Driver Gene Networks of Genomic Instability in Prostate Cancer Progression

Abstract

The less stable the cancer gene is, the faster cancer worsens. This can be used as an important measure of cancer's characteristics in various human cancers. By comparing and analyzing tumors at the genetic or gene expression level that is highly unstable in cancerous cells, we can identify changes in factors that are important to the various genetic manifestations that result in increased genomic instability (GI). This talk will describe the GI driver networks that can distinguish disease subtypes. Progress was made in four key points in the first year of the funding period. I have 1) developed a novel method for GI score computation to identify PCs with highly altered genome, 2) identified the genes (PCGI-E) significantly correlated with GI events, 3) Identified MRs that are relevant to PCGI-E gene signature regulation, and 4) developed PCGI TRN classifier that can identify the PCs with poor survival outcomes.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2023
Accession Number
AD1210179

Entities

People

  • Sungyong You

Organizations

  • Cedars-Sinai Medical Center

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Cell Physiological Processes
  • Cells
  • Chemical Synthesis
  • Chemistry
  • Computational Science
  • Data Mining
  • Gene Expression
  • Genetics
  • Genomic Instability
  • Health Services
  • Information Science
  • Machine Learning
  • Medical Personnel
  • Oncology
  • Prostate Cancer
  • Proteins
  • Proteomics

Fields of Study

  • Biology

Readers

  • Molecular Biology and Genetics
  • Molecular and genetic basis of cancer.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • Biotechnology