Bioinformatics functional analysis of let-7a,miR-34a,and miR-199a/b reveals novel insights into immune system pathways and cancer hallmarks for hepatocellular carcinoma

Abstract

Let-7a, miR-34a, and miR-199 a/b have gained a great attention as master regulators for cellular processes. In particular, these three micro-RNAs act as potential onco-suppressors for hepatocellular carcinoma. Bioinformatics can reveal the functionality of these micro-RNAs through target prediction and functional annotation analysis. In the current study, in silico analysis using innovative servers (miRror Suite, DAVID, miRGator V3.0, GeneTrail) has demonstrated the combinatorial and the individual target genes of these micro-RNAs and further explored their roles in hepatocellular carcinoma progression. There were 87 common target messenger RNAs (p ≤ 0.05) that were predicted to be regulated by the three micro-RNAs using miRror 2.0 target prediction tool. In addition, the functional enrichment analysis of these targets that was performed by DAVID functional annotation and REACTOME tools revealed two major immune-related pathways, eight hepatocellular carcinoma hallmarks–linked pathways, and two pathways that mediate interconnected processes between immune system and hepatocellular carcinoma hallmarks. Moreover, protein–protein interaction network for the predicted common targets was obtained by using STRING database. The individual analysis of target genes and pathways for the three micro-RNAs of interest using miRGator V3.0 and GeneTrail servers revealed some novel predicted target oncogenes such as SOX4, which we validated experimentally, in addition to some regulated pathways of immune system and hepatocarcinogenesis such as insulin signaling pathway and adipocytokine signaling pathway. In general, our results demonstrate that let-7a, miR-34a, and miR-199 a/b have novel interactions in different immune system pathways and major hepatocellular carcinoma hallmarks. Thus, our findings shed more light on the roles of these miRNAs as cancer silencers.

Keywords

Onco suppressor micro-RNAs hepatocellular carcinoma bioinformatics cancer hallmarks immune system novel effective therapeutics

Introduction

It has been reported that hepatocellular carcinoma (HCC) is caused by various genetic and epigenetic changes that induce multiple oncogenic pathways;^1,2 however, the major contributing mechanisms underlying these pathways are still elusive.

Micro-RNAs (miRNAs) are class of non-coding RNAs that act as master regulators of gene expression. They control several cellular pathways through modulating the expression of tens to hundreds of genes which are involved in those pathways.³ They function by controlling on/off expression switch to fine-tune protein levels. The miRNA action is mediated either by inducing target genes degradation (messenger RNA (mRNA)) or repressing their translation at the post-transcriptional level.⁴ These two different modes of action depend on whether the binding sequence of the miRNA is fully or partially complementary to the mRNA-binding site.⁵

Liver tissue is enriched with innate and adaptive immune cells that act against inflammatory responses, tumor transformation, and progression.^6,7 Examples of these cells include natural killer (NK) and T-cells, which are correlated positively with apoptosis and negatively with proliferation.⁸ In addition, liver expresses specific miRNAs, which naturally regulate its immunological processes.^9–11 For instance, these miRNAs have been shown to regulate the activation of T- and B-lymphocytes; however, they also work as functional actors in HCC initiation and progression.^12,13

The present study is a workflow based on our previous research on the top 26 differentially expressed miRNAs in HCC and their relation to cancer hallmarks.^14,15 We focused on three highly represented miRNAs—let-7a, miR-34a, and miR-199a/b—where we collectively predicted their significantly regulated target genes for further understanding of their function in HCC pathways and immune system. These three HCC-associated miRNAs were specifically selected as key candidates for the in silico analysis because of their expression and functionality mounting evidences as detailed below. Let-7a, miR-34a, and miR-199a/b have been known as the most prominent tumor suppressor miRNAs which are evolutionarily conserved and they control basic programs during cellular proliferation and differentiation.^3,16–19 However, the deregulation of these miRNAs has been shown to contribute to the development and progression of multiple cancers including HCC due to the gain of function of multiple proto-oncogenes as their miRNA-mediated repression is inhibited.^3,20,21

Let-7a has been shown as the fifth most abundantly expressed miRNA in normal liver with an average ratio in miRNome 3.3%.²¹ Nevertheless, this miRNA was found to be significantly downregulated in a group of HCC tissues and cell lines by more than 50% as compared to adjacent normal liver.^21–24 MiR-34a has been shown to be significantly downexpressed or silenced in 76% of HCC tissues as compared to adjacent normal tissues.^3,25–30 Besides, miR-34a has been determined among the miRNA signatures, which is remarkably associated with HCC invasion and metastasis.^25,31 MiR-199a/b has been shown as the third most highly expressed miRNA in normal liver with an average ratio in miRNome 4.9%.²¹ However, these two miRNAs have been found to consistently decrease in HCC tissues, as much as 82.5% and 73%, respectively, and this decrement is significantly correlated with poor survival of patients.^32–36

Let-7a expression restoration in HCC tissue has been found to inhibit tumor formation and growth; this is due to targeting multiple components of cell proliferation pathways. For instance, introduction of let-7a precursor into HepG2 cells could significantly decrease cell proliferation by 30% and increase apoptosis.^20,23,37,38 Moreover, let-7a has been evolved to post-transcriptionally control innate and adaptive immunity (e.g. production of interleukin 13 (IL-13)).^39,40 Interestingly, let-7a has been proved to be an ideal HCC therapy because cholesterol-conjugated let-7a (Chol-let-7a) was found to exhibit superior anti-HCC effects.⁴¹ These effects promote tumor cell death, autophagy, and inhibit tumor cell growth, proliferation, invasion, and metastasis. Also, the re-introduction of miR-34a in HCC has been found to induce G1 arrest, apoptosis, and senescence and found to combat tumor growth, proliferation, scattering, migration, invasion, stemness, metastasis, chemo-resistance, and recurrence, and therefore it was identified as a potent HCC suppressor and an ideal therapeutic tool.^{3,27,29,42–47} Interestingly, miR-34a has been entered phase 1 clinical trial in HCC;⁴⁸ this is after significant tumor reduction, dramatic protection from progression without toxicity, and prolonged survival of animal models.³ Besides, the interconnected functions between miR-34a and molecules of immune system in HCC have been verified through regulating the expression of chemokine CCL22 and recruitment of regulatory T (Treg) cells.⁴⁵ Also, it was found that the transfection of oncolytic adenovirus co-expressing miR-34a and interleukin 24 (IL-24) in HCC cells could effectively achieve tumor suppressive effects.⁴⁴ Furthermore, miR-34a has been demonstrated to sensitize apoptosis through CD95 network, which also provided substantial protective axis against the loss of let-7.^49,50 Similarly, restoration of miR-199a expression in HCC cells has been shown to lead to 60% decrease of the invasive potential, inhibition of anchorage-independent growth, migration, proliferation, and cell cycle.^32,34,51,52 In addition, miR-199a could sensitize HCC cells to doxorubicin and hypoxia-induced apoptosis.³² Furthermore, miR-199 has been shown to function in immune system through regulating an important target in HCC, CD44 (a trans-membrane glycoprotein involved in cell–cell interaction, cell adhesion, and migration).³⁶

In the current study, we have demonstrated some important mechanisms of the most potent miRNAs regulating pathways of immune system and major HCC hallmarks through detecting miRNA-target genes and its regulated pathways. It has been shown that HCC is characterized by multiple cancer hallmarks.^53–55 These hallmarks facilitated the elucidation of molecular pathogenesis of HCC and identification of effective therapeutics as well.⁵⁶ In this context, the aim of our current study is to perform an integrative in silico analysis for three potent tumor suppressor miRNAs—let-7a, miR-34a, and miR-199a/b—using a systematic bioinformatics approach. This is to investigate the synergy between these miRNAs on regulating specific network of protein-coding genes and pathways in HCC especially the concerted and novel ones that encompass cancer hallmarks and/or interactions with the components of the immune system. Supporting this perspective, we proceed on in vitro validating the inhibition of some novel oncogenes having critical roles in the induction and the progression of HCC. We highlight the role of some oncogenes such as SOX4 and we leave others for the next part of the study. Overall, these miRNAs may hold great promise as anti-HCC therapeutics.

Methods

We integrated different bioinformatics servers of miRNA-target prediction and functional enrichment analysis to get new insights into the multi-functions of three important tumor suppressor miRNAs—let-7a, miR-34a, and miR-199a/b—in immune system and different HCC/cancer hallmarks. Mainly, innovative servers (miRror Suite⁵⁷ and miRGator v3.0⁵⁸) were utilized to identify specific target genes from HCC expression data sets either for the combination of the miRNAs or for every individual miRNA. Then, the interactome between the miRNA and its corresponding targets in the enriched pathways and signaling interactions was determined by DAVID and GeneTrail.⁵⁹ The overall protocol of the study was presented in Figure 1.

Figure 1.

Flowchart illustrating the integrative bioinformatics analysis steps of let-7a, miR-34a, and miR-199a/b in HCC.

The three potent tumor suppressor miRNAs—let-7a, miR-34a, and miR-199a/b—were analyzed by an integrative bioinformatics approach. First, the target genes which are regulated either by the combination of these miRNAs or by every individual miRNA were determined using miRror Suite or miRGator servers. The combinatorial target prediction analysis gave 87 common targets while the individual analysis gave 34 targets of let-7a, 36 targets of miR-34a, 40 targets of miR-199a, and 35 targets of miR-199b. Second, the interaction between the miRNA and its corresponding predetermined targets in a variety of the enriched pathways and Gene Ontology (GO) terms was revealed. This was done either by the global functional enrichment analysis of the common targets of the three miRNAs by the annotation tools (DAVID, REACTOME, STRING) or the individual functional enrichment analysis of every miRNA targets by GeneTrail (Kyoto Encyclopedia of Genes and Genomes (KEGG), GO, TRANSFAC, TRANSPATH, NIA human disease gene sets). Finally, the multi-functions of the three miRNAs of interest in pathways of immune system and major HCC/cancer hallmarks were identified.

Combinatorial analysis of miRNAs by miRror suite

The miRror v2.0 tool has been analyzed for the combinatorial regulation by let-7a, miR-34a, and miR-199a/b on common genes and pathways.⁵⁷ These miRNAs recognized common binding sites of targets, and therefore they exhibited overlapping roles. The analysis included two stages. (a) Target prediction of the common genes of the three miRNAs of interest based on the miRtegrate protocol. This protocol calculated the probability of matches between miRNAs and their target genes based on the statistical common coverage for all the miRNA-target prediction databases (DBs). These DBs were PITA TOP,⁶⁰ PicTar v5.0,⁶¹ TargetScan,⁶² microcosm,⁶³ microRNA.org,⁶⁴ DIANA-microT v3.0,⁶⁵ EIMMO-MirZ,⁶⁶ miRDB,⁶⁷ RNA22,⁶⁸ and MAMI (http://34.236.212.39/microrna/getMirnaForm.do). Our analysis focused on the highly expressed genes in HCC having top ranked score of prediction 25%. The significance threshold for determining the miRNA targets based on the hypergeometric distribution with correction for multiple testing is p ≤ 0.05. In addition, these predicted targets were filtered by miRror Internal Score (miRIS) which balanced between the miRror predictions and the DBs that support target prediction. We have rerun this analysis using miRIS range from 0 to 1. Additional step of analysis has been added which included testing the target validation with the three miRNAs using specialized DBs of target validation, for example, mirecords, mirtarbase, and tarbase. (b) Statistical functional enrichment analysis of the commonly determined targets in order to reveal the coordinated function and regulation by the three miRNAs. This enrichment has been evolved by forwarding the resulted common targets from miRror suite to several resources like (a) the DAVID functional annotation tool,^69,70 for investigating the biological functions of the predetermined targets in pathways of KEGG,⁷¹ BioCarta, and GO annotations.⁷² (b) REACTOME,⁷³ a curated DB was implemented for additional pathway enrichment. Notably, for DAVID and REACTOME analyses, the significance was determined by the threshold (p ≤ 0.05) and multiple testing adjustments using false discovery rate (FDR). (c) STRING⁷⁴ DB was used to illustrate the possible protein–protein interaction network using cytoscape for the predicted targets of the miRNAs.

