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Stem Cell Res. 2018 Jan;26:1-7. doi: 10.1016/j.scr.2017.11.009. Epub 2017 Nov 21.

Predictive bioinformatics identifies novel regulators of proliferation in a cancer stem cell model.

Author information

1
Cell Cycle and Cancer Biology Research Program, Oklahoma Medical Research Foundation, 825 NE 13th Street, Oklahoma City, OK 73104, USA.
2
Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, 825 NE 13th Street, Oklahoma City, OK 73104, USA.
3
Cell Cycle and Cancer Biology Research Program, Oklahoma Medical Research Foundation, 825 NE 13th Street, Oklahoma City, OK 73104, USA. Electronic address: GJG@omrf.org.

Abstract

The cancer stem cell model postulates that tumors are hierarchically organized with a minor population, the cancer stem cells, exhibiting unlimited proliferative potential. These cells give rise to the bulk of tumor cells, which retain a limited ability to divide. Without successful targeting of cancer stem cells, tumor reemergence after therapy is likely. However, identifying target pathways essential for cancer stem cell proliferation has been challenging. Here, using a transcriptional network analysis termed GAMMA, we identified 50 genes whose correlation patterns suggested involvement in cancer stem cell division. Using RNAi depletion, we found that 21 of these target genes showed preferential growth inhibition in a breast cancer stem cell model. More detailed initial analysis of 6 of these genes revealed 4 with clear roles in the fidelity of chromosome segregation. This study reveals the strong predictive potential of transcriptional network analysis in increasing the efficiency of successful identification of novel proliferation dependencies for cancer stem cells.

KEYWORDS:

Breast cancer; Cancer stem cells; Cell cycle; Cell division; Chromosome instability; Mitosis

PMID:
29179130
PMCID:
PMC5899939
DOI:
10.1016/j.scr.2017.11.009
[Indexed for MEDLINE]
Free PMC Article

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