Deep sequencing of small RNA transcriptome reveals novel non-coding RNAs in hepatocellular carcinoma

Accepted Manuscript Deep sequencing of small RNA transcriptome reveals novel non-coding RNAs in hepatocellular carcinoma Priscilla T-Y Law, Hao Qin, Arthur K-K Ching, Keng Po Lai, Ngai Na Co, Mian He, Raymond W-M Lung, Anthony W-H Chan, Ting-Fung Chan, Nathalie Wong PII: DOI: Reference:

S0168-8278(13)00079-2 http://dx.doi.org/10.1016/j.jhep.2013.01.032 JHEPAT 4582

To appear in:

Journal of Hepatology

Received Date: Revised Date: Accepted Date:

6 July 2012 17 January 2013 21 January 2013

Please cite this article as: Law, P.T-Y., Qin, H., Ching, A.K-K., Lai, K.P., Co, N.N., He, M., Lung, R.W-M., Chan, A.W-H., Chan, T-F., Wong, N., Deep sequencing of small RNA transcriptome reveals novel non-coding RNAs in hepatocellular carcinoma, Journal of Hepatology (2013), doi: http://dx.doi.org/10.1016/j.jhep.2013.01.032

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Deep sequencing of small RNA transcriptome reveals novel non-coding RNAs in hepatocellular carcinoma Priscilla T-Y Law1,2, Hao Qin3, Arthur K-K Ching1,2, Keng Po Lai1,2, Ngai Na Co1,2, Mian He1,2, Raymond W-M Lung1, Anthony W-H Chan1, Ting-Fung Chan3*, Nathalie Wong1,2* 1

Department of Anatomical and Cellular Pathology, 2State Key Laboratory in Oncology in South China, and 3School of Life Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

*Co-Correspondence: Nathalie Wong, Dept of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, N.T., Hong Kong Phone: +852-2632-1128; Fax: +852-2637-6274; E-mail: [email protected] Ting-Fung Chan, School of Life Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong Phone: +852-3943-6876; Fax: +8522603-7246; E-mail: [email protected]

Running title:

Small RNA Sequencing in HCC

Electronic word count (including abstract, references, tables and figure legends): 4968 Number of figures and tables: 5 figures and 2 tables List of abbreviations ncRNA, non-coding RNA; miRNA, microRNA; HCC, hepatocellular carcinoma; piRNA, PIWI-interacting RNA, snoRNAs, small nucleolar RNAs; siRNAs, small interfering RNAs; qRT-PCR, quantitative reverse transcription-polymerase chain reaction; CUHK-NTEC, Chinese University 1

of Hong Kong - New Territories East Cluster; HBV, hepatitis B; a.u., arbitrary unit; RT, reverse transcription; ATP, adenosine triphosphate; MTT, 3-(4,5-dimethylthiazolyl-2)- 2,5-diphenyl tetrazolium bromide; P, probability; PI3K, phosphoinositide 3-kinase; BWA, Burrows-Wheeler Aligner; INFERNAL, Inference of RNA alignments; BLAST, Basic Local Alignment Search Tool Conflict of interest: Nil Financial support: This research was supported by the Theme-based Research Scheme from the Hong Kong Research Grants Council, Hong Kong (Ref. no. T12-403/11) Acknowledgment: The authors thank Dr. Kevin Yip, Dr. Ka-Fai To and Dr. Kwok-Wai Lo for discussions and to Vanessa Fung and Coleen Lau for technical assistance.

2

Abstract Background & Aims: Small non-coding RNAs (ncRNA) are increasingly recognized to play important roles in tumorigenesis. With the advent of deep sequencing, efforts have been put forth to profile the miRNome in a number of human malignancies. However, information on ncRNA in hepatocellular carcinoma (HCC), especially the non-microRNA transcripts, is still lacking. Methods: Small RNA transcriptome of two HCC cell lines (HKCI-4 and HKCI-8) and immortalized hepatocyte line (MIHA) were examined using Illumina massively parallel sequencing. Dysregulated ncRNAs were verified in paired HCC tumors and non-tumoral livers (n=73) by quantitative reverse transcription-polymerase chain reaction. Clinicopathologic correlations and in-vitro functional investigations were further carried out. Results: The combined bioinformatic and biological analyses showed novel presence of ncRNAs and the involvement of a new PIWI-interacting RNA (piRNA) piR-Hep1 in the liver tumorigenesis. PiR-Hep1 was found to be up-regulated in 46.6% of HCC tumors compared to their corresponding adjacent non-tumoral liver. Silencing of piR-Hep1 inhibited cell viability, motility and invasiveness with a concomitant reduction in the level of active AKT phosphorylation. In the analysis of miRNA, we showed for the first 3

time the abundant expression of miR-1323 in HCC and its distinct association in tumors arising from a cirrhotic background. Furthermore, miR-1323 overexpression in cirrhotic-HCC correlated with poorer disease-free and overall survivals of patients (P<0.009). Conclusions: Our study demonstrated the value of next-generation sequencing in dissecting the ncRNome in cancer. The comprehensive definition of transcriptome unveils virtually all types of ncRNAs and provides new insight into the liver carcinogenetic events.

