Dataset Information

Proteomics Study and Protein Biomarkers of Malignant Ventricular Arrhythmia in Acute Myocardial Infarction Patients

ABSTRACT: Acute myocardial infarction (AMI) is one of the leading causes of death. It is particularly important to predict malignant ventricular arrhythmia (MVA) timely and accurately in patients with AMI. This study aimed to explore biomarkers and to reveal possible mechanisms of MVA in AMI for clinical diagnosis. Blood samples from 190 human subjects were collected to reveal the differentially expressed proteins (DEPs) in three comparisons (AMI/control, AMI+MVA/control, and AMI+MVA/AMI) in proteomics with bioinformatics analysis and validated in new cohorts. The diagnostic value of candidate DEPs predicting AMI+MVA was also evaluated by the receiver operating characteristic curve (ROC). A total of 8,365 peptides and 460 proteins from the LC-MS/MS analysis were identified with 90 in AMI/control, 94 in AMI+MVA/control, and 43 in AMI+MVA/AMI identified as DEPs. TGFBI level had notable decreasing trend in AMI+MVA (161.9 ± 19.0 ng/ml) compared with AMI (P<0.0001) or control (P<0.0001). vWF level in AMI+MVA patients was significantly higher than that in AMI patients (P<0.01) and control (P<0.0001). ROC analysis showed that TGFBI and vWF had the strong and potential value of predicting MVA in AMI. By constructing proteomics profile to identify the protein characteristics this study found that decreased TGFBI and increased vWF are the characteristics of proteins in the MVA patients with AMI. These findings provide new insight into the diagnosis and pathogenesis of the development of MVA. Further, decreased TGFBI and increased vWF are potential predicting biomarkers for diagnosis of MVA in AMI patients.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Hai-Tao Hou

LAB HEAD: Hou Hai Tao

PROVIDER: PXD041535 | Pride | 2024-01-26

REPOSITORIES: Pride

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Proteomics study and protein biomarkers of malignant ventricular arrhythmia in acute myocardial infarction patients.

Zhang Jian-Liang JL Hou Hai-Tao HT Song Yu Y Guo Mu M Liu Xiao-Cheng XC Yang Qin Q He Guo-Wei GW

Clinical and translational medicine 20231101 11

PMID: 37962003

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Project description:Whole-genome gene expression analysis has been successfully utilized to diagnose, prognosticate, and identify potential therapeutic targets for cardiovascular disease. However, the utility of this approach to identify outcome-related genes and dysregulated pathways following first-time myocardial infarction (AMI) remains unknown and may offer a novel strategy to detect affected expressome networks that predict long-term outcome. Whole-genome microarray and targeted cytokine expression profiling on blood samples from normal cardiac function controls and first-time AMI patients within 48-hours post-MI revealed expected differential gene expression profiles enriched for inflammation and immune-response pathways in AMI patients. To determine molecular signatures at the time of AMI that could prognosticate long-term outcomes, transcriptional profiles from sub-groups of AMI patients with (n=5) or without (n=22) any recurrent events over an 18-month follow-up were compared. This analysis identified 559 differentially expressed genes. Bioinformatic analysis of this differential gene set for associated pathways revealed 1) increasing disease severity in AMI patients is associated with a decreased expression of the developmental epithelial-to-mesenchymal transition, and 2) modulation of cholesterol transport genes that include ABCA1, CETP, APOA1, and LDLR is associated with clinical outcome. In conclusion, differentially regulated genes and modulated pathways were identified that predicted recurrent cardiovascular outcomes in first-time AMI patients. This cell-based approach for risk stratification in AMI warrants a larger study to determine the role of metabolic remodeling and regenerative processes required for optimal outcomes. A validated transcriptome assay could represent a novel, non-invasive platform to anticipate modifiable pathways and therapeutic targets to optimize long-term outcome for AMI patients. The overall experimental design includes blood samples from 21 control and 31 myocardial infaction patient groups. Among the 31patients, 5 patients have recurrent events. Microarray were peformed on the blood samples and comparisons of control vs patient and patients with recurrent events vs patients without recurrent events were performed to identify differential genes related to disease or patients groups with recurrent events for the following bioinformatic analysis.

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