Abnormal Expression of Long Noncoding RNAs in ...

Physiol Biochem 2018;48:618-632 Cellular Physiology Cell © 2018 The Author(s). Published by S. Karger AG, Basel DOI: 10.1159/000491890 DOI: 10.1159/000491890 © 2018 The Author(s) www.karger.com/cpb online:July July18, 18, 2018 Published online: 2018 Published by S. Karger AG, Basel and Biochemistry Published www.karger.com/cpb Li et al.: Abnormal Long Noncoding RNAs in Primary Immune Thrombocytopenia Accepted: April 30, 2018

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Original Paper

Abnormal Expression of Long Noncoding RNAs in Primary Immune Thrombocytopenia: A Microarray Related Study Tengda Lia Mingli Gub Peng Liua Anmei Denga,c,d Cheng Qiane

Yun Liub

Jie Guob

Weiwei Zhangb

Center of Clinical Experiments, Changhai Hospital, Second Military Medical University, Shanghai,bDepartment of Laboratory Diagnosis, Changhai Hospital, Second Military Medical University, Shanghai, cNorth Sichuan Medical College, Sichuan, dShanxi University of Chinese Medicine, Xianyang, e The 100th Hospital of PLA, Suzhou, China a

Key Words Long noncoding RNA • Primary immune thrombocytopenia • Microarray • Quantitative realtime polymerase chain reaction • Co-expression network construction. Abstract Background/Aims: Long noncoding RNAs (lncRNAs) are important regulators of biological processes and they contribute to the pathological developments of various diseases, including autoimmune diseases. To gain the further understanding, we estimate the expression of lncRNAs in primary immune thrombocytopenia (ITP). Methods: In this study, microarray studies were performed to characterize expression profiles of various lncRNAs and mRNAs in blood samples collected from ITP patients. Quantitative real-time PCR (qRT-PCR) was performed to confirm the results, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and gene ontology analysis were used to provide functional annotations, co-expression network construction (CNC) analysis was made to reveal the relations between lncRNAs and their targeted genes. Results: A total of 1177 and 632 lncRNAs were significantly up-regulated or down-regulated, respectively, in “newly diagnosed ITP” patients versus healthy individuals. In addition, 1182 genes and 737 genes were up-regulated or down-regulated, respectively, in “chronic recurrent ITP” patients versus healthy individuals. In a KEGG analysis, “TNF signaling pathway-Homo sapiens (human)” was a key result. In a gene ontology analysis, “Granulocyte macrophage colony-stimulating factor production (GO:0032604, ontology: Biological process, P = 1.69577E-05)” and “coreceptor activity (GO: 0015026, ontology: molecular function, P = 4.67594E-06)” were the two most critical results. Data from qRT-PCR and receiver operating characteristic curves further demonstrated that ENST00000440492, ENST00000528366, T. Li and M. Gu contributed equally to this work. Cheng Qian and Anmei Deng

The 100th Hospital of PLA, Suzhou 215007 (China) and Changhai Hospital, Shanghai 200433(China) E-Mail [email protected], [email protected]

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Physiol Biochem 2018;48:618-632 Cellular Physiology Cell © 2018 The Author(s). Published by S. Karger AG, Basel DOI: 10.1159/000491890 and Biochemistry Published online: July 18, 2018 www.karger.com/cpb Li et al.: Abnormal Long Noncoding RNAs in Primary Immune Thrombocytopenia

NR_038920, and ENST00000552576 can efficiently distinguish different stages of ITP, especially NR_038920 and ENST00000528366. In a CNC analysis, four lncRNAs were emphasized, and NR_038920 and ENST00000528366 were both associated with proteins with important roles in autoimmune diseases. Conclusions: These results suggest that lncRNAs act through targeted genes to mediate their functions and to mediate their functions and affect the pathogenesis of ITP. © 2018 The Author(s) Published by S. Karger AG, Basel