Electronic word count: 248

Keywords :

hepatocellular

carcinoma;

piwi-interacting RNA; microRNA; biomarker

4

small

RNA

sequencing;

Introduction Non-coding regions of the human genome account for 98% of the transcriptome [1]. Although once thought to be non-functional [2], these non-coding RNAs (ncRNAs) have emerged as regulators in almost every aspect of plant and animal biology. To date, the most widely studied class of ncRNA is undoubtedly the microRNA (miRNA). It regulates gene expression post-transcriptionally through binding complementarily to regulatory sequences of target mRNA [3]. Following the discovery of the first miRNA in 1993 [3], various classes of tiny ncRNAs have been identified, including piRNAs [4], snoRNAs [5], siRNAs [4], etc. These ncRNAs have been linked to developmental, physiological and pathological processes. In particular, the dysregulation of piRNAs has recently been implicated in cancer development [6, 7]. Many earlier profiling studies of ncRNA, mostly miRNA, have been accomplished through the use of chip-based [8] or Taqman-based [9] methods. However, these platforms often cannot keep abreast with the rapid discovery of novel ncRNAs, which can reach up to 200 miRNAs per year [10]. Moreover, novel miRNA identification by extensive cloning and sequencing not

only

consumes

tremendous

effort, 5

but

is

also

biased

to

abundantly-expressed miRNAs. The advent of high throughput massively parallel sequencing has provided an unprecedented platform to study the miRNome. Moreover, bioinformatics advances have further enabled different types of ncRNA to be analyzed simultaneously. In this study, we examined the entire small transcriptome (< 30nt) in hepatocellular carcinoma (HCC) by Illumina massively parallel sequencing. Dysregulated ncRNAs identified from two HCC cell lines were further validated in an independent cohort of paired HCC and non-tumoral liver by qRT-PCR. We successfully identified a novel piRNA, piR-Hep1, which is commonly over-expressed in HCC. Furthermore, our results highlighted a prevailing expression of miR-1323 in cirrhosis-related HCC. Our study demonstrated the value of RNA sequencing in dissecting the small non-coding transcriptome and in the discovery of novel ncRNAs.

6

Materials and Methods Cell lines HCC cell lines, HKCI-4 and HKCI-8, were established from chronic hepatitis B (HBV) infected patients with underlying liver cirrhosis [11]. Early passage of each cell line were cultured according to the conditions previously described [11]. Immortalized hepatocyte cell line, MIHA, was propagated in DMEM complete medium [12].

Patients Tumorous liver tissue and matching adjacent non-tumoral liver were collected from 73 patients who underwent curative surgery for HCC at Prince of Wales Hospital, Hong Kong. Informed consent was obtained from the recruited patients and the study protocol was approved by CUHK-NTEC Clinical Research Ethics Committee. All cases were reviewed by experienced pathologist who confirmed the diagnosis of HCC and histologically examined tissue sections for concomitant liver cirrhosis or chronic hepatitis. Serological analysis for HBV surface antigen indicated a chronic hepatitis B carriage in 89.0% of patients. The demographic information of patients is tabulated in Table 1. 7

Small RNA sequencing Total RNA was isolated from HKCI-4, HKCI-8 and MIHA cells by TRIzol (Invitrogen, Carlsbad, CA) and treated with RQ1 Rnase-free Dnase (Promega Corporation, Madison, WI). The RNA quality was assured by Bioanalyzer (Agilent Technologies, CA). Small RNA library preparations and sequencings using HiSeq2000 (Illumina Inc., CA) were performed at Beijing Genomics Institute-Shenzhen [13, 14]. For each library, DNase I treated total RNA was size fractionated by polyacrylamide gel electrophoresis and small RNA fraction ranging from 18 to 30 nt was excised. RNA eluted from the polyacrylamide gel was prepared for library construction according to the Illumina Small RNA Sample Prep protocol. Briefly, extracted RNA was converted to single-stranded cDNA using Superscript II reverse transcriptase (Invitrogen) and Illumina small RNA RT-Primer following manufacturer’s instructions. The resulting cDNA was PCR-amplified using small RNA primer set from Illumina. Purified PCR product was then subjected to single-ended strand-specific sequencing to generate more than 8GB data. The sequencer images were processed to derive base calls and generation of digital data. After the removal of adaptor sequences, the reads were then used 8

for bioinformatics analysis.

Sequence mapping and ncRNA identification All samples were mapped to homo sapiens reference genome hg19 by BWA 0.5.8c [15] with default parameters. The chromosomal location of each ncRNA was defined by the left-most and right-most position of all reads that covered that specific ncRNA in the three samples. Expression level of ncRNA, expressed as arbitrary unit (a.u.), was calculated by the sum of the number of reads that cover each base along the ncRNA averaged by the length of the ncRNA. All ncRNAs, with length >10 nt and expression level >10 a.u., were extracted. NcRNAs were annotated according to 3 databases: Refseq (http://www.ncbi.nlm.nih.gov/RefSeq/), miRBase [10] release 18 and Rfam [16] 10.1.

Putative miRNA prediction Novel ncRNAs extended at 50 nt from both 5’ and 3’ ends were subjected to Infernal [17] 1.0.2 for prediction with default parameters, using Rfam [16] 10.1 as database. Sequences with known miRNA hits on the plus strand and e-values <0.001 were considered as putative novel miRNAs. 9

Putative piRNA identification Novel ncRNAs, which were not predicted as miRNAs, were searched against known

piRNA

sequences

in

nr

database

from

NCBI

(http://www.ncbi.nlm.nih.gov/) using BLAST [18]. Sequences with piRNA hits on the plus strand and e-values <0.01 were considered as putative piRNAs.

