OLOncology Letters1792-10741792-1082D.A. Spandidos10.3892/ol.2016.4972OL-0-0-4972ArticlesRNA-seq analysis identifies key long non-coding RNAs connected to the pathogenesis of alcohol-associated head and neck squamous cell carcinomaYuVicky1*SinghPranav1*RahimyElham1ZhengHao1KuoSelena Z.1KimElizabeth1Wang-RodriguezJessica2OngkekoWeg M.1Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of California San Diego, San Diego, CA 92161, USADepartment of Pathology, Veterans Administration Medical Center, University of California San Diego, San Diego, CA 92161, USACorrespondence to: Dr Weg M. Ongkeko, Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of California San Diego, 9500 Gillman Drive, La Jolla, San Diego, CA 92161, USA, E-mail: rongkeko@ucsd.edu
Alcohol consumption has been implicated in the pathogenesis of head and neck squamous cell carcinoma (HNSCC), although its mechanism is poorly understood. Recent advances in the identification and understanding of long non-coding RNAs (lncRNAs) have indicated that these molecules have a profound effect on numerous biological processes, including tumorigenesis and oncogenesis. The present authors hypothesize that alcohol-mediated dysregulation of lncRNAs is a key event in HNSCC pathogenesis. An in silico differential expression analysis utilizing RNA sequencing (RNA-seq) data from 34 HNSCC patients, which included alcohol drinkers and non-alcohol drinkers, identified a panel of lncRNAs that were dysregulated due to alcohol consumption. Normal oral keratinocytes were then exposed to ethanol and acetaldehyde to validate the RNA-seq results. Two lncRNAs that were differentially expressed due to alcohol consumption were identified from RNA-seq analysis of the clinical data: lnc-PSD4-1 and lnc-NETO-1. Oral keratinocytes exposed to alcohol and acetaldehyde demonstrated dysregulation of these two lncRNAs, thus validating the results of RNA-seq analysis. In addition, low expression of the lnc-PSD4-1 isoform, lnc-PSD4-1:14, exhibited a strong correlation with high survival rates in a Cox proportional hazards regression model. Therefore, these lncRNAs may play a key role in the early pathogenesis of HNSCC, since they are dysregulated in both clinical data and in vitro experiments mimicking the effects of alcohol use.
alcoholacetaldehydeRNA-seqlong non-coding RNAshead and neck squamous cell carcinomaepigeneticsIntroduction
Alcohol and tobacco use are associated with ≥75% of all head and neck squamous cell carcinomas (HNSCCs) (1). In addition, alcohol has been reported to independently increase the risk of cancer (2). Despite compelling evidence suggests that alcohol plays a key role in the pathogenesis of HNSCC, its molecular mechanism remains poorly understood (2). Alcohol is speculated to increase the risk of cancer by impairing DNA-repair genes or folate metabolism (2). However, previous studies have suggested that it is not alcohol, but instead its metabolite acetaldehyde, the major contributor to HNSCC progression (2,3). Acetaldehyde is produced by alcohol dehydrogenases in the intestine, kidney, liver and oral cavity, and has been proposed to exert mutagenic effects, including DNA cross-linking, chromatid exchange, aneuploidy and other chromosomal abnormalities (3). The present study specifically focuses on the effect of alcohol exposure on HNSCC, and proposes that ethanol or its derivative acetaldehyde alters the expression of key long non-coding RNAs (lncRNAs) that may be critical in the pathogenesis of HNSCC.
The human genome sequence is composed primarily of non-coding RNAs (ncRNAs), with only 2% of RNAs coding for proteins (4,5). Previously, ncRNAs were considered to be transcriptional noise (4). However, it has been recently reported that ncRNAs are important in transcriptional and post-transcriptional processes (4). Recent studies have revealed that ncRNAs influence messenger RNA (mRNA) translation and chromatin modifications (5). Since previous studies have demonstrated that alcohol is able to regulate ncRNAs (6,7), it is possible that the role of ncRNAs as epigenetic regulators could account for the effects of alcohol in the pathogenesis of HNSCC.
