Competing endogenous RNA regulatory network in papillary thyroid carcinoma
- Authors:
- Published online on: May 11, 2018 https://doi.org/10.3892/mmr.2018.9009
- Pages: 695-704
-
Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Thyroid carcinomas originate from follicular or parafollicular thyroid cells (1). Papillary thyroid carcinomas (PTC) are the most frequent histologic type of thyroid carcinoma, and account for 75–85% of reported cases (2). The incidence rate of PTC has increased rapidly in recent years, and 5–10% of patients with PTC experience a particularly aggressive development of the disease (3). The underlying molecular mechanisms of the aggressiveness and mortality in such patients remain unknown. A stratification system that may accurately and effectively identify patients with high mortality risk may have great value.
Bioinformatics-based approaches offer considerable promise for evaluating patients with high risk to specific factors (4). Long non-coding RNAs (lncRNAs) serve crucial roles in cancer biology and cancer pathways at transcriptional, post-transcriptional and epigenetic levels (5). lncRNAs generally lack conserved motifs that may be used for detection and identification; however, current highly developed sequencing technologies have made it easier to identify lncRNAs. By combining lncRNAs with known miRNAs, chromatin interactions and RNA-coding proteins, bioinformatics tools are now able to recognize and functionally annotate a large number of lncRNAs (6). Micro (mi)RNAs are post-transcriptional gene regulators that lead to translational repression, gene silencing and mRNA degradation (7). It has been reported that lncRNAs may interact with miRNAs and modulate their regulatory roles by acting as decoys (8). Computational prediction is a useful tool for the construction of miRNA-lncRNA interaction networks, and is a widespread method (9). Multiple types of non-coding RNAs (including lncRNA, circRNAs and pseudogenes) along with protein-coding mRNAs function as key competing endogenous (ce)RNAs to regulate the expression levels of other mRNAs in mammalian cells (10,11). A regulatory role of ceRNA crosstalk with cancer-associated genes has been reported (12). For example, the lncRNA hepatocellular carcinoma upregulated lncRNA was demonstrated to bind to miRNA (miR)-372 and serve a regulatory function (13).
Although the crucial roles of miRNAs, lncRNAs and ceRNAs in cell-fate determination and in various human diseases have been implicated, the regulatory interaction networks among them remain unknown. In the present study, a ceRNA regulatory network comprising miRNAs, lncRNAs and mRNAs in PTC samples was constructed, and the important nodes in the networks were extracted to establish a support vector machine (SVM) classifier.
Materials and methods
Microarray data sets and data preprocessing
miRNA, mRNA and lncRNA sequencing data of PTC samples were downloaded from The Cancer Genome Atlas (TCGA) through the National Cancer Institute Genomic Data Commons data portal (https://gdc-portal.nci.nih.gov) on 20 June, 2016. The TCGA is a data-rich resource for biological discovery that collects information from ~10,000 patient samples together with clinicopathological information across >30 types of human cancer. The present study reviewed a number of previous reports that have used data from TCGA (14,15) and, thus, have selected sequencing data based on TCGA, with the RNASeqV2 platform used for mRNA and lncRNA sequencing data, and with HiSeq2000 used for miRNA sequencing data. Following barcode matching, the mRNA, miRNA and lncRNAs were annotated using the information recorded by the Human Genome Organization Gene Nomenclature Committee (http://www.genenames.org). In addition, two gene expression data sets, GSE33630 and GSE60542, were downloaded from the Gene Expression Omnibus (GEO) database. These microarray data sets were based on the GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array platform. GSE33630 contained 49 PTC samples and 45 normal sample, while GSE60542 contained 33 PTC samples and 32 normal samples. The ‘oligo’ package in R was used to conduct the background correction and normalization as previously described (16,17).
Differentially expressed (DE)-lncRNA, -mRNA and -miRNA
DE-lncRNAs, DE-mRNAs (or DE-genes; DEGs) and DE-miRNAs between the PTC samples and the normal samples were screened using the ‘edgeR’ version 2.4 and ‘multtest’ version 1.6 packages in R (18,19). DE-lncRNAs, DE-miRNAs and DEGs were selected based on the same cut-off value of false discovery rate (FDR) adjusted by Benjamini-Hochberg method, as previously described (14,15); |log fold change (FC)| >1 and FDR <0.05 were used as cut-off criteria. Heatmaps were generated by hierarchical clustering analysis and used to identify the differences in gene expression levels between the PTC samples and the normal samples.
