Diverse correlation structures in gene expression data and their utility in improving statistical inference

It is well known that correlations in microarray data represent a serious nuisance deteriorating the performance of gene selection procedures. This paper is intended to demonstrate that the correlation structure of microarray data provides a rich source of useful information. We discuss distinct correlation substructures revealed in microarray gene expression data by an appropriate ordering of genes. These substructures include stochastic proportionality of expression signals in a large percentage of all gene pairs, negative correlations hidden in ordered gene triples, and a long sequence of weakly dependent random variables associated with ordered pairs of genes. The reported striking regularities are of general biological interest and they also have far-reaching implications for theory and practice of statistical methods of microarray data analysis. We illustrate the latter point with a method for testing differential expression of nonoverlapping gene pairs. While designed for testing a different null hypothesis, this method provides an order of magnitude more accurate control of type 1 error rate compared to conventional methods of individual gene expression profiling. In addition, this method is robust to the technical noise. Quantitative inference of the correlation structure has the potential to extend the analysis of microarray data far beyond currently practiced methods.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS120 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

Functional Effects of let-7g Expression in Colon Cancer Metastasis.

MicroRNA regulation is crucial for gene expression and cell functions. It has been linked to tumorigenesis, development and metastasis in colorectal cancer (CRC). Recently, the let-7 family has been identified as a tumor suppressor in different types of cancers. However, the function of the let-7 family in CRC metastasis has not been fully investigated. Here, we focused on analyzing the role of let-7g in CRC. The Cancer Genome Atlas (TCGA) genomic datasets of CRC and detailed data from a Taiwanese CRC cohort were applied to study the expression pattern of let-7g. In addition, in vitro as well as in vivo studies have been performed to uncover the effects of let-7g on CRC. We found that the expression of let-7g was significantly lower in CRC specimens. Our results further supported the inhibitory effects of let-7g on CRC cell migration, invasion and extracellular calcium influx through store-operated calcium channels. We report a critical role for let-7g in the pathogenesis of CRC and suggest let-7g as a potential therapeutic target for CRC treatment

Integrative multi-omics analysis identifies a prognostic miRNA signature and a targetable miR-21-3p/TSC2/ mTOR axis in metastatic pheochromocytoma/ paraganglioma

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar y los autores pertenecientes a la UAMPheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors that present variable outcomes. To date, no effective therapies or reliable prognostic markers are available for patients who develop metastatic PPGL (mPPGL). Our aim was to discover robust prognostic markers validated through in vitro models, and define specific therapeutic options according to tumor genomic features. Methods: We analyzed three PPGL miRNome datasets (n=443), validated candidate markers and assessed them in serum samples (n=36) to find a metastatic miRNA signature. An integrative study of miRNome, transcriptome and proteome was performed to find miRNA targets, which were further characterized in vitro. Results: A signature of six miRNAs (miR-21-3p, miR-183-5p, miR-182-5p, miR-96-5p, miR-551b-3p, and miR-202-5p) was associated with metastatic risk and time to progression. A higher expression of five of these miRNAs was also detected in PPGL patients’ liquid biopsies compared with controls. The combined expression of miR-21-3p/miR-183-5p showed the best power to predict metastasis (AUC=0.804, P=4.67·10-18), and was found associated in vitro with pro-metastatic features, such as neuroendocrine-mesenchymal transition phenotype, and increased cell migration rate. A pan-cancer multi-omic integrative study correlated miR-21-3p levels with TSC2 expression, mTOR pathway activation, and a predictive signature for mTOR inhibitor-sensitivity in PPGLs and other cancers. Likewise, we demonstrated in vitro a TSC2 repression and an enhanced rapamycin sensitivity upon miR-21-3p expression. Conclusions: Our findings support the assessment of miR-21-3p/miR-183-5p, in tumors and liquid biopsies, as biomarkers for risk stratification to improve the PPGL patients’ management. We propose miR-21-3p to select mPPGL patients who may benefit from mTOR inhibitorsThis work was supported by the Instituto de Salud Carlos III (ISCIII), Acción Estratégica en Salud, cofounded by FEDER, [grant number PI14/00240, PI17/01796 to M.R., PI15/00783 to A.C], the Paradifference Foundation [no grant number applicable to M.R.], the ANR [ANR-2011-JCJC-00701 MODEOMAPP to AP.G-R], the European Union [FP7/2007-2013 n° 259735, Horizon 2020 n° 633983 to AP.G-R], Epigénétique et Cancer [EPIG201303 METABEPIC to AP.G-R], the the Ligue Nationale contre le Cancer ["Cartes d'Identité des Tumeurs (CIT) program" to AP.G-R], the Institut National du Cancer, the Direction Générale de l’Offre de Soins [PRT-K 2014, COMETE-TACTIC, INCa-DGOS_8663 to AP.G-R], the Deutsche Forschungsgemeinschaft (DFG) [CRC/Transregio 205/1 “The Adrenal: Central Relay in Health and Disease“ to F.B, M.F and G.E], the Rafael del Pino Foundation [Becas de Excelencia Rafael del Pino 2017 to B.C], the Severo Ochoa Excellence Programme [project SEV-2011-0191 to M.C-F], La Caixa Foundation [B004235 to JM.R-R], the Spanish Ministry of Education, Culture and Sport [grant number FPU16/05527 to M.S.], the Site de Recherche Intégré sur le Cancer-SIRIC [CARPEM Project to N.B.] and the AECC Foundation [grant number AIO15152858 to C.M-C

SWIM: A computational tool to unveiling crucial nodes in complex biological networks

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer