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Molecular Classification of Non–Muscle-Invasive Bladder Cancer (pTa Low-Grade, pT1 Low-Grade, and pT1 High-Grade Subgroups) Using Methylation of Tumor-Suppressor Genes

2014; Elsevier BV; Volume: 16; Issue: 5 Linguagem: Inglês

10.1016/j.jmoldx.2014.04.007

ISSN

1943-7811

Autores

Raquel Sacristán, Carolina Gonzalez, J.M. Fernández Gómez, Florentino Fresno, Safwan Escaf, Marta Sänchez‐Carbayo,

Tópico(s)

Cancer-related gene regulation

Resumo

The role of epigenetics in distinguishing pathological and clinical subgroups in bladder cancer is not fully characterized. We evaluated whether methylation of tumor-suppressor genes (TSGs) would classify non–muscle-invasive (NMI) bladder cancer subgroups and predict outcome. A retrospective design included the following paraffin-embedded primary NMI tumor types (n = 251): pTa low grade (LG) (n = 79), pT1LG (n = 81), and pT1 high grade (HG) (n = 91). Methylation of 25 TSGs was measured using methylation-specific, multiplex, ligation-dependent probe amplification. The TSGs most frequently methylated in the overall series were STK11 (96.8%), MGMT2 (64.5%), RARB (63.0%), and GATA5 (63.0%). TSG methylation correlated to clinicopathological variables in each subgroup and in the overall NMI series. Methylation of RARB, CD44, PAX5A, GSTP1, IGSF4 (CADM1), PYCARD, CDH13, TP53, and GATA5 classified pTa versus pT1 tumors whereas RARB, CD44, GSTP1, IGSF4, CHFR, PYCARD, TP53, STK11, and GATA5 distinguished LG versus HG tumors. Multivariate analyses indicated that PAX5A, WT1, and BRCA1 methylation independently predicted recurrence in pTaLG, PAX6, ATM, CHFR, and RB1 in pT1LG disease; PYCARD, in pT1HG disease; and PAX5A and RB1, in the overall series. Methylation of TSGs provided a molecular classification of NMI disease according to clinicopathological factors. Furthermore, TSG methylation predicted recurrence in NMI subgroups. The role of epigenetics in distinguishing pathological and clinical subgroups in bladder cancer is not fully characterized. We evaluated whether methylation of tumor-suppressor genes (TSGs) would classify non–muscle-invasive (NMI) bladder cancer subgroups and predict outcome. A retrospective design included the following paraffin-embedded primary NMI tumor types (n = 251): pTa low grade (LG) (n = 79), pT1LG (n = 81), and pT1 high grade (HG) (n = 91). Methylation of 25 TSGs was measured using methylation-specific, multiplex, ligation-dependent probe amplification. The TSGs most frequently methylated in the overall series were STK11 (96.8%), MGMT2 (64.5%), RARB (63.0%), and GATA5 (63.0%). TSG methylation correlated to clinicopathological variables in each subgroup and in the overall NMI series. Methylation of RARB, CD44, PAX5A, GSTP1, IGSF4 (CADM1), PYCARD, CDH13, TP53, and GATA5 classified pTa versus pT1 tumors whereas RARB, CD44, GSTP1, IGSF4, CHFR, PYCARD, TP53, STK11, and GATA5 distinguished LG versus HG tumors. Multivariate analyses indicated that PAX5A, WT1, and BRCA1 methylation independently predicted recurrence in pTaLG, PAX6, ATM, CHFR, and RB1 in pT1LG disease; PYCARD, in pT1HG disease; and PAX5A and RB1, in the overall series. Methylation of TSGs provided a molecular classification of NMI disease according to clinicopathological factors. Furthermore, TSG methylation predicted recurrence in NMI subgroups. Bladder cancer is a molecular disease driven by the multistep accumulation of genetic, epigenetic, and environmental factors.1Wolff E.M. Liang G. Jones P.A. Mechanisms of Disease: genetic and epigenetic alterations that drive bladder cancer.Nat Clin Pract Urol. 2005; 2: 502-510Crossref PubMed Scopus (102) Google Scholar, 2Sánchez-Carbayo M. Cordon-Cardó C. Molecular alterations associated with bladder cancer progression.Semin Oncol. 