Ghasemi Mehdi, Alpsoy Semih, Türk Seyhan, Malkan Ümit Y, Atakan Şükrü, Haznedaroğlu İbrahim C, Güneş Gürsel, Gündüz Mehmet, Yılmaz Burak, Etgül Sezgin, Aydın Seda, Aslan Tuncay, Sayınalp Nilgün, Aksu Salih, Demiroğlu Haluk, Özcebe Osman I, Büyükaşık Yahya, Göker Hakan
Hacettepe University Faculty of Medicine, Department of Internal Medicine, Division of Hematology, Ankara, Turkey, Phone: +90 312 305 15 43, E-mail:
Turk J Haematol. 2016 Dec 1;33(4):286-292. doi: 10.4274/tjh.2015.0145. Epub 2016 Apr 18.
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.
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.
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.
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.
由于即使在新型抗骨髓瘤治疗下疾病仍会难治性复发,多发性骨髓瘤(MM)目前仍无法治愈。计算机模拟研究对于在针对MM慢性病程的临床病理斗争中做出关键决策很有效。本计算机模拟研究的目的是识别其表达谱与硼替佐米细胞毒性实验所产生的表达谱相匹配的个体基因。
我们使用计算机模拟文献挖掘方法,通过创建从相关信息中得出的元数据汇总集来识别潜在生物标志物。包含来自789个癌细胞系(包括8个骨髓瘤细胞系)表达数据的E-MTAB-783数据集,以及来自ArrayExpress获得的威康信托桑格研究所数据库的药物筛选数据,使用GeneSpring v.12.5进行“稳健多阵列分析”归一化。药物毒性数据来自癌症药物敏感性基因组学项目。为了识别其表达谱与硼替佐米细胞毒性实验所产生的表达谱相匹配的个体基因,我们使用了基于线性回归的方法,在该方法中我们搜索基因表达值与IC50数据之间的统计学显著相关性。在8个细胞系中识别出基因的交集并用于进一步分析。
我们的线性回归模型识别出73个基因,并且发现一些基因的表达水平与硼替佐米IC50值密切相关。当将所有73个基因用于层次聚类分析时,可以识别出代表相对敏感和耐药细胞的两个主要细胞簇。还研究了所有重要基因的通路和分子功能分析,以及涉及通路的基因。
我们目前计算机模拟研究的结果不仅对于理解MM的基因组学很重要而且对于更好地安排靶向抗骨髓瘤疗法(如硼替佐米)也很重要。