Academic Journal
Establishment of a Predictive Model for Mortality in Sepsis Patients Using WGCNA and Machine Learning Algorithms
| Τίτλος: | Establishment of a Predictive Model for Mortality in Sepsis Patients Using WGCNA and Machine Learning Algorithms |
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| Συγγραφείς: | Bai Y, Xu Y, Tan S, Huang Y, Huang X, Fu Z |
| Πηγή: | Journal of Inflammation Research, Vol 19, Iss Issue 1, Pp 1-13 (2026) |
| Στοιχεία εκδότη: | Dove Medical Press, 2026. |
| Έτος έκδοσης: | 2026 |
| Συλλογή: | LCC:Pathology LCC:Therapeutics. Pharmacology |
| Θεματικοί όροι: | sepsis, mortality, predictive model, genes, machine learning algorithms, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950 |
| Περιγραφή: | Yulong Bai,1,* Yubiao Xu,2,* Shihui Tan,1 Yisheng Huang,3 Xiaoxiang Huang,3 Zhaoyin Fu31Department of Emergency, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, People’s Republic of China; 2Department of General Surgery, Guangxi Medical University Kaiyuan Langdong Hospital, Nanning, 530022, People’s Republic of China; 3Department of Critical Care Medicine, The First People’s Hospital of Qinzhou, Qinzhou, 535009, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhaoyin Fu, Department of Critical Care Medicine, The First People’s Hospital of Qinzhou, No. 47 Qianjin Road, Qinzhou, 535009, People’s Republic of China, Email icudrfuzhaoyin2023@163.comBackground: Predicting mortality in sepsis patients remains a challenge in clinical settings. This study aimed to identify genes associated with sepsis-related mortality and determine the predictive value of a model using these death-related genes.Methods: We employed Weighted Gene Co-expression Network Analysis (WGCNA), differential expression analysis, and machine learning (ML) algorithms to identify genes related to sepsis mortality by analyzing the GSE185263 dataset. A predictive model was constructed base on the genes related to sepsis mortality and validated using external cohorts. Blood samples from 120 sepsis patients were collected to test the expression of key genes. The predictive value of the key genes was verified in clinical samples.Results: WGCNA results revealed that the turquoise module was related to sepsis mortality. Thirty-five death-related genes were identified by intersecting module genes with differentially expressed genes between survivor and non-survivor samples. Three ML algorithms identified TMTC1, AMOTL1, and PTGFR as key genes. The model constructed with these three genes showed moderate predictive value. External cohorts with a total of 523 samples validated the predictive value of the model at moderate to high levels. Clinical sample results indicated that all three genes were elevated in deceased sepsis patients, and the predictive value was moderate. Combining the model with the APACHE II score achieved high predictive accuracy.Conclusion: This study identified and validated three genes related to sepsis mortality and established a predictive model that performed well in distinguishing sepsis patients at high risk of death.Keywords: sepsis, mortality, predictive model, genes, machine learning algorithms |
| Τύπος εγγράφου: | article |
| Περιγραφή αρχείου: | electronic resource |
| Γλώσσα: | English |
| ISSN: | 1178-7031 |
| Relation: | https://www.dovepress.com/establishment-of-a-predictive-model-for-mortality-in-sepsis-patients-u-peer-reviewed-fulltext-article-JIR; https://doaj.org/toc/1178-7031 |
| Σύνδεσμος πρόσβασης: | https://doaj.org/article/db1a9f389aa24247bd16ee027c19b08a |
| Αριθμός Καταχώρησης: | edsdoj.b1a9f389aa24247bd16ee027c19b08a |
| Βάση Δεδομένων: | Directory of Open Access Journals |
| ISSN: | 11787031 |
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