The present study focused on lncRNAs, which are ncRNAs of >200 nucleotides in length. lncRNAs have been reported to play a critical role in cancer progression through the modification of transcription factors associated with regulation of oncogenes, tumor suppressor proteins, self-renewal and differentiation (8,9). lncRNAs regulate transcription factors by acting as chromatin modifiers and direct transcriptional regulators (8,10,11). This regulation has been demonstrated to occur either in cis (in close proximity to the transcribed lncRNA) or in trans (far from the transcription site) (12). Therefore, alcohol-dysregulated lncRNAs could play critical roles in the inhibition of tumor suppressors or the activation of oncogenes that are required for the malignant transformation of normal oral epithelial cells.
Using RNA sequencing (RNA-seq) technology, the present study identified a panel of lncRNAs that were differentially expressed between alcohol drinkers and non-alcohol drinkers among HNSCC patients. This panel was partially validated in vitro in normal oral keratinocytes treated with clinically relevant levels of ethanol and acetaldehyde.
Materials and methodsRNA-seq analysis
An in silico differential expression analysis was conducted utilizing publicly available RNA-seq libraries obtained from The Cancer Genome Atlas (TCGA; https://tcga-data.nci.nih.gov/tcga), which comprised 34 HNSCC patients, 17 of which were alcohol drinkers and 17 non-alcohol drinkers. Within each category of alcohol drinkers and non-alcohol drinkers, 12 patients were tobacco smokers and 5 were non-tobacco smokers (Table I). The libraries were generated by TCGA utilizing a Genome Analyzer IIx (Illumina Inc., San Diego, CA, USA), which resulted in paired-end RNA-seq libraries with insert sizes of 200 bp-5 kb. These RNA-seq files were next position-sorted, indexed and aligned to a human reference genome (hg19). The files were then annotated with a browser extensible data file containing 32,108 human lncRNA transcripts, which was downloaded from LNCipedia (http://www.lncipedia.org/) (13). The bedtools (http://bedtools.readthedocs.io/en/latest/) (14) utility coverageBed was then used to generate lncRNA read counts (integer values of expression levels) by calculating the number of alignments from each RNA-seq file that overlapped with each individual lncRNA provided by the annotation file from LNCipedia. A total of 13,338 lncRNAs displayed reads generated in the RNA-seq libraries.
These read counts were then utilized for lncRNA differential expression analysis, which compared alcohol drinkers vs. non-alcohol drinkers using the R/Bioconductor software package edgeR (version 3.4.2; http://www.bioconductor.org/packages). The read counts were normalized within edgeR based on the relative library sizes of each cohort. The differential expression analysis implemented in edgeR utilized an empirical Bayes estimation and exact tests based on the negative binomial distribution of the reads (15). From this comparison, a list of differentially expressed lncRNAs with false discovery rates <5% was compiled in HNSCC patients who were alcohol drinkers vs. those who were non-alcohol drinkers (Table II).
Cell culture
In vitro experiments were performed on OKF4 and OKF6, two noncancerous cell lines obtained from the laboratory of Dr James Rheinwald at Harvard Medical School (Harvard University, Boston, MA, USA). Normal oral keratinocytes from the floor of the mouth were used, since the hypothesis proposed by the present authors concerns the initial steps in the pathogenesis of alcohol-induced oropharyngeal cancer.
The oral keratinocytes were cultured in 1X Keratinocyte-serum-free medium (SFM) with L-glutamine (catalogue no. 17005-042), supplemented with 0.2 ng/ml human recombinant epidermal growth factor (EGF) type B (amino acids 1–53), 25 µg/ml bovine pituitary extract (BPE), 0.3 mM calcium chloride, 100 U/ml penicillin and 100 µg/ml streptomycin (all from Gibco; Thermo Fisher Scientific, Inc., Waltham, MA, USA), at 37°C and 5% CO2. Upon reaching 30% confluency, OKF4 and OKF6 cells were cultured with equal parts of supplemented Keratinocyte-SFM and DFK medium, which was prepared with equal parts of Dulbecco's modified Eagle's medium (catalogue no. 21068-028; Gibco; Thermo Fisher Scientific, Inc.) and Ham's F-12 nutrient mixture (catalogue no. 11765-054; Thermo Fisher Scientific, Inc.), and supplemented with 0.2 ng/ml EGF type B (amino acids 1–53), 25 µg/ml BPE, 2 mM L-glutamine, 100 U/ml penicillin and 100 µg/ml streptomycin.
