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ZNF492 and GPR149 Methylation Patterns As Prognostic Markers for Clear Cell Renal Cell Carcinoma: Array‑Based DNA Methylation Profiling

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ZNF492 and GPR149 Methylation Patterns As Prognostic Markers for Clear Cell Renal Cell Carcinoma: Array‑Based DNA Methylation Profiling ONCOLOGY REPORTS 42: 453-460, 2019 ZNF492 and GPR149 methylation patterns as prognostic markers for clear cell renal cell carcinoma: Array‑based DNA methylation profiling YONG-JUNE KIM1*, WOOYEONG JANG2*, XUAN-MEI PIAO1, HYUNG-YOON YOON1, YOUNG JOON BYUN1, JI SANG KIM1, SUNG MIN KIM1, SANG KEUN LEE1, SUNG PIL SEO1, HO WON KANG1, WON TAE KIM1, SEOK JOONG YUN1, HO SUN SHON3, KEUN HO RYU4, SANG WON KIM5, YUN-SOK HA5, GHIL SUK YOON6, SANG-CHEOL LEE1, TAE GYUN KWON5 and WUN-JAE KIM1 1Department of Urology, College of Medicine, Chungbuk National University, Cheongju, Chungcheongbuk 28644; 2Clinical Genomics Analysis Branch, National Cancer Center, Goyang, Gyeonggi 10408; 3Medical Research Institute and 4Department of Computer Science, School of Medicine, Chungbuk National University, Cheongju, Chungcheongbuk 28644; Departments of 5Urology and 6Pathology, School of Medicine, Kyungpook National University, Jung-gu, Daegu 41944, Republic of Korea Received December 13, 2018; Accepted May 2, 2019 DOI: 10.3892/or.2019.7151 Abstract. The present study aimed to identify novel methyla- of candidate genes were investigated in another TCGA tion markers of clear cell renal cell carcinoma (ccRCC) using dataset (n=153). For validation, pyrosequencing analyses microarray methylation analysis and evaluate their prognostic with ccRCC samples from our institute (n=164) and another releva nc e i n pat ient sa mples. To ident i f y ca nc er‑sp e ci fic met hyl- (n=117) were performed and the potential clinical application ated biomarkers, microarray profiling of ccRCC samples from of selected biomarkers was examined. We identified 22 CpG our institute (n=12) and The Cancer Genome Atlas (TCGA) island loci that were commonly hypermethylated in ccRCC. database (n=160) were utilized, and the prognostic relevance Kaplan-Meier analysis of TCGA data indicated that only 4/22 loci were significantly associated with disease progression. In the internal validation set, Kaplan-Meier analysis revealed that hypermethylation of two loci, zinc finger protein 492 (ZNF492) and G protein-coupled receptor 149 (GPR149), Correspondence to: Professor Wun-Jae Kim, Department of was significantly associated with shorter time‑to‑progression. Urology, College of Medicine, Chungbuk National University, 1st Chungdae-ro, Seowon-gu, Cheongju, Chungcheongbuk 28644, Multivariate Cox regression models revealed that hypermeth- Republic of Korea ylation of ZNF492 [hazard ratio (HR), 5.44; P=0.001] and E-mail: [email protected] GPR149 (HR, 7.07; P<0.001) may be independent predictors of tumor progression. Similarly, the methylation status of these Professor Tae Gyun Kwon, Department of Urology, School of two genes was significantly associated with poor outcomes in Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu 41944, Republic of Korea the independent external validation cohort. Collectively, the E-mail: [email protected] present study proposed that the novel methylation markers ZNF492 and GPR149 could be independent prognostic indica- *Contributed equally tors in patients with ccRCC. Abbreviations: CBNUH, Chungbuk National University Hospital; Introduction ccRCC, clear cell renal cell carcinoma; CI, confidence interval; gDNA, genomic DNA; GPCRs, G protein-coupled receptors; Renal cell carcinoma (RCC) is a common malignancy of the GPR149, G protein-coupled receptor 149; HR, hazard ratio; KIRC, genitourinary system, and is classified into various subtypes kidney renal clear cell carcinoma; KPNUH, Kyungpook National based on histopathological characteristics and each subtype University Hospital; NC, normal control; PSQ, pyrosequencing; has a diverse prognosis (1,2). From a clinical viewpoint, three ROC, receiver operating characteristic; AUC, area under the ROC curve; TCGA, The Cancer Genome Atlas; TWIST1, twist family main RCC subtypes are important in research: Clear cell bHLH transcription factor 1; ZNF492, zinc finger protein 492 RCC (ccRCC; 80-90% cases), papillary RCC (10-15% cases), and chromophobe RCC (4-5% cases) (1,2). The development Key words: carcinoma, methylation, microarray analysis, prognosis, of RCC is complex, and numerous parameters, including renal cell anatomical, histological, clinical and molecular factors contribute to disease outcome. Despite great improvements in understanding the major molecular mechanisms involved 454 KIM et al: DNA METHYLATION MARKERS AND PROGNOSIS IN CLEAR CELL RENAL CELL CARCINOMA in RCC, none of the conventional or molecular markers have based on pathological stage and histological subtype, as demonstrated satisfactory sensitivity and specificity