Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, six of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors’ mRNA levels. In conclusion, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research.
We have evaluated the predictive ability of a simple method combing the efficient cluster packing model of metallic glasses proposed by Miracle, chemical mixing enthalpy and the normalized configurational entropy S config =R in rapidly locating the bulk metallic glass (BMG)-forming composition region, in four multicomponent alloy systems. It is shown that the BMG-forming regions are similar in the topologically and chemically equivalent La-Al-Co and Ce-Al-Co alloy systems. However, in the topologically equivalent Zr-Ti-Cu-Ni and Zr-Ti-Al-Cu-Ni alloy systems, the BMG-forming regions are quite different due to the difference in chemical bonding between constituents. BMG formation is most probably a compromise between topological and chemical effects. This method could be a new approach to rapidly locate BMG-forming composition region in multicomponent alloy systems, in which eutectic compositions are difficult to be measured in experiments.
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