Ethanol/acetaldehyde treatments
Two independent experiments regarding cell treatments were conducted in the present study, one for ethanol and one for acetaldehyde. For both, two biological replicates were performed. For the alcohol experiments, the two cell lines were treated with increasing dosages of 200 proof ethanol (0, 20, 50 and 170 µM). The concentrations of ethanol were selected based on their toxicity, and an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was performed to determine the levels of cell proliferation at varying ethanol concentrations (Fig. 1). To represent long-term alcohol use, cells were treated every 24 h with ethanol diluted in medium (equal parts Keratinocyte-SFM and DFK) for a period of 28 days. The ethanol and the medium were replaced daily. The ethanol-treated culture plates were covered with plastic paraffin film while incubating at 37°C to minimize ethanol evaporation.
The above treatment was repeated with 17.82 M acetaldehyde (catalogue no. SHBD3908V; Sigma-Aldrich, St. Louis, MO, USA), since this is the first metabolite of ingested alcohol in the human body (3). OKF4 and OKF6 cells were treated with acetaldehyde at concentrations of 0, 75, 150, 300 and 1,000 µM for 48 h, with acetaldehyde added every 4 h and the medium (equal parts Keratinocyte-SFM and DFK) replaced every 8 h. The appropriate concentrations of acetaldehyde were determined from previous studies (16). Due to the volatility, toxicity and short half-life of acetaldehyde, cells could not be treated for 28 days (as they had been for ethanol). The plates were also covered with plastic paraffin film to minimize evaporation of acetaldehyde while incubated at 37°C. Cell lines were passaged at 30–80% confluency prior to harvesting.
Upon completion of alcohol and acetaldehyde treatments, cells were harvested, and total cell lysates were collected. RNA was extracted using SurePrep RNA Isolation kit (Thermo Fisher Scientific, Inc.). Complementary DNA was synthesized according to the manufacturer's protocol, using LncProfiler qPCR Array kit (catalogue no. RA900A-1; System Biosciences, Mountain View, CA, USA). qPCR was performed using SYBR Green reagent (Applied Biosystems; Thermo Fisher Scientific, Inc.) in a StepOnePlus Real-time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.) for 2 min at 50°C, 95°C for 10 min, and 40 cycles of 95°C for 15 sec and 60°C for 1 min. Gene expression levels with gene-specific primers (Eurofins MWG Operon, Louisville, KY, USA; Table II) and error bars were calculated utilizing the 2−ΔΔCq method (17), with non-treated cells acting as a control for the ethanol and acetaldehyde treatments, and glyceraldehyde 3-phosphate dehydrogenase serving as a control for endogenous gene expression.
Survival data analysis
Utilizing the original 34 patient cohort and clinical information provided by the TCGA, the expression levels of key dysregulated lncRNAs were correlated with the patients' long-term survival. The expression levels of the lncRNAs were classified as either increased or decreased depending on whether they fell above or below the median value. A Cox proportional hazards regression model was then applied to determine both univariate and multivariate survival hazard ratios (HRs) for the lncRNAs, based on a decreased expression (Table III). A Kaplan-Meier survival curve was used to illustrate the correlation between lncRNA expression and survival.
Results and Discussion
The purpose of the present study was to identify key lncRNAs implicated in the pathogenesis of HNSCC, since lncRNAs are known to regulate transcription factors associated with regulation of oncogenes, tumor suppressor proteins, self-renewal and differentiation (8,9). Recent studies have demonstrated that dysregulated lncRNA expression could mark the progression of a disease (18). lncRNAs may also serve as an indicator of patient survival independent of other variables (19). In addition, lncRNAs have previously been implicated in HNSCC and other types of epithelial cancer (20). Therefore, lncRNAs may aid the understanding of the molecular basis of HNSCC, thus enabling advances in early detection and identification of novel therapeutic targets aimed to improve patient prognosis.
Determination of ethanol concentrations
MTT assays were performed in the present study to determine the ethanol concentrations required for cell treatments and to evaluate cell proliferation prior and subsequent to treatment. Concentrations of 0, 20, 50 and 170 µM ethanol were selected for the experimental assays (Fig. 1). However in cell culture, 170 µM ethanol exhibited high toxicity against normal oral keratinocytes, and therefore was not used in subsequent experiments.