2007; 34: 75-84Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar DNA methylation represents the most common epigenetic alteration in cancer, in addition to miRNAs and histone modifications.3Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future.Oncogene. 2002; 21: 5427-5440Crossref PubMed Scopus (976) Google Scholar, 4Sánchez-Carbayo M. Hypermethylation in bladder cancer: biological pathways and translational applications.Tumour Biol. 2012; 33: 347-361Crossref PubMed Scopus (52) Google Scholar Hypermethylation of the CpG islands of tumor-suppressor genes (TSGs), located around gene promoters, is frequently associated with transcriptional inactivation.3Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future.Oncogene. 2002; 21: 5427-5440Crossref PubMed Scopus (976) Google Scholar Hypermethylation of TSGs, which may occur early in carcinogenesis, is clinically valuable not only for early diagnosis but also for disease stratification.1Wolff E.M. Liang G. Jones P.A. Mechanisms of Disease: genetic and epigenetic alterations that drive bladder cancer.Nat Clin Pract Urol. 2005; 2: 502-510Crossref PubMed Scopus (102) Google Scholar, 2Sánchez-Carbayo M. Cordon-Cardó C. Molecular alterations associated with bladder cancer progression.Semin Oncol. 2007; 34: 75-84Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar, 3Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future.Oncogene. 2002; 21: 5427-5440Crossref PubMed Scopus (976) Google Scholar, 4Sánchez-Carbayo M. Hypermethylation in bladder cancer: biological pathways and translational applications.Tumour Biol. 2012; 33: 347-361Crossref PubMed Scopus (52) Google Scholar Initial studies of methylation in bladder cancer evaluated individual TSGs by means of methylation-specific PCR (MS-PCR).4Sánchez-Carbayo M. Hypermethylation in bladder cancer: biological pathways and translational applications.Tumour Biol. 2012; 33: 347-361Crossref PubMed Scopus (52) Google Scholar The assessment of multiple TSGs in bladder cancer, using multiparametric techniques, is feasible using an MS, multiplex, ligation-dependent, probe-amplification assay (MS-MLPA).5Serizawa R.R. Ralfkiaer U. Dahl C. Lam G.W. Hansen A.B. Steven K. Horn T. Guldberg P. Custom-designed MLPA using multiple short synthetic probes: application to methylation analysis of five promoter CpG islands in tumor and urine specimens from patients with bladder cancer.J Mol Diagn. 2010; 12: 402-408Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar, 6Cabello M.J. Grau L. Franco N. Orenes E. Alvarez M. Blanca A. Heredero O. Palacios A. Urrutia M. Fernández J.M. López-Beltrán A. Sánchez-Carbayo M. Multiplexed methylation profiles of tumor suppressor genes in bladder cancer.J Mol Diagn. 2011; 13: 29-40Abstract Full Text Full Text PDF PubMed Scopus (40) Google Scholar, 7Agundez M. Grau L. Palou J. Algaba F. Villavicencio H. Sanchez-Carbayo M. Evaluation of the methylation status of tumour suppressor genes for predicting bacillus Calmette-Guérin response in patients with T1G3 high-risk bladder tumours.Eur Urol. 2011; 60: 131-140Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar, 8Zuiverloon T.C. Beukers W. van der Keur K.A. Munoz J.R. Bangma C.H. Lingsma H.F. Eijkemans M.J. Schouten J.P. Zwarthoff E.C. A methylation assay for the detection of non-muscle-invasive bladder cancer (NMIBC) recurrences in voided urine.BJU Int. 2012; 109: 941-948Crossref PubMed Scopus (56) Google Scholar Discrimination between methylated and unmethylated sequences in MS-MLPA is based on the annealing of probes containing a recognition site for the methylation-sensitive restriction enzyme HhaI [Haemophilus haemolyticus (ATCC 10014)]. We have previously shown that a selected panel of TSGs are clinically useful in tumor and urine samples6Cabello M.J. Grau L. Franco N. Orenes E. Alvarez M. Blanca A. Heredero O. Palacios A. Urrutia M. Fernández J.M. López-Beltrán A. Sánchez-Carbayo M. Multiplexed methylation profiles of tumor suppressor genes in bladder cancer.J Mol Diagn. 