Construction and validation of a prognostic signature for mucinous colonic adenocarcinoma based on N7-methylguanosine-related long non-coding RNAs.

Background: Mucinous colonic adenocarcinoma remains a challenging disease due to its high propensity for metastasis and recurrence. N7-methylguanosine (m7G) and long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on m7G-related lncRNA in mucinous colonic adenocarcinoma is lacking. Therefore, we sought to explore the prognostic impact of m7G-related lncRNAs in mucinous adenocarcinoma (MC) patients. Methods: In this study, Pearson analysis was used to identify m7G-related lncRNAs from transcriptome data in The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to further screen m7G-related lncRNAs and incorporate them into a prognostic signature. Based on the risk model, patients were divided into low- and high-risk groups and randomly assigned to the training set and test sets in a 6:4 ratio. Kaplan-Meier, receiver operating characteristic (ROC) curve, multivariate regression, and nomogram analyses were used to confirm the accuracy of the signature. The CIBERSORT algorithm was used to calculate the degree of immune cell infiltration (ICI). Finally, the correlation of the prognostic signature with tumor mutational burden (TMB) and immunophenotype score (IPS) was evaluated. Results: A total of 432 m7G-related lncRNAs were identified by Pearson analysis. Univariate Cox regression, LASSO regression and survival analysis were performed to further select six m7G-related lncRNAs (P<0.05): AC254629.1, LINC01133, LINC01134, MHENCR, SMIM2-AS1, and XACT. Based on the risk model, heat maps, Kaplan-Meier curves, and ROC curves were constructed, and the results showed that there were significant differences in expression levels and survival status between the two risk groups. The area under the ROC curve (AUC) values for 3-, 5-, and 10-year survival in the training set were 0.944, 0.957, and 1.000, respectively. And in the test set were 0.964, 1.000, and 1.000, respectively. Subsequently, univariate and multivariate regression analyses of clinical characteristics and risk score were performed. The results of risk score were [hazard ratio (HR): 6.458, 95% confidence interval (CI): 2.708-15.403, P<0.001; HR: 7.280, 95% CI: 2.500-21.203, P<0.001], respectively. Using the risk score as an independent prognostic factor, the AUC of it over 3, 5, and 10 years was 0.911, 0.955, and 0.961, respectively. Calibration plots for the nomogram show that the model calibration line is very close to the ideal calibration line, indicating good calibration. The level of ICI was significantly different in the different risk groups. Survival analysis showed that, regardless of TMB risk, patients with MC and a high-risk score consistently had a poor overall survival (OS). Conclusions: The m7G-related lncRNA prognostic signature has potential value for the prognosis of mucinous colonic adenocarcinoma.

Euphorbia factor L1 inhibited transport channel and energy metabolism in human colon adenocarcinoma cell line Caco-2.

Euphorbia factor L1 (EFL1) is a kind of lathyrane-type diterpenoid and is isolated from the medical herb Euphorbia lathyris L. (Euphorbiaceae); it has been reported with the toxicity that causes intestinal irritation, but the underlying mechanisms are still obscure. The objective of this study was to assess the EFL1-induced intestinal cytotoxicity in human colon adenocarcinoma Caco-2 cells. The Caco-2 cells were treated with EFL1, and the intracellular calcium ion concentration, mitochondrial membrane potential (MMP), mitochondrial permeability transition pore (mPTP), adenosine 5'-triphosphate (ATP) content, ATPase activities, TGF-ß1 concentration, and transepithelial electrical resistance (TEER) were detected. The interaction between EFL1 and the tight junction proteins Occludin, Claudin-4, Tricellulin, ZO-1, JAM-1, and E-cadherin was simulated by molecular docking. The expression of proteins involved in the energy metabolism, the ion transporters and aquaporins, the tight junction, and the F-actin cytoskeleton were detected by Western blotting and cell immunofluorescence. As a result, EFL1 decreased the intracellular Ca2+, MMP, mPTP, ATP content, and ATPase activities in the Caco-2 cells. The AMPK/SIRT1/PGC-1α signaling pathway, which regulates the energy metabolism, was inhibited. The ion transporters NEH and CFTR, as well as the aquaporins in the Caco-2 cells, were decreased. The tight junction proteins were down-regulated, and the integrity of the intestinal barrier was injured; TGF-ß1 was compensatively increased; so, the intestinal permeability was increased and was characterized by decreased TEER. The morphology of the F-actin cytoskeleton was destroyed. These findings indicated that EFL1 caused cytotoxicity in the human intestinal Caco-2 cells through mitochondrial damage, inhibition of the energy metabolism, and suppression of the ion and water molecule transporters, as well as the down-regulation tight junction and cytoskeleton protiens.

