BioMed Research International
Volume 2018 (2018), Article ID 2864120, 9 pages
https://doi.org/10.1155/2018/2864120

Research Article

Analysis of the Cancer Genome Atlas Data Reveals Novel Putative ncRNAs Targets in Hepatocellular Carcinoma

Tiago Falcon,¹ Martiela Freitas,1,2 Ana Carolina Mello,1,3 Laura Coutinho,1,3 Mario R. Alvares-da-Silva,4,5 and Ursula Matte1,2,6

¹Gene Therapy Center, Experimental Research Center, Hospital de Clínicas de Porto Alegre, 90035-903 Porto Alegre, RS, Brazil
²Post-Graduation Program on Genetics and Molecular Biology, UFRGS, 91501-970 Porto Alegre, RS, Brazil
³Graduation Program on Biotechnology/Bioinformatics, UFRGS, 91501-970 Porto Alegre, RS, Brazil
⁴Gastroenterology and Hepatology Division, Hospital de Clinicas de Porto Alegre, Brazil
⁵Graduate Program on Gastroenterology and Hepatology, Department of Internal Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
⁶Department of Genetics, UFRGS, 91501-970 Porto Alegre, RS, Brazil

Correspondence should be addressed to Ursula Matte; umatte@hcpa.edu.br

Received 23 February 2018; Accepted 17 May 2018; Published 26 June 2018

Academic Editor: Qiongzhu Dong

Copyright © 2018 Tiago Falcon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Hepatocellular carcinoma (HCC) is the prevalent type of primary liver malignancy. Different noncoding RNAs (ncRNAs) that negatively regulate gene expression, such as the microRNAs and the long ncRNAs (lncRNAs), have been associated with cell invasiveness and cell dissemination, tumor recurrence, and metastasis in HCC. To evaluate which regulatory ncRNAs might be good candidates to disrupt HCC proliferation pathways, we performed both unsupervised and supervised analyses of HCC expression data, comparing samples of solid tumor tissue (TP) and adjacent tissue (NT) of a set of patients, focusing on ncRNAs and searching for common mechanisms that may shed light in future therapeutic options. All analyses were performed using the R software. Differential expression (total RNA and miRNA) and enrichment analyses (Gene Ontology + Pathways) were performed using the package TCGABiolinks. As a result, we improved the set of lncRNAs that could be the target of future studies in HCC, highlighting the potential of FAM170B-AS1 and TTN-AS1.

1. Introduction

Epidemiologic data from the International Agency for Research on Cancer of the World Health Organization reveals that liver cancer comprises 5.6% of worldwide cancer incidence and 9.1% of all cancer-associated mortality [1]. Hepatocellular carcinoma (HCC) is the most prevalent type of primary liver malignancy [2]. The high lethality of HCC can be attributed to the lack of diagnostic markers for an early detection and late stages high heterogeneity [3]. HCC has been epidemiologically associated with chronic Hepatitis B Virus (HBV) or Hepatitis C Virus (HCV) [4], as well as alcoholic and nonalcoholic fatty liver disease, which are its major risk factors [2]. Currently, the most effective treatment is either surgical tumor resection or liver transplantation [5].

Multiple studies have shown the potential of different microRNAs (miRNAs) as prognostic and diagnostic biomarkers in many types of cancer, including HCC [6–8]. miRNAs are noncoding RNAs that negatively regulate gene expression by leading mRNAs to target degradation or translational repression after binding to its 3’UTR (for review see [9]). In cancer, their role has been either as tumor suppressors or as enhancers (oncomiRs) [10].

In HCC, different miRNAs have been associated with cell invasiveness by repressing TET gene expression, leading to silencing of several invasion-suppressors via hypermethylation [11], and cell dissemination by regulating differentiation, hence increasing metastatic potential [12]. They are even implicated in improvement of HBV and HCV viral replication and tumor-supporting mechanisms [13, 14]. This multifaceted miRNA capacity of influencing in the HCC environment proves the importance of studies describing expression profiles of miRNAs during tumor occurrence.

Another class of noncoding (nc) RNAs, the long ncRNAs (lncRNAs), are > 200 nucleotides’ RNA molecules with multiple regulatory roles that can not be inferred by their sequence. These roles comprise, among others, chromatin organization affecting the gene expression [15]. HOTAIR, an antisense lncRNA, has been associated with HCC recurrence and metastasis [16]. HULC and FTX (HCC) are also upregulated in tumoral samples [17].

