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Quantitative Proteomics Analysis Reveals Novel Insights into Mechanisms of Action of Long Noncoding RNA Hox Transcript Antisense Intergenic RNA (HOTAIR) in HeLa Cells*

2015; Elsevier BV; Volume: 14; Issue: 6 Linguagem: Inglês

10.1074/mcp.m114.043984

1535-9484

Peng Zheng, Qian Xiong, Ying Wu, Ying Chen, Zhuo Chen, Joy Fleming, Ding Gao, Lijun Bi, Feng Ge,

RNA Research and Splicing

Long noncoding RNAs (lncRNAs), which have emerged in recent years as a new and crucial layer of gene regulators, regulate various biological processes such as carcinogenesis and metastasis. HOTAIR (Hox transcript antisense intergenic RNA), a lncRNA overexpressed in most human cancers, has been shown to be an oncogenic lncRNA. Here, we explored the role of HOTAIR in HeLa cells and searched for proteins regulated by HOTAIR. To understand the mechanism of action of HOTAIR from a systems perspective, we employed a quantitative proteomic strategy to systematically identify potential targets of HOTAIR. The expression of 170 proteins was significantly dys-regulated after inhibition of HOTAIR, implying that they could be potential targets of HOTAIR. Analysis of this data at the systems level revealed major changes in proteins involved in diverse cellular components, including the cytoskeleton and the respiratory chain. Further functional studies on vimentin (VIM), a key protein involved in the cytoskeleton, revealed that HOTAIR exerts its effects on migration and invasion of HeLa cells, at least in part, through the regulation of VIM expression. Inhibition of HOTAIR leads to mitochondrial dysfunction and ultrastructural alterations, suggesting a novel role of HOTAIR in maintaining mitochondrial function in cancer cells. Our results provide novel insights into the mechanisms underlying the function of HOTAIR in cancer cells. We expect that the methods used in this study will become an integral part of functional studies of lncRNAs. Long noncoding RNAs (lncRNAs), which have emerged in recent years as a new and crucial layer of gene regulators, regulate various biological processes such as carcinogenesis and metastasis. HOTAIR (Hox transcript antisense intergenic RNA), a lncRNA overexpressed in most human cancers, has been shown to be an oncogenic lncRNA. Here, we explored the role of HOTAIR in HeLa cells and searched for proteins regulated by HOTAIR. To understand the mechanism of action of HOTAIR from a systems perspective, we employed a quantitative proteomic strategy to systematically identify potential targets of HOTAIR. The expression of 170 proteins was significantly dys-regulated after inhibition of HOTAIR, implying that they could be potential targets of HOTAIR. Analysis of this data at the systems level revealed major changes in proteins involved in diverse cellular components, including the cytoskeleton and the respiratory chain. Further functional studies on vimentin (VIM), a key protein involved in the cytoskeleton, revealed that HOTAIR exerts its effects on migration and invasion of HeLa cells, at least in part, through the regulation of VIM expression. Inhibition of HOTAIR leads to mitochondrial dysfunction and ultrastructural alterations, suggesting a novel role of HOTAIR in maintaining mitochondrial function in cancer cells. Our results provide novel insights into the mechanisms underlying the function of HOTAIR in cancer cells. We expect that the methods used in this study will become an integral part of functional studies of lncRNAs. Annotation of the human genome has revealed that, although less than 2% of the genome sequence encodes proteins (1Esteller M. Non-coding RNAs in human disease.Nat. Rev. Genet. 2011; 12: 861-874Crossref PubMed Scopus (3543) Google Scholar), at least 90% is actively transcribed into noncoding RNAs (ncRNAs) 1The abbreviations used are:ncRNAnoncoding RNAlncRNAlong noncoding RNAmiRNAmicroRNAHOTAIRhoxtranscript antisense intergenicRNAntnucleotideVIMvimentinPPIprotein–protein interactionMRMmultiple reaction monitoring. 