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Related Concept Videos

Regulation of Angiogenesis and Blood Supply01:24

Regulation of Angiogenesis and Blood Supply

Rapidly dividing tumors, embryos, and wounded tissues require more oxygen than usual, lowering the oxygen concentration in the blood. At low oxygen or hypoxic conditions, an oxygen-sensitive transcription factor called the hypoxia-inducible factor 1 or HIF1 is activated. HIF1 is a dimeric protein of alpha (ɑ) and beta (β) subunits.  Under optimal oxygen conditions, HIF1β is present in the nucleus while HIF1ɑ remains in the cytosol. HIF1ɑ is hydroxylated by prolyl hydroxylase and factor...

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Related Experiment Video

Updated: May 26, 2026

Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-Cell Multi-Omics Experiments
09:12

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Published on: October 4, 2021

Multi-Omics and Machine Learning Integration Identifies Key Endothelial Modules in Carotid Artery Stenosis.

Jiayi Wu1, Chunguang Guo2, Linfeng Zhang3

  • 1Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, China.

Journal of Clinical Laboratory Analysis
|May 25, 2026
PubMed
Summary

New biomarkers, NRP1 and XAF1, derived from endothelial cells show promise for early detection of carotid artery stenosis (CAS), a key cause of ischemic stroke.

Keywords:
biomarkerscarotid artery stenosisendothelial cellmachine learningsingle‐cell RNA sequencing

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Area of Science:

  • Biomarkers and diagnostics
  • Genomics and transcriptomics
  • Vascular biology

Background:

  • Carotid artery stenosis (CAS) is a significant risk factor for ischemic stroke.
  • Current diagnostic methods for CAS lack sufficient sensitivity for early detection.
  • There is a critical need for reliable molecular biomarkers for early CAS identification.

Purpose of the Study:

  • To identify novel endothelial cell-derived molecular biomarkers for early diagnosis of CAS.
  • To validate these biomarkers across multiple independent datasets and molecular levels.

Main Methods:

  • Integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptomics, and multi-omics data.
  • Applied Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms.
  • Validated findings across independent GEO and ZZ cohorts at single-cell, transcriptomic, and proteomic levels.

Main Results:

  • scRNA-seq revealed endothelial cell enrichment in CAS and identified 836 potential markers.
  • Integrative analysis narrowed down candidates to 80, with NRP1 and XAF1 selected by machine learning.
  • NRP1 and XAF1 demonstrated consistent upregulation and strong diagnostic performance across multi-omics validation datasets.

Conclusions:

  • NRP1 and XAF1 are identified as novel endothelial cell-derived biomarkers for CAS.
  • These biomarkers show significant potential for early screening and clinical diagnosis of carotid artery stenosis.
  • The findings highlight the utility of multi-omics integration and machine learning in biomarker discovery.