Classification of Genes Based on Age-Related Differential Expression in Breast Cancer

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specifi...

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Main Authors: Gunhee Lee, Minho Lee
Format: Article
Language:English
Published: Korea Genome Organization 2017-12-01
Series:Genomics & Informatics
Subjects:
Online Access:http://genominfo.org/upload/pdf/gi-2017-15-4-156.pdf
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spelling doaj-21aba19caef0494e88aedced95417fd92020-11-25T02:32:25ZengKorea Genome OrganizationGenomics & Informatics2234-07422017-12-0115415616110.5808/GI.2017.15.4.156499Classification of Genes Based on Age-Related Differential Expression in Breast CancerGunhee Lee0Minho Lee1 Department of Biological Science, Sangji University, Wonju 26339, Korea Catholic Precision Medicine Research Center, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaTranscriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.http://genominfo.org/upload/pdf/gi-2017-15-4-156.pdfbiomarkersbreast cancerdifferentially expressed genesgene classification
collection DOAJ
language English
format Article
sources DOAJ
author Gunhee Lee
Minho Lee
spellingShingle Gunhee Lee
Minho Lee
Classification of Genes Based on Age-Related Differential Expression in Breast Cancer
Genomics & Informatics
biomarkers
breast cancer
differentially expressed genes
gene classification
author_facet Gunhee Lee
Minho Lee
author_sort Gunhee Lee
title Classification of Genes Based on Age-Related Differential Expression in Breast Cancer
title_short Classification of Genes Based on Age-Related Differential Expression in Breast Cancer
title_full Classification of Genes Based on Age-Related Differential Expression in Breast Cancer
title_fullStr Classification of Genes Based on Age-Related Differential Expression in Breast Cancer
title_full_unstemmed Classification of Genes Based on Age-Related Differential Expression in Breast Cancer
title_sort classification of genes based on age-related differential expression in breast cancer
publisher Korea Genome Organization
series Genomics & Informatics
issn 2234-0742
publishDate 2017-12-01
description Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.
topic biomarkers
breast cancer
differentially expressed genes
gene classification
url http://genominfo.org/upload/pdf/gi-2017-15-4-156.pdf
work_keys_str_mv AT gunheelee classificationofgenesbasedonagerelateddifferentialexpressioninbreastcancer
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