OSluca

OSluca evaluates and validates prognostic biomarkers in lung cancer by analyzing gene expression profiles across multiple cohorts to produce survival analyses.

Key Features:

• Extensive Dataset Integration: Integrates 35 expression datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the Roepman study, encompassing 5,741 lung cancer patients.
• Survival Analysis Outputs: Accepts an official gene symbol and generates cohort-level and combined survival summaries, including forest plots and survival tables derived from Cox proportional regression analyses.
• Biomarker Validation: Assesses prognostic value across cohorts and has been tested on 104 previously reported prognostic biomarkers.

Scientific Applications:

• Prognostic Biomarker Validation: Assess the prognostic significance of individual genes in lung cancer using multi-cohort expression data.
• Patient Outcome Prediction: Provide hazard estimates to inform prediction and stratification of patient outcomes based on gene expression.
• Translational Research: Consolidate multi-cohort evidence to support translation of genomic biomarkers into clinical research contexts.

Methodology:

Collects long-term follow-up clinical information across datasets, applies Cox proportional regression models to estimate hazard ratios, and combines cohort results to produce forest plots and survival tables.