Related Experiment Video
Updated: May 13, 2025
Scanning Electron Microscopy of Macerated Tissue to Visualize the Extracellular Matrix
Published on: June 14, 2016
Comprehensive Plasma Proteomic Profiling Reveals Differentially Regulated Signaling Pathways Underlying Left
Heidi S Lumish1, Lorenzo R Sewanan1, Lusha W Liang1
1Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, 622 West 168 Street, PH 3 - 342, New York, NY, 10032, USA.
Insights
This study differentiated hypertrophic cardiomyopathy (HCM) from aortic stenosis (AS) using plasma proteomics and machine learning. Distinct signaling pathways, including Ras-MAPK, were identified in HCM, offering new insights into this genetic heart condition.
Area of Science:
- Cardiology
- Genetics
- Proteomics
Background:
- Hypertrophic cardiomyopathy (HCM) is a primary genetic myocardial disorder causing asymmetric left ventricular hypertrophy (LVH).
- Aortic stenosis (AS) leads to concentric LVH due to sustained pressure overload.
- Distinguishing the underlying molecular mechanisms of LVH in HCM versus AS is crucial for targeted therapies.
Purpose of the Study:
- To identify distinct signaling pathways in HCM compared to AS using plasma proteomic profiling.
- To develop and validate a machine learning model for differentiating HCM from AS based on proteomic data.
Main Methods:
- Plasma proteomic profiling was conducted on 76 HCM cases and 36 AS controls, matched for age and sex.
- A machine learning model was trained on 70% of the cohort and validated on the remaining 30% to predict HCM.
- Differential protein expression analysis was performed, followed by pathway analysis to identify regulated signaling cascades.
Main Results:
- The machine learning model achieved high accuracy in distinguishing HCM from AS (AUC = 0.90).
- Pathway analysis revealed significant differential regulation in Ras-MAPK, inflammatory, and metabolic signaling pathways between HCM and AS.
- Distinct proteomic signatures were identified that correlate with the specific etiologies of left ventricular hypertrophy.
Conclusions:
- Plasma proteomic profiling combined with machine learning can effectively differentiate HCM from AS.
- HCM is characterized by unique alterations in Ras-MAPK, inflammatory, and metabolic pathways compared to AS-induced LVH.
- These findings highlight distinct molecular underpinnings of LVH in genetic versus pressure-overload cardiomyopathies.
Abstract:
Hypertrophic cardiomyopathy (HCM) is the most common genetic myocardial disease, characterized by asymmetric left ventricular hypertrophy (LVH) due to sarcomeric mutations. Aortic stenosis (AS) results in concentric LVH, due to pressure overload. The aim of this study was to identify signaling pathways differentially regulated in HCM compared to AS, using plasma proteomic profiling. 76 HCM cases and 36 AS controls were matched by age and sex. A machine-learning (ML) model to predict HCM was built in the training set (70% cohort) and examined in the test set (30% cohort). Pathway analysis of proteins differentially expressed between HCM and AS was performed. The ML model accurately distinguished HCM from AS, with area under the receiver operating characteristic curve of 0.90 (95% CI: 0.79-1.00). Pathway analysis revealed differential regulation of Ras-MAPK, inflammatory and metabolic pathways. In conclusion, this study identified distinctive proteomic profiles and signaling pathways underlying LVH in HCM compared to AS.
