{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T11:37:35Z","timestamp":1787225855661,"version":"build-2736575974"},"reference-count":65,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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. Genet."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed.<\/jats:p>","DOI":"10.3389\/fgene.2023.1264873","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T11:53:05Z","timestamp":1699876385000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning"],"prefix":"10.3389","volume":"14","author":[{"given":"Han","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enguang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jlr.2021.100090","article-title":"The lipid biology of sepsis","volume":"62","author":"Amunugama","year":"2021","journal-title":"J. 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