Identification of alcohol-dysregulated lncRNAs from HNSCC patient samples
The expression patterns of 32,108 lncRNAs were examined in the present study, 13,338 of which were detected within the present cohort of 34 HNSCC patients. Of those 13,338 lncRNAs, 11 were differentially expressed between alcohol drinkers and non-alcohol drinkers, with several lncRNAs represented by multiple isoforms (Table II). Of the identified lncRNAs, 4 were upregulated and 7 were downregulated, with fold-changes ranging from ≥3.5 to 18.5 and false discovery rates <5%. The small panel of lncRNAs differentially expressed between alcohol drinkers and non-alcohol drinkers suggests that the mechanism by which alcohol contributes to the pathogenesis of HNSCC involves certain lncRNAs.
In vitro validation of lncRNAs differentially expressed in clinical samples
The lncRNAs identified by RNA-seq analysis in the present study were evaluated in vitro by measuring their relative expression levels in two normal keratinocyte cell lines, which were exposed to ethanol concentrations of 0, 20, 50 and 170 µM (Fig. 3) and acetaldehyde concentrations of 0, 75, 150, 300 and 1,000 µM (Fig. 4), via RT-qPCR. Of the 11 lncRNAs identified in the RNA-seq analysis, two were verified in vitro: lnc-PSD4-1 (including the isoform, lnc-PSD4-1:14) and lnc-NETO1-1, whose expression levels were increased in the treated samples, compared with the non-treated controls. These results suggest that the above lncRNAs may be important in the pathogenesis and progression of alcohol-associated HNSCC.
For the ethanol-treated OKF4 cell line, both PSD4-1 and NETO1-1 exhibited negligible changes in their expression levels when 20 µM ethanol was used, while a 2-fold increase in their expression levels was observed in 50 µM ethanol-treated cells. For OKF6 cells, 20 µM ethanol exposure resulted in a 5-fold increase in the expression levels of PSD4-1 and NETO1-1, while 50 µM ethanol exposure resulted in a 2-fold increase in their expression levels, compared with the control. The higher expression levels observed in the 20 µM ethanol-treated cells vs. the 50 µM ethanol-treated cells may be due to the 50 µM ethanol concentration being too toxic for OKF6 cells, in contrast to the OKF4 cell line, where 50 µM ethanol demonstrated the largest effect.
While the ethanol treatment used in the present study mimics the physiological levels of alcohol in the body, ethanol is less carcinogenic than its metabolic derivative acetaldehyde (3). Therefore, acetaldehyde treatment may be a more accurate in vitro model of the role of alcohol in HNSCC than ethanol treatment. In general, in acetaldehyde-treated samples, both PSD4-1 and NETO1-1 displayed higher expression levels, correlating with higher concentrations of acetaldehyde. In OKF4 cells treated with 1,000 µM acetaldehyde, NETO1-1 and PSD4-1 demonstrated a 12-fold and 16-fold increase in expression, respectively, compared with the control. In OKF6 cells treated with the same concentration of acetaldehyde, both PSD4-1 and NETO1-1 demonstrated a >100-fold increase in expression. In the present study, 1,000 µM was selected as the upper range of acetaldehyde concentration, since the treatment of the OKF4 and OKF6 cell lines was limited to only 48 h due to the acetaldehyde's volatility. This concentration is considered to be more representative of the actual exposure to alcohol experienced by patients with a long history of alcohol use.
Survival data
In addition to the aforementioned in vitro tests, long-term survival analysis correlating NETO1-1 and PSD4-1:14 expression levels and patient outcomes was conducted. NETO1-1 did not exhibit any significant correlation with patient survival. By contrast, low expression levels of PSD4-1:14 were highly correlated with overall better patient survival in both univariate (HR, 0.267150; P=0.047926) and multivariate (HR, 0.236208; P=0.034013) Cox proportional hazards regression models (Table III). The univariate Kaplan-Meier survival curve in Fig. 5 demonstrates that low expression levels of PSD4-1:14 correlate with better patient survival. Although the Kaplan-Meier survival curve is not statistically significant, it approached ~P=0.05, and would likely be statistically significant in a larger sample size.
