A microRNA expression profile for vascular invasion can predict overall survival in hepatocellular carcinoma

Accepted Manuscript A microRNA expression profile for vascular invasion can predict overall survival in hepatocellular carcinoma

Zhuo Lin, Yi-Jing Cai, Rui-Cong Chen, Shi-Hao Xu, Xiao-Dong Wang, Mei Song, Jian-Min Wu, Yu-Qun Wang, Meng-Tao Zhou, Ke-Qing Shi PII: DOI: Reference:

S0009-8981(17)30103-1 doi: 10.1016/j.cca.2017.03.026 CCA 14696

To appear in:

Clinica Chimica Acta

Received date: Revised date: Accepted date:

6 January 2017 21 March 2017 28 March 2017

Please cite this article as: Zhuo Lin, Yi-Jing Cai, Rui-Cong Chen, Shi-Hao Xu, XiaoDong Wang, Mei Song, Jian-Min Wu, Yu-Qun Wang, Meng-Tao Zhou, Ke-Qing Shi , A microRNA expression profile for vascular invasion can predict overall survival in hepatocellular carcinoma. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Cca(2017), doi: 10.1016/ j.cca.2017.03.026

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ACCEPTED MANUSCRIPT A microRNA expression profile for vascular invasion can predict overall survival in hepatocellular carcinoma Zhuo Lin1#, Yi-Jing Cai1#, Rui-Cong Chen1#, Shi-Hao Xu2, Xiao-Dong Wang1, Mei Song1, Jian-Min Wu3, Yu-Qun Wang1, Meng-Tao Zhou4*, Ke-Qing Shi1*

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1. Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical

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University, Wenzhou, Zhejiang, China.

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2. Department of Ultrasonography, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.

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3. Institute of Genomic Medicine, Wenzhou Medical University; Wenzhou, Zhejiang,

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China.

4. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou

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# Co-First author

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Medical University, Wenzhou, China

*Corresponding Author

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Ke-Qing Shi, MD

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Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University; Wenzhou, Zhejiang, China. No. 2 Fuxue lane,Wenzhou 325000, China. E-mail: [email protected]; fax: (86) 577-88078262; tel: (86) 577-57779621 Meng-Tao Zhou, MD Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University

ACCEPTED MANUSCRIPT E-mail: [email protected] and [email protected]

Author contributions Zhuo Lin: study design, data collection and analysis, interpreted data, drafted the

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manuscript. Yi-Jing Cai, data analysis and helped to draft the manuscript. Rui-Cong

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Chen and Jian-Min Wu: data collection and analysis, interpreted data, prepared figures.

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Yu-Qun Wang, Xiao-Dong Wang, Mei Song and Zhuo Lin: data collection and analysis. Meng-Tao Zhou: study design and supervision. Meng-Tao Zhou and Ke-Qing

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Shi: study design, study supervision, obtained funding and helped to draft the

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manuscript. All authors saw and approved the final version of the paper.

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List of Abbreviations:

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VI, vascular invasion; HCC, hepatocellular carcinoma; OS, overall survival; miRNA, microRNA; TCGA, The Cancer Genome Atlas; AUC, area under receiver operating

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characteristics; C-index, Concordance index; td-ROC, time-dependent receiver

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operating characteristic; LASSO, least absolute shrinkage and selection operator method; HR, Hazard ratio; CI, confidence interval.

Disclosures: All authors declare that they do not have anything to disclose regarding funding from industry with respect to this manuscript.

ACCEPTED MANUSCRIPT Acknowledgements: This work was supported by grants from the Natural Science Foundation of Zhejiang Province (LY16H160047), National Natural Sciences Foundation of China (81201589 and 81472651), Public Welfare Science and Technology Project of Wenzhou

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(Y20150307 and Y20140718), and Project of New Century 551 Talent Nurturing in

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Wenzhou.

ACCEPTED MANUSCRIPT Abstract Background: The presence of vascular invasion (VI) in pathology specimens is a well-known unfavorable prognostic factor of hepatocellular carcinoma (HCC) recurrence and

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overall survival (OS). We investigated the vascular invasion related microRNA

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(miRNA) expression profiles and potential of prognostic value in HCC.

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Methods:

MiRNA and mRNA expression data for HCC were accessed from The Cancer

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Genome Atlas (TCGA). LASSO logistic regression models were used to develop a

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miRNA-based classifier for predicting VI. The predictive capability was accessed by area under receiver operating characteristics (AUC). Concordance index (C-index)

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and time-dependent receiver operating characteristic (td-ROC) were used to

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determine its prognostic value. We validated the predictive and prognostic accuracy of this classifier in an external independent cohort of 127 patients. Functionally relevant

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targets of miRNAs were determined using miRNA target prediction, experimental

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validation and correlation of miRNA and mRNA expression data. Results:

A 16-miRNA-based classifier was developed which identified VI accurately, with AUC of 0.731 and 0.727 in TCGA set and validation cohort, respectively. C-index and td-ROC showed that the classifier was able to stratify patients into risk groups strongly associated with OS. When stratified by tumor characteristics, the classifier was still a clinically and statistically significant prognostic model. The predictive and

ACCEPTED MANUSCRIPT prognostic accuracy of the classifier was confirmed in validation cohort. Vascular invasion related miRNA/target pairs were identified by integrating expression patterns of predicted targets, which were validated in cell lines. Conclusions:

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A multi-miRNA-based classifier developed based on the presence of VI, which could

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effectively predict OS in HCC.

