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. https://doi.org/10.1038/s41598-019-52134-4

Welcome to Help Page

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.

PredictionsAnalysis



Predictions


1. RNA Based Prediction

1.1 Metastatic tumor vs Primary tumor
This tool allows the users to submit RSEM values of mRNA and predict the the cancer status i.e. metastatic or primary tumor employing RNA expression data of signature mRNAs, resulting from analysis of mRNA-seq data of Skin cutaneous melanoma(SKCM) from TCGA dataset obtained from TCGA assembler-2. This method requires RSEM values of 17 mRNA in each patient to predict patients's status. The first column is mRNA and in second column expression of corresponding mRNA in terms of RSEM value in a particular number of patients.

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1.2 Intralymphatic Tumor vs Primary Tumor
This tool allows the users to submit RSEM values of mRNA and predict the cancer status i.e. Intralymphatic tumor or primary tumor employing RNA expression data of signature mRNAs, resulting from analysis of mRNA-seq data of Skin cutaneous melanoma(SKCM) from TCGA dataset obtained from TCGA assembler-2. This method requires RSEM values of 10 mRNA in each patient to predict patient's status. The first column is mRNA and in second column expression of corresponding mRNA in terms of RSEM value in a particular number of patients.

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1.3 Lymphatic Tumor vs Primary Tumor
This tool allows the users to submit RSEM values of mRNA and predict the the cancer status i.e. lymphatic tumor or primary tumor employing RNA expression data of signature mRNAs, resulting from analysis of mRNA-seq data of Skin cutaneous melanoma(SKCM) from TCGA dataset obtained from TCGA assembler-2. This method requires RSEM values of 12 mRNA in each patient to predict patients's status. The first column is mRNA and in second column expression of corresponding mRNA in terms of RSEM value in a particular number of patients.

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1.4 Distant metastatic Tumor vs Primary tumor
This tool allows the users to submit RSEM values of mRNA and predict the the cancer status i.e. Distant metastatic or primary tumor employing RNA expression data of signature mRNAs, resulting from analysis of mRNA-seq data of Skin cutaneous melanoma(SKCM) from TCGA dataset obtained from TCGA assembler-2. This method requires RSEM values of 5 mRNA in each patient to predict patient's status. The first column is mRNA and in second column expression of corresponding mRNA in terms of RSEM value in a particular number of patients.

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1.5 Regional tumor vs Lymphatic tumor
This tool allows the users to submit RSEM values of mRNA and predict the the cancer status i.e. regional tumor or primary tumor employing RNA expression data of signature mRNAs, resulting from analysis of mRNA-seq data of Skin cutaneous melanoma(SKCM) from TCGA dataset obtained from TCGA assembler-2. This method requires RSEM values of 14 mRNA in each patient to predict patient's status. The first column is mRNA and in second column expression of corresponding mRNA in terms of RSEM value in a particular number of patients.

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1.6 Metastatic tumor vs Regional tumor
s tool allows the users to submit RSEM values of mRNA and predict the the cancer status i.e. Metastatic tumor or regional tumor employing RNA expression data of signature mRNAs, resulting from analysis of mRNA-seq data of Skin cutaneous melanoma(SKCM) from TCGA dataset obtained from TCGA assembler-2. This method requires RSEM values of 15 mRNA in each patient to predict patients's status. The first column is mRNA and in second column expression of corresponding mRNA in terms of RSEM value in a particular number of patients.

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2. miRNA Based Predictions

2.1 Metastatic Tumor vs Primary tumor
This tool allows the users to submit RSEM values of miRNA and predict the the cancer status i.e. metastatic or primary tumor employing RNA expression data of signature miRNAs, resulting from analysis of miRNA-seq data of Skin cutaneous melanoma(SKCM) from TCGA dataset obtained from TCGA assembler-2. This method requires RSEM values of 32 miRNA in each patient to predict patients's status. The first column is miRNA and in second column expression of corresponding miRNA in terms of RSEM value in a particular number of patients.

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3. Methylation Based Prediction

3.1 Metastatic Tumor vs Primary Tumor
This tool allows the users to submit beta values of methylated genes and predict the the cancer status i.e. metastatic or primary tumor employing methylation expression data of signature genes, resulting from analysis of methyl-seq data of Skin cutaneous melanoma(SKCM) from TCGA dataset obtained from TCGA assembler-2. This method requires beta values of 38 genes in each patient to predict patient's status.

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Analysis


1.
This page provides the user to get the list of total number of peptides according to a particular cancer type.

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Result
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2.
This page provides the user to get the list of all the protein names in the database and clicking.

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Result
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3.

This module displays the peptides present according to the different biofluids in the database. By clicking the different menu user can get number and additional details about the type of peptide selected.
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Result