Circulating MicroRNAs in Delayed Cerebral Infarction After Aneurysmal Subarachnoid Hemorrhage

15 Pages·2017·1.79 MB·English

ORIGINAL RESEARCH
Circulating MicroRNAs in Delayed Cerebral Infarction After
Aneurysmal Subarachnoid Hemorrhage
GangLu,MD,PhD;ManSzeWong,MSc;MarkZhiQiangXiong,PhD;ChiKwanLeung,PhD;XianWeiSu,PhD;JingYeZhou,PhD;WaiSang
Poon, MD; Vera Zhi Yuan Zheng, MSc; Wai Yee Chan, PhD; George Kwok Chu Wong, MD
Background-—Delayedcerebralinfarction(DCI)isamajorcauseofmorbiditiesafteraneurysmalsubarachnoidhemorrhage(SAH)
andtypicallystartsat day4to7afterinitialhemorrhage.MicroRNAs(miRNAs) playanimportantroleinposttranscriptionalgene
expression control, and distinctive patterns of circulating miRNA changes have been identified for some diseases. We aimed to
investigate miRNAs that characterize SAH patients with DCI compared with those without DCI.
MethodsandResults-—CirculatingmiRNAswerecollectedonday7afterSAHinhealthy,SAH-freecontrols(n=20),SAHpatients
with DCI (n=20), and SAH patients without DCI (n=20). We used the LASSO (least absolute shrinkage and selection operator)
methodofregressionanalysistocharacterizemiRNAsassociatedwithSAHpatientswithDCIcomparedwiththosewithoutDCI.In
the28dysregulatedmiRNAsassociatedwithDCIandSAH,wefoundthatacombinationof4miRNAs(miR-4532,miR-4463,miR-
1290,andmiR-4793)coulddifferentiateSAHpatientswithDCIfromthosewithoutDCIwithanareaunderthecurveof100%(95%
CI 1.000–1.000, P<0.001). This 4-miRNA combination could also distinguish SAH patients with or without DCI from healthy
controls with areas under the curve of 99.3% (95% CI 0.977–1.000, P<0.001) and 82.0% (95% CI 0.685–0.955, P<0.001),
respectively.
Conclusions-—We found a 4-miRNA combination that characterized SAH patients with DCI. The findings could guide
future mechanistic study to develop therapeutic targets. (J Am Heart Assoc. 2017;6:e005363. DOI: 10.1161/JAHA.116.
005363.)
Key Words: biomarker(cid:129)delayed cerebral infarction(cid:129)miRNA(cid:129)stroke(cid:129)subarachnoid hemorrhage
A
neurysmalsubarachnoidhemorrhage(SAH)accountsfor experience cognitive impairment and impaired quality of life
about 3% to 5% of stroke and is an important cause of and may not be able to return to their previous work.2–8
stroke in young populations, causing significant socioeco- Delayed cerebral infarction (DCI) occurs in up to 44% of
nomic burden worldwide.1 Up to two thirds of SAH patients SAHpatientsandtypicallystartsatday4to7aftertheinitial
hemorrhage.9–11DCIisawell-establishedandrelevantclinical
surrogate marker for neurological outcome after SAH.12
From the Division of Neurosurgery, Department of Surgery, Prince of Wales Numerous factors including age, initial neurological impair-
Hospital (G.L., J.Y.Z., W.S.P., V.Z.Y.Z., G.K.C.W.) and CUHK-SDU Joint
ment, intraventricular hemorrhage, SAH load, and aneurysm
Laboratory on Reproductive Genetics, School of Biomedical Sciences (G.L.,
size are reported to be associated with the development of
M.S.W.,W.Y.C.),TheChineseUniversityofHongKong,China;Bioinformatics
Unit,SDIVFR&DCentre,HongKongScienceandTechnologyParks,HongKong, DCI.13–16
China(M.Z.Q.X.,C.K.L.,X.W.S.). MicroRNAs (miRNAs) are a family of small (19–23 base
Accompanying Tables S1 and S2 are available at http://jaha.ahajournals.
pairs), noncoding, and deeply conserved RNA molecules that
org/content/6/4/e005363/DC1/embed/inline-supplementary-material-1.
