Low-Level Expression of MicroRNAs let-7d and miR-205 Are Prognostic Markers of Head and Neck Squamous Cell Carcinoma
2009; Elsevier BV; Volume: 174; Issue: 3 Linguagem: Inglês
10.2353/ajpath.2009.080731
ISSN1525-2191
AutoresGeoffrey Childs, Melissa Fazzari, Gloria Kung, Nicole Kawachi, Margaret Brandwein‐Gensler, Michael S. McLemore, Quan Chen, Robert D. Burk, Richard V. Smith, Michael B. Prystowsky, Thomas J. Belbin, Nicolas F. Schlecht,
Tópico(s)Cancer-related molecular mechanisms research
ResumoSmall noncoding microRNAs (miRNAs) have been shown to be abnormally expressed in every tumor type examined. The importance of miRNAs as potential cancer prognostic indicators is underscored by their involvement in the regulation of basic cellular processes such as cell proliferation, differentiation, and apoptosis. In this study, miRNA expression profiles of head and neck squamous cell carcinoma (HNSCC) tumor and adjacent normal tissue were examined by microarray analysis and validated by quantitative TaqMan real-time polymerase chain reaction. Using TaqMan real-time polymerase chain reaction we measured the quantitative associations between a subset of miRNAs identified on microarrays in primary tumors at diagnosis and cancer survival in a cohort of 104 HNSCC patients undergoing treatment with curative intent. The majority of miRNAs exhibiting altered expression in primary human HNSCC tumors (including miR-1, miR-133a, miR-205, and let-7d) show lower expression levels relative to normal adjacent tissue. In contrast, hsa-miR-21 is frequently overexpressed in human HNSCC tumors. Using univariate and multivariable statistical models we show that low levels of hsa-miR205 are significantly associated with loco-regional recurrence independent of disease severity at diagnosis and treatment. In addition, combined low levels of hsa-miR-205 and hsa-let-7d expression in HNSCC tumors are significantly associated with poor head and neck cancer survival Our results show that miRNA expression levels can be used as prognostic markers of head and neck cancer. Small noncoding microRNAs (miRNAs) have been shown to be abnormally expressed in every tumor type examined. The importance of miRNAs as potential cancer prognostic indicators is underscored by their involvement in the regulation of basic cellular processes such as cell proliferation, differentiation, and apoptosis. In this study, miRNA expression profiles of head and neck squamous cell carcinoma (HNSCC) tumor and adjacent normal tissue were examined by microarray analysis and validated by quantitative TaqMan real-time polymerase chain reaction. Using TaqMan real-time polymerase chain reaction we measured the quantitative associations between a subset of miRNAs identified on microarrays in primary tumors at diagnosis and cancer survival in a cohort of 104 HNSCC patients undergoing treatment with curative intent. The majority of miRNAs exhibiting altered expression in primary human HNSCC tumors (including miR-1, miR-133a, miR-205, and let-7d) show lower expression levels relative to normal adjacent tissue. In contrast, hsa-miR-21 is frequently overexpressed in human HNSCC tumors. Using univariate and multivariable statistical models we show that low levels of hsa-miR205 are significantly associated with loco-regional recurrence independent of disease severity at diagnosis and treatment. In addition, combined low levels of hsa-miR-205 and hsa-let-7d expression in HNSCC tumors are significantly associated with poor head and neck cancer survival Our results show that miRNA expression levels can be used as prognostic markers of head and neck cancer. MicroRNAs (miRNAs) are a group of noncoding 22 nucleotide RNA molecules that posttranscriptionally regulate the expression of target mRNA.1Ambros V MicroRNAs: tiny regulators with great potential.Cell. 2001; 107: 823-826Abstract Full Text Full Text PDF PubMed Scopus (1415) Google Scholar These newly discovered small RNAs regulate processes as fundamental as cellular proliferation, differentiation, and apoptosis, and a subset of miRNAs have been identified as potential diagnostic and prognostic markers in cancer.2Kent OA Mendell JT A small piece in the cancer puzzle: microRNAs as tumor suppressors and oncogenes.Oncogene. 