Bioinformatics analysis of the interactions among lncRNA, miRNA and mRNA expression, genetic mutations and epigenetic modifications in hepatocellular carcinoma
- Authors:
- Published online on: December 5, 2018 https://doi.org/10.3892/mmr.2018.9728
- Pages: 1356-1364
Abstract
Introduction
Primary liver cancer is one of the most common types of malignant tumor. Hepatocellular carcinoma (HCC) is the principal pathological type of liver cancer and is a leading cause of cancer-associated mortalities worldwide (1). HCC is characterized by high malignancy and is generally diagnosed at the middle to late stage. As a result, the prognosis of patients with HCC remains poor. The majority of patients are treated surgically; however, 60–70% of all HCC cases relapse within five years (2). Therefore, it is critical to identify novel molecular markers in order to improve the prognoses of the patients. Additionally, identification of the molecular mechanisms underlying HCC progression may help in developing novel targeted strategies to treat HCC.
In recent years, an increasing number of studies on the roles of microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) have been published. The regulatory network modulating miRNAs, lncRNAs and their target genes represents a popular topic in cancer research. miRNAs are involved in key biological processes, including cell growth, differentiation and apoptosis, and are able to induce mRNA degradation or to inhibit protein translation, altering the expression levels of the downstream proteins (3,4). lncRNAs represent a group of noncoding RNA molecules, that, in contrast to miRNAs, inhibit or activate gene expression by interacting with transcription factors or by binding to specific regions of the target mRNAs, modulating their translation (5). Via various mechanisms, lncRNAs serve important roles in numerous biological processes, including cell differentiation, cell apoptosis and the heat shock response (6). A previous study demonstrated that lncRNAs are a novel class of regulators involved in multiple human diseases by modulating gene expression at the transcriptional, post-transcriptional or epigenetic level (7). Therefore, the regulatory association between lncRNA, miRNA and mRNA, and tumor pathogenesis requires further investigation.
Genetic mutations are one of the principal factors involved in tumor occurrence and may lead to the activation of proto-oncogenes or the loss of tumor suppressor genes. Furthermore, accumulating evidence demonstrated that genetic modifications are associated with tumorigenesis (8–11). DNA methylation is a mechanism underlying epigenetic modifications, and is able to affect the DNA conformation, the chromatin structure and the stability of DNA, altering the interactions between DNA and proteins regulating gene expression (12). Epigenetic modifications, including DNA methylation and histone modifications, are used as biomarkers to study tumorigenesis and tumor progression, helping clinical diagnosis (11). Furthermore, the investigation of epigenetic modifications may contribute to the development of targeted treatments aimed to inactivate tumor suppressor genes (13). Therefore, examining the associations between genetic mutations and DNA methylation, and the occurrence, development and prognosis of HCC may improve the understanding of HCC pathogenesis and may provide novel strategies to prevent, diagnose and treat this fatal disease.
In the present study, a prognostic model was constructed and the genes involved in HCC were investigated by analyzing biological datasets from online databases. The potential interactions among lncRNAs, miRNAs and mRNAs involved in the development of HCC were investigated. Additionally, the association between RNA expression levels, and the occurrence of genetic mutations and epigenetic modifications were examined in order to identify novel potential biomarkers.
Materials and methods
Database screening
Gene expression data from HCC, relative to 374 tumor samples and 50 normal samples, were downloaded from the The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) using the key words ‘transcriptome profiling’, ‘HTSeq-Counts’ and ‘TCGA-LIHC’. The data were combined and the gene identification numbers were converted using Perl (https://www.perl.org/) in order to obtain a gene expression matrix.
Identification of differentially expressed (DE) genes
The expression matrix of miRNAs, lncRNAs and mRNAs was analyzed using the edgeR package (version 3.22.3; http://www.bioconductor.org/packages/release/bioc/html/edgeR.html) of R software (version 3.4.2) (http://www.r-project.org/). The correlations between DElncRNA-DEmiRNA pairs and DEmiRNA-DEmRNA pairs were matched using Perl. The pairs used the Spearman's test with a coefficient >0.95 were considered as co-expressed. The lncRNAs-miRNAs-mRNAs regulatory interactions were identified based on the DElncRNA-DEmiRNA and DEmiRNA-DEmRNA regulation pairs. The regulatory associations between DElncRNAs and DEmiRNAs were investigated using miRcode (http://www.mircode.org). The regulatory associations between DEmiRNAs and DEmRNAs were investigated using miRDB (http://www.mirdb.org/), miRTarBase (release 7.0) (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) and TargetScan (release 7.2) (http://www.targetscan.org/). The negatively associated DEmiRNA-DEmRNA and DElncRNA-DEmiRNA regulatory pairs were selected to construct regulatory networks. The VennDiagram package (version 1.6.20; http://cran.r-project.org/web/packages/VennDiagram/index.html) of R software was used to draw Venn diagrams.