1The abbreviations used are:ncRNAnoncoding RNAlncRNAlong noncoding RNAmiRNAmicroRNAHOTAIRhoxtranscript antisense intergenicRNAntnucleotideVIMvimentinPPIprotein–protein interactionMRMmultiple reaction monitoring.. NcRNAs, once thought to be the "dark matter" of the genome, have attracted widespread attention and are implicated in the regulation of many major biological processes impacting development, differentiation, and metabolism (2Kugel J.F. Goodrich J.A. Non-coding RNAs: key regulators of mammalian transcription.Trends Biochem. Sci. 2012; 37: 144-151Abstract Full Text Full Text PDF PubMed Scopus (98) Google Scholar). 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Long non-coding RNAs and cancer: a new frontier of translational research?.Oncogene. 2012; 31: 4577-4587Crossref PubMed Scopus (878) Google Scholar, 18Gibb E.A. Vucic E.A. Enfield K.S. Stewart G.L. Lonergan K.M. Kennett J.Y. Becker-Santos D.D. MacAulay C.E. Lam S. Brown C.J. Lam W.L. Human cancer long non-coding RNA transcriptomes.PloS One. 2011; 6: e25915Crossref PubMed Scopus (324) Google Scholar). Identification of cancer-associated lncRNAs and their interplay with target genes are now important areas of research in cancer biology; lncRNAs may be one of the missing pieces in the oncogene network puzzle. noncoding RNA long noncoding RNA microRNA hoxtranscript antisense intergenicRNA nucleotide vimentin protein–protein interaction multiple reaction monitoring. noncoding RNA long noncoding RNA microRNA hoxtranscript antisense intergenicRNA nucleotide vimentin protein–protein interaction multiple reaction monitoring. HOTAIR (Hox transcript antisense intergenic RNA), which has a length of 2158 nt and is located within the Homeobox C (HOXC) gene cluster on chromosome 12, is one of the few well-studied lncRNAs (19Zhang J. Zhang P. Wang L. Piao H.L. Ma L. Long non-coding RNA HOTAIR in carcinogenesis and metastasis.Acta Bioch. Bioph. Sin. 2014; 46: 1-5Crossref PubMed Scopus (142) Google Scholar, 20Wan Y. Chang H.Y. HOTAIR: Flight of noncoding RNAs in cancer metastasis.Cell Cycle. 2010; 9: 3391-3392Crossref PubMed Scopus (66) Google Scholar). It is unique in that it is overexpressed in the vast majority of cancer types analyzed so far and has been recognized as an oncogenic lncRNA (19Zhang J. Zhang P. Wang L. Piao H.L. Ma L. Long non-coding RNA HOTAIR in carcinogenesis and metastasis.Acta Bioch. Bioph. Sin. 2014; 46: 1-5Crossref PubMed Scopus (142) Google Scholar). Recently, HOTAIR has been shown to induce proliferation and metastasis in a variety of tumors and is a negative prognostic indicator for several cancers (19Zhang J. Zhang P. Wang L. Piao H.L. Ma L. Long non-coding RNA HOTAIR in carcinogenesis and metastasis.Acta Bioch. Bioph. Sin. 2014; 46: 1-5Crossref PubMed Scopus (142) Google Scholar, 20Wan Y. Chang H.Y. HOTAIR: Flight of noncoding RNAs in cancer metastasis.Cell Cycle. 2010; 9: 3391-3392Crossref PubMed Scopus (66) Google Scholar). Work pioneered by Howard Chang and colleagues uncovered a possible mechanism for HOTAIR in cancer (20Wan Y. Chang H.Y. HOTAIR: Flight of noncoding RNAs in cancer metastasis.Cell Cycle. 2010; 9: 3391-3392Crossref PubMed Scopus (66) Google Scholar, 21Li L. Liu B. Wapinski O.L. Tsai M.C. Qu K. Zhang J. Carlson J.C. Lin M. Fang F. Gupta R.A. Helms J.A. Chang H.Y. 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Targeted disruption of Hotair leads to homeotic transformation and gene derepression.Cell Rep. 2013; 5: 3-12Abstract Full Text Full Text PDF PubMed Scopus (254) Google Scholar, 22Gupta R.A. Shah N. Wang K.C. Kim J. Horlings H.M. Wong D.J. Tsai M.C. Hung T. Argani P. Rinn J.L. Wang Y. Brzoska P. Kong B. Li R. West R.B. van de Vijver M.J. Sukumar S. Chang H.Y. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis.Nature. 2010; 464: 1071-1076Crossref PubMed Scopus (4166) Google Scholar). Subsequent studies demonstrated that HOTAIR serves as a molecular scaffold for at least two distinct histone modification complexes, coordinating their functions in transcription repression (12Tsai M.C. Manor O. Wan Y. Mosammaparast N. Wang J.K. Lan F. Shi Y. Segal E. Chang H.Y. Long noncoding RNA as modular scaffold of histone modification complexes.Science. 