miRNA-regulated lncRNAs and genes
The regulatory pairs between miRNAs and lncRNAs were obtained using miRcode version 11 (http://www.mircode.org) and the starBase version 2.0 (http://starbase.sysu.edu.cn) database; regulatory pairs of miRNAs-target genes were collected from miRTarBase version 7.0 (http://mirtarbase.mbc.nctu.edu.tw). Gene-gene interaction pairs were obtained from the Biological General Repository for Interaction Datasets version 3.4 (http://thebiogrid.org), The Human Protein Reference Database version 9.0 (http://www.hprd.org) and the Database of Interacting Proteins version 2.5 (http://dip.doe-mbi.ucla.edu). All interaction or regulatory pairs among genes, miRNAs and lncRNAs were first collected from the related databases and then the DEGs, DE-lncRNAs and DE-miRNAs were matched with them to obtain their connections, an lncRNA-miRNA regulatory network and a miRNA-mRNA network. These two networks were integrated into a comprehensive ceRNA regulatory network to demonstrate the interactions of DE-miRNAs with DE-lncRNAs or DEGs. These networks were visualized using Cytoscape software version 3.6 (http://www.cytoscape.org/).
Feature genes in the miRNA-mRNA regulatory network
Topological structure analysis was conducted to identify the feature genes in the miRNA-mRNA regulatory network. There are four common topological properties, including degree, closeness, betweenness and PageRank, which may reflect the centrality. ‘Betweenness centrality’ (BC) is a classical measure that quantifies the importance of a vertex within a graph, because it is a ratio of the shortest paths between vertex pairs that pass through the vertex of interest (20). In a number of previous studies, gene signatures have been identified largely based on the BC (21,22); therefore, the present study used BC as the representative topological parameter to indicate node centrality. The BC of genes in the network was calculated using the following formula:
BC=∑s≠v≠tσst(V)σstWhere s, t and v indicate different nodes in the network, in which s and t are vertex pairs. σst is the shortest path numbers (the total number of the shortest paths) from node s to node t, and σst (v) is the path numbers passes node v from s to t. BC values ranged between 0 and 1, and values that were closer to 1 indicated a higher node (gene) degree in the network.
The number of DEGs in the miRNA-mRNA regulatory network is larger than that of the ceRNA network. Therefore, the feature genes were preliminarily selected in the miRNA-mRNA regulatory network to avoid missing important feature genes. The nodes in the miRNA-mRNA regulatory network with the top 100 BC values were preliminarily selected as the feature genes that may display significantly different expressions between PTC and normal tissue samples. Clustering analysis was conducted using these feature genes via clusterStab package version 3.4 in R.
SVM classifier construction for different samples
The selected feature genes were used to conduct recursive feature elimination (RFE) analysis (23), which could distinguish different samples in the training dataset. The feature genes were subsequently utilized to construct the SVM classifier. The robustness and portability of the classifier were determined using the GSE33630 and GSE60542 microarray data. The classification effect was evaluated using correct rate, sensitivity, specificity, positive predictive value, negative predictive value and area under receiver operating characteristic curve.
Function and pathway enrichment
Gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the integrated ceRNA regulatory network using the Database for Annotation, Visualization and Integrated Discovery version 6.8 (https://david.ncifcrf.gov). Fisher exact test was applied for the enrichment using the following formula:
p=∑i=0x-1(Mi)(N-MK-i)(NK)Where N is the total number of all human genes, M is the number of genes in enriched pathways and K is number of DEGs.
Results
Microarray data sets and preprocessing
A total of 506 samples comprising 447 PTC samples and 59 normal samples were obtained from TCGA, from which a total of 877 lncRNAs, 1,046 miRNAs and 1,8348 mRNAs were annotated from the sequencing data. The microarray data sets GSE33630 and GSE60542 were downloaded from the GEO database, which included 45 and 32 normal samples, as well as 49 and 33 PTC samples, respectively.
DE-lncRNAs, DEGs, and DE-miRNAs
Using thresholds of FDR <0.05 and |logFC| >1, 16 DE-lncRNAs were identified (Table I), as well as 917 DEGs and 30 DE-miRNAs. Clustering analysis of DE-lncRNAs, DE-miRNAs and DEGs suggested that tumor samples may be distinguished from normal samples based on the expression levels of DE-lncRNAs, DE-miRNAs and DEGs (Fig. 1).
