Cancer is one of the major killing diseases worldwide. Among them, breast cancer (BC) has quickly become the most commonly diagnosed malignancy of women in Taiwan during the past decade.1
Such a global disease has its own heterogeneity clinically and molecularly. Up to now, ER(−) breast cancer population still needs as many efficient therapies as ER(+) one has. The poor prognostic features for both triple negatives (TN) and ERBB2+ have been considered as the top two killing subtypes in breast cancer.2
A recent review on TN tumors3
pointed out that TN is a heterogeneous group of multiple molecular subtypes of breast cancer.
To resolve the complexity of disease like cancer using systems approaches, which have integrated transcriptomic data into molecular network, they show promise in their ability to classify tumor subtypes, predict clinical progression, and inform treatment options.4,5
We proposed to search for the transcription factors critical to a subset of ER(−) breast tumor development with the aid from the new statistical approach6
on analyzing genome-wide gene expression data of 91 infiltrating ductal carcinoma (IDCs).
Many transcription factors have been predicted to be determinants of clinical indices in 91 IDCs. Their clinical niches are under investigation. In this study, we are interested in unraveling the role(s) for the most recognized signal transduction pathways in both mammary gland and breast cancer development involving the STAT3
which is a transcription factor (TF). The Stat family of transcription factors is known to have diverse roles in mammary gland development.7
is widely overexpressed in breast cancers.11
It has been classified as a protooncogene8
and suggested as a therapeutic target of cancers in model systems.12
Up to now, roles of STAT3
in clinical breast cancer population study remain either controversial or not completely understood. For instance, the prognostic value of STAT3
in human breast cancer remains controversial and associations range from favorable to unfavorable.13
We revisit this research topic because we noticed an elevated expression of STAT3
in triple negatives as compared to that in ERBB2+ in the cohort (77A). This could be subtype enriched transcriptional activities in causing unique pathological phenotypes. We would like to use this established method of ours—CIDUGPCC to unravel the potential network activities of STAT3
in a subtype enriched manner. Network medicine may be desirable in future medicine.14,15
Here, we proposed the diagnostic and/or prognostic roles of STAT3 transcriptional regulatory network to be predicted at a global transcriptome scale in a clinical breast cancer population. Furthermore, the annotated gene activities of STAT3 subnetworks will be supported by inferred biochemical pathways, patients diagnostic result(s), clinical outcome and published research evidence by others.