2010; 329: 689-693Crossref PubMed Scopus (2593) Google Scholar). Several transcriptome-wide studies have detected extensive changes in cellular transcript levels in response to inhibition of HOTAIR, indicating that HOTAIR can regulate hundreds of genes (22Gupta R.A. Shah N. Wang K.C. Kim J. Horlings H.M. Wong D.J. Tsai M.C. Hung T. Argani P. Rinn J.L. Wang Y. Brzoska P. Kong B. Li R. West R.B. van de Vijver M.J. Sukumar S. Chang H.Y. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis.Nature. 2010; 464: 1071-1076Crossref PubMed Scopus (4166) Google Scholar, 23Kim K. Jutooru I. Chadalapaka G. Johnson G. Frank J. Burghardt R. Kim S. Safe S. HOTAIR is a negative prognostic factor and exhibits pro-oncogenic activity in pancreatic cancer.Oncogene. 2012; 32: 1616-1625Crossref PubMed Scopus (689) Google Scholar), providing insight into mechanisms underlying the function of HOTAIR in cancer cells. Although informative, transcript abundances do not necessarily reflect cellular protein levels because protein activity can be influenced by an array of post-transcriptional regulatory mechanisms and the correlation between protein and mRNA levels is generally modest (24Schwanhausser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4058) Google Scholar, 25Wu L. Candille S.I. Choi Y. Xie D. Jiang L. Li-Pook-Than J. Tang H. Snyder M. Variation and genetic control of protein abundance in humans.Nature. 2013; 499: 79-82Crossref PubMed Scopus (270) Google Scholar). It is therefore necessary to analyze cellular protein levels after inhibition of HOTAIR at the proteomics level. In a previous study, we successfully employed a quantitative proteomic approach using SILAC (stable isotope labeling by amino acids in cell culture) methodology to identify targets of miR-21 in cancer cells (26Xiong Q. Zhong Q. Zhang J. Yang M. Li C. Zheng P. Bi L.J. Ge F. Identification of novel miR-21 target proteins in multiple myeloma cells by quantitative proteomics.J. Proteome Res. 2012; 11: 2078-2090Crossref PubMed Scopus (63) Google Scholar). Here, we carried out global proteomic profiling to identify genes regulated by HOTAIR in HeLa cells. Using SILAC-based quantitative proteomics, we found that the expression of 170 proteins was dysregulated by inhibition of HOTAIR. Many interesting differentially-expressed proteins that potentially play functional roles during HOTAIR inhibition were identified. Analysis of this data at the systems level revealed major changes in proteins involved in diverse cellular components, in particular in the cytoskeleton and respiratory chain. Further functional studies revealed that vimentin (VIM), a key protein involved in the cytoskeleton, contributes to various phenotypic effects observed after inhibition of HOTAIR. By correlating our proteomic data with that from functional studies, novel insights into the mechanism underlying the function of HOTAIR in cancer cells emerge. Human cervical cancer cell line HeLa, Chang Liver, human macrophage-like cell line U937, multiple myeloma cell line U266, lung cancer cell line H1299, hepatocellular carcinoma line HepG2 and gastric cancer cell line SGC7901 were purchased from American Type Culture Collection (Manassas, VA). HeLa, Chang Liver, U937, U266, H1299, HepG2 and SGC7901 were grown in DMEM (Hyclone, Logan, UT) containing 10% fetal bovine serum (FBS) (Gibco, Gaithersburg, MD), 2 mm glutamine, 50 U/ml penicillin and 50 mg/ml streptomycin at 37 °C in a humidified atmosphere with 5% CO2. For transient transfection, HeLa cells were transfected with 10 nm of siRNA targeting HOTAIR (siHOTAIR-I or siHOTAIR-II) or negative control siRNA (siNC) using Lipofectamine RNAiMAX (Invitrogen, Gaithersburg, MD). RNA expression of HOTAIR was verified at 48 h after transfection by qRT-PCR as described below. For VIM gene knockdown, HeLa cells were transfected with 10 nm of siRNA targeting VIM (siVIM-I, siVIM-II or siVIM-III) and a negative control siRNA (siNC). Cells were harvested 48 h