Differentially-expressed ncRNAs identification NcRNA expressions of each cell line were normalized by sample size factors estimated by DESeq [19]. Pairwise comparison of ncRNA expression among different samples was then performed by DEGseq [20]. NcRNAs were considered dysregulated if they fulfill two criteria simultaneously: (1) Expressions in both HKCI-4 and HKCI-8 were consistently dysregulated at a significant level with q-value [21] at <1E-50; and (2) minimum fold change, defined

as

the

smaller

value

between

the

absolute

value

of

log2(HKCI-4)/(MIHA) and the absolute value of log2(HKCI-8)/(MIHA), at >1. 10

qRT-PCR For the estimation of ncRNA levels, specific reverse transcription (RT) primers and custom-designed or inventoried Taqman assays (Life technologies) were used. Accession number of tested ncRNAs were as follows: piR-Hep1 (#CS1RUEH), miR-1323 (#002786), miR-128 (#002216) and U6snRNA (#001973). qRT-PCR was performed as previously described [22] with endogenous U6 snRNA levels used as the normalization control. An increase in ncRNA level of > 2-fold was considered as up-regulation. Three normal liver RNA were acquired commercially (Ambion® Life Technologies, Clontech Laboratories Inc., and Stratagene) and served as reference. These samples were proven to be free of viral hepatitis infections (types B and C), and the integrity and quality of total RNA was ensured using the Agilent bioanalyser. For detection of Piwi orthologs, TaqMan assays targeting PIWIL1 (Hs01041737_m1;

Applied

Biosystems),

PIWIL2

(Hs00216263_m1),

PIWIL3 (Hs00381509_m1), PIWIL4 (Hs00908825_m1) were conducted. TaqMan assay for 18s (Hs99999901_s1) served as endogenous control.

11

Northern blot analysis Twenty micrograms of total RNA resolved on 12% polyacrylamide gel was electroblotted onto Nylon membrane. Oligonucleotides complementary to ncRNA were end-labeled with [γ-32P]-ATP by T4 polynucleotide kinase. Sequences

of

probes

were

as

follows:

5΄-TTCTCTAACCACTAGACCACCAGGGA-3΄, 5′-GCAGGGGCCATGCTAATCTTCTCTGTATCG-3′.

piR-Hep1:

and Hybridized

U6 blots

were later exposed onto radiograph film for signal visualization.

Knockdown of piR-Hep1 miRCURY LNATM inhibitor (Exiqon, Vedbaek, Denmark) against piR-Hep1 was custom-designed. Control inhibitor, with < 70% homology to all organism genomes in both NCBI and miRBase database, was used as negative control. Thirty-three nanomolar LNA inhibitor was mediated into HKCI-8 (with high endogenous level of piR-Hep1) using Lipofectamine 2000 (Invitrogen) for 24 hours according to manufacturer’s protocol. Successful knockdown was confirmed by qRT-PCR. The effect of piR-Hep1 on cell viability was examined by 3-(4,5-dimethylthiazolyl-2)-2,5- diphenyl tetrazolium bromide (MTT) assay 12

as previously described [22]. The effect of piR-Hep1 on cell migration and invasiveness were examined at 48h post-transfection by Transwell inserts (Corning Incorporated, Tewksbury, MA) and Matrigel invasion chambers (BD Biosciences, San Jose, CA), respectively. To circumvent the growth inhibitory effect of piR-Hep1 interfering with motility assays, DNA synthesis was halted by administering 1 ug/mL mitomycin C (Sigma-Aldrich, St. Louis, MO) in serum-free medium for 3 hours before transfection. Fifteen thousands and 3 x 104 transfected HKCI-8 cells in serum-free medium were then seeded onto the top chamber of the migration and invasion inserts, respectively with complete medium added to the bottom chamber as an attractant. After 48h, cells retained on top of the membrane were wiped off by a cotton swab while cells moved to the underside of the membrane were fixed and stained with hematoxylin. Cells were averaged from the enumeration of at least 10 microscopic fields. Each experiment was performed in duplicate inserts and the mean value was determined from 3 independent experiments.

Over-expression of piR-Hep1 piR-Hep1 was ectopically transfected into MIHA (with low endogenous level of

piR-Hep1).

Synthetic

single 13

stranded

piR-Hep1

(UCCCUGGUGGUCUAGUGGUUAGAGAmA)

and

control

RNA

(UCACAACCUCGGCUAGAAAGAGUAGmA)

were

acquired

from

Dharmacon Research (Lafayette, CO). Thirty-three nanomolar RNA was transfected into cells using Lipofectamine 2000 (Invitrogen). After 24h, cells were subjected to cell viability, cell migration and invasion assays as described for the knockdown experiments. The level of overexpression was assessed by qRT-PCR.

Western blot Twenty-microgram total protein was resolved on 10% SDS-PAGE, and transferred onto a nitrocellulose membrane. Specific primary antibody used included Phospho-AKT (Ser473) (#4060S, Cell Signaling), total AKT (#4691S, Cell Signaling) and β-ACTIN (#A1978, Sigma-Aldrich). Blots were visualized using ECL chemiluminescence (GE Healthcare).

Statistical analysis NcRNA expression in HCC tumors was compared with adjacent non-tumoral livers by Student's t-test and normal livers by Mann-Whitney test. Recurrence-free and overall survivals in cirrhotic HCC patients were 14

analyzed by the Kaplan-Meier method and the statistical probability (P) value was generated by Log Rank test. Clinicopathological information related to tumor progression was compared by Fisher’s exact test. The association between PIWIL2 and piR-Hep1 was evaluated by Pearson’s correlation analysis. The effects of piR-Hep1 on cell proliferation, invasion and migration were assessed by Student’s t test. All data were expressed as mean ± SEM. Statistical analyses were performed using Graphpad Prism, version 3.02 with P-value < 0.05 considered to be statistically significant.