Introduction

Primary immune thrombocytopenia (ITP), also known as idiopathic thrombocytopenic purpura, is an autoimmune disorder characterized by a decrease in platelets due to autoantibody-mediated destruction of platelets or compromised platelet production [1, 2]. It has been reported that pathological autoantibodies produced by B cells, aberrant T lymphocytes, and especially the turbulent developed megakaryocytes, significantly contribute to ITP[3-6]. Previous studies have demonstrated that most immune disorders are caused by dysregulation of gene expression [7, 8], and changes in phenotypes are often manifestations of changes in gene regulation [9-11]. In addition to coding genes, noncoding genes such as microRNAs and lncRNAs have increasingly gained attention [12-14]. LncRNAs have recently been discovered to be noncoding RNAs with lengths greater than 200 nucleotides [15]. Based on proximal protein-coding mRNAs, lncRNAs have been classified as sense lncRNAs, antisense lncRNAs, intronic lncRNAs, bidirectional lncRNAs, and long intergenic ncRNAs (lincRNAs). LncRNAs have been shown to affect every stage of a gene’s life cycle, including chromosome reconstruction, transcription, post-transcription, and intracellular metabolism [15-18]. In a recent study, the role of lncRNAs in the development of autoimmune diseases was highlighted [19]. For example, the lncRNAs, Gas5, Hotair and H19, and Tmevg1, have roles in systemic lupus erythematosus (SLE)[20, 21], rheumatoid arthritis (RA)[22, 23], and Sjögren syndrome (SS)[24], respectively. However, few studies have investigated lncRNAs Figures in ITP. Among these studies, the lncRNA, TMEVPG1, has been shown to be related to a b Th1-type transcription factors, T-bet, STAT1, and STAT4, thereby indicating the importance of lncRNAs in the pathology of ITP[25]. In the present study, a microarray analysis of lncRNAs and mRNAs in blood samples collected from newly diagnosed ITP patients (n = 3), chronic recurrent ITP patients (n = 3), and healthy individuals (n = 3) was conducted (Fig. 1). The goal was to identify differentially expressed lncRNAs which may participate in the development of ITP. In addition, quantitative real-time PCR (qRT-PCR) was performed to detect selected lncRNAs in a larger number of patient samples, and bioinformatics methods were applied to reveal potential functions. Materials and Methods

Patient selection All of the subjects enrolled in this study received treatment at No. 100 Hospital of PLA (Suzhou, Jiangsu, China) or Changhai Hospital (Shanghai, China) between October 2014 and January 2015. There were

Fig. 1. Hierarchical clustering for lncRNAs(a) and Figure 1 mRNAs(b) detected in healthy individuals(N1-3), newly diagnosed ITP cases(P1a-c), and chronic recurrent ITP patients(P2a-c).

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Physiol Biochem 2018;48:618-632 Cellular Physiology Cell © 2018 The Author(s). Published by S. Karger AG, Basel DOI: 10.1159/000491890 and Biochemistry Published online: July 18, 2018 www.karger.com/cpb Li et al.: Abnormal Long Noncoding RNAs in Primary Immune Thrombocytopenia

no statistically significant differences among the patients in regard to gender or age. Patients referred to as having “newly diagnosed ITP” (termed P1 below) included diagnosed patients without reliable predictive clinical or laboratory parameters of disease duration. The patients referred to as having “chronic recurrent ITP” (termed P2 below) were previously diagnosed with ITP that persisted for more than 12 months, as previously detailed [26]. Healthy individuals composed the N group and they were examined for the same period of time as the P1 and P2 patients. Table 1 summarizes the characteristics of the P1 (n = 30) and P2 (n = 30) patients. Pregnant patients and those with diabetic complications, cardiovascular disease, an active infection, hypertension, or other autoimmune diseases were excluded. The Ethics Committee of the People’s Liberation Army 100th Hospital and Changhai Hospital approved this research. The procedures performed were carefully explained to the patients and informed consents were signed by all of the participating patients. All experiments in the methods section were performed in accordance with approved international guidelines and ethical standards.

Table 1. Characteristics of the patients with ITP. Abbreviations: ITP: immune thrombocytopenia; PLT: platelet; IVIG: intravenous immunoglobulin; GP: glycoprotein; GC: glucocorticoid; P1: newly diagnosed ITP; P2: chronic ITP Variables

P1 No. patients(%)

P2 No. patients(%)

20 20-40

10(33.3%)

14(46.7%)

4(13.3%)

16(53.3%)

Age of the ITP patients (y) 40-60

60 Male

Course(mo)

4(13.3%) 8(26.7%) 8(26.7%)

1(P1) 3-100(P2) 1-3(P1) 100(P2)

26(86.7%)

GPIIb+

12(40.0%)

PLT count 10109/L Anti-platelet antibodies GPIIIa+

GPIb/IX+ GPIb+

Therapy GC

IVIG

4(13.3%)