Here, differently from previous works that focused on viral infection (HBV or HCV) comparing primary solid tumor tissue (TP) and adjacent tissue (NT) [6, 8, 18, 19], or focused on the mutation found [20], we in silico compared TP and NT of a set of patients in sense to discover the pathways that differentiate both groups of samples and the regulatory ncRNAs and their putative targets. As a result, we improve the set of lncRNAs that could be the target of future studies.

2. Material and Methods

All analyses were performed using the R software (v. 3.4.0) [21]. The differential expression (mRNA and miRNA) analysis was performed using the package TCGABiolinks (v. 2.7.1) [22]. First, we downloaded HCC harmonized data (hg38) from The Cancer Genome Atlas (TCGA) using the function GDCdownload with the option legacy = FALSE. We analyzed a total of 41 participants that have expression data of both primary solid tumor and adjacent tissue samples. It is worth noticing that in the database adjacent tissue is referred to as normal; however this is hardly the case as all patients were cirrhotic. Thus we use the term adjacent, as this is not a sample from a normal liver. To select these individuals, we used only the participant ID of the TCGA barcode as query barcode (e.g., participant ID in bold: TCGA-BC-A10Q-01A-11R-A131-07). The sampling comprises a group of men and women, white, black, or Asian, showing or not the presence of risk factors such as fat liver disease. Not all samples had a positive diagnostic for HBV or HCV. All data is available at TCGA web portal.

For total RNA differential expression, we followed the standard pipeline. The samples were highly correlated after an outlier check (TCGAanalyze_Preprocessing function). Except one sample (0.85 < r < 0.9) all other samples showed an r > 0.9. Then, we followed a normalization step using both GC content and gene length (TCGAanalyze_Normalization) and gene filtering by quantile (TCGAanalyze_Filtering) as recommended in [23]. Differentially expressed genes (DEGs) were accessed by the function TCGAanalyze_DEA considering a log2 fold change (logFC) of > 1 or < -1. and false discovery rate (FDR) of 0.01. Enrichment analyses of DEGs and top 10 categories’ plot were performed by the functions TCGAanalyze_EAcomplete and TCGAvisualize_EAbarplot, respectively.

Heat maps were plotted using the function heatmap.2 from package gplots (v. 3.0.1) [24] considering the gene expression information of the top genes based on significant FDR or all differentially expressed transcripts of the categories miRNA, precursor microRNA (pre-miRNA), and lncRNA. Hierarchical cluster analyses were performed using the package pvclust (v. 2.0-0) [25] with 1000 bootstrap replications. Clusters with approximately unbiased grouping support p values (%) (au – red values) of 95 were considered as statistically significant groups.

For the differentially expressed transcripts, we performed a Spearman correlation to detect which regulatory RNAs are negatively correlated with other RNAs. We accepted those with r < -0.8 and p value < 0.05 as statistically significantly correlated. These correlated transcripts were used as interactions to input the network on Cytoscape (v. 3.5.1) [26], where the edges represent the statistically significant r values. The miRNAs and their putative targets were used to predict their interaction using the online software TargetScan (release 7.1) [27]. Interactions not found in TargetScan were also tested in miRDB [28] and TarBase (v. 8) [29]. The interactions found by either TargetScan or TarBase were confirmed by two other tools: miRWalk v. 3.0 (http://mirwalk.umm.uni-heidelberg.de/) [30] considering a binding probability cut-off of 0.8, and mirDIP v. 4.1 (http://ophid.utoronto.ca/mirDIP/index.jsp) [31, 32] considering a “medium” cut-off of scores. Gene Ontology Biological Processes of the proteins associated with the network were evaluated using the Cytoscape plugin BiNGO [33]. For the interest in lncRNAs, we performed a supervised prediction model using the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) using the package pROC v. 1.11.0 [34].

3. Results and Discussion

In this study we performed a supervised analysis of HCC expression data focusing on ncRNAs searching for common mechanisms that may shed light in future therapeutic options. The majority of statistically significant differentially expressed ncRNAs are higher expressed on tumor samples, suggesting that these RNAs are necessary to tumor progression/maintenance. Additionally, tumor samples showed a more diverse expression profile in comparison to those from adjacent tissues. Such pattern has been reported also for gastric [35] and colorectal cancers [36].

We found a total of 1739 DEGs in total RNA-seq among tumor and normal samples. From these, 1276 were upregulated in tumor (Figure 1(a), Figure S1A, and Table S1). miRNA differential expression (DE) revealed 234 DE miRNAs, of which 169 were upregulated in tumor (Figure 1(b), Figure S1B, and Table S1). Other noncoding regulatory RNAs resulted in 92 pre-miRNAs (73 upregulated in tumor) and 122 lncRNAs (90 upregulated in tumor) (Figure 1(c), Table S2). Considering the fold change of DEGs and DE miRNAs, the top ten up- and downregulated genes in tumoral samples are shown in Table 1.

