Differential expression of serum microRNAs in cirrhosis that evolve into hepatocellular carcinoma related to hepatitis B virus

  • Authors:
    • Youwen Tan
    • Bin Lin
    • Yun Ye
    • Danfeng Wen
    • Li Chen
    • Xinbei Zhou
  • View Affiliations

  • Published online on: April 24, 2015     https://doi.org/10.3892/or.2015.3924
  • Pages: 2863-2870
Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations

Abstract

Circulating microRNAs (miRNAs) exist stably in body fluids and are potential biomarkers for hepatocellular carcinoma (HCC). Twenty-five patients with cirrhosis that evolved into HCC, who were treated at The Third Hospital of Zhenjiang Affiliated to Jiangsu University between January 2005 and December 2012, were enrolled. In the discovery stage, 2 serum samples pooled from 3 cirrhosis and 3 HCC samples were subjected to deep sequencing. Subsequently, differential expression of miRNAs was validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) in the serum samples from an independent cohort of 22 patients with cirrhosis and HCC. Twenty-two miRNAs showed a >2-fold upregulation (P<0.01), and 2 miRNAs showed a >2-fold downregulation (P<0.01) in the cirrhosis and HCC samples. Using the comparative Ct method, we calculated the 2-ΔΔCt for 40 candidate miRNAs in the sample sets. Eight of the 40 miRNAs demonstrated significantly differential expression levels between the disease categories. The miRNAs exhibiting differential expression were hsa-miR‑122-5p, hsa‑miR-199a-5p, hsa-miR‑486-5p, hsa‑miR‑193b-5p, hsa-miR‑206, hsa‑ miR‑141-3p, hsa‑miR‑192-5p and hsa‑miR‑26a-5p. We identified the miRNAs differentially expressed in cirrhosis that evolved into hepatitis B virus-related HCC.

Introduction

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, and is associated with persistent infection from hepatitis B virus (HBV) or hepatitis C virus (HCV). These viruses play key roles in hepatocarcinogenesis; and therefore, HCC is highly prevalent in China due to chronic HBV and HCV infection (1). HCC patients show the shortest survival time among cancer patients, with most patients dying within 12 months of HCC tumor development (2). Furthermore, only 30–40% of HCC patients are found eligible for potentially curative intervention (3) upon diagnosis, partially due to the lack of highly sensitive and specific early-detection measures. Therefore, the effective identification of new markers for HCC is urgently needed.

MicroRNAs (miRNAs) are a class of single-stranded non-coding small RNAs (19–24 nt) that regulate the gene expression network and are known to contribute to a diverse range of functions, including development, apoptosis, differentiation and oncogenesis by binding to specific target mRNAs (4). Circulating miRNAs exist stably in body fluids and were first reported as a newly identified family of miRNAs by Valadi et al in 2007 (5). It has become clear that miRNAs potentially regulate all aspects of cellular activity. Recent studies have provided clear evidence that miRNAs are abundant in the liver and modulate a diverse spectrum of liver functions, including differentiation and development, metabolism, apoptotic cell death, cell proliferation, viral infection and tumorigenesis (6,7). Deregulation of miRNA expression may be a key pathogenic mechanism in many liver diseases, such as HCC, viral hepatitis and polycystic liver disease (811).

Differential miRNA expression in HCC and non-tumor tissue has been reported in numerous studies (1217). Several differentially expressed serum miRNAs, including miR-16, miR-122, miR-21, miR-223, miR-24, miR-27a, miR-375 and let-7f have been recently reported in patients with HCC, when compared with hepatitis B patients and healthy individuals (18,19). However, the differentially expressed miRNAs found in these studies varied only between different individuals, and these differences were either investigated in control vs. HCC patients or cirrhosis vs. HCC patients. In the present study, we investigated miRNA expression profiles in cirrhosis patients who went on to develop hepatitis B virus-related HCC.

Materials and methods

Ethics statement

The present study was approved by the Medical Ethics Committee of The Third Hospital of Zhenjiang Affiliated to Jiangsu University, Zhenjiang, China, and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from each patient prior to their participation in the present study.

