期刊
BRIEFINGS IN FUNCTIONAL GENOMICS
卷 22, 期 4, 页码 351-365出版社
OXFORD UNIV PRESS
DOI: 10.1093/bfgp/elad008
关键词
ovarian cancer; transcriptional regulation; protein activity; master regulator analysis
The expression and activity of transcription factors play crucial roles in controlling normal cellular processes, while dysregulated transcription factor activity in cancer leads to abnormal gene expression related to tumorigenesis and development. Targeted therapy can reduce the carcinogenicity of transcription factors. However, most studies on ovarian cancer have focused on individual transcription factors, disregarding the need to evaluate multiple transcription factors simultaneously to assess their effects on drug therapies.
The expression and activity of transcription factors, which directly mediate gene transcription, are strictly regulated to control numerous normal cellular processes. In cancer, transcription factor activity is often dysregulated, resulting in abnormal expression of genes related to tumorigenesis and development. The carcinogenicity of transcription factors can be reduced through targeted therapy. However, most studies on the pathogenic and drug-resistant mechanisms of ovarian cancer have focused on the expression and signaling pathways of individual transcription factors. To improve the prognosis and treatment of patients with ovarian cancer, multiple transcription factors should be evaluated simultaneously to determine the effects of their protein activity on drug therapies. In this study, the transcription factor activity of ovarian cancer samples was inferred from virtual inference of protein activity by enriched regulon algorithm using mRNA expression data. Patients were clustered according to their transcription factor protein activities to investigate the association of transcription factor activities of different subtypes with prognosis and drug sensitivity for filtering subtype-specific drugs. Meanwhile, master regulator analysis was utilized to identify master regulators of differential protein activity between clustering subtypes, thereby identifying transcription factors associated with prognosis and assessing their potential as therapeutic targets. Master regulator risk scores were then constructed for guiding patients' clinical treatment, providing new insights into the treatment of ovarian cancer at the level of transcriptional regulation.
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