12(40.0%) 18(60.0%) 14(46.7%) 14(46.7%) 4(13.3%) 2(6.7%)

2(6.7%)

12(40.0%) 2(6.7%)

22(73.3%) 8(26.7%)

18(60.0%) 16(53.3%) 14(46.7%) 8(26.7%)

16(53.3%)

30(100.0%) 16(53.3%)

Patient specimens Peripheral blood (6 mL) was collected from each subject. To isolate peripheral blood mononuclear cells (PBMCs), the collected blood samples were diluted with PBS (pH 7.2–7.4, Jiru Biology Company, Shanghai, China) and then slowly added to Ficoll-Hypaque (Thermo Fisher Scientific, USA). After centrifugation at 2500 rpm for 20 min, the obtained PBMCs were washed twice with PBS, were added into 1 ml Trizol reagent (Invitrogen, USA) and then were stored at -80 °C

RNA isolation Prepared blood samples were thawed at room temperature (RT) before chloroform was added to each sample. After a brief vortexing step, the samples were centrifuged (12000 × g, 5 min, 4 °C) and the supernatants were transferred to RNase-free eppendorf tubes and mixed with isopropyl alcohol. After an incubation at RT for 30 min and another centrifugation step (12000 × g,15 min, 4 °C), the supernatants were precipitated with cold 75% alcohol and centrifuged (12000 × g, 5 min, 4 °C). The pellets were dried at RT for 2–5 min and then 20 ul of RNase-free water was added to each. From each sample, a 1 ul aliquot was analyzed for RNA concentration and purity with a Nanodrop 2000 instrument (Thermo Scientific, USA).

Microarray to obtain expression profiles for lncRNAs and mRNAs An Arraystar Human LncRNA Microarray V3.0 (Arraystar Inc., USA) was used based on its capacity to evaluate 30, 586 lncRNAs and 26, 109 coding transcripts. To prepare samples for analysis, total RNA was isolated as described above. Both RNA integrity and genomic DNA contamination were assayed by denaturing agarose gel electrophoresis performed with an Agilent Bioanalyzer 2100 instrument (Agilent Technologies, USA) by KangChen (Shanghai, China). Total RNA was purified with RNeasy Mini Kits (Qiagen, Germany) and Baseline-ZERO DNase (EPICENTRE, USA) according to the manufacturers’ protocols. A One-Color Quick Amp Labeling Kit (Agilent, USA) was used to label samples. The labeled samples were subsequently purified, along with labeled cRNA as a quality control, with RNeasy Mini Kits (Qiagen, Germany) according to the manufacturer’s protocol. Specific activity (pmol dye per g cRNA) was calculated as follows: specific activity = (pmol per ul dye)/(ug per ul cRNA). Array hybridization was performed according to the manufacturer’s protocol for the Agilent Gene Expression Hybridization Kit (Agilent, USA). The hybridized

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Physiol Biochem 2018;48:618-632 Cellular Physiology Cell © 2018 The Author(s). Published by S. Karger AG, Basel DOI: 10.1159/000491890 and Biochemistry Published online: July 18, 2018 www.karger.com/cpb Li et al.: Abnormal Long Noncoding RNAs in Primary Immune Thrombocytopenia

array was washed with Gene Expression Wash Buffer 1 and Gene Expression Wash Buffer 2 (Agilent, USA) before being scanned by an Agilent Microarray Scanner. Data were subsequently extracted and analyzed with Agilent Feature Extraction Software (V11.0.1.1).

