Tumor cells with stemness (stem-cell) features contribute to initiation and progression of hepatocellular carcinoma (HCC), but involvement of long noncoding RNAs (lncRNAs) remains largely unclear. Genome-wide analyses were applied to identify tumor-associated lncRNA-DANCR. DANCR expression level and prognostic values of DANCR were assayed in two HCC cohorts (China and Korea, n 5 135 and 223). Artificial modulation of DANCR (down-and overexpression) was done to explore the role of DANCR in tumorigenesis and colonization, and tumor-bearing mice were used to determine therapeutic effects. We found that lncRNA-DANCR is overexpressed in stem-like HCC cells, and this can serve as a prognostic biomarker for HCC patients. Experiments showed that DANCR markedly increased stemness features of HCC cells to promote tumorigenesis and intra-/ extrahepatic tumor colonization. Conversely, DANCR knockdown attenuated the stem-cell properties and in vivo interference with DANCR action led to decreased tumor cell vitality, tumor shrinkage, and improved mouse survival. Additionally, we found that the role of DANCR relied largely on an association with, and regulation of, CTNNB1. Association of DANCR with CTNNB1 blocked the repressing effect of microRNA (miR)2214, miR320a, and miR-199a on CTNNB1. This observation was confirmed in vivo, suggesting a novel mechanism of tumorigenesis involving lncRNAs, messenger RNAs, and microRNAs. Conclusions: These studies reveal a significance and mechanism of DANCR action in increasing stemness features and offer a potential prognostic marker and a therapeutic target for HCC. (HEPATOLOGY 2016;63:499-511)

Despite the prevalent studies of DNA/Chromatin related epigenetics, such as, histone modifications and DNA methylation, RNA epigenetics has not drawn deserved attention until a new affinity-based sequencing approach MeRIP-Seq was developed and applied to survey the global mRNA N6-methyladenosine (m6A) in mammalian cells. As a marriage of ChIP-Seq and RNA-Seq, MeRIP-Seq has the potential to study the transcriptome-wide distribution of various post-transcriptional RNA modifications. We have previously developed an R/Bioconductor package ‘exomePeak’ for detecting RNA methylation sites under a specific experimental condition or the identifying the differential RNA methylation sites in a case control study from MeRIP-Seq data. Compared with other relatively well studied data types such as ChIP-Seq and RNA-Seq, the study of MeRIP-Seq data is still at very early stage, and existing protocols are not optimized for dealing with the intrinsic characteristic of MeRIP-Seq data. We therein provide here a detailed and easy-to-use protocol of using exomePeak R/Bioconductor package along with other software programs for analysis of MeRIP-Seq data, which covers raw reads alignment, RNA methylation site detection, motif discovery, differential RNA methylation analysis, and functional analysis. Particularly, the rationales behind each processing step as well as the specific method used, the best practice, and possible alternative strategies are briefly discussed.

Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. The MethylTranscriptome DataBase (MeT-DB, http://compgenomics.utsa.edu/methylation/) is the first comprehensive resource for N6-methyladenosine (m6A) in mammalian transcriptome. It includes a database that records publicaly available data sets from methylated RNA immunoprecipitation sequencing (MeRIP-Seq), a recently developed technology for interrogating m6A methyltranscriptome. MeT-DB includes ∼300k m6A methylation sites in 74 MeRIP-Seq samples from 22 different experimental conditions predicted by exomePeak and MACS2 algorithms. To explore this rich information, MeT-DB also provides a genome browser to query and visualize context-specific m6A methylation under different conditions. MeT-DB also includes the binding site data of microRNA, splicing factor and RNA binding proteins in the browser window for comparison with m6A sites and for exploring the potential functions of m6A. Analysis of differential m6A methylation and the related differential gene expression under two conditions is also available in the browser. A global perspective of the genome-wide distribution of m6A methylation in all the data is provided in circular ideograms, which also act as a navigation portal. The query results and the entire data set can be exported to assist publication and additional analysis.

BackgroundCircular RNAs (circRNAs) are a new type of non-coding RNAs and their functions in gastric cancer (GC) remain unclear. Recent studies have revealed that circRNAs play an important role in cancer development and certain types of pathological responses, acting as microRNA (miRNA) sponges to regulate gene expression.MethodsCircNet was used to screen potential circRNAs and validated circYAP1 expression levels in 17 GC tissues by quantitative real-time PCR (qRT-PCR) and another 80 paired GC tissues by FISH. CircYAP1 overexpression and knockdown experiments were conducted to assess the effects of circYAP1 in vitro and in vivo, and its molecular mechanism was demonstrated by RNA in vivo precipitation assays, western blotting, luciferase assay and rescue experiments.ResultsCircYAP1 expression level was significantly lower in GC tissues than the adjacent normal tissues, and GC patients with circYAP1 low expression had shorter survival times as compared with those with circYAP1 high expression. Functionally, circYAP1 overexpression inhibited cell growth and invasion in vitro and in vivo, but its knockdown reversed these effects. Further analysis showed that circYAP1 sponged miR-367-5p to inhibit p27 Kip1 expression and GC progression.ConclusionOur findings demonstrate that circYAP1 functions as a tumor suppressor in GC cells by targeting the miR-367-5p/p27 Kip1 axis and may provide a prognostic indicator of survival in GC patients.Electronic supplementary materialThe online version of this article (10.1186/s12943-018-0902-1) contains supplementary material, which is available to authorized users.

Background The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers’ clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images. Methods We developed an automated deep learning algorithm using cascaded convolutional neural networks to detect OCSCC from photographic images. We included all biopsy-proven OCSCC photographs and normal controls of 44,409 clinical images collected from 11 hospitals around China between April 12, 2006, and Nov 25, 2019. We trained the algorithm on a randomly selected part of this dataset (development dataset) and used the rest for testing (internal validation dataset). Additionally, we curated an external validation dataset comprising clinical photographs from six representative journals in the field of dentistry and oral surgery. We also compared the performance of the algorithm with that of seven oral cancer specialists on a clinical validation dataset. We used the pathological reports as gold standard for OCSCC identification. We evaluated the algorithm performance on the internal, external, and clinical validation datasets by calculating the area under the receiver operating characteristic curves (AUCs), accuracy, sensitivity, and specificity with two-sided 95% CIs. Findings 1469 intraoral photographic images were used to validate our approach. The deep learning algorithm achieved an AUC of 0·983 (95% CI 0·973–0·991), sensitivity of 94·9% (0·915–0·978), and specificity of 88·7% (0·845–0·926) on the internal validation dataset ( n = 401), and an AUC of 0·935 (0·910–0·957), sensitivity of 89·6% (0·847–0·942) and specificity of 80·6% (0·757–0·853) on the external validation dataset ( n = 402). For a secondary analysis on the internal validation dataset, the algorithm presented an AUC of 0·995 (0·988–0·999), sensitivity of 97·4% (0·932–1·000) and specificity of 93·5% (0·882–0·979) in detecting early-stage OCSCC. On the clinical validation dataset ( n = 666), our algorithm achieved comparable performance to that of the average oral cancer expert in terms of accuracy (92·3% [0·902–0·943] vs 92.4% [0·912–0·936]), sensitivity (91·0% [0·879–0·941] vs 91·7% [0·898–0·934]), and specificity (93·5% [0·909–0·960] vs 93·1% [0·914–0·948]). The algorithm also achieved significantly better performance than that of the average medical student (accuracy of 87·0% [0·855–0·885], sensitivity of 83·1% [0·807–0·854], and specificity of 90·7% [0·889–0·924]) and the average non-medical student (accuracy of 77·2% [0...