Table I.Differentially expressed lncRNAs between papillary thyroid carcinomas samples and normal tissue samples. |
lncRNA-miRNA, miRNA-mRNA and gene-gene regulatory networks
A total of 246 lncRNA-miRNA regulatory pairs were screened from miRcode and starBase, including 19 DE-miRNAs (Table II). The constructed miRNA-lncRNA regulatory network comprised 8 DE-lncRNAs and 7 DE-miRNAs (Fig. 2). A total of 13,159 target genes of the 30 DE-miRNAs were obtained from miRTarBase database; 406 DEGs were among these target genes. The miRNA-mRNA regulatory pairs were integrated with gene-gene interaction pairs to construct a comprehensive miRNA-mRNA regulatory network (Fig. 3). The network comprised 353 nodes (miRNAs or genes) and 577 lines (interactions).
Feature genes extraction
Degree distribution of the nodes demonstrated that the constructed miRNA-regulatory network followed the characteristics of scale-free networks. A total of 100 feature genes were screened according to the BC values. The clustering analysis indicated that tumor samples may be distinguished from the normal samples based on the expression level of the top 100 feature genes ranked by BC value (Fig. 4).
SVM classifier
A total of 15 feature genes with high predictive precision (>95%) were obtained following refinement by the RFE algorithm, as demonstrated in Fig. 5A, the accuracy was >95% when there were 15 feature genes. Basic information on these 15 genes is provided in Table III. The SVM classifier constructed by the 15 genes demonstrated an accuracy of 96.05% (486/506; Fig. 5B).
In the validation test, the SVM classifier was able to correctly distinguish 44 out of 45 normal samples in the GSE33630 data set, and 47 out of 49 PTC samples, with an accuracy of 96.81% (Fig. 6A). For the GSE60542 data set, SVM classifier could correctly distinguish all the 32 normal samples and 32 out of 33 PTC samples, with the accuracy of 98.46% (Fig. 6B). As demonstrated in Table IV, the SVM classifier demonstrated a satisfied classifying characteristics (Table IV).
Table IV.Classifying characteristics of the support vector machine classifier on all downloaded data sets. |
Function and pathway enrichment
The ceRNA regulatory network comprised 365 nodes and 596 lines (Fig. 7). Genes in the ceRNA network were significantly enriched in 20 GO function terms, most of which related to neuron development and differentiation (Table V). The present study focused on the Biological Process (BP) ontologies in GO as the purpose of this current study is the RNA regulatory network of PTC. In addition, genes in the ceRNA regulatory network were significantly enriched in four KEGG pathways, including ‘neuroactive ligand-receptor interaction’, ‘metabolism of xenobiotics by cytochrome P450’, ‘drug metabolism’ and ‘cytokine-cytokine receptor interaction pathway’.
ceRNA regulatory network of selected feature genes
The ceRNA regulatory network of the selected feature genes was constructed (Fig. 8). The network provided valuable information for reveal potential RNA regulation mechanism in PTC. The upregulated expression of lncRNA HOX transcript antisense RNA (HOTAIR), surfactant associated 1, pseudogene (SFTA1P), urothelial cancer associated 1 (UCA1) or the downregulated expression of Prader-Willi region non-protein coding RNA 2 (PWRN2) and DIO3 opposite strand/antisense RNA (DIO3OS) result in the downregulation of miR-206 expression, which in turn may subsequently lead to the upregulated gene expression of kallikrein-related peptidase 10 (KLK10), dachshund family transcription factor 2 (DACH2), 5-hydroxytryptamine receptor 1D (HTR1D), zinc-finger CCHC-type containing 16 (ZCCHC16) and transient receptor potential cation channel subfamily C member 5 (TRPC5). These five genes were also revealed to be potentially significantly upregulated by the decreased expression level of miR-18b, which was induced by the upregulation of C16orf18 and the downregulation of MYCN opposite strand (MYCNOS), T cell leukemia/lymphoma 6 (TCL6) and DIO3OS.
