Abstract: In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.

Abstract: High-throughput sequencing of PCR-amplified taxonomic markers (like the 16S rRNA gene) has enabled a new level of analysis of complex bacterial communities known as microbiomes. Many tools exist to quantify and compare abundance levels or OTU composition of communities in different conditions. The sequencing reads have to be denoised and assigned to the closest taxa from a reference database. Common approaches use a notion of 97% similarity and normalize the data by subsampling to equalize library sizes. In this paper, we show that statistical models allow more accurate abundance estimates. By providing a complete workflow in R, we enable the user to do sophisticated downstream statistical analyses, whether parametric or nonparametric. We provide examples of using the R packages dada2, phyloseq, DESeq2, ggplot2 and vegan to filter, visualize and test microbiome data. We also provide examples of supervised analyses using random forests and nonparametric testing using community networks and the ggnetwork package.

Abstract: Plants in their natural habitats adapt to drought stress in the environment through a variety of mechanisms, ranging from transient responses to low soil moisture to major survival mechanisms of escape by early flowering in absence of seasonal rainfall. However, crop plants selected by humans to yield products such as grain, vegetable, or fruit in favorable environments with high inputs of water and fertilizer are expected to yield an economic product in response to inputs. Crop plants selected for their economic yield need to survive drought stress through mechanisms that maintain crop yield. Studies on model plants for their survival under stress do not, therefore, always translate to yield of crop plants under stress, and different aspects of drought stress response need to be emphasized. The crop plant model rice ( Oryza sativa) is used here as an example to highlight mechanisms and genes for adaptation of crop plants to drought stress.

Abstract: Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available data sets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

Abstract: The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.

Abstract: Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet .

Abstract: Ongoing debates surrounding Open Access to the scholarly literature are multifaceted and complicated by disparate and often polarised viewpoints from engaged stakeholders. At the current stage, Open Access has become such a global issue that it is critical for all involved in scholarly publishing, including policymakers, publishers, research funders, governments, learned societies, librarians, and academic communities, to be well-informed on the history, benefits, and pitfalls of Open Access. In spite of this, there is a general lack of consensus regarding the potential pros and cons of Open Access at multiple levels. This review aims to be a resource for current knowledge on the impacts of Open Access by synthesizing important research in three major areas: academic, economic and societal. While there is clearly much scope for additional research, several key trends are identified, including a broad citation advantage for researchers who publish openly, as well as additional benefits to the non-academic dissemination of their work. The economic impact of Open Access is less well-understood, although it is clear that access to the research literature is key for innovative enterprises, and a range of governmental and non-governmental services. Furthermore, Open Access has the potential to save both publishers and research funders considerable amounts of financial resources, and can provide some economic benefits to traditionally subscription-based journals. The societal impact of Open Access is strong, in particular for advancing citizen science initiatives, and leveling the playing field for researchers in developing countries. Open Access supersedes all potential alternative modes of access to the scholarly literature through enabling unrestricted re-use, and long-term stability independent of financial constraints of traditional publishers that impede knowledge sharing. However, Open Access has the potential to become unsustainable for research communities if high-cost options are allowed to continue to prevail in a widely unregulated scholarly publishing market. Open Access remains only one of the multiple challenges that the scholarly publishing system is currently facing. Yet, it provides one foundation for increasing engagement with researchers regarding ethical standards of publishing and the broader implications of 'Open Research'.

Abstract: Calculating solvent accessible surface areas (SASA) is a run-of-the-mill calculation in structural biology. Although there are many programs available for this calculation, there are no free-standing, open-source tools designed for easy tool-chain integration. FreeSASA is an open source C library for SASA calculations that provides both command-line and Python interfaces in addition to its C API. The library implements both Lee and Richards’ and Shrake and Rupley’s approximations, and is highly configurable to allow the user to control molecular parameters, accuracy and output granularity. It only depends on standard C libraries and should therefore be easy to compile and install on any platform. The library is well-documented, stable and efficient. The command-line interface can easily replace closed source legacy programs, with comparable or better accuracy and speed, and with some added functionality.

Abstract: Prenatal substance use is a critical public health concern that is linked with several harmful maternal and fetal consequences. The most frequently used substance in pregnancy is tobacco, followed by alcohol, cannabis and other illicit substances. Unfortunately, polysubstance use in pregnancy is common, as well as psychiatric comorbidity, environmental stressors, and limited and disrupted parental care, all of which can compound deleterious maternal and fetal outcomes. There are few existing treatments for prenatal substance use and these mainly comprise behavioral and psychosocial interventions. Contingency management has been shown to be the most efficacious of these. The purpose of this review is to examine the recent literature on the prenatal use of tobacco, alcohol, cannabis, stimulants, and opioids, including the effects of these on maternal and fetal health and the current therapeutic options.