Study design

A total of 25 patients with cirrhosis that evolved into HCC, who were treated at The Third Hospital of Zhenjiang Affiliated to Jiangsu University between January 2005 and December 2012, were enrolled in the present study. In the discovery stage, 2 serum pooled samples from 3 cirrhosis and 3 HCC status samples from the patients were subjected to deep sequencing using the Illumina HiSeq 2000 system (Illumina, Inc., San Diego, CA, USA) to identify statistically significant differential miRNA expression. Subsequently, differentially expressed miRNAs were validated by qRT-PCR in serum samples of an independent cohort that included 22 cirrhosis and HCC status samples from patients. All patients were positive for HBsAg, the surface antigen of HBV, for a period of at least 6 months and were not co-infected with other types of hepatitis viruses such as hepatitis A, C, D or E. Patients with any other liver disease, such as alcoholic, autoimmune or metabolic liver diseases were excluded. The diagnosis of HCC and cirrhosis was histopathologically confirmed. As this was a retrospective study, collection of clinical data from the medical records, pathology reports, and regular follow-up interviews with the subjects was utilized. Serum samples used in biochemical tests and then miRNA detection were from the same specimens.

Demographics and clinical features of the patients are listed in Table I. Biochemical characteristics of the patients with cirrhosis that evolved into HCC are listed in Table II.

Table I

Demographics and clinical features of the patients.

Table I

Demographics and clinical features of the patients.

No.GenderAge (years)Smoking statusAlcohol consumptionAntiviral treatmentHBeAgHBV DNAGenotype
1M43YesNoETVNNDB
2M35NoYesLAMNNDC
3M46NoNoETVNNDC
4F44YesNoETVPNDC
5M53NoYesETVNNDB
6M57YesNoETVNNDC
7M63YesNoLAMPNDC
8M37YesNoETVPNDC
9M47NoYesETVNNDC
10M53NoNoETVNNDC
11F67YesNoLAMNNDB
12M64YesNoETVNNDC
13M57NoNoETVNNDC
14M54NoNoETVPNDB
15M56NoNoLAM+ADVNNDB
16M57NoNoETVNNDC
17M54NoYesETVNNDC
18M47YesNoETVNNDC
19M45YesNoETVNNDB
20M44NoNoLAMPNDC
21M47YesNoETVNNDC
22M37NoNoETVNNDB
23F43NoNoETVPNDB
24M48YesNoETVNNDC
25M51NoNoETVNNDC

[i] M, male; F, female. LAM, lamivudine; ETV, entecavirus; ADV, adefovirus. N, negative; P, positive. ND, not determined.

Table II

Biochemical characteristics of the patients with cirrhosis that evolved into HCC.

Table II

Biochemical characteristics of the patients with cirrhosis that evolved into HCC.

VariablesScreening set
Validation set
Cirrhosis status (n=3)HCC status (n=3) P-valueaCirrhosis status (n=22)HCC status (n=22) P-valuea
TBIL13.67±6.8713.90±6.790.70116.61±9.4818.62±11.900.573
ALB40.17±3.0141.63±3.780.74541.66±2.1741.96±3.090.755
ALT35.26±13.2543.62±25.360.39235.94±21.3232.38±23.570.363
AST43.27±21.7647.25±12.640.77835.77±14.5345.59±26.450.277
ALP81.31±31.71136.73±93.250.39295.94±31.39132.88±57.560.017
GGT74.88±38.26151.16±65.740.24965.67±45.46141.68±67.46<0.001
PTA98.34±5.1399.53±3.350.86297.46±4.3698.56±3.210.764
AFP5.23±2.48126.23±73.630.1716.49±4.44101.46 ±78.40.021
PLT115.37±43.86132.26±48.340.948132.23±47.98131.28±28.420.897

[i] TBIL, total bilirubin; PLT, platelets; PTA, prothrombin activity; ALB, albumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase. The normal range of ALT and AST is 5–40 U/l, PLT is 100–300×109/l, ALB is 35–55 g/l. apaired samples test.