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Keywords: hepatocellular carcinoma; microRNA; vascular invasion; The Cancer

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Genome Atlas; time-dependent receiver operating characteristic

ACCEPTED MANUSCRIPT Introduction Hepatocellular carcinoma (HCC) is one of the most common malignant tumor globally [1]. Despite the clinical implementation of numerous therapeutic strategies, the 5-year survival of HCC still remains low [2]. It is known that vascular invasion

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(VI) is a major predictive factor of recurrence due to tumor cell dissemination and

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poor survival for HCC [3-6]. Preoperative imaging techniques for documenting VI are

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still challenging, as it is detected only at pathological examination of surgically resected specimen, which limits the influence of the diagnosis on preoperative

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decision making [7]. Much work has been devoted to preoperative estimation of VI in

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HCC. Preoperative serum alpha-fetoprotein (AFP), tumour number, tumour size, and tumour volume were closely associated with VI [8, 9]. The use of serum or tumor

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biomarkers to estimate VI risk has also been proposed [10]. However, these results

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required further prospective validation to avoid potential interobserver variability. Although some gene signatures have been identified to be associated with VI, they

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were not performed by genome-wide analysis [10, 11].

The application of microarray or high-throughput technologies which rely on thousands of pieces of bio-information and provide an accurate landscape of HCC genetic alterations has enabled researchers to measure the expression of a large number of genes in HCC for diagnosis of VI. Comprehensive multidimensional genetic and molecular profiles of large tumor populations generated by The Cancer Genome Atlas (TCGA) have enabled integrated analysis of genetic and molecular

ACCEPTED MANUSCRIPT alterations associated with tumor characteristics [12, 13]. MicroRNAs (miRNAs) are small, noncoding regulatory RNA molecules that influence a wide range of biological and pathological processes [14, 15]. They bind to the 3′-untranslated region (3′-UTR) of target mRNA to inhibit translation or promote mRNA degradation[14, 16]. Many

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miRNAs are proposed as biomarkers for VI in HCC [17, 18]. However, the sample

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size of miRNA expression profiling studies is small, and validation is lacking.

In current study, we developed a multi-miRNA-based classifier that predicts the

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presence of VI in patients with HCC using TCGA data with least absolute shrinkage

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and selection operator method (LASSO) logistic regression model, a statistical method is suitable for high-dimensional microarray data [19]. Additionally, we

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assessed the prognostic and predictive accuracy of this classifier.

Materials and methods

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TCGA datasets and patients specimens

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A total of 377 patients with liver cancer datasets were downloaded from the publicly available TCGA data portal (up to March 1st, 2016, https://tcga-data.nci.nih.gov). The exclusion criteria were set as follows: 1) samples were not HCC; 2) samples with clinical data but without miRNA sequence data; and 3) missing important clinical or biological data. Finally, 361 HCC patients were included in our study for further analysis. This study met the publication guidelines provided by TCGA.

ACCEPTED MANUSCRIPT The miRNA expression data of the corresponding patients at level 3 were downloaded from TCGA. Expression profiling was performed using the Illumina HiSeq 2000 miRNA sequencing platforms (Illumina Inc, San Diego, CA) and expression level was demonstrated as reads per million miRNA mapped data. Expression analyses were

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performed using BRB-ArrayTools (version 4.4)[20]. In brief, the miRNAs with

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missing data exceeded 20% of all subjects were excluded and the expression level of

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each individual miRNA was log2-transformed for further analysis.

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To externally validate the miRNA-based classifier, we used an independent cohort of

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127 patients with HCC recruited from the First Affiliated Hospital of Wenzhou Medical University (validation cohort). These patients were diagnosed with HCC

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between January, 2010 and August, 2016. Clinical information of each patient

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including details of pathology and outcomes with a regularly follow up was collected. Research ethics approval for this project was granted from the First Affiliated

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Hospital of Wenzhou Medical University, and written informed consents were

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obtained from all patients for the use of the biospecimens for research purposes. The HCC samples were frozen and stored in liquid nitrogen immediately after surgically resected.

miRNA-based classifier for VI HCC VI specific miRNAs were retained as differentially expressed and significant if P values were lower than 0.05 from TCGA. The hierarchical clustering analysis was

ACCEPTED MANUSCRIPT performed with Multiexperiment Viewer (version 4.9) using the average linkage method and uncentred Pearson’s correlation coefficients [21]. Each HCC VI specific miRNA in clustering analysis was standardized independently by the Z-score transformation to scale expression intensities into having a mean of 0 and a standard

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deviation of 1.

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LASSO is a popular method for regression with high-dimensional predictors. The method performs a sub-selection of miRNAs involved in VI development by

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shrinkage of the regression coefficient through imposing a penalty proportional to

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their size. This results in most potential predictors being shrunk to zero leaving a relatively small number with a weight of nonzero [22]. These miRNAs may not be the

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only potential predictors in the set, because, if two predictors exhibit co-linearity,

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LASSO will choose the one that has the strongest association with response (which is not necessarily the only causal one, especially if the difference between the two

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predictors’ degree of association with response is not significant) and the other will be

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given zero weight. LASSO can be used for optimal selection of genes in high-dimensional microarray data with a strong predictive value and low correlation between each other to prevent overfitting [19]. We used the LASSO logistic regression model to select the most useful predictive markers of all the VI specific miRNAs, and constructed multi-miRNA-based classifier for predicting VI using the sum of miRNA expression values weighted by the coefficients from the LASSO logistic regression.

