- Open Access
Exposure to ionizing radiation induced persistent gene expression changes in mouse mammary gland
© Datta et al.; licensee BioMed Central Ltd. 2012
Received: 10 May 2012
Accepted: 16 November 2012
Published: 5 December 2012
Breast tissue is among the most sensitive tissues to the carcinogenic actions of ionizing radiation and epidemiological studies have linked radiation exposure to breast cancer. Currently, molecular understanding of radiation carcinogenesis in mammary gland is hindered due to the scarcity of in vivo long-term follow up data. We undertook this study to delineate radiation-induced persistent alterations in gene expression in mouse mammary glands 2-month after radiation exposure.
Six to eight week old female C57BL/6J mice were exposed to 2 Gy of whole body γ radiation and mammary glands were surgically removed 2-month after radiation. RNA was isolated and microarray hybridization performed for gene expression analysis. Ingenuity Pathway Analysis (IPA) was used for biological interpretation of microarray data. Real time quantitative PCR was performed on selected genes to confirm the microarray data.
Compared to untreated controls, the mRNA levels of a total of 737 genes were significantly (p<0.05) perturbed above 2-fold of control. More genes (493 genes; 67%) were upregulated than the number of downregulated genes (244 genes; 33%). Functional analysis of the upregulated genes mapped to cell proliferation and cancer related canonical pathways such as ‘ERK/MAPK signaling’, ‘CDK5 signaling’, and ‘14-3-3-mediated signaling’. We also observed upregulation of breast cancer related canonical pathways such as ‘breast cancer regulation by Stathmin1’, and ‘HER-2 signaling in breast cancer’ in IPA. Interestingly, the downregulated genes mapped to fewer canonical pathways involved in cell proliferation. We also observed that a number of genes with tumor suppressor function (GPRC5A, ELF1, NAB2, Sema4D, ACPP, MAP2, RUNX1) persistently remained downregulated in response to radiation exposure. Results from qRT-PCR on five selected differentially expressed genes confirmed microarray data. The PCR data on PPP4c, ELF1, MAPK12, PLCG1, and E2F6 showed similar trend in up and downregulation as has been observed with the microarray.
Exposure to a clinically relevant radiation dose led to long-term activation of mammary gland genes involved in proliferative and metabolic pathways, which are known to have roles in carcinogenesis. When considered along with downregulation of a number of tumor suppressor genes, our study has implications for breast cancer initiation and progression after therapeutic radiation exposure.
We know from epidemiological studies that exposure to ionizing radiation (IR) is one of the major risk factors for breast cancer especially if exposure occurs at a young age[1, 2]. Exposure to radiation, which is increasingly used in diagnostic and therapeutic interventions, is unavoidable and is predicted to pose carcinogenic risk[3, 4]. Radiation induces a myriad of damage to biomolecules including DNA and the carcinogenic potential of radiation has been attributed to alterations in the expression of genes, which could induce proliferation and provide survival advantage to cells. Changes in gene expression in response to radiation have been reported to vary depending on the dose and the tissue exposed; and, there are significant differences in acute and delayed responses to IR exposure. Delayed effects of IR on gene expression are not only associated with cancer initiation and promotion in normal cells but are also known to play a critical role in the development of resistance to therapy in cancer cells. Therefore, radiation treatment of breast cancer has on the one hand the potential for cancer recurrence and on the other the risk for developing new cancer in the contralateral breast. Despite the fact that radiation exposure causes persistent changes in gene expression, data on radiation-induced long-term alterations in gene expression in mammary tissues are of limited availability. Furthermore, there are no reports in the literature of long-term microarray-based studies of changes in mouse mammary gland gene expression after exposure to radiation doses relevant to therapeutic procedures. Considering the carcinogenic potential of radiation exposure to mammary glands, it is important to understand how radiation modulates long-term gene expression changes and how these changes relate to oncogenic signaling pathways known to be involved in breast cancer initiation and progression. Therefore, the goals of this study were to characterize persistent transcriptomic alterations and explore how these alterations relate to biological functions in whole body exposed 6 to 8 week old female C57BL6/J mouse mammary glands 2 months after exposure to 2 Gy γ radiation.
Mice and irradiation
Mice (C57BL/6J, female, 6-8 week old) were purchased from Jackson Laboratories (Bar Harbor, ME, USA) and were housed at the Georgetown University’s (GU) animal facility. All the animal procedures were performed as per protocol approved by GU Animal Care and Use Committee (GUACUC). Mice were placed in a circular pie shaped, well-ventilated plastic mouse holder, the mouse holder with mice was positioned on a rotating turntable inside the irradiator, and mice were exposed to γ radiation using a 137Cs source. Radiation dose was delivered at a rate of 1 Gy/min and a total dose of 2 Gy was delivered to the whole body and the control groups were sham irradiated. After irradiation mice were returned to their home cage, and monitored regularly.
