{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T16:09:20Z","timestamp":1774973360508,"version":"3.50.1"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"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. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Apply bioinformatics combined with machine learning algorithms to screen potential biomarkers of neonatal sepsis (NS) and explore the correlation between biomarkers and immune cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Expression profiling data containing NS samples and control samples were downloaded from the Gene Expression Omnibus (GEO) database. First, the differentially expressed genes (DEGs) were found. Then gene set enrichment analysis (GSEA) was carried out to reveal the significantly enriched pathways, and the core genes within these pathways were aggregated. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to identify the gene modules significantly correlated with NS, from which the key genes were screened. The feature genes were selected by the use of three machine learning algorithms: least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and random forest cross-validation (RF-CV), and then the potential biomarkers of NS were identified by logistic regression analyses. The dataset was randomly split into training and test sets. A logistic regression model with biomarkers was constructed using the training set, and its diagnostic value was evaluated separately on both the training and test sets. In addition, immune infiltration analysis was performed using immune cell deconvolution algorithm CIBERSORT to explore the correlation between biomarkers and immune cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Overall, after overlapping the filtered DEGs, the core genes of GSEA, and the key genes of WGCNA, we detected 14 intersecting genes. Three machine learning algorithms further selected 4 feature genes, and logistic regression analyses identified ACSL1 and CD3D as potential biomarkers of NS. The classification accuracies of the logistic regression model were 0.883 and 0.902 on the training set and test set, respectively. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.961 and 0.966, and the calibration curves were close to the ideal curve. It should be noted that these AUC values were estimated within this discovery GEO cohort and require confirmation in independent external cohorts. Immune infiltration analysis showed significant changes (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.05) in the infiltration abundance of 13 immune cell types, and a strong correlation (&amp;gt;0.6) existed between ACSL1, CD3D and neutrophils, naive CD4\n                      <jats:sup>+<\/jats:sup>\n                      T cells, and CD8\n                      <jats:sup>+<\/jats:sup>\n                      T cells.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      ACSL1, CD3D are potential biomarkers of NS and may play an important role in the pathogenesis of NS by modulating immune cell functions such as neutrophils, naive CD4\n                      <jats:sup>+<\/jats:sup>\n                      T cells, and CD8\n                      <jats:sup>+<\/jats:sup>\n                      T cells. These conclusions should be regarded as exploratory biomarker discovery rather than diagnostic readiness.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1702073","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:02:41Z","timestamp":1774965761000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Bioinformatics combined with machine learning for potential biomarker screening and immune infiltration analysis in neonatal sepsis"],"prefix":"10.3389","volume":"14","author":[{"given":"Na","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Pediatrics, Wuhan Fourth Hospital","place":["Wuhan, Hubei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Tan","sequence":"additional","affiliation":[{"name":"Informatization Centre, Wuhan Vocational College of Software and Engineering","place":["Wuhan, Hubei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/S2213-2600(18)30063-8","article-title":"The global burden of paediatric and neonatal sepsis: a systematic review","volume":"6","author":"Fleischmann-Struzek","year":"2018","journal-title":"Lancet Respir Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1136\/archdischild-2020-320217","article-title":"Global incidence and mortality of neonatal sepsis: a systematic review and meta-analysis","volume":"106","author":"Fleischmann","year":"2021","journal-title":"Arch Dis Child"},{"key":"B3","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12889-023-15050-x","article-title":"Disease burden and attributable risk factors of neonatal disorders and their