Multivariable Cox regression analysis of independent prognostic factors in HCC
Univariate analysis was performed for DElncRNAs, DEmRNAs and DEmiRNAs. The DE-RNAs with P<0.001 were selected for further multivariate Cox regression analysis. A prognostic model of HCC was established, and Receiver Operating Characteristic (ROC) curves were used to test the reliability of the constructed model.
Construction of a competing endogenous (ceRNA) regulatory network and survival analysis
The integrated co-expression network of DElncRNA-DEmiRNA-DEmRNA regulatory associations was visualized using Cytoscape software (version 3.6.1) (https://cytoscape.org/). Furthermore, prognostic DEmRNAs, DElncRNAs and DEmiRNAs in the ceRNA network were identified, and Kaplan-Meier survival plots were drawn using R survival package (version 2.42–6) (https://cran.r-project.org/web/packages/survival/index.html).
Functional enrichment analysis
The BINGO plugin (version 3.0.3) (https://www.psb.ugent.be/cbd/papers/BiNGO/Tutorial.html) of Cytoscape software was used for Gene Ontology (GO) analysis (https://david.ncifcrf.gov/). KOBAS 3.0 (http://kobas.cbi.pku.edu.cn/) was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
Construction of protein-protein interaction (PPI) networks
PPI networks were constructed using Search Tool for the Retrieval of Interacting Genes/Proteins (https://string-db.org/). In order to model the network, the minimum required interaction score was set to 0.4. In analyzing the six genes associated with miRNA-424, the minimum required interaction score was set to 0.4.
Association between genetic mutations and DNA methylation
cBioPortal database (http://www.cbioportal.org/) was used to investigate the association between the genetic mutations and DNA methylation of six target genes regulated by miRNA-424 in HCC. Their association with the overall survival of the patients with HCC was further assessed by Kaplan-Meier analysis. P<0.05 was considered to indicate a statistically significant difference.
Results
Identification of DElncRNAs, DEmiRNAs and DEmRNAs associated with HCC survival
A total of 1,077 DElncRNAs, 122 DEmiRNAs and 1,985 DEmRNAs were identified by analyzing RNA-sequencing (seq) and miRNA-seq data downloaded from TCGA, and derived from 374 HCC and 50 normal liver samples. Target prediction analysis, performed using miRDB, miRTarBase and TargetScan, suggested that 747 of the DEmRNAs were potential targets of lncRNAs and miRNAs. Among the 747 DEmRNAs, 35 were identified as potential targets of 77 DElncRNAs and 16 DEmiRNAs (data not shown).
Kaplan-Meier survival analysis suggested that 13 DElncRNAs and 19 DEmRNAs were associated with the 5-year survival rate of patients with HCC (data not shown). In contrast, no miRNAs were associated with the 5-year survival rate.
Univariate analysis of DERNAs suggested that nine DElncRNAs, one DEmiRNA and 14 DEmRNAs were risk factors of HCC development (data not shown. Multivariable Cox regression analysis suggested that 12 DERNAs were associated with HCC, whereas, long intergenic non-protein coding RNA 200, miRNA-137, PDZ binding kinase (PBK) and DNA polymerase θ (POLQ) were independent prognostic factors (Table I). These 12 DERNAs were used to construct a prognostic model, and the overall survival rate of patients with a low risk score was significantly increased compared with patients with a high risk score. Furthermore, the ROC analysis performed resulted in an area under the curve of 0.756, indicating the reliability of the model (data not shown). A heat map of the 12 DERNAs suggested that their expression patterns exhibited consistency among 374 tumor samples compared with 50 normal liver samples (data not shown).
