DocumentCode :
2964181
Title :
Low- and high- agressive genetic breast cancer subtypes and significant survival gene signatures
Author :
Kuznetsov, V.A. ; Motakis, E. ; Ivshina, A.V.
Author_Institution :
Bioinf. Inst., Singapore
fYear :
2008
fDate :
1-8 June 2008
Firstpage :
4151
Lastpage :
4156
Abstract :
We characterize three small gene signatures derived consequently from the original 232-gene breast cancer aggressiveness signature which could improve biological classification and clinical assignment of ~50% of breast cancer patients having histologic grade 2 tumors . Here, we develop a novel approach to identify small gene signatures providing statistically reliable, biological important and clinical significant molecular markers.We consider three small molecular signatures which strongly represent three specific groups of genes related to (i) cell cycle /mitosis, (2) chromosome segregation and micro-tubular formation, (3) cell-cell communication, extracellular/immune signaling, and RNA binding. These results shed light on underlined biological mechanisms of low-aggressive and high-aggressive human breast cancer phenotypes and support our suggestion that re-classification of grade 2 breast tumors onto tumor grade 1-like and tumor grade 3-like subtypes can be related to two genetically and clinically distinct cancer types.
Keywords :
biological organs; cancer; cellular biophysics; genetics; macromolecules; medical computing; molecular biophysics; pattern classification; statistical analysis; tumours; RNA binding; biological classification; cell-cell communication; chromosome segregation; clinical assignment; extracellular-immune signaling; high-aggressive genetic breast cancer subtype; low-aggressive genetic breast cancer subtype; microtubular formation; mitosis; molecular signature; statistical analysis; survival gene signature; tumor; Biological cells; Breast cancer; Breast neoplasms; Cells (biology); Extracellular; Genetics; Humans; Immune system; RNA; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2008.4634396
Filename :
4634396
Link To Document :
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