Individual analysis of miRNAs by miRGator v3.0

We used miRGator to predict the putative canonical targets with significant anti-correlation of expression with let-7a, miR-34a, and miR-199a/b in a portal of deep sequencing data of HCC from The Cancer Genome Atlas- Liver Hepatocellular Carcinoma (TCGA-LIHC).^58,75 Accordingly, the analysis focused on the series of expression profiles of both miRNAs and mRNAs for the same set of samples to verify effective targeting relationship. Normalization between the data of the miRNA sequence and mRNA sequences was based on calculating the Pearson correlation coefficient and the Spearman rank correlation. Regarding the relationships between the three miRNAs of interest and their mRNA targets, our research model aimed to determine the common targets from the network viewer of at least two DBs of prediction and at most six. These DBs were PITA,⁶⁰ PicTar,⁶¹ TargetScan,⁶² microcosm,⁶³ miRDB,⁶⁷ and miRNA.org.⁷⁶ Mainly miRGator analyses depend on the highest opposite expression pattern between the miRNA and its targets which were closely placed. Consequently, a strong supporting evidence of targeting miRNA to these targets was proved. We have chosen the top 300 targets for each of these DBs. For network viewer of the miRNA–mRNA relation, the limited score ranged from 23 to 7 or 15 to 7 (score 23 or 15 showed the highest opposite expression pattern, while score 7 showed a decrease in the opposite expression pattern, and the targets were far placed from the miRNA). Interestingly, some of the predicted targets were verified in one or more DBs of target validation, for example, miRecords,⁷⁷ Tarbase,⁷⁸ and mirTarBase.⁷⁹ Consistently, we have run search in literature for all the determined miRGator targets to identify the novel ones and confirm the verified ones. Next, in order to reveal the enriched functional categories for these candidate targets, we forwarded them to GeneTrail server.⁵⁹

Functional enrichment analysis of miRNA targets by GeneTrail

We used GeneTrail to demonstrate the comprehensive regulation of the input lists of miRNA targets which were formerly determined from miRGator. This analysis covered various categories of immune system and/or major HCC/cancer hallmarks, and it based mainly on KEGG⁷¹ and GO,⁷² in addition to other interesting categories, for example, TRANSPATH⁸⁰ and TRANSFAC.⁸¹ We performed Over-Representation Analysis (ORA) comparing a reference set of all genes to the input gene list of each miRNA (test set), and calculation of significance threshold (p ≤ 0.05) relied on hypergeometric distribution and multiple testing adjustment method: FDR.

In vitro validation

As mentioned above, target prediction analysis was performed to identify panels of novel oncogenic targets of the three miRNAs of interest. Consequently, we have proceeded on in vitro validation of some of the most important targets. We verified here one of them: SOX4 as a direct target of miR-34a.

Cell culture

The HCC cell line HepG2 was used as a cell culture model for the study, and it was obtained from American Tissue Culture Collection (ATCC, designation number HB-8065™). HepG2 was cultured in Dulbecco’s Modified Eagle Medium (DMEM; Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (FBS; Invitrogen) and 1% pen/strep under normal growth conditions (typically 37°C and 5% CO₂).

In vitro transfection assay

HepG2 cells were seeded at a density of 4.0 × 10⁵ cells/well in a six-well plate 24 h before transfection. The cells were further co-transfected with 6 µg of pEGP-miR-34a expression vector (DNA plasmid) using 12 µL of TurboFect in vitro transfection reagent according to the manufacturer’s recommendations (Thermo Fisher Scientific, Lithuania). After 4 h of incubation, the medium was replaced with fresh complete medium containing 10% FBS, and gene silencing was monitored 48 h post-transfection.

Quantitative validation of SOX4

Total RNA was extracted from the transfected and un-transfected HepG2 using BIZOL Reagent (BioFlux; Hangzhou Bioer Technology Co. Ltd., Binjiang) following the manufacturer’s instructions. Thereafter, complementary DNA (cDNA) was synthesized using the RevertAid™ H Minus First Strand cDNA synthesis kit as described by the manufacturer (Thermo Fisher Scientific) with oligo(dT)₁₈ primers. Quantitative real-time polymerase chain reaction (qRT-PCR) for the expression of SOX4 transcript was performed using Universal SYBR Green PCR master mix (Applied Biosystems, Foster City, CA, USA) and specifically designed primer set: 5′-CAGCAAACCAACAATGCCGA-3′ and 5′-CTTGCACCAGCTCGGGTC-3′. All the reaction mixtures were added in 96 well plates and were put in Roche 480 thermal cycler under the following cycling conditions: 95 C for 10 min followed by 40 cycles of 95 C for 15 s, 50°C for 30 s, and 72°C for 30 s. For normalization of the expression level of SOX4, measurement of an endogenous control GAPDH mRNA was performed with the specific primer set: 5′-CAAGGTCATCCATGACAACTTG-3′ and 5′-GTCCACCACCCTGTTGCTGTAG-3′. The relative expression level was calculated by the formula 2^−ΔΔCt.⁸² The data were obtained from three independent experiments and expressed as mean ± standard error of the mean (SEM).

Results

Combinatorial targets of the miRNAs

There were 87 common predicted target mRNAs of the input miRNA set—let-7a, miR-34a, and miR-199a/b—including some novel ones (Table S1). These combinatorial targets resulted by the miRror suite DBs of target prediction and ranked according to p value with threshold 0.05. Afterward, the statistically significant and enriched pathways and GO terms of these targets have been determined by the DAVID functional annotation server and REACTOME. Overall, 12 enriched pathways have been identified. The collection of all these pathways was illustrated in Figure 2(a). Depending on our characterization of these pathways’ results, we found two pathways linked to immune system: regulation of innate and adaptive immune system pathways and cytokine–cytokine receptor interaction; we found eight pathways linked to HCC/cancer hallmarks: cellular responses to stress, cell cycle, signal transduction, gene expression, pathways in cancer, insulin signaling pathway, ERBB signaling pathway, and casitas B-lineage lymphoma (CBL)-mediated ligand-induced downregulation of epidermal growth factor (EGF) receptors; and we found two pathways that mediate interconnected dual function between immune system and major HCC/cancer hallmarks: focal adhesion and extracellular matrix signaling (ECM)-receptor interaction.

Figure 2.

Bar chart of the enriched pathways and GO terms of the predicted targets of let-7a, miR-34a, and miR-199a/b, with the logarithm p value after FDR correction. (a) Pathways according to the determined significance thresholds (p ≤ 0.05), and pathway analysis of the common target genes by REACTOME, showing the highest six statistically significant pathways. While KEGG and BioCarta pathways analysis revealed four pathways and one pathway with lower significance. (b) GO functional categories of the commonly predicted targets of the miRNAs with the logarithm of corrected p value. According to the determined significance thresholds (p ≤ 0.05) and FDR adjustment, the GO analysis by DAVID tool revealed seven enriched GO terms of the common predicted targets.

Meanwhile, REACTOME provided us with some global high statistically significant (p < 0.001) pathway categories (Figure 2(a)), which are preferably divided into subcategories in order to clarify the functional enrichment of result. Examples of these pathways included signal transduction pathway category, which includes 13 subcategories: signaling by EGFR, signaling by FGFR, signaling by insulin receptor, signaling by PDGF, signaling by vascular endothelial growth factor (VEGF), signaling by ERBB2, signaling by ERBB4, PIP3 activating AKT signaling, RAF/MAP kinase cascade, signaling by TGF-beta receptor complex, signaling by Rho GTPases, signaling by NOTCH, and signaling by WNT. In addition, regulation of immune system pathway category includes innate immune system, adaptive immune system and cytokine signaling. Furthermore, gene expression category includes pathways of metabolism of non-coding RNA and miRNA-mediated RNA cleavage. And finally, pathways in cancer category which includes signaling by EGFR, PI3K/AKT signaling in cancer, signaling by FGFR, signaling by NOTCH1, signaling by TGF-beta receptor complex. As seen from the determined pathway results, let-7a, miR-34a, and miR-199a/b have interconnected functions on key immune system pathways besides other signaling pathways involved in HCC. These modulated functions were verified through the common predicted target genes of the three miRNAs in all these pathways. Examples of the most important predicted targets are shown in Table S2.

Regarding the result of DAVID GO annotations, there were seven enriched GO terms of the key predicted targets of let-7a, miR-34a, and miR-199a/b (Figure 2(b)) with p ≤ 0.05. Mostly, these GO categories were linked to HCC/cancer hallmarks for example, cell migration, cell proliferation and only one specific category; cell adhesion which was related to immunity with the highest significance (p-value 0.0081).

Individual targets of the miRNAs

We have determined a collection of common targets for each of the three miRNAs—let-7a, miR-34a, and miR-199a/b—from miRGator network viewer by at least two or at most six DBs of target prediction and score range from 23 to 7. Notably, all of the evolved targets had critical roles in hepatocarcinogenesis, and some of them have been considered to be novel as they were predicted by programs of target prediction but were not validated before. There were 34 predicted targets of let-7a, of which 13 genes of them were considered to be novel (Table S3). Meanwhile, there were 36 targets of miR-34a, in which nine genes of them were considered to be novel (Table S4). Also, there are 40 targets of miR-199a and 35 common targets of miR-199b. Interestingly, 22 genes of miR-199a and 25 genes of miR-199b were considered to be novel (Table S5 (A, B)). The miRNA-binding sites within the 3′ Un Translated Region (UTR) of some of these novel targets were shown in Figure 3(a)–(c). Importantly, we have narrowed the list of interesting novel targets and have chosen some of them for experimental validation. For example, SOX4 was validated as a direct target of miR-34a as it will be shown later. Furthermore, we could reveal the regulatory function of let-7a, miR-34a, and miR-199a/b in immune system modulation and/or HCC/cancer hallmarks through entering the target gene list of each of these miRNAs to GeneTrail.

Figure 3.

The binding modes for the three miRNAs—let-7a, miR-34a, and miR-199a/b—and some of their novel targets. The predicted structure of the duplex between the miRNA and its target site (either 5′dominant canonical or 5′ dominant seed) was shown. (a) Representation of the mode of pairing between let-7a target sites within the 3′ UTR regions of FAM103A1 and DDX19B. (b) Representation of the mode of pairing between miR-34a target sites within the 3′ UTR regions of ACBD3 and PGAP2. (c) Representation of the mode of pairing between miR-199a/b target sites within the 3′ UTR regions of ABCA1 and ARHGAP21.

GeneTrail functional enrichment of let-7a

Overall, the 34-input target list of let-7a has yielded 15 significant KEGG pathways, 8 GO terms, and other interesting category: NIA human disease gene sets.

KEGG pathways

A total of 15 significant KEGG pathways were obtained; 3 pathways linked to immune system functions, 6 pathways linked to HCC/cancer hallmarks, and 6 pathways integrate interconnected networking between immune system and HCC hallmarks. These enriched pathways were tabulated in Table 1, where they are arranged in ascending order according to p value with threshold 0.05.

Table 1.

Enriched KEGG pathways for let-7a target genes.

KEGG pathway	Functionality of pathway	p value (FDR)
B-cell receptor signaling pathway	Immune system function	0.00159518
T-cell receptor signaling pathway	Immune system function	0.00282761
Pathways in cancer	HCC hallmarks	0.00335521
Chemokine signaling pathway	Immune system function	0.00724346
MAPK signaling pathway	HCC hallmarks	0.00848984
VEGF signaling pathway	HCC hallmarks	0.00848984
Apoptosis	HCC hallmarks	0.00963969
ERBB signaling pathway	HCC hallmarks	0.00963969
Gap junction	Integrated networking between immune system and HCC hallmarks	0.0096831
Toll-like receptor signaling pathway	Integrated networking between immune system and HCC hallmarks	0.0110663
Axon guidance	Integrated networking between immune system and HCC hallmarks	0.0162626
Insulin signaling pathway	HCC hallmarks	0.016632
Natural killer cell–mediated cytotoxicity	Integrated networking between immune system and HCC hallmarks	0.016632
Tight junction	Integrated networking between immune system and HCC hallmarks	0.016632
Regulation of actin cytoskeleton	Integrated networking between immune system and HCC hallmarks	0.0379654

KEGG: Kyoto Encyclopedia of Genes and Genomes; FDR: false discovery rate; HCC: hepatocellular carcinoma; MAPK: mitogen-activated protein kinase; VEGF: vascular endothelial growth factor.

GO terms

There were eight obtained GO terms in which the let-7a target genes had significant structural and molecular functions. These ontologies included nuclear pore, RNA localization, pore complex, nucleic acid transport, RNA transport, mRNA transport, and establishment of RNA localization. All these terms had unified significance of 0.0389916 except for nucleobase, nucleoside, nucleotide, and nucleic acid transport which had significance of 0.0491332.

Other significant category of let-7a included NIA human disease gene sets with one enriched item, neoplasms which suggested to be regulated by let-7a target genes with significance of 0.0335982.

GeneTrail functional enrichment of mir-34a

The 36-input target list of mir-34a has yielded two KEGG pathways, 22 significant GO terms, and other interesting categories: TRANSPATH and TRANSFAC.

KEGG pathways

There were two significantly enriched KEGG pathways where the mir-34a target genes have significant roles. These pathways were hematopoietic cell lineage and P53 signaling pathway with unified significance of 0.0454855.

GO terms

We have determined 22 GO terms in which mir-34a target genes have significant roles as shown in Table 2. These GO terms were arranged in ascending order according to their p value with threshold 0.05.

Table 2.

Enriched GO terms for miR-34a target genes.

GO term	p value (FDR)
Positive regulation of cell communication	0.0263287
Positive regulation of signaling pathway	0.0263287
Positive regulation of WNT receptor signaling pathway	0.0342905
Positive regulation of cellular process	0.0342905
Negative regulation of cellular process	0.0342905
Regulation of biological process	0.0342905
Regulation of metabolic process	0.0342905
Positive regulation of biological process	0.0355075
Negative regulation of apoptosis	0.0357673
Negative regulation of programmed cell death	0.0357673
Regulation of cellular process	0.0357673
Negative regulation of cell death	0.0357673
Cell fate determination	0.0369966
Cell proliferation	0.0369966
Regulation of biosynthetic process	0.0369966
Negative regulation of biological process	0.0369966
Positive regulation of signal transduction	0.04269
Positive regulation of signaling process	0.0437503
Protein stabilization	0.0461599
Biosynthetic process	0.0471492
Regulation of macromolecule biosynthetic process	0.0471492
Signaling pathway	0.0471492

GO: Gene Ontology; FDR: false discovery rate.

Other categories of miR-34a enrichment analysis included TRANSPATH with one significantly enriched pathway which is NOTCH pathway (significance 0.00738002) and TRANSFAC with one significantly enriched function: Sp1 (significance 0.0421583).

GeneTrail functional enrichment of mir-199a/b

The 40-input target list of miR-199a has yielded four significant GO terms and other interesting TRANSPATH pathway.

GO term

We have determined four specific GO terms of the miR-199a target genes with significance of 0.0282994. The most important GO term was regulation of autophagy which provided new insight into a very specific functional role for miR-199a as a potent tumor suppressor in HCC. This could be demonstrated through miR-199a regulation of two important oncogenic targets, MTOR and CISD2, as determined from our findings. While the rest three terms had structural and molecular functions and they were intracellular part, membrane-bounded organelle and intracellular membrane-bounded organelle.

The other significant category of miR-199a included TRANSPATH with one enriched pathway which is ErbB3 survival with significance of 0.000444754.

The 35-input target list of miR-199b has yielded one KEGG pathway and eight significant GO terms.

KEGG category

Adipocytokine signaling pathway (p-value of 0.0368874) is the significantly enriched KEGG pathway in which the miR-199b target genes are implicated. In addition, adipocytokine signaling pathway was found to be significantly enriched according to our results with a p value of 0.0368874.

GO category

We have determined eight GO subcategories of the miR-199b target genes as shown in Table 3. These subcategories were arranged in ascending order according to p value with threshold 0.05.

Table 3.

Enriched GO terms for miR-199b target genes.

GO term	p value (FDR)
Intracellular part	0.000470417
Membrane-bounded organelle	0.00162463
Intracellular membrane-bounded organelle	0.00162463
Intracellular organelle	0.00305257
Regulation of autophagy	0.0111249
Organelle outer membrane	0.0381585
Outer membrane	0.0382413
Cytoplasmic part	0.0386425

GO: Gene Ontology; FDR: false discovery rate.

In vitro validation results

SOX4 was validated as a direct novel target for miR-34a in the tested HepG2 cells by qRT-PCR analysis. After 48 h of pEGP-miR-34a transfection, the relative expression level of endogenous mRNA of SOX4 was significantly decreased by about 0.064% (15.6 folds) as compared to control (un-transfected cells, p < 0.05). In conclusion, this finding has shed light on the tumor suppression function of miR-34a in HCC through negative regulation of SOX4.

Discussion

The tumor suppression roles of let-7a, miR-34a, and miR-199a/b in HCC were reported in several studies.^{3,23,27,29,32,34,36,37,41,44–48,51,52} However, new functions of these miRNAs in pathways and GO categories of immune system and HCC hallmarks have been identified by our integrative analysis which is done by the bioinformatics tools; miRror Suite, DAVID, miRGator, and GeneTrail.

The enriched functions of the key common target genes by miRror

We have investigated the coordinated regulation of let-7a, miR-34a, and miR-199a/b on the targets with high statistical significance. Interestingly, examples of their novel fight against HCC included focal adhesion (represented interconnected link between immune system and HCC/cancer hallmarks) and insulin signaling (represented HCC/cancer hallmarks) (Figure 4(a) and (b)).

Figure 4.

Two pathway phenotype examples showing the coordinated regulation of let-7a, miR-34a, and miR-199a/b in immune system functions and HCC/cancer hallmarks. The three miRNAs of interest let-7a, miR-34a, and miR-199a/b regulate common target genes in focal adhesion and insulin signaling. These targets promote a variety of hepatocarcinogenic phenotypes: (a) focal adhesion and (b) insulin signaling.

Focal adhesion

Aberrant expression and altered functions of focal adhesion proteins have been found to promote HCC progression, metastasis, and epithelial–mesenchymal transition (EMT) of hepatic cancer stem cells (CSCs).^83,84 With reference to our result, focal adhesion was the highest statistically significant KEGG pathway (p value of 0.0077). It involved six predicted target genes (PAK4, FN1, PDGFRA, THBS1, CHAD, and ITGB8) as shown in Figure 4(a). PAK4 was found to be the highest significantly predicted target of let-7a, miR-34a, and miR-199a/b with p value of 0.0017. This oncogenic target has been reported to endogenously localize to adhesive structures and overexpress in HCC.^85,86 Its activation has been shown to induce the loss of focal adhesion and promote HCC cells migration and metastatic invasion.^86,87 Interestingly, it was reported that miR-199a/b target the tumor promoting PAK4 to suppress HCC growth,²¹ while our findings revealed that the three miRNAs of interest suppress the oncogenic potential of PAK4 in focal adhesion and consequently inhibit worse immune response and hallmarks of HCC. FN1 is another significantly predicted target of let-7a and miR-199a/b found in our results with p value of 0.0029. Functionally, FN1 has been found to be involved in HCC cells survival and motility,^88,89 and hence focal adhesion could be regulated through targeting FN1. Also, PDGFRA was found as a predicted target of let-7a, miR-34a, and miR-199a/b with p value of 0.0029. Interestingly, this is the first study to show the coordinated functioning of these three miRNAs in focal adhesion through regulating PDGFRA, which is known to induce HCC cells proliferation, migration, invasion, and EMT.^90–92 Besides, these miRNAs were found to modulate cytokine–cytokine receptor interaction through the same interconnected target, PDGFRA. In addition, THBS1 was found as a predicted target of let-7a, miR-34a, and miR-199a/b with p value of 0.0038. It has been identified as a matricellular adhesive glycoprotein and angiogenesis stimulator in hepatocarcinogenesis.⁹³ Its overexpression has been shown to promote HCC cells’ adhesion, survival, proliferation, migration, and invasion.^94,95 Herein, our study revealed that the three miRNAs efficiently block THBS1 and therefore act mainly as anti-angiogenic molecules. Collectively, these data implied that focal adhesion is a dual mediator between immune system and HCC hallmarks.

