m6A-Atlas v2.0: updated resources for unraveling the N6-methyladenosine (m6A) epitranscriptome among multiple species

RNA sequence analysis landscape: A comprehensive review of task types, databases, datasets, word embedding methods, and language models

Deciphering information of RNA sequences reveals their diverse roles in living organisms, including gene regulation and protein synthesis. Aberrations in RNA sequence such as dysregulation and mutations can drive a diverse spectrum of diseases including cancers, genetic disorders, and neurodegenerative conditions. Furthermore, researchers are harnessing RNA's therapeutic potential for transforming traditional treatment paradigms into personalized therapies through the development of RNA-based drugs and gene therapies. To gain insights of biological functions and to detect diseases at early stages and develop potent therapeutics, researchers are performing diverse types RNA sequence analysis tasks. RNA sequence analysis through conventional wet-lab methods is expensive, time-consuming and error prone. To enable large-scale RNA sequence analysis, empowerment of wet-lab experimental methods with Artificial Intelligence (AI) applications necessitates scientists to have a comprehensive knowledge of both DNA and AI fields. While molecular biologists encounter challenges in understanding AI methods, computer scientists often lack basic foundations of RNA sequence analysis tasks. Considering the absence of a comprehensive literature that bridges this research gap and promotes the development of AI-driven RNA sequence analysis applications, the contributions of this manuscript are manifold: It equips AI researchers with biological foundations of 47 distinct RNA sequence analysis tasks. It sets a stage for development of benchmark datasets related to 47 distinct RNA sequence analysis tasks by facilitating cruxes of 64 different biological databases. It presents word embeddings and language models applications across 47 distinct RNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 58 word embeddings and 70 language models based predictive pipelines performance values as well as top performing traditional sequence encoding based predictors and their performances across 47 RNA sequence analysis tasks.

Consistent RNA expression and RNA modification patterns in cardiotoxicity induced by Matrine and Evodiamine

Recent research has demonstrated the efficacy of traditional Chinese medicine (TCM) and its active compounds in combating cancer, leading to an increasing utilization of TCM as adjunctive therapy in clinical oncology. However, the optimal dosage of TCM remains unclear, and excessive use may result in cardiotoxicity, which poses a significant health concern for patients undergoing systemic treatment. Therefore, elucidating the underlying mechanisms of cytotoxicity induced by TCM can provide valuable insights for clinical management. In this study, we employed a comprehensive bioinformatics analysis to present sequencing data obtained from AC16 myocardial cells treated with two bioactive derived from botanical drugs: Matrine and Evodiamine. We aim to investigate the dysregulated signaling pathways associated with cardiotoxicity induced by these compounds. Based on our sequencing results, we observed consistent patterns of gene expression and epitranscriptome regulation (m6A and A-to-I modifications) across various drugs-treated AC16 cells when analyzed using KEGG pathway enrichment and gene ontology analyses. Furthermore, m6A writers VIRMA and A-to-I writers ADARB1 is consistent target of Evodiamine and Matrine. In general, our findings suggest that different Chinese botanical drugs induced cardiotoxicity may share common therapeutic strategies.

Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation

As a fundamental mechanism for gene expression regulation, post-transcriptional RNA methylation plays versatile roles in various biological processes and disease mechanisms. Recent advances in single-cell technology have enabled simultaneous profiling of transcriptome-wide RNA methylation in thousands of cells, holding the promise to provide deeper insights into the dynamics, functions, and regulation of RNA methylation. However, it remains a major challenge to determine how to best analyze single-cell epitranscriptomics data. In this study, we developed SigRM, a computational framework for effectively mining single-cell epitranscriptomics datasets with a large cell number, such as those produced by the scDART-seq technique from the SMART-seq2 platform. SigRM not only outperforms state-of-the-art models in RNA methylation site detection on both simulated and real datasets but also provides rigorous quantification metrics of RNA methylation levels. This facilitates various downstream analyses, including trajectory inference and regulatory network reconstruction concerning the dynamics of RNA methylation.

