G9A performs important roles in the progression of breast cancer through upregulating its targets

Jiang,Wenhua; Liu,Pengfei; Li,Xiaodong

doi:10.3892/ol.2017.5977

G9A performs important roles in the progression of breast cancer through upregulating its targets

Authors:
- Wenhua Jiang
- Pengfei Liu
- Xiaodong Li
View Affiliations

Affiliations: Department of Radiotherapy, Second Hospital of Tianjin Medical University, Tianjin 300211, P.R. China, Department of Lymphoma, Sino‑US Center of Lymphoma and Leukemia, Tianjin Medical University Cancer Institute and Hospital, Tianjin's National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, P.R. China
Published online on: April 3, 2017 https://doi.org/10.3892/ol.2017.5977
Pages: 4127-4132
Copyright: © Jiang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Breast cancer (BC) is the most common type of malignancy in females worldwide, however, its underlying mechanisms remain poorly understood. The present study aimed to investigate the mechanisms behind the development and progression of BC and identify potential biomarkers for it. The chromatin immunoprecipitation-DNA sequencing (ChIP‑Seq) dataset GSM1642516 and gene expression dataset GSE34925 were downloaded from the Gene Expression Omnibus database. Affy and oligo packages were used for the background correction and normalization of the gene expression dataset. Based on Limma package and the criteria of a fold change >1.41 or <0.71, and a false discovery rate adjusted P‑value <0.05, differentially-expressed genes (DEGs) in euchromatic histone lysine methyltransferase 2 (G9A) ‑knockout (KO) breast samples compared with control samples were identified. The Database for Annotation, Visualization and Integrated Analysis was used for the functional enrichment analysis of the DEGs. Bowtie 2 and model‑based analysis of ChIP‑Seq version 14 (macs14) were used for the mapping of raw reads and the identification of G9A binding sites (peaks), respectively. In addition, overlapping genes between the DEGs and genes in the peaks located in ‑3000 to 3000 bp centered in the transcription start sites (conpeaks) were screened out and microRNAs (miRNAs) believed to regulate those overlaps were identified through the TargetScan database. A total of 217 DEGs were identified in G9A‑KO samples, which were mainly involved in the biological processes and pathways associated with the inflammatory response and cancer progression. A total of 10,422 peaks, containing 1,210 conpeaks involving 1,138 genes, were identified. Among the 1,138 genes, 15 were overlapped with the DEGs, and 35 miRNAs were identified to regulate those overlaps. Insulin-induced gene 1 was regulated by 9 genes in the miRNA‑gene regulation network, which may indicate its importance in the progression of BC. The present study identified potential biomarkers of BC that may be useful in the diagnosis and treatment of patients with the disease.

