Prognostic features of signal transducer and activator of transcription 3 in an ER(+) breast cancer model system
The aberrantly expressed signal transducer and activator of transcription 3 (STAT3) predicts poor prognosis, primarily in estrogen receptor positive (ER(+)) breast cancers. Activated STAT3 is overexpressed in luminal A subtype cells. The mechanisms contributing to the prognosis and/or subtype relevant features of STAT3 in ER(+) breast cancers are through multiple interacting regulatory pathways, including STAT3-MYC, STAT3-ERα, and STAT3-MYC-ERα interactions, as well as the direct action of activated STAT3. These data predict malignant events, treatment responses and a novel enhancer of tamoxifen resistance. The inferred ...
Source: Cancer Informatics - January 21, 2014 Category: Cancer & Oncology Authors: Li-Yu D. LiuLi-Yun ChangWen-Hung KuoHsiao-Lin HwaYi-Shing LinMeei-Huey JengDon A. RothKing-Jen ChangFon-Jou Hsieh Source Type: research
OmicCircos: A Simple-to-Use R Package for the Circular Visualization of Multidimensional Omics Data
Summary: OmicCircos is an R software package used to generate high-quality circular plots for visualizing genomic variations, including mutation patterns, copy number variations (CNVs), expression patterns, and methylation patterns. Such variations can be displayed as scatterplot, line, or text-label figures. Relationships among genomic features in different chromosome positions can be represented in the forms of polygons or curves. Utilizing the statistical and graphic functions in an R/Bioconductor environment, OmicCircos performs statistical analyses and displays results using cluster, boxplot, histogram, and heatmap fo...
Source: Cancer Informatics - January 16, 2014 Category: Cancer & Oncology Authors: Ying HuChunhua YanChih-Hao HsuQing-Rong ChenKelvin NiuGeorge A. KomatsoulisDaoud Meerzaman Source Type: research
stepwiseCM: An R Package for Stepwise Classification of Cancer Samples Using Multiple Heterogeneous Data Sets
This paper presents the R/Bioconductor package stepwiseCM, which classifies cancer samples using two heterogeneous data sets in an efficient way. The algorithm is able to capture the distinct classification power of two given data types without actually combining them. This package suits for classification problems where two different types of data sets on the same samples are available. One of these data types has measurements on all samples and the other one has measurements on some samples. One is easy to collect and/or relatively cheap (eg, clinical covariates) compared to the latter (high-dimensional data, eg, gene ex...
Source: Cancer Informatics - January 2, 2014 Category: Cancer & Oncology Authors: Askar ObulkasimMark A van de Wiel Source Type: research