Insulin signaling

Insulin is strongly associated with glucose metabolism and cell proliferation, both of which are regulated by hepatocytes in physiological conditions.^96,97 While, the HCC cells have been characterized by upregulated insulin signaling which leads to hyperinsulinemia and in turn promotes the survival and the proliferation of HCC cells.^96,98 With reference to our result, insulin signaling was the second highest statistically significant KEGG pathway (p value of 0.0096). It involved five predicted target genes (MTOR, RHEB, CBL, CBLB, and PTPRF) as shown in Figure 4(b). MTOR was found to be the highest significantly predicted target of let-7a, miR-34a, and miR-199a/b with p value of 0.0001. Increased level and activity of this target have been found to be induced by insulin.^99,100 Interestingly, this signaling has been demonstrated in 60% of HCC cases, which promoted cell cycle progression, proliferation, growth, and angiogenesis.^101–105 Regarding the relation between let-7a, miR-34a, and miR-199a/b and MTOR as a direct target in insulin signaling, it has been shown that insulin stimulation inhibits the expression of miR-199a-5p.¹⁰⁶ Besides, miR-199a-3p targets MTOR in HCC cells, leading to G1 arrest, reduced invasive capability, and increased sensitivity to doxorubicin-induced apoptosis.³² Moreover, it has been shown that let-7 together with LIN28A/B regulate the interaction between insulin and MTOR.¹⁰⁷ Loss of LIN28A and overexpression of let-7 resulted in insulin resistance due to repression of the insulin-MTOR signaling.^107,108 Furthermore, miR-34a has been shown to interfere with MTOR signaling.¹⁰⁹ Collectively, based on these data, MTOR possibly acts as an important therapeutic target of let-7a, miR-34a, or miR-199a/b, and subsequently its contribution to previously mentioned HCC hallmarks will be suppressed. RHEB was found to be another significantly predicted target of the three miRNAs, and it is involved in insulin signaling with p value of 0.0023. This target has been known to be activated by its downstream target MTOR, and it is associated with cell cycle progression and growth.^110,111RHEB/MTOR signaling is overactivated in a wide variety of cancers, emphasizing their oncogenic potential.^112,113 Interestingly, insulin has been demonstrated to relieve inhibition of RHEB and increase its level which in turn activates the mTOR signaling.^114,115 Therefore, based on the determined findings, the three miRNAs of interest have major inhibitory role on the multiconnected signaling of insulin/RHEB/MTOR.

LIN28B is among the key novel targets revealed by miRror prediction, and it provides a good example for the combinatorial HCC suppression by the three miRNAs of interest. This oncogene has been found to exhibit high expression in HCC with more frequency in high-grade HCCs.¹¹⁶ In addition, it is well known to induce multiple oncogenic pathways in HCC, which promote tumor proliferation, EMT, and invasion capacity.^116–118 Interestingly, LIN28B was reported to coordinate blocking let-7 precursors from being processed through terminal uridylation and degradation.^117,119 Besides, our findings demonstrated that LIN28B is involved in regulating many of the non-coding RNA processes, including non-coding RNA/miRNA metabolic and catabolic processes, post-transcriptional regulation of gene expression, negative regulation of gene expression, gene silencing by RNA or miRNA, and production of miRNAs.

Regarding the protein–protein interaction network analysis of let-7a, miR-34a, and miR-199a/b targets which was retrieved from the STRING tool,⁷⁴ top ranking nodes of 24 interacting hub proteins (ATG4B, RHEB, TSC2, MTOR, EIF4EBP1, RICTOR, MLST8, RPTOR, RPS6KB1, AKT1, CBLB, CBL, PDGFRA, SPRY2, RANGAP1, RANBP2, RAD23B, ATAD2B, GRBS, MYEF2, SNAI1, MAP3K11, ACVR2B, and ACVR2A) were noticeably identified by cyto-hubba (Figure S1). By this interactome, the above-mentioned targets could link different pathways.

The enriched functions of the key common target genes by miRGator and GeneTrail

Individual target analysis for let-7a, miR-34a, or miR-199a/b has been conducted by miRGator platform which predicted the most prominent targets with inverse expression correlation to the miRNA of interest in HCC. Some of these targets were novel while others were validated. Our main focus was to reveal the vital regulatory roles of the three miRNAs on fine-tuning these targets. Importantly, we presented an integrative view for analyzing their novel mechanisms in some pathways and GO categories of the immune system and HCC hallmarks which have been determined by GeneTrail.

Let-7a and Toll-like receptor pathway

Normally, toll-like receptor (TLR) signaling has been known to be critical for regulating innate and adaptive immune responses in liver.^120,121 However, abnormal TLR activation was found to develop inflammation-associated hepatocarcinogenesis due to their universal high expression.^122–124 Our results revealed that let-7a regulation of TLR signaling is statistically significant (p value of 0.0110663) through two target genes (CASP8 and NFKB1). Notably, these targets were previously validated for let-7a;^77,78 however, it is the first time to analyze their mediated action in TLR-driven HCC. Based on miRGator prediction analysis, CASP8 was a predicted target of let-7a with common score of 9.749 (Table S3). It has been found that CASP8 is highly expressed in the immune system and liver cells where it has novel regulatory role in inflammasome activation and induction of compensatory proliferation which are important for HCC progression.^125,126 Thus, hepatocyte-specific deletion of CASP8 was found to be protective against inflammation-related hepatocarcinogenesis.¹²⁷ Consistently, our findings demonstrated direct targeting of CASP8 by let-7a, and consequently the HCC-promoting consequences of inflammatory responses (an emerging HCC hallmark; immune destruction) and multi-characteristics of HCC hallmarks are inhibited. Nuclear factor-kappa B1 (NF-κB1) was found to be another predicted target of let-7a with common score of 7.47 (Table S3). It has been previously shown that NF-κBs as well as TLRs are critical for the production of cytokines associated with hepatocarcinogenesis.¹²⁸ Actually, NF-κB activation was reported to be triggered by pro-inflammatory cytokines which produced through TLR signaling.¹²⁹ In addition, the activated NF-κB by TLR upregulation has been found to induce chronic pro-inflammatory response which transforms the liver stem/progenitor cells into HCC cells and promotes liver metastasis.^130,131 Collectively, based on the accumulated evidence, let-7a can act as TLR signaling antagonist as it is shown previously to inhibit the immune destruction and other routine HCC hallmarks. Thus, it may represent an important antitumor tool against HCC.

Let-7a and NK cell–mediated cytotoxicity

NK cells have been reported to account for 25%–50% of liver lymphocytes and mediate natural cytotoxicity through producing cytokines and forming the first line of liver defense against infection, stress, and tumor formation.^132,133 In HCC, NK cells have been found exclusively in areas of positive tumor apoptosis and negative tumor proliferation.⁸ However, high percentage of these cells has been shown to be defective and might be associated with the immune evasion of HCC.^134–137 Interestingly, let-7a was reported to be highly expressed in NK cells,¹³⁸ and according to our results, NK cell signaling is significantly regulated by let-7a (p value of 0.016632) through two predicted targets, NRAS and HRAS (Table S3). Although, these targets were previously validated for let-7a,^41,77–79 it is the first time to investigate their actions in NK cell signaling in HCC. NRAS and HRAS have been shown to exhibit significant upregulation in hepatocarcinogenesis, and they regulate NK group 2 member D (NKG2D) ligand expression through mitogen-activated protein kinase (MAPK)/MEK and PI3K pathways.^139–142 Therefore, direct targeting of RAS oncogenes by let-7a might provide defense mechanism against HCC because it acts as an enhancer for NK cell–mediated cytotoxicity and as an inhibitor for HCC-contributing pathways. Overall, NK cell signaling is essential as anti-HCC immunity. Thus, its augmentation may be a promising immunotherapy.

Mir-34a and positive regulation of WNT receptor signaling

The WNT receptor signaling is frequently activated in one third of all HCCs where it leads to limitless proliferation of tumor cells.^1,143–145 With reference to our results, two enriched mir-34a target genes; SOX4 and PPM1A (p value of 0.0342905) were found to have roles in positive regulation of WNT receptor signaling in HCC. Based on miRGator analysis, SOX4 was found as a novel predicted target of miR-34a with common score of 7.226. SOX4 has been recognized as one of the most highly tumor-associated genes and cancer signature genes because it is significantly elevated in multiple cancers, including HCC.^146–155 Besides, SOX4 was found to exert its oncogenic effect and metastasis-promoting capacity in stem cell compartment.^156,157 We validated this target in HepG2 cells following transfection with pEGP-miR-34a vector. It exhibited downregulation by 15.6 folds as compared to control (un-transfected cells), respectively (p < 0.05). The fundamental role of SOX4 in positive regulation of WNT receptor signaling may be explained as follows: SOX4 has been demonstrated to regulate the transcriptional activity of many downstream target genes leading to HCC initiation, progression, EMT, and recurrence.^150,158,159 For instance, SOX4 was showed to enhance WNT tumorigenic effects through direct interaction and stabilization of β-catenin/T-cell factor complex.^{150,151,160,161} These enhanced effects of SOX4 were associated with inhibition of apoptosis, cell cycle progression, proliferation, and inflammation.^{151,157,158,162} It has been found that RNA interference against SOX4 in invasive HCC greatly suppresses tumor growth, invasion, and metastasis.^151,158 This finding supported our result which revealed that SOX4 is a potent target of miR-34a and its oncogenic role in WNT receptor signaling is reversed by this miRNA. PPM1A is a member of Ser/Thr protein phosphatases,¹⁶³ mainly expressed in the cytoplasm of HCC cells and so it is positively correlated with tumor progression.¹⁶⁴PPM1A has been shown to dephosphorylate cyclin-dependent kinases to enhance cell invasion and migration.^165,166 Our study revealed that PPM1A is a predicted and validated target of mir-34a, with common score of 7.0 (Table S4). PPM1A may enhance HCC cell growth through positive regulation of WNT receptor signaling. Nevertheless, further studies are still needed to investigate its clear mechanistic role. Collectively based on the mentioned data, we could conclude that mir-34a downregulates SOX4 and PPM1A which are involved in the sustained growth and proliferation of HCC cells through positive regulation of WNT receptor signaling.

Mir-34a and negative regulation of apoptosis

It has been shown that miR-34a induces apoptosis through regulating apoptosis inhibitors.^43,167,168 Actually, miR-34a can participate in the apoptotic program triggered by p53 activation.¹⁶⁹ According to our results, five significantly enriched mir-34a target genes (NOTCH2, BIRC3, PGAP2, CD44, and SOX4) were found to act as apoptosis inhibitors in HCC (p value of 0.0357673). All these targets were predicted and validated for miR-34a except for SOX4 (Table S4). It has been shown that miR-34a inhibits NOTCH2 in order to promote apoptosis and inhibit proliferation and invasion of the tumor cells.^170,171 However, our results showed that NOTCH2 is regulated by miR-34a in HCC cells to promote the same effects. BIRC3 has been known among the anti-apoptotic transcripts which downregulated by miR-34a leading to cancer cell death.^172,173CD44 was identified as a molecular marker in multiple tumors including HCC because its aberrant overexpression has been correlated with the metastatic potential of tumors.^174–177 In addition, it was shown to elevate in many CSCs including those of liver cancer and as a result it was identified as CSC marker evading death receptor–mediated apoptosis.^178–184 Moreover, CD44 has been shown to promote apoptosis suppression in HCC to enhance adhesion-mediated proliferation, invasion, migration, and metastasis.^{174,185–187} Ectopic re-expression of miR-34a has been found to inhibit tumor cell growth and induce apoptosis¹⁸⁸ through downregulation of CD44.¹⁸⁹ In addition, miR-34a could abolish stemness of CSCs via direct targeting 90% of tumor-initiating CD44+ stem cells.^175,190,191 Nevertheless, in the absence of miR-34a expression, CD44 has been shown to negatively regulate apoptosis via extracellular and intracellular signaling and mitochondrial pathway.^192–196 Overall, our results confirmed the regulatory role of miR-34a on CD44 in HCC cells. As mentioned before, overexpression of SOX4 has been found to contribute to hepatic tumorigenesis by inhibiting p53-mediated apoptosis.¹⁶² In this context, our findings provided additional evidence for the crucial role of miR-34a in positive regulation of p53-mediated apoptosis. Altogether, we could conclude that mir-34a opposes several HCC hallmarks such as apoptotic evasion, growth signaling, and proliferation through negative regulation of key oncogenic targets in apoptosis and consequently enhances HCC cell death.