specific causes in China from 1990 to 2019 and its prediction to 2024","volume":"23","author":"Wu","year":"2023","journal-title":"BMC Public Health"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1307-14+1320","DOI":"10.14188\/j.1671-8852.2024.0628","article-title":"Trend and prediction analysis of the changing disease burden of neonatalsepsis in China and globally from 1990 to 2021","volume":"46","author":"Yin","year":"2025","journal-title":"Med J Wuhan Univ"},{"key":"B5","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1770","DOI":"10.1016\/S0140-6736(17)31002-4","article-title":"Neonatal sepsis","volume":"390","author":"Shane","year":"2017","journal-title":"Lancet"},{"key":"B7","doi-asserted-by":"publisher","first-page":"142","DOI":"10.14744\/SEMB.2020.00236","article-title":"Neonatal sepsis","volume":"54","author":"Odabasi","year":"2020","journal-title":"Sisli Etfal Hastan Tip Bul"},{"key":"B8","doi-asserted-by":"publisher","first-page":"749","DOI":"10.21037\/tp-22-369","article-title":"A narrative review of precision medicine in neonatal sepsis: genetic and epigenetic factors associated with disease susceptibility","volume":"12","author":"Dai","year":"2023","journal-title":"Transl Pediatr"},{"key":"B9","doi-asserted-by":"publisher","first-page":"m3672","DOI":"10.1136\/bmj.m3672","article-title":"Neonatal sepsis","volume":"371","author":"Kim","year":"2020","journal-title":"Br Med J"},{"key":"B10","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.clp.2021.03.012","article-title":"Biomarkers for the diagnosis of neonatal sepsis","volume":"48","author":"Cantey","year":"2021","journal-title":"Clin Perinatol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"931","DOI":"10.3760\/cma.j.cn112140-20240505-00307","article-title":"Expert consensus on diagnosis and management of neonatal bacteria sepsis (2024)","volume":"62","year":"2024","journal-title":"Chin J Pediatr"},{"key":"B12","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s40635-022-00445-8","article-title":"Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival","volume":"10","author":"Kosyakovsky","year":"2022","journal-title":"Intensive Care Med Exp"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1007\/s12519-023-00717-7","article-title":"Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric sepsis by integrating bioinformatics and machine learning","volume":"19","author":"Zhang","year":"2023","journal-title":"World J Pediatr"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1080\/14737159.2016.1222906","article-title":"Opportunities and technical challenges in next-generation sequencing for diagnosis of rare pediatric diseases","volume":"16","author":"Bacchelli","year":"2016","journal-title":"Expert Rev Mol Diagn"},{"key":"B15","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.jaci.2023.05.023","article-title":"Blood transcriptomic signature in type-2 biomarker-low severe asthma and asthma control","volume":"152","author":"Zeng","year":"2023","journal-title":"J Allergy Clin Immunol"},{"key":"B16","doi-asserted-by":"publisher","first-page":"i555","DOI":"10.1093\/bioinformatics\/bty271","article-title":"GSEA-InContext: identifying novel and common patterns in expression experiments","volume":"34","author":"Powers","year":"2018","journal-title":"Bioinformatics"},{"key":"B17","doi-asserted-by":"publisher","first-page":"135117","DOI":"10.1016\/j.ijbiomac.2024.135117","article-title":"DUSP6 protein action and related hub genes prevention of sepsis-induced lung injury were screened by WGCNA and Venn","volume":"279","author":"Song","year":"2024","journal-title":"Int J Biol Macromol"},{"key":"B18","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1186\/s12911-019-1004-8","article-title":"Comparing different supervised machine learning algorithms for disease prediction","volume":"19","author":"Uddin","year":"2019","journal-title":"BMC Med Inform Decis Mak"},{"key":"B19","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1167\/tvst.9.2.14","article-title":"Introduction to machine learning, neural networks, and deep learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"B20","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"Edoctor: machine learning and the future of medicine","volume":"284","author":"Handelman","year":"2018","journal-title":"J Intern Med"},{"key":"B21","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists","volume":"23","author":"Greener","year":"2022","journal-title":"Nat Rev Mol Cell Biol"},{"key":"B22","doi-asserted-by":"publisher","first-page":"D138","DOI":"10.1093\/nar\/gkad965","article-title":"NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update","volume":"52","author":"Clough","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"B23","doi-asserted-by":"publisher","first-page":"374","DOI":"10.3389\/fonc.2018.00374","article-title":"Prognostic