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. 2023 Apr 10;9(4):e15428. doi: 10.1016/j.heliyon.2023.e15428

Construction of a miRNA-mRNA network related to exosomes in metastatic hepatocellular carcinoma

Jiang Guo a,1, Xingang Zhou b,1, Long Cheng a, Xuesong Gao c,∗
PMCID: PMC10123261  PMID: 37101627

Abstract

Aims

This study aimed to construct a miRNA-mRNA network to elucidate the molecular mechanism of exosome function in metastatic HCC.

Methods

We explored the Gene Expression Omnibus (GEO) database and then analyzed the RNAs of 50 samples to obtain differentially expressed miRNAs (DEMs) and mRNAs (DEGs) involved in the progression of metastatic HCC. Next, a miRNA-mRNA network related to exosomes in metastatic HCC was constructed on the basis of the identified DEMs and DEGs. Finally, the function of the miRNA-mRNA network was explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Immunohistochemistry was performed to validate expression of NUCKS1 in HCC specimens. Based on the immunohistochemistry, the score of the NUCKS1 expression was calculated, and the patients were divided into high- and low-expression patients, and the differences in survival between the two groups were compared.

Results

Through our analysis, 149 DEMs and 60 DEGs were identified. In addition, a miRNA-mRNA network, including 23 miRNAs and 14 mRNAs, was constructed. Low expression of NUCKS1 was validated in the majority of HCCs compared with their matched adjacent cirrhosis specimens (P < 0.001), which was consistent with our result of differential expression analyses. HCC patients with low expression of NUCKS1 had shorter overall survival than those with high NUCKS1 expression (P = 0.0441).

Conclusions

The novel miRNA-mRNA network will provide new insights into the underlying molecular mechanisms of exosomes in metastatic HCC. NUCKS1 might serve a potential therapeutic target to restrain the development of HCC.

Keywords: Hepatocellular carcinoma, miRNA, mRNA, Exosomes, Bioinformatics

1. Introduction

Hepatocarcinoma (HCC) is one of the most common malignant tumors and ranks among the leading causes of cancer deaths worldwide [1]. Most HCC cases are diagnosed at an advanced stage, leading to an overall poor prognosis [2]. Many biological processes are reported to be involved in HCC metastasis; however, the underlying mechanisms are still largely unknown.

In recent years, extensive studies have found that exosomes may play vital roles in the occurrence and development of HCC, providing new insights into HCC metastasis [3]. Exosomes are microvesicles with a diameter of 40–150 nm and are released into the extracellular space from all cell types [4,5]. Exosomes can fuse with the membrane of recipient cells or be internalized by endocytosis. They contain a particular subset of proteins, mRNAs, microRNAs (miRNAs), other RNAs, and both cDNA and large fragments of double-stranded genomic DNA [6]. The precise function of exosomes is unknown. Exosomes are heterogeneous, especially in pathological conditions such as cancers. They are involved in intercellular communication and microenvironment regulation by mediating signaling pathways. In cancer, exosomes have been largely reported to be promoters of tumor progression. Exosomes are key components of the tumor microenvironment. They can change adaptive immunity, mediate immune/cancer cell communication, regulate pathological angiogenesis, activate fibroblasts, remodel the extracellular matrix (ECM), and promote metastasis [5]. MiRNAs constitute a class of small endogenous noncoding RNA molecules composed of ∼22 nucleotides [7]. MiRNAs play critical roles in the regulation of gene expression and a variety of cellular biological processes, such as cell proliferation, differentiation, and apoptosis; the cell cycle; cell migration; and energy metabolism [8]. MiRNAs are aberrantly expressed in a variety of cancers, indicating a close relationship between miRNAs and human cancer [9]. It has been established that miRNAs contribute to HCC growth and progression [10,11].