2006; 25: 6188-6196Crossref PubMed Scopus (614) Google Scholar, 3Calin GA Croce CM MicroRNA signatures in human cancers.Nat Rev Cancer. 2006; 6: 857-866Crossref PubMed Scopus (6521) Google Scholar, 4Cummins JM Velculescu VE Implications of micro-RNA profiling for cancer diagnosis.Oncogene. 2006; 25: 6220-6227Crossref PubMed Scopus (219) Google ScholarAmong these, recent studies have shown that overexpression of miR-21 in lung cancer tumors may act as a classic oncogene, and down-regulation of let-7 miRNA family of genes, which target the Ras oncogene, may be correlated with poor survival and relapse in non-small cell lung cancer.5Yanaihara N Caplen N Bowman E Seike M Kumamoto K Yi M Stephens RM Okamoto A Yokota J Tanaka T Calin GA Liu CG Croce CM Harris CC Unique microRNA molecular profiles in lung cancer diagnosis and prognosis.Cancer Cell. 2006; 9: 189-198Abstract Full Text Full Text PDF PubMed Scopus (2675) Google Scholar, 6Yu SL Chen HY Chang GC Chen CY Chen HW Singh S Cheng CL Yu CJ Lee YC Chen HS Su TJ Chiang CC Li HN Hong QS Su HY Chen CC Chen WJ Liu CC Chan WK Chen WJ Li KC Chen JJ Yang PC MicroRNA signature predicts survival and relapse in lung cancer.Cancer Cell. 2008; 13: 48-57Abstract Full Text Full Text PDF PubMed Scopus (710) Google Scholar Aberrant regulation of miRNAs has also revealed roles for specific genes as either tumor suppressors, when deleted or repressed, or as oncogenes when amplified or otherwise overexpressed.2Kent OA Mendell JT A small piece in the cancer puzzle: microRNAs as tumor suppressors and oncogenes.Oncogene. 2006; 25: 6188-6196Crossref PubMed Scopus (614) Google Scholar, 7Zhang L Huang J Yang N Greshock J Megraw MS Giannakakis A Liang S Naylor TL Barchetti A Ward MR Yao G Medina A O'Brien-Jenkins A Katsaros D Hatzigeorgiou A Gimotty PA Weber BL Coukos G MicroRNAs exhibit high frequency genomic alterations in human cancer.Proc Natl Acad Sci USA. 2006; 103: 9136-9141Crossref PubMed Scopus (909) Google Scholar, 8Esquela-Kerscher A Slack FJ Oncomirs—microRNAs with a role in cancer.Nat Rev Cancer. 2006; 6: 259-269Crossref PubMed Scopus (6135) Google Scholar However, miRNA expression is highly tissue-specific; separate and distinct profiles have been described for every cancer type.9Jiang J Lee EJ Gusev Y Schmittgen TD Real-time expression profiling of microRNA precursors in human cancer cell lines.Nucleic Acids Res. 2005; 33: 5394-5403Crossref PubMed Scopus (447) Google Scholar To date there have been only two studies investigating miRNAs in head and neck squamous cell carcinoma (HNSCC) immortal cell lines, neither of which assessed the role of these noncoding RNA molecules in human tissue or attempted to correlate miRNA expression with HNSCC prognosis.9Jiang J Lee EJ Gusev Y Schmittgen TD Real-time expression profiling of microRNA precursors in human cancer cell lines.Nucleic Acids Res. 2005; 33: 5394-5403Crossref PubMed Scopus (447) Google Scholar, 10Tran N McLean T Zhang X Zhao CJ Thomson JM O'Brien C Rose B MicroRNA expression profiles in head and neck cancer cell lines.Biochem Biophys Res Commun. 2007; 358: 12-17Crossref PubMed Scopus (226) Google ScholarHNSCC is the fifth most common malignancy in men worldwide and includes tumors of the oral cavity, oropharynx, and larynx. Survival rates for HNSCC have remained unchanged throughout the last 3 decades, and half of all cases die within 5 years of diagnosis.11American Cancer Society Cancer Facts and Figures 2007. American Cancer Society, Atlanta2007Google Scholar Efforts to identify prognostic biomarkers for HNSCC, including p53, EGFR, Bcl-2, MMPs, cyclins, and other markers, have proved to be primarily unsuccessful to date.12Kyzas PA Stefanou D Batistatou A Agnantis NJ Prognostic significance of VEGF immunohistochemical expression and tumor angiogenesis in head and neck squamous cell carcinoma.J Cancer Res Clin Oncol. 2005; 131: 624-630Crossref PubMed Scopus (90) Google Scholar, 13Lothaire P de Azambuja E Dequanter D Lalami Y Sotiriou C Andry G Castro Jr, G Awada A Molecular markers of head and neck squamous cell carcinoma: promising signs in need of prospective evaluation.Head Neck. 2006; 28: 256-269Crossref PubMed Scopus (124) Google Scholar, 14McShane LM Altman DG Sauerbrei W Taube SE Gion M Clark GM Reporting recommendations for tumor marker prognostic studies.J Clin Oncol. 