regulate target gene expression at a posttranscriptional level
pdf
Correspondence to: George Kwok Chu Wong, MD, 4/F, Department of by inhibiting mRNA translation17 or destabilizing mRNA
Surgery, Lui Che Woo Clinical Sciences Building, Prince of Wales Hospital, molecules.18 There are 1881 miRNAs in human genomes,
30-32 Ngan Shing Street, Shatin, NT, Hong Kong, China. E-mail: 296 of which are currently annotated as high confidence,
[email protected]
according to miRBase 21.0.19 Circulating miRNAs have been
ReceivedJanuary7,2017;acceptedMarch17,2017.
demonstrated to be promising diagnostic or prognostic
ª 2017The Authors and SDIVFR&DCentre, NationalResearchCenter for
ARTandReproductiveGenetics.PublishedonbehalfoftheAmericanHeart biomarkersforcerebrovascularconditionssuchasmyocardial
Association,Inc.,byWiley.Thisisanopenaccessarticleunderthetermsof infarction,20 atherosclerotic diseases,21 stroke,22 cerebral
the Creative Commons Attribution-NonCommercial License, which permits infarction,23 hypertension,24 intracranial aneurysms,25–28
use,distributionandreproductioninanymedium,providedtheoriginalwork
isproperlycitedandisnotusedforcommercialpurposes. and SAH.29,30
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In the current study, we aimed to investigate miRNAs that medical problem (including no smoking history and hyperten- IG
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characterized SAH patients with DCI compared with those sion). Participant characteristics are shown in Table 1. A
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without DCI. R
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Delayed Cerebral Infarction A
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Methods DCI is defined as a new cerebral infarction identified on
computed tomography after exclusion of procedure-related
Patient Recruitment and Sample Collection
infarctions.3,12 Procedure-related infarction was defined as
The study was approved by the Joint NTEC-CUHK (New new hypodensity appearing on the posttreatment computed
Territories East Cluster-Chinese University of Hong Kong) tomography at 12 to 24 hours after aneurysm treatment. All
Clinical Research Ethics Committee, and written informed recruited patients had delayed computed tomography of the
consent was obtained from all participants or their next of brain at 2 to 3 weeks after presentation available for
kins.CirculatingmiRNAswerecollectedfromhealthycontrols assessment. The diagnoses of DCI were made by consensus
(n=20),SAHpatientswithDCI(n=20)atday7afterSAH,and of 2 neuroradiologists.
SAH patients without DCI (n=20) at day 7 after SAH. SAH
patients were recruited from Prince of Wales Hospital,
Quantitative Polymerase Chain Reaction
Chinese University of Hong Kong, Hong Kong Special
Administrative Region, People’s Republic of China, between Peripheral blood samples were obtained using EDTA tubes
2012 and 2013. Ruptured cerebral aneurysms were diag- with standard procedures. Samples were placed on ice
nosed by computer tomography angiography. For SAH immediately and centrifuged at 1000g for 15 minutes at
patients, the healthy controls (n=20) were recruited from 4°C.Plasmafractionwasaliquotedandstoredat(cid:1)80°C.RNA
the family members of SAH patients with the no major isolationandquantitativereal-timepolymerasechainreaction
wereperformed,asdescribedpreviously.29Briefly,serumwas
Table 1. Participant Characteristics
preparedbyadding20%wt/volCaCl (Sigma-Aldrich)intothe
2
plasma samples at a ratio of 1:100, followed by clotting
SAH
overnight and centrifugation. Total RNA was isolated from
SAHWith Without
Control(n=20) DCI(n=20) DCI(n=20) PValue pooledserumsamplesusingthemiRNeasySerum/PlasmaKit
Demographics following the manufacturer’s instructions (Qiagen). The quan-
Age,y 50(cid:3)17 59(cid:3)12 59(cid:3)11 tity of the extracted RNA was determined using a Nanodrop
2000 UV-Vis spectrophotometer (Thermo Scientific). The
Female,% 65(13) 45(9) 65(13)
results were analyzed with the Applied Biosystems SDS
WFNSgradeonadmission,%
software for Ct values and melting curve analyses. Of the 99
1–2 (cid:4)(cid:4)(cid:4) 60(12) 75(15) 0.311
possible deregulated miRNAs associated with SAH,29 we
3–5 (cid:4)(cid:4)(cid:4) 40(8) 25(5)
selected the top 20 differentially regulated miRNAs between
CTfeatureonadmission SAH patients with and without DCI; the top 8 miRNAs
Fishergrade (cid:4)(cid:4)(cid:4) 100(20) 100(20) 1.000 between SAH patients with DCI and healthy controls (2
3/4,% overlapped); and miR-132-3p and miR-324-3p, the 2 previ-
Riskfactors,% ously reported circulating miRNAs in SAH patients29 from a
Hypertension (cid:4)(cid:4)(cid:4) 55(11) 30(6) 0.200 totalof28uniquemiRNAsforexpressionanalysis.TableS1is
Smokers (cid:4)(cid:4)(cid:4) 10(2) 0(0) 0.487 the list of quantitative polymerase chain reaction primers for
miRNA expression profiling used in this study.