Although many studies regarding miRNA expression and function in HCC have been launched, systematic and comprehensive analysis of the miRNA-mRNA regulatory network related to exosomes in HCC metastasis is still rare. We speculate that miRNAs secreted from exosomes may regulate the expression of mRNAs in HCC and participate in HCC metastasis. In the present study, the miRNA expression profile of exosomes related to HCC metastasis was first downloaded from the Gene Expression Omnibus (GEO). Then, we performed successive differential expression analyses, GO functional annotation, and KEGG enrichment. Finally, a potential miRNA-mRNA regulatory network was successfully constructed. This study contributes to the exploration of the regulatory mechanism of HCC metastasis and provides valuable insights useful for further functional research.

2. Materials & methods

2.1. The data downloaded

First, we searched the GEO database (Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geoprofiles) [12] for datasets related to metastatic hepatocarcinoma and exosomes by using the keywords “metastatic hepatocarcinoma” and “exosomes”. Next, the GSE106452, GSE6222, GSE94016 and GSE40367 datasets were downloaded. For miRNA expression profiling related to exosomes in metastatic hepatocarcinoma, 2 exosome samples from HepG2 and MHCC-97L cells (cancer cells with low levels of metastasis) and 6 exosome samples from CSQT-2 and HCC-LM3 cells (highly metastatic cancer cells) in the GSE106452 dataset were assessed (platform: GPL20712 Agilent-070156 Human miRNA). For gene/mRNA expression profiling in metastatic hepatocarcinoma, 6 liver cancer samples (HCC stage 3) and 4 liver cancer samples (HCC stage 1) were in the GSE6222 dataset (platform: GPL570 Affymetrix Human Genome U133 Plus 2.0 Array), 5 metastatic hepatocarcinoma samples and 15 nonmetastatic hepatocarcinoma samples in the GSE94016 dataset (platform: GPL15207 Affymetrix Human Gene Expression Array), and 6 metastatic hepatocarcinoma samples and 10 nonmetastatic hepatocarcinoma samples in the GSE40367 dataset (platform: GPL570 Affymetrix Human Genome U133 Plus 2.0 Array) were assessed.

2.2. DEM and DEG screening

First, the differentially expressed miRNAs (DEMs) in the exosome samples obtained from highly metastatic cancer cells and cancer cells with low metastasis were screened by GEO2R online software (http://www.ncbi.nlm.nih.gov/geo/geo2r). A P-value <0.05 and | logFC | ≥ 1 were selected as thresholds. In addition, the differentially expressed mRNAs obtained from metastatic HCC samples and 15 nonmetastatic HCC samples were screened with GEO2R online software. For the GSE6222 and GSE40367 dataset, a P-value <0.05 and | logFC | ≥ 1 were selected as the thresholds. For the GSE94016 dataset, a P-value <0.05 was considered statistically significant. The overlapping mRNAs in the GSE6222, GSE94016 and GSE40367 datasets were selected as the differentially expressed genes (DEGs).

2.3. GO and KEGG enrichment analysis of the DEGs and PPI network construction

To forecast the possible functions of the selected DEGs, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analyses were conducted with the clusterProfiler R package [13]. GO analysis involves annotations in three distinct categories: The biological process (BP), cellular component (CC) and molecular function (MF) categories. A P-value <0.05 was considered statistically significant. In addition, to better understand the relationship among the identified genes, a PPI network was established using the STRING database (http://string-db.org) [14]. PPI node pairs with a combined score ≥0.4 were selected for further analysis.

2.4. miRNA-mRNA network construction

miRNAs can bind to targeted mRNAs to promote the degradation of mRNAs. Herein, target genes of these miRNA signatures were obtained by using the miRDB (http://mirdb.org/), [15] miRTarBase (https://mirtarbase.cuhk.edu.cn/∼miRTarBase/miRTarBase_2022/php/index.php) [16] and TargetScan (https://www.targetscan.org/vert_80/) [17] databases. Genes present in all three databases were regarded as target genes of these miRNAs. Comparing predicted target genes with DEGs, only the remaining overlapping genes and their interaction pairs were used for constructing miRNA-mRNA pairs. Therefore, the miRNA-mRNA network was related to exosomes in metastatic hepatocarcinoma.

