Bioinformatic analysis of computational identified differentially expressed genes in tumor stoma of pregnancy‑associated breast cancer

Zhou,Qian; Sun,Erhu; Ling,Lijun; Liu,Xiaofeng; Zhang,Min; Yin,Hong; Lu,Cheng

doi:10.3892/mmr.2017.6947

Bioinformatic analysis of computational identified differentially expressed genes in tumor stoma of pregnancy‑associated breast cancer

Authors:
- Qian Zhou
- Erhu Sun
- Lijun Ling
- Xiaofeng Liu
- Min Zhang
- Hong Yin
- Cheng Lu
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Affiliations: Department of Breast, Nanjing Maternity and Child Health Care Hospital, Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu 210004, P.R. China, Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China
Published online on: July 12, 2017 https://doi.org/10.3892/mmr.2017.6947
Pages: 3345-3350

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Abstract

The present study aimed to screen the differentially expressed genes (DEGs) in tumor‑associated stroma of pregnancy‑associated breast cancer (PABC). By analyzing Affymetrix microarray data (GSE31192) from the Gene Expression Omnibus database, DEGs between tumor associated stromal cells and normal stromal cells in PABC were identified. Gene Ontology (GO) function and pathway enrichment analyses for the DEGs were then performed, followed by construction of a protein‑protein interaction (PPI) network. A total of 94 upregulated and 386 downregulated DEGs were identified between tumor associated stromal cells and normal stromal cells in patients with PABC. The upregulated DEGs were primarily enriched in the cytokine‑cytokine receptor interaction pathway and GO terms associated with the immune response, which included the DEGs of interleukin 18 (IL18) and cluster of differentiation 274 (CD274). The downregulated DEGs were primarily involved in GO terms associated with cell surface receptor linked signal transduction and pathways of focal adhesion and pathways in cancer. In the PPI network, nodes of jun proto‑oncogene (JUN), FBJ murine osteosarcoma viral oncogene homolog (FOS), V‑myc avian myelocytomatosis viral oncogene homolog (MYC), and alpha‑smooth muscle actin (ACTA2) had higher degrees. The hub genes of JUN, FOS, MYC and ACTA2, as well as the DEGs IL18 and CD274 that were associated with the immune response in GO terms may exert important functions in the molecular mechanisms of PABC. These genes may be used as new molecular targets in the treatment of this disease.

