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

Submission information

Submission Number: 19

Submission ID: 186660

Submission UUID: 33ff26ab-39bc-4ca9-904f-5452695e4649

Submission URI: /nci/datajamboree/abstractsubmission

Submission Update: /nci/datajamboree/abstractsubmission?token=DP5gIU6WENai2NJ6QRNCslgGRPhCDpRoMeZeKBkUvbE

Created: Wed, 07/15/2026 - 12:10

Completed: Wed, 07/15/2026 - 14:14

Changed: Wed, 07/15/2026 - 14:14

Remote IP address: 10.208.24.175

Submitted by: Anonymous

Language: English

Is draft: No

Webform: NCI Data Jamboree (Abstracts)

Submitted to: NCI Data Jamboree (Project Abstract Submission)

First Name	Hongyi
Middle Initial
Last Name	Liu
Degree(s)	Ph.D.
Position/Title/Career Status
Organization	Johns Hopkins University
Organization Address	Baltimore
Email	hliu173@jh.edu
List of Additional Authors	First Name: Yingwei Last Name: Hu Affiliation: Johns Hopkins University First Name: Mamie Last Name: Lih Affiliation: Johns Hopkins University First Name: Effram Last Name: Wei Affiliation: Johns Hopkins University First Name: Liyuan Last Name: Jiao Affiliation: Johns Hopkins University First Name: Lijun Last Name: Chen Affiliation: Johns Hopkins University First Name: Yuefan Last Name: Wang Affiliation: Johns Hopkins University First Name: Xiangning Last Name: Li Affiliation: Johns Hopkins University First Name: Zhenyu Last Name: Sun Affiliation: Johns Hopkins University First Name: Yuanyu Last Name: Huang Affiliation: Johns Hopkins University First Name: Yuanwei Last Name: Xu Affiliation: Johns Hopkins University First Name: Hui Last Name: Zhang Affiliation: Johns Hopkins University
Abstract Category	Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data
Abstract Keywords	Prostate cancer, proteomics, glycoproteomics, phosphoproteomics, crosstalk
Abstract Title	Unlocks the Biological Insights into Aggressive Prostate Cancer Using Multi-omic Approach
Abstract	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.