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Li Y, Lih TSM, Dhanasekaran SM, Mannan R, Chen L, Cieslik M, Wu Y, Lu RJH, Clark DJ, Kołodziejczak I, Hong R, Chen S, Zhao Y, Chugh S, Caravan W, Naser Al Deen N, Hosseini N, Newton CJ, Krug K, Xu Y, Cho KC, Hu Y, Zhang Y, Kumar-Sinha C, Ma W, Calinawan A, Wyczalkowski MA, Wendl MC, Wang Y, Guo S, Zhang C, Le A, Dagar A, Hopkins A, Cho H, Leprevost FDV, Jing X, Teo GC, Liu W, Reimers MA, Pachynski R, Lazar AJ, Chinnaiyan AM, Van Tine BA, Zhang B, Rodland KD, Getz G, Mani DR, Wang P, Chen F, Hostetter G, Thiagarajan M, Linehan WM, Fenyö D, Jewell SD, Omenn GS, Mehra R, Wiznerowicz M, Robles AI, Mesri M, Hiltke T, An E, Rodriguez H, Chan DW, Ricketts CJ, Nesvizhskii AI, Zhang H, Ding L. Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness. Cancer Cell 2023; 41:139-163.e17. [PMID: 36563681 PMCID: PMC9839644 DOI: 10.1016/j.ccell.2022.12.001] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/18/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022]
Abstract
Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.
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Affiliation(s)
- Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Tung-Shing M Lih
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Saravana M Dhanasekaran
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Rahul Mannan
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Marcin Cieslik
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Rita Jiu-Hsien Lu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - David J Clark
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Iga Kołodziejczak
- International Institute for Molecular Oncology, 60-203 Poznań, Poland; Postgraduate School of Molecular Medicine, Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Siqi Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yanyan Zhao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Seema Chugh
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wagma Caravan
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Nataly Naser Al Deen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Noshad Hosseini
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yuanwei Xu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA
| | - Kyung-Cho Cho
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Yuping Zhang
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chandan Kumar-Sinha
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Anna Calinawan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yuefan Wang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Shenghao Guo
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA
| | - Cissy Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Anne Le
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA; Department of Chemical and Biomolecular Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Aniket Dagar
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alex Hopkins
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hanbyul Cho
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Xiaojun Jing
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Melissa A Reimers
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Division of Medical Oncology, Department of Medicine, Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Russell Pachynski
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Division of Medical Oncology, Department of Medicine, Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Alexander J Lazar
- Departments of Pathology and Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Arul M Chinnaiyan
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Brian A Van Tine
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Gad Getz
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Feng Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | | | - W Marston Linehan
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Scott D Jewell
- Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Gilbert S Omenn
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rohit Mehra
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, 60-203 Poznań, Poland; Heliodor Swiecicki Clinical Hospital in Poznań, ul. Przybyszewskiego 49, 60-355 Poznań, Poland; Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christopher J Ricketts
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA; Department of Chemical and Biomolecular Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA.
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Zhou H, Xie T, Gao Y, Zhan X, Dong Y, Liu D, Xu Y. A novel prognostic model based on six methylation-driven genes predicts overall survival for patients with clear cell renal cell carcinoma. Front Genet 2022; 13:996291. [PMID: 36330441 PMCID: PMC9623106 DOI: 10.3389/fgene.2022.996291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 10/05/2022] [Indexed: 12/01/2022] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.
