Evidence map›Paper›PMID 42272635›Full record

ArticleFrontiers in cell and developmental biology2026

Comprehensive analysis of efferocytosis-related genes in diagnosis and immune infiltration in atherosclerosis: based on bulk and single-cell RNA sequencing data.

Jintao Qian, Junhao Zhang, Jiahui Wang, Suyu Xu, Li Yu, Chuiyu Kong, Qing Zhou

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In one paragraph

Article in Frontiers in cell and developmental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jintao Qian *Department of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Junhao Zhang *Department of Cardiothoracic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
Jiahui Wang *Department of Cardiothoracic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.
Suyu XuSchool of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China.
Li YuSchool of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China.
Chuiyu KongDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Qing ZhouDepartment of Cardiothoracic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Atherosclerosis (AS) is a widespread cardiovascular disorder that constitutes a major contributor to global morbidity and mortality, thereby imposing significant economic burdens on healthcare systems worldwide. Efferocytosis, the phagocytic removal of apoptotic cells, serves as a fundamental mechanism for maintaining tissue homeostasis during normal physiological function and for restoring equilibrium following pathological insults. Methods: This study systematically investigated the functional roles of efferocytosis across specific cell types using single-cell datasets. By integrating differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning approaches, six key genes were identified. Gene set variation analysis (GSVA) was subsequently performed to elucidate the biological pathways in which these genes are involved. Furthermore, the ssGSEA algorithm was applied to assess the association between these genes and immune cell infiltration levels. To evaluate their diagnostic potential, a nomogram was constructed based on the gene signature. Unsupervised consensus clustering revealed two distinct molecular subtypes of atherosclerosis. Finally, the protein expression levels of core EFRGs in atherosclerosis were analyzed using Western blotting. Results: Single-cell data analysis demonstrates that macrophages, vascular smooth muscle cells, and endothelial cells play potential functional roles in efferocytosis. Utilizing bulk RNA sequencing, six core efferocytosis-related genes (STAB1, ANO5, GULP1, LGR6, SCARF1, and CAMK2G) were identified, which exhibit significant diagnostic potential in atherosclerosis. Based on the expression profiles of these six genes, atherosclerosis can be stratified into two distinct molecular subtypes-subtypes A and B-with subtype B being characterized by its association with unstable plaque formation. Western blot analysis confirmed the expression trends of five proteins (ANO5, GULP1, LGR6, SCARF1, and CAMK2G) among the candidates. Conclusion: This study has revealed the potential value of efferocytosis-related biomarkers in the diagnosis of atherosclerosis (AS) and the optimization of treatment strategies, providing new theoretical basis and research perspectives for precise intervention in cardiovascular diseases.

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atherosclerosisbiomarkersefferocytosismachine learningsingle-cell

Identifiers

PMID42272635
PMCPMC13246637

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