Figures
Abstract
Large intergenic non-coding RNAs (lincRNAs) are a new class of functional transcripts, and aberrant expression of lincRNAs was associated with several human diseases. The genetic variants in lincRNA transcription factor binding sites (TFBSs) can change lincRNA expression, thereby affecting the susceptibility to human diseases. To identify and annotate these functional candidates, we have developed a database SNP@lincTFBS, which is devoted to the exploration and annotation of single nucleotide polymorphisms (SNPs) in potential TFBSs of human lincRNAs. We identified 6,665 SNPs in 6,614 conserved TFBSs of 2,423 human lincRNAs. In addition, with ChIPSeq dataset, we identified 139,576 SNPs in 304,517 transcription factor peaks of 4,813 lincRNAs. We also performed comprehensive annotation for these SNPs using 1000 Genomes Project datasets across 11 populations. Moreover, one of the distinctive features of SNP@lincTFBS is the collection of disease-associated SNPs in the lincRNA TFBSs and SNPs in the TFBSs of disease-associated lincRNAs. The web interface enables both flexible data searches and downloads. Quick search can be query of lincRNA name, SNP identifier, or transcription factor name. SNP@lincTFBS provides significant advances in identification of disease-associated lincRNA variants and improved convenience to interpret the discrepant expression of lincRNAs. The SNP@lincTFBS database is available at http://bioinfo.hrbmu.edu.cn/SNP_lincTFBS.
Citation: Ning S, Zhao Z, Ye J, Wang P, Zhi H, Li R, et al. (2014) SNP@lincTFBS: An Integrated Database of Polymorphisms in Human LincRNA Transcription Factor Binding Sites. PLoS ONE 9(7): e103851. https://doi.org/10.1371/journal.pone.0103851
Editor: Srinivas Mummidi, South Texas Veterans Health Care System and University of Texas Health Science Center at San Antonio, United States of America
Received: March 11, 2014; Accepted: July 2, 2014; Published: July 30, 2014
Copyright: © 2014 Ning et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. Our SNP@lincTFBS database is available at http://bioinfo.hrbmu.edu.cn/SNP_lincTFBS.
Funding: This work was supported by the National High Technology Research and Development Program of China [863 Program, Grant Nos. 2014AA021102], the National Natural Science Foundation of China [Grant Nos. 91129710, 61073136 and 61170154], and the Undergraduate Innovation Funds of Harbin Medical University [Grant Nos. YJSCX2012-226HLJ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Large intergenic non-coding RNAs (lincRNAs) are recently emerging as a novel class of functional non-coding RNAs, which are more than 200 nucleotides in length, derive from the intervals between protein-coding genes, have similar exon-intro-exon structure, but lack of protein-coding capacity [1]. As yet, the quantity of discriminated human lincRNA transcripts continue to increase [2], and many of them have been found to play important roles in multiple biological processes, including epigenetic regulation of protein-coding gene expression [3]–[5] and crucial action in development process [6]. Emerging evidence has also demonstrated that numerous lincRNAs were associated with a wide range of human diseases [7].
Recently, several profiling studies have revealed that dysregulated expression of lincRNAs was involved in several forms of human cancer [8]. For example, a study has reported that the expression level of lincRNA PCGEM1 was higher in prostate tumor specimens than in matched normal tissues [9]. LincRNA HOTAIR (HOX antisense intergenic RNA) can be regard as an independent cancer prognostic marker due to its significantly overexpression in breast cancer, hepatocellular cancer, colorectal cancer and laryngeal squamous cell carcinoma [10]–[12]. Another highly abundant lincRNA MALAT1 (also known as NEAT2) is originally identified as a marker for lung cancer metastasis; its expression is strongly regulated in many tumor entities including lung adenocarcinoma and hepatocellular carcinoma [13], [14]. In addition, it has been demonstrated that up-regulation of a lincRNA HULC is highly associated with the incidence of hepatitis B virus (HBV) infection [15]. However, despite a number of lincRNAs having aberrant expression in disease states, the causality that affects the expression abundance of lincRNAs has yet to be completely understood.
