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International Journal of Molecular Sciences logoLink to International Journal of Molecular Sciences
. 2026 Feb 10;27(4):1714. doi: 10.3390/ijms27041714

Let-7a and miR-34a Interplay Potent Suppressive Roles in Hepatocellular Carcinoma via Co-Targeting FNDC3B, IGF2 and SOX4

Bangly Soliman 1,2, Ahmed Fawzy Ibrahim 3, Ahmed Salem 1, Mohamed Ghazy 1,4, Mahmoud T Abo-Elfadl 5,6, Mahmoud ElHefnawi 2,*,†, Mario Flores 7,*,†
Editors: Janusz Blasiak, Duncan Ayers
PMCID: PMC12940684  PMID: 41751849

Abstract

Both let-7a and miR-34a have been repeatedly studied as pivotal suppressors for Hepatocellular carcinoma; however, their combined regulations remain to be fully elucidated. In the present study, we performed a comprehensive in silico analysis for let-7a and miR-34a using a wealth of updated tools: miRWalk, Genetrail and miRnet. In addition, our study is the first to quantify both miRs and their three predicted yet not experimentally validated oncogenic targets: FNDC3B, IGF2 and SOX4. This was assessed in HepG2 cell model following treatment by PEGP-vector expressing the miRs by MTT assay, florescence microscopy, qPCR and immune-florescence. Our bioinformatics analysis revealed a pool of common predicted hepatocarcinogenic targets shared by both let-7a and miR-34a. Importantly, three targets were identified as co-regulated through multiple canonical binding sites for each miR, and these had not been experimentally validated before. Furthermore, functional enrichment of these putative targets demonstrated their significant involvement in major and emerging HCC hallmarks, such as reprogramming of energy metabolism and evading immune destruction. These findings support our concept of simultaneous co-regulation of these oncogenes through the signaling networks and GO terms associated with both miRs. Consistently, our experimental results verified the significant overexpression of both miRs in HepG2 cells, leading to reduced tumor cell proliferation and decreased levels of the three oncogenic transcripts. Interestingly, miR-34a exhibited a superior suppression effect, reaching 38.7%, and SOX4 was identified as the most significantly downregulated target at both transcriptional and translational levels. Our findings provide new insights into the interconnected anti-HCC effects of let-7a and miR-34a and highlight the potential of applying their combined use to achieve the best therapeutic outcomes for this invasive tumor.

Keywords: hepatocellular carcinoma, silencer miRNAs, in silico analyses, in vitro verification, combined regulators, major HCC hallmarks, emerging HCC hallmarks

1. Introduction

Hepatocellular carcinoma (HCC) is the seventh most prevalent cancer globally and the second leading cause of cancer-associated mortalities [1]. Despite recent advances in HCC treatment, the effective diagnostic and therapeutic targets remain poor [2]. Thus, it is essential to more elucidate its molecular pathogenesis and develop an effective therapeutic approach against it [3].

MicroRNAs (MiRNAs) are a class of endogenously expressed noncoding RNAs [4]. Interestingly, miRNomes of HCC have identified a subset of the most differentially expressed miRs which correlates with tumor onset and progression. They could be used as biomarkers and therapeutics [5,6,7,8]. Let-7a and miR-34a are among the HCC suppressor miRs which have been reported to attain higher physiological expression in normal liver tissues compared to HCC tissues as they exhibited a significant deregulated expression in a wide panel of HCC cell lines and tissues [9,10]. In addition, the suppression of these miRs was reported to be critical for acquiring a malignant phenotype of HCC cells [11,12,13]. On the other hand, our former studies supported how the enforced re-expression of mature miRs especially let-7a and miR-34a could interfere with multiple hepatocarcinogenic properties including cell growth, proliferation, epithelial mesenchymal transition (EMT), migration and invasion [14,15,16,17,18,19,20,21,22]. Moreover, both miRs have been successfully tested in vivo, and miR-34a entered phase I clinical trials to treat liver cancer patients [23,24,25].

The miRNA-binding site within the 3′-UTR region of target genes determines the functionality of miRNA targets and evaluates their inhibitory regulation [7,26,27,28]. However, the combinatorial and individual regulation of the miRNA in HCC has emerged in our previous work of let-7a, miR-34a and miR-199a [19] and interestingly continued in the present study. This has been processed through comprehensive in silico analysis to predict the target oncogenes encompassing multi canonical binding sites for both miRs, and they were not experimentally validated before. Then, we further revealed the significantly enriched HCC pathways and Gene Ontology (GO) processes, and we studied the effects accompanied with their silencing through the combined regulation by miRs. Moreover, we quantified the anti-proliferative efficiency of both miRs in vitro via the constructed pEGP-miR expressing vectors which attained stable expression of mature let-7a and miR-34a in their native context in the tested HepG2 cells and the consequent upregulated expression of each miR and downregulated expression of the overlapping targets.

2. Results

2.1. Literature Curation Suggests Three Critical Hepatocarcinogenic Targets of Both Let-7a and miR-34a

The literature survey revealed huge proportions of oncogenic drivers contributing to HCC initiation and progression [29]. Interestingly, these hepatocarcinogenic drivers were found to be negatively regulated by some potent tumor suppressor miRs including let-7a, miR-34a, etc. as revealed by our previous studies [14,15,16,17,18,19,21,22]. Consistently, here our findings revealed the three most important candidate oncogenic targets which are widely involved in major and emerging HCC hallmarks. The major HCC hallmarks are the main and very common characteristics that the cells proceed through leading to hepatocarcinogenesis and worse progression, but the emerging hallmarks are the newly discovered characteristics that may also directly lead to the same destination. All these hallmarks can be blocked by both miRs.