2005; 23: 9067-9072Crossref PubMed Scopus (627) Google Scholar As with mRNA expression, high-throughput genomic technologies can be used to shed new light on alterations in gene expression that are associated with HNSCC carcinogenesis.15Belbin TJ Singh B Barber I Socci N Wenig B Smith R Prystowsky MB Childs G Molecular classification of head and neck squamous cell carcinoma using cDNA microarrays.Cancer Res. 2002; 62: 1184-1190PubMed Google Scholar, 16Hanna E Shrieve DC Ratanatharathorn V Xia X Breau R Suen J Li S A novel alternative approach for prediction of radiation response of squamous cell carcinoma of head and neck.Cancer Res. 2001; 61: 2376-2380PubMed Google Scholar, 17Warner GC Reis PP Jurisica I Sultan M Arora S Macmillan C Makitie AA Grenman R Reid N Sukhai M Freeman J Gullane P Irish J Kamel-Reid S Molecular classification of oral cancer by cDNA microarrays identifies overexpressed genes correlated with nodal metastasis.Int J Cancer. 2004; 110: 857-868Crossref PubMed Scopus (61) Google Scholar This is the first report to comprehensively identify and measure differentially transcribed miRNAs in HNSCC and assesses their role in cancer prognosis in humans.Materials and MethodsCollection of Samples and Patient DataFresh HNSCC tumor specimens were obtained at the time of diagnosis. Adjacent normal tissue was sampled for comparison. This was taken from the same surgical field, as often done during the tumor survey, and did not put the patient at increased or additional risk. Standard biopsy techniques were performed, as were standard surgical resection when appropriate. Additional biopsies of normal tissue adjacent to the tumor mass were taken when required. Patients were excluded if their primary tumor was too small to allow both diagnostic biopsy and biopsy for the study. Patients were recruited and consented following an institutional review board-approved protocol before inclusion in the study.Detailed clinical and demographic information was collected by physicians on all patients enrolled in the study and patients were prospectively followed-up to determine clinical outcome and disease progression. Patient information is entered on an ongoing basis into a secure clinical database system developed for the head and neck cancer program at the Albert Einstein College of Medicine. Patients undergo treatment for HNSCC, as deemed appropriate by the treating physicians blinded to miRNA results.RNA ExtractionFresh frozen tumor samples were prospectively collected from HNSCC patients at Montefiore Medical Center in the Bronx. Tumor tissue was snap-frozen in liquid nitrogen within 30 minutes of surgical resection or biopsy, and before treatment. Total RNA was extracted from an additional 50 to 100 mg of tissue using TRIzol by standardized protocol (Invitrogen, Carlsbad, CA). RNA was collected by alcohol precipitation and quantitated for microarray or real-time PCR analysis Selected samples were checked for integrity of RNAs and presence of microRNA peaks using an Agilent 2100 bioanalyzer (Agilent, Santa Clara, CA) and RNA pico chips as described by the manufacturer.MicroRNA MicroarraysMiRNA oligonucleotide microarrays used in this study were custom printed at the Albert Einstein College of Medicine Microarray Facility. We printed a custom miRNA microarray that consisted of a set of antisense probes from Ambion Inc., Austin, TX (version 1) and the same oligonucleotide set described by Croce's group19Liu CG Calin GA Meloon B Gamliel N Sevignani C Ferracin M Dumitru CD Shimizu M Zupo S Dono M Alder H Bullrich F Negrini M Croce CM An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues.Proc Natl Acad Sci USA. 2004; 101: 9740-9744Crossref PubMed Scopus (815) Google Scholar that included sense strand probes, anti-sense controls, and selected miRNA precursor-specific probes representing a total 236 unique human miRNA genes. Each oligonucleotide was triple-spotted along with Ambion controls used for probe labeling and hybridization efficiency monitoring. MiRNAs were enriched from 5 μg of total RNA by fractionation using Ambion Flashpage polyacrylamide gels followed by column purification. Purified miRNAs were labeled by addition of polyA tails followed by chemical coupling