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Affiliation(s)
- Hongmin Zhou
- Department of Urology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tiancheng Xie
- Department of Urology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yuchen Gao
- Department of Urology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiangcheng Zhan
- Department of Urology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yunze Dong
- Department of Urology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Ding Liu
- Department of Urology, Shanghai 10th People’s Hospital, Nanjing Medical University, Shanghai, China
| | - Yunfei Xu
- Department of Urology, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai, China
- *Correspondence: Yunfei Xu,
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Chen XY, Zhang Y, Chen YX, Huang ZQ, Xia XY, Yan YX, Xu MP, Chen W, Wang XL, Chen QL. MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier. Front Oncol 2021; 11:708655. [PMID: 34660276 PMCID: PMC8517330 DOI: 10.3389/fonc.2021.708655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 09/15/2021] [Indexed: 11/14/2022] Open
Abstract
Objective To develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures. Materials and Methods We retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 high-grade ccRCCs), who were randomly divided into a training set (n = 70) and a validation set (n = 29). Regions of interest (ROIs) of all tumors were manually drawn three times by a radiologist at the maximum lesion level of the cross-sectional CMP sequence images. The quantitative texture analysis software, MaZda, was used to extract texture features, including histograms, co-occurrence matrixes, run-length matrixes, gradient models, and autoregressive models. Reproducibility of the texture features was assessed with the intra-class correlation coefficient (ICC). Features were chosen based on their importance coefficients in a random forest model, while the multi-layer perceptron algorithm was used to build a classifier on the training set, which was later evaluated with the validation set. Results The ICCs of 257 texture features were equal to or higher than 0.80 (0.828–0.998. Six features, namely Kurtosis, 135dr_RLNonUni, Horzl_GLevNonU, 135dr_GLevNonU, S(4,4)Entropy, and S(0,5)SumEntrp, were chosen to develop the multi-layer perceptron classifier. A three-layer perceptron model, which has 229 nodes in the hidden layer, was trained on the training set. The accuracy of the model was 95.7% with the training set and 86.2% with the validation set. The areas under the receiver operating curves were 0.997 and 0.758 for the training and validation sets, respectively. Conclusions A machine learning-based grading model was developed that can aid in the clinical diagnosis of clear cell renal cell carcinoma using MRI images.
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Affiliation(s)
- Xin-Yuan Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yu Zhang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Yu-Xing Chen
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Zi-Qiang Huang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Xiao-Yue Xia
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yi-Xin Yan
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Mo-Ping Xu
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Wen Chen
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Xian-Long Wang
- Department of Bioinformatics, School of Basic Medical Sciences, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Qun-Lin Chen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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El Khoury LY, Fu S, Hlady RA, Wagner RT, Wang L, Eckel-Passow JE, Castle EP, Stanton ML, Thompson RH, Parker AS, Ho TH, Robertson KD. Identification of DNA methylation signatures associated with poor outcome in lower-risk Stage, Size, Grade and Necrosis (SSIGN) score clear cell renal cell cancer. Clin Epigenetics 2021; 13:12. [PMID: 33461589 PMCID: PMC7814746 DOI: 10.1186/s13148-020-00998-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 12/21/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Despite using prognostic algorithms and standard surveillance guidelines, 17% of patients initially diagnosed with low risk clear cell renal cell carcinoma (ccRCC) ultimately relapse and die of recurrent disease, indicating additional molecular parameters are needed for improved prognosis. RESULTS To address the gap in ccRCC prognostication in the lower risk population, we performed a genome-wide analysis for methylation signatures capable of distinguishing recurrent and non-recurrent ccRCCs within the subgroup classified as 'low risk' by the Mayo Clinic Stage, Size, Grade, and Necrosis score (SSIGN 0-3). This approach revealed that recurrent patients have globally hypermethylated tumors and differ in methylation significantly at 5929 CpGs. Differentially methylated CpGs (DMCpGs) were enriched in regulatory regions and genes modulating cell growth and invasion. A subset of DMCpGs stratified low SSIGN groups into high and low risk of recurrence in independent data sets, indicating that DNA methylation enhances the prognostic power of the SSIGN score. CONCLUSIONS This study reports a global DNA hypermethylation in tumors of recurrent ccRCC patients. Furthermore, DMCpGs were capable of discriminating between aggressive and less aggressive tumors, in addition to SSIGN score. Therefore, DNA methylation presents itself as a potentially strong biomarker to further improve prognostic power in patients with low risk SSIGN score (0-3).
