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Systematic gene microarray analysis of the lncRNA expression profiles in human uterine cervix carcinoma
Biomedicine & Pharmacotherapy 72 (2015) 83–90
Available online at
ScienceDirect www.sciencedirect.com
Original Article
Systematic gene microarray analysis of the lncRNA expression profiles in human uterine cervix carcinoma Jie Chen a, Ziyi Fu b, Chenbo Ji b, Pingqing Gu c, Pengfei Xu b, Ningzhu Yu a, Yansheng Kan a, Xiaowei Wu d, Rong Shen a, Yan Shen a,* a
Cervical Branch, Affiliated Nanjing Maternal and Child Health Hospital, Nanjing Medical University, Nanjing 210004, China Nanjing Maternal and Child Health Medical Institute, Affiliated Nanjing Maternal and Child Health Hospital, Nanjing Medical University, Nanjing 210004, China c Department of Clinical laboratory, Affiliated Nanjing Maternal and Child Health Hospital, Nanjing Medical University, Nanjing 210004, China d Department of Pharmacology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing 210029, China b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 January 2015 Accepted 6 April 2015
The human uterine cervix carcinoma is one of the most well-known malignancy reproductive system cancers, which threatens women health globally. However, the mechanisms of the oncogenesis and development process of cervix carcinoma are not yet fully understood. Long non-coding RNAs (lncRNAs) have been proved to play key roles in various biological processes, especially development of cancer. The function and mechanism of lncRNAs on cervix carcinoma is still rarely reported. We selected 3 cervix cancer and normal cervix tissues separately, then performed lncRNA microarray to detect the differentially expressed lncRNAs. Subsequently, we explored the potential function of these dysregulated lncRNAs through online bioinformatics databases. Finally, quantity real-time PCR was carried out to confirm the expression levels of these dysregulated lncRNAs in cervix cancer and normal tissues. We uncovered the profiles of differentially expressed lncRNAs between normal and cervix carcinoma tissues by using the microarray techniques, and found 1622 upregulated and 3026 downregulated lncRNAs (fold-change > 2.0) in cervix carcinoma compared to the normal cervical tissue. Furthermore, we found HOXA11-AS might participate in cervix carcinogenesis by regulating HOXA11, which is involved in regulating biological processes of cervix cancer. This study afforded expression profiles of lncRNAs between cervix carcinoma tissue and normal cervical tissue, which could provide database for further research about the function and mechanism of key-lncRNAs in cervix carcinoma, and might be helpful to explore potential diagnosis factors and therapeutic targets for cervix carcinoma. ß 2015 Published by Elsevier Masson SAS.
Keywords: Human uterine cervix carcinoma Microarray analysis lncRNA
1. Introduction The human uterine cervix carcinoma is one of the most commonly diagnosed malignancies which imperil women’s health worldwide, which estimated approximately 529,800 new cases diagnosed annually [1,2]. The carcinogenesis and development of cervical cancer have been linked mainly to persistent infection with at least one kind of 13 types of HPV, early sexual life, early marriage, early birth, cigarette smoking, prolific, mill production, economic conditions geographical environment and so on [3]. However, the molecular mechanisms of the tumorigenesis
* Corresponding author. Cervical Branch, Affiliated Nanjing Maternal and Child Health Hospital, Nanjing Medical University, 123 Mochou Rd, Nanjing 210004, China. Tel./fax: +86 25 522 261 59. E-mail address: [email protected] (Y. Shen). http://dx.doi.org/10.1016/j.biopha.2015.04.010 0753-3322/ß 2015 Published by Elsevier Masson SAS.
have been ignored up to now. The carcinogenesis mechanisms of many cancers, such as pancreatic cancer, lung cancer, and breast cancer, have been explored for a long time. It is necessary and emergency to uncover the brief molecular mechanism of the way of cervical cancer initiation. Recently, long non-coding RNAs (lncRNAs) have obtained significant attention in elucidating the complex mechanisms underlying malignant processes, such as tumorigenesis, drugresistance, and metastasis of kinds of cancers. On the beginning, cancer genome researches are limited principally to DNA methylation, RNA regulation, and histone modification, etc. [4], and lncRNAs were simply identified as transcriptional ‘‘noise’’ or cloning artifacts [5]. During the past decade, multiple lncRNAs have been identified, such as XIST, hotair, and H19, which were suspected as Rosetta stone in cancinogenesis process. According to the data provided by ENCODE Project Consortiumin 2012, there are
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about 9640 long non-coding RNA (lncRNA) loci in human genome [6], while the number continues to grow. Up to now, lncRNAs have been confirmed to involved in the regulation of gene transcriptional, chromatin methylation, post-transcriptional levels, and other biological progression [7], there is little information reported about the linkage between systematic expression of lncRNAs and cervical cancer. Along with the research of lncRNAs in cervical intraepithelial neoplasia [8], several well-known lncRNAs have been identified to regulate cervical cancer cell proliferation and invasion. Qin et al. investigated the role of lncRNA MEG3 in the cervical cancer, and found that MEG3 could inhibit cervical cancer cells proliferation by inducing G2/M cell cycle arrest and apoptosis [9], while the brief molecular mechanism needed further investigation. Subsequently, another research identified that lncRNA-EBIC could promote cervical cancer cell invasion by binding to EZH2 and repressing E-caderherin [10], and hotair has been conflicted to be a potential biomarker to predict poor prognosis of cervical cancer [11]. However, the systematic analysis of aberrant expression profiles of lnRNA in cervical cancer is still missing. In this study, we committed to explore the expression pattern of lncRNAs by high-throughput
microarray in cervical cancer tissues, compared to the normal tissues. We suspected to clarify the roles of differentially expressed lncRNA in cervical carcinogenesis, and provide potential biomarkers and therapeutic targets for clinical treatments. 2. Methods and materials 2.1. Tissue collection In this study, all collecting of samples were informed consent of patients at Nanjing Maternal and Child Health Care Hospital Affiliated to Nanjing Medical University (Nanjing, China). Cervix cancer was diagnosed histopathologically. These patients did not accept any local or systemic treatment. This study was approved by the Research Ethics Committee of Nanjing Medical University, China. Finally, 18 cases of cervix cancer samples and normal cervix tissues were collected. All the samples were rapidly and carefully frozen in liquid nitrogen followed by storage at –808C waiting for subsequent analysis. Randomly, 3 cervix cancer tissues and 3 normal cervix tissues were selected separately and prepared for lncRNA microarray analysis.