2.2. Bioinformatics Analysis Shows That Let-7a and miR-34a Work Combinatorially

The potential combined effect of these miRs was assessed by predicting their targets using miRWalk. We observed that there were eighty shared targets (p-value ≤ 0.05, by using Bonferroni, Table S2). We also observed that there were three predicted targets, FNDC3B, IGF2 and SOX4, that have not been validated before and that exhibit the highest coverage of target predictions scores.

Next, we noticed the presence of at least two sites of canonical hybridization for each target to both let-7a and miR-34a fragments (Figure 1). Thus, this multi-canonical binding for the same target represents novelty rather than commonality as sometimes previous researchers have shown only one binding site for a miR within its corresponding target.

Figure 1.

Figure 1

Figure 1

Figure 1

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A detailed illustration for the most predicted binding sites along the 3′UTR region of: (A) FNDC3B for let-7a, (A’) FNDC3B for miR-34a. Consistently, the same addressing style; (B) IGF2 for let-7a, (B’) IGF2 for miR-34a. (C) SOX4 for let-7a, and (C’) SOX4 for miR-34a. The binding sites are shadowed yellow, green, blue and non-binding colored red.

In addition, based on this mode of binding, both let-7a and miR-34a may predominantly function by the induced mRNA degradation and translational repression effects. For instance, the 3′UTR region of FNDC3B had five binding sites for let-7a fragment (Figure 1A) and five binding sites for miR-34a fragment (Figure 1A’), while the 3′UTR region of IGF2 had two binding sites for let-7a fragment (Figure 1B) and three binding sites for miR-34a fragment (Figure 1B’). Finally, the 3′UTR region of SOX4 had three binding sites for let-7a fragment (Figure 1C) and two binding sites for miR-34a fragment (Figure 1C’). Overall, the bioinformatics results suggest that these two miRs interplay a major role in the co-regulation of HCC via blocking the three shared hepato-carcinogens.

2.3. Downregulated HCC Pathways/GO Processes by Let-7a and miR-34a

The integrated functional analysis revealed three common HCC pathways and eight common GO processes through which both let-7a and miR-34a downregulate the central mechanisms of HCC via co-targeting FNDC3B, IGF2 and SOX4 as shown in Figure 2A,B. In addition, there are specific pathways and GO processes that have been evolved for each target and can be co-regulated by both miRs. Meanwhile, there are four specific pathways for FNDC3B, fifteen pathways for IGF2 and two pathways for SOX4 as shown in Figure 2C–E. In addition, there are nine specific GO term for FNDC3B, thirty GO term for IGF2 and twenty-eight GO term for SOX4 as listed in Table 1. Notably, some of the enriched pathways and processes lead to either the major or the emerging HCC hallmarks, while others lead to both the major and emerging HCC hallmarks as shown in the mentioned figures.

Figure 2.

Figure 2

Figure 2

Figure 2

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Common HCC pathways/GO terms involving FNDC3B, IGF2 and SOX4. (A) co-regulated pathways by both let-7a and mir-34a by their direct targeting the three targets. (B) co-regulated GO terms by both let-7a and mir-34a by their direct targeting the three targets. Specific HCC pathways involving: (C) FNDC3B. (D) IGF2. (E) SOX4. Collectively, all these pathways and GO terms were arranged in descending order according the highest significance threshold. The red text refers to both the major and emerging HCC hallmarks as a resulted fate for the mentioned pathways and GO terms, while the blue text refers to the emerging HCC hallmarks as a resulted fate end and the black text refers to the major hallmarks.

Table 1.

Variety of the specific GO processes of FNDC3B, IGF2, SOX4 which are significantly enriched by miRWalk and Gene trail tools.

Gene GO Terms p-Value
FNDC3B Transcription Factor Target (FNDC3B binds Transcription factors via its promoter) 0.0003
Positive regulation of cell fate differentiation 0.00037
Cell adhesion mediated by integrin 0.0004
Positive regulation of TGF beta production and activity 0.0005
Positive regulation of cyclin-dependent protein serine/threonine kinase activity involved in G1/S transition 0.00056
Positive regulation of cellular response to VEGF stimulus 0.0006
Positive regulation of epithelial to mesenchymal transition 0.0007
Substrate-dependent cell migration 0.00074
Positive regulation of glycogen biosynthetic process 0.00096
IGF2 Transcription factor 0.0004
Growth factor activity 0.0005
Hormone activity 0.00053
Regulation of gene expression by genetic imprinting 0.00055
Insulin like growth factor receptor binding 0.0006
Insulin receptor binding 0.00068
Insulin Resistance 0.0007
Receptor activator activity 0.00071
Receptor regulator activity 0.00075
Kinase activator activity 0.0008
Protein kinase activator activity 0.00082
Protein serine threonine kinase activator activity 0.00085
Positive regulation of protein phosphorylation 0.0009
Positive regulation of peptidyl tyrosine phosphorylation 0.00094
Positive regulation of catalytic activity 0.00096
Regulation of precursor metabolites and generation of energy 0.00097
Regulation of carbohydrate metabolic process 0.00098
Regulation of Glucose metabolic process 0.00099
Regulation of polysaccharide metabolic process 0.001
Regulation of Glycogen metabolic process 0.00147
Regulation of glucan metabolic process 0.00159
Positive regulation of cell division 0.00177
Positive regulation of mitosis 0.00196
Positive regulation of mitotic cell cycle 0.002
Positive regulation of mitotic nuclear division 0.00212
Regulation of transcription DNA dependent 0.00229
Positive regulation of leukocyte proliferation 0.00387
Positive regulation of lymphocyte proliferation 0.00402
Positive regulation of activated t cell proliferation 0.00419
Posttranslational regulation of adherens junction stability and disassembly 0.00528
SOX4 Transcription factor 0.0002
Transcriptional activator activity 0.00024
Core promoter binding 0.0003
Core promoter sequence specific DNA binding 0.00037
Nucleic acid binding and transcription factor activity 0.00039
Positive regulation of transcription DNA dependent 0.0004
RNA polymerase II binding in positive regulation of transcription 0.00042
RNA polymerase II transcription coactivator activity 0.00047
RNA polymerase II transcription cofactor activity 0.0005
Negative regulation of apoptotic process 0.00052
Negative regulation of cell death 0.00053
Signal transducer involved in cell cycle checkpoint 0.00056
Regulation of Mitotic DNA integrity checkpoint 0.00058
Regulation of G1/S transition of mitotic cell cycle 0.0006
Carbohydrate homeostasis and glucose homeostasis 0.00064
Cellular response to carbohydrate stimulus 0.00067
Positive regulation of insulin secretion 0.0007
Somatic stem cell maintenance 0.00078
Lymphoid progenitor cell differentiation 0.0008
Pro b cell differentiation 0.00084
T cell differentiation 0.00088
Positive regulation of translation 0.0009
Regulation of protein stability 0.00091
Regulation of protein export from nucleus 0.00093
N terminal protein amino acid modification 0.00095
N terminal protein amino acid acetylation 0.00098
Peptide hormone secretion 0.00099
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2.4. Coregulatory Network Enrichment of Let-7a/miR-34a

