Dataset Information

Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses.

ABSTRACT: 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.

SUBMITTER: Liu L

PROVIDER: S-EPMC7263278 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses.

Liu Liyang L Cui Haining H Xu Ying Y

Frontiers in genetics 20200519

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 <i>in vivo</i> 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 p ...[more]

PMID: 32528526

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Project description:The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

Project description:Mitochondrial dysfunction causes oxidative stress and cardiomyopathy. Oxidative stress also is a side effect of dideoxynucleoside antiretrovirals (NRTI) and is observed in NRTI-induced cardiomyopathy. We show here that treatment with the NRTI AZT {1-[(2R,4S,5S)-4-azido-5-(hydroxymethyl)oxolan-2-yl]-5-methylpyrimidine-2,4-dione} modulates cardiac gene expression epigenetically through production of mitochondrially derived reactive oxygen species. Transgenic mice with ubiquitous expression of mitochondrially targeted catalase (MCAT) and C57Bl/6 wild-type mice littermates (WT) were administered AZT (0.22 mg/day po, 35 days), and cardiac DNA and mRNA were isolated. In AZT-treated WT, 95 cardiac genes were differentially expressed compared with vehicle-treated WTs. When MCAT mice were treated with AZT, each of those 95 genes reverted toward the expression of vehicle-treated WTs. In AZT-treated WT hearts, Mthfr [5,10-methylenetetrahydrofolate reductase; a critical enzyme in synthesis of methionine cycle intermediates including S-adenosylmethionine (SAM)], was overexpressed. Steady-state abundance of SAM in cardiac extracts from AZT-treated MCAT mice increased 60% above that of vehicle-treated MCAT. No such change occurred in WT. AZT caused hypermethylation (47%) and hypomethylation (53%) of differentially methylated DNA regions in WT cardiac DNA. AZT-treated MCAT heart DNA exhibited greater hypermethylation (91%) and less hypomethylation (9%) compared with vehicle-treated MCAT controls. The gene encoding protein kinase C-? displayed multifocal epigenetic regulation caused by oxidative stress. Results show that mitochondrially derived oxidative stress in the heart hinders cardiac DNA methylation, alters steady-state abundance of SAM, alters cardiac gene expression, and promotes characteristic pathophysiological changes of cardiomyopathy. This mechanism for NRTI toxicity offers insight into long-term side effects from these commonly used antiviral agents.

Dataset Information

Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses.

Publications

Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses.

Similar Datasets

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