elevatedhsa circrna 101015,hsa circrna 101211,and hsa...

Research ArticleElevated hsa_circRNA_101015 hsa_circRNA_101211 andhsa_circRNA_103470 in the Human Blood Novel Biomarkers toEarly Diagnose Acute Pancreatitis

Chang Liu 1 Xuan Zhu 23 Xing Niu 4 Lijie Chen 4 and Chunlin Ge 2

1Department of General Surgery First Affiliated Hospital of China Medical University No 155 Nanjing North StreetHeping District Shenyang 110001 Liaoning China2Department of Pancreatic and Biliary Surgery First Affiliated Hospital of China Medical UniversityNo 155 Nanjing North Street Heping District Shenyang 110001 Liaoning China3Institute of Translational Medicine ChinaMedical University No 77 Puhe Road Shenyang North New Area Shenyang 110122Liaoning China4Department of Second Clinical College ShengJing Hospital Affiliated to China Medical University No 36 Sanhao StreetHeping District Shenyang 110004 Liaoning China

Correspondence should be addressed to Chunlin Ge gechunlin126126com

Received 10 May 2019 Revised 24 October 2019 Accepted 18 December 2019 Published 18 February 2020

Academic Editor Kui Li

Copyright copy 2020 Chang Liu et al is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Objective To explore potential biomarkers to accurately diagnose patients with acute pancreatitis (AP) at early stage and toauxiliary clinicians implement the best treatment options Methods We selected 3 patients with AP and 3 healthy controls formicroarray analysis to obtain differentially expressed circular RNAs (circRNAs) To further verify the results of the microarrayanalysis the six differentially expressed circRNAs were confirmed by quantitative polymerase chain reaction (qPCR) e di-agnostic accuracy and sensitivity of differentially expressed circRNAs were assessed using the receiver operating characteristic(ROC) curve A ceRNA network was constructed based on the 6 differentially expressed circRNAs Results ere were 25upregulated circRNAs and 26 downregulated circRNAs in the blood of patients with AP Next the qPCR verification resultsfurther confirmed three downregulated circRNAs including hsa_circRNA_002532 has_circRNA_059665 and hsa_-circRNA_104156 and three upregulated circRNAs including hsa_circRNA_101015 hsa_circRNA_101211 and hsa_-circRNA_103470 Among them hsa_circRNA_101015 hsa_circRNA_101211 and hsa_circRNA_103470 increased with theseverity of the disease ROC analysis showed that the three circRNAmodels show promise to diagnose AP And a ceRNA networkrevealed that above six circRNAs could participate in complex regulated network Conclusions Elevated hsa_circRNA_101015hsa_circRNA_101211 and hsa_circRNA_103470 could be used as novel biomarkers to diagnose AP patients

1 Introduction

AP is one of the most common gastrointestinal diseasesand patients need hospitalization [1] Although mostpatients are mild about 20 of patients develop mod-erate or severe pancreatitis with surrounding pancreastissue necrosis or multiple organ failure [2ndash4] e overallmortality rate of AP patients is about 2 but close to 30in patients with persistent organ system failure An in-creased risk of pancreatic cancer in AP patients wasobserved in a nationwide population-based matching

cohort study [5] Furthermore 17ndash22 of AP patientshave the possibility of recurrence and 8ndash16 maydevelop chronic pancreatitis [6 7] erefore early di-agnosis is necessary for AP patients

As a pancreatic inflammatory disease AP has becomeone of the leading causes of gastrointestinal disease ad-mission in the United States and many other countriesAnd the incidence rate demonstrates an increasing trendere are many factors that cause the disease such asgallstones smoking and alcohol abuse [8 9] Of courseabout 10 of patients have no cause and they are

HindawiBioMed Research InternationalVolume 2020 Article ID 2419163 12 pageshttpsdoiorg10115520202419163

idiopathic pancreatitis e patients have usually asudden onset with severe persistent abdominal pain and80 of patients are accompanied by vomiting Pain mayradiate to the back usually in the lower chest areaerefore it is critical to carry out some treatmentswithin 48 hours such as fluid resuscitation analgesia andnutritional support [10] Despite the continuous im-provement in diagnostic techniques early diagnosis isstill difficult for clinicians

erefore it is critical to explore early diagnostic bio-markers of AP [11] More and more evidence has confirmedthat circRNAs could play an important role in many diseasesCircRNAs are produced by reverse splicing of precursormRNAs from exons of thousands of genes in eukaryotes [12]At the same time circRNAs can be secreted into blood salivaand other body fluids as potential biomarkers for diseaseprediction [13] However the function of most circRNAsremains largely unknown In the ceRNA mechanismmicroRNAs (miRNAs) are important posttranscriptionalregulators of gene expression that act on target sites in theuntranslated region ofmessenger RNA (mRNA) by direct basepairing e circRNA can act as a miRNA sponge to affect theactivity of miRNAs in the regulation of mRNA expression[14 15] At the same time circRNA abnormalities can lead to avariety of diseases For example the circMTO1 could inhibitthe progression of hepatocellular carcinoma by promoting p21expression acting as a sponge of oncogenic miR-9 [16]However to date no study has explored the expression ofcircRNAs in the blood of patients with APerefore our aimwas to explore accurate biomarkers and then diagnose patientswith AP as soon as possible by detecting biomarkers in thepatientsrsquo blood

2 Materials and Methods

21 Patient Information and Diagnostic Criteria Wereviewed 60 patients who were diagnosed with AP fromApril 2018 to September 2018 at the First Affiliated Hospitalof ChinaMedical University (Shenyang China) In additionwe recruited 30 subjects who underwent routine healthchecks at the First Affiliated Hospital of China MedicalUniversity and showed no signs of disease as a controlgroup According to the ldquoGuidelines for the diagnosis andtreatment of acute pancreatitis (2014)rdquo clinically meets 2 ofthe following 3 characteristics to diagnose AP (1) abdominalpain consistent with AP (2) serum amylase andor lipaseactivity is at least 3 times higher than the upper limit ofnormal (3) abdominal imaging examination is consistentwith AP imaging changes

In clinical treatment mild acute pancreatitis (MAP)patients receive only relatively simple treatment while se-vere acute pancreatitis (SAP) patients usually require in-tensive care erefore we selected 30 MAP and 30 SAPpatients to diagnose AP patients with early stage Currentlycommon scoring standards are the APPACHE II scoringstandard the MCTSI scoring standard and BISAP scoringstandard MAP diagnostic criteria were good response tofluid supplementation without organ failure and local orsystemic complications recovery within 1-2 weeks And

APACHE-II score lt8 points or the MCTSI score lt4 pointsor BISAP lt2 are MAP Diagnostic criteria for SAP were withpersistent organ failure (48 h or more) And APACHE-IIscore ge8 points or MCTSI score ge4 points or BISAP ge2 areSAP

e exclusion criteria were as follows lt18 years of agepregnant and lactating women taking anticoagulant drugsblood system diseases tumors liver disease and gastroin-testinal bleeding patientse patientsrsquo information is shownin Table 1

en we selected 3 patients with MAP and 3 healthyparticipants for microarray analysise study was approvedby the Ethics Committee of the First Affiliated Hospital ofChina Medical University and informed consent was ob-tained from all subjects Figure 1 depicts further experi-mental design details