Abstract: Networks often contain regions of tightly connected nodes, or clusters, that highlight their shared relationships. An effective way to create a visual summary of a network is to identify clusters and annotate them with an enclosing shape and a summarizing label. Cytoscape provides the ability to annotate a network with shapes and labels, however these annotations must be created manually one at a time, which can be a laborious process. AutoAnnotate is a Cytoscape 3 App that automates the process of identifying clusters and visually annotating them. It greatly reduces the time and effort required to fully annotate clusters in a network, and provides freedom to experiment with different strategies for identifying and labelling clusters. Many customization options are available that enable the user to refine the generated annotations as required. Annotated clusters may be collapsed into single nodes using the Cytoscape groups feature, which helps simplify a network by making its overall structure more visible. AutoAnnotate is applicable to any type of network, including enrichment maps, protein-protein interactions, pathways, or social networks.

Abstract: Chronic low back pain (CLBP) is a chronic pain syndrome in the lower back region, lasting for at least 3 months. CLBP represents the second leading cause of disability worldwide being a major welfare and economic problem. The prevalence of CLBP in adults has increased more than 100% in the last decade and continues to increase dramatically in the aging population, affecting both men and women in all ethnic groups, with a significant impact on functional capacity and occupational activities. It can also be influenced by psychological factors, such as stress, depression and/or anxiety. Given this complexity, the diagnostic evaluation of patients with CLBP can be very challenging and requires complex clinical decision-making. Answering the question “what is the pain generator” among the several structures potentially involved in CLBP is a key factor in the management of these patients, since a mis-diagnosis can generate therapeutical mistakes. Traditionally, the notion that the etiology of 80% to 90% of LBP cases is unknown has been mistaken perpetuated across decades. In most cases, low back pain can be attributed to specific pain generator, with its own characteristics and with different therapeutical opportunity. Here we discuss about radicular pain, facet Joint pain, sacro-iliac pain, pain related to lumbar stenosis, discogenic pain. Our article aims to offer to the clinicians a simple guidance to identify pain generators in a safer and faster way, relying a correct diagnosis and further therapeutical approach.

Abstract: The scientific credibility of findings from clinical trials can be undermined by a range of problems including missing data, endpoint switching, data dredging, and selective publication. Together, these issues have contributed to systematically distorted perceptions regarding the benefits and risks of treatments. While these issues have been well documented and widely discussed within the profession, legislative intervention has seen limited success. Recently, a method was described for using a blockchain to prove the existence of documents describing pre-specified endpoints in clinical trials. Here, we extend the idea by using smart contracts - code, and data, that resides at a specific address in a blockchain, and whose execution is cryptographically validated by the network - to demonstrate how trust in clinical trials can be enforced and data manipulation eliminated. We show that blockchain smart contracts provide a novel technological solution to the data manipulation problem, by acting as trusted administrators and providing an immutable record of trial history.

Abstract: In the 1990s several biocontrol agents on that contained Burkholderia strains were registered by the United States Environmental Protection Agency (EPA). After risk assessment these products were withdrawn from the market and a moratorium was placed on the registration of Burkholderia-containing products, as these strains may pose a risk to human health. However, over the past few years the number of novel Burkholderia species that exhibit plant-beneficial properties and are normally not isolated from infected patients has increased tremendously. In this commentary we wish to summarize recent efforts that aim at discerning pathogenic from beneficial Burkholderia strains.

Abstract: Seven ligands bind to and activate the mammalian epidermal growth factor (EGF) receptor (EGFR/ERBB1/HER1): EGF, transforming growth factor-alpha (TGFA), heparin-binding EGF-like growth factor (HBEGF), betacellulin (BTC), amphiregulin (AREG), epiregulin (EREG), and epigen (EPGN). Of these, EGF, TGFA, HBEGF, and BTC are thought to be high-affinity ligands, whereas AREG, EREG, and EPGN constitute low-affinity ligands. This focused review is meant to highlight recent studies related to actions of the individual EGFR ligands, the interesting biology that has been uncovered, and relevant advances related to ligand interactions with the EGFR.

Abstract: Diabetes has become one of the largest global health-care problems of the 21 st century. According to the Centers for Disease Control and Prevention, the population prevalence of diabetes in the US is approaching 10% and is increasing by 5% each year. Diabetic neuropathy is the most common complication associated with diabetes mellitus. Diabetes causes a broad spectrum of neuropathic complications, including acute and chronic forms affecting each level of the peripheral nerve, from the root to the distal axon. This review will focus on the most common form, distal symmetric diabetic polyneuropathy. There has been an evolution in our understanding of the pathophysiology and the management of diabetic polyneuropathy over the past decade. We highlight these new perspectives and provide updates from the past decade of research.