The common regulatory effect of both miRs was derived from the coregulatory network of their shared targets from miRnet database. This network enrichment consisted of 63 closely interconnected hub targets including FNDC3B, IGF2 and SOX4, which were highlighted in Figure S2.

2.5. In Vitro Validation of Pre-Let-7a and Pre-miR-34a Recombinant Vectors

To validate experimentally that these tumor suppressor miRs work combinatorially, a sufficient glycerol stock of pUC 57 recombinant derivatives harboring pre-let-7a and pre-miR-34a was successfully prepared. Meanwhile, each stock was equivalent to 3 × 1010 cfu/μg. Thereafter, the pure two plasmid DNAs were obtained with high concentration and integrity as shown in Figure 3A.

Figure 3.

Figure 3

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Gel electrophoresis (A) Pre-let-7a and pre-miR-34a pUC57 recombinant derivatives which successfully purified from E. coli. The M well represents the 3000 bp marker, wells 1 and 2 represent sharp bands of each of the two intact DNA plasmids: pre-let-7a and pre-miR-34a with size ~2700 bp. (B) pUC57 derivatives and pEGP digestions. The M well represent the 3000 bp marker, well 1 represent linearized pUC57 plasmid and pre-let-7a fragment, well 2 represent linearized pUC57 plasmid and pre-miR-34a fragment, well 3 represents the pEGP original intact plasmid with approximate size 5000 bp, well 4 represents linearized pEGP-miR vector with approximate size 4700 bp.

2.6. MiRNA Expression Vector Constructs

Two miRNA expression vectors were generated as a result of ligating the pre-let-7a and pre-miR-34a (BamHI/NheI) digested fragment from the pUC57 derivatives into the pEGP (BamHI/NheI) linearized fragments (Figure 3B). Ligation products were successfully transformed. Positive transformants were selected, and pure target fragments were confirmed by Sanger sequencing such that the data of ligation products are listed in Table S3.

2.7. Transfection Efficiency Verification

We demonstrated the transfection efficiency of the two designated tumor suppressive miRs via their cationic polymer-based delivery by Turbofect reagent in HepG2 cells. Meanwhile, the formulated constructs of pre-let-7a and pre-miR-34a significantly enforce the expression of endogenous mature miRs and dramatically induce growth inhibitory effects in cultured HepG2 cells. The transiently transfected cells were validated by results of GFP fluorescence screening, cell proliferation assay and RT-qPCR assay.

2.8. Green Fluorescent Protein (GFP) Co-Expression with Let-7a and miR-34a

Fluorescence microscopy analysis demonstrated GFP expression in HepG2 cells expressing mature let-7a and miR-34a constitutively. These results confirmed higher transfection efficiency and cellular uptake of let-7a vector and miR-34a vector in HepG2 cells, respectively, 72 h and 48 h post transfection (Figure 4). This might be due to increasing the expression level of mature miRs by increasing time. Notably, only the cells with transfections greater than 50% efficiency were used for RNA extraction and further analysis.

Figure 4.

Figure 4

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GFP selected HepG2 cells transfected with pEGP-miR vectors. (A) Un-transfected HepG2 cells showing no GFP staining because they were not transfected with vector containing GFP selection marker. (B) GFP stained transfected HepG2 cells with null vector having GFP selection marker and no miRNA (negative control). Pre-miRNAs: pre-let-7a and pre-miR-34a were cloned in pEGP vector having GFP marker, so GFP staining was also observed in: (C) HepG2 cells transfected with let-7a, (D) HepG2 transfected with miR-34a. Magnification ×40. (--) Scale bar 200 µm.

2.9. Anti-Proliferative Effects of Let-7a and miR-34a in HepG2 Cells

MTT assay showed that expression of let-7a and miR-34a significantly decreases the proliferation rate of HepG2 at the indicated concentrations with the highest reduction being 32.3% and 38.7% at 200 ng, in comparison to cells transfected with null vector (Figure 5A,B). These results verified the combined anti-proliferative effects of both miRs, and the superior inhibitory effect of miR-34a rather than let-7a in the tested cells.