22 RNA Extraction and CircRNA Microarray AnalysisWe collected whole blood from 3 MAP patients and 3healthy participants Total RNA from each sample wasquantified using a NanoDrop ND-1000 (NanoDropWilmington DE USA) Sample preparation andmicroarray hybridization were performed based on thestandard protocol of Arraystar Briefly total RNA wasdigested with Rnase R (Epicenter Inc) to remove linearRNA and enrich for circular RNA en the enrichedcircular RNA was amplified by a random priming method(Arraystar Super RNA Labeling Kit Arraystar) andtranscribed into fluorescent cRNAe labeled cRNA washybridized to Arraystar Human circRNA Array V2(8 times15 K Arraystar) e labeled cRNA was purified byRNeasy Mini Kit (Qiagen) e concentration and spe-cific activity of the labeled cRNA (pmol Cy3μgcRNA)were measured by NanoDrop ND-1000 (NanoDropWilmington DE USA) 1 μg of each labeled cRNA wasfragmented by adding 5 μl of 10times blocking agent and 1 μlof 25times fragmentation buffer then the mixture was heatedat 60degC for 30 minutes and finally 25 μl of 2times hybrid-ization buffer was added to dilute the labeled cRNA 50 μlof the hybridization solution was dispensed into a spacerslide and assembled onto a circRNA expression micro-array slide Slides were incubated for 17 hours at 65degC inAgilent Hybridization Oven e hybridization array wasfixed and scanned using an Agilent Scanner G2505Cwash

Agilent Feature Extraction software (version 11011)was utilized to analyze the acquired array images Quantilenormalization and subsequent data processing were per-formed using the R software limma package (R version312)

23 Differential Expression Analysis A scatter plot is avisualization method used to assess circRNA expressionvariation Differentially expressed circRNAs with statis-tical significance (FC ge 15 and P values le005) wereidentified utilizing fold change cutoffs and volcano plotsrespectively Among them P value was calculated uti-lizing the unpaired t-test Differentially expressed

2 BioMed Research International

circRNAs between the two samples were identified by foldchange filtration Hierarchical clustering was performedto show the distinguishable circRNA expression patternsin the samples Shanghai Kangcheng Biological Engi-neering Co Ltd of the Peoplersquos Republic of Chinaconducted microarray work

24 qPCR Verification Total RNA was extracted from thewhole blood of 30 healthy participants 30 MAP patientsand 30 SAP patients according to standard proceduresTotal RNAwas reverse-transcribed into cDNA kits (RochePenzberg Germany) using random primers and Tran-scriptor First Strand cDNA Synthesis according to themanufacturerrsquos instructions 6 differentially expressedcircRNAs were measured by qPCR using a ViiA 7 Real-time PCR System (Applied Biosystems) e reactionconditions were as follows 95degC for 10 minutes and 40cycles of 95degC for 10 seconds 60degC for 60 seconds RNAlevels were normalized to human β-actin All qPCR

reactions were performed in triplicate Different primerswere designed for circRNAs rather than the more com-monly used convergent primers (Figure 2) Table 2 lists allthe primers

25 Statistical Analysis Statistical analyses were per-formed using SPSS (version 190 IBM Armonk NYUnited States) and GraghPad Prism (version 70GraphPad Software La Jolla CA United States) erelative expression level of each circRNA was expressedby fold change and converted into 2minus ΔΔCt e expressiondifference in circRNAs among SAP MAP patients andhealthy individuals and between MAP patients andposttreatment serum samples was assessed using the t-test To assess the diagnostic value ROC curve wasestablished e cut-off value of each circRNA was an-alyzed by using SPSS software Area under the ROCcurves (AUCs) were calculated to evaluate the ability ofthe differentially expressed circRNAs Due to the relative

Table 1 Demographic and clinical characteristics of the MAP patients SAP patients and the control group

Characteristic Control (n 30) MAP (n 30) SAP (n 30) Pa Pb

Age (y) 4801plusmn 1267 4826plusmn 1524 4921plusmn 1032FM (n) 1515 1218 1317MCTSI 14plusmn 093 498plusmn 104 lt0001 lt0001apache-ii 309plusmn 294 987plusmn 401 lt0001 lt0001BISAP 091plusmn 069 264plusmn 101 lt0001 lt0001aSAP versus control bSAP versus MAP

Microarray analysis of differentiallyexpressed circRNAs by 3 AP patients vs

3 healthy individuals

qPCR verification of 6 differentiallyexpressed circRNAs

ROC analysis of hsa_circRNA_101015hsa_circRNA_101211 and

hsa_circRNA_103470 in MAP patientsvs control group

The construction of ceRNA network by6 differentially expressed circRNAs

Box plot

Scatter plot

Volcanomap

The expression levels of hsa_circRNA_002532hsa_circRNA_059665 and hsa_circRNA_104156

in MAP and healthy individuals


in MAP and MAP after treatment


in healthy human MAP and SAP patients

Figure 1 A flow chart of the experimental design

BioMed Research International 3

small sample size for ROC analysis the statistical powerwas calculated using PASS (version 150) under thefollowing conditions α 005 AUC0 05 and n 30 P

value lt 005 was considered statistically significant

26 =e Construction of ceRNA Network By combiningcotargeted miRNAs we constructed a ceRNA network bycytoscape package of R language [17 18] In addition tomeasuring the number of common miRNAs each ceRNApair was subjected to a hypergeometric test which wasdefined by four parameters (i) N is the total number ofmiRNAs used to predict the target (ii) K is the e numberof miRNAs interacting with the selected gene (iii) n is thenumber of miRNAs that interact with the candidate ceRNAof the selected gene (iv) the miRNA number commonbetween the two genes [19] is test used the followingformula to calculate the P value

P 1113944

min(Kn)

ic

K

i

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦N minus K

n minus i

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦

N

n

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦

(1)

3 Results

31 Identification of Differentially Expressed CircRNAs Toinvestigate the expression profile of circRNAs in AP weused microarray analysis to perform circRNA expressionprofiling in the blood of patients with MAP and matchednormal humans e box plot visualized the dataset dis-tribution of circRNAs After normalization Figure 3(a)depicts that the log 2 ratios in the three pairs of samples arealmost identical At the same time we used a scatter plot of

ExonExon

Exon

F

R

Figure 2 Schematic representation of polymerase chain reaction primers for specific detection of circular transcripts Different polymerasechain reaction primers were designed for circRNA rather than the more commonly used convergent primers

Table 2 Primers for circRNA and mRNA levels in qPCR

Target ID Primer sequence 5prime-3prime Product size in bp

β-actin (human) F 5primeGTGGCCGAGGACTTTGATTG 3primeR 5prime CCTGTAACAACGCATCTCATATT 3prime 73

hsa_circRNA_002532 F 5primeTGGGAGTTTTCTGCTGATGAT 3primeR 5prime GGGTTTCTTTCTCATCTCTCTCA 3prime 119

hsa_circRNA_101015 F 5prime TATTGCCTTAGATCCTTCAAGTG 3primeR 5prime TAGCATCAACCAATCGCAAGT 3prime 78

hsa_circRNA_101211 F 5prime TGGTGGACGCATTTTCAGC 3primeR 5prime TCAGCACTTTGGTTAATCTTTCA 3prime 87

hsa_circRNA_103470 F 5prime TAACACGCTGGCCCATTACAAG 3primeR 5prime GTCCAGGTCCCGAAGGATGTAG 3prime 69

hsa_circRNA_059665 F 5prime AGACGCCTCCAGATGCCCTT 3primeR 5prime ACCAGACTGCAGGGACGGTGT 3prime 103

hsa_circRNA_104156 F 5prime AGAACTTCCTCGCACTGTGA 3primeR 5prime GGGAGCTATGTGAACGAACG 3prime 83


the circRNA expression profile to evaluate the changebetween the two groups (Figure 3(b)) where the values ofthe X and Y axes are the average normalized signal values(log 2 scaling) of the sample set And the green line is thefold line and the circRNA above the top green line andbelow the bottom green line indicates that the circRNAchange between the two samples is more than 15 timesNext volcano maps were used to identify differentiallyexpressed circRNAs that were statistically significant

between the two groups and are shown in Figure 3(c)Among them the vertical lines correspond to 15 times upand down and the horizontal lines represent P 005 ered dot indicates upregulated circRNAs while blue rep-resents downregulated circRNAs We found 51 differen-tially expressed circRNAs in patients with MAP (FC ge 15and P values le005) compared with normal subjectsAmong them Table 3 shows 25 upregulated circRNAs and26 downregulated circRNAs are described in Table 4 Next