Fig. 1. LncRNA microarray data of cervix cancer tissue and normal cervix tissue. The boxplot showed that after normalization, the distribution of expression values of each sample was consistent to each other (A). According the well-known online databases, including RefSeq, UCSC Knowngenes, Ensembl and many related literatures (B), totally 15,595 lncRNAs and 15,502 mRNAs were detected in the microarray. The lncRNA expression variation between the groups of normal cervix tissue and cervix cancer tissue is assessed by the method of Scatter-Plot visualization (C). LncRNA expression patterns of samples is showed as like as hierarchical clustering (D).
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2.2. RNA extraction and quality control According to the manufacturer’s protocol, total RNA was extracted from the frozen tissue block by mincing with homogenizer (IKA, Germany) and resuspending in TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Quantification and quality check were performed with Nanodrop and Agilent 2100 Bioanalyzer (Agilent Technologies), respectively. RNA quantity was measured by One drop OD-1000. 2.3. Microarray analysis Sample preparation and microarray hybridization were performed by Kangcheng Biotech, Shanghai, PR China. Briefly, RNA was purified from 1 mg total RNA after removal of rRNA (mRNAONLY Eukaryotic mRNA Isolation Kit, Epicentre). Then, each sample was amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 39 bias utilizing a random priming method. The labeled cRNAs were hybridized onto the Human LncRNA Array v2.0 (8660 K, Arraystar). After having washed the slides, the arrays were scanned by the Agilent Scanner G2505B. Agilent Feature Extraction software (version 10.7.3.1) was utilized to analyze acquired array images. Quantile normalization and subsequent data processing were carried out using the GeneSpring GX v11.5.1 software package (Agilent Technologies).
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Differentially expressed LncRNAs and mRNAs were identified through fold-change filtering (fold-change 3.0 or 0.5), standard student t-test (P < 0.05) and multiple hypothesis testing (FDR < 0.05). P values and FDR were calculated by Microsoft Excel and MATLAB, respectively. The microarray data has been deposited in NCBI Gene Expression Omnibus (GEO) and the GEO accession number is GSE55191 (http://www.ncbi.nlm.nih.gov/geo/query/acc. cgi?acc=GSE55191). Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Differentially regulated mRNAs were uploaded into the Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc. ncifcrf.gov/) to analyze the enrichment of these coding genes. The annotation summary results were shown up by this web server. 2.4. Quantitative real-time reverse transcription PCR Total RNA was isolated from tissues by the TRIzol reagent (Invitrogen, CA, USA), according to the manufacturer’s instructions. A total of 2.5 mg of RNA for each sample was reversely transcribed into cDNA by using random hexamer primer with PrimeSciptTM RT MASTER MIX (Perfect Real-Time kit TaKaRa, Japan). Primers for each lncRNA were designed according to Primer 3 (http://sourceforge. net/projects/primer3/) online and checked with Basic Local Alignment Search Tool (BLAST) of NCBI to ensure unique amplification
Fig. 2. Annotation of differentially expressed lncRNAs in cervix cancer tissues. The chromosome location data showed that the numbers of up- or downregulated lncRNAs located in the human chromosomes (A and B). The length of these dysregulated lncRNAs is mainly 200 to 2800 bp (C). D. Appeared that the relationship between these dysregulated lncRNAs and their targeted targets mainly included exon sense-overlapping, intergenic, natural antisense, intronic antisense, bidirectional, and intron senseoverlapping.
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product. Real-time PCR was performed on an Applied Biosystems ViiATM 7 Dx (Life Technologies, US) using the SYBR green method according to the manufacturer’s instructions. The PCR Reaction conditions were: adenaturation step at a denaturation step at 10 min at 95 8C, followed by 40 cycles of 15 s at 95 8C and 30 s at 60 8C and 30 s and then 30 s at 72 8C. Elative gene expression levels were quantified based on the cycle threshold (Ct) values and normalized to the internal control gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH). All the primer sequences used were DDCt shown in (Supplement Table S1). The 2 method was used to comparatively quantify the levels of mRNA. 3. Results 3.1. LncRNA microarray data of cervix cancer tissue and normal cervix tissue Randomly, 3 cervix cancer and 3 normal cervix tissues were selected and performed a standard lncRNA microarray separately. The boxplot showed that after normalization, the distribution of expression values of each sample was consistent to each other (Fig. 1A). According the well-known online databases, including RefSeq, UCSC Knowngenes, Ensembl and many related literatures (Fig. 1B), totally 15,595 lncRNAs and 15,502 mRNAs were detected in the microarray. The lncRNA expression variation between the groups of normal cervix tissue and cervix cancer tissue is assessed by the method of Scatter-Plot visualization (Fig. 1C). LncRNA expression patterns of samples is showed as like as hierarchical
clustering (Fig. 1D). Compared to the normal cervix tissue, there were 4648 lncRNAs were significantly differently expressed (> 2.0 fold), which contained 1622 upregulated and 3026 downregulated lncRNAs (Supplement material S2). 3.2. Annotation of differentially expressed lncRNAs in cervix cancer tissues In order to investigate the expression pattern of these dysregulated lncRNAs in depth, we first summarized the general signatures of these RNAs, including the chromosome location, length distribution and classification. The chromosome location data showed that the numbers of up- or downregulated lncRNAs located in the human chromosomes (Fig. 2A and B). The length of these dysregulated lncRNAs is mainly 200 to 2800 bp (Fig. 2C). Fig. 2D appeared that the relationship between these dysregulated lncRNAs and their targeted targets mainly included exon senseoverlapping, intergenic, natural antisense, intronic antisense, bidirectional, and intron sense-overlapping. Among all types of these lncRNAs, exon sense-overlapping and intergenic composed approximately 50%. 3.3. Go and pathway analysis The Gene Ontology project (http://www.geneontology.org) which covers three domains: Biological Process, Cellular Component and Molecular Function provides a controlled vocabulary to note gene and gene product attributes in any organism. Functional
Fig. 3. Go analysis. In our study, the top 10 GO terms that the associated coding gene function of upregulated lncRNAs involved: (1) cell cycle, (2) metabolic cell cycle, (3) cell cycle phase, (4) cell cycle process, (5) organelle organization, (6) organelle fission, (7) M phase of mitotic cell cycle, (8) regulation of cell cycle, (9) M phase, and (10) mitosis (A). Meanwhile, the top 10 GO terms that the associated coding gene function of down regulated lncRNAs involved: (1) regulation of biosynthetic process, (2) regulation of macromolecule biosynthetic process, (3) regulation of nitrogen compound metabolic process, (4) muscle cell migration, (5) regulation of nucleobase-containing compound metabolic process, (6) regulation of cellular biosynthetic process, (7) regulation of metabolic process, (8) regulation of cellular metabolic process, (9) regulation of cellular macromolecule biosynthetic process, and (10) regulation of RNA metabolic process (B).