Figure 5.

Figure 5

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The MTT proliferation assay revealed a growth inhibitory effect of: (A) HepG2 cells transfected with various concentrations of pEGP-null vector or pEGP-let-7a vector. (B) HepG2 cells transfected with various concentrations of pEGP-null vector or pEGP-miR-34 a vector. Each bar represents the mean ± SEM. Results were analyzed using unpaired Student’s t-test where ** = p < 0.01, * = p < 0.05.

2.10. Quantitative Validation of Let-7a and miR-34a Expression in HepG2 Cells

RT-PCR confirmed the efficiency of transfection of pEGP-let-7a and miR-34a vectors in HepG2 cells by estimating the expression level of mature let-7a and miR-34a after 48 h and 72 h of transfection (Figure 6A,B). The results indicated that there is significant upregulation of both miRs in transfected cells as compared to mock control and blank. The relative fold had an increase in let-7a after 48 h and 72 h of transfection: 3.2 and 17.7, respectively, while the relative fold had an increase in miR-34a after 48 h and 72 h of transfection: 4.3 and 20.5, respectively. The relative expression levels of these miRs were normalized to RNU6B level. As shown from figures, the upregulation of both miRs was time-dependent, and interestingly, the relative fold increase in miR-34a is higher than that of let-7a at all time points. Consequently, miR-34a may exhibit higher capacity to repress its oncogenic targets, and this will be confirmed later by qRT-PCR results of oncogenes.

Figure 6.

Figure 6

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The relative expression level of: (A) mature let-7a in the HepG2 cells. The relative let-7a expression level following 48 and 72 h of transfection in untreated HepG2 cells; HepG2 cells transfected with pEGP-null vector and HepG2 cells transfected with pEGP-let-7a. (B) mature miR-34a in the HepG2 cells. The relative miR-34a expression level following 48 and 72 h of transfection in untreated HepG2 cells; HepG2 cells transfected with pEGP-null vector and HepG2 cells transfected with pEGP-miR-34a. Each bar represents the mean ± SEM. Results were analyzed using unpaired Student’s t-test where *** = p < 0.001.

2.11. Coordinated Regulation of Let-7a and miR-34a on the mRNAs Expression Levels of FNDC3B, IGF2 and SOX4 in HepG2 Cells

FNDC3B, IGF2 and SOX4 were further verified as direct putative target genes of both let-7a and miR-34a, and they were negatively regulated in the tested cells. The HCC suppression effects of both miRs were generally inferred by cell proliferation assay and specifically by qPCR confirming the knockdown of oncogenic targets. Our computational results identified FNDC3B, IGF2 and SOX4 as three shared predicted targets of let-7a and miR-34a, and they had critical roles in HCC. In this respect, QPCR analysis validated the mRNA levels of these oncogenes after the gain-of-function of both miRs. The results indicated that over-expression of let-7a and miR-34a in HepG2 cells could significantly decrease the endogenous mRNA levels of FNDC3B, IGF2 and SOX4 as compared to mock control and blank. This marked reduction in the expression level of these oncogenes was time dependent as shown before for miRNAs (Figure 7A–D). Firstly, after 48 h of let-7a transfection, the relative expression of the target transcripts FNDC3B, IGF2 and SOX4 decreased by 12.8, 5.7 and 9.7 folds, while after 72 h, the relative expression of these transcripts decreased by 51.4, 24.5 and 44.6 folds. Secondly, after 48 h of miR-34a transfection, the relative expression of the target transcripts decreased by 10.3, 8.3 and 15.6, while after 72 h, the relative expression of these transcripts decreased by 47.3, 40.2 and 69.3 folds. The relative expression of these transcripts was normalized to GAPDH level (internal control). As shown from figures, the fold decrease in the three mRNAs is higher after 72 h of transfection, and SOX4 significantly showed the highest fold decrease. Thus, further validation of its highest inhibition at the protein level was performed using immunofluorescence technique, and the protein expression was significantly decreased as shown in Figure 8a,b.

Figure 7.

Figure 7

Figure 7

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Regulation of the mRNA expression of FNDC3B, IGF2 and SOX4 in three groups of HepG2 cells; untreated HepG2 cells; HepG2 cells transfected with pEGP-null vector and HepG2 cells transfected with (A) pEGP-let-7a vector following 48 h. transfection. and (B) pEGP-let-7a 72 h. transfection. (C) pEGP-miR-34a vector following 48 h. and (D) pEGP-miR-34a following 72 h. transfection. Each bar represents the mean ± SEM. Results were analyzed using unpaired Student’s t-test where p-values.

Figure 8.

Figure 8

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SOX4 protein expression in HepG2 treated groups. (a) Immunofluorescence photos. (b) protein expression intensities percentages. The data show high protein expression in both control and null vector groups. Significant decrease in protein expression in Let-7a and miR-34a groups. The laser setting for the blue and green fluorescence was as following: DAPI, Ex 335–383, Em 420–470 HE BFP/GFP/HcRed Ex 350–390, 460–488, 567–602, Em 395, 495, 610 Magnification was 40× (--) Scale bar 200 µm. The data were calculated on basis of 1000 cells/group. The data are expressed as mean ± SD (n = 3).

3. Discussion

Hepatocellular carcinoma (HCC) is the seventh-most common malignancy and the second leading cause of cancer-associated mortalities [1]. HCC remains difficult to diagnose and treat as a result of the rapid tumor progression [2,30]. Thus, it is critical to investigate its molecular pathogenesis, oncogenes and find effective therapeutics [3,29].