of Alexa florescent dyes using the Vana miRNA labeling kit (Ambion) and hybridized using an Ambion hybridization kit recommended for these miRNA probes. Tumor sample RNA was labeled with Alexa 645 (red) and normal counterpart RNA with Alexa 555 (green). We also conducted dye flip experiments and yellow tests (comparison of identical RNA samples with both dyes on one microarray) to confirm the specificity of our microarray hybridizations.Processing and Analysis of Microarray Gene Expression DataTumor versus normal signal intensities for each element on the array were calculated using the GenePix Pro 6.0 software package (Molecular Devices, Sunnyvale, CA). This software gives an integrated intensity per spot for each channel in addition to an integrated background count. In all subsequent analyses, we use the average background subtracted intensity for the two channels. For each spot, we calculated the mean intensity over the spot in the two channels and from this subtract the median of the background and log2 transformed. To correct for dye incorporation efficiency, fluorescence yield, and laser power used in scanning, we scaled the intensities from the two channels relative to each other, and computed an intensity-dependent normalization factor by first finding the rank invariant subset of the spots.20Tseng GC Oh MK Rohlin L Liao JC Wong WH Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects.Nucleic Acids Res. 2001; 29: 2549-2557Crossref PubMed Scopus (482) Google Scholar MiRNAs with missing signal intensity microarray expression data for two or more HNSCC cases were excluded from analysis. Furthermore, to account for labeling and hybridization efficiencies among samples, intensity ratios for each miRNA element were normalized by first taking the average value of the three spot replicates on the microarray and then correcting the values for each color channel using prespiked control oligonucleotides that are fluorescently labeled and hybridized along with the miRNA sample.21Livak KJ Schmittgen TD Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta C(T)) method.Methods. 2001; 25: 402-408Crossref PubMed Scopus (119422) Google ScholarReal-Time PCRBoth semiquantitative and quantitative real time PCR methods were used to validate and extend the microarray data. Initially, Ambion mirVana qRT-PCR primer sets were used along with TAQ polymerase as recommended by the supplier to validate microarrays. Each primer set (miR-21, miR-1, miR-133a, let-7d, miR-205, and miR-206 and 5S RNA) was individually titrated and a cycle chosen such that the PCR product visualized by agarose gel analysis was in the exponential range of amplification. Tumor and normal samples were amplified and levels of products were estimated by gel electrophoresis and normalized to 5S RNA levels. Quantitative analysis of miRNA expression from large collections of tumor and normal samples was conducted by quantitative real-time PCR TaqMan protocol using a 7900 real time PCR apparatus (Applied Biosystems, Foster City, CA) exactly as recommended by the supplier. Results were expressed as a cycle threshold (CT) value, which is inversely proportional to the sample starting copy number. Normalized (Δ CT) values were computed by subtracting the CT value (averaged across three replicates) of a small noncoding RNA control gene (RNU 48) from the raw CT value of the miRNA element. To compare tumor and paired normal tissue, we generated the ΔΔCT value by subtracting the ΔCT value of the normal tissue from the ΔCT value of the tumor. We then examined the ΔΔCT distribution for each miRNA element graphically, assuming approximately equal amplification efficiencies between tumor and normal tissue.21Livak KJ Schmittgen TD Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta C(T)) method.Methods. 