Kinase-substrate Edge Biomarkers Provide a More Accurate Prognostic Prediction in ER-negative Breast Cancer

The estrogen receptor(ER)-negative breast cancer subtype is aggressive with few treatment options available.To identify specific prognostic factors for ER-negative breast cancer,this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance,Epidemiology,and End Results(SEER)and The Cancer Genome Atlas(TCGA)databases,respectively.To identify key differential kinase-substrate node and edge biomarkers between ER-negative and ERpositive breast cancer patients,we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network.Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase-substrate node and edge features for both subtypes of breast cancer.Two promising kinase-substrate edge features,CSNK1A1-NFATC3 and SRC-OCLN,were identified for more accurate prognostic prediction in ERnegative breast cancer patients.

Profile of the breast cancer susceptibility marker rs4245739 identifies a role for miRNAs

Objective:To determine the influence of the single nucleotide polymorphism(SNP)rs4245739 on the binding and expression of micro RNAs and subsequent MDM4 expression and the correlation of these factors with clinical determinants of ER-negative breast cancers.Methods:Find Tar and miRanda were used to detect the manner in which potential micro RNAs are affected by the SNP rs4245739-flanking sequence.RNA sequencing data for ER-negative breast cancer from The Cancer Genome Atlas(TCGA)were used to compare the expression of miR-184,miR-191,miR-193a,miR-378,and MDM4 in different rs4245739 genotypes.Results:Comparison of ER-negative cancer patients with and without the expression of miR-191 as well as profile micro RNAs(miR-184,miR-191,miR-193a and miR-378 altogether)can differentiate the expression of MDM4 among different rs4245739 genotypes.Although simple genotyping alone did not reveal significant clinical relationships,the combination of genotyping and micro RNA profiles was able to significantly differentiate individuals with larger tumor size and lower number of involved lymph nodes(P<0.05)in the risk group(A allele).Conclusions:We present two novel methods to analyze SNPs within 3′UTRs that use:(i)a single miRNA marker expression and(ii)an expression profile of miRNAs predicted to bind to the SNP region.We demonstrate that the application of these two methods,in particular the miRNA profile approach,permits detection of new molecular and clinical features related to the rs4245739 variant in ER-negative breast cancer.