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. Author manuscript; available in PMC: 2012 Jun 1.
Published in final edited form as: Free Radic Biol Med. 2011 Mar 29;50(11):1565–1574. doi: 10.1016/j.freeradbiomed.2011.03.002

Differential gene expression in normal and transformed human mammary epithelial cells in response to oxidative stress

Diego F Cortes 1, Wei Sha 2, Valerie Hower 3, Greg Blekherman 1, Reinhard Laubenbacher 1,4, Steven Akman 4,5, Suzy V Torti 5,6, Vladimir Shulaev 4,7,§
PMCID: PMC3119600  NIHMSID: NIHMS290721  PMID: 21397008

Abstract

Oxidative stress plays a key role in breast carcinogenesis. To investigate whether normal and malignant breast epithelial cells differ in their responses to oxidative stress, we examined the global gene expression profiles of three cell types, representing cancer progression from a normal to a malignant stage, under oxidative stress. Normal human mammary epithelial cells (HMEC), an immortalized cell line (HMLER-1), and a tumorigenic cell line (HMLER-5), were exposed to increased levels of reactive oxygen species (ROS) by treatment with glucose oxidase. Functional analysis of the metabolic pathways enriched with differentially expressed genes demonstrates that normal and malignant breast epithelial cells diverge substantially in their response to oxidative stress. While normal cells exhibit the up-regulation of antioxidant mechanisms, cancer cells are unresponsive to the ROS insult. However, the gene expression response of normal HMEC cells under oxidative stress is comparable to that of the malignant cells under normal conditions, indicating that altered redox status is persistent in breast cancer cells, which makes them resistant to increased generation of ROS. This study discusses some of the possible adaptation mechanisms of breast cancer cells under persistent oxidative stress that differentiate them from the response to acute oxidative stress in normal mammary epithelial cells.

Keywords: Oxidative stress, breast cancer, human mammary epithelial cells, microarrays, glucose oxidase, GluOx

INTRODUCTION

Oxygen free radicals are generated continuously within mammalian cells as a direct consequence of aerobic metabolism and respiration. Reactive oxygen species (ROS) homeostasis is of critical importance for all aerobic cells, since too much oxygen results in toxicity and low oxygen levels impairs metabolism. The balance between oxidative damage and antioxidant protection controls the levels of ROS through a combination of gene regulatory, biochemical, and physiological mechanisms. An excess of oxygen-free-radical formation causes oxidative stress resulting in oxidative damage to biomolecules, lipid peroxidation, DNA damage, and carcinogenesis [1, 2].

In recent years, there have been great advances and developments in our understanding of the molecular mechanisms and factors involved in breast carcinogenesis [3]. Factors that induce persistent oxidant stress, such as inflammation, are now believed to play an important role in the prognosis and invasiveness of this and other cancers [4]. The precise mechanisms of oxidative stress generation in breast cancer cells are still not very well understood and documented, although, there are some reports on the oxidant-antioxidant profile in breast cancer patients [5-8].

Some human cancer cell lines produce large amounts of hydrogen peroxide (H2O2) while antioxidant enzymes, such as superoxide dismutase and catalase, appear to be down-regulated in cancer cells [9]. The consequences of enhanced oxidative stress in human breast cancer can also be found in elevated levels of lipid peroxidation products [10, 11]. ROS induce several kinds of DNA damage products in malignant cells including strand breaks, base modifications and DNA-protein crosslinks. Work by Frenkel et al [12] measuring circulating auto-antibodies to the oxidative DNA damage product 5-hydroxymethyl-2’-deoxyuridine (HMdU) in patients with breast or colorectal cancer, suggests that enhanced generation of oxidative DNA damage precedes and stimulates neoplasia. Others studies indicate high levels of DNA oxidation in human cancer tissues compared with corresponding controls [1]. Strong evidence suggests that carcinoma cells in vitro and in vivo are frequently under persistent oxidative stress [13]. Similarly, recent reports also indicate that high concentration of free iron in endometriotic cysts promotes carcinogenesis through iron induced persistent oxidative stress, and that malignant cells can survive a high oxidative stress environment [14].

Microarray analysis of breast cancer cell lines and tumor samples is a powerful tool to understand the global changes in gene expression associated with cancer progression, as well as for the development of profiles that can distinguish, identify and classify discreet subsets of disease and predict disease outcome or response to therapy [15-18]. Recent microarray analyses allowed the comparison of not only gene expression with respect to different phenotypes, but also the evaluation of biological functions, such as oncogenic signalling activity as well as the discovery of new breast cancer genes [17]. Several studies have reported genes that are differentially expressed in breast cancer cell lines and tumors [15-20]. However, despite the strong link between increased local oxidative stress and breast carcinogenesis, to the best of our knowledge there are no studies on the relationship between oxidative stress responses and breast cancer malignancy progression. In this regard, there are few data addressing whether malignant breast epithelial cells differ from their non-transformed counterparts with regard to their responses to oxidative stress [21].

The primary purpose of this study was to identify the characteristic gene expression profiles that distinguish the response to oxidative stress in normal and tumorigenic breast cancer cell lines using microarray analyses. Next, by analysing comprehensively the genes differentially expressed, we sought to identify pathways and gene networks significantly regulated in normal and cancer cells in response to oxidative stress. We show that 87% of the genes altered in response to oxidative stress in normal mammary epithelial cells overlap those associated with progression to malignancy. Our findings present strong evidence that persistent oxidative stress is a crucial mechanism in the progression from a normal to malignant state as the genes that are regulated by oxidative stress in normal cells are also the genes that differentiate normal from tumorigenic cell lines.

MATERIAL AND METHODS

Cell Lines

Our model for breast cancer consists of a primary human mammary epithelial cell line (HMEC) obtained from reduction mammoplasty, and two transformed mammary epithelial cell lines derived from these cells, HMLER-1 and HMLER-5, kindly provided by RA Weinberg (M.I.T., Cambridge, MA). HMLER-1 and HMLER-5 were obtained by in vivo transformation of HMEC with a series of oncogenes and cancer-associated genes, including: telomerase catalytic subunit (hTERT), SV40 large-T antigen and H-RasV12, an oncogenic allele of H-Ras [22]. HMLER-1 cells have an intermediate expression level of H-RasV12 rarely form tumors, while HMLER-5 cells have high-levels of H-RasV12 and are highly tumorigenic [22].

Glucose oxidase (GluOx) treatment and measurement of glutathione levels

To determine optimal conditions for inducing oxidative stress by GluOx treatment, HMEC subconfluent cells in the exponential phase of growth were supplied with 10 mM glucose and varying concentrations of GluOx (control, 0.02 and 0.2U) at varying time intervals (2, 4, 8 and 16 h). Following GluOx treatment cell viability was evaluated as judged by trypan blue exclusion. Cells were counted, washed and cell pellets were collected and frozen at -80 °C for glutathione analysis. Pelleted cells were extracted with 250 μl of methanol:water (50:50, v/v) acidified with 0.01M hydrochloric acid to minimize oxidation of thiols, and spiked with 4ug/ml of the isotopically labeled internal standards L-Cysteine-3,3-d2 (Cambridge Isotopes Laboratories, Inc. Andover, MA) and DL-Homocysteine-3,3,4,4-d4 (C/D/N Isotopes Inc. Pointe-Claire, Quebec, Canada). After the extraction buffer was added, cell lysis was induced by 3 cycles of 5 min incubation in dry ice, followed by 1 min sonication in ultrasonic bath. The extract was mixed with 250 μl of 5mM N-ethylmaleimide in water, vortex and incubated for 30 min at room temperature to block the free thiol groups. The cell culture lysates were centrifuged for 15 min at 13,000 rpm to remove the cellular debris.