Outcome
Infarction,% (cid:4)(cid:4)(cid:4) 65(13) 0(0) <0.001*
mRS3 mo, (cid:4)(cid:4)(cid:4) 2 1 0.034* Statistical Analyses
median
Heat maps with hierarchical clustering and principal compo-
mRS3 mo, (cid:4)(cid:4)(cid:4) 45(9) 15(3) 0.082
nent analysis (PCA) were computed and visualized using the
>2
functions pHeatmap and prcomp, respectively, from the stats
mRS3 mo, (cid:4)(cid:4)(cid:4) 55(11) 85(17)
package for R version 3.0.1 (R Foundation for Statistical
≤2
Computing). The R package pROC was used to plot and
Dataare%(N),mean(cid:3)SD,ormedian.CTindicatescomputedtomography;DCI,delayed visualize receiver operating characteristic (ROC) curves to
cerebralinfarction;mRS3mo,modifiedRankinScale3-month;SAH,subarachnoid compute the area under the curve (AUC) and confidence
hemorrhage;WFNS,WorldFederationofNeurosurgicalSocieties.
*P<0.05. intervals to evaluate the performance of the miRNA-based
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classifier.31 Different R packages and functions were used to Table 2. Performance of Individual MicroRNAs in IG
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construct the 4 classification models to identify the miRNA- Subarachnoid Hemorrhage Subtypes Classification NA
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basedclassifiersthatcoulddifferentiateSAHpatientswithor R
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without DCI after SAH—the linear support vector machine (R miRNA AUC(95%CI) PValue SE
A
package e1071, function svm, kernel=“linear”), nonlinear miR-4532 0.9475(0.873–1.000) 1.80E-08 RC
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support vector machine (R package e1071, function svm, miR-4793 0.93(0.584–1.000) 2.14E-07
kernel=“radial”), linear discriminant analysis (R package miR-1290 0.9525(0.897–1.000) 3.54E-07
MASS,functionlda),andlogisticregression(Rpackagestats,
miR-421 0.9175(0.836–0.999) 3.24E-06
function glm, family=“binomial”)—as previously described.32
miR-4492 0.895(0.798–0.992) 7.26E-06
The R package glmnet was used to fit the LASSO (least
miR-574 0.8625(0.753–0.972) 2.70E-05
absolute shrinkage and selection operator) regression model
via penalized maximum likelihood to define the miRNA-based miR-4689 0.86(0.748–0.972) 2.87E-05
classifier.33 The value k=0.07765104 based on cross- miR-4449 0.855(0.741–0.969) 3.16E-05
validation, and t tests were used to compute the P value miR-93-5p 0.835(0.713–0.957) 9.31E-05
(Tables 1 and 2). A value of P<0.05 was regarded as miR-1268 0.795(0.648–0.942) 2.69E-04
statistically significant.