Mice were euthanized by asphyxiation using CO2 as per GUACUC approved protocol and mammary glands were surgically removed 2 month after radiation exposure for RNA extraction. For RNA extraction, we used no. 4 mammary gland from the left side of each mouse and four such mice were used in each experimental group. Total RNA was isolated with Trizol reagent (Invitrogen, Carlsbad, CA) from fresh or flash frozen mammary glands followed by purification over an RNeasy column according to the manufacturer’s instructions (Qiagen, Germantown, MD) and stored at -80°C for further use. RNA quality was determined using a Bioanalyzer (Agilent Technologies, Palo Alto, CA).
Cy3-labeled cRNA was generated from 300 ng total RNA using Agilent’s Low RNA Input Linear Amplification PLUS kit. Cy3-labeled cRNA (1.65 μg) was hybridized to Agilent’s Mouse Whole Genome arrays (G4122F) with Agilent’s GEx Hybridization HI-RPM Buffer. The samples were hybridized for 16 hours in a rotisserie hybridization oven (G2545A, Agilent Technologies, Santa Clara, CA, USA), then washed with Agilent’s gene expression wash buffers and scanned with an Agilent DNA Microarray Scanner 2505A in an ozone hood. Subsequent image analysis, data extraction, and background corrections were performed with Agilent’s Feature Extraction Software 9.1. Extracted data were then analyzed using GeneSpring GX 10.0 (Agilent Technologies) to identify genes that had significantly changed expression between control and the irradiated group. Although small sample size (3 to 5 biological replicates) conventionally used in microarray experiments has its limitations, the large data sets used in comparative analysis provide enough statistical power to derive biologically meaningful inferences from the results (discussed in Prolla, 2002). Genes considered significantly (p<0.05, t-test with Benjamini-Hochberg correction) and differentially (fold change ≥2.0) regulated were listed separately.
Pathway analysis for biological functions
Significantly perturbed gene lists generated from microarray analysis were uploaded onto Ingenuity Pathway Analysis (IPA, Ingenuity Systems Inc., Redwood City, CA, USA) for assignment of biological function as well as for identifying perturbed signal transduction networks. IPA combines the uploaded data and the Ingenuity Pathways Knowledge Base (IPKB), created with information from available literature, to identify biological networks that are significantly over-represented in the gene expression data. Right-tailed Fisher's exact test is applied to determine the level of significance for each network and the p-value is displayed as score, which is the negative log of that p-value. A score of 8 indicates that there is a 1 in 108 chance that the focus genes are together in a network due to random chance alone. Furthermore, IPA uses a built-in library to identify association between the uploaded genes with the canonical pathways present in the knowledge base. In addition to providing a significance score, IPA also calculates the number of genes within a dataset that are present in each significantly perturbed pathway.
Quantitative real time PCR (qRT-PCR)
For confirmation of microarray data by qRT-PCR, cDNA was generated from 1.5 μg RNA using the RT2 First Strand Kit as per recommendation (SA Biosciences, Frederick, MD, USA). Primers for PPP4c (protein phosphatase 4, catalytic subunit; Cat# PPM28940A), MAPK12 (mitogen-activated protein kinase 12; Cat#PPM04541C), PLCG1 (phospholipase C, gamma 1; Cat#PPM004022B), and β-actin (Cat# PPM02945A) were obtained from SA Biosciences. Primers for E2F6 (Forward primer: 5’-GATGGCATCGAACTGGTGGAA-3’; reverse primer: 5’-CCCCAAAGTTGTTCAGGTCAG-3’), and ELF1 (Forward primer: 5’-TGTCCAACAGAACGACCTAGT-3’; reverse primer: 5’ CACACAAGCTAGACCAGCATAA-3’) were obtained from Eurofins MWG Operon (Huntsville, AL, USA). Primer for NFkβ, which is the nodal molecule of the top scoring molecular pathway network identified from the microarray data set by IPA, was also obtained from Eurofins MWG Operon (Forward primer: 5’-AGCACATAGATGAACTCCG-3’; reverse primer: 5’- CTGTAAAGCTGAGTTTGCG-3’). Using the RT2 SYBR Green/Fluorescein qPCR Master mix (SA Biosciences) qRT-PCR was performed on an ABI 7900HT (Applied Biosystems, Carlsbad, CA, USA) platform with temperature settings: 95°C (denaturation) for 10 min and then 40 cycles of 95°C for 15 sec, annealing/extension at 60°C for 1 min. The fold change was calculated using β-actin as an endogenous control following the comparative Ct (ΔΔCt) method as described previously. Results were expressed relative to control samples, and three biological replicates were used in each experimental group. The error bar represents standard error of mean (SEM).