Table 1: Top ten differentially expressed genes (DEGs) from total RNA-seq and microRNA RNA-seq.

Total RNA-seq DEGs

Upregulated in tumor
Downregulated in tumor

Transcript
Fold change
Transcript
Fold change

SIRT1
15.32119
PAPPA2
-5.66486

RPL28
11.29295
TNR
-4.59590

LINC0161
10.83190
SNHG21
-4.51615

CACNB3
10.71749
CALHM6
-4.44294

CREM
10.54143
LCMT1-AS
-4.39991

GOLGA8B
10.32588
BHMT2
-4.34723

SLC46A2
10.16996
PRDM1
-4.33354

LINC0044
10.10149
CPLX4
-4.29631

NUP85
9.78697
PTP4A2
-4.06166

CFAP44-AS
9.67638
GEMIN4
-3.99574

DE miRNAs

Upregulated in tumor
Downregulated in tumor

Transcript
Fold change
Transcript
Fold change

hsa-mir-767
8.99037
hsa-mir-490
-3.58293

hsa-mir-105-2
8.90799
hsa-mir-4686
-3.40137

hsa-mir-891a
8.86653
hsa-mir-1258
-2.95466

hsa-mir-105-1
8.19189
hsa-mir-139
-2.00361

hsa-mir-3923
7.94124
hsa-mir-424
-1.96325

hsa-mir-520f
7.91529
hsa-mir-4683
-1.94045

hsa-mir-520c
7.63784
hsa-mir-934
-1.92190

hsa-mir-518e
7.37922
hsa-mir-130a
-1.86453

hsa-mir-520b
7.17289
hsa-mir-873
-1.84610

hsa-mir-520a
7.00665
hsa-mir-6503
-1.76457

regulatory lncRNA.

(a)

(b)

(c)

Figure 1: Heat maps of differentially expressed noncoding regulatory RNAs. (a) miRNA. (b) Pre-miRNA. (c) LncRNA. Tumor samples in brown and adjacent samples in blue. Hierarchical clusterization based on transcript log10 scale expression.

The enrichment analysis (Gene Ontology + Pathways) revealed that the most represented pathways in differentially expressed transcripts from total RNA-seq are involved in bile metabolism, fear behavioral response, and immune-related categories (Figure 2). To infer putative expression relationship, we plotted a network based on Spearman’s correlation, considering only the negative interactions. These interactions involved a total of 18 highly correlated regulatory ncRNAs of all types (miRNA, pre-miRNA, and lncRNA) with their putative targets (Figure 3(a)). In the case of miRNAs and pre-miRNAs, the miRNA-target interactions were predicted as explained in the Material and Methods. These highly negative targets are most involved in programmed cell death, immune response, and Molybdenum cofactor biosynthesis processes (Figure 3(b)). For the lncRNAs in the network, we calculated the AUC-ROC values and found four lncRNAs with potential to correctly discriminate TP and NT samples: CCND2-AS1 (AUC = 0.792, 95% confidence interval: 0.6834-0.8903), FAM170B-AS1 (AUC = 0.917, 95% confidence interval: 0.8387-0.9758), TTN-AS1 (AUC = 0.901, 95% confidence interval: 0.84-0.9539), and SYNPR-AS1 (AUC = 0.939, 95% confidence interval: 0.8798-0..9823).

Figure 2: Enrichment analysis of differentially expressed transcripts from total RNA data. GO: Gene Ontology. The red lines represent the ratio of genes found for the pathway over the total number of genes for that specific pathway. Inside each bar, n: number of genes. Bar sizes are in agreement to the -log10 of the FDR of the enriched ontology/pathway.

(a)

(b)

Figure 3: Representative network of the negative Spearman correlation of ncRNAs and putative affected target interactions and Gene Ontology Biological Processes. (a) Representative network of Spearman’s negative correlation. Each edge represents an r < -0.8 and p value <0.05. Light blue: downregulated miRNAs in TP samples. Red: upregulated pre-miRNAs in TP samples. Light red: downregulated pre-miRNAs in TP samples. Purple: upregulated lncRNAs in TP samples. Light purple: downregulated lncRNAs in TP samples. Dark gray: upregulated heterogeneous RNAs in TP samples. Light gray: downregulated heterogeneous RNAs in TP samples. Nodes’ size represents the degree of connectivity. (b) Gene Ontology Biological Processes with statistically significant (FDR < 0.05) representation. The name of the proteins enriching each process is inside the respective piece of the pie plot.

