OSov

OSov performs consensus survival analysis of gene expression profiling and clinical follow-up data to evaluate and validate prognostic biomarkers in ovarian cancer.

Key Features:

• Integration of data: Integrates gene expression profiling data with clinical follow-up information for ovarian cancer cohorts.
• Consensus survival analysis: Performs consensus survival analysis across cohorts to assess survival associations of queried genes.
• Kaplan-Meier plot: Generates Kaplan–Meier plots showing survival probabilities over time stratified by gene expression.
• Forest plot: Produces forest plots displaying effect sizes and confidence intervals of prognostic biomarkers across studies or cohorts.
• Nomogram: Constructs nomograms to predict individual patient outcomes based on gene expression levels.
• Survival summary table: Summarizes survival statistics for queried genes in tabular form.
• Validation and performance assessment: Validated using 163 previously published prognostic biomarkers of ovarian cancer, confirming prognostic value for 72% of them.

Scientific Applications:

• Biomarker discovery: Facilitates identification of survival-associated genes in ovarian cancer cohorts using integrated gene expression and clinical data.
• Biomarker validation: Enables validation of published prognostic biomarkers across cohorts and studies, as demonstrated by the 163-biomarker assessment.
• Prognostic assessment: Supports generation of prognostic models and individual outcome predictions using Kaplan–Meier analysis, forest plots, and nomograms.
• Clinical translation: Informs prognostic assessments that can contribute to personalized treatment strategies and patient management.

Methodology:

Integrates gene expression profiling with clinical follow-up and computes Kaplan–Meier plots, forest plots, nomograms, and survival summary tables; validation was performed using 163 published prognostic biomarkers (72% confirmed).