Introduction: Breast cancer is currently the neoplastic pathology with the highest incidence in the context of pregnant women, affecting 1 in every 3,000-10,000 pregnancies per year. However, these data can be underestimated, taking into account the difficulty of obtaining the diagnosis in this scenario, since the symptoms of cancer can be confused with the natural physiological changes of pregnancy. A late diagnosis can lead to serious complications and decrease the efficiency of therapeutic measures. Anti-cancer treatment options, and in particular chemotherapy, are considered to be toxic to cells and, therefore, may cause harmful effects to the developing fetus. Complications caused by cancer and antineoplastic treatments, in part, may be related to placental dysfunction, thus leading to impaired fetal development. Even with the importance of the theme, there is a lack of information in the scientific literature regarding placental changes, especially changes and molecular mechanisms, in women with breast cancer during pregnancy. In this sense, considering the clinical relevance and the lack of knowledge at molecular levels, the objective of this study will be to study the transcriptomic profile of the placentas of women who were diagnosed with breast cancer during pregnancy and who received chemotherapy. Materials and methods: Samples of placentas already collected and stored in the Biobank of placentas at Hospital da Mulher Prof. doctor J. A. Pinotti-Caism/Unicamp both in patients with breast cancer treated with chemotherapy (case group) and in patients at usual risk (control group). For transcriptomic analyses, purified RNA from the microdissected material of placental tissue samples from patients will be converted into a cDNA library using the TruSeq Stranded mRNA LT kit (Illumina) following the manufacturer's instructions. The cDNA libraries prepared will be sequenced on a HiSeq 2500 platform (Illumina) in High Output mode, producing sequences of 200 nucleotides for each sequenced molecule, with the objective of obtaining 10 million sequences for each biological sample. Training will be carried out before carrying out the execution stages of this project, which are always carried out under the supervision of specialist researchers in the area. The in silico analysis of the data will be carried out by a Master's student who will be involved in this project, which is part of the Young invertigator Project (2021/08931-0).
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