scholarly journals Prognostic significance of α‐ and β2‐adrenoceptor gene expression in breast cancer patients

2019 ◽  
Vol 85 (9) ◽  
pp. 2143-2154 ◽  
Author(s):  
Ezequiel Mariano Rivero ◽  
Leandro Marcelo Martinez ◽  
Carlos David Bruque ◽  
Lucia Gargiulo ◽  
Ariana Bruzzone ◽  
...  
2021 ◽  
Author(s):  
Kazhaleh Mohammadi ◽  
Mahdiyeh Salimi ◽  
S. Abdolhamid Angaji ◽  
Arthur Saniotis ◽  
Foroozandeh Mahjoobi

Abstract Background Breast cancer (BC) is a heterogeneous disease that has different clinical outcomes. Bax-interacting Factor-1 (BIF-1) is a member of the endophilin B family that produces the pro-apoptotic BCL2-Associated X (BAX) protein in response to apoptotic signals. Lack of BIF-1 inhibits the intrinsic pathway of apoptosis and increases the risk of tumor genesis. The aim of the present study was to investigate the relationship between hormone receptors (ER, PR, HER2) status and different levels of BIF-1 gene expression in breast cancer patients. Methods BIF-1 gene expression was evaluated in 50 breast cancer tumors and 50 normal breast mammary tissues using SYBR Green Real Time RT-PCR technique. Multivariate and univariate analyses were used to evaluate the relationship between the prognostic significance of the BIF-1 gene using SPSS software. In this study, BIF-1 was selected as a candidate for a molecular biomarker and its expression status in breast cancer patients with hormone receptors (ER, RR, HER2) compared to patients without these hormone receptors. Results The study showed that the relative expression of BIF-1 gene in tissues of patients with hormone receptor in breast cancer compared to those without hormone receptor were not statistically significant. The expression levels of BIF-1 gene in different groups were evaluated for hormone receptor status. No significant relationship was found between BIF-1 gene expression and hormone receptors (ER, PR and HER2) (p> 0.05). Conclusion BIF-1 gene expression may be a useful prognostic marker in breast cancer.


Author(s):  
Julia Kuehn ◽  
Nancy Adriana Espinoza‐Sanchez ◽  
Felipe C. O. B. Teixeira ◽  
Mauro S. G. Pavão ◽  
Ludwig Kiesel ◽  
...  

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xu Yang ◽  
Geng-Xi Cai ◽  
Bo-Wei Han ◽  
Zhi-Wei Guo ◽  
Ying-Song Wu ◽  
...  

AbstractGene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.


2021 ◽  
Author(s):  
Yoshihisa Tokumaru ◽  
Masanori Oshi ◽  
Vijayashree V. Murthy ◽  
Eriko Katsuta ◽  
Nobuhisa Matsuhashi ◽  
...  

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