Previous studies have shown that single nucleotide polymorphisms (SNPs) in transcription factor binding sites (TFBSs) of protein-coding genes could affect gene expression by altering transcription factor binding, and participated in human diseases [16]–[20]. A recent study on a tumor suppressor lincRNA has also demonstrated that a SNP (rs944289) could predispose to papillary thyroid carcinoma through dysregulating lincRNA (PTCSC3) expression by decreasing the binding activity of both C/EBPα and C/EBPβ [21]. Thus, SNPs in the human lincRNA TFBSs can act as a set of functional variants, which may disrupt transcription factor binding, resulting in the diversity of lincRNA expression and, potentially, diverse diseases.
Furthermore, with the advent of high-throughput technologies, large-scale lincRNA annotation data, SNP data, predicted and experimentally supported TFBSs data have been generated. This provides a great opportunity to systematically identify SNPs in the human lincRNA TFBSs. For example, in the new update of NONCODE database, the lincRNA data set were expanded by collection of newly identified lincRNAs from published literatures and integration of the latest version of RefSeq and Ensembl [22]. LncRNADisease database collected experimentally supported lncRNA-disease associations and lncRNA interacting partners at various molecular levels [23]. ChIPBase database was developed to annotate and identify TFBSs and transcriptional regulatory relationships of lncRNAs and miRNAs from ChIP-Seq data [24]. In addition, the ENCODE project has compiled a large number of ChIP-Seq experiments for many human TFs in different cell lines and tissues [25]. Enriched peak regions of these ChIP-Seq data can be mapped to the promoter regions of lincRNAs, which facilitate the discovery of experimentally supported TFBSs of human lincRNAs in different cell lines and tissues, and also give us a better opportunity to identify SNPs in lincRNA TFBSs for a cell line of interest.
Therefore, to provide a beneficial annotation of these potential functional variants in human TFBSs, we developed a SNP@lincTFBS database for integrating and annotating functional SNPs in predicted lincRNA TFBSs. We identified 6,665 SNPs occurring in 6,614 TFBSs of 2,423 human lincRNAs, and provided a comprehensive and useful resource of candidate SNPs relevant to the aberrant expression of lincRNAs. The SNP@lincTFBS database will be helpful to identify functional SNPs of lincRNAs in the level of transcription and contribute to profound complex disease study.
Materials and Methods
Human lincRNA data
We obtained 6,631 human lincRNAs with genomic coordinates from the lincRNA list of GENCODE project (version 16) [26], and removed lincRNAs without unique determinate chromosomal location. Finally, 5,835 lincRNAs were contained in SNP@lincTFBS.
Identifying conserved TFBSs of human lincRNAs
We downloaded the locations and scores of conserved TFBSs from the UCSC genome browser [27]. These data were obtained by running the program tfloc (Transcription Factor binding site LOCater) on multiz46way alignments, restricting only to the July 2007 (mm9) mouse genome assembly, the November 2004 rat assembly (rn4), and the February 2009 human genome assembly (hg19). A binding site is considered to be conserved across the alignment if its score meets the threshold score for its binding matrix in all 3 species (human, mouse and rat). Transcription factor information was downloaded from the Transfac Factor database, and the score and threshold were computed with the Transfac Matrix Database (v7.0) created by Biobase [28]. Then, We defined 5 kb upstream to 1 kb downstream region of the transcription start site of each lincRNA as its promoter region refer to previous study [29]. We identified the conserved TFBSs of human lincRNAs in these regions; as a result, we identified 33,181 TFBSs in defined promoter regions of 3,839 human lincRNAs.
Identifying TFBSs of lincRNA using genome-wide ChIP-Seq data
We downloaded 690 ChIP-Seq datasets for 169 human transcription factors in different cell lines and tissues from ENCODE project [25]. These peak datasets were computed by a peak calling method (PeakSeq), which identified enriched peaks through comparing each ChIP-Seq dataset to corresponding control experiment [30]. Then, we identified the peaks that were located in the promoter regions of human lincRNAs (5 kb upstream to 1 kb downstream region of the transcription start site for each lincRNA). In total, we identified 323,256 transcription factor peaks of different transcription factors in 4,831 lincRNA promoter regions.
