Classification models of IA using neutrophil RNA expression
To our knowledge, this is the first to demonstrate IA prediction from RNA expression patterns in the blood. The prediction models we trained had a classification accuracy of up to 90% in the test dataset, a level that indicates promise for further investigation of RNA expression biomarkers for IA. Overall, classification by DLDA achieved the best performance in our data. This model consistently had the highest accuracy and AUC over multiple analyses, including cross-validation during model training (accuracy = 0.73, AUC = 0.73), independent model testing (accuracy = 0.90, AUC = 0.80) and cross-validation across the entire dataset (accuracy = 0.80, AUC = 0.84). See Additional file
1: Table S8 for a summary of the performance of all four models.
We suspect that DLDA outperformed the other methods because it best accounted for potential intersample variability of the 26 transcripts. Modeling techniques that broadly survey patterns of gene expression may afford better IA classification [
58,
59]. The DLDA method does this by (1) ranking transcripts by importance, giving more weight to the most consistently informative transcripts (unlike non-parametric approaches such as NN); and (2) using information from all transcripts to project test samples to the direction which best separates the classes. In this way, a linear combination of transcripts, which may be individually inconsistent, can generate a stable IA prediction and accommodate for potential intersample variability. Additionally, ignoring correlations between genes as DLDA does, provided a simple model and produced lower misclassification rates than more sophisticated classifiers, such as SVM.
In the current study, classifiers were developed based on 30 transcriptomes that were randomly selected from all available data (n = 40). Randomization was used so we could test the viability of IA prediction in patients who have potentially confounding covariates (comorbidities and demographics). Table
1 shows differences in smoking history between the IA and control groups in the training cohort, which may reflect an established association between the presence of an IA and smoking [
60]. It is worth noting that this study was designed differently from our previous investigation [
10]. There we identified an 82-transcript expression signature of IA by transcriptome profiling of a cohort-controlled group of patients, whereas in this study the randomly selected training cohort was not cohort-controlled. Yet, even with this difference, 10 of the 26 classifier transcripts (38%) were also a part of the previously discovered 82-transcript signature. These genes include
CYP1B1,
CD177,
ARMC12,
OLAH,
CD163,
ADTRP,
VWA8,
G0S2,
FCRL5, and
C1orf226. Notably, in qPCR validation on seven of these genes, six of them (
CYP1B1, ARMC12, OLAH, CD163, G0S2, and
FCRL5) showed consistent expression differences. These six transcripts may warrant further investigation as they may be most important for IA detection.
Biological role of classifier transcripts
The natural history of IA has been characterized by mounting inflammatory responses accompanied by progressive degradation of the aneurysmal wall [
61,
62]. This begins during aneurysm initiation, in which a combination of risk factors and hemodynamic stresses elicit pro-inflammatory changes in smooth muscle cells that lead to overproduction of matrix metalloproteinases (MMPs) that degrade the arterial extracellular matrix [
61,
62]. Once the aneurysmal sac forms, recirculating blood in the IA is conducive to inflammatory cell infiltration into the wall, which is also assisted by an increase of chemokines and cytokines in both the aneurysm wall and in the plasma within the aneurysm [
63,
64]. Recruited inflammatory infiltrates also produce MMPs that continue to degrade the aneurysm wall and advance its growth and rupture [
57,
62], which can ultimately occur when the wall is weakened to the point that it can no longer contain the force of the blood pressure [
61]. This is demonstrated by histological analyses and gene expression studies of human aneurysmal tissues, which have found increased matrix degradation proteins, inflammatory processes, and inflammatory cytokines and chemoattractant proteins in the walls of aneurysms [
65,
66].
Despite being the most abundant circulating immune cell, the role that neutrophils play in IA pathophysiology is relatively unknown. However, since neutrophils are recruited to sites of injury to coordinate the inflammatory response and attract inflammatory cells (monocytes) during other vascular pathologies [
67], we initially suspected they may play a similar role in IA. Studies suggest that neutrophils reside in intraluminal thrombi that have formed on the wall of the aneurysmal sac [
68,
69]. There, besides secreting MMP-9, activated neutrophils can release myeloperoxidase (MPO) and neutrophil gelatinase associated lipocalin (NGAL) that can indirectly promote extracellular matrix degradation and cytotoxicity. Elevated levels of MPO, a peroxidase enzyme secreted during degranulation, provoke pro-inflammatory cell signaling and oxidative stress via increased production of reactive oxygen species [
70]. Increased NGAL protects MMP-9 from degradation, thereby increasing its activity and promoting wall degeneration [
64]. Interestingly, levels of MPO and NGAL have been shown to be elevated in the peripheral blood of patients with IAs [
64,
71], which can act in an autocrine manner to promote activation of circulating neutrophils [
72,
73]. In this study, however, we did not observe significantly higher expression of the MPO or NGAL genes in circulating neutrophils, which suggests that the source of these circulating proteins could be the wall itself or other blood cells.