In summary, the present findings have demonstrated an association between alcohol-associated HNSCC and increased expression of the lncRNAs NETO1-1 and PSD4-1:14. The increased expression of NETO1-1 and PSD4-1:14 in both HNSCC patient clinical samples and in vitro models of alcohol usage suggest that these lncRNAs may act as activators of oncogenes. Previous studies have demonstrated that lncRNAs in an antisense orientation are able to control the transcription of mRNAs and oncogenes (21–23). PSD4-1:14 overlaps in an antisense orientation with paired box 8 (PAX8), which belongs to the PAX gene family, which plays a critical role in the formation of tissues and organs during embryonic development (24). PAX8 specifically is considered to activate genes involved in the formation of the thyroid gland and kidney (25,26). PAX8 has been previously characterized as a potential oncogene whose expression has been positively correlated with various types of epithelial and ovarian cancer (27–29). In addition, its overexpression has been associated with high levels of p53 (30). In those previous studies, PAX8 was identified as a biomarker that could be used to differentiate between different types of epithelial tumors (31). It is possible that PSD4-1:14 acts as a cis-regulator of PAX8, resulting in increased transcription of PAX8, although this may not be the case, and the position of PAX8 in the genome may not be associated with its interacting genes. To the best of our knowledge, PAX8 has not been extensively studied in HNSCC; however, its role in other types of epithelial cancer suggests that it is candidate oncogene involved in the pathogenesis and progression of HNSCC. The exact molecular nature of the association between PSD4-1:14 and PAX8 has not been addressed in the present study, and further characterization of this association is a potential avenue of future research.
The cause of alcohol-associated HNSCC has not been previously characterized. Based on the results of the present study, it could be proposed that NETO1-1 and PSD4-1 may be partially responsible for the pathogenesis of alcohol-associated HNSCC, which highlights the importance of lncRNAs in the molecular mechanisms underlying the pathogenesis of HNSCC. While further studies are required to understand the exact mechanisms by which these lncRNAs function, PSD4-1 and NETO1-1 may be considered promising potential biomarkers and therapeutic targets of HSNCC. Further studies on these lncRNAs could potentially lead to innovations in the prevention and treatment of alcohol-induced HNSCC.
Acknowledgements
The clinical results described in the present study are in whole or partly based on data generated by the TCGA Research Network (http://cancergenome.nih.gov).
ReferencesHashibeMBrennanPBenhamouSCastellsagueXChenCCuradoMPDal MasoLDaudtAWFabianovaEFernandezLAlcohol drinking in never users of tobacco, cigarette smoking in never drinkers and the risk of head and neck cancer: Pooled analysis in the International Head And Neck Cancer Epidemiology ConsortiumJ Natl Cancer Inst99777789200710.1093/jnci/djk17917505073BoffettaPHashibeMAlcohol and cancerLancet Oncol7149156200610.1016/S1470-2045(06)70577-016455479HomannNJousimies-SomerHJokelainenKHeineRSalaspuroMHigh acetaldehyde levels in saliva after ethanol consumption: Methodological aspects and pathogenetic implicationsCarcinogenesis1817391743199710.1093/carcin/18.9.17399328169RahimyEKuoSZOngkekoWMEvaluation of non-coding RNAs as potential targets in head and neck squamous cell carcinoma cancer stem cellsCurr Drug Targets1512471260201410.2174/138945011566614102411344625341422DengGSuiGNoncoding RNA in oncogenesis: A new era of identifying key playersInt J Mol Sci141831918349201310.3390/ijms14091831924013378ReillyMRole of non-coding RNAs in the neuroadaptation to alcoholism and fetal alcohol exposureFront Genet370201210.3389/fgene.2012.0007022715341LauferBIManthaKKleiberMLDiehlEJAddisonSMSinghSMLong-lasting alterations to DNA methylation and ncRNAs could underlie the effects of fetal alcohol exposure in miceDis Model Mech6977992201310.1242/dmm.01097523580197SpizzoRAlmeidaMIColombattiACalinGALong non-coding RNAs and cancer: A new frontier of translational research?Oncogene3145774587201210.1038/onc.2011.62122266873DingerMEAmaralPPMercerTRPangKCBruceSJGardinerBBAskarian-AmiriMERuKSoldàGSimonsCLong