Data analysis Raw data were imported into GeneSpring GX v12.1 software (Agilent, USA) for quantile standardization. Differentially expressed LncRNAs and mRNAs with statistically significant differences between each set of comparisons were identified with P-value/ false discovery rate filtering. For the differently expressed lncRNAs or mRNAs, we selected them according to the following standards: 1) the fold change was ≥ 2.0 and P-value ≤ 0.05; 2) the raw intensity was ≥200 to make the background intensity as small as possible; 3) the Xhyb column was blank to make sure that there was not non-specific detection. Hierarchical clustering and combined analysis were performed with homemade scripts. To better understand the functions of the identified lncRNAs, correlations between enhancer lncRNAs, antisense lncRNAs, lincRNAs, and nearby mRNAs were analyzed. A gene ontology (GO) analysis was performed and the categories included: Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). P-values were obtained according to Fisher’s exact test and were used to measure GO term enrichment for differentially expressed lncRNAs and mRNAs. To calculate fold enrichment of a GO term, the number of differentially expressed genes and those with an assigned GO term (“Count”), the number of genes annotated in the database (“Pop.Nits”), the number of different genes with GO annotations (“List.Total”), and the total number of genes in the database (background) with a GO annotation (“Pop.Total”), were used as follows: (Count / Pop.Nits) / (List.Total / Pop.Total), Enrichment Score = -log(P-value). A Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis showed every gene/protein or small molecule in each Pathway Map it generated. Histograms presented the enrichment scores of the pathway terms. A Volcano plot was used to select differentially expressed mRNAs and lncRNAs (for all online suppl. material, see www.karger.com/ doi/10.1159/000491890, Fig. S3a-c). A scatter plot was generated to present repeatability among the microarray data and groups (see online suppl. material, Fig. S3d-f). Probe distribution in the microarray is presented in a box plot, with changes before and after standardization shown (see online suppl. material, Fig. S3g-h). To display similar biological properties in multiple samples, hierarchical clustering was performed with Aligent GeneSpring GX software (version 11.5.1) (Fig. 1). Fig. 2. CNC analysis results for 12 lncRNAs (a) and a subset of 4 immune-related lncRNAs [ENST00000528366, and ENST00000440492, NR_038920, and ENST00000552576] and their related genes (b). (a) The bluegreen nodes represent coding genes, the red nodes represent lncRNAs. Solid lines indicate positive relationships between the lncRNAs and coding genes, and the dotted lines indicate negative relationships. (b) In this second CNC network, there were 47 nodes representing mRNAs, as well as related genes. The related genes were presented in colors that indicate their level of significance in immunity or autoimmunity (significance: blue-green > green > yellow-green > yellow).

ABHD3 ZNF454 LARP4 CDK19 KIAA0196 RHOT1 RAD51C

a

PRMT2

ERCC1

TNFRSF10B

TRPM6

PCSK5

RIN2

RAB3B CMPK2 TRAF1 KYNU MAGT1 FRAT2 SPINT2 MRPS7 GALR2

HCAR3 ZNF860

PSMD12

LIPN FAM53B GABPB1

ZNFX1 APOL3 CLMN CEA76TRAF3

ZC3HAV1 IRAK2 CASP7

B4GALT1

ALYREF

DENND4A

LYN

FAM26F

MSS51

SYTL3

CCDC58

EGLN1

CPSF2 ACYP1

LCE2C

PRRG4

NSA2 NLRC4 HBD

TMEM2

ENSA CFLAR

CXCR1

FOS

NFKB1

ISG15

KLRC4 LEPROTL1

RYBP ZFX IDO1 GBP1 HIST1H2BG SMPDL3A RAPGEF2 RNF144B PTPRJ ANKLE2 TIMM10 ARFGAP3 GHDC RNF19B RLN2 ACVR2A PELI1 CYCS REL CD36 HS3ST3B1 C11orf9 IL23A EIF1B MRPS18A NCOA4 MYCL1 MTM1 CFB CD44 LACTB RHOH ANXA5 SP140 GK5 CCR2 C18orf1 C1orf21 CLEC4D CCDC50 EIF2C4 SLC46A2 RBM41 FBLIM1 SERPINB9 LCE3D HIBCH SGMS1 UBFD1 ZNF768 DET1 KCTD12 STX11 ORC4 SCIMP SELK CR1L NCR3 RSAD2 PQLC1 NEDD9 RASGRP4 TP73 OAZ2 MITF DRAM1 GPR52 KIAA0754 ETS2 KLHL6 BAZ2B PGK1 ZNF607 ZC2HC1A ZMYM1 ANGPTL2 PRSS23 KLRC3 POLR2M SIAH1 KIF13A TBC1D2 CCDC112 MED6 UBR2 PELI2 MTMR14 CCL21 DUSP5 OXER1 LRRC25 ZBTB10 RPE GPR182 XAGE1A RAB21 FFAR2 SRRM5 PEX6 TRIP11 ENST000 MAFF PTPN1 QKI NRAS 00509463 MAGOH MAP3K8 ETF1 GLT25D1 ZCCHC6 PLK2 FLCN IFI44L WTAP GMCL1 CDK5R1 RPN2 JMY TANK ZNF720 uc010kun.2 FOXD4L3 SUFU ZNF438 TIPARP GTPBP1 SLC11A2 DUSP10 OSBPL11 KLK3

