Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer

Wang,Jinguang; Du,Qi; Li,Chen

doi:10.3892/ol.2017.7256

Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer

Authors:
- Jinguang Wang
- Qi Du
- Chen Li
View Affiliations

Affiliations: Department of Thyroid and Breast Surgery, Linyi People's Hospital, Linyi, Shandong 272100, P.R. China, Department of Prevention and Health Care, Lanshan District Center for Disease Control and Prevention, Linyi, Shandong 276000, P.R. China, Department of General Surgery, Linyi People's Hospital, Linyi, Shandong 272100, P.R. China
Published online on: October 23, 2017 https://doi.org/10.3892/ol.2017.7256
Pages: 7880-7888
Copyright: © Wang 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

Patients with the luminal B subtype of breast cancer exhibit a poor prognosis, high metastatic risk and high incidence of chemotherapy resistance. Luminal B breast cancer is sub‑classified into B1 and B2. The pathophysiological mechanism of luminal B2 breast cancer (LB2BC) progression has yet to be characterized. Therefore, the present study aimed to identify the genes involved in the pathogenesis of LB2BC. The data of 117 LB2BC expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and differentially expressed genes (DEGs) were identified by comparison with non‑tumor tissue expression profiles. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein‑protein interaction (PPI) networks were used to obtain insight into the functions of DEGs. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) analysis was performed to validate the expression level of DEGs in tissue samples. A total of 2,251 DEGs, including 759 upregulated and 1,492 downregulated genes, were identified between LB2BC and non‑tumor tissues. The top 15 upregulated and downregulated genes were used to construct a PPI network: Epidermal growth factor receptor (EGFR), fibronectin‑1 (FN1) and Polo‑like kinase‑1 had the highest connectivity degrees. KEGG analysis identified that DEGs were most significantly enriched in ‘focal adhesion’, ‘pathways in cancer’ and ‘ECM‑receptor interaction’ pathways. The results of RT‑qPCR demonstrated that EGFR was significantly downregulated in LB2BC, whereas FN1 was significantly upregulated, whereas neurotrophic receptor tyrosine kinase 2 (NTRK2) trended towards downregulation. In conclusion, the DEGs identified in the present study, including NTRK2, FN1 and EGFR, may serve pivotal roles in the tumorigenesis of LB2BC by affecting the ‘focal adhesion’, ‘pathways in cancer’ and ‘ECM‑receptor interaction’ KEGG pathways.