Identifying SNPs in the TFBSs of human lincRNA
We downloaded SNPs (common and rare variants) in public dbSNP database (build ver. 137) and identified 6,665 SNPs within 6,614 putative TFBSs of 2,423 human lincRNAs. In addition, with ChIPSeq dataset, we identified 139,576 SNPs in 304,517 transcription factor peaks of 4,813 lincRNAs. Then, we downloaded the annotation information of minor allele frequencies and others from 1000 Genomes Project (release of July 2012) datasets across 11 populations [31], and performed comprehensive annotation for these SNPs in lincRNA TFBSs. For each SNP in a lincRNA TFBS, we also extracted the flanking sequence of 30 nt up-/down-stream of the SNP position from RefSeq reference genomic sequence.
Collecting experimentally supported disease-associated SNPs in lincRNA TFBSs
We manually collected known disease-associated SNPs in lincRNAs TFBSs using PubMed to search the previous studies. We also annotated lincRNAs in SNP@lincTFBS that have been reported to be associated with diseases, and identified SNPs within their putative TFBSs. In addition, we integrated recently well-known disease-associated SNPs and disease lincRNAs into SNP@lincTFBS database.
Database implementation
SNP@lincTFBS is an online query tool developed utilizing ECLIPSE platform as the frontend, and MySQL as the backend database. The web engine was implemented using JSP technology, Struts framework and the Java connection pool Proxool, and web server was built using Apache Tomcat.
Results
Overview of the SNP@lincTFBS Database
We developed a novel integrated database named SNP@lincTFBS that allows users to perform SNP and TFBS searches in human lincRNAs. In this database, we: 1) obtained human lincRNAs, 2) identified conversed TFBSs and transcription factor peaks in defined promoter regions of human lincRNAs, 3) identified SNPs in the TFBS of lincRNA and collected experimentally supported disease-associated SNPs in lincRNA TFBSs, 4) integrated annotation information of SNP, TFBS and lincRNA. The architecture of identifying SNPs in lincRNA TFBSs is shown in Figure 1.
Currently, SNP@lincTFBS contains 8,290 entries of annotated SNP-TFBS-lincRNA associations, including 3,839 lincRNAs, 33,181 conserved TFBSs, 6,665 SNPs and 165 transcription factors. In addition, 19,878,236 entries of SNP-peak-lincRNA associations were stored in SNP@lincTFBS, including 4,831 lincRNAs, 323,256 transcription factor peaks, 139,576 SNPs and 169 transcription factors. We identified a large number of conserved TFBSs in the promoter regions of human lincRNAs and found that the distribution of SNPs in these lincRNA TFBSs was extensive (Figure 2A). Previous studies have shown that each transcription factor can bind to several TFBSs in the promoter regions of protein-coding genes, thereby controlling the transcription of genetic information from DNA to messenger RNA. We also found a similar phenomenon in human lincRNA and a transcription factor could bind to many conserved lincRNA TFBSs (∼247 lincRNA), whereas ∼20 TFBSs that have been identified SNPs within them, and every 5.3 TFBSs had a SNP for each transcription factor (Figure 2B). In addition, we observed that high frequencies of SNPs within lincRNA TFBSs to be located around lincRNA start site (Figure 2C), suggesting that these SNPs within lincRNA TFBSs might greatly affect the expression of lincRNAs.
(A) The number distribution of lincRNAs classified as chromosomes. Blue bars represent all lincRNAs. Red bars represent lincRNAs have TFBSs in their promoter regions. Green bars represent lincRNAs have SNPs in their TFBSs. (B) Statistics of lincRNA TFBSs with SNPs for each transcription factor. The quantity of lincRNA TFBSs for each transcription factor (left). The quantity of lincRNA TFBSs with SNPs for each transcription factor (middle). Density of lincRNA TFBSs with SNPs for each transcription factor (right). (C) Distribution of SNPs in lincRNA TFBSs with respect to distance to the lincRNAs. The x-axis displays the 1 kb window within 5 kb upstream to 1 kb downstream region of the start site of lincRNA and the y-axis displays the fraction of SNPs in lincRNA TFBSs located within this window.