View Figures

View References

1	Zhu J, Chen JG, Chen YS, Zhang YH, Ding LL and Chen TY: Female breast cancer survival in Qidong, China, 1972–2011: A population-based study. BMC Cancer. 14:3182014. View Article : Google Scholar : PubMed/NCBI
2	Fan L, Goss PE and Strasser-Weippl K: Current status and future projections of breast cancer in Asia. Breast Care (Basel). 10:372–378. 2015. View Article : Google Scholar : PubMed/NCBI
3	Onega T, Tosteson AN, Weiss J, Alford-Teaster J, Hubbard RA, Henderson LM, Kerlikowske K, Goodrich ME, O'Donoghue C, Wernli KJ, et al: Costs of diagnostic and preoperative workup with and without breast MRI in older women with a breast cancer diagnosis. BMC Health Serv Res. 16:762016. View Article : Google Scholar : PubMed/NCBI
4	Dare AJ, Anderson BO, Sullivan R, Pramesh CS, Andre I, Adewole IF, Badwe RA and Gauvreau CL: Surgical services for cancer care. 2015.
5	Daroudi R, Sari A Akbari, Nahvijou A, Kalaghchi B, Najafi M and Zendehdel K: The economic burden of breast cancer in Iran. Iran J Public Health. 44:1225–1233. 2015.PubMed/NCBI
6	Zhang J, He P, Xi Y, Geng M, Chen Y and Ding J: Down-regulation of G9a triggers DNA damage response and inhibits colorectal cancer cells proliferation. Oncotarget. 6:2917–2927. 2015. View Article : Google Scholar : PubMed/NCBI
7	Liu S, Ye D, Guo W, Yu W, He Y, Hu J, Wang Y, Zhang L, Liao Y, Song H, et al: G9a is essential for EMT-mediated metastasis and maintenance of cancer stem cell-like characters in head and neck squamous cell carcinoma. Oncotarget. 6:6887–6901. 2015. View Article : Google Scholar : PubMed/NCBI
8	Laumet G, Garriga J, Chen SR, Zhang Y, Li DP, Smith TM, Dong Y, Jelinek J, Cesaroni M, Issa JP and Pan HL: G9a is essential for epigenetic silencing of K(+) channel genes in acute-to-chronic pain transition. Nat Neurosci. 18:1746–1755. 2015. View Article : Google Scholar : PubMed/NCBI
9	Lee JY, Lee SH, Heo SH, Kim KS, Kim C, Kim DK, Ko JJ and Park KS: Novel function of lysine methyltransferase G9a in the regulation of Sox2 protein stability. PLoS One. 10:e01411182015. View Article : Google Scholar : PubMed/NCBI
10	Si W, Huang W, Zheng Y, Yang Y, Liu X, Shan L, Zhou X, Wang Y, Su D, Gao J, et al: Dysfunction of the reciprocal feedback loop between GATA3- and ZEB2-nucleated repression programs contributes to breast cancer metastasis. Cancer Cell. 27:822–836. 2015. View Article : Google Scholar : PubMed/NCBI
11	Liu X, Liao S, Xu Z, Zhu L, Yang F and Guo W: Identification and analysis of the porcine MicroRNA in porcine cytomegalovirus-infected macrophages using deep sequencing. PLoS One. 11:e01509712016. View Article : Google Scholar : PubMed/NCBI
12	Hu JY, Yi W, Zhang MY, Xu R, Zeng LS, Long XR, Zhou XM, Zheng XS, Kang Y and Wang HY: MicroRNA-711 is a prognostic factor for poor overall survival and has an oncogenic role in breast cancer. Oncol Lett. 11:2155–2163. 2016.PubMed/NCBI
13	Ali HO, Arroyo AB, González-Conejero R, Stavik B, Iversen N, Sandset PM, Martínez C and Skretting G: The role of microRNA −27a/b and microRNA-494 in oestrogen mediated downregulation of tissue factor pathway inhibitor α. J Thromb Haemost. 14:1226–1237. 2016. View Article : Google Scholar : PubMed/NCBI
14	Dong C, Wu Y, Yao J, Wang Y, Yu Y, Rychahou PG, Evers BM and Zhou BP: G9a interacts with Snail and is critical for Snail-mediated E-cadherin repression in human breast cancer. J Clin Invest. 122:1469–1486. 2012. View Article : Google Scholar : PubMed/NCBI
15	Gautier L, Cope L, Bolstad BM and Irizarry RA: affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
16	Stuhlmüller B: A composition of nucleic acid sequences, specific for inflammatory disease, in particular rheumatoid arthritis. EP 1795610 A1. December 6. 2005 June 13–2007.
17	Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:P32003. View Article : Google Scholar : PubMed/NCBI
18	Langmead B and Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods. 9:357–359. 2012. View Article : Google Scholar : PubMed/NCBI
19	Feng J, Liu T, Qin B, Zhang Y and Liu XS: Identifying ChIP-seq enrichment using MACS. Nat Protoc. 7:1728–1740. 2012. View Article : Google Scholar : PubMed/NCBI
20	Yu G, Wang LG and He QY: ChIPseeker: An R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics. 31:2382–2383. 2015. View Article : Google Scholar : PubMed/NCBI
21	Song X, Cheng L, Zhou T, Guo X, Zhang X, Chen YP, Han P and Sha J: Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features. Comput Biol Med. 42:1–7. 2012. View Article : Google Scholar : PubMed/NCBI
