Briefings in Functional Genomics

Stratification of ovarian cancer patients from the prospect of drug target-related transcription factor protein activity: the prognostic and genomic landscape analyses

Abstract 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.

Integrating multi-omics data to identify dysregulated modules in endometrial cancer

AbstractCancer is generally caused by genetic mutations, and differentially expressed genes are closely associated with genetic mutations. Therefore, mutated genes and differentially expressed genes can be used to study the dysregulated modules in cancer. However, it has become a big challenge in cancer research how to accurately and effectively detect dysregulated modules that promote cancer in massive data. In this study, we propose a network-based method for identifying dysregulated modules (Netkmeans). Firstly, the study constructs an undirected-weighted gene network based on the characteristics of high mutual exclusivity, high coverage and complex network topology among genes widely existed in the genome. Secondly, the study constructs a comprehensive evaluation function to select the number of clusters scientifically and effectively. Finally, the K-means clustering method is applied to detect the dysregulated modules. Compared with the results detected by IBA and CCEN methods, the results of Netkmeans proposed in this study have higher statistical significance and biological relevance. Besides, compared with the dysregulated modules detected by MCODE, CFinder and ClusterONE, the results of Netkmeans have higher accuracy, precision and F-measure. The experimental results show that the multiple dysregulated modules detected by Netkmeans are essential in the generation, development and progression of cancer, and thus they play a vital role in the precise diagnosis, treatment and development of new medications for cancer patients.