ArticleSaudi journal of biological sciences2015
Identifying new targets in leukemogenesis using computational approaches.
Article in Saudi journal of biological sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 15 citations in OpenAlex.
- Copy number variant analysis for syndromic congenital heart disease in the Chinese population.Human genomics · 2022Article
- Methylation-Driven Gene PLAU as a Potential Prognostic Marker for Differential Thyroid Carcinoma.Frontiers in cell and developmental biology · 2022Article
- A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside.Translational lung cancer research · 2021Article
- CDK9 Inhibitor Induces the Apoptosis of B-Cell Acute Lymphocytic Leukemia by Inhibiting c-Myc-Mediated Glycolytic Metabolism.Frontiers in cell and developmental biology · 2021Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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