Greater number of genes showed persistent upregulation following radiation exposure
Quantitative real time PCR confirmation of microarray data
Pathway analysis shows upregulation of metabolic and proliferative pathways
Significant upregulation of pathways involved in cell proliferation, metabolism and breast cancer
Ingenuity Canonical Pathways
ATP6V0E2, NDUFC1, COX6B1, NDUFS8, COX10, COX5A, NDUFS6, NDUFA12, COX7A1, ATP5G3
COX6B1, NDUFS8, COX10, CPT1B, COX5A, TRAK1, NDUFS6, NDUFA12, MAPK12, COX7A1
Breast cancer regulation by Stathmin1
PPP1R14C, PIK3C2B, E2F6, ADCY2, PRKCQ, CAMK2A, TUBA8, PPM1L, PPP2R5B, TUBA4A, PPP1R3A, TUBA3C/TUBA3D
PLA2G4E, PPP1R14C, PIK3C2B, TLN2, PLA2G12A, HSPB2, PPM1L, PPP2R5B, PLCG1, PPP1R3A
Cardiac β-adrenergic signaling
PPP1R14C, ADCY2, PPM1L, PPP2R5B, PDE4A, AKAP6, PPP1R3A, PDE4D
PGK1, PGM1, ALDOA, TPI1, Gapdh (includes others), LDHB
NDUFC1, NDUFS8, UFSP1, NDUFS6, NDUFA12
PIK3C2B, PRKCQ, TUBA8, TUBA4A, PLCG1, TUBA3C/TUBA3D, MAPK12
Aldosterone signaling in epithelial cells
PIK3C2B, SACS, PRKCQ, HSPA1L, DNAJC27, HSPB2, PLCG1, DNAJB5
Production of nitric oxide and reactive oxygen species in macrophages
PPP1R14C, PIK3C2B, PRKCQ, PPM1L, PPP2R5B, PLCG1, PPP1R3A, MAPK12
Fc epsilon RI signaling
PLA2G4E, PIK3C2B, PRKCQ, PLA2G12A, PLCG1, MAPK12
PLA2G4E, PIK3C2B, ADCY2, PRKCQ, PLA2G12A, GNAO1, PLCG1, MAPK12
Dopamine receptor signaling
PPP1R14C, ADCY2, PPM1L, PPP2R5B, PPP1R3A
CCR3 signaling in eosinophils
PLA2G4E, PIK3C2B, PRKCQ, PLA2G12A, CFL2, MAPK12
Type II diabetes mellitus signaling
PIK3C2B, RKCQ, PRKAB2, MAPK12, ADIPOR1, KCNJ11
PIK3C2B, THPO, PRKCQ, PLCG1
CTLA4 signaling in cytotoxic T lymphocytes
PIK3C2B, AP1S2, PPM1L, PPP2R5B, PLCG1
PPP1R14C, ADCY2, PPM1L, PPP2R5B, PPP1R3A
PIK3C2B, RXRG, SLC16A3, UCP1, PPARGC1A
Protein kinase A signaling
PPP1R14C, ADCY2, PRKCQ, CAMK2A, HIST1H1A, PDE4A, PLCG1, AKAP6, PPP1R3A, TTN, PDE4D
Synaptic long term depression
PLA2G4E, PRKCQ, PLA2G12A, PPM1L, GNAO1, PPP2R5B
Role of MAPK signaling in the pathogenesis of influenza
PLA2G4E, PLA2G12A, MAPK12, RABGEF1
Cell cycle regulation by BTG family proteins
E2F6, PPM1L, PPP2R5B
MIF regulation of innate immunity
PLA2G4E, PLA2G12A, MAPK12
PIK3C2B, E2F6, PRKCQ, CAMK2A, PLCG1
Phenylalanine, tyrosine and tryptophan biosynthesis
PIK3C2B, PRKAB2, CPT1B, PPM1L, PPP2R5B, MAPK12
PRKCQ, CAMK2A, GNAO1, PLCG1
PIK3C2B, PRKCQ, PLCG1, ITGB6
GOT1, GOT2, LDHB
PIK3C2B, CFL2, PPM1L, PPP2R5B, MAPK12, ITGB6, MYH1
Protein ubiquitination pathway
SACS, USP15, HSPA1L, USP13, UBE2B, DNAJC27, HSPB2, USP2, DNAJB5
Corticotropin releasing hormone Signaling
ADCY2, PRKCQ, GNAO1, PLCG1, MAPK12
HER-2 signaling in breast cancer
PIK3C2B, PRKCQ, PLCG1, ITGB6
Few pathways were downregulated and fewer were involved in cellular proliferation
Ingenuity Canonical Pathways
Fcγ receptor-mediated phagocytosis in macrophages and monocytes
EZR, LYN, ACTG1, PRKCB
G-Protein coupled receptor signaling
PRLHR, GPR173, PTGIR, GPRC5A, GLP2R, FZD3, GPR113, ADCY7, CELSR2, PRKCB
Factors promoting cardiogenesis in vertebrates
