A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications

• Published in: Journal of translational medicine 2023-09, Vol.21 (1), p.1-16, Article 620
• Main Authors: Zheng, Yongxin, Wang, Jinping, Ling, Zhaoyi, Zhang, Jiamei, Zeng, Yuan, Wang, Ke, Zhang, Yu, Nong, Lingbo, Sang, Ling, Xu, Yonghao, Liu, Xiaoqing, Li, Yimin, Huang, Yongbo
• Format: Article
• Language: English
• Subjects: 17β-Estradiol; Acetaminophen; Acute lung injury; Acute respiratory distress syndrome; Algorithms; Analysis; Biomarkers; Care and treatment; Clathrate compounds; Complications and side effects; Curcumin; Data mining; Datasets; Dexamethasone; Diagnosis; Drug development; Estrogen; Feature selection; Gene expression; Genetic aspects; Health aspects; Immune response; Immunosuppressive agents; Learning algorithms; Lungs; Machine learning; Methods; Mortality; Nomograms; Pathogenesis; Performance evaluation; Quality control; Quality management; Retinoic acid; Retinoids; Sepsis; Therapeutic targets; Transcriptome
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-023-04499-4

Background A significant proportion of septic patients with acute lung injury (ALI) are recognized late due to the absence of an efficient diagnostic test, leading to the postponed treatments and consequently higher mortality. Identifying diagnostic biomarkers may improve screening to identify septic patients at high risk of ALI earlier and provide the potential effective therapeutic drugs. Machine learning represents a powerful approach for making sense of complex gene expression data to find robust ALI diagnostic biomarkers. Methods The datasets were obtained from GEO and ArrayExpress databases. Following quality control and normalization, the datasets (GSE66890, GSE10474 and GSE32707) were merged as the training set, and four machine learning feature selection methods (Elastic net, SVM, random forest and XGBoost) were applied to construct the diagnostic model. The other datasets were considered as the validation sets. To further evaluate the performance and predictive value of diagnostic model, nomogram, Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) were constructed. Finally, the potential small molecular compounds interacting with selected features were explored from the CTD database. Results The results of GSEA showed that immune response and metabolism might play an important role in the pathogenesis of sepsis-induced ALI. Then, 52 genes were identified as putative biomarkers by consensus feature selection from all four methods. Among them, 5 genes (ARHGDIB, ALDH1A1, TACR3, TREM1 and PI3) were selected by all methods and used to predict ALI diagnosis with high accuracy. The external datasets (E-MTAB-5273 and E-MTAB-5274) demonstrated that the diagnostic model had great accuracy with AUC value of 0.725 and 0.833, respectively. In addition, the nomogram, DCA and CIC showed that the diagnostic model had great performance and predictive value. Finally, the small molecular compounds (Curcumin, Tretinoin, Acetaminophen, Estradiol and Dexamethasone) were screened as the potential therapeutic agents for sepsis-induced ALI. Conclusion This consensus of multiple machine learning algorithms identified 5 genes that were able to distinguish ALI from septic patients. The diagnostic model could identify septic patients at high risk of ALI, and provide potential therapeutic targets for sepsis-induced ALI. Keywords: Sepsis, Acute lung injury, Acute respiratory distress syndrome, Machine learning, Transcriptome