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<p>Reliable blood-based biomarkers for Parkinson’s disease (PD) are needed for minimally invasive diagnosis. We identified a synergistic mRNA biomarker pair, <i>AP3B1</i> and <i>BMPR2</i>, detectable in blood through an integrative multi-omics workflow. DEGs from a meta-analysis of PD versus healthy controls (HCs) were intersected with DEG-enriched pathway genes and analysed via three-step SMR to identify PD risk candidates, from which machine learning (SVM-RFE and random forest) prioritized <i>AP3B1</i> and <i>BMPR2</i>. Knockdown of each gene in SH-SY5Y-derived neurons reproduced Parkinsonian phenotypes, with protein docking and co-immunoprecipitation suggesting a direct interaction. An XGBoost model built on PPMI blood RNA-seq (<i>n</i> = 2585) using 25 established PD biomarkers (baseline AUC ~ 0.595) improved to 0.745 with addition of both <i>AP3B1</i> and <i>BMPR2</i>. qRT-PCR in a cohort of clinical blood samples confirmed their downregulation in PD. These findings support <i>AP3B1</i> and <i>BMPR2</i> as a synergistic biomarker pair with speculative biological relevance and possible translational potential.</p>

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Synergistic blood-based diagnostic value of AP3B1 and BMPR2 in Parkinson’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月28日

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  • Xiyan Zhao,
  • Li Yang,
  • Yumin Luan,
  • Tao Ding,
  • Xinglong Yang,
  • Xin Geng,
  • Tuo Zhang,
  • Jigang Pan,
  • Ziwen Xiao,
  • Wei Pan,
  • Tengxiang Chen

摘要

Reliable blood-based biomarkers for Parkinson’s disease (PD) are needed for minimally invasive diagnosis. We identified a synergistic mRNA biomarker pair, AP3B1 and BMPR2, detectable in blood through an integrative multi-omics workflow. DEGs from a meta-analysis of PD versus healthy controls (HCs) were intersected with DEG-enriched pathway genes and analysed via three-step SMR to identify PD risk candidates, from which machine learning (SVM-RFE and random forest) prioritized AP3B1 and BMPR2. Knockdown of each gene in SH-SY5Y-derived neurons reproduced Parkinsonian phenotypes, with protein docking and co-immunoprecipitation suggesting a direct interaction. An XGBoost model built on PPMI blood RNA-seq (n = 2585) using 25 established PD biomarkers (baseline AUC ~ 0.595) improved to 0.745 with addition of both AP3B1 and BMPR2. qRT-PCR in a cohort of clinical blood samples confirmed their downregulation in PD. These findings support AP3B1 and BMPR2 as a synergistic biomarker pair with speculative biological relevance and possible translational potential.