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Affiliation(s)
- Louis Y El Khoury
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.,Center for Individualized Medicine, Epigenomics Program, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Shuang Fu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.,Center for Individualized Medicine, Epigenomics Program, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.,Hematology Laboratory, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ryan A Hlady
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.,Center for Individualized Medicine, Epigenomics Program, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Ryan T Wagner
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.,Center for Individualized Medicine, Epigenomics Program, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Liguo Wang
- Division of Biomedical Statistics and Informatics, Department of Health Science Research, Mayo Clinic, Rochester, MN, USA
| | - Jeanette E Eckel-Passow
- Division of Biomedical Statistics and Informatics, Department of Health Science Research, Mayo Clinic, Rochester, MN, USA
| | - Erik P Castle
- Department of Urology, Mayo Clinic, Phoenix, AZ, USA
| | - Melissa L Stanton
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Phoenix, AZ, USA
| | | | - Alexander S Parker
- Office of Research Affairs, University of Florida, Jacksonville, FL, USA
| | - Thai H Ho
- Division of Hematology and Medical Oncology, Mayo Clinic, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA.
| | - Keith D Robertson
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA. .,Center for Individualized Medicine, Epigenomics Program, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
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Miao Y, Cao F, Li P, Liu P. DNA methylation of Hugl-2 is a prognostic biomarker in kidney renal clear cell carcinoma. Clin Exp Pharmacol Physiol 2021; 48:44-53. [PMID: 32754907 PMCID: PMC7821335 DOI: 10.1111/1440-1681.13390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 07/17/2020] [Accepted: 07/30/2020] [Indexed: 01/13/2023]
Abstract
It has been reported that loss of Hugl-2 contributes to tumour formation and progression in vitro and in vivo. However, whether Hugl-2 levels decrease during kidney renal clear cell carcinoma (KIRC) and the mechanism involved remain unknown. This study aimed to investigate whether DNA methylation of Hugl-2 reduces its expression, leading to the progression and poor prognosis of KIRC. Hugl-2 methylation and mRNA expression and KIRC clinicopathological data were extracted from The Cancer Genome Atlas (TCGA), and relationships among these factors were analyzed using UALCAN, MethHC, Wanderer and LinkedOmics web tools. We found that Hugl-2 mRNA and protein levels were reduced in KIRC tissues. Moreover, Hugl-2 mRNA levels were related to tumour grade and overall survival, and Hugl-2 methylation was increased in KIRC. According to the results of methylation-specific PCR, KIRC cells had higher Hugl-2 DNA methylation levels than HKC cells. Moreover, Hugl-2 DNA methylation correlated negatively with Hugl-2 mRNA and was also related to the pathology and T stage of KIRC patients. KIRC patients with high Hugl-2 DNA methylation also had shorter overall survival. Additionally, methylation of cg08827674, a Hugl-2 probe, was related to pathologic stage, T stage, neoplasm histologic grade, serum calcium level without laterality, M stage, N stage, and ethnicity. Furthermore, treatment with the DNA methylation inhibitor decitabine resulted in upregulation of Hugl-2 mRNA and protein levels in KIRC cell lines. These results indicate that Hugl-2 DNA methylation may be both a prognostic marker and a therapeutic target in KIRC.
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Affiliation(s)
- Yi Miao
- Center for Translational MedicineThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
- Key Laboratory for Tumor Precision Medicine of Shaanxi ProvinceThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
| | - Fang Cao
- Center for Translational MedicineThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
- Key Laboratory for Tumor Precision Medicine of Shaanxi ProvinceThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
| | - Pingping Li
- Center for Translational MedicineThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
- Key Laboratory for Tumor Precision Medicine of Shaanxi ProvinceThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
| | - Peijun Liu
- Center for Translational MedicineThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
- Key Laboratory for Tumor Precision Medicine of Shaanxi ProvinceThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
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DNA methylation profiling reveals new potential subtype-specific gene markers for early-stage renal cell carcinoma in caucasian population. QUANTITATIVE BIOLOGY 2021. [DOI: 10.15302/j-qb-021-0279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Andersson-Evelönn E, Vidman L, Källberg D, Landfors M, Liu X, Ljungberg B, Hultdin M, Rydén P, Degerman S. Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma. J Transl Med 2020; 18:435. [PMID: 33187526 PMCID: PMC7666468 DOI: 10.1186/s12967-020-02608-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 11/05/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables. METHODS A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression. RESULTS The "triple classifier" which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress (pCIP5yr) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis. CONCLUSIONS The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.