MiR-34a and cell fate determination

With reference to our findings, two enriched mir-34a target genes were found to have roles in cell fate determination (p value of 0.0369966). These targets were NOTCH2 and JAG1 (validated for mir-34a); however, it is the first time to analyze their function in HCC cell fate determination. NOTCH signaling has been shown to play a pivotal role in cell fate decision, lineage commitment of lymphocytes, and defense against tumors.¹⁹⁷ However, its dysregulation has been shown to be implicated in tumor angiogenesis and metastasis.¹⁹⁸NOTCH2 is an important receptor for membrane-bound ligands in NOTCH signaling. This receptor is involved in poor differentiation of many proliferating cells, including liver cells and self-renewal of stem cells.¹⁹⁹ Interestingly, NOTCH2 has been identified as HCC-associated oncogene, which exhibits high expression in neoplastic cells and hepatic CSCs.^200,201 Liver-specific expression and oncogenic constitutive potential of NOTCH2 intracellular domain (N2ICD) haves been shown to activate NOTCH2 signaling and upregulate the pro-proliferative genes which stimulate the proliferation of hepatocytes and sustain the stemness of liver CSCs.^200,202,203JAG1 was also a predicted target of miR-34a. It was shown to play an important role in cell fate determination through the JAG1-dependent NOTCH signaling, which is indispensable for stem cell self-renewal and differentiation.²⁰⁴ Besides, this signaling is one of the major signals required for directing CSCs and promoting stemness in solid tumors.^205–207 It has been reported that NOTCH2 and its ligand JAG1 is highly expressed in 83.3% of HCC tissues and their dysregulation leads to self-renewal of hepatic CSCs promoting hepatocarcinogenesis.²⁰³ HCC gene expression profiling has confirmed the high expression of NOTCH2 and JAG1 as hepatic progenitor cell (HPC) markers.²⁰⁸ Furthermore, it has been revealed that inhibiting either NOTCH2 or JAG1 could dramatically reduce tumor burden and growth.²⁰² Collectively, these results indicate that mir-34a suppresses the oncogenic potential of NOTCH2 and JAG1 which are key targets in HCC cell fate determination and consequently suppresses NOTCH signaling and its mediated HCC hallmarks.

Conclusion

In summary, this study highlighted the role of let-7a, mir-34a, and miR-199a/b as vital tumor suppressors in HCC. Our findings revealed that these miRNAs synergistically function together through interconnected regulatory networks consisting of common predicted target genes and enriched pathways of HCC. Also, each of these miRNAs individually regulates some specific targets and pathways. In addition, our results determined some novel regulatory actions of let-7a, mir-34a, and miR-199a/b in some pathways of immune system and HCC/cancer hallmarks (e.g. focal adhesion, insulin signaling, Toll-like receptor signaling, and adipocytokine signaling). Therefore, these miRNAs may act as effective inhibitors for HCC and may hold great promise either to be forwarded to preclinical and clinical trials or to be validated in other tumors.

Supplemental Material

supplementary_figure_TUB – Supplemental material for Bioinformatics functional analysis of let-7a, miR-34a, and miR-199a/b reveals novel insights into immune system pathways and cancer hallmarks for hepatocellular carcinoma

Supplemental material, supplementary_figure_TUB for Bioinformatics functional analysis of let-7a, miR-34a, and miR-199a/b reveals novel insights into immune system pathways and cancer hallmarks for hepatocellular carcinoma by Bangly Soliman, Ahmed Salem, Mohamed Ghazy, Nourhan Abu-Shahba and Mahmoud El Hefnawi in Tumor Biology

Supplemental Material

Supplementary_table(5)_forTUB – Supplemental material for Bioinformatics functional analysis of let-7a, miR-34a, and miR-199a/b reveals novel insights into immune system pathways and cancer hallmarks for hepatocellular carcinoma

Supplemental material, Supplementary_table(5)_forTUB for Bioinformatics functional analysis of let-7a, miR-34a, and miR-199a/b reveals novel insights into immune system pathways and cancer hallmarks for hepatocellular carcinoma by Bangly Soliman, Ahmed Salem, Mohamed Ghazy, Nourhan Abu-Shahba and Mahmoud El Hefnawi in Tumor Biology

Footnotes

Acknowledgements

Ahmed Salem, Mohamed Ghazy, and Mahmoud El Hefnawi conceived and supervised the study; Bangly Soliman and Mahmoud El Hefnawi designed the experiments; Bangly Soliman performed the in silico analysis, in vitro experiments, and the main statistical analysis; Bangly Soliman, Ahmed Salem, Mohamed Ghazy, and Mahmoud El Hefnawi analyzed the overall data; and Bangly Soliman and Mahmoud El Hefnawi were the major contributors in writing the manuscript, All authors have been involved in revising the manuscript, and all authors have given final approval of the version to be published. The authors are deeply indebted to Dr Ahmed Fawzy Mostafa, Associate Professor of Pharmaceutical Industries, National Research Center for his generous contribution in all practical experiments. Also, the authors are grateful to Engineer Saed Halim for helpful contribution in the preparation of figures and tables.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics approval and consent to participate

Ethics approval and consent to participate for the current study were not required because the present study is mainly based on bioinformatics analysis.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Research Centre (grant no. P100106) and the Ministry of Higher Education and Scientific Research (Science and Technology Development Fund [STDF], grant no. 18505), Cairo, Egypt. These grants are awarded to Mahmoud El Hefnawi and Bangly Soliman, and the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

ORCID iD

Mahmoud El Hefnawi

References

X-R

Huang

Z-G

et al . Insight into hepatocellular carcinogenesis at transcriptome level by comparing gene expression profiles of hepatocellular carcinoma with those of corresponding noncancerous liver. Proc Natl Acad Sci USA 2001; 98(26): 15089–15094.

Thorgeirsson

Grisham

JW.

Molecular pathogenesis of human hepatocellular carcinoma. Nat Genet 2002; 31(4): 339–346.

Bader

AG.

miR-34—a microRNA replacement therapy is headed to the clinic. Front Genet 2012; 3: 120.

Bartel

DP.

MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116(2): 281–297.

Brennecke

Stark

Russell

et al . Principles of microRNA-target recognition. PLoS Biol 2005; 3(3): e85.

Gao

Jeong

Tian

Liver: an organ with predominant innate immunity. Hepatology 2008; 47(2): 729–736.

Mehal

Azzaroli

Crispe

IN.

Immunology of the healthy liver: old questions and new insights. Gastroenterology 2001; 120(1): 250–260.

Chew

Tow

Teo

et al . Inflammatory tumour microenvironment is associated with superior survival in hepatocellular carcinoma patients. J Hepatol 2010; 52(3): 370–379.

L-F

Liston

MicroRNA in the immune system, microRNA as an immune system. Immunology 2009; 127(3): 291–298.

10.

Kerr

Korenblat

Davidson

NO.

MicroRNAs and liver disease. Transl Res 2011; 157(4): 241–252.

11.

Schickel

Boyerinas

Park

et al . MicroRNAs: key players in the immune system, differentiation, tumorigenesis and cell death. Oncogene 2008; 27(45): 5959–5974.

12.

Dooley

Linterman

Liston

MicroRNA regulation of T-cell development. Immunol Rev 2013; 253(1): 53–64.

13.

Wang

XW.

New kids on the block: diagnostic and prognostic microRNAs in hepatocellular carcinoma. Cancer Biol Ther 2009; 8(18): 1686–1693.

14.

ElHefnawi

Soliman

Abu-Shahba

et al . An integrative meta-analysis of microRNAs in hepatocellular carcinoma. Genomics Proteomics Bioinformatics 2013; 11(6): 354–367.

15.

Amer

Elhefnawi

El-Ahwany

et al . Hsa-miR-195 targets PCMT1 in hepatocellular carcinoma that increases tumor life span. Tumor Biol 2014; 35(11): 11301–11309.

16.

Paranjpe

The Lin28/Let-7 AXIS: an evolutionarily conserved superpathway. Curr Sci Rev 2015; 2.

17.

Sun

Wan

et al . Crosstalk between miR-34a and Notch signaling promotes differentiation in apical papilla stem cells (SCAPs). J Dent Res 2014; 93(6): 589–595.

18.

Dotto

Karine

. miR-34a/SIRT6 in squamous differentiation and cancer. Cell Cycle 2014(7): 1055–1056.

19.

Jia

et al . MiRNA-199a-3p regulates C2C12 myoblast differentiation through IGF-1/AKT/mTOR signal pathway. Int J Mol Sci 2014; 15(1): 296–308.

20.

Johnson

Esquela-Kerscher

Stefani

et al . The let-7 microRNA represses cell proliferation pathways in human cells. Cancer Res 2007; 67(16): 7713–7722.

21.

Hou

Lin

Zhou

et al . Identification of miRNomes in human liver and hepatocellular carcinoma reveals miR-199a/b-3p as therapeutic target for hepatocellular carcinoma. Cancer Cell 2011; 19(2): 232–243.

22.

Shimizu

Takehara

Hikita

et al . The let-7 family of microRNAs inhibits Bcl-xL expression and potentiates sorafenib-induced apoptosis in human hepatocellular carcinoma. J Hepatol 2010; 52(5): 698–704.

23.

Wang

Toh

et al . Lethal-7 is down-regulated by the hepatitis B virus x protein and targets signal transducer and activator of transcription 3. J Hepatol 2010; 53(1): 57–66.

24.

Jin

Yang

et al . Use of microRNA Let-7 to control the replication specificity of oncolytic adenovirus in hepatocellular carcinoma cells. PLoS ONE 2011; 6(7): e21307.

25.

Xie

et al . Diagnostic and prognostic implications of microRNAs in human hepatocellular carcinoma. Int J Cancer 2008; 123(7): 1616–1622.

26.

Mizuguchi

Mishima

Yokomuro

et al . Sequencing and bioinformatics-based analyses of the microRNA transcriptome in hepatitis B-related hepatocellular carcinoma. PLoS ONE 2011; 6(1): e15304.

27.

Dang

Luo

Rong

et al . Underexpression of miR-34a in hepatocellular carcinoma and its contribution towards enhancement of proliferating inhibitory effects of agents targeting c-MET. PLoS ONE 2013; 8(4): e61054.

28.

Yang

Zhou

et al . Analysis of sequence variations in 59 microRNAs in hepatocellular carcinomas. Mutat Res 2008; 638(1–2): 205–209.

29.