FZD3, TDGF1, PRKCB
Natural killer cell signaling
SH3BP2, Klra4 (includes others), PRKCB
Mechanisms of viral exit from host cells
Leukocyte extravasation signaling
EZR, MMP11, ACTG1, PRKCB
LYN, RPS6, PRKCB
Upregulated networks with score ≥8 are presented
Molecules in Network
26sProteasome, ACHE, AChR, Akt, AQP4, BIN1, CAMK2A, CHRNA1, Creb,ERK1/2, FRZB, HSPA1L, IDE, ISL1, KCNC1, KIAA0368, KIF1B, MYOD1, MYOG, NES, NFkB (complex),NOL3, P2RY2, PDGF BB, PGK1, PLCG1, PPARGC1A, PRKCQ, RAPSN, RNF217, RXRG, S100B, SLC37A4, SNTA1, THPO
Cell Death, Neurological Disease, Cellular Development
ALDOA, ARPP21, ATP1A1, ATP1B1, ATP2A2, ATP6V0E2, BCL6, C15orf63, CIITA, ECSIT, FXYD4, Gapdh (includes others), Gm9790/Higd1a, GNAO1, HNRNPUL1, HSP90AB1, HTT, IL5, LDLR, NAA15, NDUFA12, PKM2, PMP22, POU3F1, RAB3A, RPH3A, SACS, SLC16A3, SPI1, SQSTM1, SYN2, TPI1, TRAF6, TUBA3C/TUBA3D, XBP1
Genetic Disorder, Neurological Disease, Skeletal and Muscular Disorders
ACTA1, ACTN1, AKAP6, ATP2B1, CDK16, CPE, ELL2, FSH, FYCO1, GK, GOT1, GPCPD1, IPO13, KIDINS220, LBX1, Lh, LIF, MAP1LC3B, NANOG, NOL3, PAX6, PCSK1N, PDE4D, PGK1, PHACTR2, PPFIA4, PPP2R5B, PRKAR1A, PRKAR2A, PSMD1, Ryr1l, SNAP23, STX3, TPM1 (includes EG:22003), VAMP8
Reproductive System Development and Function, Cellular Development, Cellular Growth and Proliferation
ABCC9, ALDOA, ATP1A1, CACNA1A, CACNA1B, CACNB1, CACNB4, CDH15, CTNNB1, DLG4, GOT2, HTT, IDH2, IL5, KCNJ11, KRAS, LDHA, LDHB, MYC, PCLO, PITX2, PRKCE, SCN1B, SCN2B, SLC25A4, SLC27A1, SYT1, SYT2, TP53, TPI1, TRIO, UGP2, VDAC1
Molecular Transport, Genetic Disorder, Carbohydrate Metabolism
AHR, APAF1, ATP1B2, BMI1, CAB39, CCNG1, CHEK2, CIITA, CREBBP, E2F6, ESRRG, Estrogen Receptor, HIST1H1A, Hist2h4 (includes others), HSP90AA1, MAPK12, MCM3, NFIC, Pcp4l1, PHC1, PLA2G12A, PRNP, RAD51AP1, Rb, RING1, SFTPC, SMARCA4, SMARCD3, STK11, STRADB, STUB1, Thymidine Kinase, TP53, TUBA8, UBE2B
Cell Cycle, Cell Death, Hematological System Development and Function
ATF3, B3GALT1,Calcineurin protein(s), Calmodulin, CCNA1, CIITA, CISH, CORO6, CREB1, CYP8B1, F2, FEM1A, GDF1, Gsk3, GSTA5, HSD17B7, HSPA8, IL1B, IL1R1, INSR, KCNQ5, Mapt, OPA3, PLCG1, PMS1, PPARGC1A, PSEN1, RORA, SATB1, SERPINA3, SIX1, SLC25A25, SOD1, TUBA4A, USP13
Cell Death, Neurological Disease, Tissue Morphology
ACSL1, APOA4, BCL2, CITED2, CPT1B, CRYAB, E2F1, EYA4, FOXC2, Fundc2, GSTA3, Ins1, ITGB6, KPNA3, Ldh, LEP, NEB, NEU2, NFKB1, PCSK1, POU2F1, PPARGC1A, PPP1R3A, PTGES, PTGS2, SOX6, SP1, SPTB, TMCC2, TNF, TNIK, TRAF2, TSPO, UCP3, ZFP36
Energy Production, Lipid Metabolism, Small Molecule Biochemistry
APOA4, ATF3, CD3, CD80, CD86, CEBPB, CIITA, COL1A1, COX5A, COX6B1, CPEB3, EFCAB6, ELAVL1, Gsk3, HELT, HSD11B1, HSPB2, IDH3B, IFNG, IL4, IL1RN, IRF1, NEIL2, NEUROG1, NR3C2, NRF1, POLR2A, Rac, TACC2, TCF7L2, TRAK1, TRAM2, USP2, VLDLR, WWP2
Cellular Growth and Proliferation, Hematological System Development and Function, Tissue Development
