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Current Affairs

Machine learning model to determine spread of skin cancer

Date: 03 November 2019 Tags: Fourth Industrial Revolution

Issue

The 17 genomic signatures, which help in identifying cancerous growth were found out using Machine learning model that can discriminate metastatic from primary skin melanoma. 

 

Background

These signatures also have high sensitivity (in case tumour is metastatic), and high specificity (in case the tumour is primary). 

 

Details

  • Using the expression of 17 key genes (messenger RNAs) it is now possible to distinguish primary and metastatic cutaneous melanoma, which is the most common type of skin cancer

  • While 11 of the 17 genes have already been reported by other studies for cutaneous melanoma, it is for the first time that the potential role of remaining six genomic signatures in classifying samples as either primary or metastatic skin cutaneous cancer has been made.

  • Unlike in the case of primary skin melanoma, people with metastatic cutaneous melanoma have reduced survival rate and higher mortality rates.

  • It therefore becomes important to be able to identify and classify skin cutaneous melanoma as either primary or metastatic so correct therapeutic strategies can be chalked out and survival rates can improve in patients.

  • Six machine learning models were used to study and validate the genomic signatures.

  • They used expression profile of messenger RNA, micro RNA and methylation profile for discriminating tumour as primary or metastatic.

  • While messenger RNA outperformed microRNA in discriminating the status of the tumour, a particular microRNA was found to be a strong predictor of metastatic melanoma.

  • The genomic signatures can also help in further categorising different stages of metastasis.

  • It can tell if the tumour has spread to lymphatic nodes, which is an early stage of metastasis. Also, it can tell if the cancer has spread to distant parts of the body, which is a late stage of metastasis.

  • The researchers have further integrated the major prediction models in the webserver called CancerSPP that will help clinicians in classifying cutaneous melanoma as primary or metastatic using RNA sequence data, microRNA and methylation expression data.

  • The analysis module in the CancerSPP webserver will provide information on the role of each of the important genes in various stages of metastasis and whether the expression of a gene is up-regulated or down-regulated.