 (<i>r</i>&lt;-0.90, <i>p</i> &lt; 0.05), while transcription factors E2F4 and KLF5 (downregulated) positively regulated their expression (<i>r</i> &gt; 0.95, <i>p</i> &lt; 0.05). Clinically, SPHK1 and IRF7 positively correlated with preoperative Lysholm score (SPHK1: <i>r</i> = 0.66; IRF7: <i>r</i> = 0.61; <i>p</i> &lt; 0.05) and negatively with preoperative VAS (SPHK1: <i>r</i>=−0.58; IRF7: <i>r</i>=−0.58; <i>p</i> &lt; 0.05) in aging MI. Two drugs (decitabine, acetaminophen) targeting these biomarkers were predicted.</p> Conclusion <p>SPHK1 and IRF7 drive inflammaging via sphingolipid-immune crosstalk and epigenetic silencing, offering diagnostic and therapeutic potential.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Epigenetic silencing of SPHK1-IRF7 axis drives inflammaging in age-related meniscus degeneration via sphingolipid-immune dysregulation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月19日

查看全文
  • Shunjie Yang,
  • Hui Wang,
  • Lingcheng Wang,
  • Li Liu,
  • Peng Xu,
  • Xiaohe Tian,
  • Gang Chen

摘要

Background

Age-related meniscus injury (MI) involves inflammaging, but biomarkers and mechanisms remain unclear.

Methods

Ribonucleic acid (RNA) sequencing of meniscal tissues from 15 young (< 45 years) and 15 aging (≥ 45 years) MI patients (training dataset) identified differentially expressed inflammation-related genes (DE-IRGs). Biomarkers were screened via least absolute shrinkage and selection operator (LASSO) and support vector machines-recursive feature elimination (SVM-RFE) in the training dataset and validated through GSE191157 (testing dataset) and immunohistochemistry (IHC) staining in additional clinical specimens. Support vector machines (SVM) model and artificial neural network (ANN) model were constructed to predict the diagnostic performance of biomarkers for aging MI. Moreover, biomarkers enrichment analysis, and correlation among biomarkers, function-related genes, immune factors and immune cells were completed. Finally, transcription factor (TF)-biomarker-microRNAs (miRNAs) and competing endogenous RNA (ceRNA) regulatory networks analysis, and drug prediction were performed.

Results

IRF7 and SPHK1 were significantly downregulated in aging MI and predicted aging MI with high accuracy (area under the curve (AUC) > 0.7). Both biomarkers correlated with chemokine/cytokine pathways, immune senescence (positively with γδT cells/Th17/monocytes; negatively with LAG3/IL6R/TNFSF13), and sphingolipid metabolism (PLPP2 synergy) (|r| = 0.49 ~ 0.86, p < 0.05). Regulatory networks implicated NORAD lncRNA (upregulated) negatively regulated the expression of SPHK1 and IRF7 (r<-0.90, p < 0.05), while transcription factors E2F4 and KLF5 (downregulated) positively regulated their expression (r > 0.95, p < 0.05). Clinically, SPHK1 and IRF7 positively correlated with preoperative Lysholm score (SPHK1: r = 0.66; IRF7: r = 0.61; p < 0.05) and negatively with preoperative VAS (SPHK1: r=−0.58; IRF7: r=−0.58; p < 0.05) in aging MI. Two drugs (decitabine, acetaminophen) targeting these biomarkers were predicted.

Conclusion

SPHK1 and IRF7 drive inflammaging via sphingolipid-immune crosstalk and epigenetic silencing, offering diagnostic and therapeutic potential.