2001; 25: 402-408Crossref PubMed Scopus (119422) Google ScholarStatistical AnalysisWe analyzed the association with cancer survival in a cohort of HNSCC patients undergoing treatment with curative intent in relation to miRNA level at diagnosis. Enrollment for the clinical study began in 2002 and patients were followed prospectively after histological confirmation of SCC. Time to event was measured from treatment start to the first instance of a local or regional recurrence, disease-free and overall survival, or to the last recorded follow-up visit date for censored patients. Survival and progression-free probability curves were estimated using Kaplan-Meier estimates. Cumulative incidence curves for loco-regional recurrence were also estimated, taking into account the competing risks of death and distant metastases. We investigated the potential effect of confounding by age, gender, ethnicity, smoking history, alcohol consumption, tumor location, disease stage, TNM status, treatment modality (primary resection +/− adjuvant therapy versus neoadjuvant therapy), and prevalence of human papillomavirus (HPV) (tested by MY09/11 PCR assay).22Bauer HM Ting Y Greer CE Chambers JC Tashiro CJ Chimera J Reingold A Manos MM Genital human papillomavirus infection in female university students as determined by a PCR-based method.JAMA. 1991; 265: 472-477Crossref PubMed Scopus (900) Google Scholar, 23Hildesheim A Schiffman MH Gravitt PE Glass AG Greer CE Zhang T Scott DR Rush BB Lawler P Sherman ME Kurman RJ Manos MM Persistence of type-specific human papillomavirus infection among cytologically normal women.J Infect Dis. 1994; 169: 235-240Crossref PubMed Scopus (624) Google Scholar Adjusted models based on an a priori model containing known or suspected confounders were presented; however, we also examined all possible models using an exhaustive search of the model space. The a priori model included anatomical site (oropharynx, larynx, lip/oral), T stage (T0/T1 versus T2/T3), HPV status (HPV+ versus HPV−), and treatment (chemoradiation therapy yes/no). Hazard ratios (HRs) and corresponding confidence intervals for the adjusted models were estimated using Cox proportional hazards models. To test the null hypothesis that the regression coefficient was equal to zero, P values based on the Wald χ2 test were computed and presented for each microRNA. Each event of interest (overall survival, loco-regional recurrence, distant metastases) was modeled separately, censoring at the occurrence of any competing event, to estimate the cause-specific hazard of event. To evaluate the overall impact of each covariate across all events of interest a single proportional hazards model, stratified by failure type, was estimated. This model allowed a different baseline hazard for each event type, but also allowed us to efficiently pool information across event type to generate an overall measure of variable importance in an adjusted model. Interactions between event type and covariate were examined for each covariate of interest. Robust standard errors using the sandwich estimator of Wei and colleagues24Wei LJ Lin DY Weissfeld L Regression analysis of multivariate incomplete failure time data by modeling marginal distributions.J Am Stat Assoc. 1989; 84: 1065-1073Crossref Scopus (1440) Google Scholar were computed to correct for any model misspecification.ResultsMicroRNA Profiling of HNSCC TumorsWe used custom-spotted oligonucleotide microarrays to make direct comparisons between HNSCC tumors and adjacent normal tissue to measure aberrant regulation of specific miRNA genes in HNSCC. We were able to generate a preliminary HNSCC miRNA signature of miRNAs over- and underexpressed in tumors relative to their matched normal counterparts (Table 1). The partial list shown includes 43 human miRNAs that were expressed, on average, twofold lower in tumors versus normal samples, and 6 miRNAs that were, on average, expressed by at least twofold higher in tumors versus normals. However, we observed some variability in miRNA expression across HNSCC patients. Assessing the frequency of HNSCC tumors over- or underexpressing miRNAs compared to their adjacent normal counterpart, we found only one miRNA with at least twofold higher expression in six of eight of patient tumors examined (represented by clones for miR-21, miR-021-prec, and mmu-miR-21_AS), whereas three human miRNAs were underexpressed in six of eight patients (miR-370, miR-199a-1-prec, miR-030b-prec).Table 1List of the Highest Scoring MicroRNAs Corresponding to a Partial Expression Profile of HNSCCMicroRNAMean fold change (tumor:normal)*Tumor versus normal signal intensities for each element on the array were calculated using ratios of the mean spot intensities in