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

Atherosclerosis — the buildup of plaques inside arteries — involves a failure to properly clear away dying cells, a process called efferocytosis. When efferocytosis breaks down, dead cells accumulate inside plaques, fueling inflammation and making plaques unstable and prone to rupture. This study used two layers of genetic data — bulk RNA sequencing from tissue samples and single-cell RNA sequencing — to map which cell types are most involved in efferocytosis within atherosclerotic plaques, and to identify genes that could serve as diagnostic markers. By combining statistical methods including machine learning and network analysis, the researchers narrowed a large list of efferocytosis-related genes down to six candidates: STAB1, ANO5, GULP1, LGR6, SCARF1, and CAMK2G. These six genes were able to distinguish atherosclerosis patients from healthy controls and, importantly, to sort patients into two biological subtypes — one of which was linked to the more dangerous, unstable type of plaque. Macrophages, vascular smooth muscle cells, and endothelial cells emerged as the main cell types driving efferocytosis activity in the plaque. Protein-level confirmation via Western blotting validated five of the six gene signals, lending experimental credibility to the bioinformatic findings. The results suggest these genes could form the basis of a diagnostic panel and potentially guide more targeted treatment strategies for cardiovascular disease.

• Six core efferocytosis-related genes — STAB1, ANO5, GULP1, LGR6, SCARF1, and CAMK2G — were identified through integration of differential expression analysis, WGCNA, and machine learning as having significant diagnostic potential in atherosclerosis.
• Unsupervised consensus clustering of bulk RNA-seq data stratified atherosclerosis patients into two molecular subtypes (A and B); subtype B was specifically associated with unstable plaque formation, suggesting prognostic relevance beyond diagnosis.
• Single-cell RNA sequencing analysis implicated macrophages, vascular smooth muscle cells, and endothelial cells as the primary cell populations with functional roles in efferocytosis within atherosclerotic tissue.
• Western blot analysis confirmed the expected expression trends of five of the six candidate proteins (ANO5, GULP1, LGR6, SCARF1, and CAMK2G), providing experimental validation of the transcriptomic findings.
• A nomogram constructed from the six-gene signature demonstrated diagnostic utility, and ssGSEA analysis revealed significant associations between these genes and immune cell infiltration profiles in atherosclerotic plaques.

This was a bioinformatic and experimental study using publicly available bulk RNA sequencing and single-cell RNA sequencing datasets from atherosclerosis patients and controls. Analytical methods included differential expression analysis, weighted gene co-expression network analysis (WGCNA), machine learning-based feature selection, gene set variation analysis (GSVA), and single-sample GSEA (ssGSEA) to assess immune infiltration. A diagnostic nomogram was constructed from the six identified genes, and unsupervised consensus clustering was used to define molecular subtypes. Western blotting on atherosclerosis tissue samples was performed to validate protein expression levels of the candidate genes; specific sample sizes for the datasets are not reported in the abstract.

Because the study relies primarily on publicly available transcriptomic datasets, the exact sample sizes, cohort demographics, and tissue sources are not fully characterised in the abstract, limiting assessment of generalisability. The bioinformatic pipeline identifies association and diagnostic potential but cannot establish whether defective efferocytosis via these genes is causally driving plaque instability or is a downstream consequence of it. The study does not measure specialised pro-resolving mediators (SPMs) or other lipid-derived resolution signals, leaving open the question of whether the gene-level findings translate into functional impairment of the resolution process. Future work using loss-of-function models (e.g., macrophage-specific knockouts of GULP1 or SCARF1 in murine atherosclerosis) combined with lipidomic profiling would help establish causality and therapeutic relevance.

This study extends [§19] by providing cell-type-resolved and gene-level evidence that resolution — here enacted through efferocytosis — is an active, molecularly orchestrated process rather than passive decay, identifying macrophages and smooth muscle cells as key effectors within atherosclerotic plaques. It parallels [§20] thematically, since both concern the molecular machinery that either promotes or impairs inflammatory resolution in cardiovascular tissue, though the present study focuses on apoptotic-cell clearance genes rather than SPM biosynthesis pathways, and notably lacks lipidomic or SPM quantification data that would directly link the gene signatures to resolvin or protectin activity. The absence of omega-3 or SPM measurements also means the findings sit adjacent to but do not yet connect with the biomarker framework described in [§23], where the omega-3 index serves as a systemic proxy for resolution capacity — a future study correlating the six-gene efferocytosis signature with the omega-3 index in the same cohort would be a logical and high-value extension.

• Resolution as an active process
  Establishes the conceptual framework of resolution as an active process — directly contextualises why identifying specific efferocytosis genes matters mechanistically rather than descriptively.
• Specialized pro-resolving mediators (SPMs)
  Covers the SPM arm of resolution biology that this study omits; comparing the two highlights the gap between gene-level efferocytosis findings and lipid-mediator-level resolution evidence in atherosclerosis.
• The omega-3 index as biomarker
  The omega-3 index as a systemic biomarker of resolution capacity offers a complementary, clinically deployable readout that could be tested alongside the six-gene diagnostic panel identified here.