22	Fenga C: Occupational exposure and risk of breast cancer. Biomed Rep. 4:282–292. 2016.PubMed/NCBI
23	Inoue K and Fry EA: Novel molecular markers for breast cancer. Biomark Cancer. 8:25–42. 2016. View Article : Google Scholar : PubMed/NCBI
24	Rizk SM, Shahin NN and Shaker OG: Association between SIRT1 gene polymorphisms and breast cancer in Egyptians. PLoS One. 11:e01519012016. View Article : Google Scholar : PubMed/NCBI
25	Shen Y and Cai T: Identifying predictive markers for personalized treatment selection. Biometrics. 72:1017–1025. 2016. View Article : Google Scholar : PubMed/NCBI
26	Becht E, de Reyniès A, Giraldo NA, Pilati C, Buttard B, Lacroix L, Selves J, Sautès-Fridman C, Laurent-Puig Pa and Fridman WH: Immune and stromal classification of colorectal cancer is associated with molecular subtypes and relevant for precision immunotherapy. Clin Cancer Res. 22:4057–4066. 2016. View Article : Google Scholar : PubMed/NCBI
27	Abou-Shousha S, Moaz M, Sheta M and Motawea MA: An approach to breast cancer immunotherapy: The apoptotic activity of recombinant anti-interleukin-6 monoclonal antibodies in intact tumor microenvironment of breast carcinoma. Scand J Immunol. 83:427–437. 2016. View Article : Google Scholar : PubMed/NCBI
28	Biswas T, Efird JT, Prasad S, James SE, Walker PR and Zagar TM: Inflammatory TNBC breast cancer: Demography and clinical outcome in a large cohort of patients with TNBC. Clin Breast Cancer. 16:212–216. 2016. View Article : Google Scholar : PubMed/NCBI
29	Suárez-Arroyo IJ, Rios-Fuller TJ, Feliz-Mosquea YR, Lacourt-Ventura M, Leal-Alviarez DJ, Maldonado-Martinez G, Cubano LA and Martínez-Montemayor MM: Ganoderma lucidum combined with the EGFR tyrosine kinase inhibitor, erlotinib synergize to reduce inflammatory breast cancer progression. J Cancer. 7:500–511. 2016. View Article : Google Scholar : PubMed/NCBI
30	Yang X, Jia M, Li Z, Lu S, Qi X, Zhao B, Wang X, Rong Y, Shi J, Zhang Z, et al: Bioinformatics analysis of aggressive behavior of breast cancer via an integrated gene regulatory network. J Cancer Res Ther. 10:1013–1018. 2014. View Article : Google Scholar : PubMed/NCBI
31	Sun Y, Yuan K, Zhang P, Ma R, Zhang QW and Tian XS: Crosstalk analysis of pathways in breast cancer using a network model based on overlapping differentially expressed genes. Exp Ther Med. 10:743–748. 2015.PubMed/NCBI
32	Chen HF and Wu KJ: Epigenetics, TET proteins, and hypoxia in epithelial-mesenchymal transition and tumorigenesis. Biomedicine (Taipei). 6:12016. View Article : Google Scholar : PubMed/NCBI
33	Tsai YP, Chen HF, Chen SY, Cheng WC, Wang HW, Shen ZJ, Song C, Teng SC, He C and Wu KJ: TET1 regulates hypoxia-induced epithelial-mesenchymal transition by acting as a co-activator. Genome Biol. 15:5132014. View Article : Google Scholar : PubMed/NCBI
34	Ghose A, Kundu R, Toumeh A, Hornbeck C and Mohamed I: A review of obesity, insulin resistance, and the role of exercise in breast cancer patients. Nutr Cancer. 67:197–202. 2015. View Article : Google Scholar : PubMed/NCBI
35	Hernández-Vargas H, Rodriguez-Pinilla SM, Julián-Tendero M, Sánchez-Rovira P, Cuevas C, Antón A, Ríos MJ, Palacios J and Moreno-Bueno G: Gene expression profiling of breast cancer cells in response to gemcitabine: NF-kappaB pathway activation as a potential mechanism of resistance. Breast Cancer Res Treat. 102:157–172. 2007. View Article : Google Scholar : PubMed/NCBI
36	Einbond LS, Su T, Wu HA, Friedman R, Wang X, Jiang B, Hagan T, Kennelly EJ, Kronenberg F and Weinstein IB: Gene expression analysis of the mechanisms whereby black cohosh inhibits human breast cancer cell growth. Anticancer Res. 27:697–712. 2007.PubMed/NCBI
37	Shi Y, Tan YJ, Zeng DZ, Qian F and Yu PW: miR-203 suppression in gastric carcinoma promotes Slug-mediated cancer metastasis. Tumour Biol. Jul 21–2015.(Epub ahead of print).
38	Taipaleenmäki H, Browne G, Akech J, Zustin J, Van Wijnen AJ, Stein JL, Hesse E, Stein GS and Lian JB: Targeting of Runx2 by miR-135 and miR-203 impairs progression of breast cancer and metastatic bone disease. Cancer Res. 75:1433–1444. 2015. View Article : Google Scholar : PubMed/NCBI
39	Zhou Y, Hu HY, Meng W, Jiang L, Zhang X, Sha JJ, Lu Z and Yao Y: MEK inhibitor effective against proliferation in breast cancer cell. Tumour Biol. 35:9269–9279. 2014. View Article : Google Scholar : PubMed/NCBI
40	Li P, Guo Y, Bledsoe G, Yang Z, Chao L and Chao J: Kallistatin induces breast cancer cell apoptosis and autophagy by modulating Wnt signaling and microRNA synthesis. Exp Cell Res. 340:305–314. 2016. View Article : Google Scholar : PubMed/NCBI