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

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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.", "claims": [{"id": "claim-method", "statement": "Multivariate-adjusted logistic regression models were employed to estimate the association between baseline COVID-19 and incident POAG, reporting odds ratios (ORs) and 95% confidence intervals (CIs).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40652070", "locator": "sections[10] \"Statistical analysis\" paragraph 2", "quote": "Multivariate-adjusted logistic regression models were employed to estimate the association between baseline COVID-19 and incident POAG, reporting odds ratios (ORs) and 95% confidence intervals (CIs)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40652070", "locator": "sections[33] \"Data availability\" paragraph 2", "quote": "The data used to support this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Cross-sectional logistic regression and longitudinal Cox regression analyses consistently showed a significant positive association between COVID-19 and POAG risk, with adjusted odds ratios (ORs) ranging from 12.775 to 15.688 and hazard ratios (HRs) from 4.893 to 5.060 (all P < 0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "Cross-sectional logistic regression and longitudinal Cox regression analyses consistently showed a significant positive association between COVID-19 and POAG risk, with adjusted odds ratios (ORs) ranging from 12.775 to 15.688 and hazard ratios (HRs) from 4.893 to 5.060 (all P < 0.001)."}, "evidence": [{"asset": "source:pmid:40652070", "locator": "sections[13] \"Baseline analysis of having or not having primary open-angle glaucoma (POAG)\" paragraph 3", "quote": "Cross-sectional logistic regression and longitudinal Cox regression analyses consistently showed a significant positive association between COVID-19 and POAG risk, with adjusted odds ratios (ORs) ranging from 12.775 to 15.688 and hazard ratios (HRs) from 4.893 to 5.060 (all P < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Cross-sectional logistic regression and longitudinal Cox regression analyses consistently showed a significant positive association between COVID-19 and POAG risk, with adjusted odds ratios (ORs) ranging from 12.775 to 15.688 and hazard ratios (HRs) from 4.893 to 5.060 (all P < 0.001).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40652070"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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