GSSG and GSH were measured using pre-column AccQ·Tag™ Ultra UPLC derivatization kit (Waters Corporation, Milford, MA), following a modified protocol from [23]. Briefly, reagents for derivatization were prepared and derivatization was performed according to manufacturer’s protocol. LC-MS-PDA analysis was performed on LC-MS system comprised of Waters Acquity UPLC system (Waters Corporation, Milford, MA), equipped with the Acquity photodiode array detector, interfaced with ThermoFisher LTQ mass spectrometer (ThermoFisher Corporation, San Jose CA). UPLC separation was performed on AccQ·Tag™ Ultra column (1.7 μm, 100 mm × 2.1 mm i.d.,) from Waters. Column effluent was ionized by electrospray ionization (ESI), and the mass spectrometer was operated in full scan positive. GSSG and GSH quantitation was performed using calibration curves with isotopically labeled internal standards L-Cysteine-3,3-d2 (Cambridge Isotopes Laboratories, Inc. Andover, MA) and DL-Homocysteine-3,3,4,4-d4 (C/D/N Isotopes Inc. Pointe-Claire, Quebec, Canada), that were added to each sample. Commercially available forms of the GSSG and GSH were purchased from Sigma-Aldrich (Sigma-Aldrich, St. Louis, MO), and used to prepare the calibration curves.

Cell culture and treatment with GluOx

Cells were cultured in basal medium supplemented with 10 ng/ml of human epidermal growth factor (EGF), 0.5 μg/ml hydrocortisone, 100 U/ml penicillin-streptomycin and 10 μg/ml insulin under controlled conditions in a humidifier incubator maintained at 5% CO2 and 37°C. Triplicates of HMECs, HMELR-1 and HMLER-5 subconfluent cells in the exponential phase of growth and supplied with 10 mM glucose, were either left untreated (control) or treated with GluOx 0.2 U/ml for 2 hours. This treatment did not affect cell viability as judged by trypan blue exclusion. Cells were removed from the dishes by scraping, counted, washed and cell pellets were collected and frozen at -80°C for microarray analysis.

RNA Extraction

Total RNA was isolated from samples using Trizol Reagent, according to standard protocols (Invitrogen, Carlsbad, CA), followed by a cleanup procedure with RNeasy MinElute Cleanup Kit (Qiagen, Valencia, CA). RNA quality was assessed using UV spectra characteristics (NanoDrop, Wilmington, DE) and microanalysis (Agilent Bioanalyzer) for size and integrity of the total RNA.

Affymetrix microarrays analysis and data processing

Affymetrix U133 Plus 2.0 human oligonucleotide microarray containing over 47,000 transcripts and variants, including 38,500 well-characterized human genes was used for gene expression analysis. Preparation of in vitro cRNA, oligonucleotide array hybridization and scanning were performed according to Affymetrix (Santa Clara, CA) protocols. We generated a probe set based gene expression data file from quantified image files with the GeneChip Multi-Array Average (GCRMA) method [24] associated packages from the BioConductor tool suite (http://www.bioconductor.org/) [20, 25], using R version 2.10 (www.r-project.org) and annotated with Unigene annotations from the February 2009 mapping version of the human genome. All 18 CEL files were analysed simultaneously, yielding a data matrix of probe sets by cell lines in which each value is the calculated log abundance of each gene probe set for each cell line under oxidative stress or control treatment conditions. Background subtraction, quantile normalization and gene data summarization was done by GCRMA. Differential expression analysis was performed using the linear modelling features of the limma package [26]. The positive False Discovery Rate [27] multiple-testing adjustment was applied to correct p-values. All genes in a comparison of interest with an adjusted p-value ≤ 0.05 were considered as statistically significant regardless of the fold difference in expression level.

Pathway and functional category analysis

All genes significantly differentially expressed across different experimental conditions were analyzed in Database for Annotation, Visualization and Integrated Discovery (DAVID) version 2 (http://david.niaid.nih.gov/david/version2/index.htm) [28] to discover KEGG pathways that are significantly represented in these groups of genes. Additionally, gene network relationships and pathway analysis was performed using the Ingenuity Pathways Analysis (Ingenuity® Systems, www.ingenuity.com ) a repository database of molecular interaction, regulatory events gene to phenotype associations and chemical knowledge. With this gene expression analysis tools we identified known molecular network genes and pathways significantly represented among differentially expressed genes. Both, DAVID and Ingenuity Pathways Analysis use a Fisher’s Exact p-value to calculate the association between a particular set of differentially expressed genes and known pathways and gene networks that are most significantly enriched. A Fisher’s Exact p-value = 0 represent perfect enrichment. All pathways with a p-value ≤ 0.05 were considered strongly enriched with differentially expressed genes.

RESULTS AND DISCUSSION

We are interested in understanding the mechanism by which oxidative stress is involved in breast cancer development and progression. Accumulated evidence suggests that cancer cells seem to function with higher levels of oxidative stress in vitro and in vivo compared with their normal counterparts, and consequently have increased levels of ROS [29, 30]. In spite of evidence that cancer cells are oxidatively stressed, several important questions have not yet been addressed: (i) whether there is any difference in the oxidative stress response mechanisms of carcinogenic cells compared to their normal counterparts or whether the antioxidant system in cancer cells is suppressed; and (ii) what mechanisms, mediated by oxidative stress, in normal cells may contribute to their progression towards malignancy, if any. In an effort to further understand the mechanisms of oxidative stress associated with the progression of breast malignancy, our study concentrated on examining the gene expression profile of normal human primary mammary epithelial cells and fully tumorigenic transformed breast cancer cells in response to oxidative stress induced by GluOx.

GluOx treatment and assessment of oxidative stress levels

Cultured normal mammary epithelial cells (HMECs) were treated with varying levels of H2O2 generated in the cultured media by glucose oxidase as described in the methods section. The effects of the GluOx treatment on the levels of reduced (GSH) and oxidized (GSSG) glutathione are shown in Fig. 1. We used this information to determine the GluOx dose and minimum incubation time to induce oxidative stress. The results show that after 2h treatment with 0.2U/ml of GluOx the pool of free GSH was reduced significantly compared to untreated cells, or cells that were treated with 0.02U/ml of GluOx regardless of the incubation time. Similarly, the levels of GSSG increased after 2h treatment with 0.2U/ml of GluOx, while in untreated cells and cells treated with 0.02U/ml the levels of GSSG were below the limits of detection. We also observed that cell death increases progressively with time, in cultures treated with 0.2U/ml of GluOx. While after 2h treatment cell viability was not affect, based on trypan blue exclusion, 10%, 20% and 90% of the cells die after 4h, 8h, and 16h treatment with 0.2U/ml of GluOx, respectively.

Figure 1.

Figure 1

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The effects of the GluOx treatment on the levels of reduced (GSH) and oxidized (GSSG) glutathione in the HMEC cells. (A) GSH and GSSG levels in HMEC cells treated with 0.2U/ml of GluOx at different time intervals. (B) GSSG-to-GSH ratio in HMEC cell treated with 0.2U/ml of GluOx at different time intervals. Only 10% of the cultured cells were viable after 16h treatment and the levels of both GSH and GSSG were below the limits of detection.

As previously reported by others [31-34], glucose oxidase treatment is a standard method of generating the hydrogen peroxide that enables exposure of treated cells to a constant dose of hydrogen peroxide. Since hydrogen peroxide will decay over time, incubation with GluOx in the presence of glucose is preferable to treatment with a single bolus dose of hydrogen peroxide itself [34]. The ratio of GSSG-to-GSH is an indicator of cellular health, with GSH constituting up to 98% of cellular glutathione under normal conditions. An increased GSSG-to-GSH ratio is considered indicative of oxidative stress [35]. Our GSSG-to-GSH ratio and cell viability results indicate that a treatment with 0.2U/ml of GluOx for 2h induces adequate acute oxidative stress without affecting cell viability and is suitable to study gene expression response to oxidative stress conditions.

Identification of differential gene expression responses to oxidative stress by normal and fully malignant human mammary epithelial cells

Unlike other tissue culture models of breast cancer, the HMEC/HMLER system permits the comparison of oxidative stress-response in normal human breast epithelial cells with intermediate and fully malignant variants produced by completely defined genetic changes. We determined the influence of oxidative stress on the global gene expression profile of three cell types representing stages in the malignant progression of breast cells: normal mammary epithelial cells (HMECs), an immortalized mammary epithelial cell line expressing low levels of H-RasV12 (HMLER-1), and a fully malignant, tumorigenic cell line expressing high levels of H-RasV12 (HMLER-5). Triplicate cultures for each cell type were either grown under normal conditions or exposed to oxidative stress by treatment with glucose/GluOx. Initial data analysis of genes statistically significantly differentially expressed in response to GluOx treatment revealed significant transcriptional profile changes in normal HMECs, but markedly fewer in HMELR-1 or HMELR-5 cancer cell lines.

