Recent advances in functional annotation and prediction of the epitranscriptome

Emerging role of RNA modification and long noncoding RNA interaction in cancer

RNA modification, especially N6-methyladenosine, 5-methylcytosine, and N7-methylguanosine methylation, participates in the occurrence and progression of cancer through multiple pathways. The function and expression of these epigenetic regulators have gradually become a hot topic in cancer research. Mutation and regulation of noncoding RNA, especially lncRNA, play a major role in cancer. Generally, lncRNAs exert tumor-suppressive or oncogenic functions and its dysregulation can promote tumor occurrence and metastasis. In this review, we summarize N6-methyladenosine, 5-methylcytosine, and N7-methylguanosine modifications in lncRNAs. Furthermore, we discuss the relationship between epigenetic RNA modification and lncRNA interaction and cancer progression in various cancers. Therefore, this review gives a comprehensive understanding of the mechanisms by which RNA modification affects the progression of various cancers by regulating lncRNAs, which may shed new light on cancer research and provide new insights into cancer therapy.

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

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), 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.

Bioinformatic tools for epitranscriptomics

The epitranscriptome, defined as RNA modifications that do not involve alterations in the nucleotide sequence, is a popular topic in the genomic sciences. Because we need massive computational techniques to identify epitranscriptomes within individual transcripts, many tools have been developed to infer epitranscriptomic sites as well as to process datasets using high-throughput sequencing. In this review, we summarize recent developments in epitranscriptome spatial detection and data analysis and discuss their progression.

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

BACKGROUND

To construct and verify a novel prognostic model for thyroid cancer (THCA) based on N7-methylguanosine modification-related lncRNAs (m7G-lncRNAs) and their association with immune cell infiltration.

METHODS

In this study, we identified m7G-lncRNAs using co-expression analysis and performed differential expression analysis of m7G-lncRNAs between groups. We then constructed a THCA prognostic model, performed survival analysis and risk assessment for the THCA prognostic model, and performed independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, analysis of the regulatory relationship between prognostic differentially expressed m7G-related lncRNAs (PDEm7G-lncRNAs) and mRNAs and correlation analysis of immune cells and risk scores in THCA patients were carried out.

RESULTS

We identified 29 N7-methylguanosine modification-related mRNAs and 116 differentially expressed m7G-related lncRNAs, including 87 downregulated and 29 upregulated lncRNAs. Next, we obtained 8 PDEm7G-lncRNAs. A final optimized model was constructed consisting of 5 PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, TMEM105, SMG7-AS1 and HMGA2-AS1). Six PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, HMGA2-AS1, LINC01976, MID1IP1-AS1, and SMG7-AS1) had positive regulatory relationships with 10 PDEm7G-mRNAs, while 2 PDEm7G-lncRNAs (LINC02026 and TMEM105) had negative regulatory relationships with 2 PDEm7G-mRNAs. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with THCA. Forest maps and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Finally, we demonstrated a correlation between different immune cells and risk scores.

CONCLUSION

Our results will help identify high-risk or low-risk patients with THCA and facilitate early prediction and clinical intervention in patients with high risk and poor prognosis.

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.

A novel m7G-related lncRNA risk model for predicting prognosis and evaluating the tumor immune microenvironment in colon carcinoma

N7-Methylguanosine (m7G) modifications are a common type of posttranscriptional RNA modifications. Its function in the tumor microenvironment (TME) has garnered widespread focus in the past few years. Long non-coding RNAs (lncRNAs) played an essential part in tumor development and are closely associated with the tumor immune microenvironment. In this study, we employed a comprehensive bioinformatics approach to develop an m7G-associated lncRNA prognostic model based on the colon adenocarcinoma (COAD) database from The Cancer Genome Atlas (TCGA) database. Pearson’s correlation analysis was performed to identify m7G-related lncRNAs. Differential gene expression analysis was used to screen lncRNAs. Then, we gained 88 differentially expressed m7G-related lncRNAs. Univariate Cox analysis and Lasso regression analysis were performed to build an eight-m7G-related-lncRNA (ELFN1-AS1, GABPB1-AS1, SNHG7, GS1-124K5.4, ZEB1-AS1, PCAT6, C1RL-AS1, MCM3AP-AS1) risk model. Consensus clustering analysis was applied to identify the m7G-related lncRNA subtypes. We also verified the risk prediction effect of a gene signature in the GSE17536 test set (177 patients). A nomogram was constructed to predict overall survival rates. Furthermore, we analyzed differentially expressed genes (DEGs) between high-risk and low-risk groups. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted with the analyzed DEGs. At last, single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, MCP-COUNTER, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were utilized to discover the relationship between the risk model and the TME. Consequently, the m7G-related lncRNA risk model for COAD patients could be a viable prognostic tool and treatment target.

