Cancer stem cells are regulated by complex interactions with the components of the tumor microenvironment through networks of Cytokins and growth factors. These interactions are mediated by group of proteins and microRNAs (miRs), which are expressed or repressed. These expression levels are critical for cancer stem cell formation and expansion, enabling the promotion of tumor cell proliferation and migration, as well as for the survival of cancer recurrence and patient survival. Micro array and RNA deep sequencing (RNA-seq) provide tools with ability to generate transcriptome information, deciphering global gene expression patterns and quantifying a large dynamic range of expression levels. In this study 94 breast cancer patients were investigated based on miR and mRNA expression levels in which WDR1, APC and AKAP13 genes were identified as genes that play important role in the survival of patients and these genes differed significantly with respect to survival of patients. We used the Pearson correlation to identify the over-expressed and under-expressed genes. We demonstrated that parametric survival models can be used to model outcomes for breast cancer, and for our dataset the log-normal model demonstrated the best fit compared to other parametric models. Through the use of log-normal model, we examined how each of the identified genes influence the survival of breast cancer patients.
Analysis of survival in breast cancer patients by using different parametric models