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LASSO logistic regression model analysis was performed by R software (version 3.1.2) and the “glmnet” package. The multi-miRNA-based classifier was applied to HCC samples, and the samples were assigned to high- and low-risk groups using the

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median as a cut-off.

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Quantitative reverse transcription–polymerase chain reaction (qRT-PCR) Total RNA was extracted from frozen HCC tissues or cells using the miRcute miRNA

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isolation kit (TIANGEN, Beijing, CN) according to the manufacturer’s instructions.

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Reverse transcription were executed using the miDETECT miRNA RT Kit (RIBOBIO, Guangzhou, CN) for miRNA and FastQuant RT Kit (TIANGEN, Beijing, CN) for

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mRNA, and PCR were performed using miDETECT miRNA PCR Kit (RIBOBIO,

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Guangzhou, CN) for miRNA and SuperReal PreMix Plus PCR Kit (TIANGEN, Beijing, CN) for mRNA according to the manufacturer’s instructions. Quantitative

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real-time PCR was performed using a 7500 Real-time PCR system (Applied

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Biosystems, Carlsbad, USA). Primers for miRNA (RIBOBIO, Guangzhou, CN) and mRNA (Invitrogen, Carlsbad, USA) detection were showed in Supplementary Table 1 and Supplementary Table 2. Each sample was analyzed in triplicate, U6 and beta-actin were used for normalization. Expression level of individual miRNA or mRNA was determined by -ΔCT approach (miRNA ΔCT = CT miRNA – CT U6, mRNA ΔCT = CT mRNA – CT beta-actin). The miRNA -ΔCt values were used in the classifier algorithm to create risk scores for each patient. The patients were separated into high-

ACCEPTED MANUSCRIPT and low-risk groups (stratified by median of the score) to assess for association with overall survival (OS), recurrence and tumor free survival (TFS).

Cell culture, transfection and invasion assays

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Human HCC cell lines HepG2 and HCCLM3 cells were cultured in DMEM medium

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(Gibco, Gaithersburg, USA) containing 10% fetal bovine serum (Gibco, Gaithersburg,

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USA) at 37 °C in 5% CO2. Cells were transfected with 100nM miRNA mimic or mimic negative control (RIBOBIO, Guangzhou, CN) by Lipofectamine RNAiMAX

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Reagent (Invitrogen, Carlsbad, USA) according to the manufacturer’s instructions.

Cell invasion assays were performed by using transwell chambers (Corning,

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Tewksbury, USA) with Matrigel (Corning, Tewksbury, USA) according to the

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manufacturer’s instructions. Cells were plated into the insert of the well in serum-free medium. Medium containing 10% fetal bovine serum was added to the lower chamber.

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After 16 to 24 h incubation at 37°C, the cells remaining in the upper chamber or on

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the upper membrane were removed with a cotton swab. Cells that invaded to the lower surface of the membrane were fixed with a solution containing 0.1% crystal violet and 20% methanol. The number of cells that had invaded to the lower surface of the filter membrane was counted in five randomly chosen fields under a light microscope.

Prediction miRNA targets

ACCEPTED MANUSCRIPT The putative targets of miRNAs were predicted using three different target prediction algorithms: TargetScan v7.1 (http://www.targetscan.org), DIANA-microT v5.0 (http://diana.imis.athena-innovation.gr)

and

miRDB

(http://mirdb.org/miRDB/).

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Consensus targets were then defined as genes predicted by all the three algorithms.

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Statistical analysis

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The Chi-square test was used for categorical data. Predictive performance of the multi-miRNA-based classifier for predicting VI was tested by using the receiver

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operating characteristic (ROC) curve analysis. ROC curve analysis was performed by

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MedCalc software (version 14.0). Survival estimate for the study population were generated using the Kaplan-Meier method. The association between relevant variables

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and prognosis were assessed using Cox proportional hazards models. Variables with P

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< 0.05 in the univariate Cox regression analysis were progressed to a multivariate analysis using backward stepwise selection. Hazard ratio (HR) and 95% confidence

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interval (CI) were calculated. The performance of multi-miRNA-based classifier was

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evaluated by the concordance index (C-index) and assessed by comparing the classifier-predicted vs observed Kaplan-Meier estimates of survival probability, and bootstraps with 1000 resamples were applied to these activities using the “rms” package of R software (version 3.1.2) [23]. The time-dependent ROC (tdROC) was also used for assessing the performance of the multi-miRNA-based classifier and each independent risk factor for predicting the OS with “time ROC” package in R software[24]. A larger C-index and area under ROC (AUC) indicated more accurate

ACCEPTED MANUSCRIPT predictive stratification. P values less than 0.05 were considered statistically significant.

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Results

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Characteristics of patients in TCGA set and validation cohort

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Among the enrolled patients, 106 patients (106/361, 34.6%) in TCGA datasets and 46 patients (46/127, 36.2%) in validation cohort had VI. As summarized in Table 1, no

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significant difference was observed in the distribution of gender (P = 0.115), age (P =

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0.227), TNM staging system (P = 0.902), AJCC pathological stage (P = 0.429) and VI

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(P = 0.754).