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category enrichment based on the GO terms was evaluated for the associated coding gene of dysregulated lncRNAs. In our study, the top 10 GO terms that the associated coding gene function of upregulated lncRNAs involved:
cell cycle; metabolic cell cycle; cell cycle phase; cell cycle process; organelle organization; organelle fission; M phase of mitotic cell cycle; regulation of cell cycle; M phase; mitosis.
Meanwhile, the top 10 GO terms that the associated coding gene function of downregulated lncRNAs involved:
regulation of biosynthetic process; regulation of macromolecule biosynthetic process; regulation of nitrogen compound metabolic process; muscle cell migration; regulation of nucleobase-containing compound metabolic process; regulation of cellular biosynthetic process; regulation of metabolic process; regulation of cellular metabolic process; regulation of cellular macromolecule biosynthetic process; regulation of RNA metabolic process (Fig. 3A and B).
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By mapping genes to KEGG pathways, we performed pathway analysis (http://www.genome.jp/kegg/pathway.html). In our study, the top 10 pathway that the associated coding gene of upregulated lncRNAs involved:
cell cycle; protein processing in endoplasmic reticulum; DNA replication; lysosome; oocyte meiosis; P53 signaling pathway; pathways in cancer; spliceosome; chronic myeloid leukemia; shigellosis.
On the other hand, the top 10 pathway that the associated coding gene of downregulated lncRNAs involved:
ether lipid metabolism; alpha-linolenic acid metabolism; linoleic acid metabolism; drug metabolism-cytochrome; malaria; cytokine–cytokine receptor interaction; MAPK signaling pathway; arachidonic acid metabolism; vasal cell carcinoma; proteoglycans in cancer (Fig. 4A and B).
Fig. 4. Pathway analysis. The top 10 pathway that the associated coding gene of upregulated lncRNAs involved: (1) cell cycle; (2) protein processing in endoplasmic reticulum; (3) DNA replication; (4) lysosome; (5) oocyte meiosis; (6) P53 signaling pathway; (7) pathways in cancer; (8) spliceosome; (9) chronic myeloid leukemia; (10) shigellosis (A). On the other hand, the top 10 pathway that the associated coding gene of downregulated lncRNAs involved: (1) ether lipid metabolism; (2) alpha-linolenic acid metabolism; (3) linoleic acid metabolism; (4) drug metabolism-cytochrome; (5) malaria; (6) cytokine–cytokine receptor interaction; (7) MAPK signaling pathway; (8) arachidonic acid metabolism; (9) basal cell carcinoma; (10) proteoglycans in cancer (B).
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Fig. 5. Validation of dysregulated lncRNAs and identification of their functional annotations. We selected 18 lncRNAs for real-time PCR quantification in 20 cervix cancer and normal cervix tissues separately. The data showed that the selected lncRNAs were upregulated in cervix cancer tissues significantly, compared to the normal cervix tissues (A). The GO data showed that the target genes of these 18 lncRNAs mainly participated in the cell development, intracellular signaling cascade, regulation of transcription, angiogenesis, and other cancer-related biological process (B). Meanwhile, the pathway analysis appeared that the target genes constituted the PTEN dependent cell cycle arrest and apoptosis, AKT signaling pathway, phosphoinositides and their downstream targets, glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis, and other well-known signaling cascade pathways (C).
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3.4. Validation of dysregulated lncRNAs and identification of their functional annotations To validate the microarray results of dysregulated lncRNAs, we selected 18 lncRNAs, corresponding to the features of the lncRNAs, such as fold-change > 5, for real-time PCR quantification in 20 cervix cancer and normal cervix tissues separately. The relative quantification results were presented in Fig. 5A, which showed that the selected lncRNAs were upregulated in cervix cancer tissues significantly, compared to the normal cervix tissues. These lncRNAs might be involved in the carcinogenesis process, migration, or other malignant behavior of cervix cancer. The above 18 lncRNAs were subjected to target gene prediction and further functional analyses, including GO, pathway analysis, and the interaction of the targeted genes. The GO data showed that the target genes mainly participated in the cell development, intracellular signaling cascade, regulation of transcription, angiogenesis, and other cancer-related biological process (Fig. 5B). Meanwhile, the pathway analysis appeared that the target genes constituted the PTEN dependent cell cycle arrest and apoptosis, AKT signaling pathway, phosphoinositides and their downstream targets, glycine, serine and threonine metabolism, AminoacyltRNA biosynthesis, and other well-known signaling cascade pathways (Fig. 5C). There is little interaction among the target genes to each other, except for PDPK1 and HAND2 (Supplemental material S3). 4. Discussion The human uterine cervix cancer is one of the most commonly known malignancies in women. Many researches showed that the occurrence of cervical cancer is regulated by a crowd of molecular biology activity. But the definite molecular mechanism of cervix cancer is still far from being understood. In recent years, along with the further research, lncRNAs whichever are deemed to be simply transcriptional ‘‘noise’’ or ‘‘cloning artifacts’’ has been more and more attaching importance, such as, c-Myc-activated lncRNA CCAT1 expression contribute to colon cancer tumorigenesis and the metastatic process [12]. Long non-coding RNA MEG3 inhibits the proliferation of cervical carcinoma cells through the induction of cell cycle arrest and apoptosis [9]. Thus, we have reason to believe that lncRNAs may play potential roles in regulating molecular biology of cervix cancer, while, there is still little information about the relationship between lncRNAs expression levels and cervix cancer. Hence, it is necessary to figure out the expression profile of lncRNAs and related lncRNAs in cervix cancer, and explore the potential molecular mechanism in cervix cancer. Using a microarray assay, we detected 15,595 lncRNAs and 15,502 coding transcripts. We identified 1622 upregulated and 3026 downregulated lncRNAs in the cervix cancer compared with normal cervix uteri by set a filter of fold-change > 2.0. The Gene Ontology is a controlled vocabulary composed of > 38,000 precise defined phrases called GO terms that describe the molecular actions of gene products, the biological processes in which those actions occur and the cellular locations where they are present [13]. The existed data of our survey indicate that the main biological processes involving dysregulated lncRNAs contained many closely related to development of cervix cancer, such as, downregulated lncRNAs contained ether lipid metabolism; alphalinolenic acid metabolism; drug metabolism-cytochrome P450; malaria; cytokine–cytokine receptor interaction; MAPK singnaling pathway; arachldonic acid metabolism; basal cell carcinoma; proteoglycans in cancer. On the other hand, upregulated contained cell cycle; protein processing in endoplasmic reticulum; DNA replication; lysosome; oocyte meiosis; P53 signaling pathway;
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pathways in cancer; spliceosome; chronic myeloid leukemia; shigellosis. Microarray technology shows the expression profile of lncRNAs and human genes. Pathway analysis provides a method for gaining insight into the underlying biology of differentially expressed genes and proteins [14]. Pathway analysis show the associated gene of dysregulated lncRNAs between normal and cervix cancer mainly involved in a variety of tumor initiation and progression, such as ‘‘cell cycle’’; ‘‘DNA repication’’; ‘‘oocyte meiosis’’; ‘‘P53 signaling pathway’’; ‘‘pathways in cancer’’ and so on. For instance, the P53 signaling pathway have been clearly established as a key tumor suppressor and the ‘‘guardian of the genome’’ [15,16]. Especially in cervical cancer with a low mutation rate of p53, p53 is often inactivated by human papillomavirus (HPV) oncoprotein E6, which binds to and degrades p53 protein [17]. To further definite key-lncRNA in cervix cancer, we selected 18 genes to verify at random and found that lncRNA HOXA11-AS is near the gene HOXA11 (Homeobox genes A11), which express in several cancer. To date, it is interesting to note that there are growing research that demonstrate the protein products of HOX genes play key roles in the development of cancers. HOX were first found in the fruit fly Drosophila melanogaster, whose protein structures were confirmed in humans not until nearly 70 years later [18,19]. There were 39 HOX genes, which belong to the human genome, are structural and functional homologues of the homeotic complex of Drosophila [20]. Reports showed HOXA11 were differential expression in normal tissue vs. several cancer, such as epithelial ovarian cancers [21], bladder cancer [22], cervical cancer [23] and so on. Especially, HOX11A show significance in the methylation levels of the HSIL group when compared with healthy controls, which is crucial in the embryological development of the Mullerian Duct into the uterine cervix [18]. So, we supposed that HOXA11-AS might affect development of cervix cancer though HOXA11. In conclusion, for the first time we report the profile of differentially expressed lncRNAs between normal and cervix cancer. Network of differentially expressed lncRNAs are construced and numerous lncRNAs are involved in development and metabolism of cervix cancer. It is necessary to further study the biological progress and molecular mechanisms of the dysregulated lncRNAs. This may provide further clarify pathogenesis of cervical cancer or provide a new therapeutic target for cervix cancer by regulate key-lncRNAs. Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. Acknowledgement This study was financially supported by the National Natural Science Foundation of China (No. 81302304, 81402139, and 81272872).
Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.biopha.2015.04. 010. References [1] S.C. Chang, J.S. Woo, V. Yau, B.B. Gorzalka, L.A. Brotto, Cervical cancer screening and Chinese women: insights from focus groups, Front. Psychol. 4 (2013) 48.
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[2] A. Jemal, F. Bray, M.M. Center, J. Ferlay, E. Ward, D. Forman, Global cancer statistics, CA Cancer J. Clin. 61 (2011) 69–90. [3] M. Koskas, J.M. Rodier, J.J. Bretel, C. Bonneau, D. Luton, C. Touboul, et al., Life after uterine cancer, Rev. Prat. 64 (6) (2014) 816–820. [4] G. Egger, G. Liang, A. Aparicio, P.A. Jones, Epigenetics in human disease and prospects for epigenetic therapy, Nature 429 (2004) 457–463. [5] B. Cai, X.Q. Song, J.P. Cai, S. Zhang, HOTAIR: a cancer-related long non-coding RNA, Neoplasma 61 (4) (2014) 379–391. [6] B.E. Bernstein, E. Birney, I. Dunham, E.D. Green, C. Gunter, M. Snyder, An integrated encyclopedia of DNA elements in the human genome, Nature 489 (2012) 57–74. [7] T.R. Mercer, M.E. Dinger, J.S. Mattick, Long non-coding RNAs: insights into functions, Nat. Rev. Genet. 10 (2009) 155–159. [8] E.A. Gibb, D.D. Becker-Santos, K.S. Enfield, M. Guillaud, Niekerk Dv, J.P. Matisic, et al., Aberrant expression of long non-coding RNAs in cervical intraepithelial neoplasia, Int. J. Gynecol. Cancer. 22 (9) (2012) 1557–1563. [9] R. Qin, Z. Chen, Y. Ding, J. Hao, J. Hu, F. Guo, Long non-coding RNA MEG3 inhibits the proliferation of cervical carcinoma cells through the induction of cell cycle arrest and apoptosis, Neoplasma 60 (5) (2013) 486–492. [10] N.X. Sun, C. Ye, Q. Zhao, Q. Zhang, C. Xu, S.B. Wang, et al., Long non-coding RNAEBIC promotes tumor cell invasion by binding to EZH2 and repressing E-cadherin in cervical cancer, PLoS One 9 (7) (2014) e100340. [11] L. Huang, L.M. Liao, A.W. Liu, J.B. Wu, X.L. Cheng, J.X. Lin, et al., Overexpression of long non-coding RNA HOTAIR predicts a poor prognosis in patients with cervical cancer, Arch. Gynecol. Obstet. 290 (4) (2014) 717–723. [12] X. He, X. Tan, X. Wang, H. Jin, L. Liu, L. Ma, et al., C-Myc-activated long non-coding RNA CCAT1 promotes colon cancer cell proliferation and invasion, Tumour Biol. 35 (12) (2014) 12181–12188 [Epub ahead of print].
[13] R. Balakrishnan, M.A. Harris, R. Huntley, K. Van Auken, J.M. Cherry, A guide to best practices for Gene Ontology (GO) manual annotation. Database (Oxford) 2013, 2013 [bat054]. [14] P. Khatri, M. Sirota, A.J. Butte, Ten years of pathway analysis: current approaches and outstanding challenges, PLoS Comput. Biol. 8 (2) (2012) e1002375. [15] W. Hu, Z. Feng, A.J. Levine, The regulation of human reproduction by p53 and its pathway, Cell Cycle 8 (22) (2009) 3621–3622. [16] X. Yu, S.L. Harris, A.J. Levine, The regulation of exosome secretion: a novel function of the p53 protein, Cancer Res. 66 (9) (2006) 4795–4801. [17] M. Scheffner, B.A. Werness, J.M. Huibregtse, A.J. Levine, P.M. Howley, The E6 oncoprotein encoded by human papillomavirus types 16 and 18 promotes the degradation of p53, Cell 63 (6) (1990) 1129–1136. [18] E.B. Lewis, A gene complex controlling segmentation in Drosophila, Nature 276 (5688) (1978) [565ecom]. [19] J. Nourse, J.D. Mellentin, N. Galili, J. Wilkinson, E. Stanbridge, S.D. Smith, et al., Chromosomal translocation t(1;19) results in synthesis of a homeobox fusion mRNA that codes for a potential chimeric transcription factor, Cell 60 (4) (1990) 535–545. [20] M.P. Scott, Vertebrate homeobox gene nomenclature, Cell 71 (4) (1992) 551–553. [21] H. Fiegl, G. Windbichler, E. Mueller-Holzner, G. Goebel, M. Lechner, I.J. Jacobs, et al., HOXA11 DNA methylation – a novel prognostic biomarker in ovarian cancer, Int. J. Cancer 123 (3) (2008) 725–729. [22] H. Luo, X. Zhao, X. Wan, S. Huang, D. Wu, Gene microarray analysis of the lncRNA expression profile in human urothelial carcinoma of the bladder, Int. J. Clin. Exp. Med. 7 (5) (2014) 1244–1254. [23] S. Apostolidou, R. Hadwin, M. Burnell, A. Jones, D. Baff, N. Pyndiah, et al., DNA methylation analysis in liquid-based cytology for cervical cancer screening, Int. J. Cancer 125 (12) (2009) 2995–3002.
Available online at
ScienceDirect www.sciencedirect.com
Original Article
Systematic gene microarray analysis of the lncRNA expression profiles in human uterine cervix carcinoma Jie Chen a, Ziyi Fu b, Chenbo Ji b, Pingqing Gu c, Pengfei Xu b, Ningzhu Yu a, Yansheng Kan a, Xiaowei Wu d, Rong Shen a, Yan Shen a,* a
Cervical Branch, Affiliated Nanjing Maternal and Child Health Hospital, Nanjing Medical University, Nanjing 210004, China Nanjing Maternal and Child Health Medical Institute, Affiliated Nanjing Maternal and Child Health Hospital, Nanjing Medical University, Nanjing 210004, China c Department of Clinical laboratory, Affiliated Nanjing Maternal and Child Health Hospital, Nanjing Medical University, Nanjing 210004, China d Department of Pharmacology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing 210029, China b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 January 2015 Accepted 6 April 2015
The human uterine cervix carcinoma is one of the most well-known malignancy reproductive system cancers, which threatens women health globally. However, the mechanisms of the oncogenesis and development process of cervix carcinoma are not yet fully understood. Long non-coding RNAs (lncRNAs) have been proved to play key roles in various biological processes, especially development of cancer. The function and mechanism of lncRNAs on cervix carcinoma is still rarely reported. We selected 3 cervix cancer and normal cervix tissues separately, then performed lncRNA microarray to detect the differentially expressed lncRNAs. Subsequently, we explored the potential function of these dysregulated lncRNAs through online bioinformatics databases. Finally, quantity real-time PCR was carried out to confirm the expression levels of these dysregulated lncRNAs in cervix cancer and normal tissues. We uncovered the profiles of differentially expressed lncRNAs between normal and cervix carcinoma tissues by using the microarray techniques, and found 1622 upregulated and 3026 downregulated lncRNAs (fold-change > 2.0) in cervix carcinoma compared to the normal cervical tissue. Furthermore, we found HOXA11-AS might participate in cervix carcinogenesis by regulating HOXA11, which is involved in regulating biological processes of cervix cancer. This study afforded expression profiles of lncRNAs between cervix carcinoma tissue and normal cervical tissue, which could provide database for further research about the function and mechanism of key-lncRNAs in cervix carcinoma, and might be helpful to explore potential diagnosis factors and therapeutic targets for cervix carcinoma. ß 2015 Published by Elsevier Masson SAS.
Keywords: Human uterine cervix carcinoma Microarray analysis lncRNA
1. Introduction The human uterine cervix carcinoma is one of the most commonly diagnosed malignancies which imperil women’s health worldwide, which estimated approximately 529,800 new cases diagnosed annually [1,2]. The carcinogenesis and development of cervical cancer have been linked mainly to persistent infection with at least one kind of 13 types of HPV, early sexual life, early marriage, early birth, cigarette smoking, prolific, mill production, economic conditions geographical environment and so on [3]. However, the molecular mechanisms of the tumorigenesis
* Corresponding author. Cervical Branch, Affiliated Nanjing Maternal and Child Health Hospital, Nanjing Medical University, 123 Mochou Rd, Nanjing 210004, China. Tel./fax: +86 25 522 261 59. E-mail address: [email protected] (Y. Shen). http://dx.doi.org/10.1016/j.biopha.2015.04.010 0753-3322/ß 2015 Published by Elsevier Masson SAS.
