Empirical Phi-Discrepancies and Quasi-Empirical Likelihood: Exponential Bounds

We review some recent extensions of the so-called generalized empirical likelihood method, when the Kullback distance is replaced by some general convex divergence. We propose to use, instead of empirical likelihood, some regularized form or quasi-empirical likelihood method, corresponding to a convex combination of Kullback and χ2 discrepancies. We show that for some adequate choice of the weight in this combination, the corresponding quasi-empirical likelihood is Bartlett-correctable. We also establish some non-asymptotic exponential bounds for the confidence regions obtained by using this method. These bounds are derived via bounds for self-normalized sums in the multivariate case obtained in a previous work by the authors. We also show that this kind of results may be extended to process valued infinite dimensional parameters. In this case some known results about self-normalized processes may be used to control the behavior of generalized empirical likelihood

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories. By performing molecular analyses of 2,579 TCGA gynecological (OV, UCEC, CESC, and UCS) and breast tumors, Berger et al. identify five prognostic subtypes using 16 key molecular features and propose a decision tree based on six clinically assessable features that classifies patients into the subtypes

Differences in aberrant expression and splicing of sarcomeric proteins in the myotonic dystrophies DM1 and DM2

Aberrant transcription and mRNA processing of multiple genes due to RNA-mediated toxic gain-of-function has been suggested to cause the complex phenotype in myotonic dystrophies type 1 and 2 (DM1 and DM2). However, the molecular basis of muscle weakness and wasting and the different pattern of muscle involvement in DM1 and DM2 are not well understood. We have analyzed the mRNA expression of genes encoding muscle-specific proteins and transcription factors by microarray profiling and studied selected genes for abnormal splicing. A subset of the abnormally regulated genes was further analyzed at the protein level. TNNT3 and LDB3 showed abnormal splicing with significant differences in proportions between DM2 and DM1. The differential abnormal splicing patterns for TNNT3 and LDB3 appeared more pronounced in DM2 relative to DM1 and are among the first molecular differences reported between the two diseases. In addition to these specific differences, the majority of the analyzed genes showed an overall increased expression at the mRNA level. In particular, there was a more global abnormality of all different myosin isoforms in both DM1 and DM2 with increased transcript levels and a differential pattern of protein expression. Atrophic fibers in DM2 patients expressed only the fast myosin isoform, while in DM1 patients they co-expressed fast and slow isoforms. However, there was no increase of total myosin protein levels, suggesting that aberrant protein translation and/or turnover may also be involved