Altered antisense-to-sense transcript ratios in breast cancer.

Transcriptome profiling studies suggest that a large fraction of the genome is transcribed and many transcripts function independent of their protein coding potential. The relevance of noncoding RNAs (ncRNAs) in normal physiological processes and in tumorigenesis is increasingly recognized. Here, we describe consistent and significant differences in the distribution of sense and antisense transcripts between normal and neoplastic breast tissues. Many of the differentially expressed antisense transcripts likely represent long ncRNAs. A subset of genes that mainly generate antisense transcripts in normal but not cancer cells is involved in essential metabolic processes. These findings suggest fundamental differences in global RNA regulation between normal and cancer cells that might play a role in tumorigenesis.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

INTRODUCTION: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. METHODS: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. RESULTS: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. CONCLUSIONS: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

A model of early human embryonic stem cell differentiation reveals inter- and intracellular changes on transition to squamous epithelium.

The molecular events leading to human embryonic stem cell (hESC) differentiation are the subject of considerable scrutiny. Here, we characterize an in vitro model that permits analysis of the earliest steps in the transition of hESC colonies to squamous epithelium on basic fibroblast growth factor withdrawal. A set of markers (GSC, CK18, Gata4, Eomes, and Sox17) point to a mesendodermal nature of the epithelial cells with subsequent commitment to definitive endoderm (Sox17, Cdx2, nestin, and Islet1). We assayed alterations in the transcriptome in parallel with the distribution of immunohistochemical markers. Our results indicate that the alterations of tight junctions in pluripotent culture precede the beginning of differentiation. We defined this cell population as "specified," as it is committed toward differentiation. The transitional zone between "specified" pluripotent and differentiated cells displays significant up-regulation of keratin-18 (CK18) along with a decrease in the functional activity of gap junctions and the down-regulation of 2 gap junction proteins, connexin 43 (Cx43) and connexin 45 (Cx45), which is coincidental with substantial elevation of intracellular Ca2+ levels. These findings reveal a set of cellular changes that may represent the earliest markers of in vitro hESC transition to an epithelial phenotype, before the induction of gene expression networks that guide hESC differentiation. Moreover, we hypothesize that these events may be common during the primary steps of hESC commitment to functionally varied epithelial tissue derivatives of different embryological origins.

Functional synergies yet distinct modulators affected by genetic alterations in common human cancers.

An important general concern in cancer research is how diverse genetic alterations and regulatory pathways can produce common signaling outcomes. In this study, we report the construction of cancer models that combine unique regulation and common signaling. We compared and functionally analyzed sets of genetic alterations, including somatic sequence mutations and copy number changes, in breast, colon, and pancreatic cancer and glioblastoma that had been determined previously by global exon sequencing and SNP (single nucleotide polymorphism) array analyses in multiple patients. The genes affected by the different types of alterations were mostly unique in each cancer type, affected different pathways, and were connected with different transcription factors, ligands, and receptors. In our model, we show that distinct amplifications, deletions, and sequence alterations in each cancer resulted in common signaling pathways and transcription regulation. In functional clustering, the impact of the type of alteration was more pronounced than the impact of the kind of cancer. Several pathways such as TGF-ß/SMAD signaling and PI3K (phosphoinositide 3-kinase) signaling were defined as synergistic (affected by different alterations in all four cancer types). Despite large differences at the genetic level, all data sets interacted with a common group of 65 "universal cancer genes" (UCG) comprising a concise network focused on proliferation/apoptosis balance and angiogenesis. Using unique nodal regulators ("overconnected" genes), UCGs, and synergistic pathways, the cancer models that we built could combine common signaling with unique regulation. Our findings provide a novel integrated perspective on the complex signaling and regulatory networks that underlie common human cancers.

Integrated network analysis of transcriptomic and proteomic data in psoriasis.

BACKGROUND: Psoriasis is complex inflammatory skin pathology of autoimmune origin. Several cell types are perturbed in this pathology, and underlying signaling events are complex and still poorly understood. RESULTS: In order to gain insight into molecular machinery underlying the disease, we conducted a comprehensive meta-analysis of proteomics and transcriptomics of psoriatic lesions from independent studies. Network-based analysis revealed similarities in regulation at both proteomics and transcriptomics level. We identified a group of transcription factors responsible for overexpression of psoriasis genes and a number of previously unknown signaling pathways that may play a role in this process. We also evaluated functional synergy between transcriptomics and proteomics results. CONCLUSIONS: We developed network-based methodology for integrative analysis of high throughput data sets of different types. Investigation of proteomics and transcriptomics data sets on psoriasis revealed versatility in regulatory machinery underlying pathology and showed complementarities between two levels of cellular organization.