Construction of a ceRNA regulatory network for HCC
The regulatory associations among DElncRNAs, DEmiRNAs and DEmRNAs were analyzed by constructing a ceRNA network. A total of 290 lncRNA-miRNA pairs, 16 DEmiRNAs and 35 DEmRNAs were used to construct the ceRNA network (data not shown). The lncRNA-miRNA-mRNA regulatory network included 128 nodes and 331 edges. Among the DEmiRNAs, only miRNA-424 was downregulated. In a secondary regulatory network centered on miRNA-424, 13 DEmRNAs were identified: One was downregulated and 12 were upregulated (data not shown). Among these 13 DEmRNAs, the following six genes were associated with the overall survival of patients with HCC: E2F transcription factor 7 (E2F7), cytoplasmic polyadenylation element binding protein 3 (CPEB3), claspin (CLSPN), centrosomal protein 55 (CEP55), cell division cycle 25A (CDC25A) and cyclin E1 (CCNE1; Fig. 1). Furthermore, 29 DElncRNAS were identified, and five of them, including TSPEAR antisense RNA 1, MYLK antisense RNA 1, CLRN1 antisense RNA 1, chromosome 2 open reading frame 48 and AP002478.1, were associated with the survival rate of patients with HCC (Fig. 2). Therefore, the regulatory network of miRNA-424 was further investigated in the present study.
GO and KEGG functional enrichment analyses in HCC
Functional enrichment analysis of the mRNAs regulated by miRNA-424 resulted in five enriched GO terms. The GO terms are categorized into ‘biological process’, ‘molecular function’ and ‘cellular component’. A total of 108 nodes and 166 edges constituted a functional enrichment regulatory network (data not shown). Additionally, 13 enriched signaling pathways were identified by KEGG analysis and the most significant enriched pathways were ‘progesterone-mediated oocyte maturation’, ‘oocyte meiosis’, ‘cell cycle’ and ‘microRNAs in cancer’ (Fig. 3A).
PPI network analysis in HCC
The functional interactions were examined at the protein level by constructing PPI networks based on the DEmRNAs (Fig. 3B) and the six target genes of miRNA-424 (Fig. 3C). CDC25A presented multiple interactions in the two networks and was the central node in the miRNA-424-regulated network (Fig. 3C). Furthermore, CEP55, CLSPN, E2F7 and CCNE1 were associated with CDC25A, although no interaction with CPEB3 was identified (Fig. 3C). The interaction between CDC25A and CCNE1 presented the highest combined score, 0.981.
Analysis of genetic mutations of miRNA-424 target genes in HCC
The mutation rate in HCC was 5% for CDC25A, CEP55, CCNE1 and CPEB3, and 6% for CLSPN and E2F7, and mutations in these genes were significantly associated with vascular invasion (data not shown). The associations among the genetic mutations of the six genes were analyzed using the cBioPortal database. The results suggested that mutations in CDC25A were positively associated with mutations in CEP55, CLSPN and E2F7; however, no significant association was identified with mutations in CCNE1 or CPEB3 (Table II). Furthermore, the associations between the mutations of the six genes and the survival rates of patients with HCC were assessed by Kaplan-Meier analysis (Fig. 4). Mutations in CEP55 were associated with the overall survival and the disease-free survival rates of patients with HCC (Fig. 4B and D). CDC25A mutations were associated with the overall survival rate (Fig. 4A); however, they were not significantly associated with the disease-free survival rate (data not shown). In contrast, mutations in E2F7 were associated with the disease-free survival (Fig. 4C) and not with the overall survival rate (data not shown). Mutations in CCNE1, CLSPN and CPEB3 were not significantly associated with the survival rates (data not shown).
DNA methylation affects the biological features of HCC
The association between the levels of DNA methylation of the miRNA-424 target genes and the biological features of HCC samples were investigated (Fig. 5). The DNA methylation levels of CEP55, CDC25A and CCNE1 were negatively associated with vascular invasion. HCC samples presenting decreased levels of methylation exhibited increased vascular invasion (Fig. 5A-C). The DNA methylation levels of E2F7, CLSPN and CPEB3 were not associated with vascular invasion (data not shown). To investigate the association between DNA methylation levels and prognosis of patients with HCC, a Kaplan-Meier analysis was performed. The overall survival was increased when the methylation level of CEP55 was increased (Fig. 5D). The overall survival was decreased when the methylation level of CCNE1 was high (Fig. 5E). The disease-free survival was higher when CEP55 methylation was higher (Fig. 5F). Disease-free survival relative to CCNE1 methylation is not presented. No significant associations were identified between the DNA methylation levels of the other four genes and survival rates (data not shown).