In HMEC cells we found 11895 genes (25% of all the transcripts measured) differentially expressed in response to oxidative stress. Among them, 5966 had higher expression levels and 5929 had lower expression levels in oxidatively stressed HMECs compared to the corresponding control. Functional analysis of these gene sets revealed that pathways involved in cell cycle, energy metabolism, amino acids metabolism, protein biosynthesis, transport and degradation, fatty acid metabolism, biosynthesis and metabolism of antioxidant molecules were significantly up-regulated by oxidative stress (Table 1). Pathways associated with cancer progression, apoptosis, cell junction and several cancer signalling pathways including p53, TGF-β and Notch, were down-regulated (Table 2).

Table 1.

Significantly up-regulated pathways in normal HMEC and cancer cells in response to oxidative stress. Selection of pathways was based on Fisher Exact p-value. Annotation categories with p-value ≤ 0.05, were considered strongly enriched in up-regulated genes.

KEGG Categories HMEC HMLER-1 HMELR-5
hsa00190:Oxidative phosphorylation 0.000 * 0.001
hsa03010:Ribosome 0.000 * *
hsa03050:Proteasome 0.000 0.006 *
hsa00970:Aminoacyl-tRNA biosynthesis 0.000 * *
hsa04110:Cell cycle 0.000 * *
hsa04120:Ubiquitin mediated proteolysis 0.000 0.000 *
hsa00280:Valine, leucine and isoleucine degradation 0.000 * *
hsa00240:Pyrimidine metabolism 0.000 * *
hsa00020:Citrate cycle (TCA cycle) 0.000 * *
hsa05110:Cholera - Infection 0.001 * *
hsa00930:Caprolactam degradation 0.001 * *
hsa00230:Purine metabolism 0.004 * *
hsa00290:Valine, leucine and isoleucine biosynthesis 0.004 * *
hsa00062:Fatty acid elongation in mitochondria 0.005 * *
hsa00051:Fructose and mannose metabolism 0.006 * *
hsa00563:Glycosylphosphatidylinositol(GPI)-anchor biosynthesis 0.010 * *
hsa00770:Pantothenate and CoA biosynthesis 0.011 * *
hsa00740:Riboflavin metabolism 0.011 * *
hsa00400:Phenylalanine, tyrosine and tryptophan biosynthesis 0.013 * *
hsa00071:Fatty acid metabolism 0.015 * *
hsa04130:SNARE interactions in vesicular transport 0.019 * *
hsa00620:Pyruvate metabolism 0.032 0.035 *
hsa00450:Selenoamino acid metabolism 0.032 * *
hsa00252:Alanine and aspartate metabolism 0.032 * *
hsa00310:Lysine degradation 0.038 * *
hsa05030:Amyotrophic lateral sclerosis (ALS) 0.039 * *
hsa00030:Pentose phosphate pathway 0.042 * *
hsa00630:Glyoxylate and dicarboxylate metabolism 0.050 * *
hsa00790:Folate biosynthesis * 0.033 *
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*

Not statistically significantly represented category based on Fisher Exact p-value.

Table 2.

Significantly down-regulated pathways in normal HMEC and cancer cells in response to oxidative stress. Selection of pathways was based on Fisher Exact p-value. Annotation categories with p-value ≤ 0.05, were considered strongly enriched in down-regulated genes.

KEGG Categories HMEC HMLER-1 HMELR-5
hsa04520:Adherens junction 0.000 * 0.019
hsa04120:Ubiquitin mediated proteolysis 0.000 * *
hsa05222:Small cell lung cancer 0.001 * *
hsa04510:Focal adhesion 0.001 0.047 *
hsa04115:p53 signaling pathway 0.001 0.012 *
hsa04210:Apoptosis 0.002 * *
hsa04350:TGF-beta signaling pathway 0.002 * *
hsa04530:Tight junction 0.004 * *
hsa05212:Pancreatic cancer 0.006 * 0.018
hsa05211:Renal cell carcinoma 0.011 * *
hsa05215:Prostate cancer 0.013 * 0.033
hsa04330:Notch signaling pathway 0.017 * *
hsa04360:Axon guidance 0.025 * *
hsa04910:Insulin signaling pathway 0.030 * *
hsa05110:Cholera - Infection 0.030 * *
hsa04110:Cell cycle 0.031 0.017 *
hsa00100:Biosynthesis of steroids 0.035 * *
hsa04540:Gap junction 0.035 * *
hsa01040:Polyunsaturated fatty acid biosynthesis 0.037 * *
hsa04070:Phosphatidylinositol signaling system 0.039 * *
hsa05223:Non-small cell lung cancer 0.040 * 0.006
hsa05220:Chronic myeloid leukemia * * 0.019
hsa05213:Endometrial cancer * * 0.005
hsa04150:mTOR signaling pathway * 0.030 *
hsa05219:Bladder cancer * 0.016 *
hsa05210:Colorectal cancer * * 0.030
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*

Not statistically significantly represented category based on Fisher Exact p-value.

In HMLER-1 cell line expressing low level of oncogenic H-RasV12, 634 genes were differentially expressed in response to oxidative stress, with 346 down-regulated and 288 up-regulated. Functional analysis showed that ubiquitin mediated proteolysis, proteosome, folic acid biosynthesis and pyruvate metabolism were up-regulated (Table 1), while cell cycle, p53 and mTOR signalling pathways, and cancer progression pathways were down-regulated (Table 2).

In HMLER-5 cell line, expressing high levels of oncogenic H-RasV12, 379 genes were found regulated by oxidative stress treatment, with 265 down-regulated and 114 up-regulated. Functional analysis showed that oxidative phosphorylation was up-regulated (Table 1), and pathways in cancer progression were down-regulated (Table 2). Distribution of common genes differentially expressed in response to oxidative stress in HMEC, HMLER-1 and HMLER-5 cell types is shown in Figure 2.

Figure 2.

Figure 2

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Distribution of genes regulated by GluOx treatment in HMEC, HMLER-1 and HMLER-5 cells.

For each cell line (HMECs, HMLER-1 and HMLER-5) genes, differentially expressed in response to oxidative stress induced by GluOx treatment, were selected based on the FDR adjusted p-value ≤ 0.05.

These findings suggest that normal and tumorigenic cells differ strongly in their response to oxidative stress. In contrast, the gene expression profile of tumorigenic cell lines are comparatively unaffected by oxidative stress, while the gene expression in normal cells undergoes significant changes stimulated by the elevated intracellular levels of ROS.

Cancer progression and oxidative stress gene expression profiles overlap

Since persistent oxidative stress is believed to contribute to cancer, the failure of oxidative stress to up-regulate pathways in cancer progression was initially surprising. However, considering the low number of genes in HMLER-1 and HMLER-5 cells that responded to oxidative stress induced by GluOx treatment, we considered that the genetic changes associated with oxidative stress observed in HMEC might have already occurred in cancer cells due to a persistent oxidative stress status and high levels of ROS, making them tolerant or resistant to further oxidative treatment with GluOx. To test this hypothesis, we compared the set of genes differentially expressed in normal HMEC in response to oxidative stress with those genes associated with cancer malignancy progression in HMLERs (HMLERs control vs HMEC GluOx) (Figure 3). A set of genes associated with cancer malignancy progression identified by comparing unstressed tumorigenic HMLER cell lines with normal HMEC included genes in the TNFa/NFkB signalling pathway (i.e. AKT1 NFKBIA and MAP3K3), Wnt signalling pathway (i.e. CDC2, PIN1, TAX1BP3), and tumor suppressors (i.e. BRCA1, CDKN2A, NF1, NF2, PTEN). Further analysis of the 11895 genes differentially expressed in normal HMEC cells in response to oxidative stress, showed that 10545 (89%) are also differentially expressed during the progression to malignancy by HMLER cells (8550 or 72% by both HMLERs, 906 or 8% HMLER-5 only, and 1089 or 9% HMLER-1 only, from Figure 3). Among them we found genes with common responses--referring to those genes that were up or down-regulated in both oxidative stress and cancer malignancy progression (7182 out of 8550 – 84%); genes with opposite responses--referring to those that were up-regulated in oxidative stress, but down-regulated in malignancy progression and vice versa (1026 out of 8550 - 12%); and genes with heterogeneous response--referring to those which expression level varied, but were not particularly associated with either oxidative stress or malignancy progression exclusively (342 out of 8550 - 4%) (Supplementary Tables 1-3). Moreover, we found that several of these genes with common and heterogeneous response are part of known gene networks and pathways related to cancer (Supplementary Fig 1 Pathways in Cancer).