2011; 13: 29-40Abstract Full Text Full Text PDF PubMed Scopus (40) Google Scholar and that normal urothelium can be differentiated from bladder tumors, and we have provided prognostic and predictive assessments in pT1 high-grade (HG) tumors.7Agundez M. Grau L. Palou J. Algaba F. Villavicencio H. Sanchez-Carbayo M. Evaluation of the methylation status of tumour suppressor genes for predicting bacillus Calmette-Guérin response in patients with T1G3 high-risk bladder tumours.Eur Urol. 2011; 60: 131-140Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar We, therefore, hypothesized that these TSGs might also classify the major non–muscle-invasive (NMI) histopathological and clinical subgroups. More specifically, we aimed at evaluating whether TSG methylation would stratify pTa low-grade (LG), pT1LG, and pT1HG tumors and would contribute to prognostic assessment in each of these subgroups of patients with NMI disease. Data from patients treated for primary pTaLG (n = 79), pT1LG (n = 81), and pT1HG (n = 91) bladder tumors between 1989 and 2009 were collected under institutional review board–approved protocols at the Central Hospital of Asturias (Oviedo, Spain). Patients who were treated within this time frame were selected to obtain balanced sample subgroups that would enable the comparison of clinicopathological variables, including data from annotated follow-up for outcomes assessment. All tumors within each category were primary resections, and each n was a unique patient. Eligible patients were surgically treated with transurethral resection (TUR), a similar NMI diagnosis (transitional cell carcinoma) on histological examination, with a wide and deep primary resection with detrusor muscle in the specimen, and paraffin-embedded tissue material adequate for analysis. Patients were excluded if they had prior intravesical treatment or a tumor of the upper urinary tract. Bladder tumors were pathologically classified following the standard World Health Organization 2004 criteria, and patients were diagnosed and treated following standard clinical practice guidelines.9Babjuk M. Oosterlinck W. Sylvester R. Kaasinen E. Böhle A. Palou J. Guidelines on Non-Muscle-Invasive Bladder Cancer (TaT1 and Cis). Update March 2011. European Association of Urology, Arnhem, the Netherlands2011: 1-35Google Scholar Patients presenting with tumors with pT1LG were treated with 40 mg of mitomycin C during the 6 months after TUR. Patients with tumors with pT1HG disease received induction and maintenance courses of bacillus Calmette-Guérin (BCG). Instillation (BCG, Connaught strain) was first given after 14 days post-TUR and was repeated weekly for 6 consecutive weeks and thereafter every 2 weeks for six additional instillations within 5 to 6 months after TUR. All patients completed the mitomycin and BCG schemes. Follow-up consisted of cystoscopy with cytology at scheduled months, depending on the subgroup.9Babjuk M. Oosterlinck W. Sylvester R. Kaasinen E. Böhle A. Palou J. Guidelines on Non-Muscle-Invasive Bladder Cancer (TaT1 and Cis). Update March 2011. European Association of Urology, Arnhem, the Netherlands2011: 1-35Google Scholar Progression was defined as muscular invasion (stage T2 or higher) or metastatic disease. Patients with recurring pT1LG lesions were treated with another course of mitomycin C. Patients with pT1HG recurring tumors were treated either with BCG or cystectomy when disease progressed to invasive disease. Patients with progressing tumors with metastatic disease received adjuvant cisplatin-based chemotherapy. Paraffin-embedded tissues were macrodissected based on hematoxylin and eosin staining evaluations to ensure a minimum of 75% of tumor cells.6Cabello M.J. Grau L. Franco N. Orenes E. Alvarez M. Blanca A. Heredero O. Palacios A. Urrutia M. Fernández J.M. López-Beltrán A. Sánchez-Carbayo M. Multiplexed methylation profiles of tumor suppressor genes in bladder cancer.J Mol Diagn. 