Discussion
Using the RNA-seq data downloaded from TCGA, DE-miRNA, DEGs and DE-lncRNAs in PTC samples were identified. The identified factors were subjected to known interaction databases to obtain the lncRNA-miRNA and the miRNA-mRNA regulatory networks, as well as a comprehensive ceRNA network. By conducting BC value calculation and RFE algorithm, 15 feature genes were screened. The SVM classifier constructed by these 15 feature genes revealed an accuracy of 96.05% (486/506) for the PTC samples downloaded from TCGA, and high accuracy (96.81 and 98.46%) for the PTC samples downloaded from GEO. The lncRNA-miRNA-mRNA interaction of HOTAIR-miR-206-KLK10 (or HOTAIR-miR-206-DACH2, -HTR1D, -ZCCHC16 or -TRPC5) is some of the interactions in the ceRNA regulatory network of the selected PTC feature genes; the five genes aforementioned may also be significantly upregulated by a decrease in the expression level of miR-18b. It has been reported that miR-18b and miR-206 induce cell cycle arrest and inhibit estrogen-induced proliferation, with inhibition levels comparable to those achieved by estrogen receptor (ER)α small interfering RNA (8). Therefore, it may be concluded that miR-18b and miR-206 may serve a similar regulatory role for their target genes. In addition, genes in the comprehensive ceRNA regulatory network were mainly enriched in neuronal development and differentiation related functions, and in four pathways.
To date, the functions and roles of lncRNAs in thyroid remain to be further explored. HOTAIR is a tumor-related lncRNA, and its overexpression is believed to be associated with poor prognosis and increased invasiveness in cancer (24). The expression status of HOTAIR may be used to predict metastasis and mortality in patients with breast tumors and hepatocellular carcinoma (25,26). However, the connections between HOTAIR and PTC have not been reported previously. The inhibition of HOTAIR expression may induce the downregulation of neuronal growth-related genes (27); notably, feature genes in the comprehensive ceRNA regulatory network were related to neuron development functions. Therefore, the present study hypothesized that HOTAIR may be involved in the development of PTC by interrupting neuronal growth.
Alterations in miRNA expression levels have been identified in thyroid tumors, which indicated their involvement in thyroid carcinoma. For example, the expression of miR-146b-5p was previously revealed to be upregulated, and miR-335 expression downregulated in PTC samples (28). There are many reports about the roles of miRNA in the diagnosis and prognosis of PTC (29–31). miR-206 is a member of the skeletal muscle specific miRNA (myomiR) family, which exert important roles in the pathogenesis of various types of cancers (32). miR-206 was reported to be significantly deregulated in PTC tissues (33). miR-1 is another member in the myomiR family, and was revealed to be downregulated in aggressive PTC and associated with thyroid cell growth and migration (34). miR-1 functions as a tumor suppressor in thyroid carcinoma, targeting C-X-C motif chemokine receptor 4, cyclin D2 and stromal cell-derived factor 1α (35). Therefore, the present study hypothesized that miR-206 may serve similar roles with miR-1 in PTC.
KLK10 serves an important role in the tumor microenvironment, invasion and angiogenesis (36). KLK10 regulates normal cell growth and probably functions as a tumor suppressor (37,38). Expression of KLK10 is increased in PTC samples, and there is a specific hypomethylation of KLK10 in BRAF-mutated PTC (39).
In conclusion, ceRNA regulatory network is a good way to identify the signature RNAs for cancers. HOTAIR, miR-206, and KLK10 are potential gene markers for targeting therapy and diagnosis of PTC.
Acknowledgements
The authors appreciate the valuable suggestions from other members of their laboratories.
Funding
This study was supported by grant from the Natural Science Foundation of Shandong (grant nos. ZR2010HL018 and 2012YD18068).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
WZ conceived and designed the study. SC performed the bioinformatics analyses and was a major contributor in writing the manuscript. XF assisted in bioinformatics analysis. HG and LZ selected and downloaded the datasets. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
