Chun Wang, MD, PhD
Research Assistant Professor
Contact
Chun Wang, MD, PhD
Research Assistant Professor
Education
Medical School
PhD, Epidemiology and Health Statistics, School of Public Health, Nanjing Medical University, Nanjing, China - 2012
MS, Occupational and Environmental Health, School of Public Health, Fudan University, Shanghai, China - 2004
Most Recent Peer-Reviewed Publications
- Genomic characterization of early-stage hepatocellular carcinoma patients with Hepatitis B using circulating tumor DNA
- Prostein expression on circulating tumor cells as a prognostic marker in metastatic castration-resistant prostate cancer
- Genomic Aberrations in Circulating Tumor DNAs from Palbociclib-Treated Metastatic Breast Cancer Patients Reveal a Novel Resistance Mechanism
- Whole-exome sequencing identifies somatic mutations and intratumor heterogeneity in inflammatory breast cancer
- Integration of circulating tumor cell and neutrophil-lymphocyte ratio to identify high-risk metastatic castration-resistant prostate cancer patients
Awards
- Mentor of the top award winner in the category of Medicine & Health Coriell Institute Science Fair 2022 - 2022
- Mentor for Excellent Thesis of Undergraduate Students Jiangsu Department of Education - 2014
- Academic Excellence Award Nantong University 2004 - 2012
- Supervisor of Top 12 Environmental Protection Projects in Chinese Colleges China Environmental Protection Foundation - 2009
- Science and Technology Progress Award People's Government of Nantong City - 2008
- Excellent Mentor in the Competition 'Cherish Our Water Resources' China Environmental Protection Administration - 2004
- Distinguished Graduate Student Award Fudan University 2001 - 2003
- Distinguished Paper Award Jiangsu Preventive Medicine Association - 1999
- Excellent Lecture Award Nantong Medical College - 1998
- Distinguished Undergraduate Award Nantong Medical College 1991 - 1996
Research & Clinical Interest
Our research focuses on identifying predictive, prognostic, and racial-specific molecular biomarkers, and constructing comprehensive models for early detection and outcome prediction of solid tumors. Our study utilizes molecular epidemiology, bioinformatics, and biostatistics methods to analyze cancer genomic data from liquid biopsy samples.