In the current study, we focused on two potent HCC suppressor miRs: let-7a and miR-34a. Their levels are markedly lowered in a wide panel of HCC cell lines and tissues [10,12,13,31,32]. However, the underlying mechanisms involving the combined inhibition of both miRs for HCC still need further relevance. Therefore, we involved both in silico analysis and experimental validation of these miRs to integrate their investigation. There were multi-oncogenic targets and importantly three predicted and not experimentally validated ones: FNDC3B, IGF2 and SOX4 were determined to have more than one canonical binding site within their 3′-UTR regions for each miR. This finding provided a novel concept for the mode of binding, the number of binding sites for miR and its regulatory effect on corresponding targets. In addition, the coregulation of let-7a and miR-34a together have been supported by their shared target genes including FNDC3B, IGF2 and SOX4 in the network overview of miRnet (Figure S2). Notably, upon combining the data obtained from the literature and from our enrichment results (Table 1), these three oncogenes are all normally expressed at a physiological level to act in their normal functions according to human protein atlas. For instance, FNDC3B is a transcription factor which mediates cell adhesion and acts as a positive regulator of cell fate differentiation, growth and glycogen biosynthesis. IGF2 is a growth factor which is involved in development and growth, and it regulates insulin signaling, energy production and immune cells proliferation. SOX4 is a transcription factor which is involved in the determination of the cell fate, immune cell differentiation, glucose metabolism and insulin secretion.

However, these oncogenes were reported to attain significantly high expression in multiple HCC cells and tissues, and they mediate critical hepatocarcinogenic effects. These effects have been significantly enriched (p value < 0.05) by our functional analyses which revealed a variety of the common and specific HCC pathways and GO processes as shown above in Figure 1 and Table 1. For instance, FNDC3B emerges as an oncogenic driver which is consistent with our results via inducing cell proliferation, anchorage-independent growth, adhesion, migration and metastasis [33,34,35,36]. Additionally, it activates several pathways, including STAT3, PI3K/Akt, Rb1 and TGFβ. Functionally, FNDC3B overexpression enhances its promotor binding to a variety of transcription factors which enhance the phosphorylation of the key proteins of the mentioned pathways. In addition, its protein product was found to contain a hydrophobic C-terminal tail which localizes to the Golgi and induces its transforming function [34]. IGF2 is an important mitogenic factor according to our data, and it contributes to hepatocellular oncogenesis through an autocrine mechanism and activation of its own pathway [37,38,39]. In addition, it stimulates HCC cells growth, anti-apoptosis, angiogenesis, proliferation, survival, migration, invasion and metastasis [40,41,42]. Furthermore, it activates hepatocellular cancer stem cell populations and anchorage-independent colony formation [43,44]. Functionally, IGF2 has been reported to bind to IGF receptors which results in receptor oligomerization, activation of protein tyrosine kinase activity and phosphorylation of cellular substrates leading to downstream genes activation [45]. SOX4 has been identified as one of the most highly tumor-associated genes and one of the master promotors of EMT and metastasis [46,47,48]. Remarkably, SOX4 overexpression has been found in 63.8% of HCC samples [49]. Mechanistically, it has been shown to increase tumor cell survival, proliferation, angiogenesis via reprograming fatty acid metabolism, inhibit p53-mediated apoptosis and invasion [50,51]. Additionally, SOX4 could regulate key pathways including Wnt/beta-catenin, AKT, mTOR, energy metabolism via nucleotide biosynthesis and glucose metabolism [47,52].

Interestingly, we will discuss some novel examples showing the importance of the three oncogenes as HCC therapeutic targets and verifying their co-regulation by both miRs.

Aerobic glycolysis has been reported to be significantly upregulated in HCC owing to the Warburg effect which transcriptionally upregulates key glycolytic genes and transporters [53,54] leading to the major and emerging HCC hallmarks, particularly tumor immune escape. Thus, we suggest that the enhanced glycolytic signaling forms positive feedback loop for worse tumor progression.

The Warburg effect has been shown to provide bioenergetic, biosynthetic and redox balance advantages for cancer cells including HCC [55,56]. For instance, these proliferating cells enhance glucose uptake and its rapid fermentation as much as 10-fold more by glycolysis into lactate, and ATP where it supplies to tumor reached 70% [57]. Then, this lactate product provides an acidic microenvironment which further facilitates HCC progression [58].

Our study supported the deregulation of glycolysis/gluconeogenesis network in HCC which is induced by the overlapped interaction of three hepatocarcinogens: FNDC3B, IGF2 and SOX4, as shown in Figure 2D. HCC is among our significantly enriched KEGG pathways, and we suggest that IGF2 acts as the initiator driver for HCC because it forms a network between multiple signaling and leads to reprogramming glucose metabolism and other hallmarks of a tumor (Figure 1C). Furthermore, FNDC3B and SOX4 are demonstrated to rewire carbohydrate metabolism and homeostasis, especially glucose and insulin signaling (Table 1). Collectively, FNDC3B, IGF2 and SOX4 serve as potential metabolic targets in HCC, and as we revealed, their combined silencing by both let-7a and miR-34a could provide a novel approach to regard HCC. The coordinated inhibition of these targets by both let-7a and miR-34a can significantly dampen glycolysis (antagonize glycolytic flux) and increase gluconeogenesis resulting in ATP depletion and cell growth arrest. Interestingly, we suggest this concept of metabolic regulation by both miRs is consistent with previous reports which demonstrated elevating gluconeogenesis could both reduce acid production and increase its consumption by inhibiting glycolysis [59,60]. In addition, we support that both miRs are evidently ideal metabolic silencers for HCC via the emerged multi-targeting action of let-7a, miR-34a and miR-199a/b in insulin signaling which is highly upregulated and strongly associated with glucose metabolism in HCC as shown in our previous study [19]. In this regard, the miRNAs have been shown to modulate HCC cell metabolism by directly downregulate the expression of the rate-limiting enzymes of glycolysis or related pathways indirectly or targeting key regulatory factors, e.g., GLUTs or LDHA [54,61,62,63].

