Global gene expression analysis reveals reduced abundance of putative microRNA targets in human prostate tumours

<p>Abstract</p> <p>Background</p> <p>Recently, microRNAs (miRNAs) have taken centre stage in the field of human molecular oncology. Several studies have shown that miRNA profiling analyses offer new possibilities in cancer classification, diagnosis and prognosis. However, the function of miRNAs that are dysregulated in tumours remains largely a mystery. Global analysis of miRNA-target gene expression has helped illuminate the role of miRNAs in developmental gene expression programs, but such an approach has not been reported in cancer transcriptomics.</p> <p>Results</p> <p>In this study, we globally analysed the expression patterns of miRNA target genes in prostate cancer by using several public microarray datasets. Intriguingly, we found that, in contrast to global mRNA transcript levels, putative miRNA targets showed a reduced abundance in prostate tumours relative to benign prostate tissue. Additionally, the down-regulation of these miRNA targets positively correlated with the number of types of miRNA target-sites in the 3' untranslated regions of these targets. Further investigation revealed that the globally low expression was mainly driven by the targets of 36 specific miRNAs that were reported to be up-regulated in prostate cancer by a miRNA expression profiling study. We also found that the transcript levels of miRNA targets were lower in androgen-independent prostate cancer than in androgen-dependent prostate cancer. Moreover, when the global analysis was extended to four other cancers, significant differences in transcript levels between miRNA targets and total mRNA backgrounds were found.</p> <p>Conclusion</p> <p>Global gene expression analysis, along with further investigation, suggests that miRNA targets have a significantly reduced transcript abundance in prostate cancer, when compared with the combined pool of all mRNAs. The abnormal expression pattern of miRNA targets in human cancer could be a common feature of the human cancer transcriptome. Our study may help to shed new light on the functional roles of miRNAs in cancer transcriptomics.</p

Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy.

Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy. Two tumors underwent clonal replacement with treatment, and mathematical modeling indicates these two tumors had resistant subclones prior to treatment and rates of resistance-related genomic changes that were substantially larger than previous estimates. Our results provide a needed framework to incorporate primary tumor heterogeneity in investigating the evolution of resistance

An embryonic stem cell–like gene expression signature in poorly differentiated aggressive human tumors

Cancer cells possess traits reminiscent of those ascribed to normal stem cells. It is unclear, however, whether these phenotypic similarities reflect the activity of common molecular pathways. Here, we analyze the enrichment patterns of gene sets associated with embryonic stem (ES) cell identity in the expression profiles of various human tumor types. We find that histologically poorly differentiated tumors show preferential overexpression of genes normally enriched in ES cells, combined with preferential repression of Polycomb-regulated genes. Moreover, activation targets of Nanog, Oct4, Sox2 and c-Myc are more frequently overexpressed in poorly differentiated tumors than in well-differentiated tumors. In breast cancers, this ES-like signature is associated with high-grade estrogen receptor (ER)-negative tumors, often of the basal-like subtype, and with poor clinical outcome. The ES signature is also present in poorly differentiated glioblastomas and bladder carcinomas. We identify a subset of ES cell-associated transcription regulators that are highly expressed in poorly differentiated tumors. Our results reveal a previously unknown link between genes associated with ES cell identity and the histopathological traits of tumors and support the possibility that these genes contribute to stem cell–like phenotypes shown by many tumors

Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data

Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline) (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8

Elements and evolutionary determinants of genomic divergence between paired primary and metastatic tumors.

Can metastatic-primary (M-P) genomic divergence measured from next generation sequencing reveal the natural history of metastatic dissemination? This remains an open question of utmost importance in facilitating a deeper understanding of metastatic progression, and thereby, improving its prevention. Here, we utilize mathematical and computational modeling to tackle this question as well as to provide a framework that illuminates the fundamental elements and evolutionary determinants of M-P divergence. Our framework facilitates the integration of sequencing detectability of somatic variants, and hence, paves the way towards bridging the measurable between-tumor heterogeneity with analytical modeling and interpretability. We show that the number of somatic variants of the metastatic seeding cell that are experimentally undetectable in the primary tumor, can be characterized as the path of the phylogenetic tree from the last appearing variant of the seeding cell back to the most recent detectable variant. We find that the expected length of this path is principally determined by the decay in detectability of the variants along the seeding cell's lineage; and thus, exhibits a significant dependence on the underlying tumor growth dynamics. A striking implication of this fact, is that dissemination from an advanced detectable subclone of the primary tumor can lead to an abrupt drop in the expected measurable M-P divergence, thereby breaking the previously assumed monotonic relation between seeding time and M-P divergence. This is emphatically verified by our single cell-based spatial tumor growth simulation, where we find that M-P divergence exhibits a non-monotonic relationship with seeding time when the primary tumor grows under branched and linear evolution. On the other hand, a monotonic relationship holds when we condition on the dynamics of progressive diversification, or by restricting the seeding cells to always originate from undetectable subclones. Our results highlight the fact that a precise understanding of tumor growth dynamics is the sine qua non for exploiting M-P divergence to reconstruct the chronology of metastatic dissemination. The quantitative models presented here enable further careful evaluation of M-P divergence in association with crucial evolutionary and sequencing parameters

