Lp(a) is an independent predictor of CVD in men and women with FH. The risk of CVD is higher in those patients with an Lp(a) level >50 mg/dl and carrying a receptor-negative mutation in the LDLR gene compared with other less severe mutations.

The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks.

Background:Hereditary breast cancer comprises 5–10% of all breast cancers. Mutations in two high-risk susceptibility genes, BRCA1 and BRCA2, along with rare intermediate-risk genes and common low-penetrance alleles identified, altogether explain no more than 45% of the high-risk breast cancer families, although the majority of cases are unaccounted for and are designated as BRCAX tumours. Micro RNAs have called great attention for classification of different cancer types and have been implicated in a range of important biological processes and are deregulated in cancer pathogenesis.Methods:Here we have performed an exploratory hypothesis-generating study of miRNA expression profiles in a large series of 66 primary hereditary breast tumours by microarray analysis.Results:Unsupervised clustering analysis of miRNA molecular profiles revealed distinct subgroups of BRCAX tumours, ‘normal-like' BRCAX-A, ‘proliferative' BRCAX-B, ‘BRCA1/2-like' BRCAX-C and ‘undefined' BRCAX-D subgroup. These findings introduce a new insight in the biology of hereditary breast cancer, defining specific BRCAX subgroups, which could help in the search for novel susceptibility pathways in hereditary breast cancer.Conclusion:Our data demonstrate that BRCAX hereditary breast tumours can be sub-classified into four previously unknown homogenous groups characterised by specific miRNA expression signatures and histopathological features.

Hereditary breast cancer constitutes only 5–10% of all breast cancer cases and is characterized by strong family history of breast and/or other associated cancer types. Only ~ 25% of hereditary breast cancer cases carry a mutation in BRCA1 or BRCA2 gene, while mutations in other rare high and moderate-risk genes and common low penetrance variants may account for additional 20% of the cases. Thus the majority of cases are still unaccounted for and designated as BRCAX tumors. MicroRNAs are small non-coding RNAs that play important roles as regulators of gene expression and are deregulated in cancer. To characterize hereditary breast tumors based on their miRNA expression profiles we performed global microarray miRNA expression profiling on a retrospective cohort of 80 FFPE breast tissues, including 66 hereditary breast tumors (13 BRCA1, 10 BRCA2 and 43 BRCAX), 10 sporadic breast carcinomas and 4 normal breast tissues, using Exiqon miRCURY LNA™ microRNA Array v.11.0. Here we describe in detail the miRNA microarray expression data and tumor samples used for the study of BRCAX tumor heterogeneity (Tanic et al., 2013) and biomarkers associated with positive BRCA1/2 mutation status (Tanic et al., 2014). Additionally, we provide the R code for data preprocessing and quality control.

There is a huge quantity of information generated in Life Sciences, and it is dispersed in many databases and repositories. Despite the broad availability of the information, there is a great demand for methods that are able to look for, gather and display distributed data in a standardized and friendly way. CARGO (Cancer And Related Genes Online) is a configurable biological web portal designed as a tool to facilitate, integrate and visualize results from Internet resources, independently of their native format or access method. Through the use of small agents, called widgets, supported by a Rich Internet Application (RIA) paradigm based on AJAX, CARGO provides pieces of minimal, relevant and descriptive biological information. The tool is designed to be used by experimental biologists with no training in bioinformatics. In the current state, the system presents a list of human cancer genes. Available at http://cargo.bioinfo.cnio.es