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Elevated miR-615-3p Expression Predicts Adverse Clinical Outcome and Promotes Proliferation and Migration of Prostate Cancer Cells

2019; Elsevier BV; Volume: 189; Issue: 12 Linguagem: Inglês

10.1016/j.ajpath.2019.08.007

ISSN

1525-2191

Autores

Emma B. Laursen, Jacob Fredsøe, Linnéa Schmidt, Siri H. Strand, Helle Kristensen, Anne Rasmussen, Tina Fuglsang Daugaard, Peter Mouritzen, S. Høyer, Gitte Kristensen, Hein Vincent Stroomberg, Klaus Brasso, Martin Andreas Røder, Michael Borre, Karina D. Sørensen,

Tópico(s)

Cancer-related molecular mechanisms research

Resumo

miR-615-3p has previously been described as up-regulated in prostate cancer (PC) tissue samples compared with nonmalignant controls; however, its prognostic potential and functional role in PC remain largely unknown. In this study, we investigated the clinical and biological relevance of miR-615-3p in PC. The expression of miR-615-3p was measured in PC tissue specimens from 239 men who underwent radical prostatectomy (RP), and it was investigated if miR-615-3p could predict postoperative biochemical recurrence (BCR). These findings were subsequently validated in three independent RP cohorts (n = 222, n = 273, and n = 387) and functional overexpression studies conducted in PC cells (PC3M). High miR-615-3p expression was significantly associated with BCR in four independent PC patient cohorts (P < 0.05, log-rank test). In addition, high miR-615-3p expression was a significant predictor of PC-specific survival in univariate (hazard ratio, 3.75; P < 0.001) and multivariate (hazard ratio, 2.66; P = 0.008) analysis after adjustment for the Cancer of the Prostate Risk Assessment Post-Surgical (CAPRA-S) nomogram in a merged RP cohort (n = 734). Moreover, overexpression of miR-615-3p in PC cells (PC3M) significantly increased cell viability, proliferation, apoptosis, and migration. Together, our results suggest that miR-615-3p is a significant predictor of postoperative BCR and PC-specific survival and has oncogenic functions in PC cells. miR-615-3p has previously been described as up-regulated in prostate cancer (PC) tissue samples compared with nonmalignant controls; however, its prognostic potential and functional role in PC remain largely unknown. In this study, we investigated the clinical and biological relevance of miR-615-3p in PC. The expression of miR-615-3p was measured in PC tissue specimens from 239 men who underwent radical prostatectomy (RP), and it was investigated if miR-615-3p could predict postoperative biochemical recurrence (BCR). These findings were subsequently validated in three independent RP cohorts (n = 222, n = 273, and n = 387) and functional overexpression studies conducted in PC cells (PC3M). High miR-615-3p expression was significantly associated with BCR in four independent PC patient cohorts (P < 0.05, log-rank test). In addition, high miR-615-3p expression was a significant predictor of PC-specific survival in univariate (hazard ratio, 3.75; P < 0.001) and multivariate (hazard ratio, 2.66; P = 0.008) analysis after adjustment for the Cancer of the Prostate Risk Assessment Post-Surgical (CAPRA-S) nomogram in a merged RP cohort (n = 734). Moreover, overexpression of miR-615-3p in PC cells (PC3M) significantly increased cell viability, proliferation, apoptosis, and migration. Together, our results suggest that miR-615-3p is a significant predictor of postoperative BCR and PC-specific survival and has oncogenic functions in PC cells. Prostate cancer (PC) is the most commonly diagnosed non–skin cancer and the third leading cause of cancer-related death among men in the Western world.1Jemal A. Bray F. Center M.M. Ferlay J. Ward E. Forman D. Global cancer statistics.CA Cancer J Clin. 2011; 61: 69-90Crossref PubMed