genes of breast cancer identified by gene co-expression network analysis","volume":"8","author":"Tang","year":"2018","journal-title":"Front Oncol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"773","DOI":"10.4143\/crt.2020.974","article-title":"LASSO-based machine learning algorithm for prediction of lymph node metastasis in T1 colorectal cancer","volume":"53","author":"Kang","year":"2021","journal-title":"Cancer Res Treat"},{"key":"B25","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1186\/s12891-023-06303-2","article-title":"Determination of individual factors associated with hallux valgus using SVM-RFE","volume":"24","author":"Nakao","year":"2023","journal-title":"BMC Musculoskelet Disord"},{"key":"B26","doi-asserted-by":"publisher","first-page":"bbad002","DOI":"10.1093\/bib\/bbad002","article-title":"A review on longitudinal data analysis with random forest","volume":"24","author":"Hu","year":"2023","journal-title":"Brief Bioinform"},{"key":"B27","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1055\/a-2251-5686","article-title":"WHO-Leitlinie: versorgung von fr\u00fchgeborenen und neugeborenen mit niedrigem geburtsgewicht [WHO recommendations for care of the preterm or low-birth-weight infant]","volume":"86","author":"Ledinger","year":"2024","journal-title":"Gesundheitswesen"},{"key":"B28","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1038\/nmeth.3337","article-title":"Robust enumeration of cell subsets from tissue expression profiles","volume":"12","author":"Newman","year":"2015","journal-title":"Nat Methods"},{"key":"B29","doi-asserted-by":"publisher","first-page":"11358","DOI":"10.1038\/s41598-023-32992-9","article-title":"GPX4 Is a key ferroptosis biomarker and correlated with immune cell populations and immune checkpoints in childhood sepsis","volume":"13","author":"Qu","year":"2023","journal-title":"Sci Rep"},{"key":"B30","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1038\/s41467-021-27766-8","article-title":"Oxylipin metabolism is controlled by mitochondrial \u03b2-oxidation during bacterial inflammation","volume":"13","author":"Misheva","year":"2022","journal-title":"Nat Commun"},{"key":"B31","doi-asserted-by":"publisher","first-page":"2410","DOI":"10.3389\/fimmu.2019.02410","article-title":"Long-chain acyl-CoA synthetase 1 role in sepsis and immunity: perspectives from a parallel review of public transcriptome datasets and of the literature","volume":"10","author":"Roelands","year":"2019","journal-title":"Front Immunol"},{"key":"B32","doi-asserted-by":"publisher","first-page":"e34295","DOI":"10.1097\/MD.0000000000034295","article-title":"CD3D And CD247 are the molecular targets of septic shock","volume":"102","author":"Yang","year":"2023","journal-title":"Medicine (Baltimore)"},{"key":"B33","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1038\/nri3552","article-title":"Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy","volume":"13","author":"Hotchkiss","year":"2013","journal-title":"Nat Rev Immunol"},{"key":"B34","doi-asserted-by":"publisher","first-page":"0996","DOI":"10.34133\/research.0996","article-title":"Targeting matrix metalloproteinase-9 to alleviate T cell exhaustion and improve sepsis prognosis","volume":"8","author":"Wang","year":"2025","journal-title":"Research"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1007\/s10753-023-01803-8","article-title":"Single-cell sequence analysis combined with multiple machine learning to identify markers in sepsis patients: lILRA5","volume":"46","author":"Ning","year":"2023","journal-title":"Inflammation"},{"key":"B36","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1002\/mdr2.70010","article-title":"Comprehensive multi-omics integration reveals B cell-derived ELL2 as a novel diagnostic and prognostic biomarker in sepsis","volume":"1","author":"Wang","year":"2025","journal-title":"Med Res"},{"key":"B37","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/s10020-023-00634-5","article-title":"Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis","volume":"29","author":"Ning","year":"2023","journal-title":"Mol Med"}],"container-title":["Frontiers in Pediatrics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fped.2026.1702073\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:02:44Z","timestamp":1774965764000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fped.2026.1702073\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":37,"alternative-id":["10.3389\/fped.2026.1702073"],"URL":"https:\/\/doi.org\/10.3389\/fped.2026.1702073","relation":{},"ISSN":["2296-2360"],"issn-type":[{"value":"2296-2360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]},"article-number":"1702073"}}