Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients.
Sherry BhallaHarpreet KaurAnjali DhallGajendra P S RaghavaPublished in: Scientific reports (2019)
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/ ).
Keyphrases
- machine learning
- skin cancer
- end stage renal disease
- small cell lung cancer
- squamous cell carcinoma
- ejection fraction
- newly diagnosed
- chronic kidney disease
- genome wide
- cell proliferation
- body mass index
- prognostic factors
- physical activity
- peritoneal dialysis
- deep learning
- dna methylation
- coronary artery disease
- long noncoding rna
- poor prognosis
- big data
- cell cycle
- artificial intelligence
- weight loss
- transcription factor
- wound healing
- genome wide identification