Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles

Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. Results: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets

Transcriptional remodeling in primary hippocampal astrocytes from an Alzheimer's disease mouse model

It is well known that alterations in astrocytes occur in Alzheimer's Disease and reactive astrogliosis is one of the hallmarks of the disease. Recently, data has emerged that suggests that alterations in astrocytes may also occur early in the pathogenesis of the disease

Diacylglycerol kinase \u3b1 mediatses 17-\u3b2-estradiol-induced proliferation, motility, and anchorage-independent growth of Hec-1A endometrial cancer cell line through the G protein-coupled estrogen receptor GPR30

Increased levels of endogenous and/or exogenous estrogens are one of the well known risk factors of endometrial cancer. Diacylglycerol kinases (DGKs) are a family of enzymes which phosphorylate diacylglycerol (DAG) to produce phosphatidic acid (PA), thus turning off and on DAG-mediated and PA-mediated signaling pathways, respectively. DGK \u3b1 activity is stimulated by growth factors and oncogenes and is required for chemotactic, proliferative, and angiogenic signaling in vitro. Herein, using either specific siRNAs or the pharmacological inhibitor R59949, we demonstrate that DGK \u3b1 activity is required for 17-\u3b2-estradiol (E2)-induced proliferation, motility, and anchorage-independent growth of Hec-1A endometrial cancer cell line. Impairment of DGK \u3b1 activity also influences basal cell proliferation and growth in soft agar of Hec-1A, while it has no effects on basal cell motility. Moreover, we show that DGK \u3b1 activity induced by E2, as well as its observed effects, are mediated by the G protein-coupled estrogen receptor GPR30 (GPER). These findings suggest that DGK \u3b1 may be a potential target in endometrial cancer therapy. \ua9 2011 Elsevier Inc

Circulating microRNAs combined with PSA for accurate and non-invasive prostate cancer detection.

The dosage of prostate-specific antigen (PSA), an easily evaluable and non-invasive biomarker, has made early detection of prostate cancer (PCa) possible. However, it leads to high percentages of unnecessary biopsies and may miss aggressive tumors in men with PSA levels below 4 ng/ml. Therefore, we propose to combine circulating microRNAs (miRs) with PSA, to improve the diagnostic route for PCa. Plasma miR profiling identified candidate diagnostic miRs in a discovery cohort of 60 tumors and 60 controls (men with benign prostatic hyperplasia or healthy donors). Linear models with an empirical Bayesian approach and multivariate penalized logistic regression were applied to select tumor-associated miRs and/or clinical variables. A classifier was developed and tested on a validation cohort of 68 tumors and 174 controls consecutively collected, where miRs were evaluated by quantitative real-time polymerase chain reaction. A classifier based on miR-103a-3p, let-7a-5p and PSA could detect both overall and clinically significant tumors better than PSA alone, even in 50-69 years aged men with PSA ≤ 4 ng/ml. Even in the validation cohort, the classifier performed better than PSA alone in terms of specificity and positive predictive value, allowing to correctly identify eight out of nine tumors undetected by PSA, including three high-risk and three tumors in 50-69 years old men. Of carriers of non-malignant lesions with PSA in the 4-16 ng/ml interval, who may avoid unnecessary biopsies, 34% were correctly identified. Coupling two circulating miRs with PSA could be a useful strategy to diagnose clinically significant PCa and avoid an important fraction of unnecessary biopsies

Note illustrative della Carta Geologica d’Italia alla scala 1:50.000, foglio 078 “Breno”. Servizio Geologico d’Italia

Tectonic structure and permo-triassic stratigraphy. In the northern area, new data allow to update the lithostratigraphic subdivision of Lower Permian succession, according to the new scheme proposed by CARG Commission. Tre Valli Bresciane Group is divided into a basal coarse clastic unit (Conglomerato Basale), followed by volcanites of Lower Quartz Porphiry, and then in succession by Collio Fm., in turn divided into two informal members, one characterized by fine alluvial deposits (Pian delle Baste mb.), the other by coarser sediments (Val Dorizzo mb.). The succession is closed by partially etheropic Dosso dei Galli conglomerate and Auccia volcanite, rhyolitic ignombrites representing the final activity of Permian subsident Collio Basin