have been ignored up to now. The carcinogenesis mechanisms of many cancers, such as pancreatic cancer, lung cancer, and breast cancer, have been explored for a long time. It is necessary and emergency to uncover the brief molecular mechanism of the way of cervical cancer initiation. Recently, long non-coding RNAs (lncRNAs) have obtained significant attention in elucidating the complex mechanisms underlying malignant processes, such as tumorigenesis, drugresistance, and metastasis of kinds of cancers. On the beginning, cancer genome researches are limited principally to DNA methylation, RNA regulation, and histone modification, etc. [4], and lncRNAs were simply identified as transcriptional ‘‘noise’’ or cloning artifacts [5]. During the past decade, multiple lncRNAs have been identified, such as XIST, hotair, and H19, which were suspected as Rosetta stone in cancinogenesis process. According to the data provided by ENCODE Project Consortiumin 2012, there are
84
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about 9640 long non-coding RNA (lncRNA) loci in human genome [6], while the number continues to grow. Up to now, lncRNAs have been confirmed to involved in the regulation of gene transcriptional, chromatin methylation, post-transcriptional levels, and other biological progression [7], there is little information reported about the linkage between systematic expression of lncRNAs and cervical cancer. Along with the research of lncRNAs in cervical intraepithelial neoplasia [8], several well-known lncRNAs have been identified to regulate cervical cancer cell proliferation and invasion. Qin et al. investigated the role of lncRNA MEG3 in the cervical cancer, and found that MEG3 could inhibit cervical cancer cells proliferation by inducing G2/M cell cycle arrest and apoptosis [9], while the brief molecular mechanism needed further investigation. Subsequently, another research identified that lncRNA-EBIC could promote cervical cancer cell invasion by binding to EZH2 and repressing E-caderherin [10], and hotair has been conflicted to be a potential biomarker to predict poor prognosis of cervical cancer [11]. However, the systematic analysis of aberrant expression profiles of lnRNA in cervical cancer is still missing. In this study, we committed to explore the expression pattern of lncRNAs by high-throughput
microarray in cervical cancer tissues, compared to the normal tissues. We suspected to clarify the roles of differentially expressed lncRNA in cervical carcinogenesis, and provide potential biomarkers and therapeutic targets for clinical treatments. 2. Methods and materials 2.1. Tissue collection In this study, all collecting of samples were informed consent of patients at Nanjing Maternal and Child Health Care Hospital Affiliated to Nanjing Medical University (Nanjing, China). Cervix cancer was diagnosed histopathologically. These patients did not accept any local or systemic treatment. This study was approved by the Research Ethics Committee of Nanjing Medical University, China. Finally, 18 cases of cervix cancer samples and normal cervix tissues were collected. All the samples were rapidly and carefully frozen in liquid nitrogen followed by storage at –808C waiting for subsequent analysis. Randomly, 3 cervix cancer tissues and 3 normal cervix tissues were selected separately and prepared for lncRNA microarray analysis.
Fig. 1. LncRNA microarray data of cervix cancer tissue and normal cervix tissue. The boxplot showed that after normalization, the distribution of expression values of each sample was consistent to each other (A). According the well-known online databases, including RefSeq, UCSC Knowngenes, Ensembl and many related literatures (B), totally 15,595 lncRNAs and 15,502 mRNAs were detected in the microarray. The lncRNA expression variation between the groups of normal cervix tissue and cervix cancer tissue is assessed by the method of Scatter-Plot visualization (C). LncRNA expression patterns of samples is showed as like as hierarchical clustering (D).
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2.2. RNA extraction and quality control According to the manufacturer’s protocol, total RNA was extracted from the frozen tissue block by mincing with homogenizer (IKA, Germany) and resuspending in TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Quantification and quality check were performed with Nanodrop and Agilent 2100 Bioanalyzer (Agilent Technologies), respectively. RNA quantity was measured by One drop OD-1000. 2.3. Microarray analysis Sample preparation and microarray hybridization were performed by Kangcheng Biotech, Shanghai, PR China. Briefly, RNA was purified from 1 mg total RNA after removal of rRNA (mRNAONLY Eukaryotic mRNA Isolation Kit, Epicentre). Then, each sample was amplified and transcribed into fluorescent cRNA along the entire length of the transcripts without 39 bias utilizing a random priming method. The labeled cRNAs were hybridized onto the Human LncRNA Array v2.0 (8660 K, Arraystar). After having washed the slides, the arrays were scanned by the Agilent Scanner G2505B. Agilent Feature Extraction software (version 10.7.3.1) was utilized to analyze acquired array images. Quantile normalization and subsequent data processing were carried out using the GeneSpring GX v11.5.1 software package (Agilent Technologies).
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Differentially expressed LncRNAs and mRNAs were identified through fold-change filtering (fold-change 3.0 or 0.5), standard student t-test (P < 0.05) and multiple hypothesis testing (FDR < 0.05). P values and FDR were calculated by Microsoft Excel and MATLAB, respectively. The microarray data has been deposited in NCBI Gene Expression Omnibus (GEO) and the GEO accession number is GSE55191 (http://www.ncbi.nlm.nih.gov/geo/query/acc. cgi?acc=GSE55191). Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Differentially regulated mRNAs were uploaded into the Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc. ncifcrf.gov/) to analyze the enrichment of these coding genes. The annotation summary results were shown up by this web server. 2.4. Quantitative real-time reverse transcription PCR Total RNA was isolated from tissues by the TRIzol reagent (Invitrogen, CA, USA), according to the manufacturer’s instructions. A total of 2.5 mg of RNA for each sample was reversely transcribed into cDNA by using random hexamer primer with PrimeSciptTM RT MASTER MIX (Perfect Real-Time kit TaKaRa, Japan). Primers for each lncRNA were designed according to Primer 3 (http://sourceforge. net/projects/primer3/) online and checked with Basic Local Alignment Search Tool (BLAST) of NCBI to ensure unique amplification
Fig. 2. Annotation of differentially expressed lncRNAs in cervix cancer tissues. The chromosome location data showed that the numbers of up- or downregulated lncRNAs located in the human chromosomes (A and B). The length of these dysregulated lncRNAs is mainly 200 to 2800 bp (C). D. Appeared that the relationship between these dysregulated lncRNAs and their targeted targets mainly included exon sense-overlapping, intergenic, natural antisense, intronic antisense, bidirectional, and intron senseoverlapping.
