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.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.