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A ten-genes-based diagnostic signature for atherosclerosis

Feng Zhu1,2,3, Lili Zuo4, Rui Hu5

  • 1Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, China.

BMC Cardiovascular Disorders
|October 24, 2021
PubMed

Insights

Researchers identified 10 key genes to predict atherosclerosis, a major cause of cardiovascular disease. This discovery offers potential new diagnostic tools for atherosclerosis, aiding in early detection and management.

Area of Science:

  • Genomics
  • Biomedical research
  • Cardiovascular disease

Background:

  • Atherosclerosis is a primary cause of global cardiovascular mortality.
  • Efficient diagnostic markers for atherosclerosis remain a significant medical challenge.
  • Understanding atherosclerosis pathogenesis is crucial for developing effective treatments.

Purpose of the Study:

  • To screen for potential diagnostic genes associated with atherosclerosis.
  • To identify novel biomarkers for atherosclerosis detection.
  • To develop a predictive model for atherosclerosis diagnosis.

Main Methods:

  • Weighted gene co-expression network analysis (WGCNA) on peripheral blood gene chip data (GSE20129).
  • Functional enrichment analysis (GO, KEGG) and protein-protein interaction network construction (STRING, Cytoscape).
  • Logistic regression model development using identified hub genes.

Main Results:

  • A module of 532 atherosclerosis-related genes was identified.
  • Functional analysis revealed 235 GO terms and 44 KEGG pathways.
  • A predictive model using 10 key genes effectively distinguished atherosclerosis from normal samples.

Conclusions:

  • A predictive model based on 10 atherosclerosis-related genes was successfully developed.
  • These genes show promise as diagnostic signatures for atherosclerosis.
  • The findings contribute to advancing diagnostic research for atherosclerosis.
Abstract

Keywords:
AtherosclerosisGO analysisKEGG analysisLogistic regression diagnostic modePPI network

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