scholarly journals Prognostic biomarker identification and tumor classification in breast cancer patients by methylation and transcriptome analysis

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
Xiongdong Zhong ◽  
Guoying Zhong
2020 ◽  
Vol 46 ◽  
pp. 151507 ◽  
Author(s):  
Behnaz Motamedi ◽  
Hossain-Ali Rafiee-Pour ◽  
Mohammad-Reza Khosravi ◽  
Amirhosein Kefayat ◽  
Azar Baradaran ◽  
...  

2019 ◽  
Vol 8 (11) ◽  
pp. e1655964 ◽  
Author(s):  
Maria Esperanza Rodriguez-Ruiz ◽  
Aitziber Buqué ◽  
Michal Hensler ◽  
Jonathan Chen ◽  
Norma Bloy ◽  
...  

2017 ◽  
Vol 19 (4) ◽  
pp. 411-418 ◽  
Author(s):  
Yusufu Maimaiti ◽  
Lingling Dong ◽  
Aikebaier Aili ◽  
Maimaitiaili Maimaitiaili ◽  
Tao Huang ◽  
...  

2012 ◽  
Vol 7 ◽  
pp. BMI.S9387 ◽  
Author(s):  
Jason B. Nikas ◽  
Walter C. Low ◽  
Paul A. Burgio

Pertaining to the female population in the USA, breast cancer is the leading cancer in terms of annual incidence rate and, in terms of mortality, the second most lethal cancer. There are currently no biomarkers available that can predict which breast cancer patients will respond to chemotherapy with both sensitivity and specificity > 80%, as mandated by the latest FDA requirements. In this study, we have developed a prognostic biomarker model (complex mathematical function) that–-based on global gene expression analysis of tumor tissue collected during biopsy and prior to the commencement of chemotherapy–-can identify with a high accuracy those patients with breast cancer (clinical stages I–III) who will respond to the paclitaxel-fluorouracil-doxorubicin-cyclophosphamide chemotherapy and will experience pathological complete response (Responders), as well as those breast cancer patients (clinical stages I–III) who will not do so (Non-Responders). Most importantly, both the application and the accuracy of our breast cancer prognostic biomarker model are independent of the status of the hormone receptors ER, PR, and HER2, as well as of the ethnicity and age of the subjects. We developed our prognostic biomarker model with 50 subjects [10 responders (R) and 40 non-responders (NR)], and we validated it with 43 unknown (new and different) subjects [10 responders (R) and 33 non-responders (NR)]. All 93 subjects were recruited at five different clinical centers around the world. The overall sensitivity and specificity of our prognostic biomarker model were 90.0% and 91.8%, respectively. The nine most significant genes identified, which comprise the input variables to the mathematical function, are involved in regulation of transcription; cell proliferation, invasion, and migration; oncogenesis; suppression of immune response; and drug resistance and cancer recurrence.


2021 ◽  
Vol 17 (1) ◽  
pp. 42-52
Author(s):  
Daniel Rodrigues de Bastos ◽  
Mércia Patrícia Ferreira Conceição ◽  
Ana Paula Picaro Michelli ◽  
Jean Michel Rocha Sampaio Leite ◽  
Rafael André da Silva ◽  
...  

2020 ◽  
Vol 17 (17) ◽  
pp. 2773-2789
Author(s):  
Xudong Zhu ◽  
Yixiao Zhang ◽  
Yang Bai ◽  
Xi Gu ◽  
Guanglei Chen ◽  
...  

2021 ◽  
Author(s):  
Fariba Pishbin ◽  
Nasrin Ziamajidi ◽  
Roghayeh Abbasalipourkabir ◽  
Rezvan Najafi ◽  
Maryam Farhadian

Aim: The study aimed to explore miR-600 and WT1 expression and its potential clinical significance in breast cancer. Materials & methods: The expression of miR-600 and WT1 in tumor and non-tumor adjacent tissues in 45 breast cancer patients as well as serum level of miR-600 in these patients and 45 healthy group were analyzed. Results: The expression level of miR-600 in tumor tissue and serum of patients was significantly lower than non-tumor adjacent tissues and serum of controls, respectively, while WT1 mRNA and protein levels were higher in tumor tissues compared with non-tumor adjacent tissues. The miR-600 expression was correlated with lymph node metastasis and clinical stage. Conclusion: The miR-600 acts as tumor suppressor and a diagnostic and prognostic biomarker in breast cancer patients.


2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ashley J. Schlafstein ◽  
Allison E. Withers ◽  
Soumon Rudra ◽  
Diana Danelia ◽  
Jeffrey M. Switchenko ◽  
...  

Failure to achieve pathologic complete response is associated with poor prognosis in breast cancer patients following neoadjuvant chemotherapy (NACT). However, prognostic biomarkers for clinical outcome are unclear in this patient population. Cyclin-dependent kinase 9 (CDK9) is often dysregulated in breast cancer, and its deficiency results in genomic instability. We reviewed the records of 84 breast cancer patients from Emory University’s Winship Cancer Institute who had undergone surgical resection after NACT and had tissue available for tissue microarray analysis (TMA). Data recorded included disease presentation, treatment, pathologic response, overall survival (OS), locoregional recurrence free survival (LRRFS), distant-failure free survival (DFFS), recurrence-free survival (RFS), and event-free survival (EFS). Immunohistochemistry was performed on patient samples to determine CDK9 expression levels after NACT. Protein expression was linked with clinical data to determine significance. In a Cox proportional hazards model, using a time-dependent covariate to evaluate the risk of death between groups beyond 3 years, high CDK9 expression was significantly associated with an increase in OS (HR: 0.26, 95% CI: 0.07-0.98, p=0.046). However, Kaplan-Meier curves for OS, LRRFS, DFFS, RFS, and EFS did not reach statistical significance. The results of this study indicate that CDK9 may have a potential role as a prognostic biomarker in patients with breast cancer following NACT. However, further validation studies with increased sample sizes are needed to help elucidate the prognostic role for CDK9 in the management of these patients.


2020 ◽  
Vol 181 (3) ◽  
pp. 529-540
Author(s):  
Brian G. Hunt ◽  
Christina A. Wicker ◽  
Jennifer R. Bourn ◽  
Elyse E. Lower ◽  
Vinita Takiar ◽  
...  

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