Web interface
The SNP@lincTFBS database website includes seven modules: home, search, overview, disease lincRNA, GWAS SNP, download and help (available at http://bioinfo.hrbmu.edu.cn/SNP_lincTFBS). HOME page provides a brief description of the SNP@lincTFBS database, users can browse the high-resolution flowchart of this work to get the main idea of this database. SEARCH page provides a quick search by query of three kinds of entries: 1) a lincRNA name (Ensembl ID), 2) an SNP identifier (rs number from dbSNP), and 3) a transcription factor name. Statistic of dataset contained in the database is introduced. Search result shows lincRNA summary information and all identified TFBSs and TF peaks in promoter region of this lincRNA. SNPs in these TFBSs and TF peaks are listed below (Figure 3). OVERVIEW page provides a general overview of transcription factors stored in SNP@lincTFBS. Disease lincRNA page shows existing experimentally supported disease-associated lincRNAs with their annotations and internal links for their TFBSs and SNPs mapped within them. GWAS SNP page shows disease-associated SNPs from GWAS researches that can be mapped to the lincRNAs TFBSs, whole annotations about lincRNA and TFBS are also available by internal link. PubMed external link for relevant literature is provided. DOWNLOAD page allows users to download all data we provided at present, including TFBSs and TF peaks of lincRNA promoter regions and SNPs mapped within lincRNA TFBSs and TF peaks in the TXT format. HELP page provides detailed column label description of SNP@lincTFBS. Instruction and contact information are also obtained.
Screenshot of the main search page and corresponding result page, search as lincRNA ENSG00000177640.
Known disease SNPs in lincRNA TFBSs
The SNP@lincTFBS database was developed not only as a resource for identifying SNPs in putative TFBSs of human lincRNAs, but also as a direction for further confirmation of predicting novel disease-associated SNPs and lincRNAs. Previous studies have found that lincRNAs may tend to associated with the same diseases with the disease-associated SNPs within their TFBSs by affecting the expression of lincRNAs [21]. We found 22 known disease-associated SNPs in lincRNAs TFBSs using PubMed to search the previous studies (Table 1). For example, we found two SNPs, rs2001844 and rs6982502 in two predicted TFBSs of a lincRNA ENSG00000253111. These two SNPs were identified to be associated with the variation in the magnitude of statin-mediated reduction in total and LDL-cholesterol based on a genome-wide association study [32], thus this lincRNA might have a relationship with cholesterol-associated diseases. Further experimental validation of the role of these disease-associated SNPs in lincRNA TFBSs might provide new insights into mechanisms underlying human diseases.
We also found several lincRNAs in SNP@lincTFBS that have been reported to be associated with human diseases, and these lincRNAs had SNPs within their putative TFBSs. For example, we found human lincRNAs NAG7, MEG3, PCAT1, CASC2 and LINC00032, which were involved in nasopharyngeal carcinoma [33], glioma and bladder cancer [34], [35], prostate cancer [36], endometrial cancer [37] and melanoma [38]. We identified several SNPs in the TFBSs of these disease-associated lincRNAs. These SNPs might be potential risk SNPs for diverse diseases by regulating the expression of disease-associated lincRNAs. For example, the research on NAG7 gene involved in human nasopharyngeal carcinoma (NPC) susceptibility can be traced to more than a decade, and previous studies have found that NAG7 played a key role by means of both expression and interaction, it could inhibit proliferation and induce apoptosis in NPC cell but also stimulate NPC cell invasion [22], [33], [39]. Soon after, NAG7 gene was provided as a long intergenic non-protein coding RNA 312 (LINC00312) in HGNC (HUGO Gene Nomenclature Committee) [40]. Recently, an investigation aiming to assess the possible correlations of LINC00312 expression with NPC progression based on microarray technology has indicated that LINC00312 was significantly down-regulated in NPC tissues and it could represent a potential biomarker for metastasis, progression and prognosis in NPC [41]. In the SNP@lincTFBS database, we found a SNP (rs112175570) located within the TFBS for the transcription factor NF-κB and RelA in the promoter of LINC00312 gene (Ensembl ID: ENSG00000237697), and rs112175570 might be a potential risk SNP for nasopharyngeal carcinoma by regulating the expression of LINC00312.
Besides cancer, we also found several neurological or psychiatric disorder associated SNP in human lincRNA TFBSs. For example, we found three SNPs (rs141600967, rs111946796, rs147394431) in the TFBSs of a lincRNA, ENSG00000214548 (also known as MEG3), ENSG00000214548 has been demonstrated to be associated with multiple human diseases, including glioma and neuroblastoma [42], [43]. We found three SNPs (rs2973034, rs2973034, rs78670708) in the TFBSs of a lincRNA, ENSG00000248587 (also known as GDNF-AS1), ENSG00000248587 has been demonstrated to be associated with Alzheimer disease [44]. In addition, we found a Alzheimer's disease risk SNPs (rs6472116, p = 9.59×10−5) in a lincRNA TFBS (ENSG00000253583) [45]. Therefore, further experimental verification of this SNP might provide novel insights and lead to new treatments. Taking advantage of our database, it is possible to further investigate the mechanism of lincRNA involved in human diseases.
