No / Triple negative breast cancers (TNBC) are clinically heterogeneous, an aggressive subtype with poor diagnosis and strong resistance to therapy. There is a need to identify novel robust biomarkers with high specificity for early detection and therapeutic intervention. Microarray gene expression-based studies have offered significant advances in molecular classification and identification of diagnostic/prognostic signatures, however sample scarcity and cohort heterogeneity remains area of concern. In this study, we performed integrated analysis on independent microarray big data studies and identified a robust 880-gene signature for TNBC diagnosis. We further identified 16-gene (OGN, ESR1, GPC3, LHFP, AGR3, LPAR1, LRRC17, TCEAL1, CIRBP, NTN4, TUBA1C, TMSB10, RPL27, RPS3A, RPS18, and NOSTRIN) that are associated to TNBC tissues. The 880-gene signature achieved excellent classification accuracy ratio on each independent expression data sets with overall average of 99.06%, is an indication of its diagnostic power. Gene ontology enrichment analysis of 880-gene signature shows that cell-cycle pathways/processes are important clinical targets for triple negative breast cancer. Further verification of 880-gene signature could provide additive knowledge for better understanding and future direction of triple negative breast cancer research.

The rapid developments in the field of DNA computing reflects two substantial questions: 1. Which models for DNA based computation are really universal? 2. Which model fulfills the requirements to a universal lab-practicable programmable DNA computer that is based on one of these models? This paper introduces the functional model DNA-HASKELL focussing its lab-practicability. This aim could be reached by specifying the DNA based operations in accordiance to an analysis of molecular biological processes. The specification is determined by an abstraction level that includes nucleotides and strand end labels like 5'-phosphate. Our model is able to describe DNA algorithms for any NP-complete problem - here exemplified by the knapsacik problem - as well as it is able to simulate some established mathematical models for computation. We point out the splicing operation as an example. The computational completeness of DNA-HASKELL can be supposed. This paper is based on discussions about the potenzial and limits of DNA computing, in particular the practicability of a universal DNA computer.