Scopus (30314) Google Scholar Although localized PC is curable by radical prostatectomy (RP), approximately one-third of the patients will experience postoperative biochemical recurrence (BCR), and some will further progress into advanced metastatic PC that remains incurable.2Mottet N. Heidenreich A. Bolla M. Joniau S. Mason M.D. Matveev V. Schmid H. Van Der Kwast T.H. Wiegel T. Zattoni F. EAUGuidelines on Prostate Cancer. European Association of Urology, Arnhem, the Netherlands2015Google Scholar However, a large proportion of localized PCs are nonaggressive (indolent) and will not progress to advanced disease, even without treatment.2Mottet N. Heidenreich A. Bolla M. Joniau S. Mason M.D. Matveev V. Schmid H. Van Der Kwast T.H. Wiegel T. Zattoni F. EAUGuidelines on Prostate Cancer. European Association of Urology, Arnhem, the Netherlands2015Google Scholar Currently, treatment decisions for newly diagnosed clinically localized PC are based on serum prostate-specific antigen levels, clinical tumor stage, and Gleason grade. However, this approach lacks prognostic accuracy, leading to overtreatment of many nonaggressive PCs as well as undertreatment or delayed treatment of aggressive PCs. Hence, there is an urgent need to uncover the molecular mechanisms that drive PC aggressiveness and to identify novel prognostic biomarkers that can help guide improved and more personalized treatment decisions. miRNAs are small noncoding RNAs (approximately 22 nucleotides) that regulate posttranscriptional gene expression by translational repression and mRNA degradation. It has been estimated that approximately 60% of all human transcripts are regulated by miRNAs and that miRNAs influence key cellular processes, such as differentiation, cell cycle progression, and apoptosis.3Di Leva G. Garofalo M. Croce C.M. MicroRNAs in cancer.Annu Rev Pathol. 2014; 9: 287-314Crossref PubMed Scopus (1312) Google Scholar In addition, miRNAs have provided new insights into the understanding of human cancer biology, and evidence suggests that dysregulation of miRNA expression is involved in both PC development and treatment response.4Catto J.W.F. Alcaraz A. Bjartell A.S. De Vere White R. Evans C.P. Fussel S. Hamdy F.C. Kallioniemi O. Mengual L. Schlomm T. Visakorpi T. MicroRNA in prostate, bladder, and kidney cancer: a systematic review.Eur Urol. 2011; 59: 671-681Abstract Full Text Full Text PDF PubMed Scopus (384) Google Scholar, 5Sevli S. Uzumcu A. Solak M. Ittmann M. Ozen M. The function of microRNAs, small but potent molecules, in human prostate cancer.Prostate Cancer Prostatic Dis. 2010; 13: 208-217Crossref PubMed Scopus (39) Google Scholar, 6O 'kelly F. Marignol L. Meunier A. Lynch T.H. Perry A.S. O 'kelly F. Marignol L. Meunier A. Lynch T.H. Perry A.S. Hollywood D. MicroRNAs as putative mediators of treatment response in prostate cancer.Nat Rev Urol. 2012; 9: 397-407Crossref PubMed Scopus (32) Google Scholar, 7Esquela-Kerscher A. Slack F.J. Oncomirs: microRNAs with a role in cancer.Nat Rev Cancer. 2006; 6: 259-269Crossref PubMed Scopus (6194) Google Scholar, 8Crosby M.E. Kulshreshtha R. Ivan M. Glazer P.M. MicroRNA regulation of DNA repair gene expression in hypoxic stress.Cancer Res. 2009; 69: 1221-1229Crossref PubMed Scopus (362) Google Scholar, 9Pichler M. Calin G.A. MicroRNAs in cancer: from developmental genes in worms to their clinical application in patients.Br J Cancer. 2015; 113: 569-573Crossref PubMed Scopus (163) Google Scholar Furthermore, miRNAs hold promising biomarker potential as they exhibit both tissue-specific expression profiles and associations to tumor, node, and metastasis stage and grade in many cancer types.10Lu J. Getz G. Miska E.A. Alvarez-Saavedra E. Lamb J. Peck D. Sweet-Cordero A. Ebert B.L. Mak R.H. Ferrando A.A. Downing J.R. Jacks T. Horvitz H.R. Golub T.R. MicroRNA expression profiles classify human cancers.Nature. 