Discussion
Accumulating studies of dysregulated lincRNA expression in diverse cancers have suggested that lincRNAs might act as potential tumor suppressor genes and novel prospective therapeutic targets in cancer treatments. SNP@lincTFBS is designed to serve as a practical resource of SNPs in the TFBSs that dysregulate the expression of human lincRNAs. The database provides available genomic informations and annotations of SNPs in the TFBSs in putative promoter regions of human lincRNAs, and also a web-based interface allowed easy access to query and download flexibly. Most human lincRNAs have TFBSs in their promoter regions and the distribution of SNPs in these TFBSs of lincRNAs is widespread.
Previous studies have demonstrated that the genetic variants in the TFBSs of human lincRNA regulatory regions may change lincRNA expression, and thereby affecting the susceptibility to human diseases [21]. Thus we developed the SNP@lincTFBS database, which is devoted to the exploration and annotation of SNPs in potential TFBSs of human lincRNAs. One of the distinctive features of SNP@lincTFBS is that all SNPs that can be mapped to human lincRNA TFBSs are identified and annotated. The other databases that are related to transcriptional regulation for lncRNAs, such ChIPBase [24], only collect TF-lncRNA regulatory relationships that have been identified from ChIP-Seq data. In SNP@lincTFBS, we considered not only transcription factor of lincRNAs (like ChIPBase), but also the SNPs that affect the capability of binding to the lincRNA promoter regions of each transcription factor.
Our database has the potential to become an available resource for further studies of lincRNA function and complex disease. For example, we found several disease-associated SNPs and lincRNAs in SNP@lincTFBS, suggested the potential application of the SNP@lincTFBS in the field of disease-associated lincRNA variants. We found multiple SNPs in the TFBSs of cancer-associated lincRNAs, further experimental verification of these disease candidates might yield novel insights into disease pathophysiology. In addition, we also found multiple SNPs in the TFBSs of neurological or psychiatric disorder associated lincRNAs, this finding was consistent with previous studies, which revealed that lincRNAs played important roles in brain [5] and neuropsychiatric disorders [46]. Although the current number is limited, with the growth of interest in human lincRNAs and the availability of high-throughput technologies, the total number of disease-associated lincRNAs and SNPs will undoubtedly continue to grow, SNP@lincTFBS will become increasingly useful in future studies.
In the future, we envisage the database to be available as a semantically linked interoperable data resource. We hope that SNP@lincTFBS will be a useful tool for researchers in pertinent fields, and will benefit the functional study of human lincRNAs. With the increasing availability of genome-wide transcriptome identification and functional annotation of human lincRNAs in the public domain, we would enrich the database with this information. We will update the disease-associated lincRNAs with their annotations and disease-associated SNPs mapped to the TFBSs of lincRNAs every 4 months. SNP@lincTFBS may act as an advance resource that can provide great convenience for the research on identification of disease-associated lincRNAs or risk SNPs and the discovery of responsibility for discrepant expression abundance of lincRNAs.
Author Contributions
Conceived and designed the experiments: LW XL. Performed the experiments: SN ZZ JY. Analyzed the data: SN PW HZ RL TW JW. Wrote the paper: SN ZZ JY XL.
References
- 1. Ponting CP, Oliver PL, Reik W (2009) Evolution and functions of long noncoding RNAs. Cell 136: 629–641.
- 2. Cabili MN, Trapnell C, Goff L, Koziol M, Tazon-Vega B, et al. (2011) Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev 25: 1915–1927.
- 3. Lee JT (2012) Epigenetic regulation by long noncoding RNAs. Science 338: 1435–1439.
- 4. Khalil AM, Guttman M, Huarte M, Garber M, Raj A, et al. (2009) Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc Natl Acad Sci U S A 106: 11667–11672.
- 5. Mercer TR, Dinger ME, Sunkin SM, Mehler MF, Mattick JS (2008) Specific expression of long noncoding RNAs in the mouse brain. Proc Natl Acad Sci U S A 105: 716–721.
