Identifying new targets in leukemogenesis using computational approaches

Abstract

There is a need to identify novel targets in Acute Lymphoblastic Leukemia (ALL), a hematopoietic cancer affecting children, to improve our understanding of disease biology and that can be used for developing new therapeutics. Hence, the aim of our study was to find new genes as targets using in silico studies; for this we retrieved the top 10% overexpressed genes from Oncomine public domain microarray expression database; 530 overexpressed genes were short-listed from Oncomine database. Then, using prioritization tools such as ENDEAVOUR, DIR and TOPPGene online tools, we found fifty-four genes common to the three prioritization tools which formed our candidate leukemogenic genes for this study. As per the protocol we selected thirty training genes from PubMed. The prioritized and training genes were then used to construct STRING functional association network, which was further analyzed using cytoHubba hub analysis tool to investigate new genes which could form drug targets in leukemia. Analysis of the STRING protein network built from these prioritized and training genes led to identification of two hub genes, SMAD2 and CDK9, which were not implicated in leukemogenesis earlier. Filtering out from several hundred genes in the network we also found MEN1, HDAC1 and LCK genes, which re-emphasized the important role of these genes in leukemogenesis. This is the first report on these five additional signature genes in leukemogenesis. We propose these as new targets for developing novel therapeutics and also as biomarkers in leukemogenesis, which could be important for prognosis and diagnosis.

Keywords: Acute Lymphoblastic Leukemia (ALL); Gene prioritization; Microarray analysis; Protein interaction network; Therapeutic targets.

Figure 1

Scheme showing overview of the methodology followed in the study.

Figure 2

Directed acyclic graph showing Gene Ontology of biological processes of the 54 prioritized genes (graph obtained from WebGestalt server).

Figure 3

STRING database generated protein interaction network generated using prioritized and training protein names as query.

Figure 4

STRING Protein–Protein Interaction network, separated into 12 k-Means clusters with clusters containing LCK, MEN1, SMAD2, HDAC1, CDK9 specifically highlighted.