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Affiliation(s)
| | - Linda Vidman
- Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden
| | - David Källberg
- Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden.,Department of Statistics, USBE, Umeå University, Umeå, Sweden
| | - Mattias Landfors
- Department of Medical Biosciences, Pathology, Umeå University, 901 87, Umeå, Sweden
| | - Xijia Liu
- Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden
| | - Börje Ljungberg
- Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Umeå, Sweden
| | - Magnus Hultdin
- Department of Medical Biosciences, Pathology, Umeå University, 901 87, Umeå, Sweden
| | - Patrik Rydén
- Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden.
| | - Sofie Degerman
- Department of Medical Biosciences, Pathology, Umeå University, 901 87, Umeå, Sweden. .,Department of Clinical Microbiology, Umeå University, Umeå, Sweden.
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Lo Gullo R, Daimiel I, Morris EA, Pinker K. Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging 2020; 11:1. [PMID: 31901171 PMCID: PMC6942081 DOI: 10.1186/s13244-019-0795-6] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 09/25/2019] [Indexed: 02/07/2023] Open
Abstract
Background Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing. Main body In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis. Conclusion Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA.
| | - Isaac Daimiel
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY, 10065, USA.,Department of Biomedical Imaging and Image-guided Therapy, Molecular and Gender Imaging Service, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Wien, Austria
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9
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Cen D, Xu L, Zhang S, Chen Z, Huang Y, Li Z, Liang B. Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features. Eur Radiol 2019; 29:5415-5422. [PMID: 30877466 DOI: 10.1007/s00330-019-06049-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 01/01/2019] [Accepted: 01/29/2019] [Indexed: 01/08/2023]
Abstract
PURPOSE To investigate associations between CT imaging features, RUNX3 methylation level, and survival in clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS Patients were divided into high RUNX3 methylation and low RUNX3 methylation groups according to RUNX3 methylation levels (the threshold was identified by using X-tile). The CT scanning data from 106 ccRCC patients were retrospectively analyzed. The relationship between RUNX3 methylation level and overall survivals was evaluated using the Kaplan-Meyer analysis and Cox regression analysis (univariate and multivariate). The relationship between RUNX3 methylation level and CT features was evaluated using chi-square test and logistic regression analysis (univariate and multivariate). RESULTS β value cutoff of 0.53 to distinguish high methylation (N = 44) from low methylation tumors (N = 62). Patients with lower levels of methylation had longer median overall survival (49.3 vs. 28.4) months (low vs. high, adjusted hazard ratio [HR] 4.933, 95% CI 2.054-11.852, p < 0.001). On univariate logistic regression analysis, four risk factors (margin, side, long diameter, and intratumoral vascularity) were associated with RUNX3 methylation level (all p < 0.05). Multivariate logistic regression analysis found that three risk factors (side: left vs. right, odds ratio [OR] 2.696; p = 0.024; 95% CI 1.138-6.386; margin: ill-defined vs. well-defined, OR 2.685; p = 0.038; 95% CI 1.057-6.820; and intratumoral vascularity: yes vs. no, OR 3.286; p = 0.008; 95% CI 1.367-7.898) were significant independent predictors of high methylation tumors. This model had an area under the receiver operating characteristic curve (AUC) of 0.725 (95% CI 0.623-0.827). CONCLUSIONS Higher levels of RUNX3 methylation are associated with shorter survival in ccRCC patients. And presence of intratumoral vascularity, ill-defined margin, and left side tumor were significant independent predictors of high methylation level of RUNX3 gene. KEY POINTS • RUNX3 methylation level is negatively associated with overall survival in ccRCC patients. • Presence of intratumoral vascularity, ill-defined margin, and left side tumor were significant independent predictors of high methylation level of RUNX3 gene.