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product. Real-time PCR was performed on an Applied Biosystems ViiATM 7 Dx (Life Technologies, US) using the SYBR green method according to the manufacturer’s instructions. The PCR Reaction conditions were: adenaturation step at a denaturation step at 10 min at 95 8C, followed by 40 cycles of 15 s at 95 8C and 30 s at 60 8C and 30 s and then 30 s at 72 8C. Elative gene expression levels were quantified based on the cycle threshold (Ct) values and normalized to the internal control gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH). All the primer sequences used were DDCt shown in (Supplement Table S1). The 2 method was used to comparatively quantify the levels of mRNA. 3. Results 3.1. LncRNA microarray data of cervix cancer tissue and normal cervix tissue Randomly, 3 cervix cancer and 3 normal cervix tissues were selected and performed a standard lncRNA microarray separately. The boxplot showed that after normalization, the distribution of expression values of each sample was consistent to each other (Fig. 1A). According the well-known online databases, including RefSeq, UCSC Knowngenes, Ensembl and many related literatures (Fig. 1B), totally 15,595 lncRNAs and 15,502 mRNAs were detected in the microarray. The lncRNA expression variation between the groups of normal cervix tissue and cervix cancer tissue is assessed by the method of Scatter-Plot visualization (Fig. 1C). LncRNA expression patterns of samples is showed as like as hierarchical
clustering (Fig. 1D). Compared to the normal cervix tissue, there were 4648 lncRNAs were significantly differently expressed (> 2.0 fold), which contained 1622 upregulated and 3026 downregulated lncRNAs (Supplement material S2). 3.2. Annotation of differentially expressed lncRNAs in cervix cancer tissues In order to investigate the expression pattern of these dysregulated lncRNAs in depth, we first summarized the general signatures of these RNAs, including the chromosome location, length distribution and classification. The chromosome location data showed that the numbers of up- or downregulated lncRNAs located in the human chromosomes (Fig. 2A and B). The length of these dysregulated lncRNAs is mainly 200 to 2800 bp (Fig. 2C). Fig. 2D appeared that the relationship between these dysregulated lncRNAs and their targeted targets mainly included exon senseoverlapping, intergenic, natural antisense, intronic antisense, bidirectional, and intron sense-overlapping. Among all types of these lncRNAs, exon sense-overlapping and intergenic composed approximately 50%. 3.3. Go and pathway analysis The Gene Ontology project (http://www.geneontology.org) which covers three domains: Biological Process, Cellular Component and Molecular Function provides a controlled vocabulary to note gene and gene product attributes in any organism. Functional
Fig. 3. Go analysis. In our study, the top 10 GO terms that the associated coding gene function of upregulated lncRNAs involved: (1) cell cycle, (2) metabolic cell cycle, (3) cell cycle phase, (4) cell cycle process, (5) organelle organization, (6) organelle fission, (7) M phase of mitotic cell cycle, (8) regulation of cell cycle, (9) M phase, and (10) mitosis (A). Meanwhile, the top 10 GO terms that the associated coding gene function of down regulated lncRNAs involved: (1) regulation of biosynthetic process, (2) regulation of macromolecule biosynthetic process, (3) regulation of nitrogen compound metabolic process, (4) muscle cell migration, (5) regulation of nucleobase-containing compound metabolic process, (6) regulation of cellular biosynthetic process, (7) regulation of metabolic process, (8) regulation of cellular metabolic process, (9) regulation of cellular macromolecule biosynthetic process, and (10) regulation of RNA metabolic process (B).
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category enrichment based on the GO terms was evaluated for the associated coding gene of dysregulated lncRNAs. In our study, the top 10 GO terms that the associated coding gene function of upregulated lncRNAs involved:
cell cycle; metabolic cell cycle; cell cycle phase; cell cycle process; organelle organization; organelle fission; M phase of mitotic cell cycle; regulation of cell cycle; M phase; mitosis.
Meanwhile, the top 10 GO terms that the associated coding gene function of downregulated lncRNAs involved:
regulation of biosynthetic process; regulation of macromolecule biosynthetic process; regulation of nitrogen compound metabolic process; muscle cell migration; regulation of nucleobase-containing compound metabolic process; regulation of cellular biosynthetic process; regulation of metabolic process; regulation of cellular metabolic process; regulation of cellular macromolecule biosynthetic process; regulation of RNA metabolic process (Fig. 3A and B).
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By mapping genes to KEGG pathways, we performed pathway analysis (http://www.genome.jp/kegg/pathway.html). In our study, the top 10 pathway that the associated coding gene of upregulated lncRNAs involved:
cell cycle; protein processing in endoplasmic reticulum; DNA replication; lysosome; oocyte meiosis; P53 signaling pathway; pathways in cancer; spliceosome; chronic myeloid leukemia; shigellosis.
On the other hand, the top 10 pathway that the associated coding gene of downregulated lncRNAs involved:
ether lipid metabolism; alpha-linolenic acid metabolism; linoleic acid metabolism; drug metabolism-cytochrome; malaria; cytokine–cytokine receptor interaction; MAPK signaling pathway; arachidonic acid metabolism; vasal cell carcinoma; proteoglycans in cancer (Fig. 4A and B).
Fig. 4. Pathway analysis. The top 10 pathway that the associated coding gene of upregulated lncRNAs involved: (1) cell cycle; (2) protein processing in endoplasmic reticulum; (3) DNA replication; (4) lysosome; (5) oocyte meiosis; (6) P53 signaling pathway; (7) pathways in cancer; (8) spliceosome; (9) chronic myeloid leukemia; (10) shigellosis (A). On the other hand, the top 10 pathway that the associated coding gene of downregulated lncRNAs involved: (1) ether lipid metabolism; (2) alpha-linolenic acid metabolism; (3) linoleic acid metabolism; (4) drug metabolism-cytochrome; (5) malaria; (6) cytokine–cytokine receptor interaction; (7) MAPK signaling pathway; (8) arachidonic acid metabolism; (9) basal cell carcinoma; (10) proteoglycans in cancer (B).