4

6

8

10

12

14

16

18N

orm

aliz

ed in

tens

ity v

alue

s

Zheng1Zheng3Zheng2Test3Test1 Test2

Samples

(a)

4

6

8

10

12

14

16

Gro

up-te

st (n

orm

aliz

ed)

10 1264 16148

Group-control (normalized)

(b)

0

1

2

3

4

5

ndashlog

10 (P

val

ue)

4ndash2ndash4 20

log2 (fold change)Test vs control

(c)

Figure 3 Overview of microarray features (a) Box plots are datasets that quickly visualize circRNAs spectra (zheng normal personrsquos bloodtest blood of patients with acute pancreatitis) (b) Scatter plot showing circRNA expression variation between blood samples from patientswith AP and matched normal persons (c) Volcano maps of differentially expressed circRNAs

Table 3 25 upregulated in patients with AP (FDR false discover rate FC fold change)

CircRNA Alias P value FDR FC (abs) Chrom best_transcript Gene symbolhsa_circRNA_001747 hsa_circ_0000246 0014721747 0999852393 15883102 chr10 uc009xqp1 MCUhsa_circRNA_000761 hsa_circ_0000761 0032659244 0999852393 16521444 chr17 NM_032875 FBXL20hsa_circRNA_092519 hsa_circ_0001233 0031884015 0999852393 16043929 chr22 NM_020831 MKL1hsa_circRNA_102674 hsa_circ_0053932 0004725614 0999852393 15060131 chr2 NM_000627 LTBP1hsa_circRNA_020354 hsa_circ_0020354 0016055616 0999852393 15389787 chr10 NM_001329 CTBP2hsa_circRNA_059060 hsa_circ_0059060 0020943584 0999852393 16047327 chr2 NM_004404 SEPT2hsa_circRNA_074183 hsa_circ_0074183 0038344155 0999852393 15310808 chr5 NM_199189 MATR3hsa_circRNA_101015 hsa_circ_0000378 0023899101 0999852393 25012237 chr12 NM_002336 LRP6hsa_circRNA_100089 hsa_circ_0010501 0017554998 0999852393 17280045 chr1 NM_001397 ECE1hsa_circRNA_101386 hsa_circ_0004008 0033240163 0999852393 16859948 chr14 NM_014982 PCNXhsa_circRNA_101788 hsa_circ_0038872 0036463254 0999852393 168241 chr16 NM_004320 ATP2A1hsa_circRNA_103470 hsa_circ_0067301 0049994992 0999852393 22660602 chr3 NM_015103 PLXND1hsa_circRNA_006296 hsa_circ_0006296 0049042374 0999852393 211451 chr1 NM_004284 CHD1Lhsa_circRNA_073748 hsa_circ_0073748 004252091 0999852393 17548801 chr5 NM_005573 LMNB1hsa_circRNA_100891 hsa_circ_0023685 0006324353 0999852393 16050939 chr11 NM_002576 PAK1hsa_circRNA_404746 0036733957 0999852393 15850621 chr10 NM_018590 CSGALNACT2hsa_circRNA_005899 hsa_circ_0005899 0040331215 0999852393 15983183 chr1 NM_000081 LYSThsa_circRNA_101584 hsa_circ_0036200 0018409954 0999852393 17862666 chr15 NM_002654 PKMhsa_circRNA_103448 hsa_circ_0067029 0014194703 0999852393 17205437 chr3 NM_006810 PDIA5hsa_circRNA_004077 hsa_circ_0004077 0008137346 0999852393 19160136 chr16 NM_020927 VAT1Lhsa_circRNA_001363 hsa_circ_0000172 0031341076 0999852393 15314787 chr1 ENST00000340006 CSRP1hsa_circRNA_081881 hsa_circ_0081881 0015325331 0999852393 16956771 chr7 NM_002736 PRKAR2Bhsa_circRNA_007037 hsa_circ_0007037 0023032396 0999852393 15823312 chr9 NM_024617 ZCCHC6hsa_circRNA_101211 hsa_circ_0029407 003202411 0999852393 22688595 chr12 uc001uhy1 GLT1D1hsa_circRNA_101833 hsa_circ_0039908 004884264 0999852393 19258456 chr16 NM_017803 DUS2hsa_circRNA_100868 hsa_circ_0023255 0026079049 0999852393 16328345 chr11 NM_001876 CPT1A


we clustered all the different circRNAs to characterize theexpression pattern of circRNA e results are exhibited inFigure 4 Among them red stand for elevated and greenrepresents downregulated circRNAs

32 qPCR Verification To verify differentially expressedcircRNAs in AP we made qPCR And we selected threeupregulated circRNAs including hsa_circRNA_101015hsa_circRNA_101211 and hsa_circRNA_103470 and threedownregulated circRNAs including hsa_circRNA_002532hsa_circRNA_059665 and hsa_circRNA_104156 We vali-dated the expression of three downregulated circRNAs inMAP and normal humans And the results are shown inFigure 5 P values are 0008 (hsa_circRNA_002532) lt00001(hsa_circRNA_059665) and lt00001(hsa_circRNA_104156) respectively

To further explore the relationship between the threeupregulated circRNAs and disease severity we comparedthe expression levels in healthy individuals MAP andSAP patients respectively As shown in Figure 6 we foundthat as the condition worsened the expression levels ofhsa_circRNA_101015 (P value was 00003 lt00001 andlt00001 respectively) hsa_circRNA_101211 (P value was00014 00478 and lt00001 respectively) and hsa_circRNA_103470 (P value was 0001 lt00001 and lt00001)also increased significantly en we validated the ex-pression levels of three upregulated circRNAs in MAPpatients and MAP after treatment e results are dem-onstrated in Figure 7 After treatment the expression

levels of hsa_circRNA_101015 hsa_circRNA_101211 andhsa_circRNA_103470 in MAP were significantly declined(P value lt00001)

33 ROC Analysis of hsa_circRNA_101015hsa_circRNA_101211 and hsa_circRNA_103470 in Patientswith AP e ROC curve was constructed to assess thediagnostic significance of the three elevated circRNAs(Figure 8) RUCs of hsa_circRNA_101015 hsa_circRNA_101211 and hsa_circRNA_103470 MAP patients withhealthy people were 0768 (95 CI 0651ndash0886 Plt 0001power 097119) 0731 (95 CI 0605ndash0857 P 0002power 090168) 0770 (95 CI 0653ndash0887 Plt 0001power 097340) respectively RUC of the three circRNAscombination was 0838 (95 CI 0738ndash0937 Plt 0001power 099954) e above results show that hsa_-circRNA_101015 hsa_circRNA_101211 and hsa_circRNA_103470 can be used as biomarkers to diagnose AP atearly stage

34 =e Construction of ceRNA Network Recent evidencesuggests that circular RNA plays a crucial role in the reg-ulation of miRNA-mediated gene expression regulation byisolating miRNAs eir interaction with disease-associatedmiRNAs suggests that circular RNA is important for diseaseregulation [20] To assess the potential function of circRNAwe investigated potential miRNAs that bind to circRNAen we constructed a ceRNA network for the six