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Affiliation(s)
- Dongzhi Cen
- Department of Radiation Oncology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong Province, People's Republic of China
| | - Li Xu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & The Second Clinical College of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, 510120, Guangdong Province, People's Republic of China.
| | - Siwei Zhang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & The Second Clinical College of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, 510120, Guangdong Province, People's Republic of China.
| | - Zhiguang Chen
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & The Second Clinical College of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, 510120, Guangdong Province, People's Republic of China
| | - Yan Huang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & The Second Clinical College of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, 510120, Guangdong Province, People's Republic of China
| | - Ziqi Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & The Second Clinical College of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese Medicine, 111 Da De Lu, Guangzhou, 510120, Guangdong Province, People's Republic of China
| | - Bo Liang
- Department of Radiation Oncology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, Guangdong Province, People's Republic of China
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10
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Evelönn EA, Landfors M, Haider Z, Köhn L, Ljungberg B, Roos G, Degerman S. DNA methylation associates with survival in non-metastatic clear cell renal cell carcinoma. BMC Cancer 2019; 19:65. [PMID: 30642274 PMCID: PMC6332661 DOI: 10.1186/s12885-019-5291-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 01/07/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer and is associated with poor prognosis if metastasized. Up to one third of patients with local disease at diagnosis will develop metastasis after nephrectomy, and there is a need for new molecular markers to identify patients with high risk of tumor progression. In the present study, we performed genome-wide promoter DNA methylation analysis at diagnosis to identify DNA methylation profiles associated with risk for progress. METHOD Diagnostic tissue samples from 115 ccRCC patients were analysed by Illumina HumanMethylation450K arrays and methylation status of 155,931 promoter associated CpGs were related to genetic aberrations, gene expression and clinicopathological parameters. RESULTS The ccRCC samples separated into two clusters (cluster A/B) based on genome-wide promoter methylation status. The samples in these clusters differed in tumor diameter (p < 0.001), TNM stage (p < 0.001), morphological grade (p < 0.001), and patients outcome (5 year cancer specific survival (pCSS5yr) p < 0.001 and cumulative incidence of progress (pCIP5yr) p < 0.001. An integrated genomic and epigenomic analysis in the ccRCCs, revealed significant correlations between the total number of genetic aberrations and total number of hypermethylated CpGs (R = 0.435, p < 0.001), and predicted mitotic age (R = 0.407, p < 0.001). We identified a promoter methylation classifier (PMC) panel consisting of 172 differently methylated CpGs accompanying progress of disease. Classifying non-metastatic patients using the PMC panel showed that PMC high tumors had a worse prognosis compared with the PMC low tumors (pCIP5yr 38% vs. 8%, p = 0.001), which was confirmed in non-metastatic ccRCCs in the publically available TCGA-KIRC dataset (pCIP5yr 39% vs. 16%, p < 0.001). CONCLUSION DNA methylation analysis at diagnosis in ccRCC has the potential to improve outcome-prediction in non-metastatic patients at diagnosis.
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Affiliation(s)
- Emma Andersson Evelönn
- Department of Medical Biosciences, Umeå University, NUS, Blg 6M, 2nd floor, SE-90185 Umeå, Sweden
| | - Mattias Landfors
- Department of Medical Biosciences, Umeå University, NUS, Blg 6M, 2nd floor, SE-90185 Umeå, Sweden
| | - Zahra Haider
- Department of Medical Biosciences, Umeå University, NUS, Blg 6M, 2nd floor, SE-90185 Umeå, Sweden
| | - Linda Köhn
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Börje Ljungberg
- Department of Surgical and Perioperative Sciences, Urology and andrology, Umeå University, Umeå, Sweden
| | - Göran Roos
- Department of Medical Biosciences, Umeå University, NUS, Blg 6M, 2nd floor, SE-90185 Umeå, Sweden
| | - Sofie Degerman
- Department of Medical Biosciences, Umeå University, NUS, Blg 6M, 2nd floor, SE-90185 Umeå, Sweden
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11
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Grünwald V, Hornig M. Systemic and Sequential Therapy in Advanced Renal Cell Carcinoma. Urol Oncol 2019. [DOI: 10.1007/978-3-319-42623-5_64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Abstract
Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. While survival rates are high if RCC is diagnosed when still confined to the kidney and treated definitively, there are no specific diagnostic screening tests available and symptoms are rare in early stages of the disease. Management of advanced RCC has changed significantly with the advent of targeted therapies, yet survival is usually increased by months due to acquired resistance to these therapies. DNA methylation, the covalent addition of a methyl group to a cytosine, is essential for normal development and transcriptional regulation, but becomes altered commonly in cancer. These alterations result in broad transcriptional changes, including in tumor suppressor genes. Because DNA methylation is one of the earliest molecular changes in cancer and is both widespread and stable, its role in cancer biology, including RCC, has been extensively studied. In this review, we examine the role of DNA methylation in RCC disease etiology and progression, the preclinical use of DNA methylation alterations as diagnostic, prognostic and predictive biomarkers, and the potential for DNA methylation-directed therapies.