Regulation of programmed cell death by Brd4.

Epigenetic factor Brd4 has emerged as a key regulator of cancer cell proliferation. Targeted inhibition of Brd4 suppresses growth and induces apoptosis of various cancer cells. In addition to apoptosis, Brd4 has also been shown to regulate several other forms of programmed cell death (PCD), including autophagy, necroptosis, pyroptosis, and ferroptosis, with different biological outcomes. PCD plays key roles in development and tissue homeostasis by eliminating unnecessary or detrimental cells. Dysregulation of PCD is associated with various human diseases, including cancer, neurodegenerative and infectious diseases. In this review, we discussed some recent findings on how Brd4 actively regulates different forms of PCD and the therapeutic potentials of targeting Brd4 in PCD-related human diseases. A better understanding of PCD regulation would provide not only new insights into pathophysiological functions of PCD but also provide new avenues for therapy by targeting Brd4-regulated PCD.

m6A-TSHub: Unveiling the Context-specific m6A Methylation and m6A-affecting Mutations in 23 Human Tissues.

As the most pervasive epigenetic marker present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform m6A-TSHub for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including: (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.

Primary sequence-assisted prediction of m6A RNA methylation sites from Oxford nanopore direct RNA sequencing data.

Traditional epitranscriptome profiling approach relies on specific antibodies or chemical treatments to capture modified RNA molecules and then applies high throughput sequencing to identify their transcriptomic locations. However, due to the lack of suitable or high-quality antibodies, only a small proportion of the 170 documented RNA modifications were profiled with those approaches. Direct sequencing of native RNA molecules using Oxford Nanopore Technologies (ONT) enabled straight inspection of RNA modifications and offered a promising alternative solution. N6-methyladenosine (m6A) is known to cause characteristic changes and increased base call errors of ONT signals compared with non-modified adenosines, based on which, the m6A sites can be identified directly from ONT signals. Meanwhile, a number of studies have shown that it is possible to predict m6A sites from RNA primary sequences. Using the m6A sites revealed by Illumina technology as benchmark, we showed that, the accuracy of ONT-based m6A site prediction can be further increased by integrating additional information from the primary sequences of RNA (AUROC of 0.918), compared with using ONT signals only (AUROC 0.878 using Base call error features, and 0.804 using Tombo features), providing a new perspective for more reliable mining of the relatively noisy ONT signals.

m6AmPred: Identifying RNA N6, 2'-O-dimethyladenosine (m6Am) sites based on sequence-derived information.

N6,2'-O-dimethyladenosine (m6Am) is a reversible modification widely occurred on varied RNA molecules. The biological function of m6Am is yet to be known though recent studies have revealed its influences in cellular mRNA fate. Precise identification of m6Am sites on RNA is vital for the understanding of its biological functions. We present here m6AmPred, the first web server for in silico identification of m6Am sites from the primary sequences of RNA. Built upon the eXtreme Gradient Boosting with Dart algorithm (XgbDart) and EIIP-PseEIIP encoding scheme, m6AmPred achieved promising prediction performance with the AUCs greater than 0.954 when tested by 10-fold cross-validation and independent testing datasets. To critically test and validate the performance of m6AmPred, the experimentally verified m6Am sites from two data sources were cross-validated. The m6AmPred web server is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/m6am, and it should make a useful tool for the researchers who are interested in N6,2'-O-dimethyladenosine RNA modification.

WHISTLE: A Functionally Annotated High-Accuracy Map of Human m6A Epitranscriptome.

N6-Methyladenosine (m6A) is the most prevalent posttranscriptional modification in eukaryotes and plays a pivotal role in various biological processes, such as splicing, RNA degradation, and RNA-protein interaction. Accurately identification of the location of m6A is essential for related downstream studies. In this chapter, we introduce a prediction framework WHISTLE, which enables us to acquire so far the most accurate map of the transcriptome-wide human m6A RNA-methylation sites (with an average AUC: 0.948 and 0.880 under the full transcript or mature messenger RNA models, respectively, when tested on independent datasets). Besides, each individual m6A site was also functionally annotated according to the "guilt-by-association" principle by integrating RNA methylation data, gene expression data and protein-protein interaction data. A web server was constructed for conveniently querying the predicted RNA methylation sites and their putative biological functions. The website supports the query by genes, by GO function, table view, and the download of all the functionally annotated map of predicted map of human m6A epitranscriptome. The WHISTLE web server is freely available at: www.xjtlu.edu.cn/biologicalsciences/whistle and http://whistle-epitranscriptome.com .

m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.