View Figures

View References

1	Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI
2	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
3	Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, et al: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 98:pp. 10869–10874. 2001; View Article : Google Scholar : PubMed/NCBI
4	Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, Livasy C, Carey LA, Reynolds E, Dressler L, et al: The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 7:962006. View Article : Google Scholar : PubMed/NCBI
5	Dai X, Li T, Bai Z, Yang Y, Liu X, Zhan J and Shi B: Breast cancer intrinsic subtype classification, clinical use and future trends. Am J Cancer Res. 5:2929–2943. 2015.PubMed/NCBI
6	Ignatiadis M, Bedard P, Haibe-Kains B, Haibe-Kains B, Singhal S, Loi S, Criscitiello C, Desmedt C, Bontempi G, Piccart M, et al: A meta-analysis of gene expression profiling studies identifies clinically relevant oncogenic pathways in basal-like breast cancer. Cancer Res. 69:1062009. View Article : Google Scholar
7	Tran B and Bedard PL: Luminal-B breast cancer and novel therapeutic targets. Breast Cancer Res. 13:2212011. View Article : Google Scholar : PubMed/NCBI
8	Tomczak K, Czerwińska P and Wiznerowicz M: The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol (Pozn). 19:A68–A77. 2015.PubMed/NCBI
9	Anders S and Huber W: Differential expression analysis for sequence count data. Genome Biol. 11:R1062010. View Article : Google Scholar : PubMed/NCBI
10	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
11	Eden E, Navon R, Steinfeld I, Lipson D and Yakhini Z: GOrilla: A tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 10:482009. View Article : Google Scholar : PubMed/NCBI
12	Kanehisa M: The KEGG database. Novartis Found Symp. 247:91–103. 119–128. 244–252. 2002. View Article : Google Scholar : PubMed/NCBI
13	Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S, et al: A human protein-protein interaction network: A resource for annotating the proteome. Cell. 122:957–968. 2005. View Article : Google Scholar : PubMed/NCBI
14	Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, Stark C, Breitkreutz A, Kolas N, O'Donnell L, et al: The BioGRID interaction database: 2015 update. Nucleic Acids Res. 39(Database Issue): D470–D478. 2015. View Article : Google Scholar
15	Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, Stark C, Breitkreutz A, Kolas N, O'Donnell L, et al: The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43(Database Issue): D470–D478. 2015. View Article : Google Scholar : PubMed/NCBI
16	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
17	Schmittgen TD and Livak KJ: Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc. 3:1101–1108. 2008. View Article : Google Scholar : PubMed/NCBI
18	Li Z, Peng L, Han S, Huang Z, Shi F, Cai Z, Li X, Zhang P, Zhu H and Jin W: Screening molecular markers in early breast cancer of the same pathological types but with different prognoses using Agilent gene chip. Nan Fang Yi Ke Da Xue Xue Bao. 33:1483–1488. 2013.(In Chinese). PubMed/NCBI
19	Yu X, Liu L, Cai B, He Y and Wan X: Suppression of anoikis by the neurotrophic receptor TrkB in human ovarian cancer. Cancer Sci. 99:543–552. 2008. View Article : Google Scholar : PubMed/NCBI
20	Kupferman ME, Jiffar T, El-Naggar A, Yilmaz T, Zhou G, Xie T, Feng L, Wang J, Holsinger FC, Yu D and Myers JN: TrkB induces EMT and has a key role in invasion of head and neck squamous cell carcinoma. Oncogene. 29:2047–2059. 2010. View Article : Google Scholar : PubMed/NCBI
21	Howe EN, Cochrane DR and Richer JK: Targets of miR-200c mediate suppression of cell motility and anoikis resistance. Breast Cancer Res. 13:R452011. View Article : Google Scholar : PubMed/NCBI
22	Chen D, Wang X, Liang D, Gordon J, Mittal A, Manley N, Degenhardt K and Astrof S: Fibronectin signals through integrin α5β1 to regulate cardiovascular development in a cell type-specific manner. Dev Biol. 407:195–210. 2015. View Article : Google Scholar : PubMed/NCBI
23	Steffens S, Schrader AJ, Vetter G, Eggers H, Blasig H, Becker J, Kuczyk MA and Serth J: Fibronectin 1 protein expression in clear cell renal cell carcinoma. Oncol Lett. 3:787–790. 2012.PubMed/NCBI
24	Liu X, Ma Y, Yang W, Wu X, Jiang L and Chen X: Identification of therapeutic targets for breast cancer using biological informatics methods. Mol Med Rep. 12:1789–1795. 2015. View Article : Google Scholar : PubMed/NCBI
25	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
26	Dorman SN, Baranova K, Knoll JH, Urquhart BL, Mariani G, Carcangiu ML and Rogan PK: Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. Mol Oncol. 10:85–100. 2016. View Article : Google Scholar : PubMed/NCBI
27	Morita Y, Hata K, Nakanishi M, Omata T, Morita N, Yura Y, Nishimura R and Yoneda T: Cellular fibronectin 1 promotes VEGF-C expression, lymphangiogenesis and lymph node metastasis associated with human oral squamous cell carcinoma. Clin Exp Metastasis. 32:739–753. 2015. View Article : Google Scholar : PubMed/NCBI
28	Gutteridge RE, Ndiaye MA, Liu X and Ahmad N: Plk1 inhibitors in cancer therapy: From laboratory to clinics. Mol Cancer Ther. 15:1427–1435. 2016. View Article : Google Scholar : PubMed/NCBI
29	Maire V, Némati F, Richardson M, Vincent-Salomon A, Tesson B, Rigaill G, Gravier E, Marty-Prouvost B, De Koning L, Lang G, et al: Polo-like kinase 1: A potential therapeutic option in combination with conventional chemotherapy for the management of patients with triple-negative breast cancer. Cancer Res. 73:813–823. 2013. View Article : Google Scholar : PubMed/NCBI
30	Ou O, Huppi K, Chakka S, Gehlhaus K, Dubois W, Patel J, Chen J, Mackiewicz M, Jones TL, Pitt JJ, et al: Loss-of-function RNAi screens in breast cancer cells identify AURKB, PLK1, PIK3R1, MAPK12, PRKD2, and PTK6 as sensitizing targets of rapamycin activity. Cancer Lett. 354:336–347. 2014. View Article : Google Scholar : PubMed/NCBI
31	Serna E, Lopez-Gines C, Monleon D, Muñoz-Hidalgo L, Callaghan RC, Gil-Benso R, Martinetto H, Gregori-Romero A, Gonzalez-Darder J and Cerda-Nicolas M: Correlation between EGFR amplification and the expression of microRNA-200c in primary glioblastoma multiforme. PLoS One. 9:e1029272014. View Article : Google Scholar : PubMed/NCBI
32	Naruke A, Azuma M, Takeuchi A, Ishido K, Katada C, Sasaki T, Higuchi K, Tanabe S, Saegusa M and Koizumi W: Comparison of site-specific gene expression levels in primary tumors and synchronous lymph node metastases in advanced gastric cancer. Gastric Cancer. 18:262–270. 2015. View Article : Google Scholar : PubMed/NCBI
33	Yang CH, Chou HC, Fu YN, Yeh CL, Cheng HW, Chang IC, Liu KJ, Chang GC, Tsai TF, Tsai SF, et al: EGFR over-expression in non-small cell lung cancers harboring EGFR mutations is associated with marked down-regulation of CD82. Biochim Biophys Acta. 1852:1540–1549. 2015. View Article : Google Scholar : PubMed/NCBI
34	Park HS, Jang MH, Kim EJ, Kim HJ, Lee HJ, Kim YJ, Kim JH, Kang E, Kim SW, Kim IA and Park SY: High EGFR gene copy number predicts poor outcome in triple-negative breast cancer. Mod Pathol. 27:1212–1222. 2014. View Article : Google Scholar : PubMed/NCBI