Illumina sequencing and data analysis

Procedures and methods of sample collection, RNA isolation and Illumina sequencing were described in detail in our previous studies (20,21).

qRT-PCR validation study and data analysis

qRT-PCR-based relative quantification of miRNAs (300 μl of serum from each participant) was performed with SYBR® Premix Ex Taq (Takara, Kyoto, Japan) according to the manufacturer’s instructions using a Rotor-Gene 3000 Real-Time PCR instrument (Corbett Life Science, Sydney, Australia). miR-24 has been reported to be consistently present in human serum (22,23). Moreover, our previous experience was that miR-24 maintained stable expression levels, therefore the level of miR-24 served as an internal control in the serum miRNA relative quantitative analysis (20). The specificity of each PCR product was validated by melt curve analysis at completion of the PCR amplification cycles. All samples were analyzed in triplicate, and the cycle threshold (Ct) value was defined as the number of cycles required for the fluorescent signal to reach the threshold. Using the comparative Ct method, the relative expression levels of miRNAs in serum were calculated using the formula for 2−ΔΔCt, where ΔΔCt = [Ct (target, test) - Ct (ref, test)] - [Ct (target, calibrator) - Ct (ref, calibrator)]. All primers used were obtained from Invitrogen (Carlsbad, CA, USA).

Statistical analysis

All Illumina sequencing data were log2 transformed. The differences between samples were calculated using Chi-square and Fisher’s exact tests. Only the miRNAs with the fold-difference >2.0 and P<0.01 were considered significant. Quantitative variables were expressed as mean ± standard deviation (SD). Comparison of biochemical characteristics was conducted by paired-samples and the Mann-Whitney test was used to compare the fold-differences of candidate miRNAs upon qRT-PCR in the validation data set, between cirrhosis and HCC status. All statistical analyses were performed using SPSS software, version 21.0 (SPSS, Inc., Chicago, IL, USA). All statistical tests were two-sided and the results were considered to indicate a statistically significant result when P<0.05.

Results

Global analysis of miRNAs by deep sequencing

Illumina HiSeq 2000 sequencing of the small RNA library from the serum of the patients with cirrhosis and HCC produced 9,846,382 and 9,342,644 raw reads, respectively. After extensive preprocessing and quality control, the raw reads were eventually removed, resulting in 425,662 and 426,113 clean reads, for cirrhosis and HCC status, respectively (Table III, Fig. 1A and B). The distribution of all reads between 16–30 nt is presented in Fig. 1C. In the present study, we found that the length of miRNAs was concentrated at 20 and 22 nt. A total of 1,653 unique reads were mapped to human miRNAs or pre-miRNAs from the iRbase database, and the pre-miRNAs could be further mapped to the human genome and expressed sequence tags (ESTs).

Table III

Overview of reads from raw data to cleaned sequences.

Table III

Overview of reads from raw data to cleaned sequences.

LibraryTypeCirrhosis
HCC
Total(%) of totalUniq(%) of uniqTotal(%) of totalUniq(%) of uniq
Raw readsNA9,846,382100958,7331009,342,644100906,960100
3 ADT and length filterSequence type863,8468.77332,48534.68848,7369.08302,54333.36
Junk readsSequence type6,5830.764,6340.485,3460.062,1430.24
RfamRNA class447,5134.5459,5676.21512,4565.4955,3546.10
mRNARNA class342,6463.4847,5474.96235,4332.5246,5755.14
RepeatsRNA class76,8690.7811,0861.1698,5741.068,7560.97
rRNARNA class432,6464.3932,5743.40242,4362.5932,5463.59
tRNARNA class123,2471.2518,6441.94115,3641.2315,3641.69
snoRNARNA class32,5360.336,5370.6820,1230.224,3650.48
snRNARNA class28,3280.294,2340.4425,3460.272,4560.27
Other Rfam RNARNA class175,6851.7815,7631.6417,5630.1910,7451.18
Clean readsSequence type7,248,03173.61425,66244.407,221,26777.29426,11346.98

[i] HCC, hepatocellular carcinoma; NA, not available.

