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Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients.

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients.
 
Creator Bhalla, Sherry
Kaur, Harpreet
Dhall, Anjali
Raghava, Gajendra P S
 
Subject QR Microbiology
 
Description 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
 
Date 2019-10-31
 
Type Article
PeerReviewed
 
Relation https://www.nature.com/articles/s41598-019-52134-4
http://crdd.osdd.net/open/2478/
 
Identifier Bhalla, Sherry and Kaur, Harpreet and Dhall, Anjali and Raghava, Gajendra P S (2019) Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients. Scientific reports, 9 (1). p. 15790. ISSN 2045-2322