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VI specific miRNAs and miRNA-based classifier A total of 1046 miRNAs were identified from the TCGA set, of which 29 miRNAs

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were found to be differentially expressed between the VI and none VI. Of them, 20

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miRNAs were up-regulated and 9 were down-regulated (Supplementary Table3). There were two subtypes for let-7a and miR-9, which differed in sequence by one nucleotide. Let-7a-2 and miR-9-1 were selected for the remainder of the study. As outlined in Figure 1A, the unsupervised hierarchical clustering with the 27 miRNA expression data clearly discriminated the VI and none VI.

In order to develop miRNA-based classifier for predicting VI in HCC,

ACCEPTED MANUSCRIPT LASSO-logistic regression was performed using the 27 miRNAs expression data. Using the LASSO method, 16 miRNAs were identified with non-zero regression coefficients (Figure 1B, 1C). A risk score was created using the regression coefficients from the LASSO analysis to weight the expression value of the 16 miRNAs. where VI

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0.167E_miR-15a-5p

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0.364E_miR-140-5p

-

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0.069E_miR-550a-5p

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classifier = 0.088E_miR-452-5p - 0.071E_miR-378c + 0.020E_miR-9-5p +

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0.218E_miR-23b

+

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0.059E_let-7g-5p - 0.246E_miR-152-3p - 0.026E_miR-122-5p + 0.173E_miR-212-3p 0.013E_miR-365a-3p

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0.035E_miR-629-5p

+

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0.038E_miR-1270 - 0.256E_miR-659-3p + 0.054E_miR-3941, E_miRNA = Log2

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(expression value of miRNA).

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Accuracy of miRNA-based classifier for VI

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ROC curve analysis also showed the classifier had a good predictive accuracy in TCGA set (AUC = 0.731, 95% CI: 0.678 – 0.780; Figure 2A) and validation cohort

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(AUC = 0.727, 95% CI: 0.641 – 0.802; Figure 2B). The best cut-off values for

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miRNA-based classifier were -0.54 and 1.73 in TCGA set and validation cohort, with a sensitivity of 61.3 (95% CI: 51.4 - 70.6) and 63.0 (95% CI: 47.5 - 76.8), specificity of 76.5 (95% CI: 70.0 - 82.2) and 75.3 (95% CI: 64.5 - 84.2), respectively, in predicting VI.

Prognosis value of miRNA-based classifier Additionally, we investigated the prognostic accuracy of miRNA-based classifier

ACCEPTED MANUSCRIPT according to the cut-off value. Cox univariate analysis showed AJCC pathological stage (HR: 2.43, 95% CI: 1.68 – 3.53, P < 0.001), TNM T classification (HR: 2.55, 95% CI: 1.79 - 3.63, P < 0.001) and miRNA-based classifier (HR: 2.05, 95% CI: 1.45 2.91, P < 0.001) were associated with OS in TCGA set. After multivariable adjustment

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by these three variables, AJCC pathological stage (HR: 1.97, 95% CI: 1.36 - 2.85; P <

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0.001) and miRNA-based classifier (HR: 2.26, 95% CI: 1.56 - 3.29; P < 0.001)

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remained to be powerful and independent factors for OS (Table 2). However, Cox multivariate analysis showed the miRNA-based classifier was not an independent

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factor for TFS and reccurence (Supplementary Table 4, Supplementary Table 5).

The average predicted probability (predicted OS) was plotted against the

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Kaplan-Meier estimate (observed OS), and the dashed lines represent the ideal

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reference line for which predicted survival corresponds with actual survival. The calibration plot for the probability of 1 year, 3 year and 5 year survival showed

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excellent agreement between the prediction by miRNA-based classifier and actual

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observation in TCGA set (Figure 3A) and validation cohort (Figure 3B). The tdROC also showed that the classifier had a good prognostic accuracy in OS of HCC patients in TCGA set (Figure 3A) and validation cohort (Figure 3B). Patients with higher risk scores generally had worse survival than did those with lower risk scores in TCGA set (HR: 2.04, 95% CI: 1.41 - 2.95; P < 0.0001), which was confirmed in validation cohort (HR: 1.93, 95% CI: 1.20 - 3.11; P = 0.0001).When HCC patients stratified by clinicopathological risk factors (tumor grade and tumor stage), the miRNA-based

ACCEPTED MANUSCRIPT classifier was still a clinically and statistically significant prognostic model in overall survival in TCGA set (Figure 4) and validation cohort (Supplementary Figure 1).

The miRNA-based classifier also showed higher prognostic accuracy than tumor stage,

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and the classifier combination with tumor stage provided a more accurate prediction

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in overall survival of 1-year, 3-year and 5-year for HCC patients, wherever in TCGA

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set (Figure 5) or validation cohort (Supplementary Figure 2). Thus, the miRNA-based classifier could add the prognostic value to the clinicopathological prognostic

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characteristics.

Target genes of miRNAs

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The putative target genes of VI miRNAs were predicted by TargetScan,

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DIANA-microT and miRDB. The consensus target genes were summarized in Supplementary Table 6. MiR-23b had the highest number of consensus target genes.

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We validated the putative target genes of miR-23b (ATP6V1E1, CHST7, GCNT2,

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LHFPL2, MAPRE1, RBPMS2, SETD8, SUCO and TOX) by transfecting miR-23b mimic or negative micmic control in HepG2 and HCCLM3 cells. qRT-PCR showed that the expression level of RBPMS2 significant decreased in HepG2 and HCCLM3 cells after transfecting with miR-23b mimic (Figure 6).

miR-23b inhibits the invasion of HCC cells We also investigated whether miR-23b had an inhibitory effect of VI in HCC cells.

