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Background. Breast cancer (BRCA) is one of the most common cancers and the leading cause of cancer-related death in women. RNA-binding proteins (RBPs) play an important role in the emergence and pathogenesis of tumors. The target RNAs of RBPs are very diverse; in addition to binding to mRNA, RBPs also bind to noncoding RNA. Noncoding RNA can cause secondary structures that can bind to RBPs and regulate multiple processes such as splicing, RNA modification, protein localization, and chromosomes remodeling, which can lead to tumor initiation, progression, and invasion. Methods. (1) BRCA data were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases and were used as training and testing datasets, respectively. (2) The prognostic RBPs-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. (3) Univariate Cox proportional hazard regression was performed to identify the genes with significant prognostic value. (4) Further, we used the LASSO regression to construct a prognostic signature and validated the signature in the TCGA and ICGC cohort. (5) Besides, we also performed prognostic analysis, expression level verification, immune cell correlation analysis, and drug correlation analysis of the genes in the model. Results. Four genes (MRPL13, IGF2BP1, BRCA1, and MAEL) were identified as prognostic gene signatures. The prognostic model has been validated in the TCGA and ICGC cohorts. The risk score calculated with four genes signatures could largely predict overall survival for 1, 3, and 5 years in patients with BRCA. The calibration plot demonstrated outstanding consistency between the prediction and actual observation. The findings of online database verification revealed that these four genes were significantly highly expressed in tumors. Also, we observed their significant correlations with some immune cells and also potential correlations with some drugs. Conclusion. We constructed a 4-RBPs-based prognostic signature to predict the prognosis of BRCA patients, and it has the potential for treating and diagnosing BRCA.

Accurate genotyping is important for genetic testing. Sanger sequencing-based typing is the gold standard for genotyping, but it has been underused, due to its high cost and low throughput. In contrast, short-read sequencing provides inexpensive and high-throughput sequencing, holding great promise for reaching the goal of cost-effective and high-throughput genotyping. However, the short-read length and the paucity of appropriate genotyping methods, pose a major challenge. Here, we present RCHSBTreliable, cost-effective and high-throughput sequence based typing pipelinewhich takes short sequence reads as input, but uses a unique variant calling, haploid sequence assembling algorithm, can accurately genotype with greater effective length per amplicon than even Sanger sequencing reads. The RCHSBT method was tested for the human MHC loci HLA-A, HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1, upon 96 samples using Illumina PE 150 reads. Amplicons as long as 950bp were readily genotyped, achieving 100% typing concordance between RCHSBT-called genotypes and genotypes previously called by Sanger sequence. Genotyping throughput was increased over 10 times, and cost was reduced over five times, for RCHSBT as compared with Sanger sequence genotyping. We thus demonstrate RCHSBT to be a genotyping method comparable to Sanger sequencing-based typing in quality, while being more cost-effective, and higher throughput. (C) 2013 Wiley Periodicals, Inc.