|Year : 2018 | Volume
| Issue : 10 | Page : 622-627
Identification of potential transcription factors, long noncoding RNAs, and microRNAs associated with hepatocellular carcinoma
Hongxian Yan1, Qian Wang2, Quan Shen1, Zhaohui Li3, Jianguo Tian1, Qingfeng Jiang1, Linbo Gao4
1 Department of Hepatobiliary Surgery, Henan Provincial People's Hospital, Zhengzhou, Henan 650000, P.R. China
2 Department of Hepatobiliary Surgery, Henan Cancer Hospital, Zhengzhou, Henan 650000, P.R. China
3 Secondary Department of General Surgery, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang, Henan 471003, P.R. China
4 Laboratory of Molecular and Translational Medicine, West China Institute of Women and Children's Health; Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
|Date of Web Publication||24-Sep-2018|
Department of Hepatobiliary Surgery, Henan Provincial People's Hospital, Weiwu Road, No. 7, Zhengzhou, Henan 650000
Source of Support: None, Conflict of Interest: None
Aim: This study aimed to investigate the key transcription factors (TFs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) associated with hepatocellular carcinoma (HCC).
Materials and Methods: The datasets GSE31383 and GSE54238 were downloaded from Gene Expression Omnibus data repository. GSE31383 was used to screen differentially expressed miRNAs, and GSE54238 was used to screen differentially expressed messenger RNAs (mRNAs) and lncRNAs. ChipBase was used to identify TF-miRNA pairs. StarBase was selected to identify miRNA-mRNA and lncRNA-miRNA interactions. Kyoto Encyclopedia of Genes and Genomes pathway analysis was also conducted using Database for Annotation, Visualization, and Integrated Discovery tool.
Results: A total of 2065 mRNAs, 1050 lncRNAs, and 26 miRNAs were identified to be divergently expressed in HCC compared with normal tissues. There were 338 miRNA-mRNA and 65 lncRNA-miRNA pairs with reverse expression trend. Besides 249 TF-miRNA relationships including differentially expressed miRNA were isolated. Among them, 11 TF-miRNA had the same expression trend. Furthermore, lncRNA-miRNA-mRNA and TF-miRNA-mRNA regulatory networks were constructed. hsa-miR-497, hsa-miR-195, and hsa-miR-424 were identified as hub nodes in these two networks. Hub TFs, such as TATA box binding protein-associated factor 1 (TAF1) and hepatocyte nuclear factor 4, alpha (HNF4α), and lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) were also screened out in the network.
Conclusions: Our findings highlight the regulatory networks among TFs, lncRNAs, miRNAs, and mRNAs in HCC. Several key molecules, such as hsa-miR-195, lncRNA MALAT1 and TFs TAF1 and HNF4α, may contribute to the progression of HCC.
Keywords: Hepatocellular carcinoma, long noncoding RNA, microRNA, transcription factor
|How to cite this article:|
Yan H, Wang Q, Shen Q, Li Z, Tian J, Jiang Q, Gao L. Identification of potential transcription factors, long noncoding RNAs, and microRNAs associated with hepatocellular carcinoma. J Can Res Ther 2018;14, Suppl S3:622-7
|How to cite this URL:|
Yan H, Wang Q, Shen Q, Li Z, Tian J, Jiang Q, Gao L. Identification of potential transcription factors, long noncoding RNAs, and microRNAs associated with hepatocellular carcinoma. J Can Res Ther [serial online] 2018 [cited 2020 Jun 4];14:622-7. Available from: http://www.cancerjournal.net/text.asp?2018/14/10/622/204846
| > Introduction|| |
Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. It is the common leading cause of cancer death in the world, and the new cases of HCC in developing countries account for almost 85% of all patients. Although great efforts have been made, the prognosis of HCC is poor. Identification of key molecular markers is still urgently needed for early diagnosis and risk assessment.