Table 4 26 downregulated circRNAs in patients with AP (FDR false discover rate FC fold change)

CircRNA Alias P value FDR FC (abs) Chrom best_transcript Gene symbolhsa_circRNA_102783 hsa_circ_0007532 0006107722 0999852393 16128104 chr2 NM_015341 NCAPHhsa_circRNA_101147 hsa_circ_0028255 004131669 0999852393 16696469 chr12 NM_002973 ATXN2hsa_circRNA_407250 0047617993 0999852393 1668651 chr9 NM_004269 MED27hsa_circRNA_104923 hsa_circ_0002303 0029109485 0999852393 1545763 chr9 NM_001006617 MAPKAP1hsa_circRNA_003912 hsa_circ_0003912 0007972547 0999852393 15662106 chr19 NM_001352 DBPhsa_circRNA_406493 0035076882 0999852393 15155585 chr4 ENST00000508171 LIN54hsa_circRNA_104156 hsa_circ_0001626 0009445502 0999852393 19847946 chr6 NM_021813 BACH2hsa_circRNA_100485 hsa_circ_0006635 0030459921 0999852393 15844839 chr1 NM_014801 PCNXL2hsa_circRNA_102288 hsa_circ_0046702 0022518826 0999852393 17462888 chr18 NM_005433 YES1hsa_circRNA_003910 hsa_circ_0003910 0032896633 0999852393 20304476 chr3 ENST00000470751 SUMF1hsa_circRNA_000756 hsa_circ_0000756 0024633983 0999852393 17492495 chr17 NM_024857 ATAD5hsa_circRNA_059665 hsa_circ_0059665 002380009 0999852393 32119391 chr20 NM_015600 ABHD12hsa_circRNA_103902 hsa_circ_0006916 0016212163 0999852393 1637866 chr5 NM_004272 HOMER1hsa_circRNA_101495 hsa_circ_0003838 0025961386 0999852393 15569843 chr15 NM_173500 TTBK2hsa_circRNA_002149 hsa_circ_0001627 0008793528 0999852393 18600357 chr6 ENST00000343122 BACH2hsa_circRNA_405389 002592997 0999852393 15178686 chr15 NM_004255 COX5Ahsa_circRNA_101899 hsa_circ_0000724 0042899134 0999852393 20353244 chr16 NM_017566 KLHDC4hsa_circRNA_103772 hsa_circ_0008910 0048867847 0999852393 16283164 chr4 NM_001921 DCTDhsa_circRNA_001124 hsa_circ_0001124 0023465588 0999852393 17343183 chr2 NM_032329 ING5hsa_circRNA_003553 hsa_circ_0003553 0020016881 0999852393 15533047 chr1 NM_013441 RCAN3hsa_circRNA_002532 hsa_circ_0002532 0029871425 0999852393 18331005 chrX TCONS_00017187 XLOC_008000hsa_circRNA_102306 hsa_circ_0046995 0016459014 0999852393 19022111 chr18 NM_032142 CEP192hsa_circRNA_048000 hsa_circ_0048000 004965674 0999852393 16658957 chr18 NM_198531 ATP9Bhsa_circRNA_027904 hsa_circ_0027904 002618334 0999852393 16835986 chr12 NM_020244 CHPT1hsa_circRNA_101898 hsa_circ_0040786 0035050956 0999852393 18735586 chr16 NM_017566 KLHDC4hsa_circRNA_100993 hsa_circ_0002484 0032886032 0999852393 1519562 chr11 NM_014155 ZBTB44hsa_circRNA_040792 hsa_circ_0040792 0046578567 0999852393 18944367 chr16 NM_017566 KLHDC4


[Tes

t2 t

est]

[Tes

t3 t

est]

[Tes

t1 t

est]

[Zhe

ng1

cont

rol]

[Zhe

ng2

cont

rol]

[Zhe

ng3

cont

rol]

hsa_circRNA_103470hsa_circRNA_101833hsa_circRNA_101015hsa_circRNA_000761hsa_circRNA_074183hsa_circRNA_407250hsa_circRNA_405389hsa_circRNA_002532hsa_circRNA_003553hsa_circRNA_100485hsa_circRNA_003912hsa_circRNA_103902hsa_circRNA_101386hsa_circRNA_073748hsa_circRNA_404746hsa_circRNA_101211hsa_circRNA_001363hsa_circRNA_081881hsa_circRNA_102674hsa_circRNA_007037hsa_circRNA_100868hsa_circRNA_004077hsa_circRNA_005899hsa_circRNA_006296hsa_circRNA_092519hsa_circRNA_100089hsa_circRNA_101788hsa_circRNA_103448hsa_circRNA_101584hsa_circRNA_059060hsa_circRNA_020354hsa_circRNA_100891hsa_circRNA_001747hsa_circRNA_100993hsa_circRNA_101147hsa_circRNA_104156hsa_circRNA_002149hsa_circRNA_059665hsa_circRNA_000756hsa_circRNA_102783hsa_circRNA_406493hsa_circRNA_048000hsa_circRNA_101898hsa_circRNA_040792hsa_circRNA_101899hsa_circRNA_027904hsa_circRNA_103772hsa_circRNA_101495hsa_circRNA_102306hsa_circRNA_003910hsa_circRNA_102288hsa_circRNA_104923hsa_circRNA_001124

6 7 8Value

0

15

35

Coun

t

Color keyand histogram

Figure 4 e hierarchical cluster of the differentially expressed circRNAs


differentially expressed circRNAs In the ceRNA networkthere are 241 nodes and 831 edges (Figure 9) Amongthem for all nodes red represents microRNAs light-bluecolor represents protein_coding RNAs and at the sametime brown color represents circular RNAs Consideringall edges T-shape arrow represents directed relationshipswhile edges without arrow stand for undirected rela-tionships Specific details are shown in SupplementaryTable 1

4 Discussion

AP is an inflammatory process of the pancreas and hasbecome an increasingly common clinical disease In the

second or third week of the disease 40ndash70 of patientsdevelop infectious necrosis and are the leading cause of latedeath [21] In the United States AP accounts for $25 billionin medical expenses and the number of hospital admissionsis about 275000 per year [22] Because of the high mortalityrate of 20ndash30 in severe cases of AP early detection ofpatients whomay need to be transferred to the intensive careunit (ICU) is critical For patients with AP accurate diag-nosis appropriate triage high-quality supportive caremonitoring and treatment of complications and preventionof recurrence are also critical [23] e evaluation of bio-markers helps to further improve the identification of high-risk patients In general the clinical treatment decisions forAP depend primarily on the severity of the condition MAP

Rela

tive e

xpre

ssio

n le

vel

lowastlowastlowastlowast

00000

00001

00002

00003

00004

MAPControlhsa_circRNA_002532

(a)

Rela

tive e

xpre

ssio

n le

vel



00000

00001

00002

00003

00004

(b)

Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(c)

Figure 5 qPCR analysis of circRNA expression levels in MAP and healthy individuals including hsa_circRNA_002532hsa_circRNA_059665 and hsa_circRNA_104156 (lowastPlt 005 lowastlowastlowastPlt 0001 lowastlowastlowastlowastPlt 00001)


lowastlowastlowast


00000

00002

00004

00006

00008

Rela

tive e

xpre

ssio

n le

vel

MAP SAPControlhsa_circRNA_101015

(a)

lowastlowast

lowast


00000

00001

00002

00003

00004

00005

Rela

tive e

xpre

ssio

n le

vel


(b)



lowastlowastlowast

00000

00002

00004

00006

Rela

tive e

xpre

ssio

n le

vel


(c)