Figure 3.

Figure 3

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Distribution of genes differentially expressed in cancer cells lines (HMLER-1 and HMLER-5) relative to control HMECs and their overlap with genes regulated by oxidative stress in normal HMECs.

Genes associated with breast cancer malignancy progression were identified by comparing cancer cell lines HMLER-1 and HMELR-5 control samples to normal HMEC control. Similarly, comparing HMEC treated with GluOx relative to HMEC control identified genes regulated by GluOx in normal HMECs. More than 10000 genes associated with cancer malignancy progression are also regulated by oxidative stress. All statistically significant genes with adjusted p-value ≤ 0.05, were included.

Functional analysis of the pathways significantly enriched with genes that were induced or repressed in response to oxidative stress and cancer progression, in breast cells, is summarized in Tables 3 and 4. As expected, the overlapping distribution of genes regulated by oxidative stress and cancer results in an overlap of biochemical pathways they represent. These observations present strong evidence that adaptation to persistent oxidative stress is a crucial mechanism in the progression from a normal to malignant state as the genes that are regulated by oxidative stress in normal cells are also the genes that differentiate normal from tumorigenic cell lines.

Table 3.

Significantly up-regulated pathways in normal HMEC in response to oxidative stress and during cancer malignancy progression. Selection of pathways was based on Fisher Exact p-value. Annotation categories with p-value ≤ 0.05, were considered strongly enriched in up-regulated genes.

KEGG Categories HMEC GluOx response HMLER-1 cancer progression HMLER-5 cancer progression
hsa00190:Oxidative phosphorylation 0.000 0.000 0.000
hsa03010:Ribosome 0.000 0.000 0.000
hsa03050:Proteasome 0.000 0.000 0.000
hsa04110:Cell cycle 0.000 0.000 0.000
hsa00240:Pyrimidine metabolism 0.000 0.000 0.000
hsa00970:Aminoacyl-tRNA biosynthesis 0.000 0.032 0.013
hsa00020:Citrate cycle (TCA cycle) 0.000 0.000 0.000
hsa04120:Ubiquitin mediated proteolysis 0.000 0.000 0.000
hsa00280:Valine, leucine and isoleucine degradation 0.002 * 0.000
hsa00230:Purine metabolism 0.002 0.000 0.000
hsa00130:Ubiquinone biosynthesis 0.013 0.005 0.005
hsa00290:Valine, leucine and isoleucine biosynthesis 0.014 0.000 *
hsa00400:Phenylalanine, tyrosine and tryptophan biosynthesis 0.015 * 0.042
hsa00620:Pyruvate metabolism 0.017 0.028 0.028
hsa03020:RNA polymerase 0.018 0.007 0.006
hsa00062:Fatty acid elongation in mitochondria 0.025 * *
hsa04130:SNARE interactions in vesicular transport 0.026 0.003 0.007
hsa00071:Fatty acid metabolism 0.031 * 0.046
hsa00630:Glyoxylate and dicarboxylate metabolism 0.033 * 0.016
hsa00563:Glycosylphosphatidylinositol(GPI)-anchor biosynthesis * 0.016 *
hsa00380:Tryptophan metabolism * 0.048 0.047
hsa04115:p53 signaling pathway * 0.018 0.018
hsa00100:Biosynthesis of steroids * 0.009 0.009
hsa00770:Pantothenate and CoA biosynthesis * 0.032 0.031
hsa03022:Basal transcription factors * 0.014 0.036
hsa00030:Pentose phosphate pathway 0.017 0.036 0.036
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*

Not statistically significantly represented category based on Fisher Exact p-value.

Table 4.

Significantly down-regulated pathways in normal HMEC in response to oxidative stress and during cancer malignancy progression. Selection of pathways was based on Fisher Exact p-value. Annotation categories with p-value ≤ 0.05, were considered strongly enriched in down-regulated genes.

KEGG Categories HMEC GluOx response HMLER-1 cancer progression HMLER-5 cancer progression
hsa04520:Adherens junction 0.000 0.000 0.000
hsa04120:Ubiquitin mediated proteolysis 0.000 0.000 0.000
hsa04510:Focal adhesion 0.001 0.003 0.003
hsa04530:Tight junction 0.003 0.000 0.001
hsa05211:Renal cell carcinoma 0.003 0.033 0.017
hsa04350:TGF-beta signaling pathway 0.004 0.003 0.003
hsa04360:Axon guidance 0.034 0.005 0.003
hsa04010:MAPK signaling pathway 0.036 0.033 0.024
hsa05212:Pancreatic cancer 0.037 0.049 0.039
hsa04210:Apoptosis 0.009 * *
hsa04115:p53 signaling pathway 0.009 * *
hsa04070:Phosphatidylinositol signaling system 0.013 * *
hsa05222:Small cell lung cancer 0.025 * *
hsa05215:Prostate cancer 0.025 * *
hsa04540:Gap junction 0.032 * *
hsa01040:Polyunsaturated fatty acid biosynthesis 0.016 0.032 *
hsa00600:Sphingolipid metabolism * 0.006 0.006
hsa00563:Glycosylphosphatidylinositol(GPI)-anchor biosynthesis * 0.013 0.013
hsa04330:Notch signaling pathway * 0.013 0.014
hsa04310:Wnt signaling pathway * * 0.024
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*

Not statistically significantly represented category based on Fisher Exact p-value.

DNA Repair genes activated by oxidative stress and cancer progression

We identified 67 genes related to DNA damage signalling pathways that were common among the genes differentially expressed in HMEC in response to oxidative stress and also during cancer malignancy progression in HMELER-1 and HMLER-5 (Table 5). These 67 genes expanded several functional groups including nucleotide excision repair (NER), base excision repair (BER), mismatch excision repair (MER), DNA polymerases catalytic subunits, genes defective in diseases associated with sensitivity to DNA damaging agents, and other identified genes with DNA repair function. Of the 67 genes significantly altered, 47 were up-regulated by oxidative stress and cancer progression (e.g., tumor suppressor BRCA, 8-oxoGTPase NUDT1); ten genes were down-regulated by oxidative stress and cancer progression (i.e. ERCC6, XPA, RAD23B, ERCC4); four genes were up-regulated by oxidative stress and down-regulated by cancer progression (MGMT, RAD52, XPC, ERCC5); and two genes were down-regulated by oxidative stress and up-regulated by cancer progression (FANCL, XRCC2). For the two cancer cell lines, the similar overall trend in gene-expression changes indicated that even low expression of H-Rasv12 had resulted in the induction of genes involved in DNA damage signalling pathways.

Table 5.

DNA repair and DNA damage signalling pathway genes regulated by oxidative stress and during cancer malignancy progression. The expression values of all the genes listed is statistically significant with an adjusted p-value ≤ 0.05.