noncoding RNAs in mouse embryonic stem cell pluripotency and differentiationGenome Res1814331445200810.1101/gr.078378.10818562676GuttmanMAmitIGarberMFrenchCLinMFFeldserDHuarteMZukOCareyBWCassadyJPChromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammalsNature458223227200910.1038/nature0767219182780SaxenaACarninciPLong non-coding RNA modifies chromatin: Epigenetic silencing by long non-coding RNAsBioEssays33830839201110.1002/bies.20110008421915889RinnJLChangHYGenome regulation by long noncoding RNAsAnnu Rev Biochem81145166201210.1146/annurev-biochem-051410-09290222663078VoldersPJHelsensKWangXMentenBMartensLGevaertKVandesompeleJMestdaghPLNCipedia: A database for annotated human lncRNA transcript sequences and structuresNucleic Acids Res41(Database issue)D246D251201310.1093/nar/gks91523042674QuinlanARHallIMBEDTools: A flexible suite of utilities for comparing genomic featuresBioinformatics26841842201010.1093/bioinformatics/btq03320110278RobinsonMDMcCarthyDJSmythGKedgeR: A Bioconductor package for differential expression analysis of digital gene expression dataBioinformatics26139140201010.1093/bioinformatics/btp61619910308HomannNTillonenJMeurmanJHRintamäkiHLindqvistCRautioMJousimies-SomerHSalaspuroMIncreased salivary acetaldehyde levels in heavy drinkers and smokers: A microbiological approach to oral cavity cancerCarcinogenesis21663668200010.1093/carcin/21.4.66310753201LivakKJSchmittgenTDAnalysis of relative gene expression data using real-time quantitative PCR and the 2-δδCT methodMethods 25.44024082001PrensnerJRIyerMKBalbinOADhanasekaranSMCaoQBrennerJCLaxmanBAsanganiIAGrassoCSKominskyHDTranscriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progressionNat Biotechnol29742749201110.1038/nbt.191421804560GuptaRAShahNWangKCKimJHorlingsHMWongDJTsaiMCHungTArganiPRinnJLLong non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasisNature46410711076201010.1038/nature0897520393566YangQQDengYFLong non-coding RNAs as novel biomarkers and therapeutic targets in head and neck cancersInt J Clin Exp Pathol712861292201424817925MorrisKVLong antisense non-coding RNAs function to direct epigenetic complexes that regulate transcription in human cellsEpigenetics4296301200910.4161/epi.4.5.928219633414CarrieriCCimattiLBiagioliMBeugnetAZucchelliSFedeleSPesceEFerrerICollavinLSantoroCLong non-coding antisense RNA controls Uchl1 translation through an embedded SINEB2 repeatNature491454457201210.1038/nature1150823064229GeislerSCollerJRNA in unexpected places: Long non-coding RNA functions in diverse cellular contextsNat Rev Mol Cell Biol14699712201310.1038/nrm367924105322KentWJSugnetCWFureyTSRoskinKMPringleTHZahlerAMHausslerDThe human genome browser at UCSCGenome Res129961006200210.1101/gr.22910212045153di MaglianoM PascaDi LauroRZanniniMPax8 has a key role in thyroid cell differentiationProc Natl Acad Sci USA971314413149200010.1073/pnas.24033639711069301NarlisMGroteDGaitanYBoualiaSKBouchardMPax2 and pax8 regulate branching morphogenesis and nephron differentiation in the developing kidneyJ Am Soc Nephrol1811211129200710.1681/ASN.200607073917314325MuratovskaAZhouCHeSGoodyerPEcclesMRPaired-Box genes are frequently expressed in cancer and often required for cancer cell survivalOncogene2279897997200310.1038/sj.onc.120676612970747LauryARPeretsRPiaoHKraneJFBarlettaJAFrenchCChirieacLRLisRLodaMHornickJLA comprehensive analysis of PAX8 expression in human epithelial tumorsAm J Surg Pathol35816826201110.1097/PAS.0b013e318216c11221552115LiliacLCarcangiuMLCanevariSCăruntuIDApostolDG CiobanuDanciuMOnofriescuMAmălineiCThe value of PAX8 and WT1 molecules in ovarian cancer diagnosisRom J Morphol Embryol541727201323529305BrunnerAHRissPHeinzeGMeltzowEBrustmannHImmunoexpression of PAX 8 in endometrial cancer: Relation to high-grade carcinoma and p53Int J Gynecol Pathol30569575201110.1097/PGP.0b013e31821ac6c321979593XiangLKongBPAX8 is a novel marker for differentiating between various types of tumor, particularly ovarian epithelial carcinomasOncol Lett5735738201323425942
(A) Cell proliferation and viability in alcohol-treated cells. Cell viability with increasing levels of ethanol concentration was measured in (B) OKF4 and (C) OKF6 cell lines by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. Error bars denote standard deviation.