15

Results Small RNA sequencing Deep sequencing of small RNA libraries derived from HKCI-4 and HKCI-8 generated 13.73 and 14.73 million raw reads, respectively. More than 95% of these reads were successfully mapped to reference genome hg19 by BWA [15]. Length of mapped reads peaked at 23-24 nt, which represented the length of the majority of miRNAs (Supp. Fig.1). Our approaches to annotate known miRNAs, and prediction of novel miRNAs and piRNAs from the unannotated sequences are summarized in Supp. Fig. 2. Briefly, small RNAs were first annotated according to three databases, Refseq (Release 50), miRBase (Release 18) [10] and Rfam 10.1 [16]. Distributions of mapped reads in HKCI-4 and HKCI-8 are shown in Fig. 1A. A total of 259 and 309 known miRNAs were identified in HKCI-4 and HKCI-8, respectively, which represented 23.30% and 25.14% of reads (Fig. 1A). The annotated miRNA levels showed high concordance between HKCI-4 and HKCI-8 (r=0.714), suggesting a satisfactory consistency in the two cell lines studied. Several well-characterized HCC oncomiRNAs, including miR-423 [23], miR-221-222 cluster [9] and miR-181a-181b cluster [24] were rediscovered and showed high expression levels in the cell lines 16

(Fig. 1B). MiRNAs shown to be up-regulated in other cancer types, like miR-25 [25], miR-107 [26] and miR-320 [27] were also found at elevated expression levels.

Sequences that could not be matched to any known genes were considered novel reads. These sequences, which represented 43.71% and 55.23% of reads in HKCI-4 and HKCI-8 (Supp. Fig.2) respectively, were further subjected to INFERNAL [17] for novel miRNA prediction using the reference database Rfam 10.1 [16]. At a cut-off of e-value <0.001, 37 and 35 putative miRNAs were identified in HKCI-4 and HKCI-8, respectively. The remaining sequences were searched against known piRNAs in the NCBI nr-database by BLAST [18]. Using a cut-off of e-value <0.01, 705 and 715 putative piRNAs were defined in HKCI-4 and HKCI-8, respectively. Hepatocyte cell line MIHA, which served as a control in this study, was also subjected to small RNA sequencing and the same analysis pipeline. Pairwise comparison of ncRNA expression between HCC cell lines and MIHA was performed by DEGseq [20], which indicated 62 known miRNAs (Supp. Table 1), 7 novel miRNAs (Supp. Table 2), and 171 piRNAs and piRNA-like RNAs (Supp. Table 3) to be differentially expressed in HCC 17

cells. To further identify important ncRNA candidates, ncRNAs were prioritized according to their minimum fold change, which was as defined in the Materials and Methods section. We then attempted to carry out validation on the two most up-regulated ncRNA in each category with a minimum fold change of >10 set as the selection criterion (Table 2). For putative miRNAs, 2 most up-regulated novel miRNAs showed only a fold change of 5.62 and 5.04, respectively, and hence were not considered for further investigations. Up-regulation of putative piR-Hep1 in HCC We queried small ncRNAs sequences against known piRNA database from NCBI, and determined a number of putative piRNAs (Supp. Table 3). It was reported in C. elegans that piRNA contains a 5’ uridine bias at the first base, and a 34 nt AT-rich motif centered with a core sequence “CTGTTTCA” at about 25 nt upstream of its start site [28]. Based on these two criteria, we refined our algorithm and found that the two most up-regulated putative piRNAs both began with a guanine instead of an uridine (Table 2) and they did not contain the upstream piRNA-specific core sequence motif, together these features excluded their possibility as bona fide piRNAs. Instead, the third most up-regulated putative piRNA, designated as piR-Hep1, began with 18

an uridine and matched 6 out of the 8 nucleotides in the core sequence of the piRNA-specific motif (Fig. 2A). Moreover, the AT content of piR-Hep1 34 nt motif reached as high as 70% (Fig. 2A). Sequence alignment analysis by ClustalW further demonstrated that the first 22 nt of piR-Hep1 was conserved among four other known human piRNAs (Fig. 2A), which further supported the likelihood of piR-Hep1 being a novel piRNA. PiR-Hep1 showed elevated expression in HKCI-4 and HKCI-8 with log ratio 11.94-fold and 12.83-fold, respectively when compared to MIHA (Table 2). Northern blot analysis (Fig. 2Bi) and qRT-PCR (Supp. Fig. 3) readily confirmed up-regulation of piR-Hep1 in these two HCC cell lines. The prevalence of piR-Hep1 was further investigated in a cohort of 73 HCC tumors and paired adjacent non-tumoral livers. Quantitation of piR-Hep1 level showed up-regulations in 80.8% (59 of 73 tumors) compared to normal livers (Mann-Whitney test P=0.038), and in 34 tumors when compared to their adjacent non-malignant liver (46.6%) (Fig. 2Bii and Supp Fig. 4). Increased expressions of piR-Hep1 could also be detected in the adjacent non-tumoral livers relative to normal livers (Mann-Whitney test P=0.033). Since tumor adjacent cirrhotic or chronic hepatitis liver is often considered a pre-malignant lesion of HCC, our finding may be interpreted as piR-Hep1 19

having a role in predisposing the risk to cancer development. We further attempted to establish clinicopathologic value of piR-Hep1, but were unable to achieve significant relation, albeit a trend of increased expression with tumor size was suggested (< 5cm: 6.2+15.6 vs. > 5cm: 116.9+494.4).