Identification of novel miRNAs

In total, 14 novel miRNA genes were identified in the two disease categories. The length of these candidate miRNAs ranged from 20 to 24 nt. The localization, sequence, structure and expression profile of these miRNAs are summarized in Table IV. However, several candidates among the predicted novel miRNAs were expressed at extremely low levels.

Table IV

Novel miRNA candidates represented in the library.

Table IV

Novel miRNA candidates represented in the library.

Novel miR_namemiR_seqGenome IDStrandStartEndMFECirrhosis countHCC countP-value
PC-5p-1700_1443 TGTAGGCAAGGGAAGTCGGC gi|224514641|ref|NT_167214.1|+1160701163190.61,6134,5515.61E-42
PC-3p-8816_210 AGGACGGTGGTCATGGAAGTC gi|224589820|ref|NC_000008.10|1093045971093047100.68593,5086.89E-30
PC-3p-6084_321 ACGGGCTTGGCAGAATCAGC gi|224589807|ref|NC_000016.9|81902013819021460.52288353.41E-13
PC-5p-7578_250 AATTTCATCGTGATGGGGA gi|224589811|ref|NC_000002.11|2107644762107645370.61686052.02E-11
hsa-mir-7641-2-p3 TCGGGCCTGGTTAGTACTTGGA224589805+76070552760706040.54565593.07E-11
hsa-mir-6240-p5_1ss17GT TGCCCAGTGCTCTGAATGTC gi|224589802|ref|NC_000011.9|+77597474775975820.63005346.26E-11
PC-5p-250006_9 ATGTGATGCATCGCTTCTGT gi|224589811|ref|NC_000002.11|1706853751706854450.9283403.91E-08
hsa-mir-4459-p5_1ss4TC AGGCGGAGGTTGCAGTGAGC22458981753371348533714130.6251771.38E-06
PC-5p-156137_14 GCCGACTACAACATCCAGAA gi|224589820|ref|NC_000008.10|+1067304871067305850.8211552.81E-06
PC-3p-1704_1400 AGGGCTGGGTCGGTCGGGCTGG gi|224514641|ref|NT_167214.1|+1160701163190.62,5801401.14E-06
hsa-mir-566-p3_1ss6AG AGGCGGAGGTTGCAGTGAGC224589815+50210759502108520.531604.93E-04
PC-5p-32366_69 GTGGAAATCATGTGGGCTTT gi|224589804|ref|NC_000013.10|+61836053618361090.634498.14E-04
PC-5p-42534_51 AGAATGAAACTCAAAGGAAT gi|224589804|ref|NC_000013.10|32584468325845370.631476.86E-04
hsa-mir-451a-p3 TTTAGTAATGGTAATGGTTCT22458980827188387271884581.580251.15E-02

[i] Constitutively expressed novel miRNAs discovered by deep sequencing. Control count is the miRNA read count of cirrhosis and HCC patient count is the miRNA read count of carrier; location is the location of novel miRNA in genome (ID, start; end, strand); MFE, minimal free energy.

Analysis of differentially expressed miRNAs

When the cirrhosis and HCC status samples were compared, the differential expression levels of 127 miRNAs showed significant variability (Fig. 2). Among these, 22 miRNAs showed a >2-fold upregulation (P<0.01), and 2 miRNAs showed a >2-fold downregulation (P<0.01) in the cirrhosis and HCC patients (Table V).

Table V

Differential expression of miRNAs between HCC and cirrhosis.

Table V

Differential expression of miRNAs between HCC and cirrhosis.