N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and disease pathogenesis. We present here a comprehensive knowledgebase, m6A-Atlas, for unraveling the m6A epitranscriptome. Compared to existing databases, m6A-Atlas features a high-confidence collection of 442 162 reliable m6A sites identified from seven base-resolution technologies and the quantitative (rather than binary) epitranscriptome profiles estimated from 1363 high-throughput sequencing samples. It also offers novel features, such as; the conservation of m6A sites among seven vertebrate species (including human, mouse and chimp), the m6A epitranscriptomes of 10 virus species (including HIV, KSHV and DENV), the putative biological functions of individual m6A sites predicted from epitranscriptome data, and the potential pathogenesis of m6A sites inferred from disease-associated genetic mutations that can directly destroy m6A directing sequence motifs. A user-friendly graphical user interface was constructed to support the query, visualization and sharing of the m6A epitranscriptomes annotated with sites specifying their interaction with post-transcriptional machinery (RBP-binding, microRNA interaction and splicing sites) and interactively display the landscape of multiple RNA modifications. These resources provide fresh opportunities for unraveling the m6A epitranscriptomes. m6A-Atlas is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/atlas.

RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis.

Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.

m5UPred: A Web Server for the Prediction of RNA 5-Methyluridine Sites from Sequences.

As one of the widely occurring RNA modifications, 5-methyluridine (m5U) has recently been shown to play critical roles in various biological functions and disease pathogenesis, such as under stress response and during breast cancer development. Precise identification of m5U sites on RNA is vital for the understanding of the regulatory mechanisms of RNA life. We present here m5UPred, the first web server for in silico identification of m5U sites from the primary sequences of RNA. Built upon the support vector machine (SVM) algorithm and the biochemical encoding scheme, m5UPred achieved reasonable prediction performance with the area under the receiver operating characteristic curve (AUC) greater than 0.954 by 5-fold cross-validation and independent testing datasets. To critically test and validate the performance of our newly proposed predictor, the experimentally validated m5U sites were further separated by high-throughput sequencing techniques (miCLIP-Seq and FICC-Seq) and cell types (HEK293 and HAP1). When tested on cross-technique and cross-cell-type validation using independent datasets, m5UPred achieved an average AUC of 0.922 and 0.926 under mature mRNA mode, respectively, showing reasonable accuracy and reliability. The m5UPred web server is freely accessible now and it should make a useful tool for the researchers who are interested in m5U RNA modification.

Prediction of RNA Methylation Status From Gene Expression Data Using Classification and Regression Methods.

RNA N 6-methyladenosine (m6A) has emerged as an important epigenetic modification for its role in regulating the stability, structure, processing, and translation of RNA. Instability of m6A homeostasis may result in flaws in stem cell regulation, decrease in fertility, and risk of cancer. To this day, experimental detection and quantification of RNA m6A modification are still time-consuming and labor-intensive. There is only a limited number of epitranscriptome samples in existing databases, and a matched RNA methylation profile is not often available for a biological problem of interests. As gene expression data are usually readily available for most biological problems, it could be appealing if we can estimate the RNA methylation status from gene expression data using in silico methods. In this study, we explored the possibility of computational prediction of RNA methylation status from gene expression data using classification and regression methods based on mouse RNA methylation data collected from 73 experimental conditions. Elastic Net-regularized Logistic Regression (ENLR), Support Vector Machine (SVM), and Random Forests (RF) were constructed for classification. Both SVM and RF achieved the best performance with the mean area under the curve (AUC) = 0.84 across samples; SVM had a narrower AUC spread. Gene Site Enrichment Analysis was conducted on those sites selected by ENLR as predictors to access the biological significance of the model. Three functional annotation terms were found statistically significant: phosphoprotein, SRC Homology 3 (SH3) domain, and endoplasmic reticulum. All 3 terms were found to be closely related to m6A pathway. For regression analysis, Elastic Net was implemented, which yielded a mean Pearson correlation coefficient = 0.68 and a mean Spearman correlation coefficient = 0.64. Our exploratory study suggested that gene expression data could be used to construct predictors for m6A methylation status with adequate accuracy. Our work showed for the first time that RNA methylation status may be predicted from the matched gene expression data. This finding may facilitate RNA modification research in various biological contexts when a matched RNA methylation profile is not available, especially in the very early stage of the study.

m6Acomet: large-scale functional prediction of individual m6A RNA methylation sites from an RNA co-methylation network.