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Fig. 5. Validation of dysregulated lncRNAs and identification of their functional annotations. We selected 18 lncRNAs for real-time PCR quantification in 20 cervix cancer and normal cervix tissues separately. The data showed that the selected lncRNAs were upregulated in cervix cancer tissues significantly, compared to the normal cervix tissues (A). The GO data showed that the target genes of these 18 lncRNAs mainly participated in the cell development, intracellular signaling cascade, regulation of transcription, angiogenesis, and other cancer-related biological process (B). Meanwhile, the pathway analysis appeared that the target genes constituted the PTEN dependent cell cycle arrest and apoptosis, AKT signaling pathway, phosphoinositides and their downstream targets, glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis, and other well-known signaling cascade pathways (C).
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3.4. Validation of dysregulated lncRNAs and identification of their functional annotations To validate the microarray results of dysregulated lncRNAs, we selected 18 lncRNAs, corresponding to the features of the lncRNAs, such as fold-change > 5, for real-time PCR quantification in 20 cervix cancer and normal cervix tissues separately. The relative quantification results were presented in Fig. 5A, which showed that the selected lncRNAs were upregulated in cervix cancer tissues significantly, compared to the normal cervix tissues. These lncRNAs might be involved in the carcinogenesis process, migration, or other malignant behavior of cervix cancer. The above 18 lncRNAs were subjected to target gene prediction and further functional analyses, including GO, pathway analysis, and the interaction of the targeted genes. The GO data showed that the target genes mainly participated in the cell development, intracellular signaling cascade, regulation of transcription, angiogenesis, and other cancer-related biological process (Fig. 5B). Meanwhile, the pathway analysis appeared that the target genes constituted the PTEN dependent cell cycle arrest and apoptosis, AKT signaling pathway, phosphoinositides and their downstream targets, glycine, serine and threonine metabolism, AminoacyltRNA biosynthesis, and other well-known signaling cascade pathways (Fig. 5C). There is little interaction among the target genes to each other, except for PDPK1 and HAND2 (Supplemental material S3). 4. Discussion The human uterine cervix cancer is one of the most commonly known malignancies in women. Many researches showed that the occurrence of cervical cancer is regulated by a crowd of molecular biology activity. But the definite molecular mechanism of cervix cancer is still far from being understood. In recent years, along with the further research, lncRNAs whichever are deemed to be simply transcriptional ‘‘noise’’ or ‘‘cloning artifacts’’ has been more and more attaching importance, such as, c-Myc-activated lncRNA CCAT1 expression contribute to colon cancer tumorigenesis and the metastatic process [12]. Long non-coding RNA MEG3 inhibits the proliferation of cervical carcinoma cells through the induction of cell cycle arrest and apoptosis [9]. Thus, we have reason to believe that lncRNAs may play potential roles in regulating molecular biology of cervix cancer, while, there is still little information about the relationship between lncRNAs expression levels and cervix cancer. Hence, it is necessary to figure out the expression profile of lncRNAs and related lncRNAs in cervix cancer, and explore the potential molecular mechanism in cervix cancer. Using a microarray assay, we detected 15,595 lncRNAs and 15,502 coding transcripts. We identified 1622 upregulated and 3026 downregulated lncRNAs in the cervix cancer compared with normal cervix uteri by set a filter of fold-change > 2.0. The Gene Ontology is a controlled vocabulary composed of > 38,000 precise defined phrases called GO terms that describe the molecular actions of gene products, the biological processes in which those actions occur and the cellular locations where they are present [13]. The existed data of our survey indicate that the main biological processes involving dysregulated lncRNAs contained many closely related to development of cervix cancer, such as, downregulated lncRNAs contained ether lipid metabolism; alphalinolenic acid metabolism; drug metabolism-cytochrome P450; malaria; cytokine–cytokine receptor interaction; MAPK singnaling pathway; arachldonic acid metabolism; basal cell carcinoma; proteoglycans in cancer. On the other hand, upregulated contained cell cycle; protein processing in endoplasmic reticulum; DNA replication; lysosome; oocyte meiosis; P53 signaling pathway;
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pathways in cancer; spliceosome; chronic myeloid leukemia; shigellosis. Microarray technology shows the expression profile of lncRNAs and human genes. Pathway analysis provides a method for gaining insight into the underlying biology of differentially expressed genes and proteins [14]. Pathway analysis show the associated gene of dysregulated lncRNAs between normal and cervix cancer mainly involved in a variety of tumor initiation and progression, such as ‘‘cell cycle’’; ‘‘DNA repication’’; ‘‘oocyte meiosis’’; ‘‘P53 signaling pathway’’; ‘‘pathways in cancer’’ and so on. For instance, the P53 signaling pathway have been clearly established as a key tumor suppressor and the ‘‘guardian of the genome’’ [15,16]. Especially in cervical cancer with a low mutation rate of p53, p53 is often inactivated by human papillomavirus (HPV) oncoprotein E6, which binds to and degrades p53 protein [17]. To further definite key-lncRNA in cervix cancer, we selected 18 genes to verify at random and found that lncRNA HOXA11-AS is near the gene HOXA11 (Homeobox genes A11), which express in several cancer. To date, it is interesting to note that there are growing research that demonstrate the protein products of HOX genes play key roles in the development of cancers. HOX were first found in the fruit fly Drosophila melanogaster, whose protein structures were confirmed in humans not until nearly 70 years later [18,19]. There were 39 HOX genes, which belong to the human genome, are structural and functional homologues of the homeotic complex of Drosophila [20]. Reports showed HOXA11 were differential expression in normal tissue vs. several cancer, such as epithelial ovarian cancers [21], bladder cancer [22], cervical cancer [23] and so on. Especially, HOX11A show significance in the methylation levels of the HSIL group when compared with healthy controls, which is crucial in the embryological development of the Mullerian Duct into the uterine cervix [18]. So, we supposed that HOXA11-AS might affect development of cervix cancer though HOXA11. In conclusion, for the first time we report the profile of differentially expressed lncRNAs between normal and cervix cancer. Network of differentially expressed lncRNAs are construced and numerous lncRNAs are involved in development and metabolism of cervix cancer. It is necessary to further study the biological progress and molecular mechanisms of the dysregulated lncRNAs. This may provide further clarify pathogenesis of cervical cancer or provide a new therapeutic target for cervix cancer by regulate key-lncRNAs. Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. Acknowledgement This study was financially supported by the National Natural Science Foundation of China (No. 81302304, 81402139, and 81272872).
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