CancerSPP: Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients

CancerSPP is a comprehensive, manually curated resource that catalogs proteins involved in various human cancer signaling pathways. The database is designed to provide a centralized platform for researchers to explore the complex molecular networks that drive oncogenesis, facilitating the identification of potential therapeutic targets and biomarkers.

Web Server: http://webs.iiitd.edu.in/raghava/cancerspp/

Citation

Bhalla, S., Kaur, H., Dhall, A., & Raghava, G. P. S. (2019). Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients. Scientific Reports, 9, 15790. https://doi.org/10.1038/s41598-019-52134-4

Data can be found in release section of the Github. This dataset can also be found on Zenodo at https://doi.org/10.5281/zenodo.20079930

About the Database

Cancer is fundamentally a disease of altered cell signaling. CancerSPP consolidates scattered information from biomedical literature and biological repositories to provide a unified view of the proteins that govern cell growth, survival, and differentiation in the context of malignancy.

• Data Curation: Information is manually extracted from PubMed and integrated with data from primary repositories like UniProt and the Protein Data Bank (PDB).
• Focus: The database focuses on the functional role of proteins within established cancer-related pathways, such as PI3K/Akt, MAPK, and p53 signaling.

Key Features

1. Extensive Pathway Coverage

CancerSPP provides detailed annotations for proteins across a wide array of signaling pathways, including:

• Growth Factor Signaling: EGFR, VEGFR, and IGF-1R pathways.
• Intracellular Cascades: PI3K/Akt/mTOR, Ras/Raf/MEK/ERK, and JAK/STAT.
• Cell Cycle and Death: p53, Apoptosis, and Wnt/β-catenin signaling.

2. Protein-Centric Data

Each protein entry in CancerSPP includes:

• Structural Information: Sequence details, domain architecture, and links to experimental or predicted 3D structures.
• Functional Annotation: Biological roles, gene ontology (GO) terms, and subcellular localization.
• Cancer Association: Evidence linking the protein to specific cancer types and its role in disease progression (e.g., oncogene or tumor suppressor).

3. Mutational and Interaction Data

• Genomic Variations: Information on somatic mutations and polymorphisms reported in cancer samples.
• Protein-Protein Interactions: Curated data on interacting partners within and across pathways to illustrate network complexity.

Integrated Tools and Analysis

CancerSPP offers several modules to help users navigate and analyze signaling networks:

• Search & Browse: Query by protein name, pathway, or specific cancer type.
• Pathway Visualization: Interactive diagrams that map proteins onto signaling cascades, highlighting their interactions and regulatory roles.
• Similarity Tools: BLAST search functionality to find proteins in the database similar to a query sequence.
• Cross-References: Direct links to external databases like HGNC, Ensembl, and OMIM for deeper genomic context.

Applications

• Drug Discovery: Identifying "druggable" nodes within signaling networks for targeted therapy development.
• Systems Biology: Modeling cancer cell behavior by analyzing the interplay between multiple signaling pathways.
• Personalized Medicine: Understanding how specific protein alterations drive individual tumors to guide treatment selection.

Contact & Authors

Prof. Gajendra P. S. Raghava (Corresponding Author) raghava@iiitd.ac.in http://webs.iiitd.edu.in/raghava/

Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT Delhi), New Delhi, India.

Support

CancerSPP was developed with support from the J. C. Bose National Fellowship (DST), CSIR and DST INSPIRE and the ICMR, Government of India.