ACTA1, ACTC1, AGTR1, C14orf166, CUL5, DCT, DDX1, DMPK, DOT1L, EIF1AY, ENO1, ENPP4, FXR1, FXYD1, FZD9, Histone h3, HRASLS, JUN, MECP2, MLLT3, MYH1, MYH2, MYH4, PABPC4, RELN, SCNN1A, SGK1, STAU1, SYNCRIP, TCF4, THOC4, UCP1, UQCRC1, YWHAZ, YY1
Cellular Function and Maintenance, Molecular Transport, Cellular Assembly and Organization
ABHD5, ADAM12, ADCK3, ADM, CALCRL, CD40LG, DOK7, ENDOG, ERBB2, FILIP1, FLNA, G6pd2, GNB2L1, HIF1A, Hsp70, Hsp90, HSP90AA1, IGF1R, ITGB1, KDM3A, MUSK, NR3C1, NRG1 (includes EG:112400), PACSIN3, PAK1, PGM1, PLIN5, PRKCE, PSEN1, Rac, RAMP1, SRC, STUB1, TLN2, TTN
Cell Death, Cell-To-Cell Signaling and Interaction, Nervous System Development and Function
ADAM10, ADIPOR1, ADRA1B, CCND2, CCND3, COX7A1, CXCL10, CXCL12, CYP11A1, CYP7A1, DMD, DTNA, DUSP26, FSHB, HIPK3, HSF4, ITGB4, LCK, MAPK3, MAPRE3, MET, Mlc, NFKB2, NR5A1, PI3K (complex), PIN1, PITX1, PLEC, PRKCZ, RAC1, Ras, SOX9, SYNC, TCEA3, THRB
Cancer, Tissue Development, Embryonic Development
ABCB4, AGER, AIMP2, CD2, CD28, CD40, CD59, CD86, CD40LG, DISC1, DNM2, DYRK1B, FGFR1, GRB2, GRB14, HGF, IL2, IL12 (complex), JAK1, JAK2, MET, P38 MAPK, PDE4A, PDGFA, PDGFRB, PIK3C2B, PLCG1, PTPN2, ASA1, SH2B3, Sos, SQSTM1, WAS, ZAP70, ZFP106
Cellular Movement, Cell Death, Immune Cell Trafficking
Downregulated networks with score ≥8 are presented
Molecules in Network
AGTR1, ARHGAP17, BAMBI, BARD1, BCL9, BRCA1, BRCC3, BRE, CELSR2, FURIN, GLP2R, Gp49a/Lilrb4, HNRNPF, Hsd3b4 (includes others), IFIT2, IL12 (complex), ITGB1, JUN, mir-31, Ms4a4b (includes others), MYD88, NAB2, NR3C1, PLCG1, PLXNB1, PRKCB, RPS3A, RRAD, SEMA4D, SERPINB5, ST6GAL1, Talin, TGFB1, TIAM1, TRAF3
Cell Morphology, Cellular Growth and Proliferation, Hematological System Development and Function
ACPP, ACTG1, Actin, ADCY7, AKAP12, Akt, CAMK2B, DEFA1 (includes others), DGAT1, DHCR7, ERK1/2, EZR, FSH, GPRC5A, GRIN1, HSD17B1, KIT, LAT2, Lh, LYN, MAP2, MAP2K2, MME, MS4A2, Myosin, p85 (pik3r), PIK3AP1, PRKAR2B, PRKCB, SC4MOL, SH3BP2, SLC6A3, TDGF1, TYRO3, UNC119
Cellular Compromise, Inflammatory Response, Cell Signaling
AHR, B4GALNT1, BAZ1A, C1orf38, C5AR1, CD1D, ELF1, GHRH, HDAC1, HIPK2, IFNA1/IFNA13, IFNG,IL4, INSM2, Lyz1/Lyz2, MAFB, MMP11, MTA3, NEUROD1, Nuclear factor 1, OAS1, OAS2, PCSK1, PCSK2, PPP4C, PPP5C, RB1, Rb, Runx1, SIN3B, SLC17A2, TFCP2, TNFSF11, TP53, TYMS
Tissue Morphology, Cancer, Gene Expression
ACSL1, ADAM17, APOBEC3B, CASP7, CHSY3, CLMN, FDFT1, FDPS, HLA-C, HMGCR, HSF1, HSPA1A/HSPA1B, IDI1, Klra4 (includes others), LSS, NR1H3, PPP1R15A, RAI14, RALGPS2, RBL1, RPS6, SCNN1A, SCNN1B, SCNN1G, SQLE, SREBF2, SYMPK, TNF, TP73, TSC2, WSB1, WT1, YWHAG, YY1, ZNF267
Lipid Metabolism, Small Molecule Biochemistry, Vitamin and Mineral Metabolism
Genes from upregulated networks: A number of genes from the data set are associated with breast cancer
Role in breast cancer quoted from literature
“Tumor size was significantly higher when the ACHE gene was amplified in breast cancer.”.
heat shock 70kDa protein 1-like
“….HSPA1L and HSPA2 could represent potential biomarkers to follow up the effectiveness of 17AAG in breast cancer”.