The human transcriptome comprises not only the large numbers of protein-coding messenger RNAs (mRNAs) but also a large set of nonprotein coding transcripts, such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) that have structural, regulatory, or unknown functions. In HCC, aberrant expression profiles of miRNA made it attractive for diagnosis and prognosis. miRNA-135a contributes to the development of portal vein tumor thrombus through promoting metastasis in HCC. The expression levels of miR-124 and miR-203 are decreased in HCC cell lines and subsequently inhibit HCC cell growth via downregulating cyclin-dependent kinase 6. Moreover, increasing evidence has suggested that miRNAs and transcription factors (TFs) coordinately regulate gene expressions in response to cellular environment and signaling. Both miRNAs and TFs act as either oncogenes or tumor suppressor genes in human carcinogenesis. miRNA-34a is associated with expression of key hepatic TFs, such as hepatocyte nuclear factor 4, alpha (HNF4α) and nuclear receptor subfamily 1, Group I, member 2. Besides the critical roles of lncRNAs in contributing to chromatin modification, transcription, posttranscriptional regulation, and small RNAs processing have received great attentions., Yu et al., have conducted a review focusing on the most extensively investigated lncRNAs in HCC. A work of Han et al. also has confirmed that knockdown of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) causes cell proliferation inhibition, induces cell apoptosis, and declines migration and motility in HCC. Although there are a lot of studies about miRNAs and lncRNAs in HCC, the knowledge of regulatory relationship among miRNAs, TFs, lncRNAs, and target genes are not completely clear. Investigating of their regulatory network will be useful to understand the mechanism of tumor development and search new therapeutic targets.
Thus, we performed comprehensively analysis for the regulatory network of HCC-related genes, miRNAs, TFs, and lncRNAs in this study. Besides Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed. Our study aimed to identify potential genes, miRNAs, TFs, and lncRNAs associated with HCC, thus to reveal the possible molecular mechanisms.
| > Materials and Methods|| |
Affymetrix microarray data
The microarray data of GSE31383 deposited by Yu et al. and GSE54238 deposited by Yuan et al. were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database based on GPL10122 Homo sapiens miRNA profiling platform version 4 and GPL16955 Arraystar human lncRNA microarray V1-100309 (gene-level version), respectively. A total of 19 samples (9 human HCC tissues and 10 human healthy liver tissues) were used to the development of miRNA expression profile of GSE31383. Moreover, 56 samples (26 HCC samples, 10 normal livers, 10 chronic inflammatory livers, and 10 cirrhotic livers) were used to the development of lncRNAs and mRNAs expression profile of GSE54238. In our study, 26 HCC samples and 10 normal liver samples were used to analyze mRNAs and lncRNAs.
The raw CEL data of miRNA expression profile of GSE31383 were preprocessed for background calibration and normalization using Affy. The raw data of lncRNAs and mRNAs expression profile of GSE54238 were normalized using preprocess Core package. Moreover, the probe ID was then transformed into gene symbol based on the annotation information of array platform. If multiple probes were mapped to a given gene, the mean value was calculated as the expression value of this gene.
Identification of differentially expressed microRNAs, messenger RNAs, and long noncoding RNAs
Differentially expressed miRNAs, mRNAs, and lncRNAs in HCC samples compared with normal tissues were respectively identified using t-test in Limma package in R language. The P value was adjusted by Bonferroni multiple test. The adjusted P < 0.05 and | log fold change (FC)| >1 were considered as the cutoff value.
Regulatory interactions of transcription factor-microRNA, microRNA-messenger RNA, and long noncoding RNA-microRNA
StarBase version 2.0 (http://starbase.sysu.edu.cn/) is a database for decoding miRNA-target interaction networks from large-scale CLIP-Seq data. A total of 52306 miRNA-mRNA interactions were obtained according to the following thresholds: Pan-cancer ≥1, number of supporting Experiments ≥1, and predicted in at least three databases out of TargetScan, picTar, RNA22, PITA, and miRanda. Moreover, 10212 lncRNA-miRNA interactions were obtained according to the following thresholds: Pan-cancer ≥1 and number of supporting experiments ≥1.
ChipBase (http://deepbase.sysu.edu.cn/chipbase/) is a database for the discovery of TF binding maps and transcriptional regulatory relationships of lncRNAs and miRNAs from ChIP-Seq data. In this study, a total of 55,675 TF-miRNAs regulatory relationships were obtained according to the following thresholds: upstream: Up 5 kb, downstream: down 1 kb.
Construction of transcription factor-microRNA-messenger RNA and long noncoding RNA-microRNA-messenger RNA regulatory networks
The identified differentially expressed miRNAs and mRNAs were aligned with the miRNA-mRNA and TF-miRNA regulatory relationships from StarBase and ChipBase, respectively. Owing to the incomplete annotation of lncRNA, lncRNAs were lifted to GRCh38/hg38 coordinates using Lift Genome Annotations tool (http://genome.ucsc.edu). The differentially expressed lncRNA was then aligned with lncRNA-miRNA relationships according to the overlap region of lncRNAs on chromosomes. Finally, the TF-miRNA-mRNA and lncRNA-miRNA-mRNA regulatory networks were constructed using Cytoscape software (http://www.cytoscape.org).
Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis
KEGG (http://www.genome.ad.jp/kegg/) is a database for annotating related pathways of large-scale gene lists. Database for Annotation, Visualization, and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/) contains an integrated biological knowledge base and analytic tools for extracting biological meaning from large-scale lists of genes or proteins. In this study, the KEGG pathway enrichment analysis for target mRNAs of miRNAs was carried out by DAVID (http://david.abcc.ncifcrf.gov/). P < 0.05 was considered to be statistically significant.
| > Results|| |
Differentially expressed messenger RNAs, microRNAs, and long noncoding RNAs
A total of 2065 differentially expressed mRNAs (875 up-regulated, 1190 down-regulated), 1050 differentially expressed lncRNAs (736 up-regulated, 314 down-regulated), and 26 differentially expressed miRNAs (15 up-regulated, 11 down-regulated) were identified in HCC tissues compared with normal tissues.
Analysis of long noncoding RNA-microRNA-messenger RNA and transcription factor-microRNA-messenger RNA regulatory networks
A total of 338 miRNA-mRNA and 65 lncRNA-miRNA interactions with reverse expression trend were obtained, respectively. Besides there were 249 TF-miRNA interactions including differentially expressed miRNAs in our study. Only 20 TF-miRNA interactions involved differentially expressed TFs among them, 11 TF-miRNA interactions had the same expression trend [Table 1].
|Table 1: Transcription factor-microRNA regulatory relationships that both transcription factor and microRNA were differentially expressed|
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Then, the lncRNA-miRNA-mRNA network containing 217 nodes and 402 regulatory interactions was constructed [Figure 1]. In the network, 20 miRNAs, 20 lncRNAs, and 180 mRNAs were obtained, the degrees of which were shown in [Supplementary Table 1]. miRNAs with higher degrees included hsa-miR-21 (degree = 64), hsa-miR-195 (degree = 48), hsa-miR-497 (degree = 47), hsa-miR-378 (degree = 45), hsa-miR-424 (degree = 45), and hsa-miR-130a (degree = 43). lncRNAs with higher degrees were MALAT1 (degree = 8), nuclear paraspeckle assembly transcript 1 (nuclear-enriched abundant transcript 1 [NEAT1], degree = 7), small nucleolar RNA host gene 1 (SNHG1, degree = 6), FGD5 antisense RNA 1 (FGD5-AS1, degree = 5), and small nucleolar RNA host gene 12 (SNHG12, degree = 5).
|Figure 1: The long noncoding RNA-microRNA-messenger RNA regulatory network. Diamond represents microRNA; hexagon represents long noncoding RNA, and circle represents messenger RNA|
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In addition, the TF-miRNA-mRNA regulatory network including 286 nodes and 586 regulatory interactions was also constructed [Figure 2]. A total of 22 miRNAs, 84 TFs, and 180 mRNAs were included in this network [Supplementary Table 2]. Three miRNAs had higher degrees, including hsa-miR-497 (degree = 53), hsa-miR-195 (degree = 52), and hsa-miR-424 (degree = 51), which were also found in lncRNA-miRNA-mRNA network. Moreover, the hub TFs, such as HNF4α, degree = 9, TATA box binding protein-associated factor 1 (TAF1, degree = 8), caudal type homeobox 2 (degree = 7), and nanog homeobox (degree = 7) were identified. Notably, we found that miR-195 was regulated by TAF1 and estrogen receptor alpha.
|Figure 2: The transcription factor-microRNA-messenger RNA regulatory network. Diamond represents microRNA; triangle represents transcription factor, and circle represents messenger RNA|
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|Table 2: DAVID analysis of differentially expressed genes targeted by microRNA in hepatocellular carcinoma compared with normal tissue|
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Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis
As listed in [Table 2], 7 significant pathways enriched by mRNAs targeted by miRNAs were identified based on P < 0.05, including cell cycle, small cell lung cancer, oocyte meiosis, pathways in cancer, progesterone-mediated oocyte maturation, Fc gamma R-mediated phagocytosis, and regulation of actin cytoskeleton.
| > Discussion|| |
In this study, lncRNA-miRNA-mRNA and TF-miRNA-mRNA regulatory networks were constructed to identify the regulatory relationships among miRNAs, TFs, lncRNAs, and target genes in HCC. hsa-miR-195, hsa-miR-497, and hsa-miR-424 were identified as the hub nodes with high degrees in the two regulatory networks. Hub TFs, such as TAF1 and HNF4α and lncRNA MALAT1 were also screened out in the network, respectively. In addition, mRNAs targeted by miRNAs were markedly enriched in 7 significant pathways such as cell cycle, small cell lung cancer, and oocyte meiosis.