Heatmap of alcohol-dysregulated lncRNAs. Heatmap depicting normalized lncRNA expression levels (in the form of counts per million) across the alcohol drinker and non-alcohol drinker cohorts in 34 patients with head and neck squamous cell carcinoma. The top 100 differentially expressed lncRNAs are presented, including those whose false discovery rate was not <0.05. lnc, long non-coding.
Validation of alcohol-dysregulated lncRNAs identified in vitro with ethanol-treated cell lines. Reverse transcription-quantitative polymerase chain reaction analysis of ethanol-treated OKF4 and OKF6 cell lines demonstrated that (A) lnc-NETO1-1 and (B) lnc-PSD4-1 are dysregulated by alcohol. Error bars represent the standard deviation, as calculated by the 2−ΔΔCq method. lnc, long non-coding.
Validation of alcohol-dysregulated lncRNAs in vitro in acetaldehyde-treated cell lines. Reverse transcription-quantitative polymerase chain reaction analysis of acetaldehyde-treated OKF4 and OKF6 cell lines demonstrated that (A) lnc-NETO1-1 and (B) lnc-PSD4-1 are dysregulated by acetaldehyde. Error bars represent the standard deviation, as calculated by the 2−ΔΔCq method. lnc, long non-coding.
Kaplan-Meier survival curve for lnc-PSD4-1:14. Kaplan-Meier survival graph depicted a correlation between low expression levels of lnc-PSD4-1:14 and high survival in patients with head and neck squamous cell carcinoma. lnc, long non-coding.
Demographic characteristics of 34 patients with head and neck squamous cell carcinoma included in the present in silico analysis, with categorical breakdowns of drinking status, smoking status, vital state, gender, tumor site, stage and grade.
Variables
Total patients (%) n=34
Alcohol drinkers and tobacco smokers (%) n=12
Alcohol drinkers but non-tobacco smokers (%) n=5
Non-alcohol drinkers but tobacco smokers (%) n=12
Non-alcohol drinkers or tobacco smokers (%) n=5
Gender
Male
25 (74)
10 (83)
3 (60)
8 (67)
4 (80)
Female
9 (26)
2 (17)
2 (40)
4 (33)
1 (20)
Drinks per day
None
17 (50)
0 (0)
0 (0)
12 (100)
5 (100)
0–2
14 (41)
11 (92)
3 (60)
0 (0)
0 (0)
>2
3 (9)
1 (8)
2 (40)
0 (0)
0 (0)
Vital state
Deceased
11 (32)
4 (33)
0 (0)
6 (50)
1 (20)
Alive
23 (68)
8 (67)
5 (100)
6 (50)
4 (80)
Tumor site
Oral
24 (70)
8 (67)
3 (60)
9 (75)
4 (80)
Pharyngeal
4 (12)
1 (8)
2 (40)
0 (0)
1 (20)
Laryngeal
6 (18)
3 (25)
0 (0)
3 (25)
0 (0)
Stage
Low (I, II)
5 (15)
2 (17)
2 (40)
1 (8)
0 (0)
High (III, IV)
29 (85)
10 (83)
3 (60)
11 (92)
5 (100)
Grade
GX
1 (3)
0 (0)
1 (20)
0 (0)
0 (0)
G1-G2
26 (76)
7 (58)
4 (80)
10 (83)
5 (100)
G3-G4
7 (21)
5 (42)
0 (0)
2 (17)
0 (0)
Differentially expressed lncRNAs with FDR<0.05, including expression log fold change between drinkers and nondrinkers, gene log counts per million, and forward and reverse primers used for in vitro verification. lnc-NETO1-1 and lnc-SLC39A11-2 are represented by different splice variants and with one common forward and reverse primer.