“Among the breast cancer subtypes, nestin is highly expressed in basal breast cancer subtype”.
purinergic receptor P2Y, G-protein coupled, 2
“P2Y2 receptor-mediated modulation of estrogen-induced proliferation of breast cancer cells”.
phospholipase C, gamma 1
“Phospholipase Cgamma1 is required for metastasis development and progression”.
peroxisome proliferator-activated receptor gamma, coactivator 1 alpha
“Associations of genetic variants in the estrogen receptor coactivators PPARGC1A, PPARGC1B and EP300 with familial breast cancer”.
protein kinase C, theta
“PKCtheta promotes c-Rel-driven mammary tumorigenesis in mice”.
syntrophin, alpha 1
“….significant increase in expression of SNTA1 protein compared with the normal tissue was observed in breast carcinoma samples”.
“…hypoxia-responsive; prognostic significance in breast cancer”.
guanine nucleotide binding protein (G protein), alpha activating activity polypeptide O
“GNAO1 (Gαo) gene was identified in breast carcinomas and shown to promote oncogenic transformation when introduced into cells”.
solute carrier family 16, member 3
“Expression of SLC16A3 gene is higher in breast cancer distant metastasis….”.
triosephosphate isomerase 1
“Glycolytic cancer associated fibroblasts promote breast cancer tumor growth, without a measurable increase in angiogenesis: evidence for stromal-epithelial metabolic coupling”.
elongation factor, RNA polymerase II, 2
ELL2 is a Breast Cancer Antioestrogen Resistance (BCAR) 1 gene which control anti-oestrogen-resistant cell growth resistance (BCAR).
“We have identified cyclin G as being overexpressed in breast and prostate cancer cells”.
E2F transcription factor 6
“E2F6 represses transcription of the brca1, ctip, art27, hp1 α, and the rbap48 genes….”.
Estrogen-related receptor γ
“Estrogen-related receptor γ modulates cell proliferation and estrogen signaling in breast cancer & ERRγ mRNA was up-regulated dose-dependently by estrogen….”.
mitogen-activated protein kinase 12. Also known as ERK3; ERK6; SAPK3; PRKM12; SAPK-3; P38GAMMA
“…elevated expression of p38γ is associated with lower overall survival of patients with breast cancer”.
hydroxysteroid (17-beta) dehydrogenase 7
The stimulation of HSD17B7 expression by estradiol provides a powerful feed-forward mechanism for estradiol biosynthesis in breast cancer cells.
transforming, acidic coiled-coil containing protein 2
“This suggests that increased TACC2 may mediate an oncogenic effect on breast cancer cells and indicates that TACC2 may be a potential therapeutic target”.
DEAD (Asp-Glu-Ala-Asp) box polypeptide 1
“Here, we identify DDX1 RNA overexpression as an independent prognostic marker for early recurrence in primary breast cancer……”.
myosin, heavy chain 1, skeletal muscle, adult
Recently, MYH1 encoding skeletal muscle myosin heavy polypeptide 1 and MYH9 encoding non-muscle myosin heavy chain type A, were identified as candidate breast cancer genes in systematic analyses of the breast cancer genome.
uncoupling protein 1 (mitochondrial)
We conclude that UCP1 is up-regulated in breast cancer cell lines and primary breast as well as other tumors.
Protein kinase C and casein kinase substrate in neurons protein 3
“New targets for breast cancer treatment were identified such as ZONAB, PACSIN3, MRP8 and SUMO1, which have human homologues”.
“This gene has been identified as one of the ER status markers in the diagnosis and prognosis of breast cancer patients….”.
“Serum estradiol levels associated with specific gene expression patterns in normal breast tissue and in breast carcinomas……”.
adiponectin receptor 1
“Variants of the Adiponectin and Adiponectin Receptor 1 Genes and Breast Cancer Risk…”.
paired-like homeodomain 1
“The estrogen-regulated transcription factor PITX1 coordinates gene-specific regulation by estrogen receptor-alpha in breast cancer cells…”.
Genes from downregulated network
Role in cancer quoted from literature
cadherin, EGF LAG seven-pass G-type receptor 2
“Celsr2 was down-regulated in one cell line and in 7% of breast cancers”.
protein kinase C, beta
PRKCB has been observed to be downregulated in estrogen receptor negative breast cancer.
v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog. Also known as C-Kit; CD117
Loss of c-kit expression has been reported in 80-90% of breast cancer specimens, suggesting a possible role in the development of tumors.
microtubule-associated protein 2
“Elevation of MAP2 in breast cancer cell lines led to increased paclitaxel sensitivity” .
teratocarcinoma-derived growth factor 1. Also known as CR; CRGF; CRIPTO
“Overexpression of human Cripto-1 in transgenic mice delays mammary gland development and differentiation and induces mammary tumorigenesis”.