Excitingly, augmentation of miR-34a expression has been found to decrease the rate of glycolysis by directly targeting HK1, HK2, GPI and LDHA to inhibit glucose uptake and increase gluconeogenesis by targeting SIRT1 and PGC-1α to decrease glucose production, and consequently, it suppresses HCC progression [12,64].

Cancer cell attachment in the liver is induced by integrins [65]. Our findings revealed that FNDC3B is among the downstream signal transducers for integrins mediating HCC cells adhesion, dissociation of normal cells and further enhancement of tumor immune evasion, proliferation, migration, invasion and metastasis (Figure 2C). Consequently, upon inhibiting FNDC3B by both let-7a and miR-34a, the mentioned HCC hallmarks will be abolished.

Hedgehog pathway has been shown to contribute to the major and emerging HCC including inflammation mediated immune destruction and reprogramming of glucose metabolism [66,67]. Meanwhile, this pathway has been reported to induce glycolytic activity of liver stroma, the so-called reverse Warburg effect [67], and according to our findings, all of its events involve the upregulation of IGF2, which is efficiently repressed by both let-7a and miR-34a (Figure 2D).

Ras signaling pathway is a core network in promoting HCC hallmarks [68]. Our results revealed that both let-7a and miR-34a could co-inhibit its hepatocarcinogenic features through downregulating IGF2 (Figure 2D).

Insulin/IGF/MAPKKK cascade represents a central part of the MAPK pathway which is dominant in stimulating HCC Hallmarks and interestingly inflammation of cells [69]. Our results revealed that both let-7a and miR-34a could inhibit its overall hepatocarcinogenic effects through downregulating IGF2 (Figure 2D).

From miRs in cancer pathway (KEGG), SOX4 has been validated as a direct target for miR-335 in breast cancer metastasis [70]. However, we are first to verify its co-regulation by both let-7a and miR-34a in HCC pathway and subsequent blockade of HCC hallmarks (Figure 2E).

IL-5 signaling is another significantly enriched pathway through which SOX4 contributes to major and emerging HCC hallmarks especially innate immunity destruction (Figure 2E). Remarkably, IL-5 has been identified as an immunosuppressive cytokine which acts as a growth and differentiation factor [71]. In addition, it has been reported to activate and stabilize SOX4 tumorigenic activity [72]. In light of this, our data suggested SOX4 hepatocarcinogenic effects are enhanced by IL-5 because both are found to be upregulated in HCC tumor micro-environment, and they crosstalk together to activate the expression of genes promoting tumor hallmarks [71,73]. Therefore, both let-7a and miR-34a delivery in HCC cells could significantly immune modulate tumor progression via downregulating IL-5/SOX4 signaling axis.

We have efficiently verified the negative regulation of both let-7a and miR-34a on its three common targets in HepG2 cells by following the concept of stable overexpression of mature miRs as shown in several previous studies [74,75]. Herein, the expression vectors harboring the precursor fragments of miRs were successfully transfected into HepG2 cells and consequently, we found re-constitutive overexpression of mature let-7a and miR-34a as qualitatively screened by co-expression of GFP selection marker and quantitively estimated by qPCR.

Our results of MTT proliferation assay evaluated restoration of let-7a and miR-34a expression in HepG2 cells which decreased the rate of cellular proliferation by 32.3% and 38.7%, respectively, as shown in Figure 5. Thus, both miRs can potentially knock down their target genes in growth and proliferation signaling in HCC.

Consistently, our results of qRT-PCR confirmed the upregulated expression of mature let-7a and miR-34a levels and the silenced mRNA levels of direct targets especially after 72 h of transfection as shown in Figure 6. Thus, the synergy of tumor suppression of both miRs in HCC cells has been validated. In light of these findings, SOX4 represents the highest significant downregulated mRNA transcript as shown in Figure 7A–D and so we further confirmed its significant and highest inhibition at translational level by immunofluorescence as shown in Figure 8a, b. Therefore, SOX4 acts as the most critical oncoprotein in HCC progression.

4. Materials and Methods

4.1. Data Collection of Hepatocarcinogens

This step included surveying the literature in previous research and reviews of HCC demonstrating the most critical hepatocarcinogens which are involved in HCC onset and progression. The search terms used (oncogenes and HCC) among the determined oncogenes, FNDC3B, IGF2 and SOX4, were represented as the core oncogenes for the current study due to their emerging hepatocarcinogenic expression levels and actions [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,76,77,78,79]. Consequently, we aimed to clearly investigate their actions by both the in silico and in vitro targeting through the miRNA mode of binding with them and blocking their oncogenic roles.

4.2. In Silico Analyses of Let-7a and miR-34a

The overall flow of the in-silico analyses of the two HCC suppressor miRs, let-7a and miR-34a, were divided in three phases: target prediction, validation and functional enrichment as illustrated in Figure S1 and proceeded as follows.