2011; 13: 29-40Abstract Full Text Full Text PDF PubMed Scopus (40) Google Scholar, 7Agundez M. Grau L. Palou J. Algaba F. Villavicencio H. Sanchez-Carbayo M. Evaluation of the methylation status of tumour suppressor genes for predicting bacillus Calmette-Guérin response in patients with T1G3 high-risk bladder tumours.Eur Urol. 2011; 60: 131-140Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar Corresponding slides were digested using proteinase K (Roche Diagnostics GmbH, Mannheim, Germany) overnight before DNA extraction using standard methods. Concentration and purity of DNA samples were determined with an ND-1000 spectrophotometer (NanoDrop, Wilmington, DE). DNA quality was evaluated based on 260:280 ratios of absorbency, and integrity was checked by gel electrophoresis using the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA). The MS-MLPA ME002 probe set (MRC-Holland, Amsterdam, the Netherlands) simultaneously measured the methylation at one or two CpG dinucleotides of 25 proven or suspected TSGs. Two genes were represented by two probes, each assessing a different HhaI restriction site in the promoter region of the respective genes. Relative peak values of ligation-digestion samples were divided by relative peak values of corresponding ligation (undigested) samples, resulting in a methylation ratio (range, 0 to 1). Hypermethylation was scored when the methylation ratio was ≥0.30, corresponding to the presence of 30% of methylated DNA. The experimental procedure was performed and the results were analyzed as previously reported.6Cabello M.J. Grau L. Franco N. Orenes E. Alvarez M. Blanca A. Heredero O. Palacios A. Urrutia M. Fernández J.M. López-Beltrán A. Sánchez-Carbayo M. Multiplexed methylation profiles of tumor suppressor genes in bladder cancer.J Mol Diagn. 2011; 13: 29-40Abstract Full Text Full Text PDF PubMed Scopus (40) Google Scholar, 7Agundez M. Grau L. Palou J. Algaba F. Villavicencio H. Sanchez-Carbayo M. Evaluation of the methylation status of tumour suppressor genes for predicting bacillus Calmette-Guérin response in patients with T1G3 high-risk bladder tumours.Eur Urol. 2011; 60: 131-140Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar Associations between ratios (range, 0 to 1) of MS-MLPA methylation and clinicopathological variables were evaluated using nonparametric Wilcoxon-Mann-Whitney and Kruskal-Wallis tests, applying the Bonferroni adjustment to minimize type I error risk for multiple testing.10Dawson-Saunders B. Trapp R.G. Basic & Clinical Biostatistics.ed 2. Appleton & Lange, Norwalk, CT1994Google Scholar Clinicopathological and annotated follow-up information allowed for correlations to histopathological and clinical properties and outcomes assessments. Distributions of times to events were estimated by means of cumulative-incidence functions, to properly take into account the patients who died (competing risk) before recurrence or progression. Stratified univariate and multivariate Cox proportional hazards regression models were applied to compare actuarial events and to determine independent predictive factors.10Dawson-Saunders B. Trapp R.G. Basic & Clinical Biostatistics.ed 