No.miR_nameFold-changeFold-change (log2)Up/downmiR_seq
1hsa-miR-455-5p28.374.83Up UAUGUGCCUUUGGACUACAUCG
2hsa-let-7e-5p26.254.71Up UGAGGUAGGAGGUUGUAUAGUU
3 hsa-miR-483-5p_R−122.604.50Up AAGACGGGAGGAAAGAAGGGAG
4 hsa-miR-190b_R+122.364.48Up UGAUAUGUUUGAUAUUGGGUUU
5hsa-miR-30e-3p20.014.32Up CUUUCAGUCGGAUGUUUACAGC
6hsa-miR-486-5p17.414.12Up TCCTGTACTGAGCTGCCCCGAG
7 hsa-miR-584-5p_R−111.083.47Up UUAUGGUUUGCCUGGGACUGG
8hsa-miR-10a-5p10.453.38Up UACCCUGUAGAUCCGAAUUUGUG
9hsa-miR-574-3p9.383.23Up UGAGUGUGUGUGUGUGAGUGUGU
10 hsa-miR-193b-5p9.133.19Up CGGGGTTTTGAGGGCGAGATGA
11 hsa-miR-4532_R+28.333.06Up CCCCGGGGAGCCCGGCGCG
12hsa-miR-2067.692.94Up UGGAAUGUAAGGAAGUGUGUGG
13 hsa-miR-28-5p_R−27.432.89Up AAGGAGCUCACAGUCUUGAG
14hsa-miR-433-3p6.132.62Up AUCAUGAUGGGCUCCUCGGUGU
15 hsa-miR-3187-3p6.042.59Up TTGGCCATGGGGCTGCGCGG
16hsa-miR-98-5p5.712.51Up UGAGGUAGUAAGUUGUAUUGUU
17 hsa-miR-4433b-5p5.292.40Up AUGUCCCACCCCCACUCCUGU
18hsa-miR-497-5p4.942.30Up CAGCAGCACACTGTGGTTTGT
19 hsa-mir-1285-1-p54.602.20Up GAUCUCACUUUGUUGCCCAGG
20hsa-miR-141-3p3.291.72Up UAACACUGUCUGGUAAAGAUGG
21 hsa-miR-100-5p_R−12.991.58Up AACCCGUAGAUCCGAACUUGUG
22 hsa-miR-99b-3p_R−22.361.24Up CACCCGUAGAACCGACCUUG
32 hsa-miR-199a-5p0.50−0.99Down ACAGUAGUCUGCACAUUGGUUA
23 hsa-miR-1228-5p0.49−1.03Down GUGGGCGGGGGCAGGUGUGUG
24hsa-miR-202-3p0.48−1.05Down AGAGGTATAGGGCATGGGAA
29hsa-miR-92a-3p0.46−1.13Down TATTGCACTTGTCCCGGCCTGT
25 hsa-miR-6852-5p0.45−1.14Down CCCUGGGGUUCUGAGGACAUG
31hsa-miR-30b-5p0.45−1.14Down UGUAAACAUCCUACACUCAGCU
33hsa-miR-511-5p0.44−1.18Down GUGUCUUUUGCUCUGCAGUCA
27hsa-miR-320a0.37−1.44Down AAAAGCTGGGTTGAGAGGGCGA
28 hsa-mir-6127-p30.36−1.48Down UGAGGGAGUGGGUGGGAGG
26hsa-miR-26a-5p0.36−1.48Down UUCAAGUAAUCCAGGAUAGGCU
30hsa-miR-885-5p0.35−1.50Down TCCATTACACTACCCTGCCTCT
34hsa-miR-30a-3p0.32−1.63Down UGUAAACAUCCUCGACUGGAAG
35 hsa-miR-4454_L−20.19−2.39Down GGAUCCGAGUCACGGCACCA
36hsa-let-7c-5p0.08−3.61Down UGAGGUAGUAGGUUGUAUGGUU
37hsa-miR-30c-5p0.06−4.17Down UGUAAACAUCCUACACUCUCAGC
38hsa-let-7f-5p0.05−4.23Down UGAGGUAGUAGAUUGUAUAGUU
39hsa-miR-122-5p0.03−5.00Down UGGAGUGUGACAAUGGUGUUUG
40hsa-miR-192-5p0.03−5.00Down CUGACCUAUGAAUUGACAGCC

[i] HCC, hepatocellular carcinoma.

Validation of the differentially expressed miRNAs

We used qRT-PCR to confirm the expression of 40 candidate miRNAs that were selected from the previous step in an independent cohort consisting of 22 serum samples. The threshold value for the miRNAs was determined as Ct <35 and the detection rate >75%. We then calculated the 2−ΔΔCt of 40 candidate miRNAs in the 2 status types. Eight of the 40 miRNAs had significantly differential expression levels between the 2 statuses (Table VI). These miRNAs were hsa-miR-122-5p, hsa-miR-192-5p, hsa-miR-486-5p, hsa-miR-193b-5p, hsa-miR-206, hsa-miR-141-3p, hsa-miR-199a-5p and hsa-miR-26a-5p.