the two channels over the eight HNSCC samples tested. AS, antisense probe; prec, precursor to the mature miRNA specific oligonucleotide probe.19Underexpressed in tumors hsa-let-7f0.29 hsa-miR-10b_AS0.46 hsa-miR-124a_AS0.34 hsa-miR-142-3p0.43 hsa-miR-324-5p0.23 hsa-miR-3680.32 hsa-miR-3700.26 hsa-miR-373*Tumor versus normal signal intensities for each element on the array were calculated using ratios of the mean spot intensities in the two channels over the eight HNSCC samples tested. AS, antisense probe; prec, precursor to the mature miRNA specific oligonucleotide probe.190.44 hsa-miR-373*Tumor versus normal signal intensities for each element on the array were calculated using ratios of the mean spot intensities in the two channels over the eight HNSCC samples tested. AS, antisense probe; prec, precursor to the mature miRNA specific oligonucleotide probe.19_AS0.51 hsa-miR-422a_AS0.43 hsa-miR-422b0.16 hsa-miR-422b_AS0.46 hsa-miR-4240.27 hsa-miR-95_AS0.46 hsa-miR-99a_AS0.47 hsa-let-7a-2-prec-#10.54 hsa-let-7a-3-prec0.29 hsa-let-7d-prec0.40 hsa-miR-007-1-prec0.30 hsa-miR-007-2-prec-#10.41 hsa-miR-009-1-#10.50 hsa-miR-009-3-#10.47 hsa-miR-016b-chr30.35 hsa-miR-030a-prec-#20.45 hsa-miR-030b-prec-#10.18 hsa-miR-030b-prec-#20.45 hsa-miR-093-prec-7.1 = 093-10.40 hsa-miR-106-prec0.47 hsa-miR-125b-2-prec-#20.41 hsa-miR-128b-prec-#20.43 hsa-miR-130a-prec-#10.35 hsa-miR-135-1-prec0.33 hsa-miR-140-#10.54 hsa-miR-155-prec0.50 hsa-miR-192-2/3-#20.49 hsa-miR-199a-1-prec0.24 hsa-miR-213-prec-#20.42 hsa-miR-216-prec-#10.43 hsa-miR-218-1-prec0.36 hsa-miR-221-prec0.24 hsa-miR-224-prec0.20 miR1–20.44 miR133a-10.31Overexpressed in tumors hsa-miR-021-prec-17-#24.44 hsa-miR-024-1-prec-#11.99 hsa-miR-151-prec3.66 hsa-miR-199b-prec-#22.22 miR213.29 miR23b1.54* Tumor versus normal signal intensities for each element on the array were calculated using ratios of the mean spot intensities in the two channels over the eight HNSCC samples tested. AS, antisense probe; prec, precursor to the mature miRNA specific oligonucleotide probe.19Liu CG Calin GA Meloon B Gamliel N Sevignani C Ferracin M Dumitru CD Shimizu M Zupo S Dono M Alder H Bullrich F Negrini M Croce CM An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues.Proc Natl Acad Sci USA. 2004; 101: 9740-9744Crossref PubMed Scopus (815) Google Scholar Open table in a new tab Using a semiquantitative reverse transcription (RT)-PCR assay, we independently verified the microarray data in primary tumors and matching normal tissue for six different miRNAs from our preliminary HNSCC miRNA signature (miR-21, miR-1, miR-133a, miR-205, miR-206, and let-7d). Figure 1 shows some of the RT-PCR data comparing tumor (T) and matching normal (N) RNA from three HNSCC cases. In each case examined, we observed a general agreement between microarray and RT-PCR data. The concordance of microarray and RT-PCR data demonstrated that our microarray platform could accurately identify miRNAs that are dysregulated in HNSCC and therefore candidates for biomarker analysis of individual patient samples. Given the small initial sample size, we then set out to confirm the results on a larger sample set using quantitative real-time PCR and multivariable statistical analyses.Quantification of MicroRNA Expression in HNSCCWe selected five miRNA genes from the list of miRNA above (miR-21, miR-1, miR-133a, miR-205, and let-7d) that might fit a HNSCC signature based on evidence in the literature and the preliminary results from the TvN microarray comparisons. For example, miR-1 and miR-133a showed severely low expression levels relative to normal adjacent tissue (Table 1). We therefore hypothesized that these genes might discriminate between different HNSCC tumor behaviors. MiR-205 was also selected because it has been shown to be highly enriched in HNSCC cell lines relative to other tumor types,10Tran N McLean T Zhang X Zhao CJ Thomson JM O'Brien C Rose B MicroRNA expression profiles in head and neck cancer cell lines.Biochem Biophys Res Commun. 