4.2.1. Target Prediction Analysis

We used miRWalk database version 3 [80] to manually inspect and verify our criteria of direct targeting through predicting the target genes having canonical binding sites within the 3′ UTR regions for the two miRs according to Seok et.al 2016 and Brennecke et al., 2005 [81,82]. Our comprehensive analysis implemented the intersection of 11 target gene prediction programs with miRWalk algorithm to reveal Watson–Crick complementarity between the miRNA fragment and its corresponding sites within targets. These programs were DIANA-microT [83], miRNA.org [84], mirBridge [85], miRDB [86], miRmap [87], miRNA Map [88], PicTar [89], PITA [90], RNA22 [91], RNAhybrid [92] and Targetscan [93]. Thus, the overlap of all these algorithms should ensure consistent coverage for target prediction. Notably, the significance threshold was calculated by using Poisson distribution, and a p-value ≤ 0.05 was deemed statistically significant.

4.2.2. Target Validation Analysis

We tested if the determined set of the predicted targets were experimentally verified with both let-7a and miR-34a. This analysis was performed by filtration, and the targets have not experimentally been validated yet by using miRWalk linked resources, PubMed, PhenomiR, miR2Disease and HMDD v3.0 [94,95,96], and other high-quality databases of experimentally validated miRNA targets: miRecords [97], Tar base v9.0 [98] and miR Tar Base [99].

4.2.3. Functional Enrichment Analysis

We used the tools; miRWalk version 3 [80] and GeneTrail version 3 [100] to identify the significantly enriched pathways of the putative miRNA targets in KEGG, REACTOME and WIKI datasets. In addition, determination of GO processes were specifically endorsed in HCC ontology. As mentioned above, the miRWalk tool was based on Poisson distribution for calculation and adjustment the significance threshold, while the GeneTrail tool was based on False discovery rate (FDR), and the set threshold in both enrichment is p-value ≤ 0.05.

4.2.4. Co Regulatory Network Analysis of MiRs

We used miRnet database version 2 [101] to statistically elaborate and functionally interpret the network of the overlapping target genes of both let-7a and miR-34a.

4.3. In Vitro Experiments

4.3.1. Manipulation of Plasmids

PUC57 vectors harbor the full precursor fragment of hsa-let-7a (272 bp) and the hsa-miR-34a (310 bp) (Genscript Biotech, Piscataway, NJ, USA). Each of the synthesized inserts was flanked by BamHI and NheI restriction sites at the 5′ and 3′ ends. The two vector derivatives were transformed into DH5 α host competent cells, and then, pure vectors were isolated from the recombinant E. coli cultures by using Gene JET Plasmid Mini prep kit (Thermo Fisher Scientific, Waltham, MA, USA). The pEGP-miR- cloning and expression vector and pEGP-null vector were obtained from Cell Biolabs (San Diego, CA, USA) as bacterial glycerol stock and purified from the cultures using Gene JET Plasmid Maxi prep protocol to recover high-quality endotoxin-free plasmid suitable for transfection in the target HepG2 cells. Quantitation of all pure plasmids was conducted using NanoDrop Lite (Thermo Fisher Scientific, Waltham, MA, USA). Also, the integrity of these vectors was assessed by 1% agarose gel electrophoresis.

4.3.2. Generation of MiR Expression Constructs

The plasmids pUC57-pre-let-7a, pUC57-pre-miR-34a and pEGP-miR were double digested by fast digest BamHI and NheI (Thermo Fisher Scientific, Waltham, MA, USA) using 5 μg of Plasmid DNA according to manufacture instruction. The digested products, opened pUC 57 vectors, let-7a precursor fragment and miR-34a precursor fragment, opened pEGP-miR vector, were all separated on 2% agarose gel electrophoresis, and the target bands were purified from gel according to their sizes by Gene JET Gel Extraction kit (Thermo Fisher Scientific, Waltham, MA, USA). Next, the pre-let-7a and pre-miR-34a fragments inserted individually in the linearized pEGP-miR vectors using 3:1 molar ratio by DNA Ligase (Thermo Fisher Scientific, Waltham, MA, USA). Each insert within the generated construct was validated by Sanger sequencing using forward 5′TTTGCACCATTCTAAAGAAT3′ and reverse 5′AAACCTCTTACATCAGTTAC3′ sequencing primers.

4.3.3. HCC Cell Line Propagation

HepG2 was selected as the HCC cell culture model because it attains the major malignant properties of HCC and the two miRs of interest showed highly significant down expression in these cells. It was obtained from the American Tissue Culture Collection and was cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum (FBS) and 1% penicillin/Streptomycin, all from Lonza, (Basel, Switzerland). The cells were maintained under standard culturing conditions (37 °C, 5% CO2) in a humidified cell culture incubator.

4.3.4. Cell Transfection

HepG2 cells (250 × 103 cells per well) were seeded in six well plates and transfected with 6 μg of the recombinant vectors expressing let-7a or miR-34a or null exp. vector (negative control) using Turbofect (Thermo Fisher Scientific, Waltham, MA, USA). Similarly, untreated cells were included in all transfection studies, and all cell groups were harvested for further analyses 48 h and 72 h post-transfection.

4.3.5. Green Fluorescent Protein (GFP) Screening

All groups were screened by estimating the percentage of GFP+ cells using fluorescent microscopy (AxioImager Z2, Carl Zeiss, Jena, Germany) which detect GFP fusion markers as described previously [102].

4.3.6. Cell Proliferation Assay

The Methyl Thiazolyl Blue Tetrazolium (MTT) assay was employed to monitor proliferative ability of HepG2 cells. Cells were seeded in 96-well-plate at a density of 1 × 104 cells per well in 200 μL of full DMEM medium. After 24 h, the cells were further transfected by increasing concentrations of let-7a exp. vector or miR-34a exp. vector or null miR exp. vector (negative control) as follows: 25, 50, 100 and 200 ng by 0.4 μL of turbofect. After 96 h incubation, cells were treated with 20 μL of MTT for 4 h, and the purple formazan crystals were dissolved by 150 μL acidified iso-propanol. ELISA reader (Bio-Rad Laboratories, Hercules, CA, USA) was used to determine OD values at 590 nm.

