Integrated analysis of the potential roles of miRNA‑mRNA networks in triple negative breast cancer

Zhu,Huiru; Dai,Meiyu; Chen,Xiaoli; Chen,Xiang; Qin,Shini; Dai,Shengming

doi:10.3892/mmr.2017.6750

Integrated analysis of the potential roles of miRNA‑mRNA networks in triple negative breast cancer

Authors:
- Huiru Zhu
- Meiyu Dai
- Xiaoli Chen
- Xiang Chen
- Shini Qin
- Shengming Dai
View Affiliations

Affiliations: Department of Galactophore, The Third Affiliated Hospital of Guangxi University of Chinese Medicine, Liuzhou, Guangxi 545001, P.R. China, Department of Clinical Laboratory, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi 545005, P.R. China
Published online on: June 9, 2017 https://doi.org/10.3892/mmr.2017.6750
Pages: 1139-1146
Copyright: © Zhu 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

Triple negative breast cancer (TNBC) is a type of breast cancer where the tumor cells are negative for the estrogen, progesterone and human epidermal growth factor 2 receptors. To date, expression profiling of microRNA (miRNA/miR) and mRNA sequences have been widely applied for the diagnosis of TNBC. In the present study, an integrated analysis of miRNA‑mRNA profiling arrays was performed. A total of five dysregulated miRNAs in patients with TNBC were identified, including upregulated miR‑558 expression and downregulated miR‑320d‑1, miR‑548v, miR‑99a and miR‑21 expression. In addition, 49 potential target mRNA sequences were identified. Bioinformatics analyses were performed on the identified miRNAs and mRNAs, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes pathway and miRNA‑mRNA network analyses. A total of 31 GO terms and three signaling pathways were identified. The results indicated that the differentially expressed miRNAs and their potential target mRNAs may affect the pathogenesis of TNBC, and may therefore be considered as promising biomarkers for the early diagnosis and targeted therapy of patients with TNBC.

