Dataset Information

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability.

ABSTRACT: Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.

SUBMITTER: Zhang G

PROVIDER: S-EPMC10470306 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability.

Zhang Ge G Cui Xiaolin X Qin Zhen Z Wang Zeyu Z Lu Yongzheng Y Xu Yanyan Y Xu Shuai S Tang Laiyi L Zhang Li L Liu Gangqiong G Wang Xiaofang X Zhang Jinying J Tang Junnan J

iScience 20230809 9

Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a war ...[more]

PMID: 37664595

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Project description:Background and aimsAtherosclerosis is a major cause of heart attack and stroke. Inflammation plays a critical role in the development of atherosclerosis. Since the cytoplasmic adaptor molecule TRAF3IP2 (TRAF3-Interacting Protein 2) plays a causal role in various autoimmune and inflammatory diseases, we hypothesized that TRAF3IP2 mediates atherosclerotic plaque development.MethodsTRAF3IP2/ApoE double knockout (DKO) mice were generated by crossing TRAF3IP2(-/-) and ApoE(-/-) mice. ApoE(-/-) mice served as controls. Both DKO and control mice were fed a high-fat diet for 12 weeks. Plasma lipids were measured by ELISA, atherosclerosis by en face analysis of aorta and plaque cross-section measurements at the aortic valve region, plaque necrotic core area, collagen and smooth muscle cell (SMC) content by histomorphometry, and aortic gene expression by RT-qPCR.ResultsThe plasma lipoprotein profile was not altered by TRAF3IP2 gene deletion in ApoE(-/-) mice. While total aortic plaque area was decreased in DKO female, but not male mice, the plaque necrotic area was significantly decreased in DKO mice of both genders. Plaque collagen and SMC contents were increased significantly in both female and male DKO mice compared to respective controls. Aortic expression of proinflammatory cytokine (Tumor necrosis factor α, TNFα), chemokine (Chemokine (C-X-C motif) Ligand 1, CXCL1) and adhesion molecule (Vascular cell adhesion molecule 1, VCAM1; and Intercellular adhesion molecule 1, ICAM1) gene expression were decreased in both male and female DKO mice. In addition, the male DKO mice expressed markedly reduced levels of extracellular matrix (ECM)-related genes, including TIMP1 (Tissue inhibitor of metalloproteinase 1), RECK (Reversion-Inducing-Cysteine-Rich Protein with Kazal Motifs) and ADAM17 (A Disintegrin And Metalloproteinase 17).ConclusionsTRAF3IP2 plays a causal role in atherosclerotic plaque development and vulnerability, possibly by inducing the expression of multiple proinflammatory mediators. TRAF3IP2 could be a potential therapeutic target in atherosclerotic vascular diseases.

Project description:Vulnerable carotid atherosclerotic plaque (CAP) significantly contributes to ischemic stroke. Neovascularization within plaques is an emerging biomarker linked to plaque vulnerability that can be detected using contrast-enhanced ultrasound (CEUS). Computed tomography angiography (CTA) is a common method used in clinical cerebrovascular assessments that can be employed to evaluate the vulnerability of CAPs. Radiomics is a technique that automatically extracts radiomic features from images. This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package was used to extract radiomic features in Python. Machine learning algorithms containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP) were used to construct the models. The confusion matrix, receiver operating characteristic (ROC) curve, accuracy, precision, recall, and f-1 score were used to evaluate the performance of the models. A total of 74 patients with 110 CAPs were included. In all, 1,316 radiomic features were extracted, and 10 radiomic features were selected for machine-learning model construction. After evaluating several models on the testing cohorts, it was discovered that model_RF outperformed the others, achieving an AUC value of 0.93 (95% CI: 0.88-0.99). The accuracy, precision, recall, and f-1 score of model_RF in the testing cohort were 0.85, 0.87, 0.85, and 0.85, respectively. Radiomic features associated with the neovascularization of CAP were obtained. Our study highlights the potential of radiomics-based models for improving the accuracy and efficiency of diagnosing vulnerable CAP. In particular, the model_RF, utilizing radiomic features extracted from CTA, provides a noninvasive and efficient method for accurately predicting the vulnerability status of CAP. This model shows great potential for offering clinical guidance for early detection and improving patient outcomes.

Dataset Information

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability.

Publications

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability.

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