Abstract
Exploring the intrinsic differences among breast cancer subtypes is of crucial importance for precise diagnosis and therapeutic decision-making in diseases of high heterogeneity. The subtypes defined with several layers of information are related but not consistent, especially using immunohistochemistry markers and gene expression profiling. Here, we explored the intrinsic differences among the subtypes defined by the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 based on the decision tree. We identified 30 mRNAs and 7 miRNAs differentially expressed along the tree’s branches. The final signature panel contained 30 mRNAs, whose performance was validated using two public datasets based on 3 well-known classifiers. The network and pathway analysis were explored for feature genes, from which key molecules including FOXQ1 and SFRP1 were revealed to be densely connected with other molecules and participate in the validated metabolic pathways. Our study uncovered the differences among the four IHC-defined breast tumor subtypes at the mRNA and miRNA levels, presented a novel signature for breast tumor subtyping, and identified several key molecules potentially driving the heterogeneity of such tumors. The results help us further understand breast tumor heterogeneity, which could be availed in clinics.
Highlights
Exploring the intrinsic differences among breast cancer subtypes is of crucial importance for precise diagnosis and therapeutic decision-making in diseases of high heterogeneity
In HEBCS, the classifier using gene set[1] had a prediction accuracy of 0.8736 and 0.9066 in subtypes stratified by estrogen receptor (ER) status; it was 0.8804 and 0.8804, respectively, using gene set[2], and 0.7692 and 0.8804, respectively, using gene set[3]
We show that its expression could distinguish ER−tumors stratified by human epidermal growth factor receptor 2 (HER2) status, and have CXCL14 sharing the same expression pattern with hsa-miR-365, which together suggests the differential regulation of chemokines in breast cancer subtypes
Summary
Exploring the intrinsic differences among breast cancer subtypes is of crucial importance for precise diagnosis and therapeutic decision-making in diseases of high heterogeneity. We explored the intrinsic differences among the subtypes defined by the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 based on the decision tree. MicroRNAs, a category of small non-coding RNA molecules regulating cell function both at the transcriptional and posttranscriptional levels, complement the prognostic marker discovery using, traditionally, gene expression data[16,17] In this domain, a number of miRNAs, such as miR-7, miR-128a, miR-21017, were found differentially expressed among breast cancer subgroups. Though the “intrinsic” genes can capture the differences among the defined subtypes, they could not tell the pair-wise-subtype differences, which is essential when applied for clinical use With this aim, our study reveals the significant differences between pair-wise subgroups defined by the major IHC markers (ER, PR and HER2), integrating mRNA and miRNA expression at the transcriptional level. At the transcriptional level, breast cancer subgroups could be identified hierarchically in a pair-wise fashion based on the decision tree, indicating the hierarchical differentiation pattern of breast tumors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