miR-4497 0.8175(0.685–0.950) 3.77E-04
miR-3178 0.7875(0.640–0.935) 6.71E-04
Results miR-4674 0.765(0.602–0.928) 7.94E-04
miR-297 0.81(0.671–0.949) 9.15E-04
Hierarchical Clustering of miRNAs Associated
miR-4463 0.815(0.677–0.953) 1.03E-03
With SAH With and Without DCI
miR-4690 0.7325(0.568–0.897) 5.38E-03
We performed unsupervised hierarchical clustering to graph- miR-4459 0.73(0.560–0.900) 9.75E-03
ically represent the averaged microarray expressions of the
miR-3651 0.705(534–0.876) 1.99E-02
28 miRNAs derived from the 3 cohorts for exploratory
miR-132 0.7125(0.547–0.878) 2.37E-02
analysis: healthy controls, SAH with DCI, and SAH without
miR-4433 0.67(0.497–0.843) 7.86E-02
DCI.Thedendrogramrepresentedwiththeheatmapshowing
the entire data matrix of the 3 cohorts indicated that the miR-339-5p 0.6625(0.490–0.835) 7.95E-02
expressionprofilesofthe28miRNAsamongthe3cohortsare miR-125a 0.66(0.483–0.837) 1.58E-01
distinctive,withclearclusterdissimilarity(resultsnotshown), miR-324 0.59875(0.419–0.779) 2.29E-01
and suggested that a subset of the 28 miRNAs could miR-4440 0.5675(0.385–0.750) 4.16E-01
characterize SAH patients with DCI from those without DCI.
miR-4454 0.5075(0.321–0.694) 7.66E-01
Wenextperformedquantitativepolymerasechainreaction
miR-27b 0.49(0.304–0.676) 8.62E-01
array analyses to quantify the expression levels of the 28
miR-27a 0.485(0.299–0.671) 9.24E-01
SAH-associatedmiRNAs(Figure 1A)andusedPCAforpattern
recognitiontoevaluatethedistanceconnectivityofthe2SAH miR-15a 0.525(0.339–0.711) 9.52E-01
subtypes (Figure 1B). PCA generated 2 distinct clusters with
AUCindicatesareaunderthereceiveroperatingcharacteristiccurve.
minimal overlap of the 2 SAH subtypes, highlighting the
possibility of characterizing the disease subtypes using a chainreaction–measuredexpressionlevelsintheSAHgroups
miRNA combination. with DCI and without DCI. The results are shown in Table 2.
The AUCs of 8 miRNAs can achieve >85% (P<0.0005), and 4
ofthem—miR-4532,miR-4793,miR-1290,andmiR-421—are
miRNAs That Characterized SAH With DCI >90%.FormiR-1290,theAUC,specificity,andsensitivitywere
Given the distinctive pattern of miRNA expression profile 95.3% (95% CI 0.897–1), 0.900, and 0.850, respectively, at a
between the 2 SAH subtypes, we initially evaluated the cutoff level of 8.653.
performanceofindividualmiRNAstocharacterizeSAHpatients We postulated that a combination of miRNAs would show
with and without DCI using ROC analysis.34 Based on the characteristicsforSAHwithversuswithoutDCI.Weexamined
sensitivity (true-positive rate) and specificity (false-positive each miRNA combination of the 28 SAH-associated miRNAs,
rate)atvariedthresholdlevels,theROCanalysisallowedusto based ontheir sensitivity, specificity, andaccuracy, using the
selectpossiblyoptimalmodelsforDCIdiscrimination. 4 different classifier algorithms: linear support vector
We plotted the ROC curves for individual miRNAs from a machine, nonlinear support vector machine, linear discrimi-
totalof28miRNAsaccordingtotheirquantitativepolymerase nant analysis, and logistic regression. The results are shown
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Figure 1. A, Quantitative polymerase chain reaction array of the 28 subarachnoid hemorrhage (SAH)-
associated microRNAs. The expression levels were normalized to the control and expressed as DDCt. B,
Principal componentanalysisof the2SAHsubtypes with or withoutdelayedcerebral infarction (DCI).