Gene Name Gene Bank ID Gene Description Group HMEC - GO HMLE R-1 HMLE R-5
UNG NM_003362 Uracil-DNA glycosylase Base excision repair (BER) UP UP UP
SMUG1 BC000417 Uracil-DNA glycosylase Base excision repair (BER) UP UP UP
NTHL1 U79718 Ring-saturated or fragmented pyrimidines Base excision repair (BER) UP UP UP
PARP2 AJ236912 PARP-like enzyme Base excision repair (BER) UP UP UP
H2AFX NM_002105 Histone, phosphorylated after DNA damage Chromatin Structure UP UP UP
CHAF1A NM_005483 Chromatin assembly factor Chromatin Structure UP UP UP
ALKBH3 BF062547 1-meA dioxygenase Direct reversal of damage UP UP UP
PCNA NM_002592 Sliding clamp for pol delta and pol epsilon DNA polymerases (catalytic subunits) UP UP UP
MAD2L2 AF080398 DNA pol zeta subunit DNA polymerases (catalytic subunits) UP UP UP
FEN1 BC000323 5’ nuclease Editing and processing nucleases UP UP UP
RECQL4 NM_004260 Rothmund-Thompson syndrome Genes defective in diseases associated with sensitivity to DNA damaging agents UP UP UP
FANCA NM_000135 Involved in tolerance or repair of DNA crosslinks Genes defective in diseases associated with sensitivity to DNA damaging agents UP UP UP
FANCD2 AA579890 Involved in tolerance or repair of DNA crosslinks Genes defective in diseases associated with sensitivity to DNA damaging agents UP UP UP
FANCE NM_021922 Involved in tolerance or repair of DNA crosslinks Genes defective in diseases associated with sensitivity to DNA damaging agents UP UP UP
BLM NM_000057 Bloom syndrome helicase Genes defective in diseases associated with sensitivity to DNA damaging agents UP UP UP
RAD51C AF029669 Rad51 homolog Homologous recombination UP UP UP
SHFM1 NM_006304 BRCA2 associated Homologous recombination UP UP UP
RAD54B NM_012415 Accessory factors for recombination Homologous recombination UP UP UP
BRCA1 NM_007295 Accessory factor for transcription and recombination Homologous recombination UP UP UP
EME1 AK021607 A structure-specific DNA nuclease Homologous recombination UP UP UP
MLH1 NM_000249 MutL homologs, forming heterodimer Mismatch excision repair (MMR) UP UP UP
MSH6 D89646 Mismatch and loop recognition Mismatch excision repair (MMR) UP UP UP
DUT U90223 dUTPase Modulation of nucleotide pools UP UP UP
NUDT1 NM_002452 8-oxoGTPase Modulation of nucleotide pools UP UP UP
XRCC4 AB017445 Ligase accessory factor Non-homologous end-joining UP UP UP
XRCC5 AA205834 DNA end binding Non-homologous end-joining UP UP UP
RAD23A BF572938 Substitutes for HR23B Nucleotide excision repair (NER) UP UP UP
CDK7 L20320 Kinase subunits of TFIIH Nucleotide excision repair (NER) UP UP UP
CCNH NM_001239 Kinase subunits of TFIIH Nucleotide excision repair (NER) UP UP UP
MNAT1 NM_002431 Kinase subunits of TFIIH Nucleotide excision repair (NER) UP UP UP
GTF2H5 AV701318 Core TFIIH subunit p8 Nucleotide excision repair (NER) UP UP UP
RPA1 NM_002945 Binds DNA in preincision complex Nucleotide excision repair (NER) UP UP UP
RPA2 NM_002946 Binds DNA in preincision complex Nucleotide excision repair (NER) UP UP UP
RPA3 BC005264 Binds DNA in preincision complex Nucleotide excision repair (NER) UP UP UP
CETN2 BC005334 Binds damaged DNA as complex Nucleotide excision repair (NER) UP UP UP
ERCC1 NM_001983 5’ incision subunit Nucleotide excision repair (NER) UP UP UP
RAD1 AF074717 PCNA-like DNA damage sensors Other conserved DNA damage response genes UP UP UP
HUS1 AI968626 PCNA-like DNA damage sensors Other conserved DNA damage response genes UP UP UP
OBFC2B NM_024068 Single-stranded DNA binding protein Other identified genes with a suspected DNA repair function UP UP UP
DCLRE1B AI703304 Related to SNM1 Other identified genes with a suspected DNA repair function UP UP UP
DCLRE1A D42045 DNA crosslink repair Other identified genes with a suspected DNA repair function UP UP UP
UBE2A NM_003336 Ubiquitin-conjugating enzyme Rad6 pathway UP UP UP
UBE2B AA877765 Ubiquitin-conjugating enzyme Rad6 pathway UP UP UP
UBE2V2 U62136 Ubiquitin-conjugating complex Rad6 pathway UP UP UP
UBE2N BE262760 Ubiquitin-conjugating complex Rad6 pathway UP UP UP
RAD18 AB035274 E3 ubiquitin ligase Rad6 pathway UP UP UP
TDP1 NM_018319 Removes covalently bound TOP1-DNA complexes Repair of DNA-protein crosslinks UP UP UP
MGMT NM_002412 O6-meG alkyltransferase Direct reversal of damage UP Down Down
RAD52 NM_002879 Accessory factors for recombination Homologous recombination UP Down Down
XPC D21089 Binds damaged DNA as complex Nucleotide excision repair (NER) UP Down Down
ERCC5 NM_000123 3’ incision Nucleotide excision repair (NER) UP Down Down
FANCL NM_018062 Involved in tolerance or repair of DNA crosslinks Genes defective in diseases associated with sensitivity to DNA damaging agents Down UP UP
XRCC2 NM_005431 DNA break and crosslink repair Homologous recombination Down UP UP
MBD4 AI913365 U or T opposite G at CpG sequences Base excision repair (BER) Down Down Down
POLH NM_006502 XP variant DNA polymerases (catalytic subunits) Down Down Down
WRN NM_000553 Werner syndrome helicase / 3’ - exonuclease Genes defective in diseases associated with sensitivity to DNA damaging agents Down Down Down
ATM NM_138293 ataxia telangiectasia Genes defective in diseases associated with sensitivity to DNA damaging agents Down Down Down
PMS2L3 D38437 MutL homologs of unknown function Mismatch excision repair (MMR) Down Down Down
GTF2H3 AI569458 Core TFIIH subunit p34 Nucleotide excision repair (NER) Down Down Down
ERCC6 BF433475 Cockayne syndrome; Needed for transcription-coupled NER Nucleotide excision repair (NER) Down Down Down
XPA AW044506 Binds damaged DNA in preincision complex Nucleotide excision repair (NER) Down Down Down
RAD23B T93562 Binds damaged DNA as complex Nucleotide excision repair (NER) Down Down Down
ERCC4 AI694544 5’ incision subunit Nucleotide excision repair (NER) Down Down Down
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The association between DNA damage by oxidative stress, DNA repair genes and the characteristic of breast cancer in patients and cell lines is well documented [30, 36]. Oxidative DNA damage was reported to increase in human breast cancer tissues and transformed human breast cell lines compared to their normal counterparts [37]. Among those DNA repair or DNA damage signalling genes upregulated by oxidative stress and cancer progression, is worth mentioning RAD51C, which encodes a strand transfer protein involved in recombination repair of damage DNA, which over-expression in breast tissues has been associated with tumor progression [38]. Also, NUDT1 coding a protein that hydrolyzes oxidized purine nucleoside triphosphates, such as 8-oxo-dGTP and 2-hydroxy-dATP, to monophosphates, thereby preventing misincorpotation, was up-regulated. These are strong indicators of DNA damage and the formation of modified DNA bases, which are considered an important event in ROS-induced carcinogenesis [13, 39]. In this regard, DNA retrieved from human breast cancer specimens, as well as adjacent non-malignant tissue demonstrate elevated levels of modified purines including 8-oxoguanine, 8-oxoadenine, and FapyGua (2,6-diamino-4-hydroxy-5-formamidopyrimidine), as compared to DNA from normal breast tissues [40]. Progression from normal to malignant breast tissue is accompanied by a shift from a high ratio of ring-open (Fapy) purines to 8-oxo adducts to one favouring the 8-oxo adducts [41], reflecting a change from a reducing nuclear environment to an oxidizing one during breast carcinogenesis [42]. Furthermore, DNA derived from invasive ductal breast cancer specimens of patients with metastatic disease have a higher total content of modified purines than does DNA derived from non-metastatic breast cancers [43], suggesting a role for oxidative DNA damage in the progression to the metastatic state. Our findings of specific DNA repair genes differentially expressed during both oxidative stress and cancer progression may help explain the specific DNA modifications associated with oxidative DNA damage that occur in breast cancer cells. The similarity in expressions patterns of DNA repair genes in response to oxidative stress and cancer malignancy progression support our hypothesis that breast cancer cells are persistently exposed to high levels of ROS, and have similar oxidative stress response mechanisms compared to those found in normal HMECs under acute oxidative stress conditions.

