Highlights d Ultra-deep rRNA-depleted RNA sequencing of 144 localized prostate tumors d Fusion gene profiles differentiate localized from metastatic disease d Widespread RNA circularization events define clinically distinct tumor subtypes d Functional screening reveals pervasive circular isoformspecific essentiality

Mammalian gene expression patterns change profoundly in response to low oxygen levels. These changes in gene expression programs are strongly influenced by post-transcriptional mechanisms mediated by mRNA-binding factors: RNA-binding proteins (RBPs) and microRNAs (miRNAs). Here, we review the RBPs and miRNAs that modulate mRNA turnover and translation in response to hypoxic challenge. RBPs such as HuR (human antigen R), PTB (polypyrimidine tract-binding protein), heterogeneous nuclear ribonucleoproteins (hnRNPs), tristetraprolin, nucleolin, iron-response element-binding proteins (IRPs), and cytoplasmic polyadenylation-element-binding proteins (CPEBs), selectively bind to numerous hypoxia-regulated transcripts and play a major role in establishing hypoxic gene expression patterns. MiRNAs including miR-210, miR-373, and miR-21 associate with hypoxia-regulated transcripts and further modulate the levels of the encoded proteins to implement the hypoxic gene expression profile. We discuss the potent regulation of hypoxic gene expression by RBPs and miRNAs and their integrated actions in the cellular hypoxic response.

BackgroundImmunotherapy has become an important treatment option for patients with advanced non-small cell lung cancer (NSCLC). At present, none of these existing biomarkers can effectively stratify true responders and there is an urgent need for identifying novel biomarkers. Exosomes derived from the serum of patients with cancer have been proven to be reliable markers for cancer diagnosis. Here, we explored the possibility of using plasma-derived exosomal microRNAs as potential biomarkers for optimal selection of patients with advancedEGFR/ALKnegative NSCLC to immunotherapy.MethodsFrom June 2017 to February 2019, 30 patients with advancedEGFR/ALKwild-type (WT) NSCLC who received PD-1/PD-L1 inhibitors were enrolled. The efficacy evaluation was conducted after every three cycles of treatment according to RECIST 1.1. Plasma samples of these patients were collected before the administration of PD-1/PD-L1 inhibitors as baseline, and after every three cycles if the patients achieved partial response (PR) or complete response. Plasma from seven healthy individuals was also collected as normal control. Exosomes were prepared by ultracentrifugation followed by total RNA extraction, and exosome-derived miRNAs were profiled using small RNA next-generation sequencing followed by differential expression analysis.ResultsIn order to identify biomarker for better response, all five patients who achieved PR and four patients with progressive disease (PD) at efficacy evaluation were included for differential expression analysis. Based on unsupervised hierarchical clustering, exosomal miRNA expression profile was significantly altered in patients with NSCLC compared with normal controls with a total of 155 differentially expressed exosomal miRNAs. Interestingly, hsa-miR-320d, hsa-miR-320c, and hsa-miR-320b were identified significantly upregulated in the PD groups compared with the PR group at baseline before the treatment. In addition, we identified that hsa-miR-125b-5p, a T-cell suppressor, showed a trend of increased expression in the PD group at baseline and was significantly downregulated in the post-treatment plasma exosomes compared with pre-treatment samples of the PR patients.ConclusionPatients with NSCLC represent unique plasma exosomal miRNA profiles. Hsa-miR-320d, hsa-miR-320c, and hsa-miR-320b were identified as potential biomarkers for predicting the efficacy of immunotherapy in advanced NSCLCs. When T-cell suppressor hsa-miR-125b-5p was downregulated during the treatment, the patients may obtain increased T-cell function and respond well to immunotherapy.

Purpose The purpose of this study was to expedite the contouring process for MRI‐guided adaptive radiotherapy (MR‐IGART), a convolutional neural network (CNN) deep‐learning (DL) model is proposed to accurately segment the liver, kidneys, stomach, bowel and duodenum in 3D MR images. Methods Images and structure contours for 120 patients were collected retrospectively. Treatment sites included pancreas, liver, stomach, adrenal gland, and prostate. The proposed DL model contains a voxel‐wise label prediction CNN and a correction network which consists of two sub‐networks. The prediction CNN and sub‐networks in the correction network each includes a dense block which consists of twelve densely connected convolutional layers. The correction network was designed to improve the voxel‐wise labeling accuracy of a CNN by learning and enforcing implicit anatomical constraints in the segmentation process. Its sub‐networks learn to fix the erroneous classification of its previous network by taking as input both the original images and the softmax probability maps generated from its previous sub‐network. The parameters of each sub‐network were trained independently using piecewise training. The model was trained on 100 datasets, validated on 10 datasets and tested on the remaining 10 datasets. Dice coefficient, Hausdorff distance (HD) were calculated to evaluate the segmentation accuracy. Results The proposed DL model was able to segment the organs with good accuracy. The correction network outperformed the conditional random field (CRF), a most comparable method that is usually applied as a post‐processing step. For the 10 testing patients, the average Dice coefficients were 95.3 ± 0.73, 93.1 ± 2.22, 85.0 ± 3.75, 86.6 ± 2.69, and 65.5 ± 8.90 for liver, kidneys, stomach, bowel, and duodenum, respectively. The mean Hausdorff Distance (HD) were 5.41 ± 2.34, 6.23 ± 4.59, 6.88 ± 4.89, 5.90 ± 4.05, and 7.99 ± 6.84 mm, respectively. Manual contouring, as to correct the automatic segmentation results, was four times as fast as manual contouring from scratch. Conclusion The proposed method can automatically segment the liver, kidneys, stomach, bowel, and duodenum in 3D MR images with good accuracy. It is useful to expedite the manual contouring for MR‐IGART.