Discussion
HCC is one of the most common types of tumor, exhibiting high morbidity, malignancy and mortality rates. The occurrence and development of HCC are regulated by complex multi-factorial processes involving multiple genes, and gene regulation may be influenced by lncRNAs, miRNAs, genetic mutations and epigenetic modifications (14,15). Therefore, investigating a single factor associated with gene regulation is not sufficient to assess the prognosis of patients with HCC. Previous studies have identified genes that regulate HCC progression; however, the majority of these studies focused on single genes or investigated only one of the factors influencing gene regulation (16–18), without considering their combinatorial effect. To understand the complex molecular biological characteristics of HCC, differentially expressed lncRNAs, miRNAs and mRNAs were identified by analyzing biological datasets from TCGA database. Subsequently, lncRNA-miRNA-mRNA gene regulatory networks were constructed. To identify the independent prognostic factors that affected the biological features of HCC, the functions and the signaling pathways associated with the genes identified were investigated. Additionally, genetic mutations and epigenetic modifications were examined, and an association between DNA methylation level and tumor vascularization was identified. Furthermore, the epigenetic states of the genes examined were identified to be associated with patient survival.
Accumulating evidence has demonstrated the regulatory association between miRNAs and mRNAs, although the roles of lncRNAs have frequently been ignored. lncRNAs serve numerous roles in tumor occurrence and progression (15,19,20), whereas, miRNAs have been identified to exert their biological functions primarily by regulating gene expression. Furthermore, previous studies demonstrated that lncRNAs, miRNAs and mRNAs are able to serve important roles in tumor progression by regulating each other (21–23). Zhang et al (24) performed a comprehensive analysis of the lncRNA-miRNA-mRNA regulatory network in breast cancer by analyzing the TCGA database. The previous study by Zhang et al (24) provided important insight for future breast cancer studies, and laid the foundation for the development of novel strategies for targeting breast cancer. Previous studies investigated the role of lncRNA and miRNA regulation in HCC (14,25); however, to the best of the authors' knowledge, the lncRNA-miRNA-mRNA network in HCC has not been examined. Therefore, the present systematic analysis of the lncRNA-miRNA-mRNA regulatory network provides novel insight into the complex regulation of HCC development and progression. In the present study, univariate and multivariate Cox regression analysis based on genomic datasets from TCGA database suggested that miRNA-137, PBK, LINC00200 and POLQ are independent prognostic factors of HCC. Furthermore, Kaplan-Meier survival analyses suggested that PBK and POLQ were associated with the 5-year survival rate of patients with HCC (data not shown), and that these two genes may serve a role in carcinogenesis. He et al (26) demonstrated, by analyzing the Gene Expression Omnibus database, that PBK was associated with the prognosis of patients with HCC; however, to the best of the authors' knowledge, experimental studies of POLQ in HCC have not been conducted. Therefore, the present results provide an in silico analysis that may represent the basis for future experimental and theoretical studies aiming to investigate the prognosis of HCC. The present prognosis model of HCC may help in performing individualized diagnoses and developing targeted treatments for HCC.
A total of 77 lncRNAs, 16 DEmiRNAs and 35 DEmRNAs were identified to be involved in the constructed lncRNA-miRNA-mRNA regulation network. In total, 12 DEmiRNAs were upregulated and only one DEmiRNA, miRNA-424, was downregulated. The majority of the predicted target genes of miRNA-424, which were additionally differentially expressed in HCC, were associated with the 5-year survival rate of patients with HCC. Therefore, the miRNA-424 regulation network was selected for subsequent analyses. To assess the biological effects of the miRNA-424 regulatory network in HCC, functional enrichment analyses using GO and KEGG were performed. GO analysis suggested that the examined genes were associated with a number of ‘biological process’, ‘molecular function’ and ‘cellular component’ terms, whereas, KEGG analysis suggested that the signaling pathways ‘progesterone-mediated oocyte maturation’, ‘oocyte meiosis’, ‘cell cycle’ and ‘microRNAs in cancer’ were significantly enriched. Notably, these pathways have been previously identified to be associated with the occurrence and development of cancer (27–29). The present results suggested that the prognosis of HCC may be improved by testing the identified regulatory genes, although further experimental studies are required to verify this hypothesis.