ROS scavenging enzymes activated by oxidative stress

In addition to the increased expression levels of DNA repair genes, the levels of several ROS-scavenging enzymes were significantly altered in malignant cells and in normal HMEC treated with GluOx, suggesting aberrant regulation of redox homeostasis and stress adaptation in cancer cells. Cells control ROS levels by balancing ROS generation with their elimination by ROS-scavenging systems, such as, superoxide dismutase, glutathione peroxidase, glutaredoxin, peroxiredoxin, thioredoxin and catalase. We looked at the expression patterns of the genes coding for the major ROS-scavenging enzymes and found that all except catalase were significantly altered in their expression in response to oxidative stress treatment and cancer progression (Table 6). Of the 41 genes identified, 28 were up-regulated and 4 down-regulated by both oxidative stress treatment and cancer progression. Interestingly, seven genes including glutathione S-transferases (GSTO2, GSTM3, GSTP1, MGST2, GSTK1) and peroxiredoxin (PRDX5) were up-regulated by oxidative stress, but down-regulated during cancer progression.

Table 6.

ROS-scavenging systems expression regulated by oxidative stress in normal HMEC and during cancer malignancy progression in HMELER-1 and HMLER-5 cells

Gene Name Genebank ID Gene Description HMEC - GO HMLER-1 HMLER-5
GSTO2 AL162742 glutathione S-transferase omega 2 UP DOWN DOWN
GSTM3 AL527430 glutathione S-transferase mu 3 (brain) UP DOWN DOWN
GSTP1 NM_000852 glutathione S-transferase pi 1 UP DOWN DOWN
MGST2 NM_002413 microsomal glutathione S-transferase 2 UP DOWN DOWN
GSTK1 NM_015917 glutathione S-transferase kappa 1 UP DOWN DOWN
MGST3 AA129724 microsomal glutathione S-transferase 3 DOWN DOWN DOWN
ESD AU145746 esterase D/formylglutathione hydrolase UP UP UP
MGST3 NM_004528 microsomal glutathione S-transferase 3 UP UP UP
GPX8 AL571557 glutathione peroxidase 8 (putative) UP UP UP
GPX4 NM_002085 glutathione peroxidase 4 UP UP UP
GSR AI888037 glutathione reductase UP UP UP
GSTZ1 BC001453 glutathione transferase zeta 1 UP UP UP
ESD BC001169 esterase D/formylglutathione hydrolase UP UP UP
GSTO1 NM_004832 glutathione S-transferase omega 1 UP UP UP
GSTO1 U56250 glutathione S-transferase omega 1 UP UP UP
GPX8 AA173223 glutathione peroxidase 8 (putative) UP UP UP
GPX3 NM_002084 glutathione peroxidase 3 (plasma) UP UP UP
GSTT1 NM_000853 glutathione S-transferase theta 1 UP UP UP
SOD1 NM_000454 superoxide dismutase 1, soluble UP UP UP
GLRX3 NM_006541 glutaredoxin 3 DOWN DOWN DOWN
GLRX3 AF118652 glutaredoxin 3 UP UP UP
GLRX3 AL138831 glutaredoxin 3 UP UP UP
GLRX3 AK022131 glutaredoxin 3 UP UP UP
GLRX5 AA133341 glutaredoxin 5 UP UP UP
GLRX AF162769 glutaredoxin (thioltransferase) UP UP UP
PRDX2 AU147942 peroxiredoxin 2 DOWN DOWN DOWN
PRDX5 AI718223 peroxiredoxin 5 UP DOWN DOWN
PRDX5 AF197952 peroxiredoxin 5 UP DOWN DOWN
PRDX6 BE869583 peroxiredoxin 6 UP UP UP
PRDX6 NM_004905 peroxiredoxin 6 UP UP UP
PRDX2 L19185 peroxiredoxin 2 UP UP UP
PRDX2 L19185 peroxiredoxin 2 UP UP UP
TXNL1 AA629944 thioredoxin-like 1 DOWN DOWN DOWN
TXNL4A NM_006701 thioredoxin-like 4A UP UP UP
TXNL4B AW194729 thioredoxin-like 4B UP UP UP
TXN AF313911 thioredoxin UP UP UP
TXNRD2 AB019695 thioredoxin reductase 2 UP UP UP
TXNDC17 BC006405 thioredoxin domain containing 17 UP UP UP
TXN2 AL022313 thioredoxin 2 UP UP UP
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Superoxide dismutase SOD1, responsible for degrading superoxide radicals into oxygen and H2O2, was up-regulated by oxidative stress and by progression to cancer. Similarly, glutathione peroxidases (GPX3, GPX4, GPX8), enzymes involved in reducing lipid hydroperoxides to their corresponding alcohols and reducing H2O2 to water, were up-regulated by oxidative stress and by progression to cancer. Peroxiredoxin PRDX5 increased in oxidatively stressed HMEC but decreased in HMELR, while PDRX2 and PDRX6 were up-regulated in both oxidatively stressed normal cells and cancer cells. Members of the peroxiredoxins protein family play an antioxidant protective role in mammalian cells and also mediate signal transduction. PRDX2 particularly, may have a proliferative effect and play a role in cancer development or progression [44].

Previous work indicates that treatment of normal epithelial cells with continuous but sublethal amounts of exogenous oxidants confers resistance to higher levels of oxidative stress treatment [45]. Persistent elevated levels of ROS in cancer cells may exert selective pressure towards cells capable of adapting to oxidative stress conditions. Similar to our findings, a proteomics approach indicated that oncogenic H-ras-transformed cells expressed higher levels of antioxidant proteins such as PDRX3 and thioredoxin peroxidase (Tsa1p) compared to their non-tumorigenic parental cells [46].

Related to the ROS-scavenging enzymes system and to the cellular redox homeostasis is the pentose phosphate pathway (PPP). Our results also reveal that the PPP is significantly up-regulated in cancer cell lines and in response to oxidative stress in HMECs. The PPP occurs exclusively in the cytoplasm and is the major source of NADPH in cells, producing approximately 60% of this electron donating cofactor required, which is used to reduce glutathione via glutathione reductase (GSR). Likewise, glutaredoxins (GLRX, GLRX3 and GLRX5) and thioredoxins (TXN and TNX2), which posses an active center disulfide bond and function as electron carries in the glutathione system, depend on the availability of NADPH to go from an oxidized to reduced state. Indeed, GSR, GLRX, GLRX3, GLRX5 TXN and TNX2, were significantly up-regulated by oxidative stress in HMEC and by cancer progression in HMLER cells, which clearly suggest that increasing the pool of NADPH and GSH is a specific adaptation mechanism of breast cancer cells to elevated ROS levels. Moreover, by up-regulation of the PPP, cancer cells increase the production of ribose-5-phosphate (R5P), which is used in synthesis of nucleotides and nucleic acids, and erythrose-4-phosphate (E4P), which is used in the synthesis of aromatic amino acids.

CONCLUDING REMARKS

Although the precise pathways contributing to oxidative stress in cancer cells remain unclear, several fundamental cellular mechanisms and external factors are thought to cause oxidative stress during cancer development and disease progression. In addition to oncogenes activation and lack of functional p53, abnormal metabolism and mitochondrial dysfunction are considered two of the intrinsic factors that cause elevated levels of ROS in cancer cells [47-49]. Genes coding for subunits of the five complexes of the electron transport respiratory chain in the mitochondria were significantly up-regulated by oxidative stress and by progression to cancer. Additionally, a significant number of mitochondrial genes, involved not only in oxidative phosphorylation but also in other mitochondrial functions were up-regulated by oxidative stress and cancer progression. Furthermore, citrate cycle and pyruvate metabolism were also up-regulated, suggesting a profound modification of the energy metabolism in cancer cells and in their non-tumorigenic counterparts when exposed to oxidative stress. Although an increase in glycolysis is a well-described alteration in cancer [50], our observations that citric acid cycle and oxidative phosphorylation enzymes are also up-regulated in breast cancer cells and normal HMEC under acute oxidative stress suggest that multiple mechanisms up-regulate aerobic energy metabolism during breast cancer progression. Since our experiments were conducted in cells grown in a well-oxygenated tissue culture environment, these results may model changes in smaller, early stage tumors rather than the more hypoxic environment seen in larger tumors. Similar over-expression of energy metabolism proteins has been reported in breast, colon, ovary prostate and renal cancer cell lines and tumors [51]. Collectively, these results suggest that enhanced oxidative energy metabolism found in normal cells exposed to elevated levels of ROS is a common feature of cancer progression in different tissues, which, according to our findings, may have arisen from an adaptation to persistent high levels of ROS in cancer cells. The experiments reported herein demonstrate that normal and malignant breast epithelial cells differ substantially with regard to acute changes in gene expression in response to oxidative stress induced by elevated GluOx activity. When the gene expression profile response to oxidative stress across the different cell types was compared, there were significant differences in the number of genes regulated in normal HMEC compared to HMLER cells. While normal HMEC cells are significantly affected by oxidative stress, HMLER cells are almost unresponsive to oxidative stress exposure. However, the gene expression profile of normal HMEC cells under oxidative stress conditions shows similarity to that of the malignant HMLER under normal conditions, suggesting that HMLER cells are already in an oxidatively stressed state and are tolerant or resistant to elevated levels of ROS. Redox adaptation mechanisms remain an important concept that, to a large extent, explains the processes by which cancer cells survive under persistent endogenous oxidative stress, becoming resistant to ROS and anticancer agents.

