Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus

An underlying association between primary open-angle glaucoma (POAG) and COVID-19 has been hypothesized, but the causal link and shared mechanisms remain unclear. This study integrates epidemiological and bioinformatics approaches to investigate their relationship, aiming to identify common molecular pathways and validate clinical correlations. Epidemiological data from 3,015 participants in the CHARLS database were analyzed using multivariate logistic regression and Cox proportional hazards models to assess the association between COVID-19 and POAG, with stratification by gender, smoking status, and alcohol consumption. Concurrently, gene expression datasets from GEO (POAG: GSE27276; COVID-19: GSE171110, GSE152418) were used to identify 57 crosstalk genes (CGs) via differential expression analysis. Machine learning algorithms (LASSO, SVM-RFE, Random Forest) were applied to screen POAG diagnostic biomarkers from CGs, followed by construction of transcription factor (TF)-microRNA (miRNA)-protein-compound regulatory networks and consensus clustering to characterize COVID-19 immune microenvironment subtypes. Epidemiological analyses revealed that COVID-19 was an independent risk factor for POAG, with adjusted odds ratios (ORs) of 12.775-15.688 and hazard ratios (HRs) of 4.893-5.060 (all P < 0.001), with stronger associations in males (OR = 15.054, HR = 5.645) and smokers (OR = 29.828, HR = 9.323). HDL levels were significantly higher in POAG patients (P = 0.028), while vb001_s1 positivity was negatively associated with POAG (P < 0.001). Bioinformatics identified CGs enriched in inflammatory response, oxidative stress, and hypoxia pathways. Five potential biomarkers (CA12, ECRG4, CEBPD, HBB, HBA2) showed high diagnostic efficacy (AUC > 0.9) but limited specificity (expressed in uveitis and diabetic retinopathy). COVID-19 patients were stratified into three clusters, with Cluster B exhibiting hyperactive immune microenvironments characterized by elevated HLA class I/II and immune checkpoint genes (CTLA4, PDCD1LG2) in antigen-presenting cells and T cells. This study provides epidemiological and molecular evidence linking POAG to COVID-19, highlighting inflammation and immune dysregulation as shared mechanisms. Gender and smoking modify this association, warranting targeted POAG screening in male COVID-19 survivors and smokers. While identified biomarkers require specificity improvement, findings underscore the need for mechanistic studies (e.g., ACE2-mediated ocular injury, T-cell neuroinflammation) to validate causality and inform clinical management.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-07-12
DOI
https://doi.org/10.1038/s41598-025-10656-0
Primary Topic
Immune responses and vaccinations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus

Chungkeng Peng, Hu Long, Wanwen Su, Xin Hu
Scientific Reports
Immune responses and vaccinations
article

Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus

Chungkeng Peng, Hu Long, Wanwen Su, Xin Hu
article en

Abstract

An underlying association between primary open-angle glaucoma (POAG) and COVID-19 has been hypothesized, but the causal link and shared mechanisms remain unclear. This study integrates epidemiological and bioinformatics approaches to investigate their relationship, aiming to identify common molecular pathways and validate clinical correlations. Epidemiological data from 3,015 participants in the CHARLS database were analyzed using multivariate logistic regression and Cox proportional hazards models to assess the association between COVID-19 and POAG, with stratification by gender, smoking status, and alcohol consumption. Concurrently, gene expression datasets from GEO (POAG: GSE27276; COVID-19: GSE171110, GSE152418) were used to identify 57 crosstalk genes (CGs) via differential expression analysis. Machine learning algorithms (LASSO, SVM-RFE, Random Forest) were applied to screen POAG diagnostic biomarkers from CGs, followed by construction of transcription factor (TF)-microRNA (miRNA)-protein-compound regulatory networks and consensus clustering to characterize COVID-19 immune microenvironment subtypes. Epidemiological analyses revealed that COVID-19 was an independent risk factor for POAG, with adjusted odds ratios (ORs) of 12.775-15.688 and hazard ratios (HRs) of 4.893-5.060 (all P < 0.001), with stronger associations in males (OR = 15.054, HR = 5.645) and smokers (OR = 29.828, HR = 9.323). HDL levels were significantly higher in POAG patients (P = 0.028), while vb001_s1 positivity was negatively associated with POAG (P < 0.001). Bioinformatics identified CGs enriched in inflammatory response, oxidative stress, and hypoxia pathways. Five potential biomarkers (CA12, ECRG4, CEBPD, HBB, HBA2) showed high diagnostic efficacy (AUC > 0.9) but limited specificity (expressed in uveitis and diabetic retinopathy). COVID-19 patients were stratified into three clusters, with Cluster B exhibiting hyperactive immune microenvironments characterized by elevated HLA class I/II and immune checkpoint genes (CTLA4, PDCD1LG2) in antigen-presenting cells and T cells. This study provides epidemiological and molecular evidence linking POAG to COVID-19, highlighting inflammation and immune dysregulation as shared mechanisms. Gender and smoking modify this association, warranting targeted POAG screening in male COVID-19 survivors and smokers. While identified biomarkers require specificity improvement, findings underscore the need for mechanistic studies (e.g., ACE2-mediated ocular injury, T-cell neuroinflammation) to validate causality and inform clinical management.

Scientific ReportsVol. 15(1)
Panyu District Central Hospital (CN), Xi’an Jiaotong-Liverpool University (CN), Guangzhou Medical University (CN)
Zero hunger
Openalex Percentile: Top 81%
Immune responses and vaccinations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.