2005; 435: 834-838Crossref PubMed Scopus (8223) Google Scholar,11Fabris L. Ceder Y. Chinnaiyan A.M. Jenster G.W. Sorensen K.D. Tomlins S. Visakorpi T. Calin G.A. The potential of microRNAs as prostate cancer biomarkers.Eur Urol. 2016; 70: 312-322Abstract Full Text Full Text PDF PubMed Scopus (200) Google Scholar Moreover, miRNAs are highly stable molecules and are detectable also in archived tissue samples.12Peiró-Chova L. Peña-Chilet M. López-Guerrero J.A. García-Giménez J.L. Alonso-Yuste E. Burgues O. Lluch A. Ferrer-Lozano J. Ribas G. High stability of microRNAs in tissue samples of compromised quality.Virchows Arch. 2013; 463: 765-774Crossref PubMed Scopus (67) Google Scholar Although up-regulation of miR-615-3p in PC compared with nonmalignant prostate tissue samples has been previously reported, the possible prognostic value and biological function of miR-615-3p in PC remain largely unknown.13Kristensen H. Thomsen A.R. Haldrup C. Dyrskjøt L. Høyer S. Borre M. Mouritzen P. Ørntoft T.F. Sørensen K.D. Novel diagnostic and prognostic classifiers for prostate cancer identified by genome-wide microRNA profiling.Oncotarget. 2016; 7: 30760-30771Crossref PubMed Scopus (60) Google Scholar,14Yun S.J. Jeong P. Kang H.W. Kim Y.-H. Kim E.-A. Yan C. Choi Y.-K. Kim D. Kim J.M. Kim S.-K. Kim S.-Y. Kim S.T. Kim W.T. Lee O.-J. Koh G.-Y. Moon S.-K. Kim I.Y. Kim J. Choi Y.-H. Kim W.-J. Urinary microRNAs of prostate cancer: virus-encoded hsv1-miRH18 and hsv2-miR-H9-5p could be valuable diagnostic markers.Int Neurourol J. 2015; 19: 74-84Crossref PubMed Scopus (39) Google Scholar In the present study, using four large independent PC patient cohorts (239, 222, 273, and 387 RP patients), it was found that high miR-615-3p expression in PC tissue samples was significantly associated with postoperative BCR and poor PC-specific survival (CSS). In addition, overexpression of miR-615-3p increased the viability, proliferation, and migration of PC cells, together indicating that miR-615-3p is an oncogenic driver in PC. Expression levels of miR-615-3p were measured in tumor tissue samples from four independent patient cohorts of men treated for clinically localized PC by curatively intended RP (Table 1): cohort 1/training, n = 239; cohort 2/validation, n = 222; cohort 3/validation, n = 273; and cohort 4/validation, n = 387.Table 1Clinicopathologic Characteristics for PC Patient CohortsCharacteristicsCohort 1: training setCohort 2: validationCohort 3: validationCohort 4: validation (TCGA)SamplesRP (n = 239)RP (n = 222)RP (n = 273)RP (n = 387)Age in years, median (IQR)64.2 (35.9–74.9)63.5 (59.0–67.3)62.5 (59.2–66.6)61.0 (56.0–66.0)PSA at diagnosis in ng/mL, median (IQR)10.5 (7.1–15.7)12.3 (9.1–19.0)10.0 (6.8–14.0)7.5 (5.2–11.1)Gleason score, n (%) ≤677 (32.2)75 (33.8)111 (40.7)37 (9.6) 7126 (52.7)104 (46.8)139 (50.9)194 (50.1) 822 (9.2)34 (15.3)12 (4.4)55 (14.2) >86 (2.5)9 (4.1)11 (4.0)101 (26.1) Unknown8 (3.3)000Pathological T-stage, n (%) pT2a-c173 (72.4)137 (61.7)182 (66.7)149 (38.5) pT3a43 (18.0)65 (29.3)53 (19.4)129 (33.3) pT3b17 (7.1)20 (9.0)38 (13.9)96 (24.8) Unknown6 (2.5)0013 (3.4)Surgical margin status, n (%) Negative171 (71.5)150 (67.6)115 (42.1)251 (64.9) Positive68 (28.5)26 (26.1)158 (57.9)118 (30.5) Unknown014 (6.3)018 (4.7)Lymph node status, n (%) pN0126 (52.7)121 (54.5)106 (38.8)NA pN114 (5.9)12 (5.4)3 (1.1)NA pNx3 (1.3)2 (0.9)164 (60.1)NA Unknown96 (40.2)87 (39.2)0387 (100)CAPRA-S risk groups, n (%) Low (CAPRA-S score 0–2)63 (26.4)73 (32.9)79 (28.9)99 (25.6) Intermediate (CAPRA-S score 3–5)123 (51.5)86 (38.7)119 (43.6)139 (35.9) High (CAPRA-S score 6–12)46 (19.2)51 (23.0)75 (27.5)110 (28.4) Unknown7 (2.9)12 (5.4)039 (10.1)Recurrence status, n (%) No biochemical recurrence138 (57.7)105 (45.8)152 (55.7)356 (92.0) Biochemical recurrence101 (42.3)117 (54.2)121 (44.3)31 (8.0)Follow-up time in months, median (IQR)106.5 (89.9–121.7)139.4 (108.0–164.4)136.4 (106.5–154.5)20.9 (9.4–35.6)Survival status, n (%) Alive593 (80.8)NA Dead92 (12.5)NA PC-specific death33 (4.5)NA Survival status unknown16 (2.2)387 (100)CAPRA-S, Cancer of the Prostate Risk Assessment Post-Surgical; IQR, interquartile range; NA, not available; PC, prostate cancer; PSA, prostate-specific antigen; RP, radical prostatectomy; TCGA, The Cancer Genome Atlas; T-stage, tumor stage. Open table in a new tab CAPRA-S, Cancer of the Prostate Risk Assessment Post-Surgical; IQR, interquartile range; NA, not available; PC, prostate cancer; PSA, prostate-specific antigen; RP, radical prostatectomy; TCGA, The Cancer Genome Atlas; T-stage, tumor stage. PC tissue samples (formalin-fixed paraffin-embedded) from cohorts 1 and 2 were obtained from PC patients who underwent RP at the Department of Urology, Aarhus University Hospital (Aarhus, Denmark), between 1997 and 2005. These cohorts have also been previously described.13Kristensen H. Thomsen A.R. Haldrup C. Dyrskjøt L. Høyer S. Borre M. Mouritzen P. Ørntoft T.F. Sørensen K.D. Novel diagnostic and prognostic classifiers for prostate cancer identified by genome-wide microRNA profiling.Oncotarget. 2016; 7: 30760-30771Crossref PubMed Scopus (60) Google Scholar,15Schmidt L. Fredsøe J. Kristensen H. Strand S.H. Rasmussen A. Høyer S. Borre M. Mouritzen P. Ørntoft T. Sørensen K.D. Training and validation of a novel 4-miRNA ratio model (MiCaP) for prediction of postoperative outcome in prostate cancer patients.Ann Oncol. 2018; 29: 2003-2009Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar Before this study, patient follow-up information, including time to BCR, was updated for all patients in cohorts 1 and 2 (April 2018). PC tissue samples (formalin-fixed paraffin-embedded) from cohort 3 were obtained from PC patients who underwent RP from 2002 to 2005 at the Department of Urology, Rigshospitalet, Copenhagen University Hospital (Copenhagen, Denmark). For cohort 3, follow-up was updated in July 2017. Inclusion and exclusion criteria for cohorts 1 to 3 are reported according to the REporting Recommendations for Tumour MARKer Prognostic Studies (REMARK) guidelines in Supplemental Figure S1. Patients were excluded if they received preoperative/postoperative radiation/endocrine treatment, were lost to follow-up, and/or experienced BCR within 3 months after RP. Evaluation of tissue specimens, pathologic grading, and RNA extraction were performed, as previously described, for cohorts 1 and 216Haldrup C. Mundbjerg K. Vestergaard E.M. Lamy P. Wild P. Schulz W.A. Arsov C. Visakorpi T. Borre M. Høyer S. Orntoft T.F. Sørensen K.D. DNA methylation signatures for prediction of biochemical recurrence after radical prostatectomy of clinically localized prostate cancer.J Clin Oncol. 2013; 31: 3250-3258Crossref PubMed Scopus (112) Google Scholar and for cohort 3.17Kristensen G. Røder M.A. Berg K.D. Elversang J. Iglesias-Gato D. Moreira J. Toft B.G. Brasso K. Predictive value of combined analysis of pro-NPY and ERG in localized prostate cancer.APMIS. 2018; 126: 804-813Crossref PubMed Scopus (10) Google Scholar miR-615-3p and miR-151a-5p (for normalization) expression in cohorts 1 to 3 was profiled using the miRCURY LNA Universal RT microRNA PCR platform (Exiqon A/S, Vedbæk, Denmark), as previously described.13Kristensen H. Thomsen A.R. Haldrup C. Dyrskjøt L. Høyer S. Borre M. Mouritzen P. Ørntoft T.F. Sørensen K.D. Novel diagnostic and prognostic classifiers for prostate cancer identified by genome-wide microRNA profiling.Oncotarget. 