{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T04:05:03Z","timestamp":1793160303708,"version":"4.5.0"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Lacrimal gland enlargement is a common pathological change in patients with thyroid eye disease (TED). Tear fluid has emerged as a new source of diagnostic biomarkers, but tear-based diagnostic biomarkers for TED with high efficacy are still lacking.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>We aim to investigate genes associated with TED-associated lacrimal gland lesions. Additionally, we seek to identify potential biomarkers for diagnosing TED in tear fluid.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We obtained two expression profiling datasets related to TED lacrimal gland samples from the Gene Expression Omnibus (GEO). Subsequently, we combined the two separate datasets and conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on the obtained integrated dataset. The genes were employed for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The genes were intersected with the secretory proteins profile to get the potential proteins in the tear fluid. Machine learning techniques were then employed to identify optimal biomarkers and develop a diagnostic nomogram for predicting TED. Finally, gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted on screened hub genes to further elucidate their potential mechanisms in TED.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In our analysis of the integrated TED dataset, we identified 2,918 key module genes and 157 differentially expressed genes and finally obtained 84 lacrimal-associated key genes. Enrichment analysis disclosed that these 84 genes primarily pertain to endoplasmic reticulum organization. After intersecting with the secretory proteins, 13 lacrimal gland-associated secretory protein genes (LaSGs) were identified. The results from machine learning indicated the substantial diagnostic value of dyslexia associated gene (KIAA0319) and peroxiredoxin4 (PRDX4) in TED-associated lacrimal gland lesions. The two hub genes were chosen as candidate biomarkers in tear fluid and employed to establish a diagnostic nomogram. Furthermore, single-gene GSEA results and immune cell infiltration analysis unveiled immune dysregulation in the lacrimal gland of TED, with KIAA0319 and PRDX4 showing significant associations with infiltrating immune cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We uncovered the distinct pathophysiology of TED-associated lacrimal gland enlargement compared to TED-associated orbital adipose tissue enlargement. We have demonstrated the endoplasmic reticulum-related pathways involved in TED-associated lacrimal gland lesions and established a diagnostic nomogram for TED utilizing KIAA0319 and PRDX4 through integrated bioinformatics analysis. This contribution offers novel insights for non-invasive, prospective diagnostic approaches in the context of TED.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcell.2024.1486170","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T04:36:20Z","timestamp":1730262980000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Screening of pathologically significant diagnostic biomarkers in tears of thyroid eye disease based on bioinformatic analysis and machine learning"],"prefix":"10.3389","volume":"12","author":[{"given":"Xingyi","family":"Shu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengcheng","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruili","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"6422","DOI":"10.1109\/EMBC.2019.8857909","article-title":"Single-trial connectivity estimation through the least absolute shrinkage and selection operator","volume":"2019","author":"Antonacci","year":"2019","journal-title":"Annu. 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