Expression profiles of the individual genes corresponding to the genes generated by cytotoxicity experiments with bortezomib in multiple myeloma Multipl miyelomda bortezomib ile yapılan sitotoksisite çalışmalarında ortaya çıkan genlere karşılık gelen özgün genlerin ekspresyon profili


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Ghasemi M. , Alpsoy S., Türk S., Malkan Ü. Y. , Atakan Ş., Haznedaroğlu İ. C. , ...More

Turkish Journal of Hematology, vol.33, no.4, pp.286-292, 2016 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 4
  • Publication Date: 2016
  • Doi Number: 10.4274/tjh.2015.0145
  • Title of Journal : Turkish Journal of Hematology
  • Page Numbers: pp.286-292
  • Keywords: Myeloma and other plasma cell dyscrasias, Neoplasia, Cytogenetics, Gene therapy, Molecular hematology, COST-EFFECTIVENESS, IN-SILICO, DRUG-SENSITIVITY, CANCER-CELLS, THERAPY, PLASMACYTOMAS, DEXAMETHASONE, LENALIDOMIDE, MALIGNANCIES, SIMULATION

Abstract

© 2016, Turkish Society of Hematology. All rights reserved.Objective: Multiple myeloma (MM) is currently incurable due to refractory disease relapse even under novel anti-myeloma treatment. In silico studies are effective for key decision making during clinicopathological battles against the chronic course of MM. The aim of this present in silico study was to identify individual genes whose expression profiles match that of the one generated by cytotoxicity experiments for bortezomib. Materials and Methods: We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of metadata derived from relevant information. The E-MTAB-783 dataset containing expression data from 789 cancer cell lines including 8 myeloma cell lines with drug screening data from the Wellcome Trust Sanger Institute database obtained from ArrayExpress was “Robust Multi-array analysis” normalized using GeneSpring v.12.5. Drug toxicity data were obtained from the Genomics of Drug Sensitivity in Cancer project. In order to identify individual genes whose expression profiles matched that of the one generated by cytotoxicity experiments for bortezomib, we used a linear regression-based approach, where we searched for statistically significant correlations between gene expression values and IC50 data. The intersections of the genes were identified in 8 cell lines and used for further analysis. Results: Our linear regression model identified 73 genes and some genes expression levels were found to very closely correlated with bortezomib IC50 values. When all 73 genes were used in a hierarchical cluster analysis, two major clusters of cells representing relatively sensitive and resistant cells could be identified. Pathway and molecular function analysis of all the significant genes was also investigated, as well as the genes involved in pathways. Conclusion: The findings of our present in silico study could be important not only for the understanding of the genomics of MM but also for the better arrangement of the targeted anti-myeloma therapies, such as bortezomib.