Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Zi-Mei, Tan, Jiu-Xin, Wang, Fang, Dao, Fu-Ying, Zhang, Zhao-Yue, Lin, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
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
_version_ 1783515426959917056
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
work_keys_str_mv AT zhangzimei earlydiagnosisofhepatocellularcarcinomausingmachinelearningmethod
AT tanjiuxin earlydiagnosisofhepatocellularcarcinomausingmachinelearningmethod
AT wangfang earlydiagnosisofhepatocellularcarcinomausingmachinelearningmethod
AT daofuying earlydiagnosisofhepatocellularcarcinomausingmachinelearningmethod
AT zhangzhaoyue earlydiagnosisofhepatocellularcarcinomausingmachinelearningmethod
AT linhao earlydiagnosisofhepatocellularcarcinomausingmachinelearningmethod