Reprogramming of cellular metabolism is an emerging hallmark of neoplastic transformation. However, it is not known how metabolic gene expression in tumors differs from that in normal tissues, or whether different tumor types exhibit similar metabolic changes. Here we compare expression patterns of metabolic genes across 22 diverse types of human tumors. Overall, the metabolic gene expression program in tumors is similar to that in the corresponding normal tissues. Although expression changes of some metabolic pathways (e.g., up-regulation of nucleotide biosynthesis and glycolysis) are frequently observed across tumors, expression changes of other pathways (e.g., oxidative phosphorylation and the tricarboxylic acid (TCA) cycle) are very heterogeneous. Our analysis also suggests that the expression changes of major metabolic processes across tumors can be rationalized in terms of several principal components. On the level of individual biochemical reactions, many hundreds of metabolic isoenzymes show significant and tumor-specific expression changes. These isoenzymes are potential targets for anticancer therapy.

Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20–25 residues long than peptides in other length ranges.

Experimental data exists for only a vanishingly small fraction of sequenced microbial genes. This community page discusses the progress made by the COMBREX project to address this important issue using both computational and experimental resources.

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The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods’ reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.

Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004–17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.

Background & Aims Hepatocellular carcinoma (HCC) is among the malignancies with the highest mortality. The key regulators and their interactive network in HCC pathogenesis remain unclear. Along with genetic mutations, aberrant epigenetic paradigms, including deregulated microRNAs (miRNAs), exert profound impacts on hepatocyte transformation and tumor microenvironment remodeling; however, the underlying mechanisms are largely uncharacterized. Methods We performed RNA sequencing on HCC specimens and bioinformatic analyses to identify tumor-associated miRNAs. The miRNA functional targets and their effects on tumor-infiltrating immune cells were investigated. The upstream events, particularly the epigenetic mechanisms responsible for miRNA deregulation in HCC, were explored. Results The miR-144/miR-451a cluster was downregulated in HCC and predicted a better HCC patient prognosis. These miRNAs promoted macrophage M1 polarization and antitumor activity by targeting hepatocyte growth factor (HGF) and macrophage migration inhibitory factor (MIF). The miR-144/miR-451a cluster and EZH2, the catalytic subunit of polycomb repressive complex (PRC2), formed a feedback circuit in which miR-144 targeted EZH2 and PRC2 epigenetically repressed the miRNA genes via histone H3K27 methylation of the promoter. The miRNA cluster was coordinately silenced by distal enhancer hypermethylation, disrupting chromatin loop formation and enhancer-promoter interactions. Clinical examinations indicated that methylation of this chromatin region is a potential HCC biomarker. Conclusions Our study revealed novel mechanisms underlying miR-144/miR-451a cluster deregulation and the crosstalk between malignant cells and tumor-associated macrophages (TAMs) in HCC, providing new insights into HCC pathogenesis and diagnostic strategies.