TMEM68

ATP13A2

RDBP

SLU7

PARD6G

FAM129C

CETN3 FBXO25

ARL5B

EIF5A

NRP1 CTNNB1

ADNP2

FCAR

AQP9

PTS

AKAP5 FAM136A

TLR5

HNRNPH2

WDR73

KHDRBS1 ECD

CBR1

PFKFB3

USP43

INSIG1

NFYB IL6R

CASZ1

ULBP2

NOL7 CEP290 CXCL1

CCL3L3

CCDC152 C11orf82

CYLD

CREM

TNFAIP6 HIF1A

UAP1

RBM3

G3BP2 IFNB1

ZBTB43 DNAJB11

HECA

CDK14

PLAUR

BCL2A1 ANKRD37 TMPO TREM1

USP32 FAM20B

SAT1

FAM103A1

U2AF1 RASGEF1B NUP98

ENST00000440436 CD96 VDAC1

EGR3

FAM115C

NCK1

SGTB

FAM105B TIMM22

CYTIP

HNRNPD ELL2 RNASEH1 CDK17

SLC25A3

G0S2

uc002ubt.1

CLDN22PSMB7

ANXA2 ZNF267

CCRN4L RAB30

ARID3C ARMC8

CD33

TP53BP2

SOCS1 FAM177A1 MRGPRX3SP2 CALCA FRS2 ANKRD22 PRKAR1A CIRH1A PLOD2 EZR SPRY2 CLP1 JMJD6 CTSH FYN TSFM HAL SCGB1C1

ARRDC2

ST14 DDX6 ZFYVE26 TTLL6

SHBG SHOX KPNA5 CPA3 SULT1A4

NFAT5 FBXO3

CD68

ASPRV1

F13A1

RELL1 CTSZ TMEM66 CASP4 ADPGK SLC16A5 TMEM41B CFH BPTF MLF1IP C15orf48 CD63 FCGR1A SAMHD1 PMS1 SNX9 IRS2 CSAD POLR1C ZFP36L1 NRARP RPS4Y1

FAM111A CYP51A1

ENST000 00417932

CH25H

SUV420H1

ERICH1

H1FX PHTF2 NXT1 ATXN3L ADRB2 TBC1D2B ALAS1 SLC22A7 GADD45A MAPK6 ODC1 GIMAP8 CXCL2 ELK4 SYNRG ATP2B1 IL17RA IL1B GBP5 SPSB1 METTL3 FOSL2 CXCL3 ZNF331 DDIT3 THUMPD3 PLEKHG3 FEM1C CAPN3 RECK FAM160B1 CRIM1 RILPL2 UBE3A H2AFJ SMAP2 FCGR2A CIRBP ZNF350 SPTY2D1 ABT1 LGALS2 CRY1 SLC9A7 PLK3 GTF3C4 HAUS2 LIMK2 CEP170 ACAT2 ROCK2 TNFAIP3 KCNA2 UPF3B CDC42 STARD4 ETNK1 CARD16 CSDE1CBWD6 ZNF83 DDX47 TM2D2 HLA-DRA SAR1A UEVLD KANSL1L VAPA GCOM1HSF2 SEC14L2 PTGER3 GADD45B EID2B EIF2B3 CCL19 SOS1 NAP1L5 GBP7 SPTLC1 IFI44 YME1L1SLAMF7 FAM48A ZNF638 PFKFB4 WARS BIRC3 FAM96A WDR67 COMMD2

