Youping Deng, PhD

Youping Deng, PhD

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Full Member, Cancer Biology Program, University of Hawaiʻi Cancer Center
Co-Director, Genomics and Bioinformatics Shared Resource, University of Hawaiʻi Cancer Center

Academic Appointment(s):
Professor, Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaiʻi at Mānoa
Graduate Faculty
Program of Molecular Biosciences and Bioengineering (MBBE)
Program of Biomedical Science - Clinical Research track (BIOM)
Program of Cellular and Molecular Biology (CMB)
Program of Tropical Medicine (TM)

PhD, Molecular Pharmacology, Peking Union Medical College, Beijing, China
Post-Doctoral Fellow, Cancer and Diabetes, Wayne State University, Detroit, MI
Certified Bioinformatics Specialist/Master, National Bioinformatics Institute

Research Focus

The long-term goal of the Deng lab is to develop precision medicine for cancer using both bioinformatics and experimental approaches.

My research is mainly centered on 4 areas:

  1. New computational method development. My lab has developed a series of innovative methods including novel algorithms for data normalization, clustering, feature selection, classification, differential expression, gene function ontology, gene network modeling and so on. We are currently developing new methods for alternative splicing and DNA somatic mutation for biomarker identification based on sequencing data.

  2. Identification of non-invasive biomarkers for early detection of cancer. We are searching for novel accurate circulating biomarkers for early detection of lung cancer and breast cancer. Based on a variety of high throughput “omics” methods including small RNA-seq, metabolomics, DNA-seq, and proteomics plus bioinformatics data mining, we have found and are seeking for circulating metabolite, ncRNA, protein, and CtDNA markers for early diagnosis of cancer.

  3. Characterization of biomarkers for predicting clinical outcomes of human diseases including cancer. We are mining public “omics” data such as TCGA data as well as generating our own high-throughput data to identify biomarkers to predict clinical outcomes of human diseases including cancer. For instance, we have found novel DNA mutation and gene expression signatures to predict better response to cancer drugs such as PARP inhibitor. New cellular and animal experiments are being designed to evaluate these biomarkers and understand their mechanisms.

  4. Integrative data analysis of “omics” and clinical data. We are integrating different types of “omics” data such as genomics, transcriptomics, metabolomics, epigenomics and proteomics data, as well as clinical factors, using a systems biology approach to understand carcinogenesis, cancer development, and find better biomarkers for precision medicine.

In addition to independent research, my team also provides bioinformatics data science services, which primarily focuses on the analysis and management of high-throughput data such as microarray data, real-time PCR data, proteomics, metabolomics, multiple biomarker data and next-generation sequence data including DNA-seq, RNA-seq, Chip-seq and microbiota (metagenomics) data and so on. The core also supports routine bioinformatics applications, such as phylogenetic, protein function prediction.

Selected Publications

Foox J, Nordlund J, Lalancette C, Gong T, Lacey M, Lent S, Langhorst BW, Ponnaluri VKC, Williams L, Padmanabhan KR, Cavalcante R, Lundmark A, Butler D, Mozsary C, Gurvitch J, Greally JM, Suzuki M, Menor M, Nasu M, Alonso A, Sheridan C, Scherer A, Bruinsma S, Golda G, Muszynska A, Łabaj PP, Campbell MA, Wos F, Raine A, Liljedahl U, Axelsson T, Wang C, Chen Z, Yang Z, Li J, Yang X, Wang H, Melnick A, Guo S, Blume A, Franke V, Ibanez de Caceres I, Rodriguez-Antolin C, Rosas R, Davis JW, Ishii J, Megherbi DB, Xiao W, Liao W, Xu J, Hong H, Ning B, Tong W, Akalin A, Wang Y, Deng Y*, & Mason CE (2022). The SEQC2 epigenomics quality control (EpiQC) study. Genome Biology, 22(1):332. PMCID: PMC8650396.

Zitello E, Hernandez B, Deng Y. (2021). Viral Etiology of Solid Tumors and Immunotherapy, Gastroenterology;Sep 25:S0016-5085(21)03551-4. doi:10.1053/ j.gastro.2021.09.048. Epub ahead of print. PMID: 34582894.

Chen Y, Zitello E, Guo R, Deng Y. (2021). The function of LncRNAs and their role in the prediction, diagnosis, and prognosis of lung cancer. Clin Transl Me;Apr;11(4):e367. doi: 10.1002/ctm2.367. PMID: 33931980; PMCID: PMC8021541.

Hu L, Chen S, Fu Y, Gao Z, Long H, Ren HW, Zuo Y, Wang J, Li H, Xu QB, Yu WX, Liu J, Shao C, Hao JJ, Wang CZ, Ma Y, Wang Z, Yanagihara R, Deng Y. (2020). Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan, China. Clin Infect Dis;Nov 19;71(16):2089-2098. doi: 10.1093/cid/ciaa539. PMID: 32361738; PMCID: PMC7197620.

Deng Y, Zhu Y, Wang H, Khadka VS, Hu L, Ai J, Dou Y, Li Y, Dai S, Mason CE, Wang Y, Jia W, Zhang J, Huang G, Jiang B. (2019). Ratio-Based Method To Identify True Biomarkers by Normalizing Circulating ncRNA Sequencing and Quantitative PCR Data. Anal Chem;May 21;91(10):6746-6753. doi: 10.1021/acs.analchem.9b00821. PubMed PMID: 31002238.

Publication list via NCBI

Active Grants

Y. Deng, MPI
Pacific Center for Genome Research
09/2023 – 05/2028

Y. Deng, PI
“Circulating Lipid and miRNA Markers for Early Detection of Breast Cancer among Women with Abnormal Mammograms”
08/01/2019 – 07/31/2024

Y. Deng, PI
“Profiling Genome-wide Circulating ncRNAs for the Early Detection of Lung Cancer”
09/01/2018 – 08/31/2024

Y. Deng, Co-Leader, Genomics & Informatics Shared Resource; N. Ueno, PI
“University of Hawaii Cancer Center CCSG”
07/01/2018 – 06/30/2024

Y. Deng, PI
“Data Science Core for Biomedical Research”
05/01/2023 – 04/30/2028

Y. Deng, PI
The Hawaii Advanced Training in Artificial Intelligence for Precision Nutrition Science Research (HAIPrN)
09/2023 – 08/2028

Y. Deng, Co-I; S. Kwee, PI
“Evaluation of treatment predictors reflecting beta-catenin activation in hepatocellular carcinoma”
07/01/2021 - 06/30/2026