RESUMO
BACKGROUND: Mechanical ventilation may cause pulmonary hypertension in patients with acute lung injury (ALI), but the underlying mechanism remains elucidated. METHODS: ALI was induced in rabbits by a two-hit injury, i.e., hydrochloric acid aspiration followed by mechanical ventilation for 1 h. Rabbits were then ventilated with driving pressure of 10, 15, 20, or 25 cmH2O for 7 h. Clinicopathological parameters were measured at baseline and different timepoints of ventilation. RNA sequencing was conducted to identify the differentially expressed genes in high driving pressure ventilated lung tissue. RESULTS: The two-hit injury induced ALI in rabbits was evidenced by dramatically decreased PaO2/FiO2 in the ALI group compared with that in the control group (144.5 ± 23.8 mmHg vs. 391.6 ± 26.6 mmHg, P < 0.001). High driving pressure ventilation (20 and 25 cmH2O) significantly elevated the parameters of acute pulmonary hypertension at different timepoints compared with low driving pressure (10 and 15 cmH2O), along with significant increases in lung wet/dry ratios, total protein contents in bronchoalveolar lavage fluid, and lung injury scores. The high driving pressure groups showed more pronounced histopathological abnormalities in the lung compared with the low driving pressure groups, accompanied by significant increases in the cross-sectional areas of myocytes, right ventricular weight/body weight value, and Fulton's index. Furthermore, the expression of the genes related to ferroptosis induction was generally upregulated in high driving pressure groups compared with those in low driving pressure groups. CONCLUSIONS: A rabbit model of ventilation-induced pulmonary hypertension in ALI was successfully established. Our results open a new research direction investigating the exact role of ferroptosis in ventilation-induced pulmonary hypertension in ALI.
RESUMO
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.