Supplementary Material

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Acknowledgments

This work was supported in part by NIH (National Institutes of Health) NCI (National Cancer Institute) grants R01CA120170 (V.S) and T32 CA079448 (V.H.) and by the Virginia Bioinformatics Institute.

LIST OF ABBREVIATIONS

HMEC

human mammary epithelial cell

ROS

reactive oxygen species

hTERT

telomerase catalytic subunit

H-Rasv12

oncogenic allele of GTPase H-Ras

GluOx

glucose oxidaze

GCRMA

GeneChip Multi-Array Average

KEGG

Kyoto Encyclopaedia of Genes and Genomes

NER

nucleotide excision repair

BER

base excision repair

MER

mismatch excision repair

GSH

reduced glutathione

GSSG

oxidized glutathione

PPP

pentose phosphate pathway

GSH

reduced glutathione

GSSH

oxidized glutathione

Footnotes

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References

  • 1.Halliwell B. Oxidative stress and cancer: have we moved forward? Biochem J. 2007;401:1–11. doi: 10.1042/BJ20061131. [DOI] [PubMed] [Google Scholar]
  • 2.Poli G, Leonarduzzi G, Biasi F, Chiarpotto E. Oxidative stress and cell signalling. Curr Med Chem. 2004;11:1163–1182. doi: 10.2174/0929867043365323. [DOI] [PubMed] [Google Scholar]
  • 3.Weinberg RA. Mechanisms of malignant progression. Carcinogenesis. 2008;29:1092–1095. doi: 10.1093/carcin/bgn104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Goldberg JE, Schwertfeger KL. Proinflammatory Cytokines in Breast Cancer: Mechanisms of Action and Potential Targets for Therapeutics. Curr Drug Targets. 2010;14:13. doi: 10.2174/138945010792006799. [DOI] [PubMed] [Google Scholar]
  • 5.Kumaraguruparan R, Subapriya R, Kabalimoorthy J, Nagini S. Antioxidant profile in the circulation of patients with fibroadenoma and adenocarcinoma of the breast. Clin Biochem. 2002;35:275–279. doi: 10.1016/s0009-9120(02)00310-7. [DOI] [PubMed] [Google Scholar]
  • 6.Kumaraguruparan R, Subapriya R, Viswanathan P, Nagini S. Tissue lipid peroxidation and antioxidant status in patients with adenocarcinoma of the breast. Clin Chim Acta. 2002;325:165–170. doi: 10.1016/s0009-8981(02)00292-9. [DOI] [PubMed] [Google Scholar]
  • 7.Iscan M, Coban T, Cok I, Bulbul D, Eke BC, Burgaz S. The organochlorine pesticide residues and antioxidant enzyme activities in human breast tumors: is there any association? Breast cancer research and treatment. 2002;72:173–182. doi: 10.1023/a:1014828705281. [DOI] [PubMed] [Google Scholar]
  • 8.Portakal O, Ozkaya O, Erden Inal M, Bozan B, Kosan M, Sayek I. Coenzyme Q10 concentrations and antioxidant status in tissues of breast cancer patients. Clin Biochem. 2000;33:279–284. doi: 10.1016/s0009-9120(00)00067-9. [DOI] [PubMed] [Google Scholar]
  • 9.Pugh CW, Gleadle J, Maxwell PH. Hypoxia and oxidative stress in breast cancer. Hypoxia signalling pathways. Breast Cancer Res. 2001;3:313–317. doi: 10.1186/bcr313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Wang M, Dhingra K, Hittelman WN, Liehr JG, de Andrade M, Li D. Lipid peroxidation-induced putative malondialdehyde-DNA adducts in human breast tissues. Cancer Epidemiol Biomarkers Prev. 1996;5:705–710. [PubMed] [Google Scholar]
  • 11.Tas F, Hansel H, Belce A, Ilvan S, Argon A, Camlica H, Topuz E. Oxidative stress in breast cancer. Medical oncology (Northwood, London, England) 2005;22:11–15. doi: 10.1385/MO:22:1:011. [DOI] [PubMed] [Google Scholar]
  • 12.Frenkel K, Karkoszka J, Glassman T, Dubin N, Toniolo P, Taioli E, Mooney LA, Kato I. Serum autoantibodies recognizing 5-hydroxymethyl-2’-deoxyuridine, an oxidized DNA base, as biomarkers of cancer risk in women. Cancer Epidemiol Biomarkers Prev. 1998;7:49–57. [PubMed] [Google Scholar]
  • 13.Brown NS, Bicknell R. Hypoxia and oxidative stress in breast cancer. Oxidative stress: its effects on the growth, metastatic potential and response to therapy of breast cancer. Breast Cancer Res. 2001;3:323–327. doi: 10.1186/bcr315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Yamaguchi K, Mandai M, Toyokuni S, Hamanishi J, Higuchi T, Takakura K, Fujii S. Contents of endometriotic cysts, especially the high concentration of free iron, are a possible cause of carcinogenesis in the cysts through the iron-induced persistent oxidative stress. Clin Cancer Res. 2008;14:32–40. doi: 10.1158/1078-0432.CCR-07-1614. [DOI] [PubMed] [Google Scholar]
  • 15.Fournier MV, Martin KJ, Kenny PA, Xhaja K, Bosch I, Yaswen P, Bissell MJ. Gene expression signature in organized and growth-arrested mammary acini predicts good outcome in breast cancer. Cancer research. 2006;66:7095–7102. doi: 10.1158/0008-5472.CAN-06-0515. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Yau C, Benz CC. Genes responsive to both oxidant stress and loss of estrogen receptor function identify a poor prognosis group of estrogen receptor positive primary breast cancers. Breast Cancer Res. 2008;10:R61. doi: 10.1186/bcr2120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Kao J, Salari K, Bocanegra M, Choi YL, Girard L, Gandhi J, Kwei KA, Hernandez-Boussard T, Wang P, Gazdar AF, Minna JD, Pollack JR. Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery. PLoS One. 2009;4:e6146. doi: 10.1371/journal.pone.0006146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Ma XJ, Dahiya S, Richardson E, Erlander M, Sgroi DC. Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res. 2009;11:R7. doi: 10.1186/bcr2222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Gwinn MR, Whipkey DL, Tennant LB, Weston A. Differential gene expression in normal human mammary epithelial cells treated with malathion monitored by DNA microarrays. Environ Health Perspect. 2005;113:1046–1051. doi: 10.1289/ehp.7311. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Charafe-Jauffret E, Ginestier C, Monville F, Finetti P, Adelaide J, Cervera N, Fekairi S, Xerri L, Jacquemier J, Birnbaum D, Bertucci F. Gene expression profiling of breast cell lines identifies potential new basal markers. Oncogene. 2006;25:2273–2284. doi: 10.1038/sj.onc.1209254. [DOI] [PubMed] [Google Scholar]
  • 21.Djuric Z, Everett CK, Luongo DA. Toxicity, single-strand breaks, and 5-hydroxymethyl-2’-deoxyuridine formation in human breast epithelial cells treated with hydrogen peroxide. Free radical biology & medicine. 1993;14:541–547. doi: 10.1016/0891-5849(93)90111-7. [DOI] [PubMed] [Google Scholar]
  • 22.Elenbaas B, Spirio L, Koerner F, Fleming MD, Zimonjic DB, Donaher JL, Popescu NC, Hahn WC, Weinberg RA. Human breast cancer cells generated by oncogenic transformation of primary mammary epithelial cells. Genes Dev. 2001;15:50–65. doi: 10.1101/gad.828901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Armenta JM, Cortes DF, Pisciotta JM, Shuman JL, Blakeslee K, Rasoloson D, Ogunbiyi O, Sullivan DJ, Jr, Shulaev V. Sensitive and rapid method for amino acid quantitation in malaria biological samples using AccQ.Tag ultra performance liquid chromatography-electrospray ionization-MS/MS with multiple reaction monitoring. Anal Chem. 2010;82:548–558. doi: 10.1021/ac901790q. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4:249–264. doi: 10.1093/biostatistics/4.2.249. [DOI] [PubMed] [Google Scholar]
