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Identification of Differentially Expressed Genes and Network Analysis in the search of potential molecular signatures associated with breast cancer

Identification of Differentially Expressed Genes and Network Analysis in the search of potential molecular signatures associated with breast cancer

Marina Mendiburu-Eliçabe Garganta

Centro de Investigación del Cáncer (CIC-IBMCC), lab. 7

Date: 16/07/2020
Time: 12:30
Identification of Differentially Expressed Genes and Network Analysis in the search of potential molecular signatures associated with breast cancer

One of the work carried out in our laboratory has focused on the study of heterogeneity in the clinical evolution of patients with the same histopathological type of breast cancer, and identify which factors are responsible for the different evolution. The differences that exist between individuals in a population regarding the risk of developing breast cancer, the behavior of the disease once it has appeared, and the response to therapy are a consequence of the interaction between genetic factors and environmental factors.

Breast cancer is a complex trait that is modified by multiple intermediate phenotypes that are determined at the genetic level.

In the present work, we have studied the susceptibility and evolution of breast cancer in a murine population with controlled genetic heterogeneity generated by a backcross strategy. First, two mouse strains with different susceptibility to the development of breast cancer were crossed, the resistant strain C57BL / 6 and the FVB strain carrying the MMTV-neu transgene that causes the development of breast tumors. The F1 generation resulting from this crossing was backcrossed with the susceptible strain FVB. As a result of this crossing strategy, the ErbB2 F1BX1-neu population was generated, giving rise to genetically unique animals. The free evolution of the disease was allowed in this population, which was characterized by a series of clinical evolution pathophenotypes (latency, lifespan, number of tumors, metastases incidence, number of metastasis, duration of disease, tumor volume, tumor weight, tumor growth rate, and tumor growth speed).

Clinical pathophenotypes are determined not only by processes at the level of the organism's physiology and pathophysiology but also by the transcriptome and the genome level. For this reason, a transcriptomic analysis of breast tumors originating from transgenic mice was carried out, and we look for gene signatures associated with the clinical pathophenotypes analyzed during the evolution of breast cancer. We found a total of 21 gene signatures, some of them associated with more than one clinical pathophenotype, which include genes such as Mir9-2, Cpm, CrispId2, Spp1, and Serpini1, among others.