Functional Role of piR-Hep1 Since HKCI-8 exhibited high endogenous level of piR-Hep1 (Fig. 2Bi), it was subsequently used in the loss-of-function studies of piR-Hep1. Upon silencing by LNA inhibitor, piR-Hep1 expression in HKCI-8 was decreased to a level that similar to normal hepatocyte, MIHA (Supp Fig. 5A). In assessing the effect of knockdown piR-Hep1 on cell viability, cells transfected with piR-Hep1 inhibitor displayed a reduced cell viability by 30.36±3.10% on day 7 when compared with control inhibitor (P<0.0001) (Fig. 3A). The effects of piR-Hep1 inhibitor on cell motility and invasion were investigated in the presence of mitomycin C to eliminate interference of LNA-piR-Hep1 inhibition on cell viability (Supp. Fig.6). Cell invasion and Transwell motility of piR-Hep1 inhibited cells showed a decrease by 34.78±5.77% (P=0.005) and 18.95±5.82% (P=0.008), respectively compared to control experiments (Fig. 3B & C). 20

Contrary to piR-Hep1 inhibition experiments, overexpression of piR-Hep1 in MIHA (Supp. Fig.5B) showed no apparent effect on cell viability (Supp. Fig.7) but profound augmentations on cell migration by 214%±44% (P=0.0005) and invasion by 280%±73.7% (P=0.0014) were shown (Fig. 3D). It is plausible that MIHA being a non-tumorigenic hepatocyte cell line displays a more modest genetic background, and hence the functional effects of piR-Hep1 on motility and invasiveness may be more evident than HKCI-8. The combined results from HKCI-8 and MIHA might also suggest that the incremental genetic changes from the pre-malignant to malignant state might provide additional functional involvement of piR-Hep1, such as cell proliferation advantage.

Expression of PIWI orthologs in HCC Since current knowledge of piRNA targeting mechanism is still not well-established, we evaluated the potential functional mechanism of piR-Hep1 by first studying the human homologues of drosophila piRNA-binding protein PIWI. The human PIWI subfamily comprises of PIWIL1/HIWI,

PIWIL2/HILI,

PIWIL3,

and

PIWIL4/HIWI2

[29].

Quantitative PCR showed that only PIWIL2 and PIWIL4 were expressed in 21

normal livers. On the other hand, PIWIL1 and PIWIL3 did not show evident expression in HCC, adjacent non-tumoral liver or normal livers (all samples showed Ct >40 cycles). We further examined the expressions of PIWIL2 and PIWIL4 in HCC tumors along with their paired adjacent non-tumoral livers. The level of PIWIL2 expression determined in HCC and non-tumoral liver was compared to normal livers and found to be 1.65-fold (0.32-14.92) (median, quartiles) in tumors and 1.52-fold (0.21-3.09) in the adjacent non-malignant counterpart. Much increased PIWIL4 expression relative to normal liver was suggested at 8.37-fold (2.23-51.28) in HCC and 5.47-fold (1.59-19.13) in adjacent non-tumoral liver. We also examined the potential correlation between the expressions of PIWIL2 and PIWIL4 with the level of piR-Hep1. Pearson correlation analysis showed that only PIWIL2 expression correlated positively with the level of piR-Hep1 (r=0.424, P=0.025) (Fig. 3E), suggesting that PIWIL2 may be the interacting protein of piR-Hep1. PIWIL2 has been reported to promote tumorigenesis through the induction of signaling pathways [30]. One of its downstream target genes is AKT, which promotes a key oncogenic pathway of HCC [31]. We hence investigated the effect of piR-Hep1 on AKT activation, and found silencing of piR-Hep1 could readily reduce the level of 22

active phosphorylated AKT, whereas the total AKT level remained unchanged (Fig. 3F). Novel miR-1323 overexpression in HCC The two most up-regulated known miRNAs were miR-1323 and miR-128, which showed minimum fold change of 12.09 and 11.01, respectively (Table 2). Preliminary estimation of miR-128 expression did not suggest a significant difference between the tumor and non-tumoral counterpart (P=0.227; Supp. Fig.8); hence we focused our subsequent investigations on miR-1323. Quantitation of miR-1323 expression levels in a cohort of 73 HCC cases by qRT-PCR showed up-regulation in 39 tumors when compared to their adjacent non-malignant livers (53.4%; paired t-test P=0.046) (Fig. 4A). Up to 43.6% (17/39) of these cases showed a remarkable increase of miR-1323 by > 50-fold in tumors. Interestingly, clinicopathological correlations showed that miR-1323 is more frequently up-regulated in HCC arising from liver cirrhosis than chronic hepatitis (Fig. 4B, P=0.05). Furthermore, Kaplan-Meier survival analysis showed that miR-1323 up-regulation in cirrhotic-HCC patients correlated with a shorter recurrence-free (P=0.005) and overall survival (P=0.009) (Fig. 4C). A significant increase in the number of cases with 23

miR-1323 up-regulation was also suggested in advanced Stage T2/T3 tumors compared to early Stage T1 HCCs (P=0.041; Fig. 4D).