Tie

et al . miR-34a inhibits migration and invasion by down-regulation of c-Met expression in human hepatocellular carcinoma cells. Cancer Lett 2009; 275(1): 44–53.

30.

Tryndyak

Ross

Beland

et al . Down-regulation of the microRNAs miR-34a, miR-127, and miR-200b in rat liver during hepatocarcinogenesis induced by a methyl-deficient diet. Mol Carcinog 2009; 48(6): 479–487.

31.

Budhu

Jia

Forgues

et al . Identification of metastasis-related microRNAs in hepatocellular carcinoma. Hepatology 2008; 47(3): 897–907.

32.

Fornari

Milazzo

Chieco

et al . MiR-199a-3p regulates mTOR and c-Met to influence the doxorubicin sensitivity of human hepatocarcinoma cells. Cancer Res 2010; 70(12): 5184–5193.

33.

Jia

Cheng

Qian

et al . Lentivirus-mediated overexpression of microRNA-199a inhibits cell proliferation of human hepatocellular carcinoma. Cell Biochem Biophys 2012; 62(1): 237–244.

34.

Song

Gao

Yang

et al . MiR-199a regulates cell proliferation and survival by targeting FZD7. PLoS ONE 2014; 9(10): e110074.

35.

Duan

Wang

Gong

et al . ER stress negatively modulates the expression of the miR-199a/214 cluster to regulates tumor survival and progression in human hepatocellular cancer. PLoS ONE 2012; 7(2): e31518.

36.

Henry

Park

Jiang

et al . miR-199a-3p targets CD44 and reduces proliferation of CD44 positive hepatocellular carcinoma cell lines. Biochem Biophys Res Commun 2010; 403(1): 120–125.

37.

Tsang

Kwok

TT.

Let-7a microRNA suppresses therapeutics-induced cancer cell death by targeting caspase-3. Apoptosis 2008; 13(10): 1215–1222.

38.

Barh

Let-7 replacement therapy: applicability in cancer. Canc Ther 2008; 6(2): 969–984.

39.

Zhou

Liu

et al . MicroRNA-98 and let-7 confer cholangiocyte expression of cytokine-inducible Src homology 2-containing protein in response to microbial challenge. J Immunol 2009; 183(3): 1617–1624.

40.

Kumar

Ahmad

Sharma

et al . Let-7 microRNA-mediated regulation of IL-13 and allergic airway inflammation. J Allergy Clin Immunol 2011; 128(5): 1077–1085.e1–10.

41.

Liu

Xia

Dai

et al . Cholesterol-conjugated let-7a mimics: antitumor efficacy on hepatocellular carcinoma in vitro and in a preclinical orthotopic xenograft model of systemic therapy. BMC Cancer 2014; 14: 889.

42.

Tarasov

Jung

Verdoodt

et al . Differential regulation of microRNAs by p53 revealed by massively parallel sequencing: miR-34a is a p53 target that induces apoptosis and G1-arrest. Cell Cycle 2007; 6(13): 1586–1593.

43.

Hermeking

p53 enters the microRNA world. Cancer Cell 2007; 12(5): 414–418.

44.

Lou

Chen

et al . Oncolytic adenovirus co-expressing miRNA-34a and IL-24 induces superior antitumor activity in experimental tumor model. J Mol Med 2013; 91(6): 715–725.

45.

Yang

Feng

et al . TGF-beta-miR-34a-CCL22 signaling-induced Treg cell recruitment promotes venous metastases of HBV-positive hepatocellular carcinoma. Cancer Cell 2012; 22(3): 291–303.

46.

Cheng

Zhou

Xie

et al . The impact of miR-34a on protein output in hepatocellular carcinoma HepG2 cells. Proteomics 2010; 10(8): 1557–1572.

47.

Zhao

Kelnar

Bader

AG.

In-depth analysis shows synergy between erlotinib and miR-34a. PLoS ONE 2014; 9(2): e89105.

48.

Mirna Therapeutics, Inc. Mirna Therapeutics Is First to Advance MicroRNA into the Clinic for Cancer (Press Release). Austin, TX: Mirna Therapeutics, Inc., 2013.

49.

Misso

Di Martino

De Rosa

et al . Mir-34: a new weapon against cancer? Mol Ther Nucleic Acids 2014; 3: e194.

50.

Hau

Ceppi

Peter

ME.

CD95 is part of a let-7/p53/miR-34 regulatory network. PLoS ONE 2012; 7(11): e49636.

51.

Shen

Cicinnati

Zhang

et al . Role of microRNA-199a-5p and discoidin domain receptor 1 in human hepatocellular carcinoma invasion. Mol Cancer 2010; 9: 227.

52.

Lee

Kim

SG.

Hypoxia inducible factor-1α inhibition by protopophyrin IX through miR-199a-5p upregulation in mesenchymal-typed hepatocellular carcinoma. FASEB J 2014; 28: 1052.7.

53.

Hanahan

Weinberg

RA.

The hallmarks of cancer. Cell 2000; 100(1): 57–70.

54.

Lazebnik

What are the hallmarks of cancer?

Nat Rev Cancer 2010; 10(4): 232–233.

55.

Hanahan

Weinberg

RA.

Hallmarks of cancer: the next generation. Cell 2011; 144(5): 646–674.

56.

Liu

Jiang

Guan

XY.

The genetic and epigenetic alterations in human hepatocellular carcinoma: a recent update. Protein Cell 2014; 5(9): 673–691.

57.

Friedman

Naamati

Linial

MiRror: a combinatorial analysis web tool for ensembles of microRNAs and their targets. Bioinformatics 2010; 26(15): 1920–1921.

58.

Cho

Jang

Jun

et al . MiRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting. Nucleic Acids Res 2013; 41: D252–D257.

59.

Backes

Keller

Kuentzer

et al . GeneTrail—advanced gene set enrichment analysis. Nucleic Acids Res 2007; 35: W186–W192.

60.

Kertesz

Iovino

Unnerstall

et al . The role of site accessibility in microRNA target recognition. Nat Genet 2007; 39(10): 1278–1284.

61.

Krek

Grun

Poy

et al . Combinatorial microRNA target predictions. Nat Genet 2005; 37(5): 495–500.

62.

Lewis

Burge

Bartel

DP.

Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005; 120(1): 15–20.

63.

Griffiths-Jones

Grocock

van Dongen

et al . miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 2006; 34: D140–D144.

64.

Betel

Wilson

Gabow

et al . The microRNA.org resource: targets and expression. Nucleic Acids Res 2008; 36: D149–D153.

65.

Maragkakis

Reczko

Simossis

et al . DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res 2009; 37: W273–W276.

66.

Hausser

Berninger

Rodak

et al . MirZ: an integrated microRNA expression atlas and target prediction resource. Nucleic Acids Res 2009; 37: W266–W272.

67.

Wang

miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 2008; 14(6): 1012–1017.

68.

Miranda

Huynh

Tay

et al . A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell 2006; 126(6): 1203–1217.

69.

Huang

Sherman

Lempicki

RA.

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2008; 4(1): 44–57.

70.

Huang

Sherman

Lempicki

RA.

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009; 37(1): 1–13.

71.

Kanehisa

Goto

Kawashima

et al . The KEGG resource for deciphering the genome. Nucleic Acids Res 2004; 32: D277–D280.

72.

Camon

Magrane

Barrell

et al . The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res 2004; 32: D262–D266.

73.

Vastrik

D’Eustachio

Schmidt

et al . Reactome: a knowledge base of biologic pathways and processes. Genome Biol 2007; 8(3): R39.

74.

Szklarczyk

Franceschini

Kuhn

et al . The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011; 39: D561–D568.

75.

TCGA, http://cancergenome.nih.gov/ (accessed 2012).

76.

Betel

Koppal

Agius

et al . Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 2010; 11(8): R90.

77.

Xiao

Zuo

Cai

et al . miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 2009; 37: D105–D110.

78.

Papadopoulos

Reczko

Simossis

et al . The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res 2009; 37: D155–D158.

79.

Hsu

Lin

et al . miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 2011; 39: D163–D169.

80.

Krull

Voss

Choi

et al . TRANSPATH: an integrated database on signal transduction and a tool for array analysis. Nucleic Acids Res 2003; 31(1): 97–100.

81.

Matys

Fricke

Geffers

et al . TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res 2003; 31(1): 374–378.

82.

Livak

Schmittgen

TD.

Analysis of relative gene expression data using real-time quantitative PCR and the 2^−ΔΔCT method. Methods 2001; 25(4): 402–408.

83.

Yam

Tse

IO.

Role and significance of focal adhesion proteins in hepatocellular carcinoma. J Gastroenterol Hepatol 2009; 24(4): 520–530.

84.

Tsai

Hsu

et al . MicroRNA-122 plays a critical role in liver homeostasis and hepatocarcinogenesis. J Clin Invest 2012; 122(8): 2884–2897.

85.

Zhang

Viklund

et al . P21-activated kinase 4 interacts with integrin alpha v beta 5 and regulates alpha v beta 5-mediated cell migration. J Cell Biol 2002; 158(7): 1287–1297.

86.

H-T

Lai

W-L

Liu

H-F

et al . PAK4 phosphorylates p53 at serine 215 to promote liver cancer metastasis. Cancer Res 2016; 76(19): 5732–5742.

87.

Claire

Wells

Ridley

AJ.

PAK4 is activated via PI3K in HGF-stimulated epithelial cells. J Cell Sci 2002; 115(Pt 20): 3947–3956.

88.

Soikkeli

Podlasz

Yin

et al . Metastatic outgrowth encompasses COL-I, FN1, and POSTN up-regulation and assembly to fibrillar networks regulating cell adhesion, migration, and growth. Am J Pathol 2010; 177(1): 387–403.

89.

Liu

Zhou

et al . Characterization of genome-wide TFCP2 targets in hepatocellular carcinoma: implication of targets FN1 and TJP1 in metastasis. J Exp Clin Cancer Res 2015; 34(1): 6.

90.

Wei

Zhang

L-N

et al . Overexpression of platelet-derived growth factor receptor alpha promotes tumor progression and indicates poor prognosis in hepatocellular carcinoma. Oncotarget 2014; 5(21): 10307–10317.

91.

Zhu

Pan

Zhang

et al . MiR-146a enhances angiogenic activity of endothelial cells in hepatocellular carcinoma by promoting PDGFRA expression. Carcinogenesis 2013; 34(9): 2071–2079.

92.

Stock

Monga

Tan

et al . Platelet-derived growth factor receptor-α: a novel therapeutic target in human hepatocellular cancer. Mol Cancer Ther 2007; 6(7): 1932–1941.

93.

Poon

Chung

Cheung

et al . Clinical significance of thrombospondin 1 expression in hepatocellular carcinoma. Clin Cancer Res 2004; 10(12 Pt 1): 4150–4157.

94.

Zhang

Guo

et al . Identification of biomarkers for hepatocellular carcinoma using network-based bioinformatics methods. Eur J Med Res 2013; 18(1): 35.

95.

Qian

Tuszynski

GP.

Expression of thrombospondin-1 in cancer: a role in tumor progression. Proc Soc Exp Biol Med 1996; 212(3): 199–207.