after transfection and VIM gene knockdown was assessed by Western blotting. All the siRNAs were purchased from GenePharma Co. Ltd. (Shanghai, China). siRNA sequences are listed in supplemental Table S1. A human VIM expression plasmid (pVIM) (Catalogue NO.: EX-D0114-M13) and control plasmid (pEGFP) were purchased from GeneCopoeia, Inc. (Rockville, MD). The VIM and control plasmids were expressed in HeLa cells by transient transfection using Lipofectamine 2000 Reagent (Invitrogen), according to the manufacturer's protocol. Cells were collected 48 h after transfection and overexpression of VIM was confirmed by Western blotting. For stable transfection, HeLa cells were transfected with a plasmid expressing small interfering RNA molecules targeting HOTAIR. A pGPU6/GFP/Neo siRNA Expression Vector kit was purchased from GenePharma Co., Ltd. The RNAi sequences used were listed in supplemental Table S1 and a scrambled sequence (GTTCTCCGAACGTGTC ACGT) which has no significant homology to human gene sequences was used as a control. HeLa cells were transfected with pGPU6/GFP/Neo/HOTAIR or pGPU6/GFP/Neo/control using Lipofectamine 2000 Reagent (Invitrogen) and were then selected for neomycin resistance for 3 weeks, at which point one clone was selected from the pGPU6/GFP/Neo/control or pGPU6/GFP/Neo/HOTAIR transfected cells. Cells selected from pGPU6/GFP/Neo/control transfected HeLa cells were designated as HeLa-NC cells, and those from the pGPU6/GFP/Neo/HOTAIR transfected HeLa cells as HeLa-KD cells. The expression level of HOTAIR was determined by qRT-PCR. Total RNA was extracted from cultured cells using Trizol reagent (Invitrogen) according to the manufacturer's protocol. RNA was reverse transcribed into first strand cDNA using a RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, MA), and quantitative PCR was carried out using SYBR Green PCR Master Mix (Roche Diagnostics Ltd, Mannheim, Germany) and a LightCycler 480 Real-Time PCR system (Roche). The GAPDH gene was used as an endogenous control gene for normalizing the expression of target genes. Each sample was analyzed in triplicate. The thermo cycling program consisted of holding at 95 °C for 5 min, followed by 40 cycles of 10 s at 95 °C, 30 s at 60 °C and 30 s at 72 °C. Melting-curve data were then collected to verify PCR specificity and the absence of primer dimers. Primer sequences are listed in supplemental Table S1. Transiently or stably transfected HeLa cells (1 × 106) were harvested 48 h after transfection, washed three times in phosphate-buffered saline (PBS), and then stained with Annexin V-FITC and PI according to the manufacturer's instructions (Beyotime, Haimen, China). Samples were acquired on a FACScan flow cytometer (Becton Dickinson, San Jose, CA) and analyzed with the BD FACSDiva software 6.0 (Becton Dickinson). Transiently or stably transfected HeLa cells (1 × 106) were harvested, washed three times with ice-cold PBS and fixed with 70% ethanol overnight at 4 °C. Cells were stained with PI (Beyotime), and a cell cycle profile was determined using a BD FACSAria III Cell Sorting System (Becton Dickinson). Ten thousand events were acquired for each sample, and cell cycle distributions were determined using ModFit LT software (Becton Dickinson). Experiments were performed in triplicate. Results are presented as the percentage of cells in a particular phase. A Cell Counting Kit-8 (Boster, Wuhan, China) was used to determine cell proliferation. Briefly, transiently or stably transfected HeLa cells (1 × 103) were plated in triplicate in 96-well plates. 