m 6 Aexpress-BHM: predicting m6A regulation of gene expression in multiple-groups context by a Bayesian hierarchical mixture model

As the most abundant RNA modification, N6-methyladenosine (m6A) plays an important role in various RNA activities including gene expression and translation. With the rapid application of MeRIP-seq technology, samples of multiple groups, such as the involved multiple viral/ bacterial infection or distinct cell differentiation stages, are extracted from same experimental unit. However, our current knowledge about how the dynamic m6A regulating gene expression and the role in certain biological processes (e.g. immune response in this complex context) is largely elusive due to lack of effective tools. To address this issue, we proposed a Bayesian hierarchical mixture model (called m6Aexpress-BHM) to predict m6A regulation of gene expression (m6A-reg-exp) in multiple groups of MeRIP-seq experiment with limited samples. Comprehensive evaluations of m6Aexpress-BHM on the simulated data demonstrate its high predicting precision and robustness. Applying m6Aexpress-BHM on three real-world datasets (i.e. Flaviviridae infection, infected time-points of bacteria and differentiation stages of dendritic cells), we predicted more m6A-reg-exp genes with positive regulatory mode that significantly participate in innate immune or adaptive immune pathways, revealing the underlying mechanism of the regulatory function of m6A during immune response. In addition, we also found that m6A may influence the expression of PD-1/PD-L1 via regulating its interacted genes. These results demonstrate the power of m6Aexpress-BHM, helping us understand the m6A regulatory function in immune system.

Role of main RNA modifications in cancer: N6-methyladenosine, 5-methylcytosine, and pseudouridine

Cancer is one of the major diseases threatening human life and health worldwide. Epigenetic modification refers to heritable changes in the genetic material without any changes in the nucleic acid sequence and results in heritable phenotypic changes. Epigenetic modifications regulate many biological processes, such as growth, aging, and various diseases, including cancer. With the advancement of next-generation sequencing technology, the role of RNA modifications in cancer progression has become increasingly prominent and is a hot spot in scientific research. This review studied several common RNA modifications, such as N⁶-methyladenosine, 5-methylcytosine, and pseudouridine. The deposition and roles of these modifications in coding and noncoding RNAs are summarized in detail. Based on the RNA modification background, this review summarized the expression, function, and underlying molecular mechanism of these modifications and their regulators in cancer and further discussed the role of some existing small-molecule inhibitors. More in-depth studies on RNA modification and cancer are needed to broaden the understanding of epigenetics and cancer diagnosis, treatment, and prognosis.

Effects of Simulated Microgravity on the Physiology of Stenotrophomonas maltophilia and Multiomic Analysis

Many studies have shown that the space environment plays a pivotal role in changing the characteristics of conditional pathogens, especially their pathogenicity and virulence. However, Stenotrophomonas maltophilia, a type of conditional pathogen that has shown to a gradual increase in clinical morbidity in recent years, has rarely been reported for its impact in space. In this study, S. maltophilia was exposed to a simulated microgravity (SMG) environment in high-aspect ratio rotating-wall vessel bioreactors for 14days, while the control group was exposed to the same bioreactors in a normal gravity (NG) environment. Then, combined phenotypic, genomic, transcriptomic, and proteomic analyses were conducted to compare the influence of the SMG and NG on S. maltophilia. The results showed that S. maltophilia in simulated microgravity displayed an increased growth rate, enhanced biofilm formation ability, increased swimming motility, and metabolic alterations compared with those of S. maltophilia in normal gravity and the original strain of S. maltophilia. Clusters of Orthologous Groups (COG) annotation analysis indicated that the increased growth rate might be related to the upregulation of differentially expressed genes (DEGs) involved in energy metabolism and conversion, secondary metabolite biosynthesis, transport and catabolism, intracellular trafficking, secretion, and vesicular transport. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the increased motility might be associated the upregulation of differentially expressed proteins (DEPs) involved in locomotion, localization, biological adhesion, and binding, in accordance with the upregulated DEGs in cell motility according to COG classification, including pilP, pilM, flgE, flgG, and ronN. Additionally, the increased biofilm formation ability might be associated with the upregulation of DEPs involved in biofilm formation, the bacterial secretion system, biological adhesion, and cell adhesion, which were shown to be regulated by the differentially expressed genes (chpB, chpC, rpoN, pilA, pilG, pilH, and pilJ) through the integration of transcriptomic and proteomic analyses. These results suggested that simulated microgravity might increase the level of corresponding functional proteins by upregulating related genes to alter physiological characteristics and modulate growth rate, motility, biofilm formation, and metabolism. In conclusion, this study is the first general analysis of the phenotypic, genomic, transcriptomic, and proteomic changes in S. maltophilia under simulated microgravity and provides some suggestions for future studies of space microbiology.