- 6. Guttman M, Donaghey J, Carey BW, Garber M, Grenier JK, et al. (2011) lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature 477: 295–300.
- 7. Wapinski O, Chang HY (2011) Long noncoding RNAs and human disease. Trends Cell Biol 21: 354–361.
- 8. Maruyama R, Suzuki H (2012) Long noncoding RNA involvement in cancer. BMB Rep 45: 604–611.
- 9. Bialkowska-Hobrzanska H, Driman DK, Fletcher R, Harry V, Razvi H (2006) Expression of human telomerase reverse transcriptase, Survivin, DD3 and PCGEM1 messenger RNA in archival prostate carcinoma tissue. Can J Urol 13: 2967–2974.
- 10. Kumar V, Westra HJ, Karjalainen J, Zhernakova DV, Esko T, et al. (2013) Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet 9: e1003201.
- 11. Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, et al. (2010) Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464: 1071–1076.
- 12. Nie Y, Liu X, Qu S, Song E, Zou H, et al. (2013) Long non-coding RNA HOTAIR is an independent prognostic marker for nasopharyngeal carcinoma progression and survival. Cancer Sci 104: 458–464.
- 13. Gutschner T, Hammerle M, Eissmann M, Hsu J, Kim Y, et al. (2013) The noncoding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells. Cancer Res 73: 1180–1189.
- 14. Gutschner T, Hammerle M, Diederichs S (2013) MALAT1 - a paradigm for long noncoding RNA function in cancer. J Mol Med (Berl) 91: 791–801.
- 15. Matouk IJ, Abbasi I, Hochberg A, Galun E, Dweik H, et al. (2009) Highly upregulated in liver cancer noncoding RNA is overexpressed in hepatic colorectal metastasis. Eur J Gastroenterol Hepatol 21: 688–692.
- 16. Hata J, Matsuda K, Ninomiya T, Yonemoto K, Matsushita T, et al. (2007) Functional SNP in an Sp1-binding site of AGTRL1 gene is associated with susceptibility to brain infarction. Hum Mol Genet 16: 630–639.
- 17. Blanton SH, Burt A, Garcia E, Mulliken JB, Stal S, et al. (2010) Ethnic Heterogeneity of IRF6 AP-2a Binding Site Promoter SNP Association With Nonsyndromic Cleft Lip and Palate. Cleft Palate Craniofac J 47: 574–577.
- 18. Badano I, Stietz SM, Schurr TG, Picconi AM, Fekete D, et al. (2012) Analysis of TNFalpha promoter SNPs and the risk of cervical cancer in urban populations of Posadas (Misiones, Argentina). J Clin Virol 53: 54–59.
- 19. Jung M, Cho BC, Lee CH, Park HS, Kang YA, et al. (2012) EGFR polymorphism as a predictor of clinical outcome in advanced lung cancer patients treated with EGFR-TKI. Yonsei Med J 53: 1128–1135.
- 20. Kohanbash G, Ishikawa E, Fujita M, Ikeura M, McKaveney K, et al. (2012) Differential activity of interferon-alpha8 promoter is regulated by Oct-1 and a SNP that dictates prognosis of glioma. Oncoimmunology 1: 487–492.
- 21. Jendrzejewski J, He H, Radomska HS, Li W, Tomsic J, et al. (2012) The polymorphism rs944289 predisposes to papillary thyroid carcinoma through a large intergenic noncoding RNA gene of tumor suppressor type. Proc Natl Acad Sci U S A 109: 8646–8651.
- 22. Yang Z, Zhou L, Wu LM, Lai MC, Xie HY, et al. (2011) Overexpression of long non-coding RNA HOTAIR predicts tumor recurrence in hepatocellular carcinoma patients following liver transplantation. Ann Surg Oncol 18: 1243–1250.
- 23. Chen G, Wang Z, Wang D, Qiu C, Liu M, et al. (2013) LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res 41: D983–986.
- 24. Yang JH, Li JH, Jiang S, Zhou H, Qu LH (2013) ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data. Nucleic Acids Res 41: D177–187.
- 25. The Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661–678.
- 26. Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, et al. (2012) GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 22: 1760–1774.
- 27. Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, et al. (2003) The UCSC Genome Browser Database. Nucleic Acids Res 31: 51–54.
