Identification of new subtypes and potential genetic signatures in triple-negative breast cancer using weighted gene co-expression network analysis.

OBJECTIVE: Triple-negative breast cancer (TNBC) is a heterogeneous disease with aggressive behavior and poor prognosis. Here, we used gene expression profiling to define new subtypes of TNBC, which may improve prevention and treatment through personalized medicine. MATERIALS AND METHODS: Gene expression profiles from the public datasets GSE76250, GSE61724, GSE61723, and GES76275 were subjected to co-expression analysis to identify differentially expressed genes (DEGs) between TNBC and non-TNBC tissues. Consistency clustering was used to define TNBC subtypes, whose correlation with gene modules was analyzed. Enrichment analysis was used to identify module genes' biological functions and pathways. Single-sample gene set enrichment analysis was used to assess immune cell infiltration in the different TNBC subtypes, and the ChAMP package was used to examine methylation sites in TNBC. RESULTS: A total of 4,958 DEGs in TNBC were identified, which showed the same expression differences across all datasets as in the dataset GSE76250 and clustered into 9 co-expression modules. TNBC samples clustered into two subtypes based on nine hub genes from the modules. Class I showed the most significant correlation with module 1, whose genes were related mainly to interleukin-1 response, while class II showed the most significant correlation with module 6, whose genes were related mainly to the transforming growth factor-ß pathway. Class I was significantly enriched in cell cycle and DNA replication, and tumors of this subtype showed lower immune cell infiltration than class II tumors. Tumor infiltration by Th2 cells correlated positively with the expression of MCM10 and negatively with the expression of PREX2. A greater methylation of CIDEC, DLC1, EDNRB, EGR2 and SRPK1 correlated with better prognosis. CONCLUSIONS: Class I TNBC, for which a useful biomarker is MCM10, may be associated with a worse prognosis than class II TNBC, for which PREX2 may serve as a biomarker.

Identification of novel kinase inhibitors by targeting a kinase-related apoptotic protein-protein interaction network in HeLa cells.

OBJECTIVES: Protein kinases orchestrate activation of signalling cascades in response to extra- and intracellular stimuli for regulation of cell proliferation. They are directly involved in a variety of diseases, particularly cancers. Systems biology approaches have become increasingly important in understanding regulatory frameworks in cancer, and thus may facilitate future anti-cancer discoveries. Moreover, it has been suggested and confirmed that high-throughput virtual screening provides a novel, effective way to reveal small molecule protein kinase inhibitors. Accordingly, we aimed to identify kinase targets and novel kinase inhibitors. MATERIALS AND METHODS: A series of bioinformatics methods, such as network construction, molecular docking and microarray analyses were performed. RESULTS: In this study, we computationally constructed the appropriate global human protein-protein interaction network with data from online databases, and then modified it into a kinase-related apoptotic protein-protein interaction network. Subsequently, we identified several kinases as potential drug targets according to their differential expression observed by microarray analyses. Then, we predicted relevant microRNAs, which could target the above-mentioned kinases. Ultimately, we virtually screened a number of small molecule natural products from Traditional Chinese Medicine (TCM)@Taiwan database and identified a number of compounds that are able to target polo-like kinase 1, cyclin-dependent kinase 1 and cyclin-dependent kinase 2 in HeLa cervical carcinoma cells. CONCLUSIONS: Taken together, all these findings might hopefully facilitate discovery of new kinase inhibitors that could be promising candidates for anti-cancer drug development.