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BMC Bioinformatics. 2018 Nov 30;19(Suppl 15):436. doi: 10.1186/s12859-018-2421-x.

SWIM tool application to expression data of glioblastoma stem-like cell lines, corresponding primary tumors and conventional glioma cell lines.

Fiscon G1,2, Conte F1,2, Paci P3,4.

Author information

1
Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185, Italy.
2
SysBio Centre for Systems Biology, Rome, Italy.
3
Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Via dei Taurini 19, Rome, 00185, Italy. paola.paci@iasi.cnr.it.
4
SysBio Centre for Systems Biology, Rome, Italy. paola.paci@iasi.cnr.it.

Abstract

BACKGROUND:

It is well-known that glioblastoma contains self-renewing, stem-like subpopulation with the ability to sustain tumor growth. These cells - called cancer stem-like cells - share certain phenotypic characteristics with untransformed stem cells and are resistant to many conventional cancer therapies, which might explain the limitations in curing human malignancies. Thus, the identification of genes controlling the differentiation of these stem-like cells is becoming a successful therapeutic strategy, owing to the promise of novel targets for treating malignancies.

METHODS:

Recently, we developed SWIM, a software able to unveil a small pool of genes - called switch genes - critically associated with drastic changes in cell phenotype. Here, we applied SWIM to the expression profiling of glioblastoma stem-like cells and conventional glioma cell lines, in order to identify switch genes related to stem-like phenotype.

RESULTS:

SWIM identifies 171 switch genes that are all down-regulated in glioblastoma stem-like cells. This list encompasses genes like CAV1, COL5A1, COL6A3, FLNB, HMMR, ITGA3, ITGA5, MET, SDC1, THBS1, and VEGFC, involved in "ECM-receptor interaction" and "focal adhesion" pathways. The inhibition of switch genes highly correlates with the activation of genes related to neural development and differentiation, such as the 4-core OLIG2, POU3F2, SALL2, SOX2, whose induction has been shown to be sufficient to reprogram differentiated glioblastoma into stem-like cells. Among switch genes, the transcription factor FOSL1 appears as the brightest star since: it is down-regulated in stem-like cells; it highly negatively correlates with the 4-core genes that are all up-regulated in stem-like cells; the promoter regions of the 4-core genes harbor a consensus binding motif for FOSL1.

CONCLUSIONS:

We suggest that the inhibition of switch genes in stem-like cells could induce the deregulation of cell communication pathways, contributing to neoplastic progression and tumor invasiveness. Conversely, their activation could restore the physiological equilibrium between cell adhesion and migration, hampering the progression of cancer. Moreover, we posit FOSL1 as promising candidate to orchestrate the differentiation of cancer stem-like cells by repressing the 4-core genes' expression, which severely halts cancer growth and might affect the therapeutic outcome. We suggest FOSL1 as novel putative therapeutic and prognostic biomarker, worthy of further investigation.

KEYWORDS:

Bioinformatics; Cancer stem cell; Glioblastoma; Graph theory

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