CancerProteome

CancerProteome characterizes cancer proteomes by integrating and re-analyzing MS-based quantitative and post-translational modification (PTM) proteomics to support identification of protein markers, analysis of altered signaling, and drug-sensitivity assessment.

Key Features:

• Extensive data integration: Manually curates and re-analyzes publicly available MS-based quantification and PTM proteomes comprising 7,406 samples across 21 cancer types, covering 31,120 proteins and 4,111 microproteins.
• Quantitative proteome analysis: Computes and compares protein abundances across samples and cancer types using re-analyzed MS-based quantification data.
• PTM proteome analysis: Analyzes post-translational modification (PTM) levels across samples to profile PTM-specific alterations in cancer.
• Functional enrichment studies: Performs functional enrichment analyses to link protein and PTM changes to biological pathways and processes.
• Protein-protein association networks: Constructs association networks by integrating known interactions with co-expression signatures.
• Correlation evaluations: Evaluates correlations between protein abundances and corresponding transcript levels or PTM levels to investigate molecular mechanisms.
• Drug sensitivity and clinical relevance analyses: Associates proteomic and PTM profiles with drug sensitivity metrics and clinical relevance analyses.

Scientific Applications:

• Biomarker discovery: Identification of potential tumor protein and microprotein markers for diagnosis or prognosis based on quantitative and PTM proteomic profiles.
• Mechanistic investigation: Elucidation of altered signaling pathways and molecular mechanisms in carcinogenesis via protein, PTM, and correlation analyses.
• Therapeutic target exploration: Prioritization of candidate therapeutic targets and assessment of drug sensitivity relationships using integrated proteomic and clinical analyses.

Methodology:

Manually curates and re-analyzes publicly available MS-based quantification and PTM proteomes and applies modules for quantitative proteome analysis, PTM proteome analysis, functional enrichment, protein-protein association network construction (integrating known interactions with co-expression signatures), correlation evaluations between protein abundances and transcripts or PTMs, and drug sensitivity and clinical relevance analyses.