IIIT researchers develop novel predictive model to identify melanoma progression

IIIT researchers develop novel predictive model to identify melanoma progression

Scientists from Indraprastha Institute of Information Technology (IIIT), New Delhi have developed a method to distinguish primary and metastatic cutaneous melanoma using the expression of six novel putative markers for the disease.

The markers involving-ESM1, NFATC3, C7orf4, CDK14, ZNF827, and ZSWIM7 and their role in segregating the melanoma is described in a recent article published in Scientific Reports.

The researchers also identified how substantial genes like C7, MMP3, KRT14, LOC642587, CASP7, S100A7 and miRNAs hsa-mir-205 and hsa-mir-203b may contribute to the oncogenesis of the melanoma.

The six novel key genes will add to the previously identified 11 genes (messenger RNAs), which has been used in the segregation of metastatic and primary tumors. The scientists further developed machine learning Support Vector Classification with Weight (SVC-W) models using the expression of the 17 mRNAs to distinguish the same.

These genomic signatures proved to have high sensitivity and specificity in case of metastatic and primary tumours respectively. Of the six models one SVC-W model showed a high accuracy of over 89% in discriminating metastatic from primary skin melanoma, says the researchers.

They also explored expression profile of mRNA, micro RNA and methylation profile in discriminating tumour as primary or metastatic.

The messenger RNA expression profile was found to perform better than micro RNA and methylation profile of the patients in general for an exception of one particular microRNA,says the authors in a news source.

The researchers further developed a webserver called CancerSPP  (http://webs.iiitd.edu.in/raghava/cancerspp/) integrating the major prediction and analysis models of the genes in evaluating the tumor samples of skin cancer melanoma.

This can help clinicians in identifying cutaneous melanoma as primary or metastatic utilizing the RNA sequence data, microRNA and methylation expression data. It will also help in knowing the different stages of the metastatic samples.

The dangerous skin cancer, melanoma, occurs when the pigment-producing cells that give colour to the skin become cancerous. Unlike in the case of primary cutaneous melanoma, patients with metastatic skin melanoma have reduced survival rate and higher mortality rates.

It therefore becomes important to be able to identify and classify skin cutaneous melanoma so as to provide accurately tailored therapeutic strategies for improving the survival rates of patients.

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