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Related Concept Videos

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Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Related Experiment Video

Updated: May 8, 2026

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
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Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

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Predicting time to ovarian carcinoma recurrence using protein markers.

Ji-Yeon Yang1, Kosuke Yoshihara, Kenichi Tanaka

  • 1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77230-1402, USA.

The Journal of Clinical Investigation
|August 16, 2013
PubMed
Summary

A new protein expression index, PROVAR, accurately predicts ovarian cancer recurrence risk and survival. This protein-based model outperforms gene expression methods, offering improved insights into tumor biology and clinical predictions.

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Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
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Published on: November 2, 2014

Related Experiment Videos

Last Updated: May 8, 2026

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
09:08

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
08:55

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Published on: November 2, 2014

Area of Science:

  • Oncology
  • Proteomics
  • Biomarker Discovery

Background:

  • Ovarian cancer frequently recurs, necessitating improved prediction of treatment outcomes.
  • Accurate prediction models are crucial for developing targeted therapeutic strategies and improving patient survival.

Purpose of the Study:

  • To develop and validate a novel protein expression-based index for predicting tumor recurrence and survival in ovarian cancer patients.
  • To compare the predictive performance of the protein-based index against existing gene expression-based models.

Main Methods:

  • Generated ovarian carcinoma protein expression profiles using reverse-phase protein arrays on 412 TCGA cases.
  • Constructed a Protein-driven index of OVARian cancer (PROVAR).
  • Validated PROVAR in an independent cohort of 226 high-grade serous ovarian carcinomas.

Main Results:

  • PROVAR significantly stratified patients into high-risk and low-risk groups for tumor recurrence.
  • PROVAR accurately predicted short-term versus long-term survival.
  • The protein-based PROVAR demonstrated superior predictive capacity for tumor progression compared to gene expression-based models.

Conclusions:

  • The PROVAR index offers a robust, protein-based approach for predicting ovarian cancer recurrence and survival.
  • Protein expression profiling provides valuable insights into ovarian cancer biology and recurrence mechanisms.
  • PROVAR, potentially combined with clinical factors like BRCA mutation status, may enhance clinical decision-making for ovarian cancer management.