2016; 7: 30760-30771Crossref PubMed Scopus (60) Google Scholar Missing or nondetected values were set to quantitation cycle (Cq) 42. Written informed consent was obtained from all patients from Aarhus University Hospital (cohorts 1 and 2), and the study was approved by the regional scientific ethical committee and the Danish Data Protection Agency (file numbers 2000/0299 and 2013-41-2041). For patients from Rigshospitalet, Copenhagen University Hospital (cohorts 3), the Danish National Committee on Health Research Ethics (journal number H-6-2014-111) approved the use of archived tissue specimens for this research project. For cohort 4, PC tissue samples were collected from RP patients by The Cancer Genome Atlas (TCGA) consortium at multiple centers in the United States and Europe and analyzed by small RNA sequencing.18The Cancer Genome Atlas Research NetworkThe molecular taxonomy of primary prostate cancer.Cell. 2015; 163: 1011-1025Abstract Full Text Full Text PDF PubMed Scopus (1859) Google Scholar,19Clark K. Vendt B. Smith K. Freymann J. Kirby J. Koppel P. Moore S. Phillips S. Maffitt D. Pringle M. Tarbox L. Prior F. The cancer imaging archive (TCIA): maintaining and operating a public information repository.J Digit Imaging. 2013; 26: 1045-1057Crossref PubMed Scopus (2032) Google Scholar Normalized miRNA profiling data and clinical data were retrieved from TCGA data portal18The Cancer Genome Atlas Research NetworkThe molecular taxonomy of primary prostate cancer.Cell. 2015; 163: 1011-1025Abstract Full Text Full Text PDF PubMed Scopus (1859) Google Scholar for 499 PC patients using the GDC Data Transfer Tool and Firehose from the Broad Institute Genome Data Analysis Center. Clinical information and miRNA data were available for 476 patients. Eight patients were excluded because of missing clinical/BCR data, and another 81 patients were excluded because the patient experienced BCR within 3 months of RP, leaving 387 patients eligible for the final analysis. Unless stated otherwise, statistical analyses were conducted in R version 3.5.1 using R Studio version 1.1.463.20R Core TeamR: A Language and Environment for Statistical Computing.2018Google Scholar Initially, miR-615-3p expression levels in cohorts 1 to 3 were normalized to a stably expressed miRNA (miR-151a-5p), previously identified by the NormFinder algorithm.13Kristensen H. Thomsen A.R. Haldrup C. Dyrskjøt L. Høyer S. Borre M. Mouritzen P. Ørntoft T.F. Sørensen K.D. Novel diagnostic and prognostic classifiers for prostate cancer identified by genome-wide microRNA profiling.Oncotarget. 2016; 7: 30760-30771Crossref PubMed Scopus (60) Google Scholar,21Wang L. Liu Y. Du L. Li J. Jiang X. Zheng G. Qu A. Wang H. Wang L. Zhang X. Liu H. Pan H. Yang Y. Wang C. Identification and validation of reference genes for the detection of serum microRNAs by reverse transcription-quantitative polymerase chain reaction in patients with bladder cancer.Mol Med Rep. 2015; 12: 615-622Crossref PubMed Scopus (38) Google Scholar Normalization was performed according to the following: ΔCq = CqmiR-151a-5p − CqmiR-615-3p. In cohort 4, normalized miRNA sequencing data were obtained from TCGA data portal and log2 transformed by the following: Reads per million (RPM)log = log2 (RPM + 1).18The Cancer Genome Atlas Research NetworkThe molecular taxonomy of primary prostate cancer.Cell. 2015; 163: 1011-1025Abstract Full Text Full Text PDF PubMed Scopus (1859) Google Scholar Associations between miR-615-3p and routine clinicopathologic variables were assessed by the CAPRA-S risk nomogram, which includes preoperative prostate-specific antigen, pathologic Gleason score, surgical margin status, extracapsular extension, seminal vesicle invasion, and lymph node invasion, using nonparametric Wilcoxon rank-sum tests.22Cooperberg M.R. Hilton J.F. Carroll P.R. The CAPRA-S score: a straightforward tool for improved prediction of outcomes after radical prostatectomy.Cancer. 