to be previously described (14,15). Nuclear differentiation was considered as a prognostic indicator of RCC (1,2). Thus, the graded according to the Fuhrman Nuclear Grade system (16). majority of studies have focused on the development of suit- All patients were followed-up and managed according to able biomarkers to improve diagnosis and prognostication in standard recommendations (1). Progression was defined as RCC. lymph node involvement and/or metastatic disease as deter- As a result of recent advancements in understanding of mined by a computed tomography scan or bone scan. epigenetic modifications, DNA methylation is now regarded as one of the key findings in the development and progression of DNA methylation profiling. Genomic DNA (gDNA) from cancer. In RCC, several DNA methylation biomarkers associ- patient's samples was extracted according to the manufacture's ated with tumor development, histological subtypes, prognosis protocol by using the Wizard Genomic DNA Purification and response to therapy have been reported (3-13); however, System (Promega Corporation, Madison, WI, USA). the majority of these markers were identified in cohorts exam- Bisulfite‑modified gDNA was prepared using the EZ DNA ined in single-center studies with variable results. Therefore, Methylation-Lightning kit (Zymo Research, Irvine, CA, USA) their use in routine clinical practice is limited. The develop- according to the manufacturer's instructions. The methylation ment of high-throughput bioinformatics technology, such status was assayed using the Infinium HumanMethylation450 as genome-wide methylation pattern analysis, now enables BeadChip array (Infinium Methylation 450K; Illumina, Inc., tumor‑specific novel molecular markers to be identified from San Diego, CA, USA), which assesses the methylation status numerous samples that can readily be compared with other of >480,000 CpG sites distributed across the whole genome. cohorts via database sharing. A total of 24 matched DNA samples (pairs of NC and ccRCC Establishing the association between biomarkers and from 12 patients) were used for DNA methylation profiling. clinical outcomes in RCC is likely to prove useful for devel- Fluorescence signals corresponding to C- or T-nucleotides oping effective treatment strategies and improving clinical were measured (GenomeStudio Methylation Module, V1.8, management. The aim of the present study was to identify Illumina, Inc., San Diego, CA, USA), and the data were used to novel biomarkers that could predict the outcomes of RCC assign a quantitative measure of methylation of specific CpG using genome-wide DNA methylation microarray data derived islands (β value): Ranging from 0 (complete unmethylation) from patients analyzed in our institute and from The Cancer to 1 (complete methylation). The complete sets of microarray Genome Atlas (TCGA), and to evaluate their clinical applica- methylation data are available online (http://www.ncbi.nlm. tion in ccRCC cases with long-term follow-up. nih.gov/geo/; accession no. GSE92482. Materials and methods Array profiling data from TCGA. For comparisons with the array data from the selected CBNUH patient samples and to Subjects and sample collection. A total of 355 human investigate the prognostic relevance of the identified methyl- kidney specimens were obtained from patients (age ated CpG islands, the TCGA kidney renal clear cell carcinoma range: 30-83 years, sample collection: February 1999 to (KIRC) level 3 data set, also obtained using the Infinium September 2015) with primary, histologically-confirmed HumanMethylation450 BeadChip, was investigated (https:// ccRCC who underwent radical or partial nephrectomy cancergenome.nih.gov/). Methylation profiles of 313 ccRCC from two independent institutes including: i) 201 human patient samples were available within the KIRC dataset. The kidney specimens [164 ccRCC and 37 matched normal dataset was divided into two groups: i) Training set, 160 pairs control (NC)] from Chungbuk National University of ccRCC and NC matched samples; and ii) test set, an addi- Hospital (CBNUH); and ii) 154 human kidney specimens tion 153 ccRCC samples without matched NC samples. The (117 ccRCC and 37 matched NC) from Kyungpook National training set data were used for comparisons with the candidate University Hospital (KPNUH). To enhance the homogeneity CpG sites identified with the CBNUH samples, and the test set of the study population, patients were enrolled into the data were used for assessing the prognostic relevance of those study according to the following criteria: i) clinical and candidates. Baseline characteristics of the KIRC patients are pathological stage T1-T4 without lymph node or distant provided in Table SI. metastasis; and ii) a minimum follow-up period of 3 months. Patients whose samples had a positive surgical margin at Pyrosequencing (PSQ) analysis. The DNA methylation final pathology were not included
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