The DEGs’ enrichment analysis suggested that bile metabolism and fear behavioral response immune-related categories are the most represented pathways. Immune-related categories are usually disrupted in cancer. For example, CD274, upregulated in our TP samples, confers immune resistance to tumor cells by the inactivating cytotoxic T-cell [37]. DACT1, which encodes for an antagonist of beta catenin 1, and DVL2, a dishevelled protein family member, are respectively, down- and upregulated in tumor tissues, suggesting that the Wnt signaling is active [38]. Additionally, CDK14 and GSK3B are upregulated, reinforcing the Wnt signaling activation, which is related to cell polarity category [39]. This signaling pathway has been associated with malignant transformation [40].

GO group classified as fear behavioral response includes a series of genes neurotransmitters (such as glutamate, dopamine, and serotonin receptors), which comes as no surprise since many studies have shown the impact of serotonin, GABA, and sympathetic neurotransmitters in hepatocyte proliferation [41–43]. It also includes MECP2 and transcription factors associated with chromatin remodeling. Finally, bile acids are also known to act as potential carcinogens and deregulation of bile acids homeostasis has been linked to HCC formation [44].

Another transcription factor, FXR2, is supposed to act as heterodimer (or larger complexes) with TP53 or FXR1, suppressing tumor development. However, TP53 and FXR1 expressions were not detected after normalization process. Still, FXR2 can interact as homodimer or as a larger complex [45]. The absence of FXR1 expression could be a consequence of GSK3B upregulation, once FXR1 phosphorylation by GSK3B leads to FXR1 downregulation [46].

From the negative correlation network (Figure 3), we can highlight the immune-related categories, as occurred in the DEGs enrichment analysis. The expression of NAT1 has been recently reported to show high expression in breast cancer and be associated with steroid biosynthetic pathway genes [47]. Here, NAT1 is also upregulated in TP samples. This gene’s expression is also negatively correlated with two lncRNAs, both antisense RNAs: SYNPR-AS1 and CCND2-AS1. CCND2-AS1 is known to promote glioma cell proliferation by activating Wnt/β-catenin signaling [48], but it is downregulated in HCC. CASP1, usually downregulated in cancer cells once it promotes apoptosis [49], showed a high expression pattern in TP samples and is negatively correlated with CCND2-AS1. NLRP1 expression is associated with tumor inflammasomes and suppression of apoptosis in metastatic melanoma [50]. However, NLRP1 is downregulated in our tumor data and negatively correlated with the pre-miRNA MIR3667. This miRNA is known to disrupt the oncogenetic activity of PCAT-1/MYC in prostate cancer [51] and thus its low expression in TP samples is expected. GRIK2, correlated with tumor progress [52], ERC1, which is upregulated in TP samples and its expression is associated with tumor progression once it is necessary to focal adhesion turnover [53], and PRDX3, whose overexpression is highly connected to prostate cancer [54] by protecting tumoral cells from oxidative stress [55], are all upregulated in TP samples of HCC and negatively correlated with the expression of the antisense RNA FAM170B-AS1. ERC1 is also negatively correlated with SYNPR-AS1, hsa-mir-139, which is known to play antitumoral roles in HCC [56], and the pre-miRNA MIR320A plays antitumoral roles in breast cancer [57]. SLC17A8 is upregulated in prostate cancer [58] and in HCC and negatively correlated with SYNPR-AS1. HSD3B7 is associated with bile acid and did not change its expression in CTNNB1 mutated HCC samples [59]. However, here HSD3B7 is upregulated in TP samples and negatively correlated with LINC01493.

NLRP1/CASP1 form a complex that induces pyroptosis [60], a cell death dependent on CASP1 and associated with many pathological conditions, including cancer [61]. Bearing in mind that we can not interpret gene expression as active protein production or enzymatic activity, still it seems like pyroptosis pathway is disrupted in HCC in comparison to other cancer types and that CCND2-AS1 might play a role by regulating CASP1 expression in this process.

AQP9 overexpression decreased the PIK3CB levels in normal tissues, reducing the cell proliferative potential by increasing FOXO1 levels and reducing PCNA expression [62]. In HCC, AQP9 levels are low [63] inducing PIK3CB activity and cell proliferation [62]. In agreement with these authors, AQP9 is downregulated in our TP samples profile, while PIK3CB is upregulated. PCNA is also upregulated but did not pass the logFC cut-off. AQP9 is negatively correlated with TTN-AS1, which was recently described as an oncogene highly expressed in esophageal squamous cell carcinoma progression and metastasis [64].

