OSbrca

OSbrca performs prognostic survival analysis of mRNA biomarkers in breast cancer by integrating gene expression and clinical follow-up data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) across more than 7,400 cases to generate Kaplan-Meier plots, hazard ratios, and log-rank P-values.

Key Features:

• Dataset integration: Integrates gene expression profiles with clinical follow-up information from TCGA and GEO covering over 7,400 breast cancer cases.
• Survival analysis outputs: Computes Kaplan-Meier survival plots along with corresponding hazard ratios and log-rank P-values for specified genes.
• Cross-cohort evaluation: Enables assessment of prognostic significance across multiple cohorts derived from TCGA and GEO.
• Validation of published biomarkers: Includes validation by reassessing the prognostic potency of 128 previously published breast cancer biomarkers.

Scientific Applications:

• Identification of prognostic mRNA biomarkers: Screen and quantify associations between candidate genes and patient survival outcomes.
• Development and evaluation of prognostic signatures: Evaluate prognostic performance of single genes or gene sets across integrated cohorts.
• Independent validation of published biomarkers: Reassess previously reported biomarkers to confirm prognostic relevance.
• Quantitative survival statistics: Provide hazard ratios and log-rank P-values to support biomarker-driven stratification analyses.

Methodology:

Integrates gene expression profiles with clinical follow-up from TCGA and GEO (over 7,400 cases) and computes Kaplan-Meier survival plots with corresponding hazard ratios and log-rank P-values, with validation performed by reassessing 128 published breast cancer biomarkers.