  • 25.Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, Clark L, Bayani N, Coppe JP, Tong F, Speed T, Spellman PT, DeVries S, Lapuk A, Wang NJ, Kuo WL, Stilwell JL, Pinkel D, Albertson DG, Waldman FM, McCormick F, Dickson RB, Johnson MD, Lippman M, Ethier S, Gazdar A, Gray JW. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer cell. 2006;10:515–527. doi: 10.1016/j.ccr.2006.10.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3 doi: 10.2202/1544-6115.1027. Article3. [DOI] [PubMed] [Google Scholar]
  • 27.Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences of the United States of America. 2003;100:9440–9445. doi: 10.1073/pnas.1530509100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Dennis G, Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID:Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4:P3. [PubMed] [Google Scholar]
  • 29.Szatrowski TP, Nathan CF. Production of large amounts of hydrogen peroxide by human tumor cells. Cancer Res. 1991;51:794–798. [PubMed] [Google Scholar]
  • 30.Powell CL, Swenberg JA, Rusyn I. Expression of base excision DNA repair genes as a biomarker of oxidative DNA damage. Cancer letters. 2005;229:1–11. doi: 10.1016/j.canlet.2004.12.002. [DOI] [PubMed] [Google Scholar]
  • 31.Lin F, Girotti AW. Hyperresistance of leukemia cells to photodynamic inactivation after long-term exposure to hemin. Cancer research. 1996;56:4636–4643. [PubMed] [Google Scholar]
  • 32.Das A, Hazra TK, Boldogh I, Mitra S, Bhakat KK. Induction of the human oxidized base-specific DNA glycosylase NEIL1 by reactive oxygen species. The Journal of biological chemistry. 2005;280:35272–35280. doi: 10.1074/jbc.M505526200. [DOI] [PubMed] [Google Scholar]
  • 33.Stewart GD, Lowrie AG, Riddick AC, Fearon KC, Habib FK, Ross JA. Dermcidin expression confers a survival advantage in prostate cancer cells subjected to oxidative stress or hypoxia. Prostate. 2007;67:1308–1317. doi: 10.1002/pros.20618. [DOI] [PubMed] [Google Scholar]
  • 34.Miccadei S, Di Venere D, Cardinali A, Romano F, Durazzo A, Foddai MS, Fraioli R, Mobarhan S, Maiani G. Antioxidative and apoptotic properties of polyphenolic extracts from edible part of artichoke (Cynara scolymus L.) on cultured rat hepatocytes and on human hepatoma cells. Nutr Cancer. 2008;60:276–283. doi: 10.1080/01635580801891583. [DOI] [PubMed] [Google Scholar]
  • 35.Pastore A, Piemonte F, Locatelli M, Lo Russo A, Gaeta LM, Tozzi G, Federici G. Determination of blood total, reduced, and oxidized glutathione in pediatric subjects. Clin Chem. 2001;47:1467–1469. [PubMed] [Google Scholar]
  • 36.Synowiec E, Stefanska J, Morawiec Z, Blasiak J, Wozniak K. Association between DNA damage, DNA repair genes variability and clinical characteristics in breast cancer patients. Mutat Res. 2008;648:65–72. doi: 10.1016/j.mrfmmm.2008.09.014. [DOI] [PubMed] [Google Scholar]
  • 37.Musarrat J, Arezina-Wilson J, Wani AA. Prognostic and aetiological relevance of 8-hydroxyguanosine in human breast carcinogenesis. Eur J Cancer. 1996;32A:1209–1214. doi: 10.1016/0959-8049(96)00031-7. [DOI] [PubMed] [Google Scholar]
  • 38.Dosanjh MK, Collins DW, Fan W, Lennon GG, Albala JS, Shen Z, Schild D. Isolation and characterization of RAD51C, a new human member of the RAD51 family of related genes. Nucleic Acids Res. 1998;26:1179–1184. doi: 10.1093/nar/26.5.1179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Breimer LH. Molecular mechanisms of oxygen radical carcinogenesis and mutagenesis: the role of DNA base damage. Mol Carcinog. 1990;3:188–197. doi: 10.1002/mc.2940030405. [DOI] [PubMed] [Google Scholar]
  • 40.Malins DC, Haimanot R. Major alterations in the nucleotide structure of DNA in cancer of the female breast. Cancer research. 1991;51:5430–5432. [PubMed] [Google Scholar]
  • 41.Malins DC, Holmes EH, Polissar NL, Gunselman SJ. The etiology of breast cancer. Characteristic alteration in hydroxyl radical-induced DNA base lesions during oncogenesis with potential for evaluating incidence risk. Cancer. 1993;71:3036–3043. doi: 10.1002/1097-0142(19930515)71:10<3036::aid-cncr2820711025>3.0.co;2-p. [DOI] [PubMed] [Google Scholar]
  • 42.Steenken S. Purine-Bases, Nucleosides, and Nucleotides - Aqueous-Solution Redox Chemistry and Transformation Reactions of Their Radical Cations and E- and Oh Adducts. Chemical Reviews. 1989;89:503–520. [Google Scholar]
  • 43.Malins DC, Polissar NL, Gunselman SJ. Progression of human breast cancers to the metastatic state is linked to hydroxyl radical-induced DNA damage. Proceedings of the National Academy of Sciences of the United States of America. 1996;93:2557–2563. doi: 10.1073/pnas.93.6.2557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Ji H, Reid GE, Moritz RL, Eddes JS, Burgess AW, Simpson RJ. A two-dimensional gel database of human colon carcinoma proteins. Electrophoresis. 1997;18:605–613. doi: 10.1002/elps.1150180344. [DOI] [PubMed] [Google Scholar]
  • 45.Choi J, Liu RM, Forman HJ. Adaptation to oxidative stress: quinone-mediated protection of signaling in rat lung epithelial L2 cells. Biochem Pharmacol. 1997;53:987–993. doi: 10.1016/s0006-2952(96)00867-2. [DOI] [PubMed] [Google Scholar]
  • 46.Young TW. Activation of antioxidant pathways in ras-mediated oncogenic transformation of human surface ovarian epithelial cells revealed by functional proteomics and mass spectrometry. Cancer Res. 2004;64:4577–4584. doi: 10.1158/0008-5472.CAN-04-0222. [DOI] [PubMed] [Google Scholar]
  • 47.Irani K. Mitogenic signaling mediated by oxidants in Ras-transformed fibroblasts. Science. 1997;275:1649–1652. doi: 10.1126/science.275.5306.1649. [DOI] [PubMed] [Google Scholar]
  • 48.Brandon M, Baldi P, Wallace DC. Mitochondrial mutations in cancer. Oncogene. 2006;25:4647–4662. doi: 10.1038/sj.onc.1209607. [DOI] [PubMed] [Google Scholar]
  • 49.Horn HF, Vousden KH. Coping with stress: multiple ways to activate p53. Oncogene. 2007;26:1306–1316. doi: 10.1038/sj.onc.1210263. [DOI] [PubMed] [Google Scholar]
  • 50.Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324:1029–1033. doi: 10.1126/science.1160809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Ertel A, Verghese A, Byers SW, Ochs M, Tozeren A. Pathway-specific differences between tumor cell lines and normal and tumor tissue cells. Mol Cancer. 2006;5:55. doi: 10.1186/1476-4598-5-55. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

01
NIHMS290721-supplement-01.xls (2.2MB, xls)
02
NIHMS290721-supplement-02.doc (85KB, doc)
03
NIHMS290721-supplement-03.xls (237KB, xls)
04
NIHMS290721-supplement-04.doc (165KB, doc)

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