PDQ Cancer Information Summaries: Health Professional VersionPDQ Cancer Information Summaries. Bethesda (MD): 2002 | |
Sosa JA and Udelsman R: Papillary thyroid cancer. Surg Oncol Clin N Am. 15:585–601. 2006. View Article : Google Scholar : PubMed/NCBI | |
Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini F, Randolph GW, Sawka AM, Schlumberger M, et al: 2015 American Thyroid Association Management Guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: The American Thyroid Association Guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid. 26:1–133. 2016. View Article : Google Scholar : PubMed/NCBI | |
Xing M, Haugen BR and Schlumberger M: Progress in molecular-based management of differentiated thyroid cancer. Lancet. 381:1058–1069. 2013. View Article : Google Scholar : PubMed/NCBI | |
Song X, Wang B, Bromberg M, Hu Z, Konigsberg W and Garen A: Retroviral-mediated transmission of a mouse VL30 RNA to human melanoma cells promotes metastasis in an immunodeficient mouse model. Proc Natl Acad Sci USA. 99:6269–6273. 2002. View Article : Google Scholar : PubMed/NCBI | |
Yotsukura S, duVerle D, Hancock T, Natsume-Kitatani Y and Mamitsuka H: Computational recognition for long non-coding RNA (lncRNA): Software and databases. Brief Bioinform. 18:9–27. 2017. View Article : Google Scholar : PubMed/NCBI | |
Bartel DP: MicroRNAs: Target recognition and regulatory functions. Cell. 136:215–233. 2009. View Article : Google Scholar : PubMed/NCBI | |
Salmena L, Poliseno L, Tay Y, Kats L and Pandolfi PP: A ceRNA hypothesis: The Rosetta Stone of a hidden RNA language? Cell. 146:353–358. 2011. View Article : Google Scholar : PubMed/NCBI | |
Jeggari A, Marks DS and Larsson E: miRcode: A map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics. 28:2062–2063. 2012. View Article : Google Scholar : PubMed/NCBI | |
Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, Maier L, Mackowiak SD, Gregersen LH, Munschauer M, et al: Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 495:333–338. 2013. View Article : Google Scholar : PubMed/NCBI | |
Hansen TB, Jensen TI, Clausen BH, Bramsen JB, Finsen B, Damgaard CK and Kjems J: Natural RNA circles function as efficient microRNA sponges. Nature. 495:384–388. 2013. View Article : Google Scholar : PubMed/NCBI | |
Karreth FA, Tay Y, Perna D, Ala U, Tan SM, Rust AG, DeNicola G, Webster KA, Weiss D, Perez-Mancera PA, et al: In vivo identification of tumor-suppressive PTEN ceRNAs in an oncogenic BRAF-induced mouse model of melanoma. Cell. 147:382–395. 2011. View Article : Google Scholar : PubMed/NCBI | |
Wang J, Liu X, Wu H, Ni P, Gu Z, Qiao Y, Chen N, Sun F and Fan Q: CREB up-regulates long non-coding RNA, HULC expression through interaction with microRNA-372 in liver cancer. Nucleic Acids Res. 38:5366–5383. 2010. View Article : Google Scholar : PubMed/NCBI | |
Wang H, Niu L, Jiang S, Zhai J, Wang P, Kong F and Jin X: Comprehensive analysis of aberrantly expressed profiles of lncRNAs and miRNAs with associated ceRNA network in muscle-invasive bladder cancer. Oncotarget. 7:86174–86185. 2016. 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 | |
Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D and Altman RB: Missing value estimation methods for DNA microarrays. Bioinformatics. 17:520–525. 2001. View Article : Google Scholar : PubMed/NCBI | |
Rao Y, Lee Y, Jarjoura D, Ruppert AS, Liu CG, Hsu JC and Hagan JP: A comparison of normalization techniques for microRNA microarray data. Stat Appl Genet Mol Biol. 7:Article222008. View Article : Google Scholar : PubMed/NCBI | |
Robinson MD, McCarthy DJ and Smyth GK: edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 26:139–140. 2010. View Article : Google Scholar : PubMed/NCBI | |
Li D and Dye TD: Power and stability properties of resampling-based multiple testing procedures with applications to gene oncology studies. Comput Math Methods Med. 2013:6102972013. View Article : Google Scholar : PubMed/NCBI | |
Kivimäki I, Lebichot B, Saramäki J and Saerens M: Two betweenness centrality measures based on Randomized Shortest Paths. Sci Rep. 6:196682016. View Article : Google Scholar : PubMed/NCBI | |
Breitkreutz D, Hlatky L, Rietman E and Tuszynski JA: Molecular signaling network complexity is correlated with cancer patient survivability. Proc Natl Acad Sci USA. 109:9209–9212. 2012. View Article : Google Scholar : PubMed/NCBI | |