10 μl cell proliferation reagents were added to each well and cells were incubated for 2 h at 37 °C. Cell numbers were estimated by measuring the optical density (OD) at 450 nm. Absorbance of cell-free wells containing medium was set as zero. Transiently or stably transfected HeLa cells were seeded into a six-well plate and allowed to grow to 70% confluence in complete medium. Cell monolayers were wounded using a plastic tip (1 mm) that touched the plate. Cells were then washed with PBS to remove debris, transfected and incubated for 24 h. The number of cells migrating to the wound surface and the average distance cells migrated was determined under an inverted microscope at designated time points. Transwell chambers (Corning, 8.0 μm pore size) coated with Matrigel (BD Biosciences, Bedford, MA) were used to measure the invasiveness of cancer cells. In brief, transiently or stably transfected HeLa cells (2 × 105) were plated in the upper chamber in serum-free media. The bottom chamber was covered with media containing 10% FBS. After incubating for 48 h, cells that migrated to the bottom of the chamber insert was fixed in methanol for 15 min and stained with Giemsa stain. Invading cells were photographed and counted on the stained membrane under a microscope. Each membrane was divided into four quadrants and the average from four quadrants was calculated. HeLa cells were grown in SILAC DMEM Medium (Pierce Biotechnology, Rockford, IL) containing 10% FBS, and either the 13C6-l-lysine (heavy) or 12C6-l-lysine (light) for more than seven generations before harvesting, to ensure high labeling efficiency (>99%). Heavy-labeled cells were transfected with 10 nm siHOTAIR-I and light-labeled HeLa cells were transfected with 10 nm siNC. After incubating for 48 h, cells were washed three times with ice-cold PBS, transferred to clean 1.5 ml Eppendorf tubes and lysed with RIPA lysis buffer (50 mm Tris-HCl, 150 mm NaCl, 0.1% SDS, 1% Nonidet P-40, 0.5% sodium deoxycholate, 1 mm PMSF, 100 mm leupeptin, and 2 mg/ml aprotinin, pH 8.0) on ice for 15 min. Cellular debris was removed by centrifugation at 13,200 × g for 30 min at 4 °C. Protein concentration was measured using a BCA Protein Assay Kit (TIANGEN, Beijing, China). The "Light" and "Heavy" lysates were mixed in a 1:1 ratio based on protein weight (100 μg of each), boiled in SDS-PAGE sample buffer, separated by 12% SDS-PAGE and stained with Coomassie Brilliant Blue. The entire gel lane was cut into 30 sections for in-gel digestion. Excised sections were chopped into small pieces of 1 mm2, washed in deionized water and completely destained using 100 mm ammonium bicarbonate (NH4HCO3) in 50% acetonitrile (ACN). A reduction step was performed in 100 mm dl-Dithiothreitol (DTT) at 37 °C for 1 h. The proteins were alkylated in 50 mm iodoacetamide (IAA) at room temperature for 1 h in the dark. Gel sections were first washed in deionized water, then ACN, and finally dried in a SpeedVac system (Thermo Fisher Scientific). Digestion was carried out using 20 μg/ml sequencing grade modified trypsin (Promega, Madison, WI) in 50 mm NH4HCO3 at 4 °C for 45 min and then incubated at 37 °C overnight. The supernatants were transferred into a 200 μl microcentrifuge tube and the gels were extracted twice with extraction buffer (67% ACN containing 2.5% trifluoroacetic acid). The peptide extracted and the supernatant of the gel slices were combined and then dried in a SpeedVac. The dried peptides from each gel slice were reconstituted in 5% ACN/0.1% formic acid and analyzed using a maXis impact UHR-QTOF system (Bruker Daltonics, Bremen, Germanys) coupled to a Dionex Ultimate 3000 nano-flow HPLC (Dionex, Sunnyvale, CA). Peptide mixtures from each gel slice were first desalted online using a C18 PepMap trap column (300 μm i.d., 5 cm long, LC Packings), and eluted to a C18 PepMap analytical column (75 μm i.d., 15 cm long, LC Packings). Peptides were separated using a linear gradient from 10–50% of solvent B (98% ACN, 2% water and 0.1% formic acid) over 45 min at a flow rate of 300 nl/min at room temperature, where solvent A was water containing 2% ACN and 0.1% formic acid. Two biological replicates were analyzed. TOF-MS screening measurements were all performed on a predefined 50–2200 m/z acquisition window at 2500 TOF summations (∼2 Hz). CID MS/MS acquisition was performed over the same 50–2200 m/z window with three intensity-binned precursors of charge +2 to +4, and at least 1000 counts selected for fragmentation. Accumulation times for MS/MS were also intensity-binned from a maximum of 5000 summations (∼1 Hz, if