Dataset Information

Circulating neutrophil transcriptome may reveal intracranial aneurysm signature.

ABSTRACT: Background: Unruptured intracranial aneurysms (IAs) are typically asymptomatic and undetected except for incidental discovery on imaging. Blood-based diagnostic biomarkers could lead to improvements in IA management. This exploratory study examined circulating neutrophils to determine whether they carry RNA expression signatures of IAs.

Methods: Blood samples were collected from patients receiving cerebral angiography. Eleven samples were collected from patients with IAs and 11 from patients without IAs as controls. Samples from the two groups were paired based on demographics and comorbidities. RNA was extracted from isolated neutrophils and subjected to next-generation RNA sequencing to obtain differential expressions for identification of an IA-associated signature. Bioinformatics analyses, including gene set enrichment analysis and Ingenuity Pathway Analysis, were used to investigate the biological function of all differentially expressed transcripts.

Results: Transcriptome profiling identified 258 differentially expressed transcripts in patients with and without IAs. Expression differences were consistent with peripheral neutrophil activation. An IA-associated RNA expression signature was identified in 82 transcripts (p<0.05, fold-change ?2). This signature was able to separate patients with and without IAs on hierarchical clustering. Furthermore, in an independent, unpaired, replication cohort of patients with IAs (n = 5) and controls (n = 5), the 82 transcripts separated 9 of 10 patients into their respective groups.

Conclusion: Preliminary findings show that RNA expression from circulating neutrophils carries an IA-associated signature. These findings highlight a potential to use predictive biomarkers from peripheral blood samples to identify patients with IAs.

SUBMITTER: Tutino VM

PROVIDER: S-EPMC5771622 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Circulating neutrophil transcriptome may reveal intracranial aneurysm signature.

Tutino Vincent M VM Poppenberg Kerry E KE Jiang Kaiyu K Jarvis James N JN Sun Yijun Y Sonig Ashish A Siddiqui Adnan H AH Snyder Kenneth V KV Levy Elad I EI Kolega John J Meng Hui H

PloS one 20180117 1

<h4>Background</h4>Unruptured intracranial aneurysms (IAs) are typically asymptomatic and undetected except for incidental discovery on imaging. Blood-based diagnostic biomarkers could lead to improvements in IA management. This exploratory study examined circulating neutrophils to determine whether they carry RNA expression signatures of IAs.<h4>Methods</h4>Blood samples were collected from patients receiving cerebral angiography. Eleven samples were collected from patients with IAs and 11 from ...[more]

PMID: 29342213

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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:BackgroundIntracranial aneurysm (IA) accounts for a substantial source of non-traumatic subarachnoid hemorrhage, with inflammation postulated as a potential factor in its pathogenesis. The present study aims at evaluating the association between circulating inflammatory cytokines and risk of IA under a bidirectional two-sample Mendelian randomization (MR) design.MethodsFor primary analysis, summary statistics of inflammatory regulators was obtained from a genome-wide association study (GWAS) comprising 8293 Finnish participants. Summary data of IA were extracted from a GWAS which comprised 7495 cases and 71,934 controls in European descent. For targeted analysis, summary statistics were extracted from two proteomic studies, which recruit 3301 and 5368 European participants, respectively. Summary data of IA were acquired from FinnGen study with 5342 cases and 342,673 controls. We employed inverse variance weighted (IVW) method as main approach, with sensitivity analyses using weighted median, MR-Egger, and MR-PRESSO methods. Reverse MR analyses were conducted to minimize bias from reverse causality.ResultsNo causation of cytokines with IA subtypes was identified in both primary and targeted analysis after Bonferroni correction. In primary analysis, vascular endothelial growth factor (VEGF) and fibroblast growth factor basic (bFGF) levels were suggestively associated with aneurysmal subarachnoid hemorrhage (aSAH) [VEGF → aSAH: OR = 1.15, 95%CI 1.04-1.26, P = 0.005; bFGF → aSAH: OR = 0.62, 95% CI 0.42-0.92, P = 0.02]. Statistical significance failed to replicate in targeted analysis. Instead, suggestive protective effects for aSAH were identified in FGF-9 (FGF-9 → aSAH: OR = 0.74, 95% CI 0.62-0.89, P = 0.001) and FGF-16 (FGF-16 → aSAH: OR = 0.84, 95% CI 0.72-0.97, P = 0.017). Furthermore, reverse analyses identified suggestive effect of unruptured IA on RANTES, MIF, GRO-alpha, FGF-16, and FGF-19. Result remained robust after applying sensitivity tests.ConclusionsNo causality of inflammatory biomarkers on the risk of IA subtypes was identified. Future large-scale studies are in need to evaluate the temporal dynamics of cytokines in conjunction with IA.

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

Circulating neutrophil transcriptome may reveal intracranial aneurysm signature.

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Circulating neutrophil transcriptome may reveal intracranial aneurysm signature.

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