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Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method
Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancer-related death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods h...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122481/ https://www.ncbi.nlm.nih.gov/pubmed/32292778 http://dx.doi.org/10.3389/fbioe.2020.00254 |
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author | Zhang, Zi-Mei Tan, Jiu-Xin Wang, Fang Dao, Fu-Ying Zhang, Zhao-Yue Lin, Hao |
author_facet | Zhang, Zi-Mei Tan, Jiu-Xin Wang, Fang Dao, Fu-Ying Zhang, Zhao-Yue Lin, Hao |
author_sort | Zhang, Zi-Mei |
collection | PubMed |
description | Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancer-related death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods have been used for discriminating HCC from cirrhosis tissues in patients without HCC (CwoHCC). However, the recognition successful rates are still far from satisfactory. In this study, we applied a computational approach that based on machine learning method to a set of microarray data generated from 1091 HCC samples and 242 CwoHCC samples. The within-sample relative expression orderings (REOs) method was used to extract numerical descriptors from gene expression profiles datasets. After removing the unrelated features by using maximum redundancy minimum relevance (mRMR) with incremental feature selection, we achieved “11-gene-pair” which could produce outstanding results. We further investigated the discriminate capability of the “11-gene-pair” for HCC recognition on several independent datasets. The wonderful results were obtained, demonstrating that the selected gene pairs can be signature for HCC. The proposed computational model can discriminate HCC and adjacent non-cancerous tissues from CwoHCC even for minimum biopsy specimens and inaccurately sampled specimens, which can be practical and effective for aiding the early HCC diagnosis at individual level. |
format | Online Article Text |
id | pubmed-7122481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71224812020-04-14 Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method Zhang, Zi-Mei Tan, Jiu-Xin Wang, Fang Dao, Fu-Ying Zhang, Zhao-Yue Lin, Hao Front Bioeng Biotechnol Bioengineering and Biotechnology Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancer-related death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods have been used for discriminating HCC from cirrhosis tissues in patients without HCC (CwoHCC). However, the recognition successful rates are still far from satisfactory. In this study, we applied a computational approach that based on machine learning method to a set of microarray data generated from 1091 HCC samples and 242 CwoHCC samples. The within-sample relative expression orderings (REOs) method was used to extract numerical descriptors from gene expression profiles datasets. After removing the unrelated features by using maximum redundancy minimum relevance (mRMR) with incremental feature selection, we achieved “11-gene-pair” which could produce outstanding results. We further investigated the discriminate capability of the “11-gene-pair” for HCC recognition on several independent datasets. The wonderful results were obtained, demonstrating that the selected gene pairs can be signature for HCC. The proposed computational model can discriminate HCC and adjacent non-cancerous tissues from CwoHCC even for minimum biopsy specimens and inaccurately sampled specimens, which can be practical and effective for aiding the early HCC diagnosis at individual level. Frontiers Media S.A. 2020-03-27 /pmc/articles/PMC7122481/ /pubmed/32292778 http://dx.doi.org/10.3389/fbioe.2020.00254 Text en Copyright © 2020 Zhang, Tan, Wang, Dao, Zhang and Lin. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Zhang, Zi-Mei Tan, Jiu-Xin Wang, Fang Dao, Fu-Ying Zhang, Zhao-Yue Lin, Hao Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method |
title | Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method |
title_full | Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method |
title_fullStr | Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method |
title_full_unstemmed | Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method |
title_short | Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method |
title_sort | early diagnosis of hepatocellular carcinoma using machine learning method |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122481/ https://www.ncbi.nlm.nih.gov/pubmed/32292778 http://dx.doi.org/10.3389/fbioe.2020.00254 |
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