Evidence map›Paper›PMID 39085562›Full record

ArticleMolecular biotechnology2025

Identification of Key Efferocytosis-Related Genes and Mechanisms in Diabetic Retinopathy.

Yu Qian, Yanwen Jia

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Article in Molecular biotechnology, 2025. 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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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.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Yu QianDepartment of Ophthalmology, The First People's Hospital of Zhaoqing, 9 Donggang East Road, Zhaoqing, 526060, Guangdong, China. 13822657191@163.com.ORCID http://orcid.org/0009-0006-7386-1241
Yanwen JiaDepartment of Ophthalmology, Changzhou Second People's Hospital Affiliated Nanjing Medical University, Changzhou, 213004, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to explore the key efferocytosis-related genes in diabetic retinopathy (DR) and their regulatory mechanisms. Public DR-related gene expression datasets, GSE160306 (training) and GSE60436 (validation), were downloaded. Differentially expressed efferocytosis-related genes (DEERGs) were analyzed using differential expression analysis and weighted gene co-expression network analysis. Functional enrichment analysis was conducted. Moreover, efferocytosis-related signature genes were identified using machine learning analysis, and their expression levels and diagnostic value were analyzed. Furthermore, nomograms were constructed; immune cell infiltration was analyzed; and gene set enrichment analysis, transcriptional regulation analysis, and small-molecule drug (SMD) prediction of efferocytosis-related signature genes were performed. In total, 36 DEERGs were identified in DR, and were markedly enriched in multiple functions, such as visual system development. Through further machine learning analysis, two efferocytosis-related signature genes, Ferritin Light Chain (FTL) and Fc Gamma Binding Protein (FCGBP), were identified, and were found to be upregulated in DR samples and showed high diagnostic performance for DR. A nomogram constructed using FTL and FCGBP accurately predicted the risk of DR. Moreover, the level of infiltration of immature B cells was positively correlated with FTL and FCGBP expression levels. Multiple transcription factors (TFs), such as CCCTC-Binding Factor (CTCF) and KLF Transcription Factor 9 (KLF9), were found to interact with both FTL and FCGBP. In addition, FTL can be targeted by miRNAs, such as miR-22-3p, and FCGBP can be targeted by miR-7973. In addition, both FTL and FCGBP can be targeted by SMDs, such as bisphenol A. Key efferocytosis-related genes, such as FTL and FCGBP, may promote DR development. Detecting or targeting FTL and FCGBP may aid in the prevention, diagnosis, and treatment of DR.

Indexed as

Diabetic RetinopathyPhagocytosisEfferocytosisGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansMachine LearningNomogramsDiabetic retinopathyDiagnosisEfferocytosisMachine learningWeighted gene co-expression network analysis

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Registered trials

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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 Socratic graph.