van den Heuvel MP and Sporns O: Network hubs in the human brain. Trends Cogn Sci. 17:683–696. 2013. View Article : Google Scholar : PubMed/NCBI | |
Baur B and Bozdag S: A feature selection algorithm to compute gene centric methylation from probe level methylation data. PLoS One. 11:e01489772016. View Article : Google Scholar : PubMed/NCBI | |
Berrondo C, Flax J, Kucherov V, Siebert A, Osinski T, Rosenberg A, Fucile C, Richheimer S and Beckham CJ: Expression of the long non-coding RNA HOTAIR correlates with disease progression in bladder cancer and is contained in bladder cancer patient urinary exosomes. PLoS One. 11:e01472362016. View Article : Google Scholar : PubMed/NCBI | |
Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, Tsai MC, Hung T, Argani P, Rinn JL, et al: Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 464:1071–1076. 2010. View Article : Google Scholar : PubMed/NCBI | |
Geng YJ, Xie SL, Li Q, Ma J and Wang GY: Large intervening non-coding RNA HOTAIR is associated with hepatocellular carcinoma progression. J Int Med Res. 39:2119–2128. 2011. View Article : Google Scholar : PubMed/NCBI | |
Ono H, Motoi N, Nagano H, Miyauchi E, Ushijima M, Matsuura M, Okumura S, Nishio M, Hirose T, Inase N, et al: Long noncoding RNA HOTAIR is relevant to cellular proliferation, invasiveness, and clinical relapse in small-cell lung cancer. Cancer Med. 3:632–642. 2014. View Article : Google Scholar : PubMed/NCBI | |
Zhang J, Liu Y, Liu Z, Wang XM, Yin DT, Zheng LL, Zhang DY and Lu XB: Differential expression profiling and functional analysis of microRNAs through stage I–III papillary thyroid carcinoma. Int J Med Sci. 10:585–592. 2013. View Article : Google Scholar : PubMed/NCBI | |
Suresh R, Sethi S, Ali S, Giorgadze T and Sarkar FH: Differential expression of MicroRNAs in papillary thyroid carcinoma and their role in racial disparity. J Cancer Sci Ther. 7:145–154. 2015.PubMed/NCBI | |
Chen YT, Kitabayashi N, Zhou XK, Fahey TJ III and Scognamiglio T: MicroRNA analysis as a potential diagnostic tool for papillary thyroid carcinoma. Mod Pathol. 21:1139–1146. 2008. View Article : Google Scholar : PubMed/NCBI | |
Nikiforova MN, Tseng GC, Steward D, Diorio D and Nikiforov YE: MicroRNA expression profiling of thyroid tumors: Biological significance and diagnostic utility. J Clin Endocrinol Metab. 93:1600–1608. 2008. View Article : Google Scholar : PubMed/NCBI | |
McCarthy JJ: MicroRNA-206: The skeletal muscle-specific myomiR. Biochim Biophys Acta. 1779:682–691. 2008. View Article : Google Scholar : PubMed/NCBI | |
Liu X, He M, Hou Y, Liang B, Zhao L, Ma S, Yu Y and Liu X: Expression profiles of microRNAs and their target genes in papillary thyroid carcinoma. Oncol Rep. 29:1415–1420. 2013. View Article : Google Scholar : PubMed/NCBI | |
Yip L, Kelly L, Shuai Y, Armstrong MJ, Nikiforov YE, Carty SE and Nikiforova MN: MicroRNA signature distinguishes the degree of aggressiveness of papillary thyroid carcinoma. Ann Surg Oncol. 18:2035–2041. 2011. View Article : Google Scholar : PubMed/NCBI | |
Leone V, D'Angelo D, Rubio I, de Freitas PM, Federico A, Colamaio M, Pallante P, Medeiros-Neto G and Fusco A: MiR-1 is a tumor suppressor in thyroid carcinogenesis targeting CCND2, CXCR4, and SDF-1alpha. J Clin Endocrinol Metab. 96:E1388–E1398. 2011. View Article : Google Scholar : PubMed/NCBI | |
Borgoño CA, Michael IP and Diamandis EP: Human tissue kallikreins: Physiologic roles and applications in cancer. Mol Cancer Res. 2:257–280. 2004.PubMed/NCBI | |
Liu XL, Wazer DE, Watanabe K and Band V: Identification of a novel serine protease-like gene, the expression of which is down-regulated during breast cancer progression. Cancer Res. 56:3371–3379. 1996.PubMed/NCBI | |
Goyal J, Smith KM, Cowan JM, Wazer DE, Lee SW and Band V: The role for NES1 serine protease as a novel tumor suppressor. Cancer Res. 58:4782–4786. 1998.PubMed/NCBI | |
Mancikova V, Buj R, Castelblanco E, Inglada-Pérez L, Diez A, de Cubas AA, Curras-Freixes M, Maravall FX, Mauricio D, Matias-Guiu X, et al: DNA methylation profiling of well-differentiated thyroid cancer uncovers markers of recurrence free survival. Int J Cancer. 135:598–610. 2014. View Article : Google Scholar : PubMed/NCBI |