96.

Nakamuta

Yada

Taketomi

et al . 575 alteration in lipid and glucose (insulin signaling pathway) metabolism-related gene expression in hepatocellular carcinoma has implications for treatment using lipid modulators. J Hepatol 2010; 52: S228.

97.

Meivar-Levy

Sapir

Berneman

et al . Human liver cells expressing albumin and mesenchymal characteristics give rise to insulin-producing cells. J Transplant 2011; 2011: 252387.

98.

Chettouh

Lequoy

Fartoux

et al . Hyperinsulinemia and insulin signalling in the pathogenesis and the clinical course of hepatocellular carcinoma. Liver Int 2015; 35: 2203–2217.

99.

Stadlbauer

Brunmair

Szocs

et al . The effects of amino acids on glucose metabolism of isolated rat skeletal muscle are independent of insulin and the mTOR/S6K pathway. Am J Physiol Endocrinol Metab 2009; 297(3): E785–E792.

100.

Tremblay

Marette

Amino acid and insulin signaling via the mTOR/p70 S6 kinase pathway. A negative feedback mechanism leading to insulin resistance in skeletal muscle cells. J Biol Chem 2001; 276(41): 38052–38060.

101.

Chen

. Novel therapeutic targets for hepatocellular carcinoma treatment. In: Lau

(ed.) Hepatocellular carcinoma: basic research. Rijeka: InTech, 2012. ISBN: 978-53-51.

102.

Villanueva

Chiang

Newell

et al . Pivotal role of mTOR signaling in hepatocellular carcinoma. Gastroenterology 2008; 135(6): 1972–198411.

103.

Sahin

Kannangai

Adegbola

et al . mTOR and P70 S6 kinase expression in primary liver neoplasms. Clin Cancer Res 2004; 10(24): 8421–8425.

104.

Tan

Zhang

et al . Activation of Akt-mTOR-p70S6K pathway in angiogenesis in hepatocellular carcinoma. Oncol Rep 2008; 20(4): 713–719.

105.

Zhou

Huang

et al . The mTOR pathway is associated with the poor prognosis of human hepatocellular carcinoma. Med Oncol 2009; 27(2): 255–261.

106.

Rane

Sayed

et al . An antagonism between the AKT and beta-adrenergic signaling pathways mediated through their reciprocal effects on miR-199a-5p. Cell Signal 2010; 22(7): 1054–1062.

107.

Frost

RJA

Olson

. Control of glucose homeostasis and insulin sensitivity by the Let-7 family of microRNAs. Proc Natl Acad Sci USA 2011; 108(52): 21075–21080.

108.

Zhu

Shyh-Chang

Segre

et al . The Lin28/let-7 axis regulates glucose metabolism. Cell 2011; 147(1): 81–94.

109.

Shin

Pan

Zhong

XP.

Regulation of mast cell survival and function by tuberous sclerosis complex 1. Blood 2012; 119(14): 3306–3314.

110.

Avruch

Hara

Lin

et al . Insulin and amino-acid regulation of mTOR signaling and kinase activity through the Rheb GTPase. Oncogene 2006; 25(48): 6361–6372.

111.

Aspuria

Tamanoi

The Rheb family of GTP-binding proteins. Cell Signal 2004; 16(10): 1105–1112.

112.

Mavrakis

Zhu

Silva

et al . Tumorigenic activity and therapeutic inhibition of Rheb GTPase. Genes Dev 2008; 22(16): 2178–2188.

113.

Nardella

Chen

Salmena

et al . Aberrant Rheb-mediated mTORC1 activation and Pten haploinsufficiency are cooperative oncogenic events. Genes Dev 2008; 22(16): 2172–2177.

114.

Jaeschke

Hartkamp

Saitoh

et al . Tuberous sclerosis complex tumor suppressor-mediated S6 kinase inhibition by phosphatidylinositide-3-OH kinase is mTOR independent. J Cell Biol 2002; 159(2): 217–224.

115.

Garami

Zwartkruis

Nobukuni

et al . Insulin activation of Rheb, a mediator of mTOR/S6K/4E-BP signaling, is inhibited by TSC1 and 2. Mol Cell 2003; 11(6): 1457–1466.

116.

Wang

Chen

Yuan

et al . Lin-28B expression promotes transformation and invasion in human hepatocellular carcinoma. Carcinogenesis 2010; 31(9): 1516–1522.

117.

Guo

Chen

Ito

et al . Identification and characterization of lin-28 homolog B (LIN28B) in human hepatocellular carcinoma. Gene 2006; 384: 51–61.

118.

You

Liu

Zhang

et al . Hepatitis B virus X protein upregulates Lin28A/Lin28B through Sp-1/c-Myc to enhance the proliferation of hepatoma cells. Oncogene 2014; 33(4): 449–460.

119.

Piskounova

Polytarchou

Thornton

et al . Lin28A and Lin28B inhibit let-7 microRNA biogenesis by distinct mechanisms. Cell 2011; 147(5): 1066–1079.

120.

Beutler

BA.

TLRs and innate immunity. Blood 2009; 113(7): 1399–1407.

121.

Schreibelt

Tel

Sliepen

et al . Toll-like receptor expression and function in human dendritic cell subsets: implications for dendritic cell-based anti-cancer immunotherapy. Cancer Immunol Immunother 2010; 59(10): 1573–1582.

122.

Seki

Brenner

DA.

Toll-like receptors and adaptor molecules in liver disease: update. Hepatology 2008; 48(1): 322–335.

123.

Maeda

Omata

Inflammation and cancer: role of nuclear factor-kappaB activation. Cancer Sci 2008; 99(5): 836–842.

124.

Yang

Zheng

QC.

Emerging role of Toll-like receptor 4 in hepatocellular carcinoma. J Hepatocell Carcinoma 2015; 2: 11–17.

125.

McCall

Uppal

Jaffee

et al . The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res 2011; 39: D1011–D1015.

126.

Gurung

Kanneganti

TD.

Novel roles for caspase-8 in IL-1beta and inflammasome regulation. Am J Pathol 2015; 185(1): 17–25.

127.

Liedtke

Bangen

Freimuth

et al . Loss of caspase-8 protects mice against inflammation-related hepatocarcinogenesis but induces non-apoptotic liver injury. Gastroenterology 2011; 141(6): 2176–2187.

128.

Maeda

NF-κB, JNK, and TLR signaling pathways in hepatocarcinogenesis. Gastroenterol Res Pract 2010; 2010: 367694.

129.

Karin

Greten

FR.

NF-kappaB: linking inflammation and immunity to cancer development and progression. Nat Rev Immunol 2005; 5(10): 749–759.

130.

French

Oliva

French

et al . Alcohol, nutrition and liver cancer: role of Toll-like receptor signaling. World J Gastroenterol 2010; 16(11): 1344–1348.

131.

Maeda

Hikiba

Sakamoto

et al . Ikappa B kinaseβ/nuclear factor-κB activation controls the development of liver metastasis by way of interleukin-6 expression. Hepatology 2009; 50(6): 1851–1860.

132.

Sun

Xiao

et al . Natural killer cell dysfunction in hepatocellular carcinoma and NK cell-based immunotherapy. Acta Pharmacol Sin 2015; 36(10): 1191–1199.

133.

Ishiyama

Ohdan

Ohira

et al . Difference in cytotoxicity against hepatocellular carcinoma between liver and periphery natural killer cells in humans. Hepatology 2006; 43(2): 362–372.

134.

Hoechst

Voigtlaender

Ormandy

et al . Myeloid derived suppressor cells inhibit natural killer cells in patients with hepatocellular carcinoma via the NKp30 receptor. Hepatology 2009; 50(3): 799–807.

135.

Cai

Zhang

Zhou

et al . Functional impairment in circulating and intrahepatic NK cells and relative mechanism in hepatocellular carcinoma patients. Clin Immunol 2008; 129(3): 428–437.

136.

Jinushi

Takehara

Tatsumi

et al . Impairment of natural killer cell and dendritic cell functions by the soluble form of MHC class I-related chain A in advanced human hepatocellular carcinomas. J Hepatol 2005; 43(6): 1013–1020.

137.

Chuang

Liu

Chang

WY.

Natural killer cell activity in patients with hepatocellular carcinoma relative to early development and tumor invasion. Cancer 1990; 65(4): 926–930.

138.

Beaulieu

Bezman

Lee

et al . MicroRNA function in NK-cell biology. Immunol Rev 2013; 253(1): 40–52.

139.

Calvisi

Ladu

Gorden

et al . Ubiquitous activation of Ras and Jak/Stat pathways in human HCC. Gastroenterology 2006; 130(4): 1117–1128.

140.

Calvisi

Ladu

Conner

et al . Inactivation of Ras GTPase-activating proteins promotes unrestrained activity of wild-type Ras in human liver cancer. J Hepatol 2011; 54(2): 311–319.

141.

Liu

Tan

et al . Ras activation induces expression of Raet1 family NK receptor ligands. J Immunol 2012; 189(4): 1826–1834.

142.

Chan

Smyth

Martinet

Molecular mechanisms of natural killer cell activation in response to cellular stress. Cell Death Differ 2014; 21(1): 5–14.

143.

Takigawa

Brown

AMC

. WNT signaling in liver cancer. Curr Drug Targets 2008; 9(11): 1013–1024.

144.

Laurent-Puig

Legoix

Bluteau

et al . Genetic alterations associated with hepatocellular carcinomas define distinct pathways of hepatocarcinogenesis. Gastroenterology 2001; 120(7): 1763–1773.

145.

Peng

Tang

et al . Recent advances of miRNA involvement in hepatocellular carcinoma and cholangiocarcinoma. Open J Intern Med 2012; 2(3): 135–162.

146.

Castillo

Matheu

Mariani

et al . Novel transcriptional targets of the SRY-HMG box transcription factor SOX4 link its expression to the development of small cell lung cancer. Cancer Res 2012; 72(1): 176–186.

147.

Zhang

Liang

Lei

et al . SOX4 induces epithelial-mesenchymal transition and contributes to breast cancer progression. Cancer Res 2012; 72(17): 4597–4608.

148.

Liu

Ramachandran

Ali Seyed

et al . Sex-determining region Y Box 4 is a transforming oncogene in human prostate cancer cells. Cancer Res 2006; 66(8): 4011–4019.

149.

McCracken

Kim

et al . An alternative pathway for expression of p56lck from type I promoter transcripts in colon carcinoma. Oncogene 1997; 15(24): 2929–2937.

150.

Liao

Sun

Chau

et al . Identification of SOX4 target genes using phylogenetic footprinting-based prediction from expression microarrays suggests that overexpression of SOX4 potentiates metastasis in hepatocellular carcinoma. Oncogene 2008; 27(42): 5578–5589.

151.

Chen

Zhang

et al . Methylation-mediated repression of microRNA 129-2 enhances oncogenic SOX4 expression in HCC. Liver Int 2013; 33(3): 476–486.

152.

Suzuki

Shen

Akagi

et al . New genes involved in cancer identified by retroviral tagging. Nat Genet 2002; 32(1): 166–174.

153.