Table VI

Expression profiles of candidate miRNAs upon qRT-PCR in the validation set.

Table VI

Expression profiles of candidate miRNAs upon qRT-PCR in the validation set.

No.miR_nameHCC vs. cirrhosis
P-valueFold-change
1hsa-miR-455-5p0.1081.58
2hsa-let-7e-5p0.0634.50
3 hsa-miR-483-5p_R−10.1083.19
4 hsa-miR-190b_R+10.2463.47
5hsa-miR-30e-3pNDND
6 hsa-miR-486-5p0.0062.42
7 hsa-miR-584-5p_R−10.1083.25
8hsa-miR-10a-5p0.1441.43
9hsa-miR-574-3pNDND
10 hsa-miR-193b-5p0.0082.64
11 hsa-miR-4532_R+20.0231.23
12 hsa-miR-2060.0033.64
13 hsa-miR-28-5p_R−20.0692.45
14hsa-miR-433-3p0.1081.64
15 hsa-miR-3187-3pNDND
16hsa-miR-98-5p0.7231.63
17 hsa-miR-4433b-5pNDND
18hsa-miR-497-5p<0.0011.33
19 hsa-mir-1285-1-p50.0121.64
20 hsa-miR-141-3p0.0012.44
21 hsa-miR-100-5p_R−10.2891.54
22 hsa-miR-99b-3p_R−20.1252.14
23 hsa-miR-1228-5p0.2682.54
24hsa-miR-202-3pNDND
25 hsa-miR-6852-5pNDND
26 hsa-miR-26a-5p0.0020.64
27hsa-miR-320a0.0560.53
28 hsa-mir-6127-p30.1080.77
29hsa-miR-92a-3p0.2560.87
30hsa-miR-885-5p0.4420.65
31hsa-miR-30b-5p0.2560.45
32 hsa-miR-199a-5p<0.0010.53
33hsa-miR-511-5p0.2840.45
34hsa-miR-30a-3p0.0840.54
35 hsa-miR-4454_L−2NDND
36hsa-let-7c-5p0.0730.65
37hsa-miR-30c-5p0.0770.76
38hsa-let-7f-5p0.1120.78
39 hsa-miR-122-5p<0.0010.12
40 hsa-miR-192-5p<0.0010.24

[i] ND, not determined; miRNA Ct value >35 and detection rate <75%. HCC, hepatocellular carcinoma. Bold text, significant differences.

Discussion

Since the discovery of circulating miRNAs, several studies have been conducted to investigate their potential as novel biomarkers in body fluid. Circulating miRNAs have already been shown to be relevant biomarkers for cancer detection, applicable in non-invasive diagnostic testing and have demonstrated several other successful applications (2427). To date, three methods have mainly been used for the analysis of the expression profiles of circulating miRNAs in serum: qRT-PCR, microarray and next-generation sequencing technology (28). Although qRT-PCR has been widely employed for miRNA quantification, it is only capable of detecting a limited number of miRNAs at any one time. Microarray analysis, a high-throughput method, is capable of detecting only known fragments and is not suitable for detection of low-abundant miRNAs or for distinguishing between miRNAs with single nucleic acid polymorphisms. Compared to these techniques, next-generation sequencing technology appears to be more suitable for miRNA profiling. Thus, the Roche 454 genome Sequencer, the Illumina genome Analyzer and the ABI SOLiD System sequencing platforms have become widely available and used over the past few years.

Several studies have shown that many miRNAs are dysregulated in HCC (17,29,30) and have also considered the potential of circulating miRNA levels to affect HCC progression. The high stability of miRNAs in circulation suggests them for use as potentially ideal biomarkers, particularly for early-stage detection (4). Various studies have observed and explored the upregulation of circulating miR-21 (18,31), miR-222 (31) and miR-223 (32) in the serum/plasma of HBV- or HCV-associated HCC patients.

