{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T14:45:21Z","timestamp":1788878721976,"version":"build-2803163510"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"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> The precise diagnostic and prognostic biological markers were needed in immunotherapy for sepsis. Considering the role of necroptosis and immune cell infiltration in sepsis, differentially expressed necroptosis-related genes (DE-NRGs) were identified, and the relationship between DE-NRGs and the immune microenvironment in sepsis was analyzed.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> Machine learning algorithms were applied for screening hub genes related to necroptosis in the training cohort. CIBERSORT algorithms were employed for immune infiltration landscape analysis. Then, the diagnostic value of these hub genes was verified by the receiver operating characteristic (ROC) curve and nomogram. In addition, consensus clustering was applied to divide the septic patients into different subgroups, and quantitative real-time PCR was used to detect the mRNA levels of the hub genes between septic patients (SP) (<jats:italic>n<\/jats:italic> = 30) and healthy controls (HC) (<jats:italic>n<\/jats:italic> = 15). Finally, a multivariate prediction model based on heart rate, temperature, white blood count and 4 hub genes was established.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> A total of 47 DE-NRGs were identified between SP and HC and 4 hub genes (<jats:italic>BACH2<\/jats:italic>, <jats:italic>GATA3<\/jats:italic>, <jats:italic>LEF1<\/jats:italic>, and <jats:italic>BCL2<\/jats:italic>) relevant to necroptosis were screened out <jats:italic>via<\/jats:italic> multiple machine learning algorithms. The high diagnostic value of these hub genes was validated by the ROC curve and Nomogram model. Besides, the immune scores, correlation analysis and immune cell infiltrations suggested an immunosuppressive microenvironment in sepsis. Septic patients were divided into 2 clusters based on the expressions of hub genes using consensus clustering, and the immune microenvironment landscapes and immune function between the 2 clusters were significantly different. The mRNA levels of the 4 hub genes significantly decreased in SP as compared with HC. The area under the curve (AUC) was better in the multivariate prediction model than in other indicators.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> This study indicated that these necroptosis hub genes might have great potential in prognosis prediction and personalized immunotherapy for sepsis.<\/jats:p>","DOI":"10.3389\/fgene.2023.1158029","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T06:28:02Z","timestamp":1680762482000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning"],"prefix":"10.3389","volume":"14","author":[{"given":"Han","family":"She","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruibo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanlin","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibin","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinming","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.celrep.2016.03.037","article-title":"Vaccination with necroptotic cancer cells induces efficient anti-tumor immunity","volume":"15","author":"Aaes","year":"2016","journal-title":"Cell Rep."},{"key":"B2","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.1136\/gutjnl-2020-321767","article-title":"RIPK3 acts as a lipid metabolism regulator contributing to inflammation and carcinogenesis in non-alcoholic fatty liver disease","volume":"70","author":"Afonso","year":"2021","journal-title":"Gut"},{"key":"B3","doi-asserted-by":"publisher","first-page":"108495","DOI":"10.1016\/j.intimp.2021.108495","article-title":"Ethyl pyruvate attenuates isoproterenol-induced myocardial infarction in rats: Insight to TNF-alpha-mediated apoptotic and necroptotic signaling interplay","volume":"103","author":"Al-Botaty","year":"2022","journal-title":"Int. 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