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Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

ABSTRACT: BACKGROUND:Intracranial 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. METHODS:Neutrophil 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. RESULTS:Feature 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. CONCLUSIONS:We 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.

SUBMITTER: Poppenberg KE

PROVIDER: S-EPMC7565814 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

Poppenberg Kerry E KE Tutino Vincent M VM Li Lu L Waqas Muhammad M June Armond A Chaves Lee L Jiang Kaiyu K Jarvis James N JN Sun Yijun Y Snyder Kenneth V KV Levy Elad I EI Siddiqui Adnan H AH Kolega John J Meng Hui H

Journal of translational medicine 20201015 1

<h4>Background</h4>Intracranial 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.<h4>Met ...[more]

PMID: 33059716

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Project description:BACKGROUND:Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA. METHODS:Neutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate?<?0.05, fold change???1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls). RESULTS:The training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve?=?0.80 and accuracy?=?0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort. CONCLUSIONS:Our findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.

Project description:BackgroundThe presence of an intracranial aneurysm is contraindicated to recombinant tissue plasminogen activator (r-tPA) treatment for acute ischemic stroke. However, it is difficult to exclude asymptomatic intracranial aneurysms by using conventional, noncontrast head computed tomography (CT), which is the only neuroimaging suggested before r-tPA. Recent case reports and series have shown that administering r-tPA to patients with a pre-existing aneurysm does not increase the bleeding risk. However, Asians are known to have a relatively higher bleeding risk, and little evidence is available regarding the risk of using r-tPA on Asian patients with intracranial aneurysms.MethodsMedical records from the Shuang Ho hospital stroke registration between July 2010 and December 2014 were retrospectively reviewed, and 144 patients received r-tPA. Unruptured intracranial aneurysms were detected using CT, or magnetic resonance or conventional angiography after r-tPA. The primary and secondary outcomes were the difference in overall intracranial hemorrhage (ICH) and symptomatic ICH after r-tPA. The differences were analyzed using Fisher's exact or Mann-Whitney U tests, and p < 0.05 was defined as the statistical significance.ResultsA total of 144 patients were reviewed, and incidental unruptured intracranial aneurysms were found in 11 of them (7.6%). No significant difference was observed in baseline demographic data between the aneurysm and nonaneurysm groups. Among patients with an unruptured aneurysm, two had giant aneurysms (7.7 and 7.4 mm, respectively). The bleeding risk was not significant different between aneurysm group (2 out of 11, 18%) with nonaneurysm group (7 out of 133, 5.3%) (p = 0.14). None of the patients with an unruptured aneurysm had symptomatic ICH, whereas one patient without an aneurysm exhibited symptomatic ICH.ConclusionsThe presence of an unruptured intracranial aneurysm did not significantly increase the risk of overall and symptomatic ICH in Taiwanese patients after they received r-tPA.

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Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

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Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm.

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