lncRNAs
log2 FC
log2 CPM
P-value
FDR
Forward primer
Reverse primer
lnc-SLC39A11-2:7
4.194151
3.878865
1.94E-07
0.001085
5′-CGATGTGTCTCTTTTTCCCGT-3′
5′-AGAAGGCTGAACCAGACGAC-3′
lnc-SLC39A11-2:5
4.140367
3.885084
2.44E-07
0.001085
5′-CGATGTGTCTCTTTTTCCCGT-3′
5′-AGAAGGCTGAACCAGACGAC-3′
lnc-SLC39A11-2:6
4.142305
3.884648
2.46E-07
0.001085
5′-CGATGTGTCTCTTTTTCCCGT-3′
5′-AGAAGGCTGAACCAGACGAC-3′
lnc-LAMB3-1:1
−2.581367
6.606811
2.54E-06
0.008414
5′-TAAGGCTGTGTGTCCTGGTT-3′
5′-TCCTCTGTTCCATACCATCACT-3′
lnc-CCL18-1:1
−2.190396
5.522936
5.69E-06
0.015064
5′-AGAAGTCATACCCCAACCCAA-3′
5′-CGAATCAGTTAGCAAGAGGCA-3′
lnc-NETO1-1:9
3.351077
3.585717
1.56E-05
0.022272
5′-TCTGCCCCCACATCATTTCT-3′
5′-TCCAGTGATTAGGGCTTGAACT-3′
lnc-NETO1-1:3
3.364047
3.615927
1.63E-05
0.022272
5′-TCTGCCCCCACATCATTTCT-3′
5′-TCCAGTGATTAGGGCTTGAACT-3′
lnc-NETO1-1:2
3.362404
3.616193
1.64E-05
0.022272
5′-TCTGCCCCCACATCATTTCT-3′
5′-TCCAGTGATTAGGGCTTGAACT-3′
lnc-NETO1-1:4
3.347929
3.625721
1.68E-05
0.022272
5′-TCTGCCCCCACATCATTTCT-3′
5′-TCCAGTGATTAGGGCTTGAACT-3′
lnc-NETO1-1:6
3.34735
3.624247
1.68E-05
0.022272
5′-TCTGCCCCCACATCATTTCT-3′
5′-TCCAGTGATTAGGGCTTGAACT-3′
lnc-PSD4-1:14
1.669653
1.020086
2.74E-05
0.032967
5′-GCTGATGGCAAGGGATAGCA-3
5′-CTGGCTTCCTTCACCCAAAA-3′
lnc-SPANXA2-2:1
2.367287
2.927082
3.36E-05
0.037051
5′-ACCAACTCTCCTGATTTCCTCA-3′
5′-CTGGGGCTGTCCTGTTTTTA-3′
lnc-AC002472.13.1-1:1
−1.862271
1.547773
4.26E-05
0.041177
5′-CAGGATGGAGTGGAGCCTTC-3′
5′-TCTGGTAGAAAAAGGGATGGGT-3′
lnc-ERC1-1:2
−3.066857
4.256589
4.98E-05
0.041177
5′-TAGCAAGAGAGCGAAGTCCC-3′
5′-GTGTTTGGAGGAGGAAGGGT-3′
lnc-FBXL14-1:1
−3.066857
4.256589
4.98E-05
0.041177
5′-GTGTTTGGAGGAGGAAGGGT-3′
5′-TAGCAAGAGAGCGAAGTCCC-3′
lnc-ERC1-1:3
−3.066857
4.256589
4.98E-05
0.041177
5′-TAGCAAGAGAGCGAAGTCCC-3′
5′-GTGTTTGGAGGAGGAAGGGT-3′
lnc-KTN1-AS1-1:6
−2.463648
1.922281
5.81E-05
0.042703
5′-GCTCCAGGCTAAGGTAATGAGA-3′
5′-CTGTGGCTCTATTCCCCATCT-3′
lnc, long non-coding; FC, fold-change; CPM, counts per million; FDR, false discovery rate.
Cox proportional hazards regression model for the lncRNA PSD4-1:14. Survival information, including HR and P-value, for lnc-PSD4-1:14 in both univariate and multivariate models demonstrates a strong correlation between low expression of PSD4-1:14 and improved overall survival.
Low expression
Univariate HR (95% CI)
P-value
Multivariate HR (95% CI)
P-value
lnc-PSD4-1:14
0.267150
0.047926
0.236208
0.034013
(0.072234–0.988021)
(0.062212–0.896836)
lnc, long non-coding; HR, hazard ratio; CI, confidence interval.