in Figure 2. All 3 readouts improved while increasing the accordingtotheLASSOregressionmodelandtheROCanalysis
number of miRNAs contained in each classifier. using the following formula: ln(Y/1(cid:1)Y)=(cid:1)4.29759355+
WenextperformedtheLASSOregressionanalysisinvolving miR-446390.16589606+miR-453290.43352951+miR-47939
penalizing the absolute size of the regression coefficients to 0.09569081+miR-129090.2602013. PCA analysis indicated
determine the accuracy using a combination of miRNAs to thatthe4-miRNAcombinationcouldproduce2nonoverlapping
distinguishSAHwithandwithoutDCI(Figure 3A).Theresults clusters to unambiguously differentiate SAH with and without
are shown in Figure 3B. ROC analysis indicated that the AUC DCI.
achieved 100% (95% CI 1–1, P<0.0001) using 4 miRNAs, Interestingly, the 4 miRNAs were strongly repressed in
namely, miR-4463, miR-4532, miR-4793, and miR-1290, SAH blood samples with DCI but activated in the non-DCI
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Figure 2. Four classification models, namely, linear support vector machine (L-SVM), nonlinear support
vector machine(non-L-SVM), lineardiscriminant analysis(LDA),andlogisticregression (LR)models, were
applied to construct microRNA (miRNA) classifiers. The performance of the miRNA-based classifiers was
determinedbyleave-one-outcross-validation. Thex-axis denotesthenumberofmiRNAscontainedinthe
classifier,whereasthey-axisindicatestheperformanceoftheclassifiersincludingsensitivity(greencircle),
specificity(bluesquare),andaccuracy(redtriangle).IneachgroupwiththesamemiRNAnumber,onlythe
classifier displaying thebestperformance ispresented.
samples compared with healthy controls (Figure 4). The demonstrated that a number of miRNAs could offer good to
biphasic expressions of these 4 miRNAs in the SAH patients excellentdiscriminatorypowerbetweenSAHwithandwithout
withorwithoutDCIalsoprovideddiscrimination withanAUC DCI (Table 2). LASSO regression analysis and PCA analysis
>80% by ROC analyses to differentiate the SAH patients with furtherdemonstratedthata4-miRNAcombination(miR-4463,
orwithoutDCIfromhealthycontrolswithAUCsof99.3%(95% miR-4532, miR-4793, and miR-1290) could produce 2
CI 0.977–1, P<0.0001) and 82.0% (95% CI 0.685–0.955, nonoverlapping clusters to unambiguously differentiate SAH
P<0.0005), respectively. The results supported the disease- with and without DCI, with an AUC of 100% (95% CI 1–1,
specific nature of the 4 miRNAs. P<0.0001) (Figure 3). In addition, the classifier can also
distinguish the healthy controls from each SAH subtype with
or without DCI (Figure 4), highlighting its disease specificity.
Discussion
Recently, studies of miRNAs in serum and cerebrospinal
In this study, both the heat map and PCA analysis suggested fluididentifiedbiomarkersassociatedwithoccurrenceofSAH,
that a combination of miRNAs could characterize SAH including miR-92a, let-7b, miR-204-5p, miR-223-3p, miR-337-
patients with DCI from those without DCI (Figure 1). We 5p,miR-451a,miR-489,miR-508-3p,miR-514-3p,miR-516-5p,
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Figure 3. A,LASSO(leastabsoluteshrinkageandselectionoperator)regressionanalysis,(B)thebox-and-
whiskerplot(upperpanel),receiveroperatingcharacteristic(ROC)curve(bottomleftpanel),andprincipal
component analysis (bottom right panel) of the 4 microRNAs (miR-4532, miR-4463, miR-1290, and miR-
4793-3p)inthesubarachnoidhemorrhagegroupswithandwithoutdelayedcerebralinfarction(DCI)inthe
classification algorithm. AUCindicates area underthecurve.
miR-548, miR-599, miR-937, miR-1224-3p and miR- couldbepotentialbiomarkersforSAH.29,35,36Styllisandothers
1301.29,35,36OurpreviousgenomewideserummiRNAexpres- investigated cerebrospinal fluid miRNAs in 10 SAH patients
sionprofilingalsosuggestedthatmiR-132-3pandmiR-324-3p with and 10 without cerebral vasospasm and identified miR-
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Figure 4. Box-and-whisker plots(left panel)andreceiveroperating characteristic curves(rightpanel)of
the 4 microRNAs (miR-4532, miR-4463, miR-1290, and miR-4793-3p) in (A) subarachnoid hemorrhage
(SAH)patientswithdelayedcerebralinfarction(DCI)vscontroland(B)SAHpatientswithoutDCI(non-DCI)
vscontrol, using theRpackage.AUC indicatesarea underthecurve.