Sci-ModoM: a quantitative database of transcriptome-wide high-throughput RNA modification sites

We present Sci-ModoM, the first next-generation RNome database offering a holistic view of the epitranscriptomic landscape. Sci-ModoM has a simple yet powerful interface, underpinned by FAIR data principles, a standardized nomenclature, and interoperable formats, fostering the use of common standards within the epitranscriptomics community. Sci-ModoM provides quantitative measurements per site and dataset, enabling users to assess confidence levels based on score, coverage, and stoichiometry. Data in Sci-ModoM is directly traceable to its sources. Users can Search and Compare over six million modifications across 156 datasets, Browse or download datasets, and retrieve metadata. A comparison tool offers a novel and unique opportunity to compare modifications site-wise across datasets, with the ability to securely upload and compare user data against latest published research. Sci-ModoM empowers researchers, including non-experts, to access a broad spectrum of recent quantitative RNA modification data, thereby enhancing the utility and impact of latest discoveries, and opening new avenues in biological and medical research.

A combined deep learning framework for mammalian m6A site prediction

N6-methyladenosine (m6A) is the most prevalent chemical modification in eukaryotic mRNAs and plays key roles in diverse cellular processes. Precise localization of m6A sites is thus critical for characterizing the functional roles of m6A in various conditions and dissecting the mechanisms governing its deposition. Here, we design a combined framework of Transformer architecture and recurrent neural network, deepSRAMP, to identify m6A sites using sequence-based and genome-derived features. As a result, deepSRAMP achieves a notably enhanced performance compared to its predecessor, SRAMP, the most-used predictor in this field. Moreover, based on multiple benchmark datasets, deepSRAMP greatly outperforms other state-of-the-art m6A predictors, including WHISTLE and DeepPromise, with an average 16.1% and 18.3% increase in AUROC and a 43.9% and 46.4% increase in AUPRC. Finally, deepSRAMP can be successfully exploited on mammalian m6A epitranscriptome mapping under diverse cellular conditions and can potentially reveal differential m6A sites among transcript isoforms of individual genes.

Interpretable deep cross networks unveiled common signatures of dysregulated epitranscriptomes across 12 cancer types

Cancer is a complex and multifaceted group of diseases characterized by uncontrolled cell growth that leads to the formation of malignant tumors. Recent studies suggest that N6-methyladenosine (m6A) RNA methylation plays pivotal roles in cancer pathology by influencing various cellular processes. However, the degree to which these mechanisms are shared across different cancer types remains unclear. In this study, we analyze an expansive array of 167 m6A epitranscriptome profiles covering 12 distinct cancer types and their originating normal tissues. We trained 12 distinct, cancer type-specific interpretable deep cross network models, which successfully distinguish between specific pairs of normal and cancer m6A contexts using integrated information from both the sequences and curated genomic knowledge. Interestingly, cross-cancer type testing indicated the existence of shared genomic patterns across various cancers at the epitranscriptome level. A pan-cancer model was subsequently developed to identify these shared patterns that could not be observed in a single cancer type. Our analysis uncovered, for the first time, a common epitranscriptome signature shared across multiple cancer types, particularly associated with RNA hybridization process and aberrant splicing. This highlights the importance of a comprehensive understanding of the pan-cancer epitranscriptome and holding potential implications in the development of RNA methylation-based therapeutics for various cancers.

Multiomics profiling uncovers curdione-induced reproductive toxicity in HTR-8/SVneo cells

The assessment of medication toxicity and safety is pivotal during pregnancy. Curdione, a sesquiterpene compound extracted from Curcumae Radix, displays beneficial properties in terms of anti-inflammatory, tumor growth suppression, and anti-coagulative effects. However, its reproductive toxicity and precise mechanism remain unclear. This study aims to explore the mechanism of curdione-induced toxicity damage in HTR-8/SVneo cells through the epigenetics, proteomics, and metabolomics, and experimental verification. The results showed that curdione elicited alterations in m6A modification, gene expression, protein levels, and cellular metabolism of HTR-8/SVneo cells. Additionally, curdione induces oxidative stress, mitochondrial and DNA damage, while also downregulating the expression of Wnt6, β-catenin, ZO-1, and CLDN1 proteins. Curdione has the potential to modulate oxidative stress, mitochondrial dysfunction, and disruption of tight junctions via the Wnt/β-catenin signaling pathway, which contributes to cellular damage in HTR-8/SVneo cells.