- 28. Karolchik D, Kuhn RM, Baertsch R, Barber GP, Clawson H, et al. (2008) The UCSC Genome Browser Database: 2008 update. Nucleic Acids Res 36: D773–779.
- 29. Jarinova O, Stewart AF, Roberts R, Wells G, Lau P, et al. (2009) Functional analysis of the chromosome 9p21.3 coronary artery disease risk locus. Arterioscler Thromb Vasc Biol 29: 1671–1677.
- 30. Rozowsky J, Euskirchen G, Auerbach RK, Zhang ZD, Gibson T, et al. (2009) PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat Biotechnol 27: 66–75.
- 31. Broadbent HM, Peden JF, Lorkowski S, Goel A, Ongen H, et al. (2008) Susceptibility to coronary artery disease and diabetes is encoded by distinct, tightly linked SNPs in the ANRIL locus on chromosome 9p. Hum Mol Genet 17: 806–814.
- 32. Barber MJ, Mangravite LM, Hyde CL, Chasman DI, Smith JD, et al. (2010) Genome-wide association of lipid-lowering response to statins in combined study populations. PLoS One 5: e9763.
- 33. Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57.
- 34. Ying L, Huang Y, Chen H, Wang Y, Xia L, et al. (2013) Downregulated MEG3 activates autophagy and increases cell proliferation in bladder cancer. Mol Biosyst 9: 407–411.
- 35. Volders PJ, Helsens K, Wang X, Menten B, Martens L, et al. (2012) LNCipedia: a database for annotated human lncRNA transcript sequences and structures. Nucleic Acids Res 41: D246–251.
- 36. Prensner JR, Iyer MK, Balbin OA, Dhanasekaran SM, Cao Q, et al. (2011) Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression. Nat Biotechnol 29: 742–749.
- 37. Baldinu P, Cossu A, Manca A, Satta MP, Sini MC, et al. (2004) Identification of a novel candidate gene, CASC2, in a region of common allelic loss at chromosome 10q26 in human endometrial cancer. Hum Mutat 23: 318–326.
- 38. Pujana MA, Ruiz A, Badenas C, Puig-Butille JA, Nadal M, et al. (2007) Molecular characterization of a t(9;12)(p21;q13) balanced chromosome translocation in combination with integrative genomics analysis identifies C9orf14 as a candidate tumor-suppressor. Genes Chromosomes Cancer 46: 155–162.
- 39. Tan C, Peng C, Huang YC, Zhang QH, Tang K, et al. (2002) Effects of NPC-associated gene NAG7 on cell cycle and apoptosis in nasopharyngeal carcinoma cells. Ai Zheng 21: 449–455.
- 40. Gray KA, Daugherty LC, Gordon SM, Seal RL, Wright MW, et al. (2013) Genenames.org: the HGNC resources in 2013. Nucleic Acids Res 41: D545–552.
- 41. Zhang W, Huang C, Gong Z, Zhao Y, Tang K, et al. (2013) Expression of LINC00312, a long intergenic non-coding RNA, is negatively correlated with tumor size but positively correlated with lymph node metastasis in nasopharyngeal carcinoma. J Mol Histol 44(5): 545–554.
- 42. Wang P, Ren Z, Sun P (2012) Overexpression of the long non-coding RNA MEG3 impairs in vitro glioma cell proliferation. J Cell Biochem 113: 1868–1874.
- 43. Astuti D, Latif F, Wagner K, Gentle D, Cooper WN, et al. (2005) Epigenetic alteration at the DLK1-GTL2 imprinted domain in human neoplasia: analysis of neuroblastoma, phaeochromocytoma and Wilms' tumour. Br J Cancer 92: 1574–1580.
- 44. Airavaara M, Pletnikova O, Doyle ME, Zhang YE, Troncoso JC, et al. (2011) Identification of novel GDNF isoforms and cis-antisense GDNFOS gene and their regulation in human middle temporal gyrus of Alzheimer disease. J Biol Chem 286: 45093–45102.
- 45. Hollingworth P, Sweet R, Sims R, Harold D, Russo G, et al. (2012) Genome-wide association study of Alzheimer's disease with psychotic symptoms. Mol Psychiatry 17: 1316–1327.
- 46. Qureshi IA, Mattick JS, Mehler MF (2010) Long non-coding RNAs in nervous system function and disease. Brain Res 1338: 20–35.