2.5. Functional enrichment analysis of the miRNA-mRNA network

Next, we studied the potential biological processes and pathways of the miRNA-mRNA network. Using the clusterProfiler R package, we performed GO functional enrichment analysis. The KOBAS database (http://kobas.cbi.pku.edu.cn/) [18] was subsequently utilized to identify the KEGG pathway enrichment of the miRNA-mRNA network. For GO and KEGG pathway enrichment analysis, P-value <0.05 was considered statistically significant.

2.6. Immunohistochemistry (IHC) analysis

Paraffin embedded liver tissues were obtained from department of pathology of Beijing Ditan Hospital, including 4 health control (HC) liver tissues, 5 focal nodular hyperplasia (FNH) of the liver tissues, 44 HBV related HCC tissues, and 44 paracancerous tissues (para-HCC). For the immunohistochemical detection of NUCKS1, 10% buffered formalin-fixed, paraffin-embedded sections were deparaffinized then rehydrated. Slides were incubated with primary antibodies against rabbit polyclonal NUCKS1 (ab84710, Abcam, Cambridge, UK, 1:500) at 4 °C overnight. An EnVision Chem Detection Kit (DaKo Cytomation, CA) secondary antibody was used at room temperature for 30 min. The staining intensity was semi-quantitatively scored. The staining index was calculated by multiplying the intensity score of NUCKS1 positive staining (negative = 0, weak = 1, moderate = 2, and strong = 3) by the proportion of stained cells (<5% = 0, 5–25% = 1, 26–50% = 2, 51–75% = 3, and >75% = 4). Positive immune-reactivity was considered if staining intensity was more than +2.

2.7. Statistical analysis

Statistical analyses were performed using GraphPad Prism 7 (San Diego, CA, USA). Fisher's exact test and Chi-square test for independence were utilized for categorical responses and an independent samples t-test for comparing NUCKS1 expression in HC, FHN, para-HCC and HCC, respectively. NUCKS1 expression in HCC and adjacent cirrhosis tissue were compared using a paired t-test. The Kaplan-Meier method and log-rank test were used for survival analysis. A P-value <0.05 was considered statistically significant.

3. Results

3.1. DEM and DEG screening

An illustration of the data was shown in Fig. 1. One hundred forty-nine DEMs (84 up- and 65 downregulated genes) were screened (Fig. 2A). For the DEGs, 2521 differentially expressed mRNAs (718 up- and 1803 downregulated mRNAs) were identified in the GSE6222 dataset (Figs. 2B), 2612 differentially expressed mRNAs (1747 up- and 865 downregulated mRNAs) were identified in the GSE94016 dataset (Figs. 2C), and 1597 differentially expressed mRNAs (1037 up- and 560 downregulated mRNAs) were identified in the GSE40367 dataset (Fig. 2D).

Fig. 1.

Fig. 1

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Workflow of this study.

Fig. 2.

Fig. 2

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Volcano plot of the DEMs and DEGs. (A) volcano plot of differentially expressed miRNAs; (B–D) volcano plot of differentially expressed genes.

3.2. GO and KEGG enrichment analyses of the DEGs and PPI network construction

The overlapped mRNAs in the GSE6222, GSE94016 and GSE40367 datasets were selected as DEGs. Sixty DEGs were identified (Fig. 3A). As shown in Fig. 3B, for the GO-BP analysis of the identified DEGs, cell cycle arrest, regulation of protein stability, cytoplasmic microtubule organization, interleukin−27, and interleukin−35 were the five most significant GO-BP terms. The GO-CC analysis suggested that the five terms associated with the greatest gene enrichment were early endosome, focal adhesion, cell−substrate junction, basal part of the cell and cytoplasmic stress granules. In addition, cytokine receptor binding, nucleoside−triphosphatase regulator activity, hormone receptor binding, phosphoprotein phosphatase activity and guanyl−nucleotide exchange factor activity were the five most significant GO-MF terms. As shown in Fig. 3C, the KEGG pathway enrichment analysis indicated that the five most-enriched pathways were human papillomavirus infection, the PI3K−Akt signaling pathway, Th17 cell differentiation, toxoplasmosis, signaling pathways regulating pluripotency of stem cells, and transcriptional misregulation in cancer.