2. Appleton & Lange, Norwalk, CT1994Google Scholar, 11Kim H.T. Cumulative incidence in competing risks data and competing risks regression analysis.Clin Cancer Res. 2007; 13: 559-565Crossref PubMed Scopus (377) Google Scholar For analysis of recurrence, only patients with available follow-up data (recurrence or alive with no evidence of disease) were considered. For analysis of progression, only patients with available follow-up data (progression to invasive disease or alive with no evidence of disease) were considered. For analysis of disease-specific survival, only patients with available follow-up data (disease-specific death or alive with no evidence of disease) were included. For analysis of overall survival, only patients with available follow-up data (all-cause death or alive with no evidence of disease) were included. Patients alive at the last follow-up or lost to follow-up were censored. Survival curves were plotted using the cumulative-incidence methodology.11Kim H.T. Cumulative incidence in competing risks data and competing risks regression analysis.Clin Cancer Res. 2007; 13: 559-565Crossref PubMed Scopus (377) Google Scholar The positive predictive value (PPV) of the presence of methylation of each TSG was estimated for the clinical outcomes of recurrence, progression, and disease-specific survival. Statistical analyses were performed using PASW Statistics version 18.0.1 (IBM SPSS Statistics, IBM Corporation, Armonk, NY), and R for Statistical Computing version 2.9.1 (R Foundation, Vienna, Austria). The tumor characteristics and clinical follow-up of all of the cases under analysis and within each pathological subgroup are summarized in Table 1. The TSGs most frequently methylated in the overall NMI series were STK11 (96.8%), MGMT2 (64.5%), RARB (63.0%), and GATA5 (63.0%). Considering all NMIs together, correlations of methylation to stage were found with RARB (P = 0.027), CD44 (P = 0.002), PAX5A (P = 0.018), GSTP1 (P < 0.001), IGSF4 (P < 0.001), PYCARD (P < 0.001), CDH13 (P = 0.028), TP53 (P = 0.001), and GATA5 (P = 0.001); and to tumor grade, with RARB (P < 0.001), CD44 (P < 0.001), GSTP1 (P < 0.001), IGSF4 (P = 0.005), CHFR (P = 0.032), PYCARD (P < 0.001), TP53 (P < 0.001), STK11 (P = 0.028), and GATA5 (P < 0.001). Distributions of methylation in the full series and within each subgroup are shown in Table 2. Significant correlations between methylation of the following TSGs and clinicopathological variables were observed in the NMI overall series: RARB (P = 0.018), CD44 (P = 0.001), PYCARD (P < 0.001), STK11 (P = 0.048), and GATA5 (P = 0.006) and the presence of associated cis; GSTP1 (P = 0.034) and PYCARD (P = 0.045) and tumor size; and VHL (P = 0.021), CD44 (P = 0.047), IGSF4 (P = 0.044), BRCA2 (P = 0.034), and THBS1 (P = 0.050) and focality (Table 3).Table 1Summary of the Clinical and Follow-Up Characteristics in Each Subset of NMI Cases and All the Patients Analyzed TogetherCriterionpTaLG (n = 79)pT1LG (n = 81)pT1HG (n = 91)All NMI (n = 251)Sex [no. (%)] Male64 (81.0)66 (81.5)83 (91.2)213 (84.9) Female15 (19.0)15 (18.55)8 (8.8)38 (15.1)Age [no. (%)] <65 years25 (31.6)28 (24.6)27 (29.7)80 (31.9) ≥65 years54 (68.4)53 (64.4)64 (70.3)171 (68.1)Associated CIS [no. (%)] Absent79 (100)81 (100)63 (69.2)223 (88.8) Present0028 (39.6)28 (11.2)Tumor size [no. (%)] <3 cm67 (84.8)55 (67.9)55 (60.4)177 (70.5) ≥3 cm12 (15.2)26 (32.1)36 (39.6)74 (29.5)Focality [no. (%)] Únique60 (76.0)51 (63.0)50 (55.0)161 (64.1) Multiple19 (24.0)30 (37.0)41 (45.0)90 (35.9)Outcome Recurrence (no.)464537128Median (range) time to recurrence (months)27 (3–111)35 (3–107)43 (3–114)35 (3–114) Progression (no.)242531Median (range) time to progression (months)58 (3–129)65 (3–129)50 (3–130)58 (3–130) Death15233977Cancer-specific death (no.)211316 Time to cancer-specific death (months)46∗Range is not provided due to the small number of cases. and 26∗Range is not provided due to the small number of cases. (n = 2)102∗Range is not provided due to the small number of cases. (n = 1)16 (5–126)24 (5–129)Death for other reasons (no.)13222661 Median (range) time to death for other reasons (months)32 (4–68)43.5 (3–144)30.5 (12–180)33 (3–180) SurvivalOverall survivors (no.)645852174 Median (range) overall survival (months)53 (3–129)60 (3–129)52 (3–131)55 (3–153)Disease-free survivors (no.)29263994 Median (range) disease-free survival (months)59.5 (25–218)71 (27–218)69 (40–235)64 (25–235)∗ Range is not provided due to the small number of cases. Open table in a new tab Table 2Methylation Frequencies in the Full Series and in Each NMI SubgroupTSGOverall(n = 251)pTaLG(n = 79)pT1LG(n = 81)pT1HG(n = 91)STK11243 (96.8)78 (98.7)∗P ≤ 0.05 for association between methylation and stage and grade.80 (98.8)∗P ≤ 0.05 for association between methylation and stage and grade.85 (93.4)∗P ≤ 0.05 for association between methylation and stage and grade.MGMT-2162 (64.5)51 (64.6)53 (65.4)58 (63.7)GATA5158 (63.0)61 (77.2)∗P ≤ 0.05 for association between methylation and stage and grade.58 (71.6)∗P ≤ 0.05 for association between methylation and stage and grade.39 (42.9)∗P ≤ 0.05 for association between methylation and stage and grade.RARB158 (63.0)57 (72.1)∗P ≤ 0.05 for association between methylation and stage and grade.63 (77.8)∗P ≤ 0.05 for association between methylation and stage and grade.38 (41.8)∗P ≤ 0.05 for association between methylation and stage and grade.CD44139 (55.4)55 (69.6)∗P ≤ 0.05 for association between methylation and stage and grade.59 (72.8)∗P ≤ 0.05 for association between methylation and stage and grade.25 (27.5)∗P ≤ 0.05 for association between methylation and stage and grade.PYCARD131 (52.2)62 (78.5)∗P ≤ 0.05 for association between methylation and stage and grade.58 (71.6)∗P ≤ 0.05 for association between methylation and stage and grade.11 (12.1)∗P ≤ 0.05 for association between methylation and stage and grade.WT1126 (50.2)43 (54.4)41 (50.6)42 (46.2)CDH13116 (46.2)29 (36.7)∗P ≤ 0.05 for association between methylation and stage and grade.39 (48.2)∗P ≤ 0.05 for association between methylation and stage and grade.48 (52.8)∗P ≤ 0.05 for association between methylation and stage and grade.BRCA1115 (45.8)40 (50.6)34 (42.0)41 (45.1)MSH6115 (45.8)32 (40.5)39 (48.2)44 (48.4)GSTP1112 (44.6)49 (62.2)∗P ≤ 0.05 for association between methylation and stage and grade.45 (55.6)∗P ≤ 0.05 for association between methylation and stage and grade.18 (19.8)∗P ≤ 0.05 for association between methylation and stage and grade.TP5387 (34.7)39 (49.4)∗P ≤ 0.05 for association between methylation and stage and grade.29 (35.8)∗P ≤ 0.05 for association between methylation and stage and grade.19 (21.9)∗P ≤ 0.05 for association between methylation and stage and grade.PAX5A85 (33.9)19 (24.1)∗P ≤ 0.05 for association between methylation and stage and grade.32 (39.5)∗P ≤ 0.05 for association between methylation and stage and grade.34 (37.4)∗P ≤ 0.05 for association between methylation and stage and grade.IGSF456 (22.3)7 (8.9)∗P ≤ 0.05 for association between methylation and stage and grade.20 (24.7)∗P ≤ 0.05 for association between methylation and stage and grade.29 (31.9)∗P ≤ 0.05 for association between methylation and stage and grade.BRCA254 (21.5)11 (13.9)19 (23.5)24 (26.4)ESR150 (19.9)18 (22.8)19 (23.5)13 (14.3)MGMT43 (17.1)11 (13.9)21 (25.9)11 (12.1)ATM24 (9.6)7 (8.9)11 (13.6)6 (6.6)TP7324 (9.5)4 (5.1)12 (14.8)8 (8.8)PAX622 (8.8)4 (5.1)10 (12.3)8 (8.8)THBS122 (8.8)5 (6.3)6 (7.4)11 (12.1)PTEN20 (8.0)5 (6.3)15 (18.5)5 (5.5)VHL18 (7.2)6 (7.6)5 (6.2)7 (7.7)RB1-215 (6.0)2 (2.5)8 (9.9)5 (5.5)CDKN2A14 (5.6)4 (5.1)8 (9.9)2 (2.2)RB114 (5.6)4 (5.1)7 (8.6)3 (3.3)CHFR12 (4.8)2 (2.5)∗P ≤ 0.05 for association between methylation and stage and grade.9 (11.1)∗P ≤ 0.05 for association between methylation and stage and grade.1 (1.1)∗P ≤ 0.05 for association between methylation and stage and grade.Data are expressed as no. (%).