E74-like factor 1
“All of the mouse and most human mammary tumors also displayed decreased expression of genes known to inhibit cell proliferation, including NFKBIA (IKBalpha), GADD45B, and CDKN1A (p21); transcription-related genes such as CEBP, JUN, JUNB, and ELF1;….”.
runt-related transcription factor 1
“Furthermore, we found that RUNX1 expression was reduced in high-grade primary breast tumors compared to low/mid-grade tumors”.
“Gene polymorphisms in TYMS, MTHFR, p53 and MDR1 as risk factors for breast cancer: a case–control study”.
Mammary gland is sensitive to the carcinogenic actions of radiation and epidemiological data strongly correlates radiation exposure and breast cancer[1, 2]. Here we demonstrate that exposure of 6 to 8 week old female mice to 2 Gy of γ radiation, a dose relevant to fractionated radiotherapy, induced long-term gene expression changes in the mammary glands. These changes are associated with molecular pathways that have the potential to enhance the chance of malignant transformation. We show that even after two months, a significant number of genes remain perturbed and that the majority of them are upregulated. Broadly, these genes are categorized into two major interdependent cellular functions – proliferation and metabolism. Upregulation of proliferative functions supported by induction of adaptive energy producing pathways is also observed in cancer cells. Cancer, described as a chronic and multifactorial disease, was not observed in these mice within the study duration of 2 month. However, we did observe alterations in signaling pathways that may be considered potential carcinogenic precursors events.
The top network with a score of 44 associated 28 genes from the data set and literature indicates that at least 12 (ACHE, HSPA1L, MYOD1, NES, NOL3, P2RY2, PGK1, PLCG1, PPARGC1A, PRKCQ, S100B, and SNTA1) of them are involved in human cancer (genes relevant to breast cancer are presented with relevant references in Tables 5 and6). Indeed, dysregulation of ACHE, HSPA1L, NES, P2RY2, PLCG1, PPARGC1A, PRKCQ, S100B, and SNTA1 genes have been implicated in breast cancer progression and metastasis due to higher proliferation and increase cell migration (Tables 5 and6). Cellular metabolism and transportation related genes (ALDOA, SLC16A3, TPI1, LDHB, KCNQ5, and PGM1), which are implicated in human cancer, also remained upregulated even 2-month after radiation exposure. Importantly, induction of glycolytic enzymes like aldolase A (ALDOA), lactate dehydrogenase (LDH), triosephosphate isomerase 1 (TPI1), and phosphoglucomutase 1 (PGM1), we believe, is supporting cellular growth and development resulting from upregulated proliferative signaling pathways. It has been proposed that in a ‘Reverse Warburg Effect’ the glycolytic pathway is upregulated in stromal cells generating pyruvate/lactate, which can then be utilized by adjacent epithelial cells for oxidative phosphorylation in mitochondria. Indeed, upregulation of ‘oxidative phosphorylation’ and ‘mitochondrial dysfunction’ are the two top canonical pathways observed in the IPA and are reported to be associated with breast cancer. While stromal cell niche is known to play important roles in mammary gland carcinogenesis, we are yet to determine whether there are differential alterations of gene expression in stromal vs. epithelial cells and would require separate microarray analysis of stromal and epithelial compartment in mammary glands. However, considered along with the fact that in a whole boy irradiated mice all the tissue compartments are exposed to radiation, our results led us to speculate that differential alterations of gene expression in stromal vs. epithelial cells could occur in mammary gland to promote radiation carcinogenesis and is supportive of what has been reported in the literature[48, 49]. Interestingly, both ALDOA and SLC16A3 are also suggested to be hypoxia responsive and is upregulated in hypoxic breast cancer with poor outcome. SLC16A3 is known to allow efflux of lactate, which we believe is produced in excess due to radiation-induced upregulation of glycolytic enzymes. Increased glycolysis may have served as a trigger for increased mitochondrial oxidative metabolism observed in our results, and could lead to oxidative stress as has been indicated by upregulation of canonical pathways involved in ‘mitochondrial dysfunction’ and ‘production of nitric oxide and reactive oxygen species in macrophages’. Interestingly, we also observed upregulation of UCP1 (uncoupling protein 1), a member of the UCP protein family known to uncouple respiratory chain events from ATP production. UPC1 is known to limit reactive oxygen species (ROS) production, which could result in oxidative stress below apoptotic threshold, and enhance cellular survival in the presence of irreversible injury to respiration[32, 50]. The presence of radiation-induced mitochondrial dysfunction and consequent oxidative injury to cells are further exemplified by the upregulation of ENDOG, which has been shown to be involved in cell death in the absence of caspases.
