ADAR1-mediated RNA editing promotes B cell lymphomagenesis

Non‐m6A RNA modifications in haematological malignancies

Dysregulated RNA modifications, stemming from the aberrant expression and/or malfunction of RNA modification regulators operating through various pathways, play pivotal roles in driving the progression of haematological malignancies. Among RNA modifications, N6-methyladenosine (m6A) RNA modification, the most abundant internal mRNA modification, stands out as the most extensively studied modification. This prominence underscores the crucial role of the layer of epitranscriptomic regulation in controlling haematopoietic cell fate and therefore the development of haematological malignancies. Additionally, other RNA modifications (non-m6A RNA modifications) have gained increasing attention for their essential roles in haematological malignancies. Although the roles of the m6A modification machinery in haematopoietic malignancies have been well reviewed thus far, such reviews are lacking for non-m6A RNA modifications. In this review, we mainly focus on the roles and implications of non-m6A RNA modifications, including N4-acetylcytidine, pseudouridylation, 5-methylcytosine, adenosine to inosine editing, 2'-O-methylation, N1-methyladenosine and N7-methylguanosine in haematopoietic malignancies. We summarise the regulatory enzymes and cellular functions of non-m6A RNA modifications, followed by the discussions of the recent studies on the biological roles and underlying mechanisms of non-m6A RNA modifications in haematological malignancies. We also highlight the potential of therapeutically targeting dysregulated non-m6A modifiers in blood cancer.

RNA editing enzymes: structure, biological functions and applications

With the advancement of sequencing technologies and bioinformatics, over than 170 different RNA modifications have been identified. However, only a few of these modifications can lead to base pair changes, which are called RNA editing. RNA editing is a ubiquitous modification in mammalian transcriptomes and is an important co/posttranscriptional modification that plays a crucial role in various cellular processes. There are two main types of RNA editing events: adenosine to inosine (A-to-I) editing, catalyzed by ADARs on double-stranded RNA or ADATs on tRNA, and cytosine to uridine (C-to-U) editing catalyzed by APOBECs. This article provides an overview of the structure, function, and applications of RNA editing enzymes. We discuss the structural characteristics of three RNA editing enzyme families and their catalytic mechanisms in RNA editing. We also explain the biological role of RNA editing, particularly in innate immunity, cancer biogenesis, and antiviral activity. Additionally, this article describes RNA editing tools for manipulating RNA to correct disease-causing mutations, as well as the potential applications of RNA editing enzymes in the field of biotechnology and therapy.

Genetic and transcriptomic analyses of diffuse large B-cell lymphoma patients with poor outcomes within two years of diagnosis

Despite the improvements in clinical outcomes for DLBCL, a significant proportion of patients still face challenges with refractory/relapsed (R/R) disease after receiving first-line R-CHOP treatment. To further elucidate the underlying mechanism of R/R disease and to develop methods for identifying patients at risk of early disease progression, we integrated clinical, genetic and transcriptomic data derived from 2805 R-CHOP-treated patients from seven independent cohorts. Among these, 887 patients exhibited R/R disease within two years (poor outcome), and 1918 patients remained in remission at two years (good outcome). Our analysis identified four preferentially mutated genes (TP53, MYD88, SPEN, MYC) in the untreated (diagnostic) tumor samples from patients with poor outcomes. Furthermore, transcriptomic analysis revealed a distinct gene expression pattern linked to poor outcomes, affecting pathways involved in cell adhesion/migration, T-cell activation/regulation, PI3K, and NF-κB signaling. Moreover, we developed and validated a 24-gene expression score as an independent prognostic predictor for treatment outcomes. This score also demonstrated efficacy in further stratifying high-risk patients when integrated with existing genetic or cell-of-origin subtypes, including the unclassified cases in these models. Finally, based on these findings, we developed an online analysis tool ( https://lymphprog.serve.scilifelab.se/app/lymphprog ) that can be used for prognostic prediction for DLBCL patients.

Identifying CD1c as a potential biomarker by the comprehensive exploration of tumor mutational burden and immune infiltration in diffuse large B cell lymphoma

Background

Tumor mutational burden (TMB) is a valuable prognostic biomarker. This study explored the predictive value of TMB and the potential association between TMB and immune infiltration in diffuse large B-cell lymphoma (DLBCL).

Methods

We downloaded the gene expression profile, somatic mutation, and clinical data of DLBCL patients from The Cancer Genome Atlas (TCGA) database. We classified the samples into high-and low-TMB groups to identify differentially expressed genes (DEGs). Functional enrichment analyses were performed to determine the biological functions of the DEGs. We utilized the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm to estimate the abundance of 22 immune cells, and the significant difference was determined by the Wilcoxon rank-sum test between the high- and low-TMB group. Hub gene had been screened as the prognostic TMB-related immune biomarker by the combination of the Immunology Database and Analysis Portal (ImmPort) database and the univariate Cox analysis from the Gene Expression Omnibus (GEO) database including six DLBCL datasets. Various database applications such as Tumor Immune Estimation Resource (TIMER), CellMiner, konckTF, and Genotype-Tissue Expression (GTEx) verified the functions of the target gene. Wet assay confirmed the target gene expression at RNA and protein levels in DLBCL tissue and cell samples.

Results

Single nucleotide polymorphism (SNP) occurred more frequently than insertion and deletion, and C > T was the most common single nucleotide variant (SNV) in DLBCL. Survival analysis showed that the high-TMB group conferred poor survival outcomes. A total of 62 DEGs were obtained, and 13 TMB-related immune genes were identified. Univariate Cox analysis results illustrated that CD1c mutation was associated with lower TMB and manifested a satisfactory clinical prognosis by analysis of large samples from the GEO database. In addition, infiltration levels of immune cells in the high-TMB group were lower. Using the TIMER database, we systematically analyzed that the expression of CD1c was positively correlated with B cells, neutrophils, and dendritic cells and negatively correlated with CD8+ T cells, CD4+ T cells, and macrophages. Drug sensitivity showed a significant positive correlation between CD1c expression level and clinical drug sensitivity from the CellMiner database. CREB1, AHR, and TOX were used to comprehensively explore the regulation of CD1c-related transcription factors and signaling pathways by the KnockTF database. We searched the GETx database to compare the mRNA expression levels of CD1c between DLBCL and normal tissues, and the results suggested a significant difference between them. Moreover, wet experiments were conducted to verify the high expression of CD1c in DLBCL at the RNA and protein levels.

Conclusions

Higher TMB correlated with poor survival outcomes and inhibited the immune infiltrates in DLBCL. Our results suggest that CD1c is a TMB-related prognostic biomarker.

FLARE: a fast and flexible workflow for identifying RNA editing foci

BACKGROUND

Fusion of RNA-binding proteins (RBPs) to RNA base-editing enzymes (such as APOBEC1 or ADAR) has emerged as a powerful tool for the discovery of RBP binding sites. However, current methods that analyze sequencing data from RNA-base editing experiments are vulnerable to false positives due to off-target editing, genetic variation and sequencing errors.

RESULTS

We present FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I. We applied FLARE to C-to-U editing data from a RBFOX2-APOBEC1 STAMP experiment, to show that our approach attains high specificity for detecting RBFOX2 binding sites. We also applied FLARE to detect regions of exogenously introduced as well as endogenous A-to-I editing.

CONCLUSIONS

FLARE is a fast and flexible workflow that identifies significantly edited regions from RNA-seq data. The FLARE codebase is available at https://github.com/YeoLab/FLARE .