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10 results on '"Saraygord-Afshari, Neda"'

9. The Application of Bi-clustering and Bayesian Network for Gene Sets Network Construction in Breast Cancer Microarray Data.

Author

Sohrabi, Ahmad, Saraygord-Afshari, Neda, and Roudbari, Masoud

Subjects
*INFERENTIAL statistics, *MICROARRAY technology, *CANCER patients, *GENE expression profiling, *DESCRIPTIVE statistics, *STATISTICAL models, *CLUSTER analysis (Statistics), *BREAST tumors, *ALGORITHMS
Abstract

Background: Breast cancer is one of the most prevalent types of cancer in Iranian women and the second cause of death in women worldwide. Gene mutations are the key determinants of the disease; therefore, the genetic study of this disease is of paramount importance. One of the genetic evaluation methods of this disease is microarray technology, which allows the examination of the simultaneous expression of thousands of genes. Clustering is the method for analyzing high-dimension data, which we used in the present research for collecting similar genes in separated clusters. Method: A descriptive and inferential statistical analysis was carried out to evaluate unsupervised learning models of gene expression analysis and five bi-clustering methods (including PLAID (PL), Fabia, Bimax, Cheng & Church (CC), and Xmotif) were compared. For this purpose, we obtained the microarray gene expression data for lapatinib-resistant breast cancer cell lines from previously published research. The enrichment efficacy of the clusters was evaluated with gene ontology, and the results of these five models were compared with the Jaccard index, variance stability, least-square error, and goodness of fit indices. Furthermore, the results of the best model were assessed for building a genes sets network with Bayesian networks. Results: After preprocessing, clustering was performed on the data with the dimension (4710 x 18) of the genes. Four models, except for CC, successfully found bi-clusters in the data set. The data evaluation revealed that the results of the models were almost the same, but the PL model performed better than the others, finding 11 bi-clusters; this model was used to build the network of gene sets. Conclusion: According to the results, the PL method was suitable for clustering the data. Accordingly, it could be recommended for data analysis. In addition, the gene sets network formed on gene expression data was incompetent. [ABSTRACT FROM AUTHOR]

Published
2022
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10. Newly Developed Targeted Therapies Against the Androgen Receptor in Triple-Negative Breast Cancer: A Review

Author

Choupani, Edris, Mahmoudi Gomari, Mohammad, Zanganeh, Saeed, Nasseri, Sherko, Haji-allahverdipoor, Kaveh, Rostami, Neda, Hernandez, Yaeren, Najafi, Safa, Saraygord-Afshari, Neda, and Hosseini, Arshad

Abstract

Among different types of breast cancers (BC), triple-negative BC (TNBC) amounts to 15% to 20% of breast malignancies. Three principal characteristics of TNBC cells are (i) extreme aggressiveness, (ii) absence of hormones, and (iii) growth factor receptors. Due to the lack or poor expression of the estrogen receptor, human epidermal growth factor receptor 2, and progesterone receptor, TNBC is resistant to hormones and endocrine therapies. Consequently, chemotherapy is currently used as the primary approach against TNBC. Expression of androgen receptor (AR) in carcinoma cells has been observed in a subset of patients with TNBC; therefore, inhibiting androgen signaling pathways holds promise for TNBC targeting. The new AR inhibitors have opened up new therapy possibilities for BC patients carrying AR-positive TNBC cells. Our group provides a comprehensive review of the structure and function of the AR and clinical evidence for targeting the cell’s nuclear receptor in TNBC. We updated AR agonists, inhibitors, and antagonists. We also presented a new era of genetic manipulating CRISPR/Cas9 and nanotechnology as state-of-the-art approaches against AR to promote the efficiency of targeted therapy in TNBC.Significance StatementThe lack of effective treatment for triple-negative breast cancer is a health challenge. The main disadvantages of existing treatments are their side effects, due to their nonspecific targeting. Molecular targeting of cellular receptors, such as androgen receptors, increased expression in malignant tissues, significantly improving the survival rate of breast cancer patients.

Published
2023
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