Dataset Information

Deep Proteomic Analysis for Non-Invasive Left Atrial Appendage Thrombus Prediction in Patients with Non-Valvular Atrial Fibrillation

ABSTRACT: Methods: Deep proteomic analysis on plasma from 8 non-valvular AF patients categorized into thrombus and control groups based on LAAT presence was conducted. Identified biomarkers were validated using enzyme-linked immunosorbent assay in a cohort of 179 patients, with clinical, transthoracic echocardiography, and transesophageal echocardiography data collected. A Lasso regression-based predictive model was constructed. Results: The Thrombus group had higher CHA2DS2-VASc scores, larger left atrial appendage thrombus (LAA) diameter, and lower LAA flow velocity. Deep proteomic analysis identified 30 differentially expressed proteins, with MYL4, PCYOX1, and DCN selected as diagnostic biomarkers (P<0.0001). The model included MYL4, PCYOX1, DCN, LAA cauliflower morphology, and CHA2DS2-VASc score. The model's area under the curve is 0.983. Additionally, net reclassification improvement and integrated discrimination improvement indices were also calculated, yielding values of 1.797 and 0.740, respectively, indicating a substantial improvement in risk prediction and classification over the conventional CHA2DS2-VASc model alone. Conclusions: Elevated MYL4, decreased PCYOX1 and DCN levels, and cauliflower LAA morphology are independent risk factors. Incorporating these variables into the prediction model improves the diagnostic performance of the LAAT prediction model, potentially offering a non-invasive and clinically valuable approach to stroke prevention. Keywords: Atrial Fibrillation; Thrombosis; Proteomics

ORGANISM(S): Homo Sapiens

SUBMITTER: Yang Li

PROVIDER: PXD047778 | iProX | Tue Dec 12 00:00:00 GMT 2023

REPOSITORIES: iProX

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Project description:Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction.

Dataset Information

Deep Proteomic Analysis for Non-Invasive Left Atrial Appendage Thrombus Prediction in Patients with Non-Valvular Atrial Fibrillation

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