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Identification of transcription factors that may reprogram lung adenocarcinoma

Authors :
Yu-Hang Zhang
Tao Huang
Yu-Dong Cai
Chenglin Liu
Source :
Artificial Intelligence in Medicine. 83:52-57
Publication Year :
2017
Publisher :
Elsevier BV, 2017.

Abstract

The method can identify the core transcription factors that regulate lung adenocarcinoma associated genes.Seven core transcription factors are detected, and have been reported to relate to tumorigenesis of lung adenocarcinoma.The discovered functional core set may reverse malignant transformation and reprogram cancer cells. BackgroundLung adenocarcinoma is one of most threatening disease to human health. Although many efforts have been devoted to its genetic study, few researches have been focused on the transcription factors which regulate tumor initiation and progression by affecting multiple downstream gene transcription. It is proved that proper transcription factors may mediate the direct reprogramming of cancer cells, and reverse the tumorigenesis on the epigenetic and transcription levels. MethodsIn this paper, a computational method is proposed to identify the core transcription factors that can regulate as many as possible lung adenocarcinoma associated genes with as little as possible redundancy. A greedy strategy is applied to find the smallest collection of transcription factors that can cover the differentially expressed genes by its downstream targets. The optimal subset which is mostly enriched in the differentially expressed genes is then selected. ResultsSeven core transcription factors (MCM4, VWF, ECT2, RBMS3, LIMCH1, MYBL2 and FBXL7) are detected, and have been reported to contribute to tumorigenesis of lung adenocarcinoma. The identification of the transcription factors provides a new insight into its oncogenic role in tumor initiation and progression, and benefits the discovery of functional core set that may reverse malignant transformation and reprogram cancer cells.

Details

ISSN :
09333657
Volume :
83
Database :
OpenAIRE
Journal :
Artificial Intelligence in Medicine
Accession number :
edsair.doi.dedup.....f288634e1c2b2b195ce9be882cabe542