ACCEPTED MANUSCRIPT Transwell assays showed the invasion capacities of HepG2 and HCCLM3 cells significantly decreased after transfecting with miR-23b mimic (Figure 7). miR-23-3p attenuates the invasion capacity of HCC cells.

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Discussion

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VI is described as intrahepatic dissemination that initiates when tumor cells have

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developed an advanced phenotype to invade blood vessels and begins the metastatic process [25]. Consequently, it is associated with poor prognosis after surgical

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treatments [13]. Using LASSO regression, this study has identified a 16-miRNA

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classifier for VI prediction. ROC curve analysis showed the AUC was 0.731 in TCGA set and 0.727 in validation cohort, indicated that the miRNA-based classifier has

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clinical application value for VI prediction. When this miRNA-based classifier

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combined with some other biomarkers, such as AFP, tumour number, tumour size, it would provide a very accurate prediction for VI. Meanwhile, this classifier has

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potential applicability to the clinic for OS assessment, and combination with tumor

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stage has generated a powerfully accurate assessment approach for OS in HCC patients. Futhermore, we analyzed the targets of the 16 miRNAs and validated the fuctional role of miR-23b in HCC. The classifier ultimately may facilitate confidence in treatment decisions and recognize candidates for new therapies.

Some studies screening biomarkers of VI in HCC were from the perspective of genes in recent years. To date, however, studies have attempted to define gene signatures

ACCEPTED MANUSCRIPT predicting VI due to tumor cell dissemination using mostly small set of patients. Furthermore, reliable validation employing sufficient number of independent set of patients was lacking. In previous studies of screening VI specific miRNAs in HCC, the sample cases for high-throughput sequencing were usually small, and the results

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were often not consistent among those studies [17]. There are 1046 miRNAs from

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miRNA expression profiles of HCC in TCGA set, and an appropriate method would

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be needed to screen the useful miRNA makers from the high-dimentional datasets. Therefore, we used the TCGA datasets including 306 HCC patients with VI data to

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screen the miRNA signatures. The sample cases for screening in this study were much

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larger than previous studies and the results would be more credible. High-throughput sequencing had a higher credibility and a stronger sensitivity for quantitative

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detection of the gene expression [26]. The miRNA expression profiles of TCGA were

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obtained by the second-generation high-throughput sequencing which has a powerful accuracy in gene expression detection. In addition, the predictive or prognostic value

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of the miRNA-based classifier was validated in an independent cohort. There is a

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limitation in our study, the miRNA-based classifier have not been validated in blood specimens, which have a potential clinic applicability to determine VI. Further work need to be done before this miRNA-based classifier in clinical application.

Previous studies have been limited by small number of miRNAs screened, small sample sizes, lack of independent validation, and the use of inappropriate statistical methods to mine miRNA microarray data. The use of the LASSO logistic regression

ACCEPTED MANUSCRIPT model which is suitable for high-dimensional microarray data allowed us to integrate multiple miRNAs into one tool, which has significantly greater prognostic accuracy than that of single miRNAs alone.

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The biological function of the most miRNAs used in our classifier have been

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investigated in previous studies. miR-122, a highly abundant and liver-specific

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miRNA, acts as a tumor suppressor affecting HCC intrahepatic metastasis by angiogenesis suppression via regulation of ADAM17 [27]. miR-23b mediates

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urokinase and c-met downmodulation and a decreased migration of human HCC cells

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[28]. miR-550a markedly promotes HCC cell migration and invasion by directly targeting cytoplasmic polyadenylation element-binding protein 4 [29]. miR-9

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enhances HCC migration and invasion through regulation of Kruppel like factor 17

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[30]. In the classifier, miR-1270, miR-659 and miR-3941 have not been previously reported to associate with HCC biology. Although miR-1270 has not been reported to

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have a role in HCC, it involved in the interferon-α1 (IFN-α1) antisence RNA

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competing endogenous RNA network to regulate the expression of IFN-α1 [31], which has antiproliferative activity for tumor [32]. It suggests a potential role of miR-1270 in HCC biology. In this study, the expression level of RBPMS2 was significant decreased after transfecting with miR-23b mimic. RBPMS2 is a member of the RNA recognition motif (RRM)-containing protein family and is up-regulated in gastrointestinal stromal tumors compared to normal gastrointestinal tissues [33], which indicated that miR-23b may involve in HCC progression by RBPMS2.

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In conclusion, our findings showed that the miRNA-based classifier developed by LASSO approach could effectively identify VI and classify patients into groups at low and high risk of OS in HCC. Moreover, the classifier could add the prognostic value

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to tumor stage, making it a potentially valuable biomarker signature in clinical

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practice. Analysis of targets of these miRNAs has identified potential key players in

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miRNA-mRNA interaction in HCC progression.

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VI networks in HCC, providing a resource for investigating the roles of

ACCEPTED MANUSCRIPT Referrences: [1] L.A. Torre, R.L. Siegel, E.M. Ward, A. Jemal, Global Cancer Incidence and Mortality Rates and Trends--An Update, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research,

PT

cosponsored by the American Society of Preventive Oncology 25(1) (2016) 16-27.

