OScc

OScc performs survival analysis to validate prognostic biomarkers in cervical cancer using integrated expression profiling and clinical follow-up datasets.

Key Features:

• Data Integration: Integrates four distinct datasets of expression profiling and clinical follow-up data sourced from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), comprising 690 patients.
• Survival Analysis: Generates Kaplan-Meier survival curves and computes log-rank p-values and hazard ratios for genes of interest.
• Subgroup Analysis: Performs subgroup analyses across patient demographics or clinical characteristics to assess biomarker performance in specific cohorts.
• Cross-Dataset Validation: Enables validation and comparison of biomarker prognostic value across integrated datasets.
• Implementation: Implemented using R software.

Scientific Applications:

• Preliminary Biomarker Assessment: Facilitates initial evaluation of candidate prognostic biomarkers in cervical cancer cohorts.
• Biomarker Validation: Supports cross-dataset validation of prognostic associations to corroborate findings across GEO and TCGA data.
• Clinical Outcome Analysis: Assists in analyzing associations between gene expression and patient outcomes to inform studies of cervical cancer progression and prognosis.

Methodology:

Integrates four GEO and TCGA expression profiling datasets with clinical follow-up data and performs survival analyses by generating Kaplan-Meier curves, conducting log-rank tests, estimating hazard ratios, and executing subgroup and cross-dataset comparisons; implemented in R.