Downregulation of miRNAs is also a common finding in HBV-related HCC; in this case, these miRNAs act as tumor-suppressor genes. The pathological mechanisms of tumor-suppressive miRNAs is involved in cell cycle arrest, increased apoptosis and eventual reductions in tumor angiogenesis and metastasis by inhibiting migration and invasion. Among these downregulated miRNAs, miR-122 and miR-199 appear to be particularly important in HCC (3335). In the present study, we also found that the miRNA downregulated in cirrhosis status that evolved into HCC was miR-122, a liver-specific miRNA that is abundant in the liver and plays an important role in regulating hepatocyte development and differentiation (36,37). The overexpression of miR-122 has been found to induce apoptosis and suppress proliferation in the human liver carcinoma cell lines Hepg2 and Hep3B in vitro (38), and has been demonstrated in vivo directly by the generation of miR-122-knockout mice in liver cancer (39,40).

The present study revealed that serum hsa-miR-486-5p, hsa-miR-193b-5p, hsa-miR-206, hsa-miR-141-3p, hsa-miR-199a-5p, hsa-miR-122-5p, hsa-miR-192-5p and hsa-miR-26a-5p were potential circulating markers for HCC diagnosis, and 4 of these 8 miRNAs (miR-122, miR-199, miR-192 and miR-26a) in the present study have been previously reported to show differential expression (19,41,42).

At the circulating blood level, Xu et al (18) reported that miR-21, miR-122 and miR-223 could be utilized in discriminating HCC patients from a healthy group. Qu et al (43) found that miR-16 has moderate diagnostic accuracy in HCC. Li et al (14) reported an extraordinarily high diagnostic accuracy for serum miRNA profiles in the diagnosis of HCC [area under the curve (AUC) = 0.97-1.00] with miR-10a, miR-125b, miR-223, miR-23a, miR-23b, miR-342-3p, miR-375, miR-423, miR-92a and miR-99a. However, the need for different markers for different group comparisons with different critical values in their study (HCC vs. healthy, HCC vs. HBV, healthy vs. HBV, healthy vs. HCV and HBV vs. HCV) raised concerns about the robustness of these markers.

In our previous study (20), we established a logistic model of miRNAs for the diagnosis of HCC in a larger sample size and independent validation set. However, in the previous study, the cirrhosis and HCC patients were different individuals. While the present study was limited by the sample size, its innovation was the successful investigation of two phases of disease status in the same individuals.

Acknowledgments

The authors thank LC Bio-Tech Inc. for the expert technical assistance. This study was supported by the Natural Science Foundation of Jiangsu Province, China (BK2011151) (http://www.jstd.gov.cn/), the Medical Project of the Health Department, Jiangsu Province (H201248) (http://www.jswst.gov.cn/), the Preventive Medicine Research Projects of Jiangsu Province (Y2012016) (http://www.jswst.gov.cn/), and the Social Development Project of Zhenjiang City (SH201346) (http://kjj.zhenjiang.gov.cn/).

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June 2015
Volume 33 Issue 6

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Online ISSN:1791-2431

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Copy and paste a formatted citation
APA
Tan, Y., Lin, B., Ye, Y., Wen, D., Chen, L., & Zhou, X. (2015). Differential expression of serum microRNAs in cirrhosis that evolve into hepatocellular carcinoma related to hepatitis B virus. Oncology Reports, 33, 2863-2870. https://doi.org/10.3892/or.2015.3924
MLA
Tan, Y., Lin, B., Ye, Y., Wen, D., Chen, L., Zhou, X."Differential expression of serum microRNAs in cirrhosis that evolve into hepatocellular carcinoma related to hepatitis B virus". Oncology Reports 33.6 (2015): 2863-2870.
Chicago
Tan, Y., Lin, B., Ye, Y., Wen, D., Chen, L., Zhou, X."Differential expression of serum microRNAs in cirrhosis that evolve into hepatocellular carcinoma related to hepatitis B virus". Oncology Reports 33, no. 6 (2015): 2863-2870. https://doi.org/10.3892/or.2015.3924