2011; 117: 5039-5046Crossref PubMed Scopus (302) Google Scholar P < 0.05 was considered significant. To investigate prognostic potential, patients in cohort 1 (training) were dichotomized according to miR-615-3p expression levels based on the optimal cutoff identified by receiver operating characteristic curve analysis of BCR status and using Youden's J statistic in the pROC package.23Turck N. Vutskits L. Sanchez-Pena P. Robin X. Hainard A. Gex-Fabry M. Fouda C. Bassem H. Mueller M. Lisacek F. Puybasset L. Sanchez J.-C. pROC: an open-source package for R and S+ to analyze and compare ROC curves.BMC Bioinformatics. 2011; 8: 12-77Google Scholar The cutoff fraction identified in cohort 1 was subsequently used and tested in cohorts 2 and 3. Because of the difference in expression data type (ΔCq values in cohorts 1 to 3; RPMlog in cohort 4), a separate cutoff was used in cohort 4, as identified by receiver operating characteristic curve analysis of BCR status. Univariate and multivariate Cox regression as well as Kaplan-Meier analyses were used to evaluate the prognostic potential of miR-615-3p expression using the survival package24Therneau T.M. Grambsch P.M. Modeling Survival Data: Extending the Cox Model. Springer, New York, NY2000Crossref Google Scholar with postoperative BCR (prostate-specific antigen ≥ 0.2 ng/mL) or CSS as end points. Patients not having experienced BCR were censored at their last normal prostate-specific antigen test result. Last known patient survival status was obtained using information from the Central Office of Civil Registration by personal identification number in May 2018 for cohorts 1 and 2 and in July 2017 for cohort 3. Predictive accuracy was determined using Harrell's concordance index (C-index). All functional studies of miR-615-3p overexpression were conducted in the human cell line PC3M, whereas miR-615-3p inhibition was performed in the human cell line PNT1A. The cell lines were selected because of a low and high endogenous expression of miR-615-3p, respectively (data not shown). PC3M was a kind gift from Prof. Raymond C. Bergan (Department of Medicine, Northwestern University, Chicago, IL), received in October 2011. PNT1A cells are immortalized prostate epithelial cells and are described by Degeorges et al.25Degeorges A. Hoffschir F. Cussenot O. Gauville C. Le Duc A. Dutrillaux B. Calvo F. Recurrent cytogenetic alterations of prostate carcinoma and amplification of c-myc or epidermal growth factor receptor in subclones of immortalized pnt1 human prostate epithelial cell line.Int J Cancer. 1995; 62: 724-731Crossref PubMed Scopus (47) Google Scholar The cell lines were authenticated by short tandem repeat profiling with IdentiCell using the GenePrint 10 system (Promega, Madison, WI) in October 2012 and July 2014, respectively, and no cross contamination was observed. Before all experiments, cells were further confirmed negative for Mycoplasma contamination using the Mycoplasma qPCR Detection Kit (TaKaRa, Shiga, Japan), according to the manufacturer's instructions. Cells were cultured in RPMI 1640 medium with l-glutamine (Lonza, Basel, Switzerland) and supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin (Gibco, Grand Island, NY) at 37°C and 5% CO2. To ectopically overexpress miR-615-3p, PC3M cells were reverse transfected using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA), according to the manufacturer's instructions. In brief, PC3M cells were transfected with mirVana miR-615-3p mimic (product MC117319) or negative control (mirVanaTM miRNA Mimic, Negative Control number 1; catalog number 4464058) (Ambion, Life Technologies, Carlsbad, CA) at a final miR-615-3p mimic or negative control concentration of 20 nmol/L, respectively. To inhibit miR-615-3p expression, PNT1A cells were transfected using the same reverse