RI

[2] A. Forner, J.M. Llovet, J. Bruix, Hepatocellular carcinoma, Lancet 379(9822)

SC

(2012) 1245-55.

[3] S. Jonas, W.O. Bechstein, T. Steinmuller, M. Herrmann, C. Radke, T. Berg, U.

NU

Settmacher, P. Neuhaus, Vascular invasion and histopathologic grading determine

Hepatology 33(5) (2001) 1080-6.

MA

outcome after liver transplantation for hepatocellular carcinoma in cirrhosis,

D

[4] S. Roayaie, I.N. Blume, S.N. Thung, M. Guido, M.I. Fiel, S. Hiotis, D.M. Labow,

PT E

J.M. Llovet, M.E. Schwartz, A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma, Gastroenterology

CE

137(3) (2009) 850-5.

AC

[5] K.J. Halazun, M. Najjar, R.M. Abdelmessih, B. Samstein, A.D. Griesemer, J.V. Guarrera, T. Kato, E.C. Verna, J.C. Emond, R.S. Brown, Jr., Recurrence After Liver Transplantation for Hepatocellular Carcinoma: A New MORAL to the Story, Annals of surgery (2016). [6] P.H. Liu, C.Y. Hsu, C.Y. Hsia, Y.H. Lee, Y.H. Huang, C.W. Su, F.Y. Lee, H.C. Lin, T.I. Huo, Proposal and validation of a new model to estimate survival for hepatocellular carcinoma patients, European journal of cancer 63 (2016) 25-33.

ACCEPTED MANUSCRIPT [7] J. Bruix, G.J. Gores, V. Mazzaferro, Hepatocellular carcinoma: clinical frontiers and perspectives, Gut 63(5) (2014) 844-55. [8] A. Cucchetti, F. Piscaglia, A.D. Grigioni, M. Ravaioli, M. Cescon, M. Zanello, G.L. Grazi, R. Golfieri, W.F. Grigioni, A.D. Pinna, Preoperative prediction of

PT

hepatocellular carcinoma tumour grade and micro-vascular invasion by means of

RI

artificial neural network: a pilot study, Journal of hepatology 52(6) (2010) 880-8.

SC

[9] K. Shirabe, T. Toshima, K. Kimura, Y. Yamashita, T. Ikeda, T. Ikegami, T. Yoshizumi, K. Abe, S. Aishima, Y. Maehara, New scoring system for prediction of

NU

microvascular invasion in patients with hepatocellular carcinoma, Liver international :

MA

official journal of the International Association for the Study of the Liver 34(6) (2014) 937-41.

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[10] B. Minguez, Y. Hoshida, A. Villanueva, S. Toffanin, L. Cabellos, S. Thung, J.

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Mandeli, D. Sia, C. April, J.B. Fan, A. Lachenmayer, R. Savic, S. Roayaie, V. Mazzaferro, J. Bruix, M. Schwartz, S.L. Friedman, J.M. Llovet, Gene-expression

CE

signature of vascular invasion in hepatocellular carcinoma, Journal of hepatology

AC

55(6) (2011) 1325-31.

[11] J. Feng, R. Zhu, C. Chang, L. Yu, F. Cao, G. Zhu, F. Chen, H. Xia, F. Lv, S. Zhang, L. Sun, CK19 and Glypican 3 Expression Profiling in the Prognostic Indication for Patients with HCC after Surgical Resection, PloS one 11(3) (2016) e0151501. [12] C.C. Alifrangis, U. McDermott, Reading between the lines; understanding drug response in the post genomic era, Molecular oncology 8(6) (2014) 1112-9.

ACCEPTED MANUSCRIPT [13] S. Diederichs, L. Bartsch, J.C. Berkmann, K. Frose, J. Heitmann, C. Hoppe, D. Iggena, D. Jazmati, P. Karschnia, M. Linsenmeier, T. Maulhardt, L. Mohrmann, J. Morstein, S.V. Paffenholz, P. Ropenack, T. Ruckert, L. Sandig, M. Schell, A. Steinmann, G. Voss, J. Wasmuth, M.E. Weinberger, R. Wullenkord, The dark matter

PT

of the cancer genome: aberrations in regulatory elements, untranslated regions, splice

RI

sites, non-coding RNA and synonymous mutations, EMBO molecular medicine 8(5)

SC

(2016) 442-57.

[14] M.S. Ebert, P.A. Sharp, Roles for microRNAs in conferring robustness to

NU

biological processes, Cell 149(3) (2012) 515-24.

MA

[15] J.T. Mendell, E.N. Olson, MicroRNAs in stress signaling and human disease, Cell 148(6) (2012) 1172-87.

D

[16] L.P. Lim, N.C. Lau, P. Garrett-Engele, A. Grimson, J.M. Schelter, J. Castle, D.P.

PT E

Bartel, P.S. Linsley, J.M. Johnson, Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs, Nature 433(7027) (2005) 769-73.

CE

[17] K.Q. Shi, Z. Lin, X.J. Chen, M. Song, Y.Q. Wang, Y.J. Cai, N.B. Yang, M.H.

