Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer t...

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Autores principales:	ShiJian Ding, Hao Li, Yu-Hang Zhang, XianChao Zhou, KaiYan Feng, ZhanDong Li, Lei Chen, Tao Huang, Yu-Dong Cai
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	pan-cancer study feature selection classification algorithm decision rule biomarker Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b6ecdcbcfc4042e28b83231f75ac0449
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id	oai:doaj.org-article:b6ecdcbcfc4042e28b83231f75ac0449
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spelling	oai:doaj.org-article:b6ecdcbcfc4042e28b83231f75ac04492021-12-01T18:49:54ZIdentification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines2296-634X10.3389/fcell.2021.781285https://doaj.org/article/b6ecdcbcfc4042e28b83231f75ac04492021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcell.2021.781285/fullhttps://doaj.org/toc/2296-634XThere are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.ShiJian DingHao LiYu-Hang ZhangXianChao ZhouKaiYan FengZhanDong LiLei ChenTao HuangTao HuangYu-Dong CaiFrontiers Media S.A.articlepan-cancer studyfeature selectionclassification algorithmdecision rulebiomarkerBiology (General)QH301-705.5ENFrontiers in Cell and Developmental Biology, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pan-cancer study feature selection classification algorithm decision rule biomarker Biology (General) QH301-705.5
spellingShingle	pan-cancer study feature selection classification algorithm decision rule biomarker Biology (General) QH301-705.5 ShiJian Ding Hao Li Yu-Hang Zhang XianChao Zhou KaiYan Feng ZhanDong Li Lei Chen Tao Huang Tao Huang Yu-Dong Cai Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines
description	There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.
format	article
author	ShiJian Ding Hao Li Yu-Hang Zhang XianChao Zhou KaiYan Feng ZhanDong Li Lei Chen Tao Huang Tao Huang Yu-Dong Cai
author_facet	ShiJian Ding Hao Li Yu-Hang Zhang XianChao Zhou KaiYan Feng ZhanDong Li Lei Chen Tao Huang Tao Huang Yu-Dong Cai
author_sort	ShiJian Ding
title	Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines
title_short	Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines
title_full	Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines
title_fullStr	Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines
title_full_unstemmed	Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines
title_sort	identification of pan-cancer biomarkers based on the gene expression profiles of cancer cell lines
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b6ecdcbcfc4042e28b83231f75ac0449
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_version_	1718404715931238400

Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines

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