protocol with mirVana miR-615-3p inhibitor (product MH11731) or negative control (mirVana miRNA Inhibitor, Negative Control number 1; catalog number 4464076) (Thermo Fisher Scientific) at an equal concentration of 20 nmol/L. For all experiments, Lipofectamine was diluted 1:1000. No evidence of Lipofectamine toxicity was observed using alamarBlue Cell Viability assays (Invitrogen, Carlsbad, CA) (data not shown). Before initiating functional experiments, transfection efficiency was assessed using a Cy3-labeled pre-miR Negative Control (catalog number AM17120; Ambion, Applied Biosystems, Waltham, MA) and was near to 100% at 48 hours after transfection for both PC3M and PNT1A. Cell viability was assessed by a Rezasurin assay (alamarBlue Cell Viability Reagent) and measured using the Synergy HT reader (BioTek, Winooski, VT). In brief, PC3M cells were reverse transfected in a 96-well plate using 4000 cells per well. At 48 hours after transfection, PC3M cells were incubated with 10% alamarBlue Cell Viability Reagent for 3 hours before measurement of absorbance. All experiments were performed in four technical replicates and repeated three times. Cell proliferation was estimated using the xCELLigence RTCA DP system (ACEA Biosciences, Inc., San Diego, CA). Cells were subjected to xCELLigence immediately after reverse transfection at 4000 cells per well and analyzed for approximately 100 hours (two independent experiments with two and five technical replicates). Cell apoptosis was assessed by a caspase 3/7 activity assay, as described previously.26Christensen L.L. Holm A. Rantala J. Kallioniemi O. Rasmussen M.H. Ostenfeld M.S. Dagnaes-Hansen F. Øster B. Schepeler T. Tobiasen H. Thorsen K. Sieber O.M. Gibbs P. Lamy P. Hansen T.F. Jakobsen A. Riising E.M. Helin K. Lubinski J. Hagemann-Madsen R. Laurberg S. Ørntoft T.F. Andersen C.L. Functional screening identifies miRNAs influencing apoptosis and proliferation in colorectal cancer.PLoS One. 2014; 9: e96767Crossref PubMed Scopus (50) Google Scholar Briefly, PC3M cells were reverse transfected with miR-615-3p mimic or scrambled control in 24-well plates at 40,000 cells per well. Forty-eight hours after transfection, cells were treated with the apoptosis-inducing agent staurosporine (1 μmol/L) for 2 hours before caspase 3/7 activity in cell lysates was measured by the liberation of 7-amino-4-trifluoromethyl coumarin (AFC) (excitation, 400 nm; emission, 489 nm) from the substrate Ac-DEVD-AFC (Enzo Life Sciences, Inc., Farmingdale, NY) using the Synergy HT reader (BioTek). All experiments were performed in triplicate and repeated twice. To monitor cell migration, wound healing assays (scratch assays) were performed. PC3M cells were seeded and reverse transfected (miR-615-3p mimic or scrambled control) in 6-well plates at 300,000 cells per well. Forty-eight hours after transfection, four scratches were made per well using a 100-μL pipette tip. After scratching, the medium was changed to avoid reattachment of cells in the wound (gap area). Cell migration toward the wound was documented using a light-optical microscope (Axiovert40; Carl Zeiss, Oberkocken, Germany) with ×10 magnification. Images were taken at 0, 4, and 8 hours after performing the scratch. Three images were taken per well, and the width of the gap was measured at three locations per image using the ImageJ software version 1.51j8 (NIH, Bethesda, MD; https://imagej.nih.gov/ij) (n = 9 measurements/well).27Schneider C.A. Rasband W.S. Eliceiri K.W. NIH Image to ImageJ: 25 years of image analysis.Nat Methods. 