View Figures

View References

1	Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al: Molecular portraits of human breast tumours. Nature. 406:747–752. 2000. View Article : Google Scholar : PubMed/NCBI
2	Pierobon M and Frankenfeld CL: Obesity as a risk factor for triple-negative breast cancers: A systematic review and meta-analysis. Breast Cancer Res Treat. 137:307–314. 2013. View Article : Google Scholar : PubMed/NCBI
3	Foulkes WD, Smith IE and Reis-Filho JS: Triple-negative breast cancer. N Engl J Med. 363:1938–1948. 2010. View Article : Google Scholar : PubMed/NCBI
4	Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y and Pietenpol JA: Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 121:2750–2767. 2011. View Article : Google Scholar : PubMed/NCBI
5	Blows FM, Driver KE, Schmidt MK, Broeks A, van Leeuwen FE, Wesseling J, Cheang MC, Gelmon K, Nielsen TO, Blomqvist C, et al: Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: A collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med. 7:e10002792010. View Article : Google Scholar : PubMed/NCBI
6	Cui Q, Yu Z, Pan Y, Purisima EO and Wang E: MicroRNAs preferentially target the genes with high transcriptional regulation complexity. Biochem Biophys Res Commun. 352:733–738. 2007. View Article : Google Scholar : PubMed/NCBI
7	Kim VN and Nam JW: Genomics of microRNA. Trends Genet. 22:165–173. 2006. View Article : Google Scholar : PubMed/NCBI
8	Rodriguez A, Griffiths-Jones S, Ashurst JL and Bradley A: Identification of mammalian microRNA host genes and transcription units. Genome Res. 14:1902–1910. 2004. View Article : Google Scholar : PubMed/NCBI
9	Yang L, Feng Y, Qi P, Xu S and Zhou Y: Mechanism of serum miR-21 in the pathogenesis of familial and triple negative breast cancer. J Biol Regul Homeost Agents. 30:1041–1045. 2016.PubMed/NCBI
10	Zhang G, Liu Z, Han Y, Wang X and Yang Z: Overexpression of miR-509 increases apoptosis and inhibits invasion via suppression of tumor necrosis factor-α in triple-negative breast cancer Hs578T cells. Oncol Res. 24:233–238. 2016. View Article : Google Scholar : PubMed/NCBI
11	Mathe A, Wong-Brown M, Morten B, Forbes JF, Braye SG, Avery-Kiejda KA and Scott RJ: Novel genes associated with lymph node metastasis in triple negative breast cancer. Sci Rep. 5:158322015. View Article : Google Scholar : PubMed/NCBI
12	Agarwal V, Bell GW, Nam JW and Bartel DP: Predicting effective microRNA target sites in mammalian mRNAs. Elife. 4:2015. View Article : Google Scholar :
13	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
14	da W Huang, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009.PubMed/NCBI
15	da W Huang, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI
16	Sam LT, Mendonca EA, Li J, Blake J, Friedman C and Lussier YA: PhenoGO: An integrated resource for the multiscale mining of clinical and biological data. BMC bioinformatics. 10 Suppl 2:S82009. View Article : Google Scholar : PubMed/NCBI
17	Labi V and Erlacher M: How cell death shapes cancer. Cell Death Dis. 6:e16752015. View Article : Google Scholar : PubMed/NCBI
18	Zhang C, Wang S, Israel HP, Yan SX, Horowitz DP, Crockford S, Gidea-Addeo D, Chao KS Clifford, Kalinsky K and Connolly EP: Higher locoregional recurrence rate for triple-negative breast cancer following neoadjuvant chemotherapy, surgery and radiotherapy. Springerplus. 4:3862015. View Article : Google Scholar : PubMed/NCBI
19	Kim S, Park HS, Kim JY, Ryu J, Park S and Kim SI: Comparisons of oncologic outcomes between triple-negative breast cancer (TNBC) and Non-TNBC among patients treated with breast-conserving therapy. Yonsei Med J. 57:1192–1198. 2016. View Article : Google Scholar : PubMed/NCBI
20	Shi KQ, Lin Z, Chen XJ, Song M, Wang YQ, Cai YJ, Yang NB, Zheng MH, Dong JZ, Zhang L and Chen YP: Hepatocellular carcinoma associated microRNA expression signature: Integrated bioinformatics analysis, experimental validation and clinical significance. Oncotarget. 6:25093–25108. 2015. View Article : Google Scholar : PubMed/NCBI
21	Dai M, Chen X, Mo S, Li J, Huang Z, Huang S, Xu J, He B, Zou Y, Chen J and Dai S: Meta-signature LncRNAs serve as novel biomarkers for colorectal cancer: Integrated bioinformatics analysis, experimental validation and diagnostic evaluation. Sci Rep. 7:465722017. View Article : Google Scholar : PubMed/NCBI
22	An N, Shi X, Zhang Y, Lv N, Feng L, Di X, Han N, Wang G, Cheng S and Zhang K: Discovery of a novel immune gene signature with profound prognostic value in colorectal cancer: A model of cooperativity disorientation created in the process from development to cancer. PloS One. 10:e01371712015. View Article : Google Scholar : PubMed/NCBI
23	Volinia S and Croce CM: Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA. 110:7413–7417. 2013. View Article : Google Scholar : PubMed/NCBI
24	Zhang X, Peng Y, Jin Z, Huang W, Cheng Y, Liu Y, Feng X, Yang M, Huang Y, Zhao Z, et al: Integrated miRNA profiling and bioinformatics analyses reveal potential causative miRNAs in gastric adenocarcinoma. Oncotarget. 6:32878–32889. 2015.PubMed/NCBI
25	Lovering RC, Camon EB, Blake JA and Diehl AD: Access to immunology through the gene ontology. Immunology. 125:154–160. 2008. View Article : Google Scholar : PubMed/NCBI
26	Xiong H, Li Q, Liu S, Wang F, Xiong Z, Chen J, Chen H, Yang Y, Tan X, Luo Q, et al: Integrated microRNA and mRNA transcriptome sequencing reveals the potential roles of miRNAs in stage I endometrioid endometrial carcinoma. PLoS One. 9:e1101632014. View Article : Google Scholar : PubMed/NCBI
27	Li C, Nong Q, Solanki MK, Liang Q, Xie J, Liu X and Li Y, Wang W, Yang L and Li Y: Differential expression profiles and pathways of genes in sugarcane leaf at elongation stage in response to drought stress. Sci Rep. 6:256982016. View Article : Google Scholar : PubMed/NCBI
28	Ma G, Luo Y, Zhu H, Luo Y, Korhonen PK, Young ND, Gasser RB and Zhou R: MicroRNAs of Toxocara canis and their predicted functional roles. Parasit Vectors. 9:2292016. View Article : Google Scholar : PubMed/NCBI
29	Ha M and Kim VN: Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol. 15:509–524. 2014. View Article : Google Scholar : PubMed/NCBI
30	Lapkina-Gendler L, Rotem I, Pasmanik-Chor M, Gurwitz D, Sarfstein R, Laron Z and Werner H: Identification of signaling pathways associated with cancer protection in Laron syndrome. Endocr Relat Cancer. 23:399–410. 2016. View Article : Google Scholar : PubMed/NCBI
31	Shi KQ, Lin Z, Chen XJ, Song M, Wang YQ, Cai YJ, Yang NB, Zheng MH, Dong JZ, Zhang L and Chen YP: Hepatocellular carcinoma associated microRNA expression signature: Integrated bioinformatics analysis, experimental validation and clinical significance. Oncotarget. 6:25093–25108. 2015. View Article : Google Scholar : PubMed/NCBI
32	Geraldo MV and Kimura ET: Integrated analysis of thyroid cancer public datasets reveals role of post-transcriptional regulation on tumor progression by targeting of immune system mediators. PLoS One. 10:e01417262015. View Article : Google Scholar : PubMed/NCBI