Abstract: The discovery of the myofibroblast has allowed definition of the cell responsible for wound contraction and for the development of fibrotic changes. This review summarizes the main features of the myofibroblast and the mechanisms of myofibroblast generation. Myofibroblasts originate from a variety of cells according to the organ and the type of lesion. The mechanisms of myofibroblast contraction, which appear clearly different to those of smooth muscle cell contraction, are described. Finally, we summarize the possible strategies in order to reduce myofibroblast activities and thus influence several pathologies, such as hypertrophic scars and organ fibrosis.

Abstract: The mammalian target of rapamycin, mTOR, plays key roles in cell growth and proliferation, acting at the catalytic subunit of two protein kinase complexes: mTOR complexes 1 and 2 (mTORC1/2). mTORC1 signaling is switched on by several oncogenic signaling pathways and is accordingly hyperactive in the majority of cancers. Inhibiting mTORC1 signaling has therefore attracted great attention as an anti-cancer therapy. However, progress in using inhibitors of mTOR signaling as therapeutic agents in oncology has been limited by a number of factors, including the fact that the classic mTOR inhibitor, rapamycin, inhibits only some of the effects of mTOR; the existence of several feedback loops; and the crucial importance of mTOR in normal physiology.

Abstract: Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox, TCGAbiolinks.

Abstract: Metals play important roles in the human body, maintaining cell structure and regulating gene expression, neurotransmission, and antioxidant response, to name a few. However, excessive metal accumulation in the nervous system may be toxic, inducing oxidative stress, disrupting mitochondrial function, and impairing the activity of numerous enzymes. Damage caused by metal accumulation may result in permanent injuries, including severe neurological disorders. Epidemiological and clinical studies have shown a strong correlation between aberrant metal exposure and a number of neurological diseases, including Alzheimer’s disease, amyotrophic lateral sclerosis, autism spectrum disorders, Guillain–Barre disease, Gulf War syndrome, Huntington’s disease, multiple sclerosis, Parkinson’s disease, and Wilson’s disease. Here, we briefly survey the literature relating to the role of metals in neurodegeneration.

Abstract: The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.

Abstract: In reproductive medicine little progress has been achieved regarding the clinical management of patients with a reduced ovarian reserve or poor ovarian response (POR) to stimulation with exogenous gonadotropins -a frustrating experience for clinicians as well as patients. Despite the efforts to optimize the definition of this subgroup of patients, the existing POR criteria unfortunately comprise a heterogeneous population and, importantly, do not offer any recommendations for clinical handling. Recently, the POSEIDON group ( Patient- Oriented Strategies Encompassing Individualize D Oocyte Number) proposed a new stratification of assisted reproductive technology (ART) in patients with a reduced ovarian reserve or unexpected inappropriate ovarian response to exogenous gonadotropins. In brief, four subgroups have been suggested based on quantitative and qualitative parameters, namely, i. Age and the expected aneuploidy rate; ii. Ovarian biomarkers (i.e. antral follicle count [AFC] and anti-Mullerian hormone [AMH]), and iii. Ovarian response - provided a previous stimulation cycle was performed. The new classification introduces a more nuanced picture of the "low prognosis patient" in ART, using clinically relevant criteria to guide the physician to most optimally manage this group of patients. The POSEIDON group also introduced a new measure for successful ART treatment, namely, the ability to retrieve the number of oocytes needed for the specific patient to obtain at least one euploid embryo for transfer. This feature represents a pragmatic endpoint to clinicians and enables the development of prediction models aiming to reduce the time-to-pregnancy (TTP). Consequently, the POSEIDON stratification should not be applied for retrospective analyses having live birth rate (LBR) as endpoint. Such an approach would fail as the attribution of patients to each Poseidon group is related to specific requirements and could only be made prospectively. On the other hand, any prospective approach (i.e. RCT) should be performed separately in each specific group.

Abstract: The growing cell wall in plants has conflicting requirements to be strong enough to withstand the high tensile forces generated by cell turgor pressure while selectively yielding to those forces to induce wall stress relaxation, leading to water uptake and polymer movements underlying cell wall expansion. In this article, I review emerging concepts of plant primary cell wall structure, the nature of wall extensibility and the action of expansins, family-9 and -12 endoglucanases, family-16 xyloglucan endotransglycosylase/hydrolase (XTH), and pectin methylesterases, and offer a critical assessment of their wall-loosening activity

