Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses
Quantitative assessment of the intracellular oxidative stress level is a very important problem since it is the basis for elucidation of the fundamental causes of metabolic changes in diseased human cells, particularly cancer. However, the problem proves to be very challenging to solve in vivo becau...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2020-05-01
|
Series: | Frontiers in Genetics |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2020.00494/full |
id |
doaj-750a7f2e948c4a12ba5e23d4efbb90a1 |
---|---|
record_format |
Article |
spelling |
doaj-750a7f2e948c4a12ba5e23d4efbb90a12020-11-25T02:21:24ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-05-011110.3389/fgene.2020.00494537623Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data AnalysesLiyang Liu0Liyang Liu1Haining Cui2Ying Xu3Ying Xu4College of Physics, Jilin University, Changchun, ChinaDepartment of Biochemistry and Molecular Biology, Institute of Bioinformatics, The University of Georgia, Athens, GA, United StatesCollege of Physics, Jilin University, Changchun, ChinaDepartment of Biochemistry and Molecular Biology, Institute of Bioinformatics, The University of Georgia, Athens, GA, United StatesCancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun, ChinaQuantitative assessment of the intracellular oxidative stress level is a very important problem since it is the basis for elucidation of the fundamental causes of metabolic changes in diseased human cells, particularly cancer. However, the problem proves to be very challenging to solve in vivo because of the complex nature of the problem. Here a computational method is presented for predicting the quantitative level of the intracellular oxidative stress in cancer tissue cells. The basic premise of the predictor is that the genomic mutation level is strongly associated with the intracellular oxidative stress level. Based on this, a statistical analysis is conducted to identify a set of enzyme-encoding genes, whose combined expression levels can well explain the mutation rates in individual cancer tissues in the TCGA database. We have assessed the validity of the predictor by assessing it against genes that are known to have anti-oxidative functions for specific types of oxidative stressors. Then the applications of the predictor are conducted to illustrate its utility.https://www.frontiersin.org/article/10.3389/fgene.2020.00494/fulloxidative stressgenomic mutationtranscriptomic datacancerTCGA data analysiscomputational prediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liyang Liu Liyang Liu Haining Cui Ying Xu Ying Xu |
spellingShingle |
Liyang Liu Liyang Liu Haining Cui Ying Xu Ying Xu Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses Frontiers in Genetics oxidative stress genomic mutation transcriptomic data cancer TCGA data analysis computational prediction |
author_facet |
Liyang Liu Liyang Liu Haining Cui Ying Xu Ying Xu |
author_sort |
Liyang Liu |
title |
Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses |
title_short |
Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses |
title_full |
Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses |
title_fullStr |
Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses |
title_full_unstemmed |
Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses |
title_sort |
quantitative estimation of oxidative stress in cancer tissue cells through gene expression data analyses |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2020-05-01 |
description |
Quantitative assessment of the intracellular oxidative stress level is a very important problem since it is the basis for elucidation of the fundamental causes of metabolic changes in diseased human cells, particularly cancer. However, the problem proves to be very challenging to solve in vivo because of the complex nature of the problem. Here a computational method is presented for predicting the quantitative level of the intracellular oxidative stress in cancer tissue cells. The basic premise of the predictor is that the genomic mutation level is strongly associated with the intracellular oxidative stress level. Based on this, a statistical analysis is conducted to identify a set of enzyme-encoding genes, whose combined expression levels can well explain the mutation rates in individual cancer tissues in the TCGA database. We have assessed the validity of the predictor by assessing it against genes that are known to have anti-oxidative functions for specific types of oxidative stressors. Then the applications of the predictor are conducted to illustrate its utility. |
topic |
oxidative stress genomic mutation transcriptomic data cancer TCGA data analysis computational prediction |
url |
https://www.frontiersin.org/article/10.3389/fgene.2020.00494/full |
work_keys_str_mv |
AT liyangliu quantitativeestimationofoxidativestressincancertissuecellsthroughgeneexpressiondataanalyses AT liyangliu quantitativeestimationofoxidativestressincancertissuecellsthroughgeneexpressiondataanalyses AT hainingcui quantitativeestimationofoxidativestressincancertissuecellsthroughgeneexpressiondataanalyses AT yingxu quantitativeestimationofoxidativestressincancertissuecellsthroughgeneexpressiondataanalyses AT yingxu quantitativeestimationofoxidativestressincancertissuecellsthroughgeneexpressiondataanalyses |
_version_ |
1724866402810068992 |