Fig. 3.

Fig. 3

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(A) Venn diagram of the DEGs. It was screened out using the VennDiagram R package. (B) GO analysis of the DEGs. (C) KEGG analysis of the DEGs. (D) PPI network of the DEGs.

Next, we mapped these DEGs using the STRING database. Using the information in this public database, we constructed a PPI network of these gene products. Sixteen nodes were ultimately obtained (Fig. 3D).

3.3. The miRNA-mRNA network

As illustrated in Fig. 4A, a miRNA-mRNA network, including 23 miRNAs (e.g., hsa-miR-424-5p, hsa-miR-4739 and hsa-miR-532-5p) and 14 mRNAs (e.g., NUCKS1, ZFHX3, and TGFBR1), was constructed. In this miRNA network (Fig. 4B and C), 9 miRNAs (e.g., hsa-miR-4739, hsa-miR-6785-5p and hsa-miR-6867-5p) and 3 mRNAs (KMT2A, TGFBR1 and PPM1H) were upregulated in metastatic hepatocarcinoma, while 14 miRNAs (e.g., hsa-miR-424-5p, hsa-miR-27a-3p and hsa-miR-30e-3p) and 11 mRNAs (e.g., IL6ST, SLC11A2 and NUCKS1) were downregulated in metastatic hepatocarcinoma. These results suggest that the miRNAs carried by exosomes may regulate the expression of certain mRNAs in metastatic hepatocarcinoma.

Fig. 4.

Fig. 4

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(A) MiRNA-mRNA network. (B) Heat map of miRNAs in the MiRNA-mRNA network. (C) Heat map of mRNAs in the MiRNA-mRNA network.

3.4. Functional enrichment analysis of the miRNA-mRNA network

To better understand the miRNA-mRNA network relationships, the Enrichr database was utilized to perform GO function and KEGG pathway enrichment analyses. The 30 most-enriched GO items are listed in Fig. 5A and Suppl. Table 1. The GO-BP analysis revealed that the miRNA-mRNA network was significantly enriched with genes involved in cell cycle arrest, response to transforming growth factor beta, peptidyl−serine, interleukin−27, interleukin−35, etc. The GO-CC analysis revealed that the miRNA-mRNA network was significantly enriched in genes involved in the plasma membrane signaling receptor complex, the lysosomal membrane, the lytic vacuole membrane, early endosomes, the vacuolar membrane, etc. The GO-MF analysis revealed that the miRNA-mRNA network was significantly enriched in genes involved in phosphoprotein phosphatase activity, cytokine receptor binding, phosphatase activity, phosphoric ester hydrolase activity, protein serine/threonine phosphatase activity, etc. A KEGG pathway enrichment analysis was also conducted with this miRNA-mRNA network. As indicated in Fig. 5B and suppl. Table 2, this miRNA-mRNA network was significantly enriched in the mTOR signaling pathway, osteoclast differentiation, pancreatic cancer, signaling pathways regulating pluripotency of stem cells and Th17 cell differentiation.

Fig. 5.

Fig. 5

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(A) GO analysis of the miRNA-mRNA network. (B) KEGG analysis of the miRNA-mRNA network.