24

Discussion The importance of small ncRNAs is increasingly recognized due to their widespread occurrence and diverse functions as regulatory molecules. The advent

of

massively

parallel

sequencing

enables

researchers

to

comprehensively profile the small non-coding transcriptome and unveil previously unknown transcribing templates of the genome. In this study, we utilized deep sequencing to carry out in-depth analysis of the small non-coding transcriptome of two HCC cell lines and an immortalized normal hepatocyte. Our study was able to substantiate other earlier observations in which several well-characterized HCC oncomiRNAs, including miR-423 [23], miR-221-222 cluster [9] and miR-181a-181b cluster [24] were rediscovered and showed high expression levels in the two cell lines tested. Moreover, we were able to extend our understanding on ncRNAs by revealing novel involvements of a new piRNA, piR-Hep1, and miR-1323 in HCC. High expression of miR-21 is frequently described in HCC [32], whereas down-regulations of miR-122 and miR-29 have been reported in this malignancy [33-35]. We were unable to highlight common up-regulations of miR-21 in this study despite profound transcription of miR-21 could actually 25

be detected in HKCI-8 and HKCI-4 (Supp Table 1). This is due to the fact that MIHA also expressed elevated level of miR-21, and the ratio scored between HCC cells and MIHA has annulled the overall effect. Recent RNA-seq study has shown high expressions of miR-21 in human normal liver and non-tumoral liver adjacent to HCC [33]. Hence, the detection of high miR-21 expression in immortalized hepatocyte MIHA is probable. Moreover, it is likely that the process of immortalization which induces limitless replication and active proliferation is facilitated by the up-regulation of miR-21 through its role in promoting cell cycle progression [36]. Reports on miR-122, on the other hand, showed a decrease in expression in ~50% of HCC [33,34]. We found miR-122 to be under-expressed in HKCI-8 but over-expressed in HKCI-4 relative to MIHA (data not shown). Indeed, elevated miR-122 expression has also been detected in HCC tumors and adjacent non-tumoral liver [37]. While it is generally considered that miR-122 is abundantly expressed in the liver, because concordant deregulation of miR-122 was not observed in both cell lines, which was one of our filtering criteria, it was hence not highlighted in the Supp Table 1. Likewise, miR-29 is also known to be commonly down-regulated in HCC patients [35]. However, we found up-regulation of miR-29a in both HKCI-4 26

and HKCI-8 relative to MIHA (Supp Table 1). In line with our finding, up-regulation of miR-29a has also been found in 1/3 of HCC cases, where a functional implication on cell migration was further demonstrated [38]. PiRNA is a relatively new class of small ncRNAs of 26 – 32 nt in length, with its underlying downstream targeting mechanism remains largely elusive. PiRNAs were originally believed to play a role in maintaining germline stability through transposon repression [39]. Here, we showed that piR-Hep1, a novel piRNA, is up-regulated in HCC tumors and have roles in promoting HCC viability and invasiveness. Similarly, piR-651 [6] and piR-823 [7] were also dysregulated in gastric cancer and has been implicated in the control of cancer cell proliferation. In human, four members of the PIWI subfamily, PIWIL1/HIWI, PIWIL2/HILI, PIWIL3, and PIWIL4/HIWI2, have been identified and shown to interact with piRNA to regulate tumorigenesis [29]. In particular, we showed here that PIWIL2 is positively associated with the expression of piR-Hep1 (Fig. 3E). PIWIL2 has been documented in a wide variety of tumors as an oncogene [30]. In colon cancer, knockdown of PIWIL2 attenuated cell migration and invasion abilities via the regulation of MMP9 transcription in-vitro, and inhibited tumor growth in-vivo [40]. Moreover, association between PIWIL2 overexpression and up-regulation of 27

AKT were identified in human testicular germ cell tumor and mouse breast tumor tissues. Since PI3K/AKT signaling is one of the key oncogenic pathways in HCC, we further explored and showed a causal link between piR-Hep1 overexpression and the augmentation of PI3K/AKT signaling. In-vitro functional studies have previously demonstrated the importance of AKT pathway in HCC cell proliferation, motility and invasiveness [31]. In addition, AKT phosphorylation has been reported to be a risk factor for early disease recurrence and poor prognosis in HCC [41]. Reduction of phosphor-AKT level upon piR-Hep1 silencing might infer a role for PI3K/AKT signaling in mediating the proliferative and invasive properties of this novel piRNA. Up-regulation of miR-1323 ranked the most significant event in our two cell lines studied. However, its role remains largely elusive despite its discovery by large scale cloning experiment in 2006 [42]. In this study, we were able to establish a novel association of miR-1323 overexpression with cirrhosis-related HCC. About 70-90% of HCC are developed from a cirrhotic liver, which usually arises from chronic infection of hepatitis virus, alcohol consumption, or metabolic liver diseases. The elevation of miR-1323 in cirrhosis-associated HCC might have an implication on its potential 28

contribution in cancer development from the putative pre-malignant lesion of liver cirrhosis. More importantly, when focused on cirrhotic patients, we were able to establish a significant link between increased endogenous miR-1323 level and poorer overall and recurrence-free survivals of patients. These findings highlighted the potential prognostic value of miR-1323, especially in cirrhosis-related HCC patients. Though small RNA sequencing has been extensively performed in both solid and hematological malignancies [43], most studies focused on cataloging the miRNome with few attempts to investigate the functional effects and clinical relevance of identified candidates, especially the non-miRNA transcripts. Our study demonstrates novel involvements of piR-Hep1 and miR-1323 in hepatocarcinogenesis. These findings not only broaden current understanding of small non-coding transcriptome in HCC, but also support the notion that ncRNAs, including miRNAs and piRNAs, play an active part in the carcinogenetic process.