Hierarchical cluster analysis of the differentially expressed total transcripts, miRNAs, pre-miRNAs, and lncRNAs, shows that statistically significant groups are created in all cases, discriminating most adjacent from tumoral samples. This kind of distinction was not found when trying to differentiate samples also by viral types (HBV or HCV) (data not shown). It is worth noticing that DEG, but especially ncRNA analysis, was able to perfectly discriminate between TP and NT, although it was not able to separate HBV and HCV-infected samples. This suggests that the mechanisms depicted here are common to HCC regardless of its causative injury. Even though risk factors for HCC are well-known, it remains as an important cause of death worldwide. Although tumor surveillance in cirrhotics is highly recommended by international guidelines [65], late diagnosis is quite common. Moreover, advanced liver disease and parenchymal dysfunction further prevent curative therapies [66].

4. Conclusions

Our data suggests that neither HBV nor HCV infection changes overall gene expression (including those genes encoding for ncRNAs) in TP samples. Pyroptosis pathway is misregulated in HCC if compared to other cancer types and the lncRNA CCND2-AS1 might be involved in this misregulation, revealing a singular characteristic of HCC. Additionally, FAM170B-AS1 and TTN-AS1 emerge as new candidates to tests to disrupt HCC homeostasis by turning cancer cells susceptible to oxidative stress or affecting cancer cell proliferation, respectively. Also, these lncRNAs show remarkable expression signatures, differentiating TP from NT samples with high AUC-ROC values.

Data Availability

All data used in this work is publicly available at The Cancer Genome Atlas (TCGA) database <https://cancergenome.nih.gov/>.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Tiago Falcon and Martiela Freitas contributed equally to this work.

Acknowledgments

The authors thank the Programa Nacional de Pós-Doutorado (PNPD) CAPES/HCPA for the fellowship provided to Tiago Falcon, and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the fellowship provided to Martiela Freitas and Ursula Matte. We also thank FIPE/HCPA for financial support.

Supplementary Materials

Figure S1: volcano plot representing differentially expressed transcripts (primary solid tumor x normal tissues). (A.) Total RNA. (B.) MicroRNA. Blue dots: upregulated transcripts in tumor samples. Red dots: downregulated transcripts in tumor samples. Black dots: statistically nonsignificant expressed transcripts. logFC: log2 fold change. Vertical gray lines: cut-off of logFC < -1 and logFC > 1. Horizontal purple line: FDR cut-off of 0.01. Table S1: differentially expressed data from total RNA. logFC: log2 fold change. Table S2: differentially expressed miRNAs. logFC: log2 fold change. (Supplementary Materials)

References

J. Ferlay, I. Soerjomataram, M. Ervik et al., “GLOBOCAN 2012 v1.0,” in Cancer Incidence and Mortality Worldwide: IARC CancerBase, no. 11, International Agency For Research on Cancer, Lyon, France, 2013, Accessed on: January 19th, 2018, http://globocan.iarc.fr.

K. Taniguchi, L. R. Roberts, I. N. Aderca et al., “Mutational spectrum of β-catenin, AXIN1, and AXIN2 in hepatocellular carcinomas and hepatoblastomas,” Oncogene, vol. 21, no. 31, pp. 4863–4871, 2002.

T. Thurnherr, W.-C. Mah, Z. Lei, Y. Jin, S. G. Rozen, and C. G. Lee, “Differentially expressed mirnas in hepatocellular carcinoma target genes in the genetic information processing and metabolism pathways,” Scientific Reports, vol. 6, Article ID 20065, 2016.

M. Anzola, “Hepatocellular carcinoma: Role of hepatitis B and hepatitis C viruses proteins in hepatocarcinogenesis,” Journal of Viral Hepatitis, vol. 11, no. 5, pp. 383–393, 2004.

Q. Y. Xie, A. Almudevar, C. L. Whitney-Miller, C. T. Barry, and M. N. McCall, “A microRNA biomarker of hepatocellular carcinoma recurrence following liver transplantation accounting for within-patient heterogeneity,” BMC Medical Genomics, vol. 9, no. 1, article 18, 2016.

G. Diaz, M. Melis, A. Tice et al., “Identification of microRNAs specifically expressed in hepatitis C virus-associated hepatocellular carcinoma,” International Journal of Cancer, vol. 133, no. 4, pp. 816–824, 2013.

M. Pichler and G. A. Calin, “MicroRNAs in cancer: from developmental genes in worms to their clinical application in patients,” British Journal of Cancer, vol. 113, no. 4, pp. 569–573, 2015.