4.3.7. Total RNA Isolation

Total RNA was isolated from cells by Biazol reagent (Qiagen, Hilden, Germany) as described previously [103]. Afterwards, 2 µg of total RNA was digested with 2 µL of DNase I (Thermo Fisher Scientific, Waltham, MA, USA) to remove any genomic DNA.

4.3.8. Quantitative Reverse Transcriptase-PCR (qRT-PCR)

Initially, single stranded complementary DNA (cDNA) was synthesized from 2 μg of denatured template RNA and 1 μL of oligo (dT)18 primer for mRNAs or 1 μL of stem-loop reverse transcription specific primers for miRs which were designed as described previously [104] using Revert Aid™ H Minus M-MuLV Reverse Transcription kit (Thermo Fisher Scientific, Waltham, MA, USA). Then, qRT-PCR of total reaction volume 25 μL was synthesized, which consisted of 2.5 μL of template cDNA of either miRNAs or mRNAs, 12.5 μL of 2X Universal SYBR Green PCR master mix (Applied Biosystems, Waltham, MA, USA), 2.5 μL of forward primer and 2.5 μL of reverse primer. All reactions were run in light cycler 480 II (Roche, Basel, Switzerland) as follow: 95.0 °C for 10 min, 40 cycles (95.0 °C for 15 s, 60.0 °C for 1 min). All Primer sequences of RT and qPCR were listed in Table S1. Notably, the 2-DDCT method [105] was used for the relative quantitation of the expression level of mature miRNAs and mRNAs, and normalization of data was conducted by endogenous controls RNU6B and GAPDH.

4.3.9. Immunofluorescence Assay for SOX4

HepG2 cells in different groups were fixed with 4% Paraformaldehyde in a chamber slide. Then the cells were incubated with 3% goat serum, 0.1% BSA and 0.1% Triton (blocking solution) for 1 h at RT. Primary antibody human anti-SOX4 (PA5-72852, Thermofisher) (1∶100) was incubated with cells overnight at 4 °C. After thoroughly washing, the slides were incubated with Fluorescein-Conjugated Goat Anti-Rabbit IgG (H + L) (Alexa fluor488, Abcam, Cambridge, UK) (1∶100) for 1 h. Nuclei were counterstained with 0.5 g/mL Hoechst 33342 (Life Technology, Carlsbad, CA, USA) in the dark for 5 min. After mounting, the slides were photographed with a AxioImager Z2 fluorescent microscope equipped with Zen11 Blue Edition software, Zeiss, Jena, Germany. This software measures the fluorescence intensities per pixel. We measured the fluorescence intensities per 1000 cells/treatment. The threshold for positive green fluorescence was set at intensity >= 600 and at frequency >= 6000. By assuming the 1000 cells have a positive expression of the protein, i.e., positive green fluorescence, all treatments including the Null and the Control were quantified and converted to percentage. This approach was repeated three independent times. Statistically, one-way ANOVA was equipped followed by Tukey’s multiple comparisons test at a 95% confidence interval.

4.4. Statistical Analysis

All the data were statistically analyzed using Graph Pad Prism 8, and unpaired t-test was used to compare gene expression between two groups, while one-way ANOVA followed by Tukey’s multiple comparisons was used for immunofluorescence estimation. Data were presented as mean ± SEM from three independent experiments which were included in biological triplicates. The p-values *** = p < 0.001, ** = p < 0.01 and * = p < 0.05 were considered statistically significant.

5. Conclusions

New notions of HCC repression can be successfully revealed through the two master HCC inhibitors, let-7a and miR-34a. Our integrative work underscored their potency to combinatorially block major and emerging HCC hallmarks. This combined effect appears to arise from their putatively co-regulated hepatocarcinogenic targets, particularly the predicted and not yet experimentally verified ones: FNDC3B, IGF2 and SOX4. These putative oncogenes were efficiently attenuated by both let-7a and miR-34a, leading to the suppression of their mediated hepatocarcinogenesis in the tested HepG2 cells. Thus, these miRs worked cooperatively to achieve superior anti-HCC effects.

Collectively, our findings support that both miRs may hold great promise for miRNA replacement therapy. Furthermore, additional studies involving more HCC cell lines, in vivo models and preclinical trials are warranted to ensure their efficient and safe application in the near future.

Abbreviations

The following abbreviations are used in this manuscript:

HCC Hepatocellular carcinoma
miR MicroRNA
EMT Epithelial mesenchymal transition
GO Gene ontology
MTT Methyl Thiazolyl Blue Tetrazolium
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Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms27041714/s1.

ijms-27-01714-s001.zip (16.6MB, zip)

Author Contributions

Conceived and supervised the study: A.F.I., A.S., M.G., M.F. and M.E. Designed experiments: A.F.I., B.S., M.F. and M.E. Performed the in-silico analyses, in vitro experiments and the main statistical analysis: A.F.I., M.T.A.-E. and B.S. Analyzed the overall data: B.S., A.S., M.G., M.F. and M.E. The major contributors in writing the manuscript: B.S., M.T.A.-E., M.F. and M.E. All authors have been involved in revising the manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

Author Ahmed Fawzy Ibrahim was employed by the company Attana AB. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Statement

This work was supported by a grant from the Academy of Scientific Research Technology PRISM program grant 5202, Cairo, Egypt. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Associated Data

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Supplementary Materials

ijms-27-01714-s001.zip (16.6MB, zip)

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

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