Recent data have linked hypoxia, a classic feature of the tumor microenvironment, to the function of specific microRNAs (miRNAs); however, whether hypoxia affects other types of noncoding transcripts is currently unknown. Starting from a genomewide expression profiling, we demonstrate for the first time a functional link between oxygen deprivation and the modulation of long noncoding transcripts from ultraconserved regions, termed transcribed-ultraconserved regions (T-UCRs). Interestingly, several hypoxia-upregulated T-UCRs, henceforth named 'hypoxia-induced noncoding ultraconserved transcripts' (HINCUTs), are also overexpressed in clinical samples from colon cancer patients. We show that these T-UCRs are predominantly nuclear and that the hypoxia-inducible factor (HIF) is at least partly responsible for the induction of several members of this group. One specific HINCUT, uc.475 (or HINCUT-1) is part of a retained intron of the host protein-coding gene, O-linked N-acetylglucosamine transferase, which is overexpressed in epithelial cancer types. Consistent with the hypothesis that T-UCRs have important function in tumor formation, HINCUT-1 supports cell proliferation specifically under hypoxic conditions and may be critical for optimal O-GlcNAcylation of proteins when oxygen tension is limiting. Our data gives a first glimpse of a novel functional hypoxic network comprising protein-coding transcripts and noncoding RNAs (ncRNAs) from the T-UCRs category.

BackgroundAs an important oncogenic miRNA, microRNA-21 (miR-21) is associated with various malignant diseases. However, the precise biological function of miR-21 and its molecular mechanism in hypertrophic scar fibroblast cells has not been fully elucidated.Methodology/Principal FindingsQuantitative Real-Time PCR (qRT-PCR) analysis revealed significant upregulation of miR-21 in hypertrophic scar fibroblast cells compared with that in normal skin fibroblast cells. The effects of miR-21 were then assessed in MTT and apoptosis assays through in vitro transfection with a miR-21 mimic or inhibitor. Next, PTEN (phosphatase and tensin homologue deleted on chromosome ten) was identified as a target gene of miR-21 in hypertrophic scar fibroblast cells. Furthermore, Western-blot and qRT-PCR analyses revealed that miR-21 increased the expression of human telomerase reverse transcriptase (hTERT) via the PTEN/PI3K/AKT pathway. Introduction of PTEN cDNA led to a remarkable depletion of hTERT and PI3K/AKT at the protein level as well as inhibition of miR-21-induced proliferation. In addition, Western-blot and qRT-PCR analyses confirmed that hTERT was the downstream target of PTEN. Finally, miR-21 and PTEN RNA expression levels in hypertrophic scar tissue samples were examined. Immunohistochemistry assays revealed an inverse correlation between PTEN and hTERT levels in high miR-21 RNA expressing-hypertrophic scar tissues.Conclusions/SignificanceThese data indicate that miR-21 regulates hTERT expression via the PTEN/PI3K/AKT signaling pathway by directly targeting PTEN, therefore controlling hypertrophic scar fibroblast cell growth. MiR-21 may be a potential novel molecular target for the treatment of hypertrophic scarring.

As the speed of mass spectrometers, sophistication of sample fractionation, and complexity of experimental designs increase, the volume of tandem mass spectra requiring reliable automated analysis continues to grow. Software tools that quickly, effectively, and robustly determine the peptide associated with each spectrum with high confidence are sorely needed. Currently available tools that postprocess the output of sequence-database search engines use three techniques to distinguish the correct peptide identifications from the incorrect: statistical significance re-estimation, supervised machine learning scoring and prediction, and combining or merging of search engine results. We present a unifying framework that encompasses each of these techniques in a single model-free machinelearning framework that can be trained in an unsupervised manner. The predictor is trained on the fly for each new set of search results without user intervention, making it robust for different instruments, search engines, and search engine parameters. We demonstrate the performance of the technique using mixtures of known proteins and by using shuffled databases to estimate false discovery rates, from data acquired on three different instruments with two different ionization technologies. We show that this approach outperforms machine-learning techniques applied to a single search engine's output, and demonstrate that combining search engine results provides additional benefit. We show that the performance of the commercial Mascot tool can be bested by the machine-learning combination of two open-source tools X!Tandem and OMSSA, but that the use of all three search engines boosts performance further still. The Peptide identification Arbiter by Machine Learning (PepArML) unsupervised, model-free, combining framework can be easily extended to support an arbitrary number of additional searches, search engines, or specialized peptide-spectrum match metrics for each spectrum data set. PepArML is open-source and is available from http://peparml.sourceforge.net.

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