Vincent M Tutino1,2,3,4,5, Kerry E Poppenberg6,7, Robert J Damiano6,8, Tatsat R Patel6,8, Muhammad Waqas6,7, Adam A Dmytriw9, Kenneth V Snyder6,7, Adnan H Siddiqui6,7,10, James N Jarvis11,12. 1. Canon Stroke and Vascular Research Center, 875 Ellicott Street, Buffalo, NY, 14214, USA. vincentt@buffalo.edu. 2. Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA. vincentt@buffalo.edu. 3. Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA. vincentt@buffalo.edu. 4. Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, USA. vincentt@buffalo.edu. 5. Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA. vincentt@buffalo.edu. 6. Canon Stroke and Vascular Research Center, 875 Ellicott Street, Buffalo, NY, 14214, USA. 7. Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA. 8. Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA. 9. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. 10. Department of Radiology, University at Buffalo, Buffalo, NY, USA. 11. Department of Pediatrics, University at Buffalo, Buffalo, NY, USA. 12. Genetics, Genomics, and Bioinformatics Program, University at Buffalo, Buffalo, NY, USA.
Abstract
BACKGROUND AND OBJECTIVE: Long non-coding RNAs (lncRNAs) may serve as biomarkers for complex disease states, such as intracranial aneurysms. In this study, we investigated lncRNA expression differences in the whole blood of patients with unruptured aneurysms. METHODS: Whole blood RNA from 67 subjects (34 with aneurysm, 33 without) was used for next-generation RNA sequencing. Differential expression analysis was used to define a signature of intracranial aneurysm-associated lncRNAs. To estimate the signature's ability to classify aneurysms and to identify the most predictive lncRNAs, we implemented a nested cross-validation pipeline to train classifiers using linear discriminant analysis. Ingenuity pathway analysis was used to study potential biological roles of differentially expressed lncRNAs, and lncRNA ontology was used to investigate ontologies enriched in signature lncRNAs. Co-expression correlation analysis was performed to investigate associated differential protein-coding messenger RNA expression. RESULTS: Of 4639 detected lncRNAs, 263 were significantly different (p < 0.05) between the two groups, and 84 of those had an absolute fold-change ≥ 1.5. An eight-lncRNA signature (q < 0.35, fold-change ≥ 1.5) was able to separate patients with and without aneurysms on principal component analysis, and had an estimated accuracy of 70.9% in nested cross-validation. Bioinformatics analyses showed that networks of differentially expressed lncRNAs (p < 0.05) were enriched for cell death and survival, connective tissue disorders, carbohydrate metabolism, and cardiovascular disease. Signature lncRNAs shared ontologies that reflected regulation of gene expression, signaling, ubiquitin processing, and p53 signaling. Co-expression analysis showed correlations with messenger RNAs related to inflammatory responses. CONCLUSIONS: Differential expression in whole blood lncRNAs is detectable in patients harboring aneurysms, and reflects expression/signaling regulation, and ubiquitin and p53 pathways. Following validation in larger cohorts, these lncRNAs may be potential diagnostic targets for aneurysm detection by blood testing.
BACKGROUND AND OBJECTIVE: Long non-coding RNAs (lncRNAs) may serve as biomarkers for complex disease states, such as intracranial aneurysms. In this study, we investigated lncRNA expression differences in the whole blood of patients with unruptured aneurysms. METHODS: Whole blood RNA from 67 subjects (34 with aneurysm, 33 without) was used for next-generation RNA sequencing. Differential expression analysis was used to define a signature of intracranial aneurysm-associated lncRNAs. To estimate the signature's ability to classify aneurysms and to identify the most predictive lncRNAs, we implemented a nested cross-validation pipeline to train classifiers using linear discriminant analysis. Ingenuity pathway analysis was used to study potential biological roles of differentially expressed lncRNAs, and lncRNA ontology was used to investigate ontologies enriched in signature lncRNAs. Co-expression correlation analysis was performed to investigate associated differential protein-coding messenger RNA expression. RESULTS: Of 4639 detected lncRNAs, 263 were significantly different (p < 0.05) between the two groups, and 84 of those had an absolute fold-change ≥ 1.5. An eight-lncRNA signature (q < 0.35, fold-change ≥ 1.5) was able to separate patients with and without aneurysms on principal component analysis, and had an estimated accuracy of 70.9% in nested cross-validation. Bioinformatics analyses showed that networks of differentially expressed lncRNAs (p < 0.05) were enriched for cell death and survival, connective tissue disorders, carbohydrate metabolism, and cardiovascular disease. Signature lncRNAs shared ontologies that reflected regulation of gene expression, signaling, ubiquitin processing, and p53 signaling. Co-expression analysis showed correlations with messenger RNAs related to inflammatory responses. CONCLUSIONS: Differential expression in whole blood lncRNAs is detectable in patients harboring aneurysms, and reflects expression/signaling regulation, and ubiquitin and p53 pathways. Following validation in larger cohorts, these lncRNAs may be potential diagnostic targets for aneurysm detection by blood testing.
Authors: Vincent M Tutino; Sarah Fricano; Kirsten Frauens; Tatsat R Patel; Andre Monteiro; Hamid H Rai; Muhammad Waqas; Lee Chaves; Kerry E Poppenberg; Adnan H Siddiqui Journal: Genes (Basel) Date: 2021-10-14 Impact factor: 4.141