Poppenberg Kerry E, Chien Aichi, Santo Briana A, Baig Ammad A, Monteiro Andre, Dmytriw Adam A, Burkhardt Jan-Karl, Mokin Maxim, Snyder Kenneth V, Siddiqui Adnan H, Tutino Vincent M
Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY 14203, USA.
Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, USA.
J Pers Med. 2023 Jan 31;13(2):266. doi: 10.3390/jpm13020266.
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 ( = 46) and testing cohorts ( = 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 -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 ( = 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.
检测后，确定哪些颅内动脉瘤（IA）会破裂至关重要。我们假设循环血液中的RNA表达反映IA的生长速率，作为不稳定性和破裂风险的替代指标。为此，我们对66例IA患者的血液样本进行了RNA测序，同时还计算了预测动脉瘤轨迹（PAT），这是一种量化IA未来生长速率的指标。我们使用PAT得分中位数将数据集分为更稳定和更可能快速生长的IA。然后将数据集随机分为训练组（n = 46）和测试组（n = 20）。在训练中，差异表达的蛋白质编码基因被确定为在至少50%的样本中表达（TPM > 0.5）、p值< 0.05（基于采用Benjamini-Hochberg校正的修正F统计量）且绝对倍数变化≥ 1.5的基因。使用Ingenuity通路分析构建基因关联网络并进行本体术语富集分析。然后使用MATLAB分类学习器在训练中采用5折交叉验证来评估差异表达基因的建模能力。最后，将模型应用于保留的独立测试组（n = 20）以评估其预测能力。总之，我们检查了66例IA患者的转录组，其中33个IA“生长”（PAT≥4.6），33个更“稳定”。将数据集分为训练组和测试组后，我们在训练中确定了39个差异表达基因（11个在“生长”IA中表达降低，28个表达增加）。模型基因很大程度上反映了机体损伤和异常以及细胞间信号传导和相互作用。使用子空间判别集成模型进行的初步建模在训练中的AUC为0.85，在测试中的AUC为0.86。总之，循环血液中的转录组表达确实可以区分“生长”和“稳定”的IA病例。由这些差异表达基因构建的预测模型可用于评估IA的稳定性和破裂潜力。