Our data shows that circulating neutrophils from patients with IA are peripherally activated. From gene ontology analysis on all 95 differentially expressed genes (q < 0.05), we found that they have dysregulated inflammatory and defense responses, and increased signaling and response to stimuli. Increased IL-1 signal transduction through receptors
IL1R1 and
IL1R2 has been shown to play a major role in neutrophil activation [
74‐
76]. Increased neutrophil activation was also evidenced through greater expression levels of several CD antigens, namely
CD36,
CD99L2,
CD163, and
CD177. Specifically,
CD177 is a marker of neutrophil activation that plays a role in migration [
77], and
CD99L2 is involved in neutrophil recruitment to inflamed tissues [
78]. These findings are consistent with the results of our previous cohort-controlled study [
10], which also showed increased peripheral leukocyte activation in neutrophils from IA patients.
In the subset of the 26 classifier transcripts, neutrophil activation was reflected through the roles of five genes (
CD177,
IL18R1,
PVRL2,
PDE9A, and
PTGES). Like
CD177,
IL18R1 has been shown to be involved in neutrophil activation as well as migration via IL-18 signaling [
79].
Nectin-
2 (
PVRL2), a membrane glycoprotein, is involved in cell adhesion [
80], and has been shown to have increased expression in the aneurysm wall tissue [
81]. Similarly,
PDE9A (a cGMP-specific phosphodiesterase) is also involved in cell adhesion [
80,
82] and, as demonstrated by Li et al. [
83], is regulated by two of the most active transcription factors in the IA tissue. Furthermore, lower
PTGES expression may be partially responsible for increasing the lifespan of neutrophils, because it is involved in p53-induced apoptosis [
84]. We suspect that capturing neutrophil activation responses involved in IA is the reason why the 26-transcript biomarker was able to detect IA.
Besides these five genes, nine other transcripts (
CD163, TGS1, CYP26B1, ADTRP, OCLN, OLAH,
C1QL1, FCRL5, and
IGSF23) in the 26-transcript classifier reflect complex inflammatory processes, albeit not specifically attributed to neutrophil activation. For example,
CD163, which is abundant in the walls of IAs (but typically associated with macrophages [
85,
86]), has been shown to be increased in neutrophils during sepsis [
87] and thus could be contributing to vascular inflammation in IA. Expression differences of other transcripts, like
TGS1 and
CYP26B1 (that are differentially expressed in tuberculosis [
88] and juvenile idiopathic arthritis [
12], respectively) could be related to neutrophil responses to intravascular perturbations during IA. Other transcripts—such as
ADTRP (expressed by macrophages in coronary artery plaques) [
89],
OCLN (increased in activated T-lymphocytes and in whole blood during sepsis) [
90,
91],
OLAH (increased in peripheral blood mononuclear cells during non-small cell lung cancer) [
92],
C1QL1 (a complement component decreed in glioblastoma multiform) [
93],
FCRL5 (an immunoglobulin receptor that regulates B cell response to antigen) [
94], and
IGSF23 (decreased during the inflammatory response associated with mycoestrogen exposure) [
95]—are involved in inflammation but have not been reported to be differentially expressed in neutrophils. The roles of the remaining model transcripts (e.g.,
C1orf226,
LOC100506229,
MTRNR2L10) in neutrophils are unknown. Further study into the roles of these transcripts in IA may be warranted, as they could represent novel predictive targets in neutrophil RNA expression.
Taken together, our results suggest that circulating neutrophils are peripherally activated in patients with IA, which leads to a change in their RNA expression profile. We postulate this activation could be caused by contact with inflamed aneurysm tissue that often contains denuded regions and plaque or thrombus [
96‐
98]. Alternatively, the activation may be caused by chemokines and cytokines released from the aneurysm. Chalouhi et al. [
63] demonstrated that blood inside IAs contain increased concentrations of the chemokines MCP-1, RANTES, MIG, IP-10, and exotoxin, and chemoattractant cytokines, including IL-8 and IL-17. Either of the above two scenarios may explain why expression differences of the 26 classifier transcripts were more pronounced in patients with larger IAs, since larger IAs provide greater luminal surface area for either contact or release of inflammatory mediators. It would be interesting to conduct a longitudinal study of patients with growing aneurysms to ascertain the relationship between aneurysm development and the effect on gene expression in circulating neutrophils.
Limitations
Due to our small sample size, the results in this study are rather preliminary. However, we have confidence in the discovered classifier transcripts for the following reasons. (a) The classifier identified patients with IA in an independent testing cohort with 90% accuracy. (b) qPCR confirmed expression differences in an independent validation cohort. (c) Our post hoc power analysis demonstrated > 0.78 power. In the future, we could further increase reliability in the model transcripts by decreasing the number of features in the data and increasing sample size.
Secondly, our classifier was derived from a population of patients who had different rates of demographic factors and comorbidities between aneurysm and control patients. It is unclear whether the presence of these confounding factors contributes to the differential neutrophil expression we detected. We are currently conducting research on larger cohorts by including multiple control groups with different vascular pathologies (including extracranial aneurysms such as abdominal aortic aneurysm) and immunological conditions (both of which were excluded in the current study) to narrow down transcripts specific to IA. Finally, although we collected basic demographics and information about comorbidities, including hypertension and diabetes, there could be others factors, such as the presence of immune-metabolism mediators in the blood that could affect gene expression in circulating neutrophils. Efforts should be made to collect higher fidelity patient health metadata.