29

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36

Figure legends Fig. 1. Distribution of small non-coding RNAs in HKCI-4 and HKCI-8. (A) Pie chart shows the percentage mapped reads in each ncRNA category of HKCI-4 and HKCI-8. Annotated miRNAs constitute 23% and 25%, respectively, whereas sequences that cannot be matched to known genes are considered novel sRNAs and represent 23% and 39%, respectively in HKCI-4 and HKCI-8. Other novel ncRNAs identified include piRNA and piRNA-like RNA. (B) Concordant expression of annotated miRNAs in HKCI-4 and HKCI-8 is observed when compared to MIHA. Figures show most points, whose coordinates represent the genes’ expression in two samples, distribute near the line y=x. Except for miR-30d and miR-191 which were found at a near linear ratio, other miRNAs annotated represent those consistently up-regulated or down-regulated in both HKCI-4 and HKCI-8

Fig. 2. Up-regulation of novel piRNA, piR-Hep1, in HCC. (A) (i) The first 22 nt of piR-Hep1 is conserved among four other human piRNAs. (ii) A total of 2,684 and 4,907 reads can be mapped to piR-Hep1 in HKCI-4 and HKCI-8 respectively, with less than 0.3% mismatch rate. An AT-rich 5’-motif with core sequence “CTGTTTCA“ and a 5’-uridine first base specific to piRNA can be readily identified in piR-Hep1. (B) (i) 37

Northern blot showed distinct up-regulation of piR-Hep1 in HKCI-4 and HKCI-8 compared to immortalized hepatocyte MiHA. Bar chart shows quantitation of band intensities from 3 independent experiments. (B) (ii) qPCR showed high levels of piR-Hep1 in HCC tumors (T) compared to normal livers (NL) (Mann-Whitney test; *P=0.038) and an increased trend relative to adjacent non-tumoral liver (Paired T-test N.S., not significant). PiR-Hep1 expression in non-tumoral liver (NT) also showed significant difference relative to normal livers (NL) (Mann-Whitney test; P=0.033). Data shown on scatter plot presented with mean+SEM.

Fig. 3. Tumor-promoting properties of piR-Hep1. PiR-Hep1 knockdown in HKCI-8 reduced (A) cell viability on day 3, 5, 7 post-transfection, and (B) decreased cell invasiveness and motility (T-test; ***P<0.0005; **P<0.01). (C) Representative images of HKCI-8 cells invaded through the underside of Matrigel membrane in control and piR-Hep1 inhibitor-treated groups are shown. (D) Ectopic expression of piR-Hep1 in MIHA increased cell invasiveness and motility (T-test; ***P<0.0005; **P<0.01). (E) Pearson’s correlation analysis showed a positive correlation between PIWIL2 and piR-Hep1 expressions in HCC 38

tumors (P=0.025). (F) Western blot suggested a decrease in the level of active pAKTSer473 upon piR-Hep1 silencing in HKCI-8, whereas total AKT level remained unchanged. Bar chart shows quantitation of band intensities from 3 independent experiments (T-test; **P<0.01.)

Fig. 4. miR-1323 up-regulation in cirrhosis-associated HCC correlated with dismal survival and advanced staging. (A) qRT-PCR analysis showing significant up-regulation of miR-1323 was observed in HCC tumors (T) compared to adjacent non-malignant livers (NT) (Paired T-test; *P<0.05) and normal livers (NL) (Mann-Whitney test; *P<0.05). (B) More profound up-regulation of miR-1323 was observed in cirrhosis-associated HCC tumors (Paired T-test; *P=0.05, N.S., not significant). Data shown on scatter plots presented with mean+SEM (C) Kaplan-Meier analysis on cirrhotic HCC patients showed that high miR-1323 expressions (solid line) correlated with shorter recurrence-free (Log Rank test; **P=0.005) and overall survival (Log Rank test; **P=0.009). (D) High miR-1323 expressions also correlated with advanced-stage HCC (Fisher’s exact test; *P<0.05). 39

Fig. 1

Expressions of ncRNAs in HKCI-4 and HKCI-8

(A)

HKCI-4 Coding gene 6%

rRNA 1%

Other ncRNAs 5%

tRNA 25%

miRNA 23%

piRNA-like RNA 12% piRNA 5%

(B)

HKCI-8 Coding gene 6% Novel sRNA 23%

Other ncRNAs 3% piRNA-like RNA 7%

rRNA 1%

tRNA 13%

Novel sRNA 39% miRNA 25%

piRNA 5%

Fig. 2

Up-regulation of novel piRNA, piR-Hep1, in HCC

(A) (i)

piR-43771 piR-43772

-TCCCTGGTGGTCTAGTGGTTAGGATTCGGCA-TCCCTGGTGGTCTAGTGGTTAGGATTCGGCAC

piR-60565

TTCCCTGGTGGTCTAGTGGTTAGGATTCGGC--

piR-43770

-TCCCTGGTGGTCTAGTGGTTAGGATA------

piR-Hep1

-TCCCTGGTGGTCTAGTGGTTAGAGAA-----**********************

(ii) Aligned sequencing reads

HKCI-4

…

HKCI-8

…

Genomic region of ATP1B1 intron 4

46 nt

…TATCCAGATAACAGTTTCTATAACATAAACTCTTTAAAAAAGTAAAAATGGAAAAATATATTTTTTCCCTGGTGGTCTAGTGGTTAG…

Promoter

AT-rich region (~ 15 nt)

Spacer region (~ 25 nt)

HKCI-8

HKC-4

(ii) MIHA

(B) (i)

piRNA start

40 30 20

piR-Hep1 (26nt)

1 0.8 0.6

N.S.

