NCI Data Jamboree (Project Abstract Submission): Submission #19

serial: '19'
sid: '186660'
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uri: /nci/datajamboree/abstractsubmission
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data:
  list_of_additional_authors:
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Yingwei
      last_name: Hu
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Mamie
      last_name: Lih
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Effram
      last_name: Wei
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Liyuan
      last_name: Jiao
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Lijun
      last_name: Chen
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Yuefan
      last_name: Wang
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Xiangning
      last_name: Li
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Zhenyu
      last_name: Sun
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Yuanyu
      last_name: Huang
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Yuanwei
      last_name: Xu
    - add_author_letters: ''
      affiliation: 'Johns Hopkins University'
      first_name: Hui
      last_name: Zhang
  category: 'Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data'
  degree_s_: Ph.D.
  email: hliu173@jh.edu
  first_name: Hongyi
  keywords_abstracts: 'Prostate cancer, proteomics, glycoproteomics, phosphoproteomics, crosstalk'
  last_name: Liu
  middle_initial: ''
  organization: 'Johns Hopkins University'
  organization_address:
    address: ''
    address_2: ''
    city: Baltimore
    country: ''
    postal_code: ''
    state_province: ''
  summary: |-
    Scientific Questions: This project integrates large-scale multi-omics to address: (1) how multi-layer genomics, proteomics (TMT/DIA), and post-translational modifications (PTMs) correlate globally; (2) how to digitally deconvolve tumor microenvironment (TME) cell fractions; and (3) how to map the causal directional regulatory cascades between cell-surface glycosylation and intracellular phosphorylation to identify therapeutic targets.
    Community Significance: Prostate cancer has profound molecular heterogeneity. Providing a reproducible, open-source computational framework for integrating mass-spectrometry-based proteomics and dual-PTM networks allows the broader cancer research community to uncover targetable biological pathways and patient subclusters obscured in genomic-only studies.
    Datasets & Tools: We leverage clinical cohorts (244 samples) with comprehensive transcriptomics, global proteomics (TMT/DIA), and deeply enriched PTMs (phospho, intact glycopeptide, ubiquitin, p-Tyr, acetyl). Key informatics tools include xCell/CIBERSORTx for cellular deconvolution, Random Forest and Lasso-logistic regression for tumor grading classifiers (NAT vs. Low/High Gleason grades), human interactome mapping for protein-protein interaction (PPI) topology, and Bayesian modeling for causal dual-PTM network inference.
  title: ''
  ttile: 'Unlocks the Biological Insights into Aggressive Prostate Cancer Using Multi-omic Approach'