Previously, we reported that exposure to 2 Gy radiation led to persistent increase in estrogen levels in serum and estrogen receptors in mammary glands at 2- and 12-month after radiation exposure and was associated with activation of PI3K-Akt proliferative pathways. Microarray data showing upregulation of 17β hydroxysteroid dehydrogenase 7 (HSD17B7), an enzyme involved in conversion of weakly estrogenic estrone to biologically active estradiol in mammary glands, is reported to be induced by high serum estradiol and is in agreement with our earlier observations of increased serum estradiol after radiation. While systemic estradiol mediates its effects in mammary gland via ERα, higher expression of HSD17B7 could provide a ‘positive feed-forward’ mechanism for enhanced tissue estradiol level and could explain activation of cellular proliferative signals observed by us. Upregulation of estrogen-related receptor γ (ESRRG), which is induced by estrogen to enhance cell proliferation, provides further evidence of agreement between microarray data and our previous results. Importantly, even 2-month after exposure to 2 Gy radiation a high number of transcripts involved in cellular growth and development including strongly mitogenic MAPK12 remain significantly upregulated and are presented in Table 5.
Along with upregulation of genes involved in cellular growth and development, we also observed that a number of genes with tumor suppressor function (GPRC5A, ELF1, NAB2, Sema4D, ACPP, MAP2, RUNX1) persistently remained downregulated in response to radiation exposure further tilting the balance in favor of transformation (Table 6)[43, 53–58]. In contrast, a few genes (TDGF1, GHRH, and BCL9) reported to be involved in promoting cell proliferation are downregulated which highlights the fact that cellular transformation is a multistage process with a sustained tug of war between tumor suppressor and tumor promoter. An important observation of our study was the downregulation of BRCC3 (BRCA1/BRCA2-containing complex, subunit 3), a protein known to play key a role in the repair of DNA damage through its E3 ubiquitin ligase activity on the repair factors and may predispose the mammary gland cells to misrepair and genomic instability. A number of other downregulated genes positively affecting cellular growth and proliferation have been observed in our study and are listed in Table 6.
Molecular pathway networks identified by IPA allowed us to understand how genes in our data sets spatially relate to oncogenic precursor events relevant to breast cancer. The top-scoring pathway has NFkβ and Akt (Figure 4A) as the central molecules and both are known to be involved in breast carcinogenesis[60, 61]. Another high scoring upregulated network identified ERBB2, an important membrane bound receptor tyrosine kinase, as the nodal molecule (Figure 8B) and is known to crosstalk with NFkβ and PI3K-Akt to promote breast cancer. One of the important observations in our study was the recognition of inflammation related factors in the IPA network and a number of pathway showed IL-5 (Figure 4B), IL-1β (Figure 6B), TNF (Figure 7A), IL-4, and INFγ (Figure 7B) as the central molecules highlighting an already existing relationship between inflammation and breast cancer[63, 64]. Reproductive hormones LH and FSH are the central molecules in an upregulated pathway network (Figure 5A) and are known to regulate estrogen, which is strongly associated with risk of developing breast cancer. Indeed, in an earlier study, we have shown that exposure to a radiation dose (2 Gy) used in the current study significantly increased serum estrogen level, urinary oncogenic estrogen metabolite level, mammary gland estrogen receptor α (ERα) level, and cellular proliferation in mice. Estrogen has also been reported to influence TGFβ activity negatively, and interestingly our downregulated data sets mapped to a pathway, which has TGFβ as the nodal molecule (Figure 10A). In our earlier study, we focused on exploring one pathway, the PI3K-Akt pathway, in relation to increased estrogen and ERα and showed that radiation not only upregulated PI3K and Akt activity but also induced their downstream effectors leading to increased mammary gland cell proliferation determined by phospho-histone H3 staining. Current microarray based complete gene expression study after radiation exposure not only showed upregulation of additional pathways already discussed but also showed increased expression of genes associated with pathways having TP53 (Figure 6A), AGTR1 (Figure 8A), P38-MAPK and PLCG1 (Figure 9B), PI3K complex and MAPK3 (Figure 9A) as central molecule. The molecular pathways, which IPA identified to be significantly associated with the genes from the microarray data sets have been implicated in human cancer including breast cancer (Tables 5 and6) and provide evidence towards carcinogenic potential of therapeutic radiation exposure.
Exposure to diagnostic and therapeutic radiation has increased considerably over time[3, 68, 69] and knowledge of the molecular pathways that remain perturbed long-term after radiation exposure could inform us to devise strategies to tackle the carcinogenic potential of radiation. Taken together the microarray data presented here not only correlate with our earlier observations on long-term effects of radiation exposure on mammary gland pathophysiology, but they also further expand on the persistently perturbed molecular networks with potential for transformation after exposure to a clinically relevant radiation dose.
This study supported in part by NASA Grant# NNX07AH70G. DRH was supported, in part, by Grant Number T32 CA009078 from the NCI.
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