Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma.
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.


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.


CONCLUSIONS
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.
Mehdi
M
Ghasemi
Semih
S
Alpsoy
Seyhan
S
Türk
Ümit Y
ÜY
Malkan
Şükrü
Ş
Atakan
Hacettepe University Faculty of Medicine, Department of Internal Medicine, Division of Hematology, Ankara, Turkey, Phone: +90 312 305 15 43, E-mail: ichaznedaroglu@gmail.com.
İbrahim C
İC
Haznedaroğlu
Gürsel
G
Güneş
Mehmet
M
Gündüz
Burak
B
Yılmaz
Sezgin
S
Etgül
Seda
S
Aydın
Tuncay
T
Aslan
Nilgün
N
Sayınalp
Salih
S
Aksu
Haluk
H
Demiroğlu
Osman I
OI
Özcebe
Yahya
Y
Büyükaşık
Hakan
H
Göker
D000970:Antineoplastic Agents / Q000494:pharmacology*; D015415:Biomarkers; D000069286:Bortezomib / Q000494:pharmacology*; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D030561:Databases, Nucleic Acid; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects*; D006801:Humans; D020128:Inhibitory Concentration 50; D058977:Molecular Sequence Annotation; D009101:Multiple Myeloma / Q000188:drug therapy / Q000235:genetics*; D061988:Proteasome Inhibitors / Q000494:pharmacology*; D059467:Transcriptome
D000970:Antineoplastic Agents; D015415:Biomarkers; D061988:Proteasome Inhibitors; D000069286:Bortezomib