WDR48

ANKRD28

PANK3

TRIM8

PVRL1

GNAL CRY2

LCORL CCDC132 CKAP4

CYorf15A PTPRO SKP1

MS4A6A C19orf48

GUCY1A3

DDIT4 VSTM1 SDR39U1

NCBP2

NACA CHST12 AKR1C3

APLP2

IGJ

NRG1 GPR132 SETD9 B3GNT5 LTBP1 KRCC1 ABCA1 NGLY1 SH2D2A NCF4 NRG3 IFT46 FBXO33 TYW5 ACOT1

ARMC10

TMEM154

OPRM1 LILRA5

SOWAHD CES1

NSUN5

VMP1

ZNF432

NOS1AP AHSP ACAA1 C3orf33 PLXNB3 ITGA6 HCAR2 NR_038920 DOCK8 PLXDC2

ACP1

SLC6A6

PDE4B

SLC39A8

ARID5B CLEC4E

CHCHD4

KIF3C

SLC5A2

C6orf225

HIVEP1

AGAP1 P2RY13

HERPUD1

PHACTR1

ARL3 PINX1

CTTN

SAMD9DNAJC18

DERL2

RIC8B

B9D2 SULT1A3 RAB31 RGS19 TRIM27 PHF21A SLC35A1 NOL6 OR51E1 SEC14L4 NUDT16 ARID4B CD8A SLC22A15 MEGF9 CLCN3 NFKBIE AHSA1 NLRP12 FAM133A MCM6 KIAA1429 SERPINF1 NT5C3 UBAP1 OMA1 IL8 EML4 RAB13 LIN7A GALK2 CD5 SH2D1A AUTS2 HBG2 NFKBIA RNMT EDDM3B SPOCK2 ZNF404 YES1 CD55 SLC7A1 NUDT7ABCD3 HLA-DRB5 NGFRAP1 CDKN2B

CBWD1

SFXN1PDE6B

CCDC18

RAB22A CHST15

LRRC59

NANS

PNMAL1

TEN1

ENST00000528366

BACE1 SH2D3C

PPHLN1

PRDM1 MGAT1 ILF3 APAF1 PLEKHM1 DTNB SRD5A1 SNCA

DAB2

TNFSF8

DNAJB6

CDPF1

IRAK4

FBXO22 ERAP2 PYGL

MTX1

PADI4

DNAJB9

KCNJ2

SCN5A MSRB1

PNLIPRP2 CXCR4

METTL7A FAM173B

ZNF281

CDH22

CTSW

ICOS

PPP1R16B

CCDC121 ZNF320

BTG3 BCL2L12 CUL4A

ETV3

TAF9B

RIMBP3C

MFSD6L

STAG2

RBM38

TBC1D22A

BPGM

SPTLC2 MPP2 NUDT17 TET2

EN1 THEMIS ARSA ICA1 OR5B21 SLAMF6 RND2 CSTF3 FGFBP1 DCAF12L2

NOP56

TRIQK

HSPA9

MOSPD2 CD28

TRIM49L1 CTNNAL1

HIST1H1C

ST20 SNAPC3 COA3

PRX

SPAG9 LDHA AGAP9

EIF2AK2

MYZAP

DENND2D

BRWD1 PTPN6

DGKG

SAP30

PIAS2

EAF1

LTA

TEX30

CCRL2

SOD2 C17orf96 CBWD2 HNMT

PGBD2

SRGN

MKKS

TIMM23

CCL3

CD69

CD274

ENST00000412084 TFB1M

KLHDC4

PNO1

IFNE

HBA2 RGS3

CBWD3

ME2

CDC37L1

NDUFV3

RAMP1 TMEM244 TTLL11

NOD2

C1orf63

RGS16

TNFRSF10C CTSG

CD79B

TCEANC2 CCDC99

ZC3H12A

IFIT1

NAT8B

CD86 HLA-DRB1

ANKRD12 RPS6KA2

TMLHE LCN2

TOMM5

TRAF3IP3 SZRD1

KIAA1919 KLK15

PILRA CCR7

ENST00000436506

CCL18

BMP2K

USP47

ATG4C

C1orf43

GTPBP4

ZNF175

C1orf162YPEL3

LRP5L

IL18

KLHL7

RAB3D

PMAIP1

MX1

TMEM39A

PQBP1

GIT2

ZNF674 CDC42EP3

IFIT2

CXCL11

TMCO3

CDC42SE2

ACOT9 HIAT1

WIPF1

MIS18BP1

EREG

SLC8A1

ZNF322 JARID2 ZBED4 C14orf149 S100PBPSGOL2 EXOC4 AHSA2 ZNF540 SLC31A1 ASCC3 CLHC1 BICD2 PID1 CYTH4 ZNF283 IER3 C9orf69 MCFD2 TYSND1 ATP6V0A1

