Reference: Bhalla, S., Kaur, H., Dhall, A., Raghava, GPS. Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients. Sci Rep 9, 15790 (2019) doi:10.1038/s41598-019-52134-4.
CancerSPP: A Webserver for predicting the progression of skin cancer

Welcome to CancerSPP

CancerSPP is a web-bench for skin cutaneous melanoma (SKCM) progression prediction. It is established for the prediction and analysis of primary and metastatic tumor of SKCM using signature genes expression data (derived from RNA-seq, miRNA and methylation RSEM expression quantification values) . Further, prediction module is used to predict multiple states of metastatic samples from primary tumor samples. Analysis module allow the user to analyze RNA-seq expression profiles data in primary and metastatic states of SKCM.

Major Features


Analysis Tool

This module allow the users to distinguish various SKCM states of metastatic samples from primary tumor samples i.e. Subcutaneous Tissue or Regional Cutaneous (P2) v/s Primary tumor (P1), Regional Lymph Node tumor (M1) v/s Primary tumor (P1), Distant Metastasis (M2) v/s Primary tumor (P1), Regional (P1P2) vs Lymphatic tumors (M1) and Metastatic tumors (M1M2) v/s Regional tumor (P1P2) using RESM expression quantification values of signature genes. Furthermore, the user can choose desired model for the classification of skin cutaneous melanoma samples by submitting respective RESM value.

This module facilitates users to evaluates significant role of signature genes in different melanoma states (primary, regional metastatic, lymph node metastatic and distinct metastatic) on the basis of RNA-seq expression profiles. Its p-value indicates whether there is significant difference between primary and metastatic states of SKCM. Further, it provides threshold-based MCC for each signature gene and mean expression value for distinct primary and metastatic states of SKCM.