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Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients
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Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients
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Journal Article
Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients
2019
Overview
The metastatic Skin Cutaneous Melanoma (SKCM) has been associated with diminished survival rates and high mortality rates worldwide. Thus, segregating metastatic melanoma from the primary tumors is crucial to employ an optimal therapeutic strategy for the prolonged survival of patients. The SKCM mRNA, miRNA and methylation data of TCGA is comprehensively analysed to recognize key genomic features that can segregate metastatic and primary tumors. Further, machine learning models have been developed using selected features to distinguish the same. The Support Vector Classification with Weight (SVC-W) model developed using the expression of 17 mRNAs achieved Area under the Receiver Operating Characteristic (AUROC) curve of 0.95 and an accuracy of 89.47% on an independent validation dataset. This study reveals the genes
C7, MMP3, KRT14, LOC642587, CASP7
,
S100A7
and miRNAs hsa-mir-205 and hsa-mir-203b as the key genomic features that may substantially contribute to the oncogenesis of melanoma. Our study also proposes genes
ESM1, NFATC3, C7orf4, CDK14, ZNF827
, and
ZSWIM7
as novel putative markers for cutaneous melanoma metastasis. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP (
http://webs.iiitd.edu.in/raghava/cancerspp/
).
Publisher
Nature Publishing Group UK,Nature Publishing Group
Subject