The previous studies have shown that miR-195 is frequently downregulated in HCC as well as other cancers such as human ovarian cancer and cervical cancer. Upregulation of miR-195 induces apoptosis of HCC cells via increasing large tumor suppressor kinase 2. Xu et al. found that ectopic expression of miR-195 dramatically inhibited tumorigenicity in vitro and blocked G1/S transition in human HCC cells. miR-195 is also associated with angiogenesis and metastasis in HCC through regulating vascular endothelial growth factor, vav 2 guanine nucleotide exchange factor, and cell division cycle 42. In our TF-miRNA-mRNA network, miR-195 was regulated by TAF1. It has been shown that TAF1 is up-regulated in HBV-HCCs from patients with liver cirrhosis and plays a role in G1/S transition of the cell cycle. Moreover, cell cycle pathway was also enriched by the target mRNAs of miRNAs. Therefore, we suggest that a deregulation of the regulatory relationship of TAF1-miR-195-mRNA may be related to HCC. TAF1 and miR-195 may be key molecules involved in HCC development.
In addition to significantly differentially expressed miRNA, this study identified an important miRNA (miR-122) that was not significantly, differentially expressed at present. miR-122 is described as a liver-specific miRNA, which is the most abundant miRNA in the liver accounting for approximately 70% of the total miRNA population. The studies have reported that miR-122 levels are frequently reduced in HCC compared to normal liver, and low miR-122 levels correlate with poor prognosis., In this study, the expression of miR-122 was also downregulated in HCC samples compared with controls, but the difference was not significant, which may be due to the individual variation. Even so, we have to recognize the important role of miR-122 in HCC.
In addition, HNF4α was the hub TF with the highest degree in the network, which was interacted with 9 miRNAs. HNF4α is a component of nuclear receptor superfamily, and the binding position of its homodimer is located at the direct repeat elements upstream of target genes. Battle et al. demonstrated that HNF4α could regulate the expressions of a number of proteins involved in cell junction assembly and adhesion. Accompany with HNF4α decreasing, cell polarity loss, cell-cell and cell-extracellular matrix adhesion reduction, telomerase activity increasing, and liver-specific gene expression inhibition are found in HCC. Furthermore, Yin et al. confirmed that cancer stem cells transfected with HNF4α could differentiate into mature hepatocytes, which was followed by cell apoptosis, cell cycle arrest, and cellular senescence. Taken together, we speculate that HNF4α may be a key regulator in the development of HCC.
Furthermore, lncRNA MALAT1 was hub node which could be interacted with 8 miRNAs in the network. MALAT1, also known as NEAT2, is associated with cell mobility in lung adenocarcinoma cells by regulating the expression of motility-related genes. In HCC patients with high level of MALAT1, the risk of tumor recurrence is significantly increased after liver transplantation. In addition, Yang et al. revealed that MALAT1 could bind to methylated and unmethylated polycomb 2 protein, thereby contributing to the relocation of polycomb bodies and interchromatin granules in response to growth signals. Hence, we speculate that MALAT1 may also play important roles in liver cancer cell and could be as attractive therapy target for the treatment of HCC.
In addition to MALAT1, lncRNAs of NEAT1, SNHG1, FGD5-AS1, and SNHG12 also had hither degrees (≥5) in the lncRNA-miRNA-mRNA network, which may also play roles in HCC progression. Interestingly, NEAT1 and SNHG1 have been found to be critical modulators of several cancers including HCC.,, In addition, SNHG12 has a role in cell proliferation and migration and has been suggested to promotes cell proliferation and migration in human osteosarcoma cells. Recently, Yu et al. found that FGD5-AS1 was differentially regulated in nonsmall-cell lung cancer. Although the roles of SNHG12 and FGD5-AS1 in HCC have not been fully investigated, we speculate that they may play roles in HCC progression given their roles in other cancers.
| > Conclusions|| |
Our findings highlight the regulatory networks among TFs, lncRNAs, miRNAs, and mRNAs. Several key molecules, such as hsa-miR-195, lncRNA MALAT1, NEAT1, SNHG1, FGD5-AS1 and SNHG12, and TFs TAF1 and HNF4α, may be existed in the progression of HCC. However, in vitro experimental validation and in vivo function validations have not been investigated in this study. Further studies are urgently needed to confirm our findings.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2]