W. Ding, H. Yang, S. Gong et al., “Candidate miRNAs and pathogenesis investigation for hepatocellular carcinoma based on bioinformatics analysis,” Oncology Letters, vol. 13, no. 5, pp. 3409–3414, 2017.

S. Kuersten and E. B. Goodwin, “The power of the 3′ UTR: Translational control and development,” Nature Reviews Genetics, vol. 4, no. 8, pp. 626–637, 2003.

C. Chen, “MicroRNAs as oncogenes and tumor suppressors,” The New England Journal of Medicine, vol. 353, no. 17, pp. 1768–1771, 2005.

K.-H. Chuang, C. L. Whitney-Miller, C.-Y. Chu et al., “MicroRNA-494 is a master epigenetic regulator of multiple invasion-suppressor microRNAs by targeting ten eleven translocation 1 in invasive human hepatocellular carcinoma tumors,” Hepatology, vol. 62, no. 2, pp. 466–480, 2015.

C. Coulouarn, V. M. Factor, J. B. Andersen, M. E. Durkin, and S. S. Thorgeirsson, “Loss of miR-122 expression in liver cancer correlates with suppression of the hepatic phenotype and gain of metastatic properties,” Oncogene, vol. 28, no. 40, pp. 3526–3536, 2009.

H. Li, J.-D. Jiang, and Z.-G. Peng, “MicroRNA-mediated interactions between host and hepatitis C virus,” World Journal of Gastroenterology, vol. 22, no. 4, pp. 1487–1496, 2016.

X. Sun, S. Zhang, and X. Ma, “Prognostic value of MicroRNA-125 in various human malignant neoplasms: a meta-analysis,” Clinical Laboratory, vol. 61, no. 11, pp. 1667–1674, 2015.

E. Bernstein and C. D. Allis, “RNA meets chromatin,” Genes & Development, vol. 19, no. 14, pp. 1635–1655, 2005.

Y. J. Geng, S. L. Xie, Q. Li, J. Ma, and G. Y. Wang, “Large intervening non-coding RNA HOTAIR is associated with hepatocellular carcinoma progression,” Journal of International Medical Research, vol. 39, no. 6, pp. 2119–2128, 2011.

Z.-S. Niu, X.-J. Niu, and W.-H. Wang, “Long non-coding RNAs in hepatocellular carcinoma: potential roles and clinical implications,” World Journal of Gastroenterology, vol. 23, no. 32, pp. 5860–5874, 2017.

Y. Murakami, T. Yasuda, K. Saigo et al., “Comprehensive analysis of microRNA expression patterns in hepatocellular carcinoma and non-tumorous tissues,” Oncogene, vol. 25, no. 17, pp. 2537–2545, 2006.

A. Salvi, E. Abeni, N. Portolani, S. Barlati, and G. De Petro, “Human hepatocellular carcinoma cell-specific miRNAs reveal the differential expression of miR-24 and miR-27a in cirrhotic/non-cirrhotic HCC,” International Journal of Oncology, vol. 42, no. 2, pp. 391–402, 2013.

The Cancer Genome Atlas Research Network, “Comprehensive and integrative genomic characterization of hepatocellular carcinoma,” Cell, vol. 169, no. 7, pp. 1327–1341, 2017.

R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2017, https://www.R-project.org/.

A. Colaprico, T. C. Silva, C. Olsen et al., “TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data,” Nucleic Acids Research, vol. 44, no. 8, p. e71, 2016.

J. H. Bullard, E. Purdom, K. D. Hansen, and S. Dudoit, “Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments,” BMC Bioinformatics, vol. 11, article 94, 2010.

G. R. Warnes, B. Bolker, L. Bonebakker et al., “Various R Programming Tools for Plotting Data,” R package version 3.0.1, 2016, https://CRAN.R-project.org/package=gplots.

R. Suzuki and H. Shimodaira, “Pvclust: an R package for assessing the uncertainty in hierarchical clustering,” Bioinformatics, vol. 22, no. 12, pp. 1540–1542, 2006.

P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software Environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003.

V. Agarwal, G. W. Bell, J.-W. Nam, and D. P. Bartel, “Predicting effective microRNA target sites in mammalian mRNAs,” eLife, vol. 4, no. 2015, Article ID e05005, 2015.

N. Wong and X. Wang, “miRDB: an online resource for microRNA target prediction and functional annotations,” Nucleic Acids Research, vol. 43, no. 1, pp. D146–D152, 2015.

D. Karagkouni, M. D. Paraskevopoulou, S. Chatzopoulos et al., “DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions,” Nucleic Acids Research, vol. 46, no. D1, pp. D239–D245, 2018.