PPI analysis identified that CDC25A, CEP55, CLSPN, CCNE1 and E2F7 were associated with the prognosis of HCC, further suggesting that the occurrence and development of HCC is a multi-factor process involving numerous genes and proteins. CDC25A was identified to be associated with CCNE1, in agreement with previous studies (30–33). Notably, CDC25A and CCNE1 were identified to be associated with the development of HCC.
Accumulating evidence demonstrated that genetic mutations are associated with the occurrence and prognosis of tumors (34–36), including HCC (37–39). In the present study, it was identified that mutations in miRNA-424 target genes were associated with biological features of HCC. Mutations in CEP55, CDC25A and E2F7 were identified to affect overall survival and disease-free survival. Notably, these genes may represent potential novel biomarkers for assessing the prognosis of patients with HCC, and may be considered potential targets for treating HCC. Epigenetic analysis suggested that differential methylation states of CDC25A, CCNE1 and CEP55 were associated with the occurrence, prognosis and biological features of HCC. Previous studies demonstrated that DNA methylation serves an important role in cancer (10,11,40). In the present study, the level of DNA methylation of CDC25A, CCNE1 and CEP55 was negatively associated with vascular invasion in HCC. Furthermore, the methylation state of CCNE1 and CEP55 was associated with the prognosis of HCC. The present findings identified a number of novel genes potentially involved in HCC development and progression that may represent novel biomarkers and novel targets for gene therapy, resulting in an improved prognosis of HCC.
Acknowledgements
The authors would like to thank Dr Li Ting (Department of Clinical Medical Oncology, Fujian Provincial Hospital, Fuzhou, China) and Dr Wang Yan (Department of Hepatobiliary Surgery, Guilin Medical University, Guilin, China) for their expert technical assistance.
Funding
The present study was supported in part by The National Natural Science Foundation of China (grant nos. 81430014 and 81771674; China) and by The 111 Project (grant no. D17011.
Availability of data and materials
The genetic transcriptome of hepatocellular carcinoma and the methylation data were downloaded from the TCGA data portal (https://portal.gdc.cancer.gov/). All datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
SH designed and conducted the study. CL performed all bioinformatics analysis and wrote the manuscript. GY analyzed the data and performed the statistical analyses. ZH and YZ participated in literature retrieval. XQ and HY made contributions to the design of this study and revised the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
|
Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Tang Z: Treatment strategies for cancer. Chin J Clin Hepatol. 27:337–339. 2011.(In Chinese). | |
|
Bartel DP: MicroRNAs: Target recognition and regulatory functions. Cell. 136:215–233. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Krol J, Loedige I and Filipowicz W: The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet. 11:597–610. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Mercer TR and Mattick JS: Structure and function of long noncoding RNAs in epigenetic regulation. Nat Struct Mol Biol. 20:300–307. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Quinn JJ and Chang HY: Unique features of long non-coding RNA biogenesis and function. Nat Rev Genet. 17:47–62. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Wapinski O and Chang HY: Long noncoding RNAs and human disease. Trends Cell Biol. 21:354–361. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Rozalski R, Gackowski D, Siomek-Gorecka A, Banaszkiewicz Z and Olinski R: Urinary measurement of epigenetic DNA modifications: A non-invasive assessment of the whole-body epigenetic status in healthy subjects and colorectal cancer patients. ChemistryOpen. 5:550–553. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Shakeri H, Fakhrjou A, Nikanfar A and Mohaddes-Ardebili SM: Methylation analysis of BRCA1 and APC in breast cancer and it's relationship to clinicopathological features. Clin Lab. 62:2333–2337. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Peng D, Zhang H and Sun G: The relationship between P16 gene promoter methylation and gastric cancer: A meta-analysis based on Chinese patients. J Cancer Res Ther. 10 Suppl:S292–S295. 2014. View Article : Google Scholar | |