AC

Zheng, J.Z. Dong, L. Zhang, Y.P. Chen, Hepatocellular carcinoma associated microRNA expression signature: integrated bioinformatics analysis, experimental validation and clinical significance, Oncotarget 6(28) (2015) 25093-108. [18] C.L. Hung, C.S. Yen, H.W. Tsai, Y.C. Su, C.J. Yen, Upregulation of MicroRNA-19b predicts good prognosis in patients with hepatocellular carcinoma presenting with vascular invasion or multifocal disease, BMC cancer 15 (2015) 665. [19] H. Sun, S. Wang, Penalized logistic regression for high-dimensional DNA

ACCEPTED MANUSCRIPT methylation data with case-control studies, Bioinformatics 28(10) (2012) 1368-75. [20] Y. Zhao, R. Simon, BRB-ArrayTools Data Archive for human cancer gene expression: a unique and efficient data sharing resource, Cancer informatics 6 (2008) 9-15.

PT

[21] Z. Lin, S.H. Xu, H.Q. Wang, Y.J. Cai, L. Ying, M. Song, Y.Q. Wang, S.J. Du,

RI

K.Q. Shi, M.T. Zhou, Prognostic value of DNA repair based stratification of

SC

hepatocellular carcinoma, Scientific reports 6 (2016) 25999.

[22] T.T. Wu, Y.F. Chen, T. Hastie, E. Sobel, K. Lange, Genome-wide association

NU

analysis by lasso penalized logistic regression, Bioinformatics 25(6) (2009) 714-21.

MA

[23] K.Q. Shi, Y.J. Cai, Z. Lin, J.Z. Dong, J.M. Wu, X.D. Wang, M. Song, Y.Q. Wang, Y.P. Chen, Development and Validation of a Prognostic Nomogram for

PT E

hepatology (2016).

D

Acute-on-Chronic Hepatitis B Liver Failure, Journal of gastroenterology and

[24] P. Blanche, J.F. Dartigues, H. Jacqmin-Gadda, Estimating and comparing

CE

time-dependent areas under receiver operating characteristic curves for censored

AC

event times with competing risks, Statistics in medicine 32(30) (2013) 5381-97. [25] K. Okuda, Natural history of hepatocellular carcinoma including fibrolamellar and hepato-cholangiocarcinoma variants, Journal of gastroenterology and hepatology 17(4) (2002) 401-5. [26] S.A. Byron, K.R. Van Keuren-Jensen, D.M. Engelthaler, J.D. Carpten, D.W. Craig, Translating RNA sequencing into clinical diagnostics: opportunities and challenges, Nature reviews. Genetics 17(5) (2016) 257-71.

ACCEPTED MANUSCRIPT [27] W.C. Tsai, P.W. Hsu, T.C. Lai, G.Y. Chau, C.W. Lin, C.M. Chen, C.D. Lin, Y.L. Liao, J.L. Wang, Y.P. Chau, M.T. Hsu, M. Hsiao, H.D. Huang, A.P. Tsou, MicroRNA-122, a tumor suppressor microRNA that regulates intrahepatic metastasis of hepatocellular carcinoma, Hepatology 49(5) (2009) 1571-82.

PT

[28] A. Salvi, C. Sabelli, S. Moncini, M. Venturin, B. Arici, P. Riva, N. Portolani, S.M.

RI

Giulini, G. De Petro, S. Barlati, MicroRNA-23b mediates urokinase and c-met

The FEBS journal 276(11) (2009) 2966-82.

SC

downmodulation and a decreased migration of human hepatocellular carcinoma cells,

NU

[29] Q. Tian, L. Liang, J. Ding, R. Zha, H. Shi, Q. Wang, S. Huang, W. Guo, C. Ge, T.

MA

Chen, J. Li, X. He, MicroRNA-550a acts as a pro-metastatic gene and directly targets cytoplasmic polyadenylation element-binding protein 4 in hepatocellular carcinoma,

D

PloS one 7(11) (2012) e48958.

PT E

[30] Z. Sun, Q. Han, N. Zhou, S. Wang, S. Lu, C. Bai, R.C. Zhao, MicroRNA-9 enhances migration and invasion through KLF17 in hepatocellular carcinoma,

CE

Molecular oncology 7(5) (2013) 884-94.

AC

[31] T. Kimura, S. Jiang, N. Yoshida, R. Sakamoto, M. Nishizawa, Interferon-alpha competing endogenous RNA network antagonizes microRNA-1270, Cellular and molecular life sciences : CMLS 72(14) (2015) 2749-61. [32]

T.

Taniguchi,

A.

Takaoka,

A weak

signal

for

strong

responses:

interferon-alpha/beta revisited, Nature reviews. Molecular cell biology 2(5) (2001) 378-86. [33] I. Hapkova, J. Skarda, C. Rouleau, A. Thys, C. Notarnicola, M. Janikova, F.

ACCEPTED MANUSCRIPT Bernex, M. Rypka, J.M. Vanderwinden, S. Faure, J. Vesely, P. de Santa Barbara, High expression of the RNA-binding protein RBPMS2 in gastrointestinal stromal tumors,

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CE

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D

MA

NU

SC

RI

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Experimental and molecular pathology 94(2) (2013) 314-21.

ACCEPTED MANUSCRIPT TABLES Table 1. Clinical characteristics of patients in The Cancer Genome Atlas set and independent validation set. Table 2. Univariate and multivariate analyses of microRNA-based classifier for

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overall survival.

