Dataset Information

Whole blood transcriptome biomarkers of unruptured intracranial aneurysm.

ABSTRACT: BACKGROUND:The rupture of an intracranial aneurysm (IA) causes devastating subarachnoid hemorrhages, yet most IAs remain undiscovered until they rupture. Recently, we found an IA RNA expression signature of circulating neutrophils, and used transcriptome data to build predictive models for unruptured IAs. In this study, we evaluate the feasibility of using whole blood transcriptomes to predict the presence of unruptured IAs. METHODS:We subjected RNA from peripheral whole blood of 67 patients (34 with unruptured IA, 33 without IA) to next-generation RNA sequencing. Model genes were identified using the least absolute shrinkage and selection operator (LASSO) in a random training cohort (n = 47). These genes were used to train a Gaussian Support Vector Machine (gSVM) model to distinguish patients with IA. The model was applied to an independent testing cohort (n = 20) to evaluate performance by receiver operating characteristic (ROC) curve. Gene ontology and pathway analyses investigated the underlying biology of the model genes. RESULTS:We identified 18 genes that could distinguish IA patients in a training cohort with 85% accuracy. This SVM model also had 85% accuracy in the testing cohort, with an area under the ROC curve of 0.91. Bioinformatics reflected activation and recruitment of leukocytes, activation of macrophages, and inflammatory response, suggesting that the biomarker captures important processes in IA pathogenesis. CONCLUSIONS:Circulating whole blood transcriptomes can detect the presence of unruptured IAs. Pending additional testing in larger cohorts, this could serve as a foundation to develop a simple blood-based test to facilitate screening and early detection of IAs.

SUBMITTER: Poppenberg KE

PROVIDER: S-EPMC7647097 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Whole blood transcriptome biomarkers of unruptured intracranial aneurysm.

Poppenberg Kerry E KE Li Lu L Waqas Muhammad M Paliwal Nikhil N 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 Tutino Vincent M VM

PloS one 20201106 11

<h4>Background</h4>The rupture of an intracranial aneurysm (IA) causes devastating subarachnoid hemorrhages, yet most IAs remain undiscovered until they rupture. Recently, we found an IA RNA expression signature of circulating neutrophils, and used transcriptome data to build predictive models for unruptured IAs. In this study, we evaluate the feasibility of using whole blood transcriptomes to predict the presence of unruptured IAs.<h4>Methods</h4>We subjected RNA from peripheral whole blood of ...[more]

PMID: 33156839

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Project description: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.

Project description:The 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.Medical 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.A 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.The 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.

Project description:After detection, identifying which intracranial aneurysms (IAs) will rupture is imperative. We hypothesized that RNA expression in circulating blood reflects IA growth rate as a surrogate of instability and rupture risk. To this end, we performed RNA sequencing on 66 blood samples from IA patients, for which we also calculated the predicted aneurysm trajectory (PAT), a metric quantifying an IA's future growth rate. We dichotomized dataset using the median PAT score into IAs that were either more stable and more likely to grow quickly. The dataset was then randomly divided into training (n = 46) and testing cohorts (n = 20). In training, differentially expressed protein-coding genes were identified as those with expression (TPM > 0.5) in at least 50% of the samples, a q-value < 0.05 (based on modified F-statistics with Benjamini-Hochberg correction), and an absolute fold-change ≥ 1.5. Ingenuity Pathway Analysis was used to construct networks of gene associations and to perform ontology term enrichment analysis. The MATLAB Classification Learner was then employed to assess modeling capability of the differentially expressed genes, using a 5-fold cross validation in training. Finally, the model was applied to the withheld, independent testing cohort (n = 20) to assess its predictive ability. In all, we examined transcriptomes of 66 IA patients, of which 33 IAs were "growing" (PAT ≥ 4.6) and 33 were more "stable". After dividing dataset into training and testing, we identified 39 genes in training as differentially expressed (11 with decreased expression in "growing" and 28 with increased expression). Model genes largely reflected organismal injury and abnormalities and cell to cell signaling and interaction. Preliminary modeling using a subspace discriminant ensemble model achieved a training AUC of 0.85 and a testing AUC of 0.86. In conclusion, transcriptomic expression in circulating blood indeed can distinguish "growing" and "stable" IA cases. The predictive model constructed from these differentially expressed genes could be used to assess IA stability and rupture potential.

Dataset Information

Whole blood transcriptome biomarkers of unruptured intracranial aneurysm.

Publications

Whole blood transcriptome biomarkers of unruptured intracranial aneurysm.

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