3.5. NUCKS1 expression in liver tissues

There is very limited information in the literature on the functions of NUCK1 in HCC. The expression of NUCKS1 was significantly lower in the HCC tissues than in the nontumorous liver tissues (Fig. 6), which was consistent with our results in the bioinformatic analysis. Therefore, NUCKS1 related to prognosis was chosen for validation. Immunohistochemistry was performed to validate expression of NUCKS1 in liver tissues. NUCKS1-positive staining was observed in the nucleus of liver cells. High expression of NUCKS1 was found in HC (Fig. 6A). The result showed the similar expression of NUCKS1 in FNH of the liver compared with HC (Fig. 6D, P = 0.207). Low expression of NUCKS1 was seen in the HCC compared with their matched adjacent cirrhosis specimens (Fig. 6B and C, P = 0.011). There was a statistical difference at 5-year survival observed when comparing the low and high expression groups, HCC patients with low expression of NUCKS1 had shorter overall survival than those with high NUCKS1 expression (P = 0.0441, Fig. 7). The clinical characteristics of patients was shown in Table 1.

Fig. 6.

Fig. 6

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(A) NUCKS1 expression in health control. (B) NUCKS1 expression in HCC and adjacent cirrhosis tissue. The left side of fibrous septum is HCC tissue and the right side is adjacent cirrhosis tissue. (C) NUCKS1 expression in HCC and adjacent cirrhosis tissue. (P < 0.001). (D) NUCKS1 expression in HC, FHN, para-HCC and HCC (ns: no significant. *: <0.05. HC vs. FHN, P = 0.207; FHN vs. para-HCC, P = 0.033; para-HCC vs. HCC, P = 0.011).

Fig. 7.

Fig. 7

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Survival curves for the low and high expression groups of NUCKS1 in HCC cohort.

Table 1.

NUCKS1 expression and clinical variables of patients.

Clinicopathological features HC FNH HCC Para-HCC P
Age (years) 40.0 ± 6.8 36.2 ± 6.2 51.4 ± 1.6 51.4 ± 1.6 0.029
Gender 0.102
 Male 2 4 43 43
 Female 3 1 10 10
AFP (ng/mL) 0.173
 >400 0 0 15 15
 <400 5 5 38 38
NUCKS1 expression (scores) 11.6 ± 0.2 10.2 ± 1.0 5.5 ± 0.4 7.4 ± 0.4 <0.001
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Abbreviations: HC: health control, FNH: focal nodular hyperplasia, HCC: hepatocellular carcinoma, para-HCC: paracancerous tissue, AFP: alpha-fetoprotein.

4. Discussion

HCC is characterized by rapid progression, unhindered metastasis and recurrence; however, the molecular mechanism underlying HCC metastasis has not been fully identified. Studies have demonstrated that exosome-mediated transfer of long noncoding RNA (lncRNA), miRNA, mRNA, or protein contributes to HCC metastasis [19,20]. However, study of a miRNA-mRNA network related to exosomes in metastatic hepatocarcinoma has rarely been performed.

In this study, we identified miRNAs and mRNAs that were differentially expressed in liver cells characterized by low and high levels of metastasis. We performed GO and KEGG pathway analyses and identified the functions in which the DEGs were involved. The GO biological process analysis results suggested that these specific DEGs were associated with cell cycle arrest, regulation of protein stability, cytoplasmic microtubule organization, etc. Cell cycle arrest is involved in the regulation of HCC cell proliferation [21,22]. Furthermore, we identified 14 mRNAs and 23 miRNAs that were significantly related to HCC metastasis. We constructed a miRNA and mRNA regulatory network. We performed GO and KEGG pathway enrichment analyses of this network. A few hub genes were highlighted in the regulatory network. We found miR-424-5p was downregulated in our bioinformatics analyses. The function of miRNA has been investigated in numerous studies, with their impact on altered oncogenes and tumor suppressors. Previous studies have yielded conflicting evidence regarding the role of miR-424-5p in cancers. miR-424-5p expression was found to be decreased in HCC tissues and associated with AFP, TNM stage, intrahepatic metastasis and poor overall survival in HCC [23]. The upregulation of miR-424-5p inhibited cell proliferation [24]. Meantime, miR-424-5p could promote proliferation and migration of a few other cancer cells [25,26].