Figure 6 qPCR analysis of circRNA expression levels in healthy human MAP SAP patients including hsa_circRNA_101015hsa_circRNA_101211 and hsa_circRNA_103470 (lowastPlt 005 lowastlowastlowastPlt 0001 lowastlowastlowastlowastPlt 00001)


00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el

PosttreatmentMAPhsa_circRNA_101015

(a)


00000

00001

00002

00003

00004

Rela

tive

expr

essio

nlev

el


(b)


00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(c)

Figure 7 qPCR analysis of circRNA expression levels in MAP and MAP after treatment including hsa_circRNA_101015hsa_circRNA_101211 and hsa_circRNA_103470 (lowastPlt 005 lowastlowastlowastPlt 0001 lowastlowastlowastlowastPlt 00001)


is usually self-limiting and does not cause death HoweverSAP can develop rapidly leading to multiple organ failureand becoming life threatening erefore the treatment ofMAP patients is relatively simple and requires only a shorthospitalization while the treatment of SAP patients usuallyinvolves intensive care Whether it is MAP or SAP quickand accurate diagnosis is still very difficult

Accurate diagnosis of AP may allow for effective treat-ment to begin earlier At present the accuracy of differentscoring systems is not high and a unique model is needed[24] ere are several laboratory tests such as blood ureanitrogen creatinine and hematocrit [25] However there isvirtually no laboratory test that consistently and accuratelypredicts the severity of AP earlier [26 27] Many predictivesystems use CT findings but CTevidence of SAP lags behindclinical findings and early CT studies may underestimate theseverity of the disease e scoring system is complex andcumbersome therefore these scoring systems are not asubstitute for the clinicianrsquos experience assessment

In recent years circRNA has received wide attention as anew class of endogenous and regulatory noncoding RNAs Atthe same time with the widespread use of RNA sequencing(RNA-seq) technology and bioinformatics prediction a largenumber of circRNAs have been identified So far no study hasexamined the role of circRNA in AP Considering thatcircRNA is involved in a variety of diseases it is necessary toexplore differences in the expression of circRNA in patientswith AP Microarrays are an effective tool for analyzingcircRNA erefore in our study we made full use of

microarray technology and selected three patients with MAPand three healthy individuals Our aim was to explore thedifferential expression of circRNA in the patientsrsquo blood withAP to diagnose AP patients as soon as possible As a result wefound 25 upregulated circRNAs and 26 downregulatedcircRNAs e vast majority of circRNAs have not beenstudied and require more in-depth exploration

To further validate the six circRNAs in AP we performedqPCR analysis e results of qPCR studies provide novelbiomarkers for molecular diagnosis and evaluation in APree circRNAs including hsa_circRNA_002532hsa_circRNA_059665 and hsa_circRNA_104156 weredownregulated in MAP compared with healthyindividuals ree upregulated circRNAs includinghsa_circRNA_101015 hsa_circRNA_101211 andhsa_circRNA_103470 significantly increased expressionlevels as the condition worsens Furthermore the expressionlevels of hsa_circRNA_101015 hsa_circRNA_101211 andhsa_circRNA_103470 were significantly reduced aftertreatmentis result suggests that the above three circRNAsmay be involved in the development of AP and showpromise as potential biomarkers for AP To evaluate thespecificity and sensitivity of the hsa_circRNA_101015hsa_circRNA_101211 and hsa_circRNA_103470 we per-formed a ROC curve for further validation e RUC was0768 0731 and 0770 respectively and the combined RUCvalues of the three circRNAs were 0838erefore the threecircRNAs can be used as diagnostic biomarkers for AP withhigh sensitivity

However the pathogenesis of AP remains unknownerefore the 6 differentially expressed circRNAs were selectedto construct a ceRNA network including three upregulatedhsa_circRNA_101015 hsa_circRNA_101211 and hsa_circRNA_103470 and three downregulated hsa_circRNA_002532 hsa_circRNA_059665 and hsa_circRNA_104156 In theceRNAnetwork we found a complex regulatory network for theabove six circRNAs However the above six differentiallyexpressed circRNAs excluding hsa_circRNA_104156 have notbeen reported Considering that circRNAs can act as sponges ofmiRNAs it is of importance to explore miRNAs in AP Moreimportantly miRNAs play a critical role in the progression ofAP For example a previous study found that miR-92b miR-10a and miR-7 are downregulated in the blood of patients withAP and can be used to distinguish between patients with AP andhealthy cases In addition the expression level of miR-551b-5pdistinguishes between MAP and SAP [28] However these sixcircRNAs cannot interact with above these miRNAs In theceRNA network hsa_circRNA_101015 could become spongesof several miRNAs such as hsa-miR-135ab and hsa-miR-543 Ithas been reported that hsa-miR-135 was upregulated in theserum of mice with acute pancreatitis [29] Furthermore it hasreported that hsa-miR-135ab binding polymorphism can re-duce the expression of CD133 and reduce the risk of lung cancerand improve the prognosis of patients [30] Abnormalities inhsa-miR-543 can lead to increased proliferation of multiplecancers such as esophageal cancer prostate cancer and oste-osarcoma [31ndash33] In the ceRNA network hsa-miR-135 couldbe a target of hsa_circRNA_101015 It has been found that hsa-miR-216a was downregulated in the serum of mice with acute

1 ndash specificity100806040200

Sens

itivi

ty

10

08

06

04

02

00

Reference linecircRNA-panel AUC = 0838 P lt 0001circRNA_103470 AUC = 0770 P lt 0001circRNA_101211 AUC = 0731 P = 0002circRNA_101015 AUC = 0768 P lt 0001

Figure 8 ROC analysis of hsa_circRNA_101015 hsa_circRNA_101211and hsa_circRNA_103470 in MAP patients versuscontrol group


pancreatitis [34] Elevated serum hsa-miR-122 has been con-sidered as a noninvasive marker for acute pancreatitis whichcould become a target miRNA of hsa_circRNA_059665 asshown in the ceRNAnetwork [35]Moreover hsa-miR-148a hasbeen validated to be lowly expressed in acute pancreatitis [36]Our results showed that hsa-miR-148a could be a potentialtarget miRNA of hsa_circRNA_059665 Furthermore hsa-miR-148a could inhibit autophagy through IL-6STAT3 axis in AP inacute pancreatitis According to the ceRNA network hsa_circRNA_104156 could be considered as a sponge of hsa-miR-148a

erefore we inferred that the six circRNAs couldparticipate in pathogenic process of AP However specificresearch mechanisms need to be explored In the future wewould continue to research the molecular mechanisms ofhsa_circRNA_101015 hsa_circRNA_101211 and hsa_circRNA_103470 in AP

5 Conclusion

After microarray analysis and qPCR we found threedownregulated circRNAs including hsa_circRNA_002532hsa_circRNA_059665 and hsa_circRNA_104156 and threeupregulated circRNAs including hsa_circRNA_101015hsa_circRNA_101211 and hsa_circRNA_103470 in the blood

of AP patients e ceRNA network revealed that the sixcircRNAs could play a critical role in AP erefore elevatedhsa_circRNA_101015 hsa_circRNA_101211 and hsa_circRNA_103470 can be used as biomarkers in the blood todiagnose AP at early stage e molecular mechanisms ofelevated hsa_circRNA_101015 hsa_circRNA_101211 andhsa_circRNA_103470 in the pathological processes of APwould be explored in our further experiments

Abbreviations

AP Acute pancreatitiscircRNAs Circular RNAsqPCR Quantitative polymerase chain reactionROC Receiver operating characteristicSAP Severe acute pancreatitisMAP Mild acute pancreatitismiRNAs MicroRNAsAUCs Area under the ROC curves