SLC4A1

FGL2

CDV3

HBA1

RAB12

PLXNB2

CD300LF PPP1R21

UTY

ENST00000552576

ZNF268

RGS2

RHOBTB2 DNAJA3

AMICA1CNTLN TACC3 PTEN C20orf196

FIG4 KIAA0907 NEK4 FGR

ENST000004 40492

VPS35

HEATR3

ENST00000521756

MS4A14

COG8 CAMP

DCAF12

PICALM

MAST3

KRTAP21-1

OR7D2

CPNE8

PLG CXCR7 ZNF701

ARL13B

MGAT4A

CEACAM21

HESX1 SERPINB8

LILRA3 IL1RN

GGCT

ADAM28

FAM198B JAK2 GLS RAB27B

CLEC7A

EIF2A ARRDC1

APBB1IP

GPR137B

HDAC8 ZNF668

KIAA0319

RMI1 ZFHX3

FAM82A1

BRCA1

FBXL3

DEXI

PARVG

DYSF

SKP2

TIMMDC1 ZNF467 LCE1C

C6orf99

FUT3

FAM188A PIM3

CSNK1G3 PPP1R17

DBI

S100A9 TRNT1

CAPNS2

BPI

FARP2

BRD8

AREG

MYO1F

IER2

LPCAT3

ABHD5 KDM5D

NAIP HBB

ICAM1 IFIT3 RABGEF1 RAP2B CHST2 KANSL2 SRC GRAMD3 DOT1L LIMS2 HSPA5 ARL8B OASL IFIT5 NHLRC1 RIPK2 TWISTNB

FOXC1

NFATC3

CR1

LYST

NQO2

FCRL5

TMEM131

RELB EZH2

S100A8

FBXL4 CNOT7

STX3

FKBP15

MICU1 BIRC2

MAPRE1

CDA

SUCLG1 ZFAND5

MNDA

DTD1 GBP4 GSTO1 TRAF3IP2 USP12 AKR1C4

TNFAIP8

SNX10

ATP13A3

TK2 ATP6V0A4

ERN1

NFE2

MMP25 BCL2L1

ECE1 GOLGA7

PLGLB2

TSPO RAD50

IL1R2

SP110

DEFA1B

ZNF295

NR4A3

ZNF410

RHBDD2

TFDP2

RCL1

TLR10

APOBEC3D

AGTPBP1

621

Physiol Biochem 2018;48:618-632 Cellular Physiology Cell © 2018 The Author(s). Published by S. Karger AG, Basel DOI: 10.1159/000491890 and Biochemistry Published online: July 18, 2018 www.karger.com/cpb Li et al.: Abnormal Long Noncoding RNAs in Primary Immune Thrombocytopenia

Synthesis of cDNA and qRT-PCR For the synthesis of cDNA, an annealing mixture composed of 1.5 ug RNA, 1 ul oligo(dT)18 (0.5 ug/ul), 1.6 ul dNTPs mix (2.5 mM) (HyTest Ltd., China), and 10.4 ul RNA-free H2O was used. After the samples were incubated in the annealing mixture in a water bath at 65 °C for 5 min, and then in an ice bath for 2 min, the samples were subjected to a short centrifugation. The following reagents were then added in this order: 4 ul 5X First-Strand Buffer (Invitrogen, USA), 1 ul 0.1M DTT, 0.3 ul RNase Inhibitor (Epicentre, USA), and 0.2 ul SuperScript III RT (Invitrogen, USA). The samples were incubated at 37 °C for 1 min, at 50 °C for 60 min, and then at 70 °C 15 min in a thermostatic water tank (DK-8D, Senxin Instruments Inc., Shanghai, China). For immediate use, the samples were placed on ice, while samples were stored at -20 °C for later use. PCR reactions were performed with 2X PCR Master Mix (Arraystar, USA) according to the manufacturer’s protocol. See online suppl. material, Table S2 provides a list of the primers used. β-Actin was detected as a reference gene and relative quantification was performed to measure gene expression levels. PCR amplification was subsequently performed to verify selected genes from the lncRNA microarray analysis in 30 patients with “newly diagnosed ITP” (P1) and 30 patients with “chronic recurrent ITP” (P2) (Table 1). ROC curves and areas under the ROC curves (AUCs) for the qRT-PCR results of the selected genes were generated with GraphPad Prism 6.0 software (GraphPad, USA).

Co-expression network construction Normalized intensities of coding genes were calculated to perform a co-expression network construction (CNC) analysis. When there were varying intensity values for different transcripts of the same gene, the median value of these intensities was used as the expressed value of the gene. The Pearson correlation coefficient (pcc) of the normalized intensities of the selected lncRNAs and all of their related genes was also calculated. When [abs(pcc)] was > 0.998 and false discovery rate (FDR) was