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Affiliation(s)
- Brittany N Lasseigne
- HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, AL, 35806-2908, USA.
| | - James D Brooks
- Department of Urology, Stanford University Medical Center, 300 Pasteur Drive, Stanford, CA, 94305-5118, USA
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Duan H, Yan Z, Chen W, Wu Y, Han J, Guo H, Qiao J. TET1 inhibits EMT of ovarian cancer cells through activating Wnt/β-catenin signaling inhibitors DKK1 and SFRP2. Gynecol Oncol 2017; 147:408-417. [PMID: 28851501 DOI: 10.1016/j.ygyno.2017.08.010] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Revised: 08/05/2017] [Accepted: 08/08/2017] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Epithelial ovarian cancer (EOC) is the deadliest type of ovarian cancer, but the mechanisms contributing to its tumorigenesis are not well understood. Herein, we will elucidate the role of Ten-eleven translocation 1 (TET1) in EOC development. METHODS The expression of TET1 in EOC cell lines and primary samples was examined by western blot and immunohistochemistry. The biological role of ectopic TET1 overexpression was revealed by a series of in vitro functional studies. Its downstream signaling pathway was predicted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of microarray data. The methylation level and expression of Wnt/β-catenin signaling inhibitors Dikkopf 1 (DKK1) and secreted Fzd receptor protein 2 (SFRP2) were examined by Chromatin immunoprecipitation (ChIP) assay, Epimark™ 5hmC and 5mC level analysis and quantitative RT-PCR. Small interference RNA (siRNA) technology was used to investigate the biological roles of DKK1 and SFRP2. RESULTS TET1 expression was inversely correlated with clinical stage in patients with EOC by tissue microarray (TMA). TET1 expression was undetected in 6 types of EOC cell lines. Ectopic expression of TET1 inhibited colony formation, cell migration and invasion in SKOV3 and OVCAR3 cells. Furthermore, TET1 overexpression reversed the epithelial-mesenchymal transition (EMT) process of SKOV3 cells. Mechanistically, TET1 potently inhibited canonical Wnt/β-catenin signaling by demethylating and upregulating two upstream antagonists of this pathway, SFRP2 and DKK1, which was associated with inhibition of EMT and cancer cell metastasis. CONCLUSION This study uncovers that TET1 has potent tumor-suppressive effects in EOC by activating Wnt/β-catenin signaling inhibitors DKK1 and SFRP2.
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Affiliation(s)
- Hongying Duan
- Key Laboratory of Assisted Reproduction, Ministry of Education, Center of Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, PR China
| | - Zhiqiang Yan
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, PR China
| | - Wei Chen
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, PR China
| | - Yu Wu
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, PR China
| | - Jinsong Han
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, PR China
| | - Hongyan Guo
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, PR China
| | - Jie Qiao
- Key Laboratory of Assisted Reproduction, Ministry of Education, Center of Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, PR China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, PR China; Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, PR China.
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14
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Grünwald V, Hornig M. Systemic and Sequential Therapy in Advanced Renal Cell Carcinoma. Urol Oncol 2017. [DOI: 10.1007/978-3-319-42603-7_64-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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