Xiao-Hong Mao1, Qiang Ye1, Guo-Bing Zhang1, Jin-Ying Jiang1, Hong-Ying Zhao1, Yan-Fei Shao1, Zi-Qi Ye2, Zi-Xue Xuan3, Ping Huang4. 1. Department of Pharmacy, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China. 2. Department of Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China. 3. Department of Pharmacy, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China. xuanzixue0222@163.com. 4. Department of Pharmacy, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China. huangping1841@zjcc.org.cn.
Abstract
BACKGROUND: Aberrant DNA methylation is significantly associated with breast cancer. METHODS: In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. RESULTS: In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994-1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976-1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. CONCLUSIONS: Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer.
BACKGROUND: Aberrant DNA methylation is significantly associated with breast cancer. METHODS: In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. RESULTS: In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994-1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976-1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. CONCLUSIONS: Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer.
Entities:
Keywords:
Biomarkers; Breast cancer; Diagnosis; Methylation; Prognosis
Authors: Bradley M Downs; Claudia Mercado-Rodriguez; Ashley Cimino-Mathews; Chuang Chen; Jing-Ping Yuan; Eunice Van Den Berg; Leslie M Cope; Fernando Schmitt; Gary M Tse; Syed Z Ali; Danielle Meir-Levi; Rupali Sood; Juanjuan Li; Andrea L Richardson; Marina B Mosunjac; Monica Rizzo; Suzana Tulac; Kriszten J Kocmond; Timothy de Guzman; Edwin W Lai; Brian Rhees; Michael Bates; Antonio C Wolff; Edward Gabrielson; Susan C Harvey; Christopher B Umbricht; Kala Visvanathan; Mary Jo Fackler; Saraswati Sukumar Journal: Clin Cancer Res Date: 2019-07-12 Impact factor: 12.531
Authors: Lingtao Jin; Jaemoo Chun; Chaoyun Pan; Dan Li; Ruiting Lin; Gina N Alesi; Xu Wang; Hee-Bum Kang; Lina Song; Dongsheng Wang; Guojing Zhang; Jun Fan; Titus J Boggon; Lu Zhou; Jeanne Kowalski; Cheng-Kui Qu; Conor E Steuer; Georgia Z Chen; Nabil F Saba; Lawrence H Boise; Taofeek K Owonikoko; Fadlo R Khuri; Kelly R Magliocca; Dong M Shin; Sagar Lonial; Sumin Kang Journal: Cancer Cell Date: 2018-07-19 Impact factor: 31.743
Authors: Andrea Sartore-Bianchi; Federica Di Nicolantonio; Ludovic Barault; Alessio Amatu; Giulia Siravegna; Agostino Ponzetti; Sebastian Moran; Andrea Cassingena; Benedetta Mussolin; Chiara Falcomatà; Alexandra M Binder; Carmen Cristiano; Daniele Oddo; Simonetta Guarrera; Carlotta Cancelliere; Sara Bustreo; Katia Bencardino; Sean Maden; Alice Vanzati; Patrizia Zavattari; Giuseppe Matullo; Mauro Truini; William M Grady; Patrizia Racca; Karin B Michels; Salvatore Siena; Manel Esteller; Alberto Bardelli Journal: Gut Date: 2017-10-05 Impact factor: 23.059
Authors: Darshan S Chandrashekar; Bhuwan Bashel; Sai Akshaya Hodigere Balasubramanya; Chad J Creighton; Israel Ponce-Rodriguez; Balabhadrapatruni V S K Chakravarthi; Sooryanarayana Varambally Journal: Neoplasia Date: 2017-07-18 Impact factor: 5.715
Authors: Tim C de Ruijter; Kim M Smits; Maureen J Aarts; Irene E G van Hellemond; Leander Van Neste; Bart de Vries; Petronella G M Peer; Jürgen Veeck; Manon van Engeland; Vivianne C G Tjan-Heijnen Journal: Diagn Progn Res Date: 2019-10-17