|
Stephen JK, Chen KM, Merritt J, Chitale D, Divine G and Worsham MJ: Methylation markers for early detection and differentiation of follicular thyroid cancer subtypes. Cancer Clin Oncol. 4:1–12. 2015.PubMed/NCBI | |
|
Furukawa R, Hachiya T, Ohmomo H, Shiwa Y, Ono K, Suzuki S, Satoh M, Hitomi J, Sobue K and Shimizu A: Intraindividual dynamics of transcriptome and genome-wide stability of DNA methylation. Sci Rep. 6:264242016. View Article : Google Scholar : PubMed/NCBI | |
|
Liu XN, Liu XH, Yu XX, et al: DNA methylation of tumor suppressor genes and hepatocellular carcinoma. China Cancer. 26:727–732. 2017.(In Chinese). | |
|
Wang F, Ying HQ, He BS, Pan YQ, Deng QW, Sun HL, Chen J, Liu X and Wang SK: Upregulated lncRNA-UCA1 contributes to progression of hepatocellular carcinoma through inhibition of miR-216b and activation of FGFR1/ERK signaling pathway. Oncotarget. 6:7899–7917. 2015.PubMed/NCBI | |
|
Zhang Z, Weaver DL, Olsen D, deKay J, Peng Z, Ashikaga T and Evans MF: Long non-coding RNA chromogenic in situ hybridisation signal pattern correlation with breast tumour pathology. J Clin Pathol. 69:76–81. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
He JH, Han ZP, Liu JM, Zhou JB, Zou MX, Lv YB, Li YG and Cao MR: Overexpression of long non-coding RNA MEG3 inhibits proliferation of hepatocellular carcinoma Huh7 cells via negative modulation of miRNA-664. J Cell Biochem. 118:3713–3721. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Huang BP, Lin CS, Wang CJ and Kao SH: Upregulation of heat shock protein 70 and the differential protein expression induced by tumor necrosis factor-alpha enhances migration and inhibits apoptosis of hepatocellular carcinoma cell HepG2. Int J Med Sci. 14:284–293. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Ye Y, Wei Y, Xu Y, Li Y, Wang R, Chen J, Zhou Y, Fu Z, Chen Y, Wang X, et al: Induced MiR-1249 expression by aberrant activation of Hedegehog signaling pathway in hepatocellular carcinoma. Exp Cell Res. 355:9–17. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Wang J, Cao L, Wu J and Wang Q: Long non-coding RNA SNHG1 regulates NOB1 expression by sponging miR-326 and promotes tumorigenesis in osteosarcoma. Int J Oncol. 52:77–88. 2018.PubMed/NCBI | |
|
Zeng S, Xie X, Xiao YF, Tang B, Hu CJ, Wang SM, Wu YY, Dong H, Li BS and Yang SM: Long noncoding RNA LINC00675 enhances phosphorylation of vimentin on Ser83 to suppress gastric cancer progression. Cancer Lett. 412:179–187. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
He JH, Han ZP, Zou MX, Wang L, Lv YB, Zhou JB, Cao MR and Li YG: Analyzing the LncRNA, miRNA and mRNA regulatory network in prostate cancer with bioinformatics software. J Comput Biol. 25:146–157. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Jiang H, Ma R, Zou S, Wang Y, Li Z and Li W: Reconstruction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in rheumatoid arthritis. Mol Biosyst. 13:1182–1192. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Li DY, Chen WJ, Luo L, Wang YK, Shang J, Zhang Y, Chen G and Li SK: Prospective lncRNA-miRNA-mRNA regulatory network of long non-coding RNA LINC00968 in non-small cell lung cancer A549 cells: A miRNA microarray and bioinformatics investigation. Int J Mol Med. 40:1895–1906. 2017.PubMed/NCBI | |
|