Role of Co in the Electrocatalytic Activity of Monolayer Ternary NiFeCo-Double Hydroxide Nanosheets for Oxygen Evolution Reaction

Monolayer nanosheets have gained significant attention as functional materials and also in photo/electrocatalysis due to their unique physical/chemical properties, abundance of highly exposed coordination sites, edges, and corner sites, motivating the pursuit of highly active monolayer nanosheets. NiFe-based layered double hydroxide (NiFe-LDH) nanosheets have been regarded as the most efficient electrocatalysis for oxygen evolution. However, the limited catalytic active site and the stacking layer limited the performance. Therefore, by introducing highly electroactive Co ions into monolayer NiFe-LDH, the obtained ternary NiFeCo-LDH monolayer structure possessed an increased concentration of defect (oxygen and metal vacancies), providing enough unsaturated coordination sites, benefitting the electrocatalytic water oxidation, as also explained by the density functional theory (DFT). This work reported an efficient strategy for the synthesis of ternary monolayer LDH in the application of energy conversion and storage

High-Adhesive Flexible Electrodes and Their Manufacture: A Review

All human activity is associated with the generation of electrical signals. These signals are collectively referred to as electrical physiology (EP) signals (e.g., electrocardiogram, electroencephalogram, electromyography, electrooculography, etc.), which can be recorded by electrodes. EP electrodes are not only widely used in the study of primary diseases and clinical practice, but also have potential applications in wearable electronics, human&ndash;computer interface, and intelligent robots. Various technologies are required to achieve such goals. Among these technologies, adhesion and stretchable electrode technology is a key component for rapid development of high-performance sensors. In last decade, remarkable efforts have been made in the development of flexible and high-adhesive EP recording systems and preparation technologies. Regarding these advancements, this review outlines the design strategies and related materials for flexible and adhesive EP electrodes, and briefly summarizes their related manufacturing techniques

Research Progress of Biomimetic Memristor Flexible Synapse

With the development of the Internet of things, artificial intelligence, and wearable devices, massive amounts of data are generated and need to be processed. High standards are required to store and analyze this information. In the face of the explosive growth of information, the memory used in data storage and processing faces great challenges. Among many types of memories, memristors have received extensive attentions due to their low energy consumption, strong tolerance, simple structure, and strong miniaturization. However, they still face many problems, especially in the application of artificial bionic synapses, which call for higher requirements in the mechanical properties of the device. The progress of integrated circuit and micro-processing manufacturing technology has greatly promoted development of the flexible memristor. The use of a flexible memristor to simulate nerve synapses will provide new methods for neural network computing and bionic sensing systems. In this paper, the materials and structure of the flexible memristor are summarized and discussed, and the latest configuration and new materials are described. In addition, this paper will focus on its application in artificial bionic synapses and discuss the challenges and development direction of flexible memristors from this perspective

Modeling Read Counts for CNV Detection in Exome Sequencing Data

Varying depth of high-throughput sequencing reads along a chromosome makes it possible to observe copy number variants (CNVs) in a sample relative to a reference. In exome and other targeted sequencing projects, technical factors increase variation in read depth while reducing the number of observed locations, adding difficulty to the problem of identifying CNVs. We present a hidden Markov model for detecting CNVs from raw read count data, using background read depth from a control set as well as other positional covariates such as GC-content. The model, exomeCopy, is applied to a large chromosome X exome sequencing project identifying a list of large unique CNVs. CNVs predicted by the model and experimentally validated are then recovered using a cross-platform control set from publicly available exome sequencing data. Simulations show high sensitivity for detecting heterozygous and homozygous CNVs, outperforming normalization and state-of-the-art segmentation methods.