Evidence map›Paper›PMID 39710725›Full record

ArticleDiabetology & metabolic syndrome2024

Unveiling the role of lipopolysaccharide-related genes in diabetic retinopathy: identification of key biomarkers and immune infiltration analysis.

Hongyi Sun, Shaohua Liu, Chao Wei

Abstract read
In one paragraph

Article in Diabetology & metabolic syndrome, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Hongyi SunDepartment of Ophthalmology, The Second Hospital, Cheeloo College of Medicine, Shandong University, No. 247, Beiyuan Street, Jinan City, Shandong Province, China.
Shaohua LiuDepartment of Ophthalmology, The Second Hospital, Cheeloo College of Medicine, Shandong University, No. 247, Beiyuan Street, Jinan City, Shandong Province, China.
Chao WeiDepartment of Ophthalmology, The Second Hospital, Cheeloo College of Medicine, Shandong University, No. 247, Beiyuan Street, Jinan City, Shandong Province, China. weichao24001@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGrowing evidence suggests a link between systemic lipopolysaccharide (LPS) exposure and worsening diabetic retinopathy (DR). This study aims to investigate DR's pathogenesis by analyzing LPS-related genes (LRGs) through bioinformatics.

methodsThe CTD database was utilized to identify LRGs. The datasets associated with DR were acquired from the GEO database. The Venn diagram was used to identify the differentially expressed LRGs (DLRGs), and the putative molecular mechanism of these DLRGs was investigated through functional enrichment analysis. We used WGCNA, Lasso regression, and RF to identify hub DLRGs. The expression levels of these hub DLRGs were validated in an independent dataset (GSE102485) and cell experiments. Employing the CIBERSORT algorithm, we examined the infiltration of 22 distinct immune cell types in DR and assessed the association between key DLRGs and immune infiltrates through correlation analysis.

resultsA total of 71 DLRGs were detected. These genes exhibited significant enrichment in pathways associated with inflammation. In addition, the in-depth analysis uncovered that five hub DLRGs (STK33 and EPHX2) linked to bacterial LPS displayed noteworthy diagnostic potential for individuals diagnosed with DR. The hub DLRGs expression in the high glucose-induced DR model was confirmed by qRT-PCR analysis. Furthermore, examination of immune infiltration indicated a significant association between these five genes and the extent of immune cell infiltration.

conclusionSTK33 and EPHX2 serve as biomarkers related to bacterial LPS. Exploring these genes in-depth could provide innovative ideas and a foundation for comprehending the progression of the disease and developing targeted treatments for DR.

Indexed as

Bacterial LPSDiabetic retinopathyDiagnostic biomarkersImmune cell infiltrationInflammation

Identifiers

PMID39710725
PMCPMC11665249

What Socratic holds

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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.