Data Availability

e data used to support the findings of this study are in-cluded within the supplementary information file

Figure 9 e ceRNA network of 6 differentially expressed circRNAs including hsa_circRNA_101015 hsa_circRNA_103470hsa_circRNA_059665 hsa_circRNA_002532 hsa_circRNA_101211 hsa_circRNA_104156


Disclosure

Chang Liu and Xuan Zhu are co-first authors

Conflicts of Interest

e authors declare no conflicts of interest

Authorsrsquo Contributions

Chang Liu and Xuan Zhu contributed equally to this article

Supplementary Materials

Supplementary Table 1 the details of the ceRNA network(Supplementary Materials)

References

[1] S M van Dijk N D L Hallensleben H C van Santvoortet al ldquoAcute pancreatitis recent advances through rando-mised trialsrdquo Gut vol 66 no 11 pp 2024ndash2032 2017

[2] P A Banks M L Freeman and Practice Parameters Com-mittee of the American College of Gastroenterology ldquoPracticeguidelines in acute pancreatitisrdquo =e American Journal ofGastroenterology vol 101 no 10 pp 2379ndash2400 2006

[3] C D Johnson and M Abu-Hilal ldquoPersistent organ failureduring the first week as a marker of fatal outcome in acutepancreatitisrdquo Gut vol 53 no 9 pp 1340ndash1344 2004

[4] R Mofidi M D Duff S J Wigmore K K MadhavanO J Garden and R W Parks ldquoAssociation between earlysystemic inflammatory response severity of multiorgandysfunction and death in acute pancreatitisrdquo British Journal ofSurgery vol 93 no 6 pp 738ndash744 2006

[5] J Kirkegard D Cronin-Fenton U Heide-Joslashrgensen andF V Mortensen ldquoAcute pancreatitis and pancreatic cancerrisk a nationwide matched-cohort study in DenmarkrdquoGastroenterology vol 154 no 6 pp 1729ndash1736 2018

[6] U Ahmed Ali Y Issa J C Hagenaars et al ldquoRisk of recurrentpancreatitis and progression to chronic pancreatitis after afirst episode of acute pancreatitisrdquo Clinical Gastroenterologyand Hepatology vol 14 no 5 pp 738ndash746 2016

[7] D Yadav M OʼConnell and G I Papachristou ldquoNaturalhistory following the first attack of acute pancreatitisrdquoAmerican Journal of Gastroenterology vol 107 no 7pp 1096ndash1103 2012

[8] P G LankischM Apte and P A Banks ldquoAcute pancreatitisrdquo=e Lancet vol 386 no 9988 pp 85ndash96 2015

[9] O Sadr-Azodi A Andren-Sandberg N Orsini and A WolkldquoCigarette smoking smoking cessation and acute pancreatitisa prospective population-based studyrdquo Gut vol 61 no 2pp 262ndash267 2012

[10] S Stigliano H Sternby E de Madaria G Capurso andM S Petrov ldquoEarly management of acute pancreatitis areview of the best evidencerdquo Digestive and Liver Diseasevol 49 no 6 pp 585ndash594 2017

[11] S M Staubli D Oertli C A Nebiker et al ldquoLaboratorymarkers predicting severity of acute pancreatitisrdquo CriticalReviews in Clinical Laboratory Sciences vol 52 no 6pp 273ndash283 2015

[12] X Li L Yang and L-L Chen ldquoe biogenesis functions andchallenges of circular RNAsrdquo Molecular Cell vol 71 no 3pp 428ndash442 2018

[13] J Salzman ldquoCircular RNA expression its potential regulationand functionrdquo Trends in Genetics vol 32 no 5 pp 309ndash3162016

[14] T B Hansen T I Jensen B H Clausen et al ldquoNatural RNAcircles function as efficient microRNA spongesrdquo Naturevol 495 no 7441 pp 384ndash388 2013

[15] M Bezzi J Guarnerio and P P Pandolfi ldquoA circular twist onmicroRNA regulationrdquo Cell Research vol 27 no 12pp 1401-1402 2017

[16] D Han J Li H Wang et al ldquoCircular RNA circMTO1 acts asthe sponge of microRNA-9 to suppress hepatocellular car-cinoma progressionrdquo Hepatology vol 66 no 4pp 1151ndash1164 2017

[17] A J Enright B John U Gaul T Tuschl C Sander andD S Marks ldquoMicroRNA targets in Drosophilardquo GenomeBiology vol 5 no 1 p R1 2003

[18] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012

[19] J-H Li S Liu H Zhou L-H Qu and J-H Yang ldquostarBasev20 decoding miRNA-ceRNA miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq datardquoNucleic Acids Research vol 42 no D1 pp D92ndashD97 2014

[20] S Ghosal S Das R Sen P Basak and J ChakrabartildquoCirc2Traits a comprehensive database for circular RNApotentially associated with disease and traitsrdquo Frontiers inGenetics vol 4 p 283 2013

[21] P A Banks T L Bollen C Dervenis et al ldquoClassification ofacute pancreatitismdash2012 revision of the Atlanta classificationand definitions by international consensusrdquo Gut vol 62no 1 pp 102ndash111 2013

[22] A Nieminen M Maksimow P Mentula et al ldquoCirculatingcytokines in predicting development of severe acute pan-creatitisrdquo Critical Care vol 18 no 3 p R104 2014

[23] C E Forsmark S Swaroop Vege and C M Wilcox ldquoAcutepancreatitisrdquo =e New England Journal of Medicine vol 375no 20 pp 1972ndash1981 2016

[24] J H Cho T N Kim and H H Chung ldquoComparison ofscoring systems in predicting the severity of acute pancrea-titisrdquo World Journal of Gastroenterology vol 21 no 8pp 2387ndash2394 2015

[25] C E Forsmark and D Yadav ldquoPredicting the prognosis ofacute pancreatitisrdquo Annals of Internal Medicine vol 165no 7 pp 523-524 2016

[26] S Tenner J Baillie J DeWitt and S S Vege ldquoAmericancollege of gastroenterology guideline management of acutepancreatitisrdquo =e American Journal of Gastroenterologyvol 108 no 9 pp 1400ndash1415 2013

[27] Working Group IAPAPA Acute Pancreatitis GuidelinesldquoIAPAPA evidencebased guidelines for the management ofacute pancreatitisrdquo Pancreatology vol 13 no 4 pp e1ndashe152013

[28] P Liu L Xia W-l Zhang et al ldquoIdentification of serummicroRNAs as diagnostic and prognostic biomarkers for acutepancreatitisrdquo Pancreatology vol 14 no 3 pp 159ndash166 2014

[29] T Qin Q Fu Y-f Pan et al ldquoExpressions of miR-22 andmiR-135a in acute pancreatitisrdquo Journal of Huazhong Uni-versity of Science and Technology [Medical Sciences] vol 34no 2 pp 225ndash233 2014

[30] M Cheng L Yang R Yang et al ldquoA microRNA-135abbinding polymorphism in CD133 confers decreased riskand favorable prognosis of lung cancer in Chinese by re-ducing CD133 expressionrdquo Carcinogenesis vol 34 no 10pp 2292ndash2299 2013


[31] H Zhao C Diao X Wang et al ldquoMiR-543 promotes mi-gration invasion and epithelial-mesenchymal transition ofesophageal cancer cells by targeting phospholipase A2 groupIVArdquo Cellular Physiology and Biochemistry vol 48 no 4pp 1595ndash1604 2018