BACKGROUND: Over one hundred different types of post-transcriptional RNA modifications have been identified in human. Researchers discovered that RNA modifications can regulate various biological processes, and RNA methylation, especially N6-methyladenosine, has become one of the most researched topics in epigenetics. RESULTS: To date, the study of epitranscriptome layer gene regulation is mostly focused on the function of mediator proteins of RNA methylation, i.e., the readers, writers and erasers. There is limited investigation of the functional relevance of individual m6A RNA methylation site. To address this, we annotated human m6A sites in large-scale based on the guilt-by-association principle from an RNA co-methylation network. It is constructed based on public human MeRIP-Seq datasets profiling the m6A epitranscriptome under 32 independent experimental conditions. By systematically examining the network characteristics obtained from the RNA methylation profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m6A sites were identified. These are biological functions that may be regulated at epitranscriptome layer via reversible m6A RNA methylation. The results were further validated on a soft benchmark by comparing to a random predictor. CONCLUSIONS: An online web server m6Acomet was constructed to support direct query for the predicted biological functions of m6A sites as well as the sites exhibiting co-methylated patterns at the epitranscriptome layer. The m6Acomet web server is freely available at: www.xjtlu.edu.cn/biologicalsciences/m6acomet .

DRUM: Inference of Disease-Associated m6A RNA Methylation Sites From a Multi-Layer Heterogeneous Network.

Recent studies have revealed that the RNA N 6-methyladenosine (m6A) modification plays a critical role in a variety of biological processes and associated with multiple diseases including cancers. Till this day, transcriptome-wide m6A RNA methylation sites have been identified by high-throughput sequencing technique combined with computational methods, and the information is publicly available in a few bioinformatics databases; however, the association between individual m6A sites and various diseases are still largely unknown. There are yet computational approaches developed for investigating potential association between individual m6A sites and diseases, which represents a major challenge in the epitranscriptome analysis. Thus, to infer the disease-related m6A sites, we implemented a novel multi-layer heterogeneous network-based approach, which incorporates the associations among diseases, genes and m6A RNA methylation sites from gene expression, RNA methylation and disease similarities data with the Random Walk with Restart (RWR) algorithm. To evaluate the performance of the proposed approach, a ten-fold cross validation is performed, in which our approach achieved a reasonable good performance (overall AUC: 0.827, average AUC 0.867), higher than a hypergeometric test-based approach (overall AUC: 0.7333 and average AUC: 0.723) and a random predictor (overall AUC: 0.550 and average AUC: 0.486). Additionally, we show that a number of predicted cancer-associated m6A sites are supported by existing literatures, suggesting that the proposed approach can effectively uncover the underlying epitranscriptome circuits of disease mechanisms. An online database DRUM, which stands for disease-associated ribonucleic acid methylation, was built to support the query of disease-associated RNA m6A methylation sites, and is freely available at: www.xjtlu.edu.cn/biologicalsciences/drum.

WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA-protein interaction. We report here a prediction framework WHISTLE for transcriptome-wide m6A RNA-methylation site prediction. When tested on six independent datasets, our approach, which integrated 35 additional genomic features besides the conventional sequence features, achieved a major improvement in the accuracy of m6A site prediction (average AUC: 0.948 and 0.880 under the full transcript or mature messenger RNA models, respectively) compared to the state-of-the-art computational approaches MethyRNA (AUC: 0.790 and 0.732) and SRAMP (AUC: 0.761 and 0.706). It also out-performed the existing epitranscriptome databases MeT-DB (AUC: 0.798 and 0.744) and RMBase (AUC: 0.786 and 0.736), which were built upon hundreds of epitranscriptome high-throughput sequencing samples. To probe the putative biological processes impacted by changes in an individual m6A site, a network-based approach was implemented according to the 'guilt-by-association' principle by integrating RNA methylation profiles, gene expression profiles and protein-protein interaction data. Finally, the WHISTLE web server was built to facilitate the query of our high-accuracy map of the human m6A epitranscriptome, and the server is freely available at: www.xjtlu.edu.cn/biologicalsciences/whistle and http://whistle-epitranscriptome.com.