H. Dweep, C. Sticht, P. Pandey, and N. Gretz, “MiRWalk—database: prediction of possible miRNA binding sites by 'walking' the genes of three genomes,” Journal of Biomedical Informatics, vol. 44, no. 5, pp. 839–847, 2011.

E. A. Shirdel, W. Xie, T. W. Mak, and I. Jurisica, “NAViGaTing the micronome—using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs,” PLoS ONE, vol. 6, no. 2, Article ID e17429, 2011.

T. Tokar, C. Pastrello, A. E. Rossos et al., “mirDIP 4.1—integrative database of human microRNA target predictions,” Nucleic Acids Research, vol. 46, no. D1, pp. D360–D370, 2018.

S. Maere, K. Heymans, and M. Kuiper, “BiNGO : a Cytoscape plugin to assess over-representation of gene ontology categories in biological networks,” Bioinformatics, vol. 21, no. 16, pp. 3448-3449, 2005.

X. Robin, N. Turck, A. Hainard et al., “pROC: an open-source package for R and S+ to analyze and compare ROC curves,” BMC Bioinformatics, vol. 12, article 77, 2011.

X. Chen, S. Y. Leung, S. T. Yuen et al., “Variation in gene expression patterns in human gastric cancers,” Molecular Biology of the Cell, vol. 14, no. 8, pp. 3208–3215, 2003.

L. Chen, Z. Zhu, W. Gao, Q. Jiang, J. Yu, and C. Fu, “Systemic analysis of different colorectal cancer cell lines and TCGA datasets identified IGF-1R/EGFR-PPAR-CASPASE axis as important indicator for radiotherapy sensitivity,” Gene, vol. 627, pp. 484–490, 2017.

R. S. Herbst, J. C. Soria, and M. Kowanetz, “Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients,” Nature, vol. 515, no. 7528, pp. 563–567, 2014.

J. Wen, Y. J. Chiang, C. Gao et al., “Loss of Dact1 disrupts planar cell polarity signaling by altering dishevelled activity and leads to posterior malformation in mice,” The Journal of Biological Chemistry, vol. 285, no. 14, pp. 11023–11030, 2010.

T. Zhan, N. Rindtorff, and M. Boutros, “Wnt signaling in cancer,” Oncogene, vol. 36, no. 11, pp. 1461–1473, 2017.

P. Polakis, “Wnt signaling in cancer,” Cold Spring Harbor Perspectives in Biology, vol. 4, no. 5, Article ID a008052, 2012.

M. Lesurtel, C. Soll, B. Humar, and P.-A. Clavien, “Serotonin: a double-edged sword for the liver?” The Surgeon, vol. 10, no. 2, pp. 107–113, 2012.

J. Shilpa, B. T. Roshni, R. Chinthu, and C. S. Paulose, “Role of GABA and serotonin coupled chitosan nanoparticles in enhanced hepatocyte proliferation,” Journal of Materials Science: Materials in Medicine, vol. 23, no. 12, pp. 2913–2921, 2012.

X. Wen, H. Huan, X. Wang et al., “Sympathetic neurotransmitters promote the process of recellularization in decellularized liver matrix via activating the IL-6/Stat3 pathway,” Biomedical Materials, vol. 11, no. 6, Article ID 065007, 2016.

X. Wang, X. Fu, C. Van Ness, Z. Meng, X. Ma, and W. Huang, “Bile acid receptors and liver cancer,” Current Pathobiology Reports, vol. 1, no. 1, pp. 29–35, 2013.

Y. Zhang, J. P. O'Connor, M. C. Siomi et al., “The fragile X mental retardation syndrome protein interacts with novel homologs FXR1 and FXR2,” EMBO Journal, vol. 14, no. 21, pp. 5358–5366, 1995.

T. Del'Guidice, C. Latapy, A. Rampino et al., “FXR1P is a GSK3β substrate regulating mood and emotion processing,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 112, no. 33, pp. E4610–E4619, 2015.

X. Zhang, S. M. Carlisle, M. A. Doll et al., “High N-acetyltransferase 1 (NAT1) expression is associated with estrogen receptor expression in breast tumors, but is not under direct regulation by estradiol, 17beta-diol, or dihydrotestosterone in breast cancer cells,” Journal of Pharmacology and Experimental Therapeutics, vol. 364, no. 3, 2018.

H. Zhang, D.-L. Wei, L. Wan, S.-F. Yan, and Y.-H. Sun, “Highly expressed lncRNA CCND2-AS1 promotes glioma cell proliferation through Wnt/β-catenin signaling,” Biochemical and Biophysical Research Communications, vol. 482, no. 4, pp. 1219–1225, 2017.