ACCEPTED MANUSCRIPT FIGURE LEGENDS Figure 1: Construction of the sixteen-miRNA-based classifier. (A) Hierarchical clustering of HCC with or without vascular invasion using 27 differentially expressed miRNAs using Euclidean distance and average linkage

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clustering. Every row represents an individual gene, and each column represents an

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individual sample. Pseudocolours indicate transcript levels from low to high on a log

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2 scale from –3 to 3 standardized independently by the Z-score transformation, ranging from a low association strength (dark, black) to high (bright, red, or green).

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(B) LASSO coefficient profiles of the 27 vascular invasion associated miRNAs. A

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vertical line is drawn at the value chosen by 10-fold cross-validation. (C) Ten-time cross-validation for tuning parameter selection in the LASSO model.

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Figure 2. ROC analysis displaying the ability of miRNA-based classifier to

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discriminate vascular invasion in TCGA set (A) and validation set (B). ROC, receiver operator characteristic; TCGA, The Cancer Genome Atlas

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Figure 3. Calibration plots, time-dependent ROC curves and Kaplan-Meier

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survival for the sixteen-miRNA-based classifier for overall survival in TCGA set (A) and validation set (B). We used AUCs at 1, 3, and 5 years to assess prognostic accuracy for overall survival, and calculated P values using the log-rank test. AUC, area under the curve; OS, overall survival; ROC, receiver operator characteristic; TCGA, The Cancer Genome Atlas Figure 4. Kaplan-Meier survival analysis in TCGA set according to the

ACCEPTED MANUSCRIPT sixteen-miRNA-based classifier stratified by clinicopathological risk factors. (A, B) Tumor grade; (C, D) Tumor stage. High risk and low risk of sixteen-miRNA-based classifier was calculated according to the cut-off value for vascular invasion.

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TCGA, The Cancer Genome Atlas

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Figure 5. Time-dependent ROC curves compare the prognostic accuracy of the

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sixteen-based-miRNA classifier with clinicopathological risk factors in TCGA set. (A) 1 year overall survival; (B) 3 year overall survival; (C) 5 year overall survival.

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Comparisons of the prognostic accuracy by the six-miRNA-based classifier, tumor

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stage, the classifier and tumor stage combined.

AUC, area under the curve. TCGA, The Cancer Genome Atlas

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Figure 6. Expression levels of miR-23b and predictive target genes after

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transfection.

(A) HepG2; (B) HCCLM3. Error bars represent the mean ± S.D. of triplicate

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experiments. *P < 0.05.

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Figure 7. miR-23b reduces invasion in HCC cells. (A) HepG2 ; (B) HCCLM3. Error bars represent the mean ± S.D. of triplicate experiments. *P < 0.05.

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ACCEPTED MANUSCRIPT Table 1. Clinical characteristics of patients in The Cancer Genome Atlas set and independent validation set

246 (68.1%) 115 (31.9%)

96 (75.6%) 31 (24.4%)

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225 (62.3%) 136 (37.7%)

86 (67.7%) 41 (32.3%)

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224 (62.6%) 134 (37.4%)

92 (72.4%) 35 (27.6%)

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250 (73.7%) 89 (26.3%)

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106 (34.6%) 200 (65,4%) 172/313 (55.0%) 127/361 (35.2%)

TCGA, The Cancer Genome Atlas

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validation set (n = 127)

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Gender Male (n, %) Female (n, %) Age 65 years or younger Older than 65 years Tumor grade G1 + G2 (n, %) G3 + G4 (n, %) TNM staging system (T) T1+T2 (n, %) T3+T4 (n, %) AJCC pathological stage I + II (n, %) III + IV (n, %) Vascular invasion Present (n, %) Absent (n, %) Recurrence (n, %) Death (n, %)

TCGA set (n = 361)

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Clinicopathological variables

P value

91 (71.7%) 36 (28.3%)

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89 (70.1%) 38 (29.9%)

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46 (36.2%) 81 (63.8%) 29/81 (35.8%) 72/127 (56.7%)

0.754 0.002 <0.001

ACCEPTED MANUSCRIPT Table 2. Univariate and multivariate analyses of miRNA-based classifier for overall survival. Multivariate analysis

95% CI

P value

1.23 0.83 1.05

0.87 – 1.76 0.58 – 1.19 0.73 – 1.51

0.241 0.307 0.784

2.43

1.68 – 3.53

< 0.001

2.55

1.79 – 3.63

< 0.001

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0.90 – 2.09

0.137

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< 0.001

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HR, Hazard ratio; CI, confidence interval

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95% CI

P value

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1.56 – 3.29

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HR

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Age (> 65 vs. ≤ 65) Gender (male vs. female) Tumor grade (G3 + G4 vs. G1 + G2) Pathological stage (III + IV vs. I + II) T classification (T3 + T4 vs. T1 + T2) Vascular invasion (present vs. absent) miRNA-based classifier (High vs. Low risk)

Univariate analysis

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ACCEPTED MANUSCRIPT Highlights: 1. A 16-miRNA-based and accurate classifier for identifying vascular invasion in hepatocellular cellularcarcinoma (HCC) were developed and validated. 2. Concordance index and time-dependent receiver operating characteristic showed

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that the classifier was able to stratify patients into risk groups strongly associated with

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overall survival.

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3. When stratified by tumor characteristics, the classifier was still a clinically and statistically significant prognostic model.

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4. The miRNA-based classifier also showed higher prognostic accuracy than tumor

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stage, and the classifier combination with tumor stage provided a more accurate

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prediction in overall survival.