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Cancer Survival Analysis

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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Updated: Jun 3, 2025

Heterotypic Three-dimensional In Vitro Modeling of Stromal-Epithelial Interactions During Ovarian Cancer Initiation and Progression

Heterotypic Three-dimensional In Vitro Modeling of Stromal-Epithelial Interactions During Ovarian Cancer Initiation and Progression

Published on: August 28, 2012

Learning to Train and to Explain a Deep Survival Model with Large-Scale Ovarian Cancer Transcriptomic Data.

Elena Spirina Menand^1,2, Manon De Vries-Brilland^2,3, Leslie Tessier²

¹Laboratoire Angevin de Recherche en Ingénierie des Systèmes (EA7315), Université d'Angers, 49035 Angers, France.

|January 8, 2025

Summary

This summary is machine-generated.

Deep learning models predict ovarian cancer survival using gene expression data. These models identify molecular pathways that stratify patients into high-risk and low-risk groups, aiding personalized treatment strategies.

Keywords:

RNA-seq TCGA deep learning molecular pathways ovarian cancer survival analysis

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Last Updated: Jun 3, 2025

Heterotypic Three-dimensional In Vitro Modeling of Stromal-Epithelial Interactions During Ovarian Cancer Initiation and Progression

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

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

Published on: January 12, 2020

Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Ovarian cancer presents poor outcomes and limited therapeutic options globally.
Novel biomarkers are crucial for stratifying patients and predicting treatment response.
Gene expression data offers potential for developing predictive outcome models.

Purpose of the Study:

To develop deep learning-based outcome predictors using ovarian cancer gene expression data.
To identify molecular pathways associated with patient survival.
To stratify ovarian cancer patients into distinct risk groups for personalized therapy.

Main Methods:

Utilized The Cancer Genome Atlas (TCGA) ovarian cancer transcriptomic data (372 patients, ~16,600 genes).
Trained and evaluated deep learning survival models.
Interpreted model outputs to derive gene contributions and molecular pathways.
Validated pathway-based stratification on in-house (12 patients) and external (274 patients) datasets.

Main Results:

Identified molecular pathways enabling stratification of TCGA patients into high-risk and low-risk groups (p=0.025).
Validated stratification efficacy on an in-house dataset (p=0.229) and an external dataset (p=0.006).
Demonstrated interpretability of deep learning models for uncovering survival-associated biological processes.

Conclusions:

Deep learning models analyzing RNA-seq data can predict survival in ovarian cancer patients.
These models effectively detect and interpret gene sets linked to survival outcomes.
This approach offers a new avenue for biomarker discovery and personalized treatment in ovarian cancer.