Abstract: About 2,500 papers dated 2014-2016 were recovered by searching the PubMed database for Streptomyces, which are the richest known source of antibiotics. This review integrates around 100 of these papers in sections dealing with evolution, ecology, pathogenicity, growth and development, stress responses and secondary metabolism, gene expression, and technical advances. Genomic approaches have greatly accelerated progress. For example, it has been definitively shown that interspecies recombination of conserved genes has occurred during evolution, in addition to exchanges of some of the tens of thousands of non-conserved accessory genes. The closeness of the association of Streptomyces with plants, fungi, and insects has become clear and is reflected in the importance of regulators of cellulose and chitin utilisation in overall Streptomyces biology. Interestingly, endogenous cellulose-like glycans are also proving important in hyphal growth and in the clumping that affects industrial fermentations. Nucleotide secondary messengers, including cyclic di-GMP, have been shown to provide key input into developmental processes such as germination and reproductive growth, while late morphological changes during sporulation involve control by phosphorylation. The discovery that nitric oxide is produced endogenously puts a new face on speculative models in which regulatory Wbl proteins (peculiar to actinobacteria) respond to nitric oxide produced in stressful physiological transitions. Some dramatic insights have come from a new model system for Streptomyces developmental biology, Streptomyces venezuelae, including molecular evidence of very close interplay in each of two pairs of regulatory proteins. An extra dimension has been added to the many complexities of the regulation of secondary metabolism by findings of regulatory crosstalk within and between pathways, and even between species, mediated by end products. Among many outcomes from the application of chromosome immunoprecipitation sequencing (ChIP-seq) analysis and other methods based on "next-generation sequencing" has been the finding that 21% of Streptomyces mRNA species lack leader sequences and conventional ribosome binding sites. Further technical advances now emerging should lead to continued acceleration of knowledge, and more effective exploitation, of these astonishing and critically important organisms.

Abstract: Central neural circuits orchestrate the behavioral and autonomic repertoire that maintains body temperature during environmental temperature challenges and alters body temperature during the inflammatory response and behavioral states and in response to declining energy homeostasis. This review summarizes the central nervous system circuit mechanisms controlling the principal thermoeffectors for body temperature regulation: cutaneous vasoconstriction regulating heat loss and shivering and brown adipose tissue for thermogenesis. The activation of these thermoeffectors is regulated by parallel but distinct efferent pathways within the central nervous system that share a common peripheral thermal sensory input. The model for the neural circuit mechanism underlying central thermoregulatory control provides a useful platform for further understanding of the functional organization of central thermoregulation, for elucidating the hypothalamic circuitry and neurotransmitters involved in body temperature regulation, and for the discovery of novel therapeutic approaches to modulating body temperature and energy homeostasis.

Abstract: Sequencing reads overlapping polymorphic sites in diploid mammalian genomes may be assigned to one allele or the other. This holds the potential to detect gene expression, chromatin modifications, DNA methylation or nuclear interactions in an allele-specific fashion. SNPsplit is an allele-specific alignment sorter designed to read files in SAM/BAM format and determine the allelic origin of reads or read-pairs that cover known single nucleotide polymorphic (SNP) positions. For this to work libraries must have been aligned to a genome in which all known SNP positions were masked with the ambiguity base 'N' and aligned using a suitable mapping program such as Bowtie2, TopHat, STAR, HISAT2, HiCUP or Bismark. SNPsplit also provides an automated solution to generate N-masked reference genomes for hybrid mouse strains based on the variant call information provided by the Mouse Genomes Project. The unique ability of SNPsplit to work with various different kinds of sequencing data including RNA-Seq, ChIP-Seq, Bisulfite-Seq or Hi-C opens new avenues for the integrative exploration of allele-specific data.

Abstract: Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox, TCGAbiolinks.

Abstract: Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality and has an increasing incidence worldwide HCC can be induced by multiple etiologies, is influenced by many risk factors, and has a complex pathogenesis Furthermore, HCCs exhibit substantial heterogeneity, which compounds the difficulties in developing effective therapies against this highly lethal cancer With advances in cancer biology and molecular and genetic profiling, a number of different mechanisms involved in the development and progression of HCC have been identified Despite the advances in this area, the molecular pathogenesis of hepatocellular carcinoma is still not completely understood This review aims to elaborate our current understanding of the most relevant genetic alterations and molecular pathways involved in the development and progression of HCC, and anticipate the potential impact of future advances on therapeutic drug development

Abstract: Overactivation of Wnt signaling is a hallmark of colorectal cancer (CRC). The Wnt pathway is a key regulator of both the early and the later, more invasive, stages of CRC development. In the normal intestine and colon, Wnt signaling controls the homeostasis of intestinal stem cells (ISCs) that fuel, via proliferation, upward movement of progeny cells from the crypt bottom toward the villus and differentiation into all cell types that constitute the intestine. Studies in recent years suggested that cancer stem cells (CSCs), similar to ISCs of the crypts, consist of a small subpopulation of the tumor and are responsible for the initiation and progression of the disease. Although various ISC signature genes were also identified as CRC markers and some of these genes were even demonstrated to have a direct functional role in CRC development, the origin of CSCs and their contribution to cancer progression is still debated. Here, we describe studies supporting a relationship between Wnt-regulated CSCs and the progression of CRC.