precursor ≤ 1 × 103 ion counts) to a minimum of 2000 summations (∼2.5 Hz, if precursor ≥ 2 × 104 ion counts). To test the effects of increasing the accumulation time, additional experiments were also performed using either a minimal time (2500 summations, ∼2 Hz) or a maximal time (15,000 summations, ∼0.33 Hz). An optimized set of isolation windows was used based on the precursor m/z to achieve at least 90% precursor recovery prior to fragmentation. Selected precursors that had been analyzed >2 times were actively excluded from analysis for 15s. Ion transmission optimization for MS/MS was also performed on four key parameters for the collision cell and the ion cooler cell (RF guide voltages CCRF and ICRF, transfer time ICTT, and prepulse time ICPP). Raw files were processed using LC/MS software DataAnalysis 4.0 SP4 (Bruker Daltonics) and converted into XML files. The XML file of each MS/MS run was imported to Proteinscape v3.0 (Bruker Daltonics). Protein identification was performed by searching the MS/MS data on a local Mascot server v2.4 (Matrix Science, London, UK) against the IPI human 3.87 database (including 91,491 entries). Search parameters used were as follows: enzyme specificity, trypsin/with no proline restriction; maximum missed cleavages, 2; carbamidomethyl (+57.0215 Da, Cys) as fixed modification; oxidation (+15.9949 Da, Met), Lys (+6.0201 Da, SILAC heavy amino acid) as variable modifications; precursor ion mass tolerance, 0.1 Da; and MS/MS mass tolerance, 0.1 Da. The false discovery rate (FDR) was set to 1% using Mascot Percolator (an algorithm that uses semi-supervised machine learning to improve the discrimination between correct and incorrect spectrum identifications). Detection of at least two matching peptides per protein was set as a requirement for unambiguous identification. In each independent technical replicate, the relative quantification of the proteins was performed by WARP-LC 3.0 (Bruker Daltonics) and Proteinscape v3.0 (Bruker Daltonics). The average peak area ratio of the "control"/"treated sample" was calculated for all the peptides by ProteinScape v3.0. Peptide ratios were normalized by dividing by the overall median of all peptides. When ratios for individual peptide matches were combined into ratios for protein hits, the Grubbs' method was used for detecting and removing outliers, and protein ratios were calculated as the geometric mean of the ratios of corresponding peptides. Protein ratios from replicate experiments were averaged. In all subsequent data analyses, we used only those proteins that were identified in both of the two independent experiments. Protein ratios were log2 transformed and the frequency distribution of the quantified proteins was calculated to determine differentially expressed proteins. Classification of HOTAIR-regulated proteins was performed using PANTHER (Protein Analysis Through Evolutionary Relationships) (http://www.pantherdb.org), which classifies genes and proteins by their functions (27Mi H. Dong Q. Muruganujan A. Gaudet P. Lewis S. Thomas P.D. PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium.Nucleic Acids Res. 2010; 38: D204-D210Crossref PubMed Scopus (466) Google Scholar). GO classification of the differentially expressed proteins were also performed using DAVID Bioinformatics Resources 6.7 (28Huang D.W. Sherman B.T. Lempicki R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Nat. Protoc. 2009; 4: 44-57Crossref PubMed Scopus (25338) Google Scholar, 29Huang D.W. Sherman B.T. Lempicki R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.Nucleic Acids Res. 2009; 37: 1-13Crossref PubMed Scopus (10266) Google Scholar). The protein–protein interaction (PPI) network of HOTAIR-regulated proteins was built by searching against the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database version 9.1(30Franceschini A. Szklarczyk D. Frankild S. Kuhn M. Simonovic M. Roth A. Lin J. Minguez P. Bork P. von Mering C. Jensen L.J. STRING v9.1: protein-protein interaction networks, with increased coverage and integration.Nucleic Acids Res. 2013;