Oncotarget

Research Papers:

Transcriptome evolution from breast epithelial cells to basal-like tumors

Gabriel Santpere, Ana Alcaráz-Sanabria, Verónica Corrales-Sánchez, Atanasio Pandiella, Balázs Győrffy and Alberto Ocaña _

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Oncotarget. 2018; 9:453-463. https://doi.org/10.18632/oncotarget.23065

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Abstract

Gabriel Santpere1, Ana Alcaráz-Sanabria2, Verónica Corrales-Sánchez2, Atanasio Pandiella3, Balázs Győrffy4,5 and Alberto Ocaña2,6

1Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA

2Translational Research Unit and CIBERONC, Albacete University Hospital, Albacete, Spain

3Cancer Research Center and CIBERONC, CSIC-University of Salamanca, Salamanca, Spain

4MTA TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary

5Semmelweis University 2nd Department of Pediatrics, Budapest, Hungary

6Regional Biomedical Research Center (CRIB), Castilla La Mancha University, Albacete, Spain

Correspondence to:

Alberto Ocaña, email: [email protected]

Keywords: breast cancer; transcriptomic evolution; carcinoma in situ

Received: August 02, 2017     Accepted: November 14, 2017     Published: December 08, 2017

ABSTRACT

In breast cancer, it is unclear the functional modifications at a transcriptomic level that are associated with the evolution from epithelial cells and ductal carcinoma in situ (DCIS) to basal-like tumors. By applying weighted gene co-expression network analysis (WGCNA), we identified 17 gene co-expression modules in normal, DCIS and basal-like tumor samples. We then correlated the expression pattern of these gene modules with disease progression from normal to basal-like tumours and found eight modules exhibiting a high and statistically significant correlation. M4 included genes mainly related to cell cycle/division and DNA replication like CCNA2 or CDK1. The M7 module included genes linked with the immune response showing top hub genes such as CD86 or PTPRC. M10 was found specifically correlated to DCIS, but not to basal-like tumor samples, and showed enrichment in ubiquitination or ubiquitin-like processes. We observed that genes in some of these modules were associated with clinical outcome and/or represented druggable opportunities, including AURKA, AURKB, PLK1, MCM2, CDK1, YWHAE, HSP90AB1, LCK, or those targeting ubiquitination. In conclusion, we describe relevant gene modules related to biological functions that can influence survival and be targeted pharmacologically.


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