*

10 1

10 0

piR-Hep1 levels

snU6 Relative piR-Hep1 expression

*

10 -1 10 -2 10 -3 10 -4 10 -5

0.4

10 -6

0.2

10-7

0

MIHA HKCI-4 MIHA HKCI4

HKCI-8 HKCI8

NL

NT

T

(n=3)

(n=73)

(n=73)

Tumor-promoting properties of piR-Hep1

(A)

(B) Control inhibitor piR-Hep1 inhibitor

Relative cell viability

1400%

(C) **

120% 120

1200%

80% 80

1000% 800%

60% 60

***

600%

40% 40

400%

20% 20

***

200%

0% 0

0%

1

3

5

7

Day

**

100 100%

100% 100

% invaded cells

***

120 120%

% migrated cells

1600%

Control Inhibitor + Mitomycin C

(D)

80 80% 60 60% 40 40% 20 20% 0 0%

piR-Hep1 inhibitor + Mitomycin C

Control inhibitor + Mitomycin C

Control Inhibitor + Mitomycin C

(F)

**

300

% migrated cells

% invaded cells

350 300 250 200 150 100

***

250 200 150 100

Control

piR-Hep1

0

12

Control

piR-Hep1

Control inhibitor

piR-Hep1 inhibitor

pAKTSer473 Total AKT

7

β-ACTIN 2

-4

50

50 0

Relative expression of PIWIL2 (log2)

(E) 400

piR-Hep1 inhibitor + Mitomycin C

piR-Hep1 inhibitor + Mitomycin C

0

4

8

-3

-8

r = 0.424 P = 0.025

Relative expression of piR-Hep1 (log2)

Relative intensity of pAKT/b-ACTIN

Fig. 3

120% 100% 80% 60% 40% 20% 0%

**

Control inhibitor

piR-Hep1 inhibitor

Fig. 4

miR-1323 up-regulation in cirrhosis-associated HCC correlated with dismal survival and advanced staging

(A)

(C) Recurrence-free survival

* * 10 0

miR-1323 levels

10 -1

10 -2 10 -3 10 -4

1.0

0.5

0.0 0

24

48

72

96

120

Months

10 -5 10 -6

NT

T

(n=73)

(n=73)

(B) N.S.

*

Overall survival

NL

(n=3)

1.0

0.5

0.0

10 0

0

24

48

72

96

120

Months

(D)

10 -1 10 -2

* 100

10 -4

80

% of cases

10 -3

10 -5 10 -6

10 -7

Presence of miR-1323 over-expression(n=25) Absence of miR-1323 over-expression(n=17)

**P = 0.009

10 -7

miR-1323 levels

Presence of miR-1323 over-expression(n=25) Absence of miR-1323 over-expression(n=17)

**P = 0.005

Presence of miR-1323 over-expression Absence of miR-1323 over-expression

60 40 20 0

NT

T

Chronic Hepatitis (n=31)

NT

T

Cirrhosis (n=42)

Early Advanced T1 T2/T3 (n=29)

(n=13)

Stage

Table 1

Demographic information of HCC primary cohort Number of patients (n = 73) Gender Male Female Age (years) < 40 >40 Hepatitis B surface antigen Positive Negative Cirrhosis Present Absent Histology grade Well-differentiated Moderately-differentiated Poorly-differentiated † Stage I II III Number of lesions Multiple Solitary Macrovascular invasion Present Absent Microvascular invasion Present Absent

†

64 9 8 65 65 8 42 31 9 55 9 46 9 18 21 52 5 68 14 59

HCC staging was classified according to tumor-node-metastasis system by American Joint Committee on Cancer

Table 2

Known and novel ncRNAs up-regulated in HCC †

ncRNA

Chromosomal location

Known miRNAs miR-1323 chr19: 54175222-54175294(+) miR-128 chr3: 35785968-35786051(+) Novel piRNAs piR-Hep1 chr1:169096981-169097006(+) n/a chr1:163620037-163620062(+) Novel pi-like-RNAs n/a chr1:17439783-17439810(-) n/a chrX:86465288-86465315(+) Putative novel miRNAs n/a chr6:1900181-1900208(+) n/a chr6:185884-185926(-)

†

Sequence (5΄ - 3΄)

Expression level

HKCI-4 Fold change, q-value Log2

HKCI-8 Fold change, q-value Log2

5’ piRNAspecific motif

HKCI-4

HKCI-8

MIHA

ucaaaacugaggggcauuuucu ucacagugaaccggucucuuu

5165.1 686.7

55682.5 1016.7

1.2 0.3

12.1 11.0

0.0E+00 1.6E-96

15.5 11.6

0.0E+00 1.7E-129

12.1 11.0

n/a n/a

ucccugguggucuagugguuagagaa ucccugguggucuaguggcuaggata

1967.5 293.0

3638.7 481.7

0.00 8.6

11.9 5.1

4.5E-234 1.0E-68

12.8 5.8

0.0E+00 4.9E-114

11.9 5.09

Present Present

guucccugguggucuagugguuagaaaa guaguuuugguauaguggugagcauaac

5385.2 2062.6

10363.7 2474.3

0.00 0.00

13.4 12.0

0.0E+00 2.2E-242

14.3 12.3

0.0E+00 4.9E-277

13.4 12.0

Absent Absent

guagaggagauggcgcaggggacacggg gagucaggggguguagaucagugguaga gcgcgugcuucgcau

652.3

1336.3

13.2

5.6

4.9E-154

6.7

0.0E+00

5.6

n/a

3012.9

803.9

24.5

6.9

0.0E+00

5.0

2.2E-186

5.0

n/a

Expression level corresponds to the number of reads mapped normalized by the length of the ncRNAs

††

Min fold change

Minimum fold change refers to the minimum value between the absolute value of log2(HKCI-4)/(MIHA) and the absolute value of log2(HKCI-8)/(MIHA)

††