D. R. McIlwain, T. Berger, and T. W. Mak, “Caspase functions in cell death and disease,” Cold Spring Harbor Perspectives in Biology, vol. 5, no. 4, Article ID a008656, 2013.

Z. Zhai, W. Liu, M. Kaur et al., “NLRP1 promotes tumor growth by enhancing inflammasome activation and suppressing apoptosis in metastatic melanoma,” Oncogene, vol. 36, pp. 3820–3830, 2017.

J. R. Prensner, W. Chen, S. Han et al., “The long non-coding RNA PCAT-1 promotes prostate cancer cell proliferation through cMyc,” Neoplasia, vol. 16, no. 11, pp. 900–908, 2014.

C. Wu, Y. Lu, H. Li et al., “Glutamate receptor, ionotropic, kainate 2 silencing by DNA hypermethylation possesses tumor suppressor function in gastric cancer,” International Journal of Cancer, vol. 126, no. 11, pp. 1097–1215, 2010.

V. Astro, D. Tonoli, S. Chiaretti et al., “Liprin-α1 and ERC1 control cell edge dynamics by promoting focal adhesion turnover,” Scientific Reports, vol. 6, Article ID 33653, 2016.

A. Basu, H. Banerjee, H. Rojas et al., “Differential expression of peroxiredoxins in prostate cancer: consistent upregulation of PRDX3 and PRDX4,” The Prostate, vol. 71, no. 7, pp. 755–765, 2011.

H. C. Whitaker, D. Patel, W. J. Howat et al., “Peroxiredoxin-3 is overexpressed in prostate cancer and promotes cancer cell survival by protecting cells from oxidative stress,” British Journal of Cancer, vol. 109, no. 4, pp. 983–993, 2013.

W. Gu, X. Li, and J. Wang, “MiR-139 regulates the proliferation and invasion of hepatocellular carcinoma through the WNT/TCF-4 pathway,” Oncology Reports, vol. 31, no. 1, pp. 397–404, 2014.

J. Yu, J.-G. Wang, L. Zhang et al., “MicroRNA-320a inhibits breast cancer metastasis by targeting metadherin,” Oncotarget , vol. 7, no. 25, pp. 38612–38625, 2016.

K. Raza and R. Jaiswal, “Reconstruction and analysis of cancer specific gene regulatory networks from gene expression profiles,” International Journal on Bioinformatics & Biosciences, vol. 3, no. 2, pp. 25–34, 2013.

S. Sekine, R. Ogawa, H. Ojima, and Y. Kanai, “Expression of SLCO1B3 is associated with intratumoral cholestasis and CTNNB1 mutations in hepatocellular carcinoma,” Cancer Science, vol. 102, no. 9, pp. 1742–1747, 2011.

S. L. Masters, M. Gerlic, D. Metcalf et al., “NLRP1 inflammasome activation induces pyroptosis of hematopoietic progenitor cells,” Immunity, vol. 37, no. 6, pp. 1009–1023, 2012.

T. Bergsbaken, S. L. Fink, and B. T. Cookson, “Pyroptosis: host cell death and inflammation,” Nature Reviews Microbiology, vol. 7, no. 2, pp. 99–109, 2009.

W.-G. Zhang, C.-F. Li, M. Liu et al., “Aquaporin 9 is down-regulated in hepatocellular carcinoma and its over-expression suppresses hepatoma cell invasion through inhibiting epithelial-to-mesenchymal transition,” Cancer Letters, vol. 378, no. 2, pp. 111–119, 2016.

C.-F. Li, W.-G. Zhang, M. Liu et al., “Aquaporin 9 inhibits hepatocellular carcinoma through upregulating FOXO1 expression,” Oncotarget , vol. 7, no. 28, pp. 44161–44170, 2016.

C. Lin, S. Zhang, Y. Wang et al., “Functional role of a novel long noncoding RNA TTN-AS1 in esophageal squamous cell carcinoma progression and metastasis,” Clinical Cancer Research, vol. 24, no. 2, pp. 486–498, 2018.

J. K. Heimbach, L. M. Kulik, R. S. Finn et al., “AASLD guidelines for the treatment of hepatocellular carcinoma,” Hepatology, vol. 67, no. 1, pp. 358–380, 2018.

J. Balogh, D. Victor, E. H. Asham et al., “Hepatocellular carcinoma: a review,” Journal of Hepatocellular Carcinoma, vol. 3, pp. 41–53, 2016.