Scholarlink

<p>The role of RNA N6-methyladenosine (m6A) modifications in modulating the immune microenvironment during ischemic stroke (IS) pathogenesis remains poorly characterized. This investigation systematically explores m6A-mediated immune regulation in IS and identifies critical immune-related biomarkers. Transcriptomic profiles from 108 IS samples were analyzed to discern m6A regulatory patterns. Single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) quantified immune cell infiltration and pathway activity across IS subtypes and controls. Weighted gene co-expression network analysis (WGCNA) identified m6A-associated gene modules. Two complementary machine learning approaches were applied to identify the key immune-related genes implicated in IS pathogenesis. The robustness of these findings was subsequently confirmed through a comprehensive meta-analysis integrating six independent datasets. Eight dysregulated m6A regulators distinguished IS from controls. Unsupervised clustering delineated two distinct m6A modification patterns (Clusters A/B) with divergent immune landscapes: Cluster B exhibited heightened infiltration of natural killer cells, eosinophils, and activated CD4 T cells, coupled with IL6/JAK/STAT3 pathway activation, whereas Cluster A demonstrated enrichment of immature dendritic cells and monocytes, alongside oxidative phosphorylation signaling. WGCNA identified a conserved immune-related module (black module, <i>r</i> = 0.68 with Cluster B) containing 322 co-expressed genes. Cross-validation by machine learning nominated five candidate biomarkers (<i>ABCA1</i>, <i>CPD</i>, <i>PRRG4</i>, <i>WDR46</i>, C19orf24) showing consistent expression trends in internal validation cohorts (Control vs. IS and Cluster A vs. B). External validation via meta-analysis confirmed <i>WDR46</i> as a protective factor against IS susceptibility (odds ratio [OR] = 0.74, 95% confidence interval [CI]: 0.57–0.97), while <i>CPD</i> (OR = 1.46, 95% CI: 1.01–2.10) and <i>ABCA1</i> (OR = 1.57, 95% CI: 1.12–2.22) were significantly associated with increased IS susceptibility, establishing their roles as risk genes. Subsequent RT-qPCR analysis in clinical samples further validated the results of the aforementioned external validation. Moreover, ROC analysis revealed an AUC of 0.88 (95% CI: 0.82–0.94) for <i>ABCA1</i>, 0.82 (95% CI: 0.74–0.89) for <i>WDR46</i>, and 0.90 (95% CI: 0.84–0.96) for <i>CPD</i>. This study establishes m6A epitranscriptomic remodeling as a pivotal orchestrator of immune microenvironment heterogeneity in IS. The identification of <i>ABCA1</i>, <i>CPD</i>, and <i>WDR46</i> as promising biomarkers not only enhances the potential for precise diagnosis but also provides actionable targets for immunomodulatory therapy in IS.</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

Integrated analysis of m6A regulator mediated RNA methylation and key immune related genes in ischemic stroke

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月28日

查看全文
  • Peng-Fei Zheng,
  • Cheng-Cheng Huang,
  • Chang-Lu Wang,
  • Ting Zhou,
  • Hong-Wei Pan,
  • Jin He,
  • Xiu-Qin Hong,
  • Li-Zhong Huang,
  • Jing-Jing Rong,
  • Xiang-Jiang Shi

摘要

The role of RNA N6-methyladenosine (m6A) modifications in modulating the immune microenvironment during ischemic stroke (IS) pathogenesis remains poorly characterized. This investigation systematically explores m6A-mediated immune regulation in IS and identifies critical immune-related biomarkers. Transcriptomic profiles from 108 IS samples were analyzed to discern m6A regulatory patterns. Single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) quantified immune cell infiltration and pathway activity across IS subtypes and controls. Weighted gene co-expression network analysis (WGCNA) identified m6A-associated gene modules. Two complementary machine learning approaches were applied to identify the key immune-related genes implicated in IS pathogenesis. The robustness of these findings was subsequently confirmed through a comprehensive meta-analysis integrating six independent datasets. Eight dysregulated m6A regulators distinguished IS from controls. Unsupervised clustering delineated two distinct m6A modification patterns (Clusters A/B) with divergent immune landscapes: Cluster B exhibited heightened infiltration of natural killer cells, eosinophils, and activated CD4 T cells, coupled with IL6/JAK/STAT3 pathway activation, whereas Cluster A demonstrated enrichment of immature dendritic cells and monocytes, alongside oxidative phosphorylation signaling. WGCNA identified a conserved immune-related module (black module, r = 0.68 with Cluster B) containing 322 co-expressed genes. Cross-validation by machine learning nominated five candidate biomarkers (ABCA1, CPD, PRRG4, WDR46, C19orf24) showing consistent expression trends in internal validation cohorts (Control vs. IS and Cluster A vs. B). External validation via meta-analysis confirmed WDR46 as a protective factor against IS susceptibility (odds ratio [OR] = 0.74, 95% confidence interval [CI]: 0.57–0.97), while CPD (OR = 1.46, 95% CI: 1.01–2.10) and ABCA1 (OR = 1.57, 95% CI: 1.12–2.22) were significantly associated with increased IS susceptibility, establishing their roles as risk genes. Subsequent RT-qPCR analysis in clinical samples further validated the results of the aforementioned external validation. Moreover, ROC analysis revealed an AUC of 0.88 (95% CI: 0.82–0.94) for ABCA1, 0.82 (95% CI: 0.74–0.89) for WDR46, and 0.90 (95% CI: 0.84–0.96) for CPD. This study establishes m6A epitranscriptomic remodeling as a pivotal orchestrator of immune microenvironment heterogeneity in IS. The identification of ABCA1, CPD, and WDR46 as promising biomarkers not only enhances the potential for precise diagnosis but also provides actionable targets for immunomodulatory therapy in IS.