27a-3p, miR-516a-5p, miR-566, and miR-1197 as potential pathways including Wnt signaling pathway, hedgehog and
biomarkers for cerebralvasospasm but not DCI.36 The identi- oxytocin signaling pathways. These pathways are broadly
fication of miRNAs associated with DCI after SAH has been involved in neurogenesis and nervous system development,
understudied, hindering understanding and development of suggesting that these 4 miRNAs may function as causative
therapeuticstrategiesforDCIafterSAH. factors in DCI pathology associated with SAH (Table S2).
Given that the downstream targets and functional speci- The4novelmiRNAs—miR-4463,miR-4532,miR-4793,and
ficities ofthe 4miRNAs (miR-4463, miR-4532,miR-4793and miR-1290—have not been associated with SAH pathology.
miR-1290) are uncharacterized, we performed gene-centric miR-4532 has been associated with breast cancer cell
analysistoidentifytheircommonanduniquepotentialtargets chemotherapy resistance.37 miR-4793 expression was ele-
using 3 algorithms: TargetScan, miRanda, and PITA. Consid- vated in liver metastases of sporadic colorectal cancer
erable overlaps of their downstream targets were shared by patients.38miR-1290hasbeenwidelyassociatedwithdifferent
these 4 miRNAs, inferring that these miRNAs may act on types of cancers including colorectal cancer, cervical cancer,
common regulatory pathways mediated by their shared non–small cell lung cancer, breast cancer, hepatocellular
targets for the development of DCI after SAH (Figure 5). carcinoma,gastriccancer,laryngealcarcinoma,lymphoblastic
KEGG (Kyoto Encyclopedia of Genes and Genomes) and gene leukemia, esophageal squamous cell carcinoma, lung adeno-
ontology analyses also consistently revealed that the targets carcinoma, prostate cancer, pancreatic cancer, and bladder
of these 4 miRNAs associated with multiple developmental carcinoma, as well as oral submucosal fibrosis, nonalcoholic
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mechanisticstudytofindtherapeutictargets.Fifth,therecould IG
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be a limitation in diagnosing DCI with computed tomography A
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hypodensity,asmagneticresonanceimagingwasnotroutinely R
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performedinourpatients.Sixth,validationwithanotherpatient E
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cohort would be warranted to confirm our findings. Seventh, RC
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cerebrospinal fluid samples were not collected for miRNA
analysisandshouldbeconsideredinfuturestudy.
Ourworkwasimportantasitprovidesanunderstandingof
thecharacteristicsofcirculating miRNAsassociated withDCI
afterSAH.The4miRNAsmightplaydistinctrolesinDCIafter
SAH. Further investigations into their roles might bring new
therapeutic targets for DCI after SAH. Future time-course
studies of miRNAs might also identify SAH patients at risk of
subsequent DCI development for stringent monitoring in
neurocritical care units and timely treatment.
Conclusions
Wefounda4-miRNAcombination(miR-4532,miR-4463,miR-
1290, and miR-4793) that characterized SAH patients with
DCI. The findings could guide future mechanistic study to
develop therapeutic targets.
Sources of Funding
This work was supported by the Chinese University of Hong
Kong Direct Grant for Research (MD11782) and a collabora-
tive funding from SDIVF R&D Centre.
Figure 5. A,Gene-centricanalysisoftheputativetargetsofthe
4microRNAs(miRNAs;miR-4532,miR-4463,miR-1290,andmiR-
4793-3p) using TargetScan, miRanda, and PITA. B, Topological
Disclosures
networkviewofthedownstreamtargetssharedbythe4miRNAs,
constructed byCytoscape. None.
fatty liver disease, and chronic rhinosinusitis.39–41 miR-4463 References
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