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)

Presenter Information

First Name Hongyi

Middle Initial {Empty}

Last Name Liu

Degree(s) Ph.D.

Position/Title/Career Status {Empty}

Organization Johns Hopkins University

Organization Address Baltimore

Email hliu173@jh.edu

Additional Authors

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 Information

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.