∗ P ≤ 0.05 for association between methylation and stage and grade. Open table in a new tab Table 3Distribution among Clinical Variables in Each Subgroup and the Overall Series for Those TSGs Significantly Associated with Clinical VariablesNMI Subgroup/TSGAgeSexTumor sizeFocalityAssociated cis<65 years≥65 yearsMaleFemale<3 cm≥3 cm1 tm≥2 tmPresentAbsentOverall STK1178 (97.5)165 (96.4)205 (96.2)38 (100)170 (96.0)73 (98.7)156 (96.9)87 (96.7)25 (89.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.218 (97.8)∗P ≤ 0.05 for clinical variables significantly associated with TSG. RARB53 (66.3)105 (61.4)136 (63.9)22 (57.9)117 (66.1)41 (55.4)101 (62.7)57 (63.3)12 (42.8)∗P ≤ 0.05 for clinical variables significantly associated with TSG.146 (65.5)∗P ≤ 0.05 for clinical variables significantly associated with TSG. GATA552 (65.0)106 (62.0)131 (61.5)27 (71.1)110 (62.2)48 (64.9)101 (62.7)57 (63.3)11 (39.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.147 (65.9)∗P ≤ 0.05 for clinical variables significantly associated with TSG. CD4448 (60.0)91 (53.2)120 (56.3)19 (50.0)98 (55.4)41 (55.4)96 (59.6)∗P ≤ 0.05 for clinical variables significantly associated with TSG.43 (47.8)∗P ≤ 0.05 for clinical variables significantly associated with TSG.7 (25.0)∗P ≤ 0.05 for clinical variables significantly associated with TSG.132 (59.2)∗P ≤ 0.05 for clinical variables significantly associated with TSG. PYCARD45 (56.3)86 (50.3)111 (52.1)19 (50.0)99 (55.9)∗P ≤ 0.05 for clinical variables significantly associated with TSG.32 (43.2)∗P ≤ 0.05 for clinical variables significantly associated with TSG.86 (53.4)45 (50.0)4 (14.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.127 (5.7)∗P ≤ 0.05 for clinical variables significantly associated with TSG. GSTP136 (45.0)76 (44.4)92 (43.2)20 (52.6)86 (48.6)∗P ≤ 0.05 for clinical variables significantly associated with TSG.26 (35.1)∗P ≤ 0.05 for clinical variables significantly associated with TSG.75 (46.6)37 (41.1)8 (28.6)104 (46.4) IGSF419 (23.8)37 (21.6)48 (22.5)9 (23.7)39 (22.0)17 (23.0)30 (18.6)∗P ≤ 0.05 for clinical variables significantly associated with TSG.26 (28.9)∗P ≤ 0.05 for clinical variables significantly associated with TSG.8 (28.6)48 (21.5) BRCA217 (21.3)37 (21.6)45 (21.1)9 (23.7)41 (23.2)13 (17.6)28 (17.4)∗P ≤ 0.05 for clinical variables significantly associated with TSG.26 (28.9)∗P ≤ 0.05 for clinical variables significantly associated with TSG.10 (35.7)44 (19.7) THBS15 (6.3)17 (9.9)19 (8.9)3 (7.9)17 (9.6)5 (6.8)8 (5.0)∗P ≤ 0.05 for clinical variables significantly associated with TSG.14 (15.6)∗P ≤ 0.05 for clinical variables significantly associated with TSG.3 (10.7)19 (8.5) VHL4 (5.0)14 (8.2)15 (7.0)3 (7.9)13 (7.3)5 (6.8)7 (4.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.11 (12.2)∗P ≤ 0.05 for clinical variables significantly associated with TSG.4 (14.3)14 (6.3)pTaLGn = 25n = 54n = 64n = 15n = 67n = 12n = 60n = 19 MGMT-219 (76.0)32 (59.3)42 (65.6)9 (60.0)46 (68.7)5 (41.7)35 (58.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.16 (84.2)∗P ≤ 0.05 for clinical variables significantly associated with TSG. GATA518 (72,00)43 (79.6)47 (73.4)14 (93.3)50 (74.6)11 (91.7)43 (71.7)∗P ≤ 0.05 for clinical variables significantly associated with TSG.18 (94.7)∗P ≤ 0.05 for clinical variables significantly associated with TSG. PAX5A1 (4.0)∗P ≤ 0.05 for clinical variables significantly associated with TSG.18 (33.