2007; 358: 12-17Crossref PubMed Scopus (226) Google Scholar and therefore represents a cell-type-specific molecule that might regulate important downstream targets. Using primers for these candidate gene species, we then performed quantitative TaqMan real-time PCR analysis of an independent set of 104 HNSCC patient samples.Patients were recruited from the Bronx, a high-risk area of New York City with a high incidence of head and neck cancer. Clinical characteristics of 104 patients with primary HNSCC tumors are shown in Table 2, and RNA prepared from these samples was used for our quantitative real-time PCR measurements. Matched tumor and normal adjacent tissue miRNA expression measurements were available on 95 of the 105 HNSCC patients tested. RNU 48 RNA expression levels in all samples were used as a control for the amount of RNA used in the RT-PCR reactions. Measurements of RNU 48 in tumor samples was consistent with a Gaussian distribution with a mean of 22.8 cycles, whereas normal samples were slightly skewed toward larger CT values, and thus had a higher mean (CT = 23.6). Measurements of the normalized miR-205 RNA expression levels in tumors versus normal tissue from the same patients by real-time PCR revealed a significant difference and indicated that tumors averaged ∼3.1-fold less miR-205 than normal tissue (ΔΔCT = +1.63, P value <0.001). In contrast, miR-21 expression levels were higher in tumors versus normals by ∼1.5-fold on average (ΔΔCT = −0.58, P = 0.002) (Figure 2). Furthermore, looking at the distribution in miRNA expression across patients, two–fold lower miR-205 levels were detected in 25% of tumors, whereas two-fold higher miR-205 expression was seen in only 4.5% of cases. Let-7d showed a similar distribution, with 26% and 2% of cases showing lower and higher let-7d expression in tumor versus normals, respectively. In contrast, equal proportions (15%) of tumors showed two–fold lower or higher miR-21 expression relative to their matched adjacent normal tissue. Interestingly, miR-205, miR-21, and let-7d demonstrated minor differences in expression levels with respect to tumor site within the head and neck, although these were within the limits of what may be considered biological variability (Table 3).Table 2Demographic and Clinical Characteristics of HNSCC PatientsCharacteristicN%Sex Male7168% Female3332%Age 606361%Ethnicity Hispanic2625% Non-Hispanic7774%Race White6562% African-American/Black3029% Other22%Anatomic site Oral cavity3130% Oropharynx3231% Hypopharynx99% Larynx3231%Tumor Stage*Some cases pending histological confirmation. Totals may not add up to 104 because of missing data. I/II2423% III/IV8077%T status I/II4644% III/IV5654%N status I/II4040% III/IV5654%Treatment Primary chemo/RT4543% Primary surgery3332% Surgery -> chemo2221%Smoking history Current4644% Former3938% Never1716% Missing22%HPV status HPV16−5957% HPV16+3736% HPV status missing88% Total104100%* Some cases pending histological confirmation. Totals may not add up to 104 because of missing data. Open table in a new tab Figure 2Distribution of tumor versus normal differences in normalized microRNA levels for miR-205, miR-21, and let-7d. Matched tumor and normal fold differences in microRNA TaqMan ΔΔCT levels normalized to the RNU48 control gene shown.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Table 3Difference in Tumor MicroRNA Levels by Anatomic SiteAnatomic sitemiR-205 (n = 104)Effect (SE)*Effect estimates and standard errors (SEs) for relative differences in microRNA expression are shown by β coefficient based on a linear regression model showing the difference in normalized ΔCT tumor levels using oropharynx as the reference, adjusting for tumor site.miR-21 (n = 104)Effect (SE)*Effect estimates and standard errors (SEs) for relative differences in microRNA expression are shown by β coefficient based on a linear regression model showing the difference in normalized ΔCT tumor levels using oropharynx as the reference, adjusting for tumor site.let-7d (n = 104)Effect (SE)*Effect estimates and standard errors (SEs) for relative differences in microRNA expression are shown by β coefficient based on a linear regression model showing the difference in normalized ΔCT tumor levels using oropharynx as the reference, adjusting for tumor site.miR-133 (n = 41)Effect (SE)*Effect estimates and standard errors (SEs) for relative differences in microRNA expression are shown by β coefficient based on a linear regression model showing the difference
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