OSppc

OSppc integrates proteomic, transcriptomic, and clinical follow-up data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) across over 8,000 cancer cases and 33 malignancies to analyze 14,038 proteins (including phosphorylated variants) and assess protein expression, phosphorylation, survival associations, and mRNA–protein relationships.

Key Features:

• Data Integration: Aggregates TCGA and CPTAC proteomic, transcriptomic, and clinical follow-up datasets covering >8,000 cases across 33 malignancies and 14,038 proteins including phosphorylated variants.
• Survival Analysis: Computes hazard ratios with 95% confidence intervals, generates Kaplan–Meier survival curves, and reports log-rank p-values for proteins or mRNA.
• Differential Analysis: Produces box plots to compare protein expression levels across tissues and between normal and cancerous states.
• Correlation Analysis: Provides scatter plots with Pearson and Spearman correlation coefficients to evaluate relationships between protein levels and corresponding mRNA expression.

Scientific Applications:

• Prognostic biomarker evaluation: Assess prognostic significance of protein biomarkers across multiple cancer types using survival statistics.
• Proteome–transcriptome integration: Investigate correlations between proteomic data and gene expression profiles to inform molecular mechanism studies.
• Target identification and validation: Identify and validate potential therapeutic targets and clinically significant functional genes based on differential expression and correlation with outcomes.

Methodology:

Employs reverse-phase protein arrays (RPPAs) and mass spectrometry to analyze protein expressions and phosphorylated variants and integrates clinical follow-up data for survival analyses.