Rhodes

Shanker

et al . Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 2004; 101(25): 9309–9314.

154.

Aaboe

Birkenkamp-Demtroder

Wiuf

et al . SOX4 expression in bladder carcinoma: clinical aspects and in vitro functional characterization. Cancer Res 2006; 66(7): 3434–3442.

155.

Andersen

Christensen

Thorsen

et al . Dysregulation of the transcription factors SOX4, CBFB and SMARCC1 correlates with outcome of colorectal cancer. Br J Cancer 2009; 100(3): 511–523.

156.

Van der Flier

Sabates-Bellver

Oving

et al . The intestinal Wnt/TCF signature. Gastroenterology 2007; 132(2): 628–632.

157.

Vervoort

van Boxtel

Coffer

PJ.

The role of SRY-related HMG box transcription factor 4 (SOX4) in tumorigenesis and metastasis: friend or foe?

Oncogene 2013; 32(29): 3397–3409.

158.

van Zijl

Zulehner

Petz

et al . Epithelial-mesenchymal transition in hepatocellular carcinoma. Future Oncol 2009; 5(8): 1169–1179.

159.

Jafarnejad

Ardekani

Ghaffari

et al . Pleiotropic function of SRY-related HMG box transcription factor 4 in regulation of tumorigenesis. Cell Mol Life Sci 2013; 70(15): 2677–2696.

160.

Lee

Ahn

Yoon

et al . Sox4 stimulates ß-catenin activity through induction of CK2. Oncol Rep 2011; 25(2): 559–565.

161.

Sinner

Kordich

Spence

et al . Sox17 and Sox4 differentially regulate beta-catenin/T-cell factor activity and proliferation of colon carcinoma cells. Mol Cell Biol 2007; 27(22): 7802–7815.

162.

Hur

Rhim

Jung

et al . SOX4 overexpression regulates the p53-mediated apoptosis in hepatocellular carcinoma: clinical implication and functional analysis in vitro. Carcinogenesis 2010; 31(7): 1298–1307.

163.

Das

Helps

Cohen

et al . Crystal structure of the protein serine/threonine phosphatase 2C at 2.0 A resolution. EMBO J 1996; 15(24): 6798–6809.

164.

Wang

Yang

et al . Expression of PTEN, PPM1A and P-Smad2 in hepatocellular carcinomas and adjacent liver tissues. World J Gastroenterol 2007; 13(34): 4554–4559.

165.

Saadat

Kikuchi

Assignment of the gene encoding magnesium-dependent protein phosphatase 1alpha (PPM1A) to human chromosome 14q22–>q23 and rat chromosome 6q24 by fluorescence in situ hybridization. Cytogenet Genome Res 2005; 108(4): 363.

166.

Zhang

Zhou

Lin

et al . Protein phosphatase 1A (PPM1A) is involved in human cytotrophoblast cell invasion and migration. Histochem Cell Biol 2009; 132(2): 169–179.

167.

Sun

Liu

et al . Downregulation of CCND1 and CDK6 by miR-34a induces cell cycle arrest. FEBS Lett 2008; 582(10): 1564–1568.

168.

Wei

Song

Durinck

et al . The MYCN oncogene is a direct target of miR-34a. Oncogene 2008; 27(39): 5204–5213.

169.

Emanuela Boštjančič

. MicroRNAs and lncRNAs as tumour suppressors. In: Cheng

(ed.) Future aspects of tumor suppressor gene. Rijeka: InTech, 2013. ISBN: 978-953-51-1063-7.

170.

de Antonellis

Medaglia

Cusanelli

et al . MiR-34a targeting of Notch ligand delta-like 1 impairs CD15+/CD133+ tumor-propagating cells and supports neural differentiation in medulloblastoma. PLoS ONE 2011; 6(9): e24584.

171.

Silber

Jacobsen

Ozawa

et al . miR-34a repression in proneural malignant gliomas upregulates expression of its target PDGFRA and promotes tumorigenesis. PLoS ONE 2012; 7(3): e33844.

172.

Chang

Wentzel

Kent

et al . Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. Mol Cell 2007; 26(5): 745–752.

173.

Liston

Fong

Korneluk

RG.

The inhibitors of apoptosis: there is more to life than Bcl2. Oncogene 2003; 22(53): 8568–8580.

174.

Wang

XW.

Clinical implications of cancer stem cell biology in hepatocellular carcinoma. Semin Oncol 2012; 39(4): 461–472.

175.

Liu

Kelnar

Liu

et al . The microRNA miR-34a inhibits prostate cancer stem cells and metastasis by directly repressing CD44. Nat Med 2011; 17(2): 211–215.

176.

Olsson

Honeth

Bendahl

et al . CD44 isoforms are heterogeneously expressed in breast cancer and correlate with tumor subtypes and cancer stem cell markers. BMC Cancer 2011; 11: 418.

177.

Heyse

Malcherczyk

Moll

et al . CD44: survival and metastasis in chondrosarcoma. Osteoarthritis Cartilage 2010; 18(6): 849–856.

178.

Allouche

Charrad

Bettaieb

et al . Ligation of the CD44 adhesion molecule inhibits drug-induced apoptosis in human myeloid leukemia cells. Blood 2000; 96(3): 1187–1190.

179.

Bates

Edwards

Burns

et al . A CD44 survival pathway triggers chemoresistance via lyn kinase and phosphoinositide 3-kinase/Akt in colon carcinoma cells. Cancer Res 2001; 61(13): 5275–5283.

180.

Yasuda

Tanaka

Fujii

et al . CD44 stimulation down-regulates Fas expression and Fas-mediated apoptosis of lung cancer cells. Int Immunol 2001; 13(10): 1309–1319.

181.

Fujita

Kitagawa

Nakamura

et al . CD44 signaling through focal adhesion kinase and its anti-apoptotic effect. FEBS Lett 2002; 528(1–3): 101–108.

182.

Henke

Bitterman

Roongta

et al . Induction of fibroblast apoptosis by anti-CD44 antibody: implications for the treatment of fibroproliferative lung disease. Am J Pathol 1996; 149(5): 1639–1650.

183.

Lakshman

Subramaniam

Jothy

CD44 negatively regulates apoptosis in murine colonic epithelium via the mitochondrial pathway. Exp Mol Pathol 2004; 76(3): 196–204.

184.

Hauptschein

Sloan

Torella

et al . Functional proteomic screen identifies a modulating role for CD44 in death receptor-mediated apoptosis. Cancer Res 2005; 65(5): 1887–1896.

185.

Zhu

Hao

Yan

et al . Cancer stem/progenitor cells are highly enriched in CD133+CD44+ population in hepatocellular carcinoma. Int J Cancer 2010; 126(9): 2067–2078.

186.

Hirohashi

Yamamoto

Uenishi

et al . CD44 and VEGF expression in extrahepatic metastasis of human hepatocellular carcinoma. Hepatogastroenterology 2004; 51(58): 1121–1123.

187.

Endo

Terada

Protein expression of CD44 (standard and variant isoforms) in hepatocellular carcinoma: relationships with tumor grade, clinicopathologic parameters, p53 expression, and patient survival. J Hepatol 2000; 32(1): 78–84.

188.

Desbarats

Newell

MK.

Fas engagement accelerates liver regeneration after partial hepatectomy. Nat Med 2000; 6(8): 920–923.

189.

Pramanik

Campbell

Karikari

et al . Restitution of tumor suppressor microRNAs using a systemic nanovector inhibits pancreatic cancer growth in mice. Mol Cancer Ther 2011; 10(8): 1470–1480.

190.

Nalls

Tang

Rodova

et al . Targeting epigenetic regulation of miR-34a for treatment of pancreatic cancer by inhibition of pancreatic cancer stem cells. PLoS ONE 2011; 6(8): e24099.

191.

Wilson

Venkatasubrahmanyam

Jia

et al . MicroRNA profiling of human-induced pluripotent stem cells. Stem Cells Dev 2009; 18(5): 749–758.

192.

Bates

Elith

Thorne

et al . Engagement of variant CD44 confers resistance to anti-integrin antibody-mediated apoptosis in a colon carcinoma cell line. Cell Adhes Commun 1998; 6(1): 21–38.

193.

Toole

Stamenkovic

Induction of apoptosis of metastatic mammary carcinoma cells in vivo by disruption of tumor cell surface CD44 function. J Exp Med 1997; 186(12): 1985–1996.

194.

Stamenkovic

Merlin, a “magic” linker between the extracellular cues and intracellular signaling pathways that regulate cell motility, proliferation, and survival. Curr Protein Pept Sci 2010; 11(6): 471–484.

195.

Jijiwa

Demir

Gupta

et al . CD44v6 regulates growth of brain tumor stem cells partially through the AKT-mediated pathway. PLoS ONE 2011; 6(9): e24217.

196.

Trochon

Mabilat

Bertrand

et al . Evidence of involvement of CD44 in endothelial cell proliferation, migration and angiogenesis in vitro. Int J Cancer 1996; 66(5): 664–668.

197.

Gogoi

Dar

Chiplunkar

SV.

Involvement of Notch in activation and effector functions of gammadelta T cells. J Immunol 2014; 192(5): 2054–2062.

198.

Leong

Karsan

Recent insights into the role of Notch signaling in tumorigenesis. Blood 2006; 107(6): 2223–2233.

199.

Artavanis-Tsakonas

Rand

Lake

RJ.

Notch signaling: cell fate control and signal integration in development. Science 1999; 284(5415): 770–776.

200.

Dill

Tornillo

Fritzius

et al . Constitutive Notch2 signaling induces hepatic tumors in mice. Hepatology 2013; 57(4): 1607–1619.

201.

Gao

Song

Chen

et al . Deregulated expression of Notch receptors in human hepatocellular carcinoma. Dig Liver Dis 2008; 40(2): 114–121.

202.

Zhu

Wang

et al . C8orf4 negatively regulates self-renewal of liver cancer stem cells via suppression of NOTCH2 signalling. Nat Commun 2015; 6: 7122.

203.

Huntzicker

Hotzel

Choy

et al . Differential effects of targeting Notch receptors in a mouse model of liver cancer. Hepatology 2015; 61(3): 942–952.

204.

Mancini

SJC

Mantei

Dumortier

et al . Jagged1-dependent Notch signaling is dispensable for hematopoietic stem cell self-renewal and differentiation. Blood 2005; 105(6): 2340–2342.

205.

Ayyanan

Civenni

Ciarloni

et al . Increased Wnt signaling triggers oncogenic conversion of human breast epithelial cells by a Notch-dependent mechanism. Proc Natl Acad Sci USA 2006; 103(10): 3799–3804.

206.

Balint

Xiao

Pinnix

et al . Activation of Notch1 signaling is required for β-catenin–mediated human primary melanoma progression. J Clin Invest 2005; 115(11): 3166–3176.

207.

Mishra

Banker

Murray

et al . Liver stem cells and hepatocellular carcinoma. Hepatology 2009; 49(1): 318–329.

208.

Govaere

Komuta

Berkers

et al . Keratin 19: a key role player in the invasion of human hepatocellular carcinomas. Gut 2014; 63(4): 674–685.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.62 MB

0.29 MB