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Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Supp...

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Autores principales: Kumar, Sugandh, Patnaik, Srinivas, Dixit, Anshuman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518185/
https://www.ncbi.nlm.nih.gov/pubmed/33024622
http://dx.doi.org/10.7717/peerj.9656
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author Kumar, Sugandh
Patnaik, Srinivas
Dixit, Anshuman
author_facet Kumar, Sugandh
Patnaik, Srinivas
Dixit, Anshuman
author_sort Kumar, Sugandh
collection PubMed
description Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.
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spelling pubmed-75181852020-10-05 Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC) Kumar, Sugandh Patnaik, Srinivas Dixit, Anshuman PeerJ Bioinformatics Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities. PeerJ Inc. 2020-09-22 /pmc/articles/PMC7518185/ /pubmed/33024622 http://dx.doi.org/10.7717/peerj.9656 Text en ©2020 Kumar et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Kumar, Sugandh
Patnaik, Srinivas
Dixit, Anshuman
Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)
title Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)
title_full Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)
title_fullStr Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)
title_full_unstemmed Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)
title_short Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)
title_sort predictive models for stage and risk classification in head and neck squamous cell carcinoma (hnscc)
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518185/
https://www.ncbi.nlm.nih.gov/pubmed/33024622
http://dx.doi.org/10.7717/peerj.9656
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