Nuclear casein kinase and cyclin-dependent kinase substrate 1 (NUCKS1) is a nuclear and highly posttranslationally modified protein, which is localized on chromosome 1q32.1 [[27], [28], [29]]. NUCKS1 is a vertebrate-specific, ubiquitously expressed, and multifunctional protein [30]. NUCKS1 regulates chromatin accessibility at target promoters to enable the recruitment of RNA polymerase Ⅱ [31]. NUCKS1 transcriptionally controls the SKP2-p21/p27 axis, which integrates mitogenic and DNA damage signalling at the G1/S transition [32]. In addition, NUCKS1 is known to be a phosphorylation substrate for CDK2 and CDK1, which affect cell cycle progression and proliferation [[33], [34], [35]]. NUCKS1 has been proved to be overexpressed in many cancers, such as gastric cancer [36], lung cancer [37] and HCC [38]. However, the physiological functions of NUCKS1 are still very limited. Cheong et al. reported the correlation between NUCKS1 and HCC and the potential of this protein to be an immunodiagnostic biomarker for HCC in 2016 [39]. They found NUCKS1 was significantly overexpressed in 37 of 70 cases (52.8%) in HCC specimens by Western blot analysis. NUCKS1 overexpression was seen in 44 of 52 cases (84.6%) of HCC in comparison with their matched nontumor liver specimens by immunohistochemistry. Zhang et al. detected the significantly up-regulated mRNA expression in HCC compared to the adjacent non-tumor tissues. And high NUCKS1 expression was closely associated with tumor differentiation, TNM stage, vascular invasion and metastasis (P < 0.05) [38]. Unlike previous studies, we found that NUCKS1 expression was down-regulated in HCC tissues, which was consistent with our results of differential expression analyses. Furthermore, the statistical difference at 5-year survival between the low and high expression of NUCKS1 groups was also observed (Fig. 7). The reasons that our result differ from previous research may be as follows. First, the etiology and the severity of the liver disease were different. Zhang did not report the etiology of HCC and the expression of NUCKS1 in normal liver tissue. Although most liver tissues were HBV related HCC, the nature of adjacent tissues has not been reported in Cheong's study. The liver tissues were all from HBV related liver cirrhosis progression to HCC in our study. Second, Zhang detected 57 of 126 cases (45.2%) had high expression of NUCKS1 by QRT-PCR. Cheong found NUCKS1 was significantly overexpressed in 37 of 70 cases (52.8%) in HCC specimens by Western blot analysis. It was clear that the expression of NUCKS1 were regulated by the translation and post-translational modification. Therefore, the expression of NUCKS1 in HCC needs to be further analyzed according to the etiology, differentiation and post-translational modification level of HCC.

There are some limitations to our study. First, although the expression and effect of the miRNAs and mRNAs have been previously reported in HCC, most of the miRNA-mRNA pairs potentially contributing to HCC metastasis in the network remain unclear. Second, because this experimental validation was carried in a single center, the risk of bias was unavoidable. It is recommended that large multicenter studies be conducted to assess the relationship between NUCKS1 and prognosis in HCC patients.

5. Conclusions

Our research elucidated the unknown mechanistic miRNA-mRNA regulatory network related to exosomes in hepatocarcinoma metastasis. NUCKS1 might serve a potential therapeutic target to restrain the development of HCC.

Ethic approval

The study was approved by the Beijing Ditan hospital institutional review board (2019-044-001).

Declarations

Author contribution statement

Jiang Guo: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Xingang Zhou: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Long Cheng: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Xuesong Gao: Conceived and designed the experiments.

Data availability statement

Data included in article/supplementary material/referenced in article.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding statement

This study was funded by High level public health technology talent construction project (Backbone-02-26), and Sample Library Program of Beijing Ditan Hospital (201901).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e15428.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.docx (20.1KB, docx)
Multimedia component 2
mmc2.docx (18KB, docx)

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