[32] Y Du X-h Liu H-c Zhu L Wang J-z Ning andC-c Xiao ldquoMiR-543 promotes proliferation and epithelial-mesenchymal transition in prostate cancer via targetingRKIPrdquo Cellular Physiology and Biochemistry vol 41 no 3pp 1135ndash1146 2017

[33] L-H Wang H-C Tsai Y-C Cheng et al ldquoCTGF promotesosteosarcoma angiogenesis by regulating miR-543angio-poietin 2 signalingrdquo Cancer Letters vol 391 pp 28ndash37 2017

[34] X-X Zhang L-H Deng W-W Chen et al ldquoCirculatingmicroRNA 216 as a marker for the early identification ofsevere acute pancreatitisrdquo =e American Journal of theMedical Sciences vol 353 no 2 pp 178ndash186 2017

[35] P Lu F Wang J Wu et al ldquoElevated serum miR-7 miR-9miR-122 and miR-141 are noninvasive biomarkers of acutepancreatitisrdquo Disease Markers vol 2017 Article ID 72934598 pages 2017

[36] B Miao W-j Qi S-w Zhang et al ldquomiR-148a suppressesautophagy by down-regulation of IL-6STAT3 signaling incerulein-induced acute pancreatitisrdquo Pancreatology vol 19no 4 pp 557ndash565 2019


idiopathic pancreatitis e patients have usually asudden onset with severe persistent abdominal pain and80 of patients are accompanied by vomiting Pain mayradiate to the back usually in the lower chest areaerefore it is critical to carry out some treatmentswithin 48 hours such as fluid resuscitation analgesia andnutritional support [10] Despite the continuous im-provement in diagnostic techniques early diagnosis isstill difficult for clinicians

erefore it is critical to explore early diagnostic bio-markers of AP [11] More and more evidence has confirmedthat circRNAs could play an important role in many diseasesCircRNAs are produced by reverse splicing of precursormRNAs from exons of thousands of genes in eukaryotes [12]At the same time circRNAs can be secreted into blood salivaand other body fluids as potential biomarkers for diseaseprediction [13] However the function of most circRNAsremains largely unknown In the ceRNA mechanismmicroRNAs (miRNAs) are important posttranscriptionalregulators of gene expression that act on target sites in theuntranslated region ofmessenger RNA (mRNA) by direct basepairing e circRNA can act as a miRNA sponge to affect theactivity of miRNAs in the regulation of mRNA expression[14 15] At the same time circRNA abnormalities can lead to avariety of diseases For example the circMTO1 could inhibitthe progression of hepatocellular carcinoma by promoting p21expression acting as a sponge of oncogenic miR-9 [16]However to date no study has explored the expression ofcircRNAs in the blood of patients with APerefore our aimwas to explore accurate biomarkers and then diagnose patientswith AP as soon as possible by detecting biomarkers in thepatientsrsquo blood

2 Materials and Methods

21 Patient Information and Diagnostic Criteria Wereviewed 60 patients who were diagnosed with AP fromApril 2018 to September 2018 at the First Affiliated Hospitalof ChinaMedical University (Shenyang China) In additionwe recruited 30 subjects who underwent routine healthchecks at the First Affiliated Hospital of China MedicalUniversity and showed no signs of disease as a controlgroup According to the ldquoGuidelines for the diagnosis andtreatment of acute pancreatitis (2014)rdquo clinically meets 2 ofthe following 3 characteristics to diagnose AP (1) abdominalpain consistent with AP (2) serum amylase andor lipaseactivity is at least 3 times higher than the upper limit ofnormal (3) abdominal imaging examination is consistentwith AP imaging changes

In clinical treatment mild acute pancreatitis (MAP)patients receive only relatively simple treatment while se-vere acute pancreatitis (SAP) patients usually require in-tensive care erefore we selected 30 MAP and 30 SAPpatients to diagnose AP patients with early stage Currentlycommon scoring standards are the APPACHE II scoringstandard the MCTSI scoring standard and BISAP scoringstandard MAP diagnostic criteria were good response tofluid supplementation without organ failure and local orsystemic complications recovery within 1-2 weeks And

APACHE-II score lt8 points or the MCTSI score lt4 pointsor BISAP lt2 are MAP Diagnostic criteria for SAP were withpersistent organ failure (48 h or more) And APACHE-IIscore ge8 points or MCTSI score ge4 points or BISAP ge2 areSAP

e exclusion criteria were as follows lt18 years of agepregnant and lactating women taking anticoagulant drugsblood system diseases tumors liver disease and gastroin-testinal bleeding patientse patientsrsquo information is shownin Table 1

en we selected 3 patients with MAP and 3 healthyparticipants for microarray analysise study was approvedby the Ethics Committee of the First Affiliated Hospital ofChina Medical University and informed consent was ob-tained from all subjects Figure 1 depicts further experi-mental design details

22 RNA Extraction and CircRNA Microarray AnalysisWe collected whole blood from 3 MAP patients and 3healthy participants Total RNA from each sample wasquantified using a NanoDrop ND-1000 (NanoDropWilmington DE USA) Sample preparation andmicroarray hybridization were performed based on thestandard protocol of Arraystar Briefly total RNA wasdigested with Rnase R (Epicenter Inc) to remove linearRNA and enrich for circular RNA en the enrichedcircular RNA was amplified by a random priming method(Arraystar Super RNA Labeling Kit Arraystar) andtranscribed into fluorescent cRNAe labeled cRNA washybridized to Arraystar Human circRNA Array V2(8 times15 K Arraystar) e labeled cRNA was purified byRNeasy Mini Kit (Qiagen) e concentration and spe-cific activity of the labeled cRNA (pmol Cy3μgcRNA)were measured by NanoDrop ND-1000 (NanoDropWilmington DE USA) 1 μg of each labeled cRNA wasfragmented by adding 5 μl of 10times blocking agent and 1 μlof 25times fragmentation buffer then the mixture was heatedat 60degC for 30 minutes and finally 25 μl of 2times hybrid-ization buffer was added to dilute the labeled cRNA 50 μlof the hybridization solution was dispensed into a spacerslide and assembled onto a circRNA expression micro-array slide Slides were incubated for 17 hours at 65degC inAgilent Hybridization Oven e hybridization array wasfixed and scanned using an Agilent Scanner G2505Cwash

Agilent Feature Extraction software (version 11011)was utilized to analyze the acquired array images Quantilenormalization and subsequent data processing were per-formed using the R software limma package (R version312)

23 Differential Expression Analysis A scatter plot is avisualization method used to assess circRNA expressionvariation Differentially expressed circRNAs with statis-tical significance (FC ge 15 and P values le005) wereidentified utilizing fold change cutoffs and volcano plotsrespectively Among them P value was calculated uti-lizing the unpaired t-test Differentially expressed















Box plot

Scatter plot

Volcanomap












P 1113944

min(Kn)

ic

K

i

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦N minus K

n minus i

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦

N

n

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦

(1)

3 Results


ExonExon

Exon

F

R














4

6

8

10

12

14

16

18N

orm

aliz

ed in

tens

ity v

alue

s


Samples

(a)

4

6

8

10

12

14

16

Gro

up-te

st (n

orm

aliz

ed)

10 1264 16148


(b)

0

1

2

3

4

5

ndashlog

10 (P

val

ue)

4ndash2ndash4 20


(c)














[Tes

t2 t

est]

[Tes

t3 t

est]

[Tes

t1 t

est]

[Zhe

ng1

cont

rol]

[Zhe

ng2

cont

rol]

[Zhe

ng3

cont

rol]


6 7 8Value

0

15

35

Coun

t





4 Discussion



Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(a)

Rela

tive e

xpre

ssio

n le

vel



00000

00001

00002

00003

00004

(b)

Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(c)



lowastlowastlowast


00000

00002

00004

00006

00008

Rela

tive e

xpre

ssio

n le

vel


(a)

lowastlowast

lowast


00000

00001

00002

00003

00004

00005

Rela

tive e

xpre

ssio

n le

vel


(b)



lowastlowastlowast

00000

00002

00004

00006

Rela

tive e

xpre

ssio

n le

vel


(c)



00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(a)


00000

00001

00002

00003

00004

Rela

tive

expr

essio

nlev

el


(b)


00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(c)










Sens

itivi

ty

10

08

06

04

02

00






5 Conclusion



Abbreviations


Data Availability




Disclosure








References




















































Box plot

Scatter plot

Volcanomap












P 1113944

min(Kn)

ic

K

i

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦N minus K

n minus i

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦

N

n

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦

(1)

3 Results


ExonExon

Exon

F

R














4

6

8

10

12

14

16

18N

orm

aliz

ed in

tens

ity v

alue

s


Samples

(a)

4

6

8

10

12

14

16

Gro

up-te

st (n

orm

aliz

ed)

10 1264 16148


(b)

0

1

2

3

4

5

ndashlog

10 (P

val

ue)

4ndash2ndash4 20


(c)














[Tes

t2 t

est]

[Tes

t3 t

est]

[Tes

t1 t

est]

[Zhe

ng1

cont

rol]

[Zhe

ng2

cont

rol]

[Zhe

ng3

cont

rol]


6 7 8Value

0

15

35

Coun

t





4 Discussion



Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(a)

Rela

tive e

xpre

ssio

n le

vel



00000

00001

00002

00003

00004

(b)

Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(c)



lowastlowastlowast


00000

00002

00004

00006

00008

Rela

tive e

xpre

ssio

n le

vel


(a)

lowastlowast

lowast


00000

00001

00002

00003

00004

00005

Rela

tive e

xpre

ssio

n le

vel


(b)



lowastlowastlowast

00000

00002

00004

00006

Rela

tive e

xpre

ssio

n le

vel


(c)



00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(a)


00000

00001

00002

00003

00004

Rela

tive

expr

essio

nlev

el


(b)


00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(c)










Sens

itivi

ty

10

08

06

04

02

00






5 Conclusion



Abbreviations


Data Availability




Disclosure








References










































P 1113944

min(Kn)

ic

K

i

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦N minus K

n minus i

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦

N

n

⎡⎢⎢⎢⎣ ⎤⎥⎥⎥⎦

(1)

3 Results


ExonExon

Exon

F

R














4

6

8

10

12

14

16

18N

orm

aliz

ed in

tens

ity v

alue

s


Samples

(a)

4

6

8

10

12

14

16

Gro

up-te

st (n

orm

aliz

ed)

10 1264 16148


(b)

0

1

2

3

4

5

ndashlog

10 (P

val

ue)

4ndash2ndash4 20


(c)














[Tes

t2 t

est]

[Tes

t3 t

est]

[Tes

t1 t

est]

[Zhe

ng1

cont

rol]

[Zhe

ng2

cont

rol]

[Zhe

ng3

cont

rol]


6 7 8Value

0

15

35

Coun

t





4 Discussion



Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(a)

Rela

tive e

xpre

ssio

n le

vel



00000

00001

00002

00003

00004

(b)

Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(c)



lowastlowastlowast


00000

00002

00004

00006

00008

Rela

tive e

xpre

ssio

n le

vel


(a)

lowastlowast

lowast


00000

00001

00002

00003

00004

00005

Rela

tive e

xpre

ssio

n le

vel


(b)



lowastlowastlowast

00000

00002

00004

00006

Rela

tive e

xpre

ssio

n le

vel


(c)



00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(a)


00000

00001

00002

00003

00004

Rela

tive

expr

essio

nlev

el


(b)


00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(c)










Sens

itivi

ty

10

08

06

04

02

00






5 Conclusion



Abbreviations


Data Availability




Disclosure








References









































4

6

8

10

12

14

16

18N

orm

aliz

ed in

tens

ity v

alue

s


Samples

(a)

4

6

8

10

12

14

16

Gro

up-te

st (n

orm

aliz

ed)

10 1264 16148


(b)

0

1

2

3

4

5

ndashlog

10 (P

val

ue)

4ndash2ndash4 20


(c)














[Tes

t2 t

est]

[Tes

t3 t

est]

[Tes

t1 t

est]

[Zhe

ng1

cont

rol]

[Zhe

ng2

cont

rol]

[Zhe

ng3

cont

rol]


6 7 8Value

0

15

35

Coun

t





4 Discussion



Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(a)

Rela

tive e

xpre

ssio

n le

vel



00000

00001

00002

00003

00004

(b)

Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(c)



lowastlowastlowast


00000

00002

00004

00006

00008

Rela

tive e

xpre

ssio

n le

vel


(a)

lowastlowast

lowast


00000

00001

00002

00003

00004

00005

Rela

tive e

xpre

ssio

n le

vel


(b)



lowastlowastlowast

00000

00002

00004

00006

Rela

tive e

xpre

ssio

n le

vel


(c)



00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(a)


00000

00001

00002

00003

00004

Rela

tive

expr

essio

nlev

el


(b)


00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(c)










Sens

itivi

ty

10

08

06

04

02

00






5 Conclusion



Abbreviations


Data Availability




Disclosure








References
















































[Tes

t2 t

est]

[Tes

t3 t

est]

[Tes

t1 t

est]

[Zhe

ng1

cont

rol]

[Zhe

ng2

cont

rol]

[Zhe

ng3

cont

rol]


6 7 8Value

0

15

35

Coun

t





4 Discussion



Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(a)

Rela

tive e

xpre

ssio

n le

vel



00000

00001

00002

00003

00004

(b)

Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(c)



lowastlowastlowast


00000

00002

00004

00006

00008

Rela

tive e

xpre

ssio

n le

vel


(a)

lowastlowast

lowast


00000

00001

00002

00003

00004

00005

Rela

tive e

xpre

ssio

n le

vel


(b)



lowastlowastlowast

00000

00002

00004

00006

Rela

tive e

xpre

ssio

n le

vel


(c)



00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(a)


00000

00001

00002

00003

00004

Rela

tive

expr

essio

nlev

el


(b)


00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(c)










Sens

itivi

ty

10

08

06

04

02

00






5 Conclusion



Abbreviations


Data Availability




Disclosure








References








































4 Discussion



Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(a)

Rela

tive e

xpre

ssio

n le

vel



00000

00001

00002

00003

00004

(b)

Rela

tive e

xpre

ssio

n le

vel


00000

00001

00002

00003

00004


(c)



lowastlowastlowast


00000

00002

00004

00006

00008

Rela

tive e

xpre

ssio

n le

vel


(a)

lowastlowast

lowast


00000

00001

00002

00003

00004

00005

Rela

tive e

xpre

ssio

n le

vel


(b)



lowastlowastlowast

00000

00002

00004

00006

Rela

tive e

xpre

ssio

n le

vel


(c)



00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(a)


00000

00001

00002

00003

00004

Rela

tive

expr

essio

nlev

el


(b)


00000

00001

00002

00003

00004

00005

Rela

tive

expr

essio

nlev

el


(c)










Sens

itivi

ty

10

08

06

04

02

00






5 Conclusion



Abbreviations


Data Availability




Disclosure








References














































Sens

itivi

ty

10

08

06

04

02

00






5 Conclusion



Abbreviations


Data Availability




Disclosure








References









































5 Conclusion



Abbreviations


Data Availability




Disclosure








References







































Disclosure








References







































elevatedhsa circrna 101015,hsa circrna 101211,and hsa...

Documents