Zhang Y, Li Y, Wang Q, Zhang X, Wang D, Tang HC, Meng X and Ding X: Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis. Mol Med Rep. 16:5113–5120. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang J, Fan D, Jian Z, Chen GG and Lai PB: Cancer specific long noncoding RNAs show differential expression patterns and competing endogenous RNA potential in hepatocellular carcinoma. PLoS One. 10:e01410422015. View Article : Google Scholar : PubMed/NCBI | |
|
He B, Yin J, Gong S, Gu J, Xiao J, Shi W, Ding W and He Y: Bioinformatics analysis of key genes and pathways for hepatocellular carcinoma transformed from cirrhosis. Medicine (Baltimore). 96:e69382017. View Article : Google Scholar : PubMed/NCBI | |
|
Kuznetsov VA, Tang Z and Ivshina AV: Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets. BMC Genomics. 18 Suppl 6:S6922017. View Article : Google Scholar | |
|
Fu Z, Han X, Du J, Han X, Liu W, Shao S and Liu X: Euphorbia lunulata extract acts on multidrug resistant gastric cancer cells to inhibit cell proliferation, migration and invasion, arrest cell cycle progression, and induce apoptosis. J Ethnopharmacol. 212:8–17. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
El-Daly SM, Abba ML and Gamal-Eldeen AM: The role of microRNAs in photodynamic therapy of cancer. Eur J Med Chem. 142:550–555. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Xu X, Yamamoto H, Sakon M, Yasui M, Ngan CY, Fukunaga H, Morita T, Ogawa M, Nagano H, Nakamori S, et al: Overexpression of CDC25A phosphatase is associated with hypergrowth activity and poor prognosis of human hepatocellular carcinomas. Clin Cancer Res. 9:1764–1772. 2003.PubMed/NCBI | |
|
Yuan P, Li J, Zhou F, Huang Q, Zhang J, Guo X, Lyu Z, Zhang H and Xing J: NPAS2 promotes cell survival of hepatocellular carcinoma by transactivating CDC25A. Cell Death Dis. 8:e27042017. View Article : Google Scholar : PubMed/NCBI | |
|
Jiang W, Huang H, Ding L, Zhu P, Saiyin H, Ji G, Zuo J, Han D, Pan Y, Ding D, et al: Regulation of cell cycle of hepatocellular carcinoma by NF90 through modulation of cyclin E1 mRNA stability. Oncogene. 34:4460–4470. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang X, Hu S, Zhang X, Wang L, Zhang X, Yan B, Zhao J, Yang A and Zhang R: MicroRNA-7 arrests cell cycle in G1 phase by directly targeting CCNE1 in human hepatocellular carcinoma cells. Biochem Biophys Res Commun. 443:1078–1084. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Bahrami A, Hesari A, Khazaei M, Hassanian SM, Ferns GA and Avan A: The therapeutic potential of targeting the BRAF mutation in patients with colorectal cancer. J Cell Physiol. 233:2162–2169. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Vietsch EE, Graham GT, McCutcheon JN, Javaid A, Giaccone G, Marshall JL and Wellstein A: Circulating cell-free DNA mutation patterns in early and late stage colon and pancreatic cancer. Cancer Genet. 218–219. 39–50. 2017. | |
|
Zhang K, Zhou J, Zhu X, Luo M, Xu C, Yu J, Deng M, Zheng S and Chen Y: Germline mutations of PALB2 gene in a sequential series of Chinese patients with breast cancer. Breast Cancer Res Treat. 166:865–873. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Hayashi T, Yamashita T, Okada H, Nio K, Hara Y, Nomura Y, Hayashi T, Asahina Y, Yoshida M, Oishi N, et al: Sporadic PCDH18 somatic mutations in EpCAM-positive hepatocellular carcinoma. Cancer Cell Int. 17:942017. View Article : Google Scholar : PubMed/NCBI | |
|
Jiao J, Niu W, Wang Y, Baggerly K, Ye Y, Wu X, Davenport D, Almeda JL, Betancourt-Garcia MM, Forse RA, et al: Prevalence of Aflatoxin-associated TP53R249S mutation in hepatocellular carcinoma in hispanics in South texas. Cancer Prev Res (Phila). 11:103–112. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Riordan JD, Feddersen CR, Tschida BR, Beckmann PJ, Keng VW, Linden MA, Amin K, Stipp CS, Largaespada DA and Dupuy AJ: Chronic liver injury alters driver mutation profiles in hepatocellular carcinoma in mice. Hepatology. 67:924–939. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Tommasi S and Besaratinia A: A versatile assay for detection of aberrant DNA methylation in bladder cancer. Methods Mol Biol. 1655:29–41. 2018. View Article : Google Scholar : PubMed/NCBI |
