Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Journal of Translational Medicine

Hui Meng¹  Vincent M. Tutino²  Kerry E. Poppenberg³  Elad I. Levy⁴  Adnan H. Siddiqui⁴  Kenneth V. Snyder⁵  John Kolega⁶  Lu Li⁷  Lee Chaves⁸  Muhammad Waqas⁹  Armond June^1,10  Kaiyu Jiang^1,11  Yijun Sun^1,12  James N. Jarvis^1,13 

[1] Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, 875 Ellicott Street, 14214, Buffalo, NY, USA;Department of Biomedical Engineering, University of Buffalo, Buffalo, USA;Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Mechanical & Aerospace Engineering, University At Buffalo, Buffalo, NY, USA;Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, 875 Ellicott Street, 14214, Buffalo, NY, USA;Department of Biomedical Engineering, University of Buffalo, Buffalo, USA;Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, 875 Ellicott Street, 14214, Buffalo, NY, USA;Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, 875 Ellicott Street, 14214, Buffalo, NY, USA;Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, 875 Ellicott Street, 14214, Buffalo, NY, USA;Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, 875 Ellicott Street, 14214, Buffalo, NY, USA;Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Computer Science and Engineering, University of Buffalo, Buffalo, USA;Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Genetics, Genomics, and Bioinformatics Program, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Genetics, Genomics, and Bioinformatics Program, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Microbiology and Immunology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Genetics, Genomics, and Bioinformatics Program, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, Buffalo, USA;

关键词: Intracranial aneurysm;    Neutrophil;    Transcriptomics;    Machine learning;    Inflammation;    Prediction model;   

DOI  :  10.1186/s12967-020-02550-2

来源: Springer

【 摘 要 】

BackgroundIntracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods.MethodsNeutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction.ResultsFeature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance.ConclusionsWe improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

【 授权许可】

CC BY