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.15 (23.4)4 (26.7)15 (22.4)4 (33.3)12 (20.0)7 (36.8)pT1LGn = 28n = 53n = 66n = 15n = 55n = 26n = 51n = 30 CD4421 (75.0)38 (71.7)52 (78.8)∗P ≤ 0.05 for clinical variables significantly associated with TSG.7 (46.7)∗P ≤ 0.05 for clinical variables significantly associated with TSG.37 (67.3)22 (84.6)38 (74.5)21 (70.0) GATA521 (75.0)37 (69.8)51 (77.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.7 (46.7)∗P ≤ 0.05 for clinical variables significantly associated with TSG.36 (65.5)22 (84.6)36 (70.6)22 (73.3) RARB21 (75.0)42 (79.2)55 (83.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.8 (53.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.41 (74.5)22 (84.6)39 (76.5)∗P ≤ 0.05 for clinical variables significantly associated with TSG.24 (80.0)∗P ≤ 0.05 for clinical variables significantly associated with TSG. PYCARD19 (67.9)39 (73.6)51 (77.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.7 (46.7)∗P ≤ 0.05 for clinical variables significantly associated with TSG.35 (63.6)23 (88.5)34 (66.7)24 (80.0) BRCA112 (42.9)22 (41.5)26 (39.4)8 (53.3)19 (34.5)∗P ≤ 0.05 for clinical variables significantly associated with TSG.15 (57.7)∗P ≤ 0.05 for clinical variables significantly associated with TSG.21 (41.2)13 (43.3) CDH139 (32.1)∗P ≤ 0.05 for clinical variables significantly associated with TSG.30 (56.6)∗P ≤ 0.05 for clinical variables significantly associated with TSG.31 (47.0)8 (53.3)26 (47.3)13 (50.0)20 (39.2)∗P ≤ 0.05 for clinical variables significantly associated with TSG.19 (63.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG. THBS11 (3.6)5 (9.4)5 (7.6)1 (6.7)5 (9.1)1 (3.9)1 (2.0)∗P ≤ 0.05 for clinical variables significantly associated with TSG.5 (16.7)∗P ≤ 0.05 for clinical variables significantly associated with TSG. PAX60∗P ≤ 0.05 for clinical variables significantly associated with TSG.10 (18.9)∗P ≤ 0.05 for clinical variables significantly associated with TSG.10 (15.2)06 (10.9)4 (15.4)7 (13.7)3 (10.0)pT1HGn = 27n = 64n = 83n = 8n = 55n = 36n = 50n = 41n = 28n = 63 STK1125 (92.6)60 (87.0)77 (92.8)8 (100)49 (89.1)∗P ≤ 0.05 for clinical variables significantly associated with TSG.36 (100)∗P ≤ 0.05 for clinical variables significantly associated with TSG.46 (92.0)39 (95.1)25 (89.3)60 (5.2) BRCA28 (29.6)16 (23.2)21 (25.3)3 (37.5)17 (30.9)7 (19.4)9 (18.0)∗P ≤ 0.05 for clinical variables significantly associated with TSG.15 (36.6)∗P ≤ 0.05 for clinical variables significantly associated with TSG.10 (35.7)14 (22.2) GSTP16 (22.2)12 (17.4)16 (19.3)2 (25.0)15 (27.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.3 (8.3)∗P ≤ 0.05 for clinical variables significantly associated with TSG.11 (22.0)7 (17.1)8 (28.6)1 (15.9) PYCARD5 (18.5)6 (8.7)9 (10.8)2 (25.0)10 (18.2)∗P ≤ 0.05 for clinical variables significantly associated with TSG.1 (2.8)∗P ≤ 0.05 for clinical variables significantly associated with TSG.6 (12.0)5 (12.2)4 (14.3)7 (11.1) VHL2 (7.4)5 (7.3)6 (7.2)1 (12.5)6 (10.1)1 (2.8)1 (2.0)∗P ≤ 0.05 for clinical variables significantly associated with TSG.6 (14.6)∗P ≤ 0.05 for clinical variables significantly associated with TSG.4 (14.3)3 (4.8)Data are expressed as no. (%).∗ P ≤ 0.05 for clinical variables significantly associated with TSG. Open table in a new tab Data are expressed as no. (%). Data are expressed as no. (%). The most frequently methylated genes were as follows: in pTaLG, STK11 (98.7%), PYCARD (78.5%), GATA5 (77.2%), RARB (72.1%), CD44 (69.6%), MGMT2 (64.6%), and GSTP1 (62.2%); in pT1LG, STK11 (98.8%), RARB (77.8%), CD44 (72.8%), PYCARD (71.6%), GATA5 (71.6%), and MGMT2 (65.4%); and

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