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. 2020 May 19:11:494.
doi: 10.3389/fgene.2020.00494. eCollection 2020.

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

Affiliations

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

Liyang Liu et al. Front Genet. .

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.

Keywords: TCGA data analysis; cancer; computational prediction; genomic mutation; oxidative stress; transcriptomic data.

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Figures

Figure 1
Figure 1
Distributions of the number of point mutations across all samples for each of the 14 cancer types, where the x-axis represents the mutation rate and the y-axis denotes the frequency of mutation rate across the tissue samples in each cancer type. (A) BLCA; (B) BRCA; (C) COAD; (D) ESCA; (E) HNSC; (F) KICH; (G) KIRC; (H) KIRP; (I) LIHC; (J) LUAD; (K) LUSC; (L) PRAD; (M) STAD; (N) THCA.
Figure 2
Figure 2
Scatter plots for mutation rates vs. predicted values in each of the 14 cancer types. For each panel, the x-axis represents the predicted mutation rates and the y-axis denotes the actual mutation rates. (A) BLCA; (B) BRCA; (C) COAD; (D) ESCA; (E) HNSC; (F) KICH; (G) KIRC; (H) KIRP; (I) LIHC; (J) LUAD; (K) LUSC; (L) PRAD; (M) STAD; (N) THCA.
Figure 3
Figure 3
Boxplots of predicted oxidative stress levels across 4 cancer stages (when data are available) along with matching controls for each of the 14 cancer types. (A) BLCA; (B) BRCA; (C) COAD; (D) ESCA; (E) HNSC; (F) KICH; (G) KIRC; (H) KIRP; (I) LIHC; (J) LUAD; (K) LUSC; (L) PRAD; (M) STAD; (N) THCA.

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