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1	Sathian B, Nagaraja SB, Banerjee I, Sreedharan J, De A, Roy B, Rajesh E, Senthilkumaran S, Hussain SA and Menezes RG: Awareness of breast cancer warning signs and screening methods among female residents of Pokhara valley, Nepal. Asian Pac J Cancer Prev. 15:4723–4726. 2014. View Article : Google Scholar : PubMed/NCBI
2	Rovera F, Frattini F, Coglitore A, Marelli M, Rausei S, Dionigi G, Boni L and Dionigi R: Breast cancer in pregnancy. Breast J. 16:(Suppl 1). S22–S25. 2010. View Article : Google Scholar : PubMed/NCBI
3	Abenhaim HA, Azoulay L, Holcroft CA, Bure LA, Assayag J and Benjamin A: Incidence, risk factors, and obstetrical outcomes of women with breast cancer in pregnancy. Breast J. 18:564–568. 2012. View Article : Google Scholar : PubMed/NCBI
4	Antoniou A, Pharoah P, Narod S, Risch HA, Eyfjord JE, Hopper JL, Loman N, Olsson H, Johannsson O, Borg A, et al: Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: A combined analysis of 22 studies. Am J Hum Genet. 72:1117–1130. 2003. View Article : Google Scholar : PubMed/NCBI
5	Cullinane CA, Lubinski J, Neuhausen SL, Ghadirian P, Lynch HT, Isaacs C, Weber B, Moller P, Offit K, Kim-Sing C, et al: Effect of pregnancy as a risk factor for breast cancer in BRCA1/BRCA2 mutation carriers. Int J Cancer. 117:988–991. 2005. View Article : Google Scholar : PubMed/NCBI
6	Kotsopoulos J, Lubinski J, Lynch HT, Klijn J, Ghadirian P, Neuhausen SL, Kim-Sing C, Foulkes WD, Moller P, Isaacs C, et al: Age at first birth and the risk of breast cancer in BRCA1 and BRCA2 mutation carriers. Breast Cancer Res Treat. 105:221–228. 2007. View Article : Google Scholar : PubMed/NCBI
7	Gao L, Ding H, Li L and Hu H: A clinicopathological study of pregnancy-associated breast cancer and breast cancer with pregnancy-like change. Chin J Clin Exper Pathol. 20:29–34. 2003.(In Chinese).
8	Douglas MR, Morrison KE, Salmon M and Buckley CD: Why does inflammation persist: A dominant role for the stromal microenvironment? Expert Rev Mol Med. 4:1–18. 2002. View Article : Google Scholar : PubMed/NCBI
9	Harvell DM, Kim J, O'Brien J, Tan AC, Borges VF, Schedin P, Jacobsen BM and Horwitz KB: Genomic signatures of pregnancy-associated breast cancer epithelia and stroma and their regulation by estrogens and progesterone. Horm Cancer. 4:140–153. 2013. View Article : Google Scholar : PubMed/NCBI
10	Wiseman BS and Werb Z: Stromal effects on mammary gland development and breast cancer. Science. 296:1046–1049. 2002. View Article : Google Scholar : PubMed/NCBI
11	Edgar R, Domrachev M and Lash AE: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30:207–210. 2002. View Article : Google Scholar : PubMed/NCBI
12	Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U and Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI
13	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
14	Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI
15	Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The Gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
16	Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
17	Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC and Lempicki RA: The DAVID gene functional classification tool: A novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8:R1832007. View Article : Google Scholar : PubMed/NCBI
18	Von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P and Snel B: STRING: A database of predicted functional associations between proteins. Nucleic Acids Res. 31:258–261. 2003. View Article : Google Scholar : PubMed/NCBI
19	Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks T, Julien P, Roth A, Simonovic M, et al: STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37:(Database issue). D412–D416. 2009. View Article : Google Scholar : PubMed/NCBI
20	He X and Zhang J: Why do hubs tend to be essential in protein networks? PLoS Genet. 2:e882006. View Article : Google Scholar : PubMed/NCBI
21	Andersson TM, Johansson AL, Hsieh CC, Cnattingius S and Lambe M: Increasing incidence of pregnancy-associated breast cancer in Sweden. Obstet Gynecol. 114:568–572. 2009. View Article : Google Scholar : PubMed/NCBI
22	Vogt PK: Fortuitous convergences: The beginnings of JUN. Nat Rev Cancer. 2:465–469. 2002. View Article : Google Scholar : PubMed/NCBI
23	Behrens A, Sibilia M and Wagner EF: Amino-terminal phosphorylation of c-Jun regulates stress-induced apoptosis and cellular proliferation. Nat Genet. 21:326–329. 1999. View Article : Google Scholar : PubMed/NCBI
24	Rauscher FD III, Voulalas P, Franza B Jr and Curran T: Fos and Jun bind cooperatively to the AP-1 site: Reconstitution in vitro. Genes Dev. 2:1687–1699. 1988. View Article : Google Scholar : PubMed/NCBI
25	Rao ChV, Li X, Manna SK, Lei ZM and Aggarwal BB: Human chorionic gonadotropin decreases proliferation and invasion of breast cancer MCF-7 cells by inhibiting NF-kappaB and AP-1 activation. J Biol Chem. 279:25503–25510. 2004. View Article : Google Scholar : PubMed/NCBI
26	Zhang Y, Pu X, Shi M, Chen L, Song Y, Qian L, Yuan G, Zhang H, Yu M, Hu M, et al: Critical role of c-Jun overexpression in liver metastasis of human breast cancer xenograft model. BMC Cancer. 7:1452007. View Article : Google Scholar : PubMed/NCBI
27	Facchini LM and Penn LZ: The molecular role of Myc in growth and transformation: Recent discoveries lead to new insights. FASEB J. 12:633–651. 1998.PubMed/NCBI
28	Hayashi J, Aoki H, Kajino K, Moriyama M, Arakawa Y and Hino O: Hepatitis C virus core protein activates the MAPK/ERK cascade synergistically with tumor promoter TPA, but not with epidermal growth factor or transforming growth factor alpha. Hepatology. 32:958–961. 2000. View Article : Google Scholar : PubMed/NCBI
29	Santoni-Rugiu E, Falck J, Mailand N, Bartek J and Lukas J: Involvement of Myc activity in a G1/S-promoting mechanism parallel to the pRb/E2F pathway. Mol Cell Biol. 20:3497–3509. 2000. View Article : Google Scholar : PubMed/NCBI
30	Chen Y, McGee J, Chen X, Doman TN, Gong X, Zhang Y, Hamm N, Ma X, Higgs RE, Bhagwat SV, et al: Identification of druggable cancer driver genes amplified across TCGA datasets. PloS One. 9:e982932014. View Article : Google Scholar : PubMed/NCBI
31	Lee HW, Park YM, Lee SJ, Cho HJ, Kim DH, Lee JI, Kang MS, Seol HJ, Shim YM, Nam DH, et al: Alpha-smooth muscle actin (ACTA2) is required for metastatic potential of human lung adenocarcinoma. Clin Cancer Res. 19:5879–5889. 2013. View Article : Google Scholar : PubMed/NCBI
32	Lambrechts A, Van Troys M and Ampe C: The actin cytoskeleton in normal and pathological cell motility. Int J Biochem Cell Biol. 36:1890–1909. 2004. View Article : Google Scholar : PubMed/NCBI
33	Fritz G and Kaina B: Rho GTPases: Promising cellular targets for novel anticancer drugs. Curr Cancer Drug Targets. 6:1–14. 2006. View Article : Google Scholar : PubMed/NCBI
34	Asztalos S, Gann PH, Hayes MK, Nonn L, Beam CA, Dai Y, Wiley EL and Tonetti DA: Gene expression patterns in the human breast after pregnancy. Cancer Prev Res (Phila). 3:301–311. 2010. View Article : Google Scholar : PubMed/NCBI
35	Ma XJ, Dahiya S, Richardson E, Erlander M and Sgroi DC: Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res. 11:R72009. View Article : Google Scholar : PubMed/NCBI
36	Wenfeng Y: Interleukin-18 anti-hepatocellular carcinoma in nude mice transplanted mechanism. Soochow University. 2009.(In Chinese).
37	Thompson RH, Gillett MD, Cheville JC, Lohse CM, Dong H, Webster WS, Krejci KG, Lobo JR, Sengupta S, Chen L, et al: Costimulatory B7-H1 in renal cell carcinoma patients: Indicator of tumor aggressiveness and potential therapeutic target. Proc Natl Acad Sci USA. 101:17174–17179. 2004. View Article : Google Scholar : PubMed/NCBI