2012; 9: 671-675Crossref PubMed Scopus (35269) Google Scholar For each individual well, all measurements of gap width were averaged. Migration was calculated as follows: % migration = (gap width after 4 or 8 hours)/(gap width after 0 hours). All experiments were performed in triplicate and repeated at least twice. mRNA expression data were retrieved from TCGA data portal18The Cancer Genome Atlas Research NetworkThe molecular taxonomy of primary prostate cancer.Cell. 2015; 163: 1011-1025Abstract Full Text Full Text PDF PubMed Scopus (1859) Google Scholar for 297 PC patients and log2 transformed by the following: RPMlog = log2 (RPM + 1). Of these 297 patients, a total of 212 had matched miRNA-sequencing data (ie, overlapped with patients included in cohort 4) and were used for further analysis. Associations between potential target transcripts and miR-615-3p expression levels were further assessed using Pearson's product-moment correlation. To investigate enriched gene sets in patients with high miR-615-3p expression compared with patients with low miR-615-3p expression, patients in cohort 4 with both miRNA and mRNA data were dichotomized according to the median miR-615-3p expression level. Gene Set Enrichment Analysis was conducted using the gage package and the Gene Ontology (GO) terms.28Luo W. Friedman M. Shedden K. Hankenson K. Woolf P. GAGE: generally applicable gene set enrichment for pathway analysis.BMC Bioinformatics. 2009; 10: 161Crossref PubMed Scopus (792) Google Scholar To evaluate the strength of the analysis, a random split between the patients in cohort 4 was also performed using the sample function in R; and Gene Set Enrichment Analysis was performed on these two groups as well. Gene sets were considered significant if the false-discovery rate q < 0.05 (adjustment of the global P value using the Benjamini and Hochberg procedure). To investigate the prognostic potential of miR-615-3p, its expression level was initially measured in RP tissue specimens from patients with clinically localized PC (cohort 1, training set, n = 239); and these findings were subsequently tested and validated in three additional independent RP patient cohorts (cohorts 2, 3, and 4: n = 222, n = 273, and n = 387, respectively). Patient characteristics for all four cohorts are summarized in Table 1. First, when compared with the established postoperative CAPRA-S nomogram, miR-615-3p was significantly up-regulated in high-risk versus low-risk tumors in cohorts 1, 2, and 4 (P < 0.05, Wilcoxon rank-sum test) (Figure 1), whereas this association was borderline significant in cohort 3 (Figure 1). This suggests that high miR-615-3p expression is associated with worse prognosis in PC. To further evaluate this, patients in cohort 1 (training) were stratified into a high- versus low-risk group based on miR-615-3p expression. In cohort 1, high miR-615-3p expression was significantly associated with BCR in Kaplan-Meier analysis (P = 0.046) (Figure 2A) and in univariate Cox regression analysis [hazard ratio (HR) = 1.61; P = 0.048] (Table 2). Although miR-615-3p did not remain significant in multivariate Cox regression analysis after adjustment for CAPRA-S (P = 0.21) (Table 2), addition of miR-615-3p to CAPRA-S in a multivariate model increased Harrell's C-index from 0.675 to 0.684 in cohort 1 (Table 2), suggesting slightly improved predictive accuracy.Table 2Univariate and Multivariate Cox-Regression Analysis of BCR and CSS Using miR-615-3p in Four RP CohortsVariableCharacteristicUnivariateMultivariateHR (95% CI)P valueC-indexHR (95% CI)P valueC-index∗C-index based on CAPRA-S nomogram only.C-index†C-index based on final model, including miR-615-3p and CAPRA-S.Cohort 1, training set n = 239, 101 with BCR CAPRA-SLowRef0.680.6750.684Intermediate2.30 (1.22–4.34)1.02 × 10−22.28 (1.21–4.30)1.12 × 10−2High7.37 (3.81–14.25)3.06 × 10−97.11 (3.66–13.80)6.87 × 109 miR-615-3pLow vs high1.61 (1.01–2.57)4.77 × 10−20.541.38 (0.84–2.26)0.21Cohort 2, n = 222, 117 with BCR CAPRA-SLowRef0.720.7230.

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