Epigenomic biomarkers insights in PBMCs for prognostic assessment of ECMO-treated cardiogenic shock patients

• Published in: Clinical epigenetics 2024-10, Vol.16 (1), p.137-14, Article 137
• Main Authors: Hsiao, Yi-Jing, Chiang, Su-Chien, Wang, Chih-Hsien, Chi, Nai-Hsin, Yu, Hsi-Yu, Hong, Tsai-Hsia, Chen, Hsuan-Yu, Lin, Chien-Yu, Kuo, Shuenn-Wen, Su, Kang-Yi, Ko, Wen-Je, Hsu, Li-Ming, Lin, Chih-An, Cheng, Chiou-Ling, Chen, Yan-Ming, Chen, Yih-Sharng, Yu, Sung-Liang
• Format: Article
• Language: English
• Subjects: Adult; Aged; Biomarkers - blood; Cardiogenic shock; Care and treatment; DNA Methylation - genetics; ECMO; Epigenesis, Genetic; Epigenome; Epigenomics - methods; Extracorporeal Membrane Oxygenation - methods; Female; Health aspects; Hospital Mortality; Humans; Kaplan-Meier Estimate; Leukocytes, Mononuclear - metabolism; Male; Methylation; Middle Aged; Mortality; Prognosis; Prognostic biomarker; Prospective Studies; ROC Curve; Shock, Cardiogenic - blood; Shock, Cardiogenic - genetics; Shock, Cardiogenic - therapy
• ISSN: 1868-7083; 1868-7075; 1868-7083
• DOI: 10.1186/s13148-024-01751-6

As the global use of extracorporeal membrane oxygenation (ECMO) treatment increases, survival rates have not correspondingly improved, emphasizing the need for refined patient selection to optimize resource allocation. Currently, prognostic markers at the molecular level are limited. Thirty-four cardiogenic shock (CS) patients were prospectively enrolled, and peripheral blood mononuclear cells (PBMCs) were collected at the initiation of ECMO (t0), two-hour post-installation (t2), and upon removal of ECMO (tr). The PBMCs were analyzed by comprehensive epigenomic assays. Using the Wilcoxon signed-rank test and least absolute shrinkage and selection operator (LASSO) regression, 485,577 DNA methylation features were analyzed and selected from the t0 and tr datasets. A random forest classifier was developed using the t0 dataset and evaluated on the t2 dataset. Two models based on DNA methylation features were constructed and assessed using receiver operating characteristic (ROC) curves and Kaplan-Meier survival analyses. The ten-feature and four-feature models for predicting in-hospital mortality attained area under the curve (AUC) values of 0.78 and 0.72, respectively, with LASSO alpha values of 0.2 and 0.25. In contrast, clinical evaluation systems, including ICU scoring systems and the survival after venoarterial ECMO (SAVE) score, did not achieve statistical significance. Moreover, our models showed significant associations with in-hospital survival (p