Comprehensive Profiling of Transcriptome and m6A Epitranscriptome Uncovers the Neurotoxic Effects of Yunaconitine on HT22 Cells

Objective

To explore different mRNA transcriptome patterns and RNA N6-methyladenosine (m6A) alteration in yunaconitine (YA)-treated HT22 mouse hippocampal neuron, and uncover the role of abnormal mRNA expression and RNA m6A modification in YA-induced neurotoxicity.

Methods

HT22 cells were treated with 0, 5, 10, and 50 μM of YA for 72 h to evaluate their viability and GSH content. Subsequently, mRNA-seq and MeRIP-seq analyses were performed on HT22 cells treated with 0 and 10 μM YA for 72 h, and molecular docking was used to simulate interactions between YA and differentially expressed m6A regulators. The mitochondrial membrane potential was examined using the JC-10 probe, and RT-qPCR was conducted to verify the expression levels of differentially expressed m6A regulatory factors, as well as to assess alterations in the mRNA expression levels of antioxidant genes.

Results

YA treatment significantly reduced the viability of HT22 cells and decreased GSH content. The mRNA-seq analysis obtained 1018 differentially expressed genes, KEGG and GO enrichment results of differentially expressed genes mainly comprise the nervous system development, cholinergic synapse, response to oxidative stress, and mitochondrial inner membrane. A total of 7 differentially expressed m6A regulators were identified by MeRIP-seq. Notably, molecular docking results suggested a stable interaction between YA and most of the differentially expressed m6A regulators.

Conclusion

This study showed that YA-induced HT22 cell damage was associated with the increased methylation modification level of target gene m6A and abnormal expression of m6A regulators.

Exploration of N6-methyladenosine modification in ascorbic acid 2-glucoside constructed stem cell sheets

The aim of this study was to explore the mechanism of bone marrow stem cells (BMSCs) sheets constructed with different doses of Ascorbic acid 2-glucoside (AA-2G) in conjunction with N6-methyladenosine (m6A)-associated epigenetic genes analysing transcriptome sequencing data. Experimental groups of BMSCs induced by different AA-2G concentrations were set up, and the tissue structures were observed by histological staining of cell slices and scanning electron microscopy. Expression patterns of DEGs were analysed using short-time sequence expression mining software, and DEGs associated with m6A were selected for gene ontology analysis and pathway analysis. The protein-protein interaction (PPI) network of DEGs was analysed and gene functions were predicted using the search tool of the Retrieve Interacting Genes database. There were 464 up-regulated DEGs and 303 down-regulated DEGs between the control and high-dose AA-2G treatment groups, and 175 up-regulated DEGs and 37 down-regulated DEGs between the low and high-dose AA-2G treatment groups. The profile 7 exhibited a gradual increase in gene expression levels over AA-2G concentration. In contrast, profile 0 exhibited a gradual decrease in gene expression levels over AA-2G concentration. In the PPI network of m6A-related DEGs in profile 7, the cluster of metallopeptidase inhibitor 1 (Timp1), intercellular adhesion molecule 1 (Icam1), insulin-like growth factor 1 (Igf1), matrix metallopeptidase 2 (Mmp2), serpin family E member 1 (Serpine1), C-X-C motif chemokine ligand 2 (Cxcl2), galectin 3 (Lgals3) and angiopoietin-1 (Angpt1) was the top hub gene cluster. The expression of all hub genes was significantly increased after AA-2G intervention (P < 0.05), and the expression of Igf1 and Timp1 increased with increasing intervention concentration. The m6A epigenetic modifications were involved in the AA-2G-induced formation of BMSCs. Igf1, Serpine1 and Cxcl2 in DEGs were enriched for tissue repair, promotion of endothelial and epithelial proliferation and regulation of apoptosis.

Examining the evidence for mutual modulation between m6A modification and circular RNAs: current knowledge and future prospects

The resistance of cancer cells to treatment significantly impedes the success of therapy, leading to the recurrence of various types of cancers. Understanding the specific mechanisms of therapy resistance may offer novel approaches for alleviating drug resistance in cancer. Recent research has shown a reciprocal relationship between circular RNAs (circRNAs) and N6-methyladenosine (m6A) modification, and their interaction can affect the resistance and sensitivity of cancer therapy. This review aims to summarize the latest developments in the m6A modification of circRNAs and their importance in regulating therapy resistance in cancer. Furthermore, we explore their mutual interaction and exact mechanisms and provide insights into potential future approaches for reversing cancer resistance.

Interaction of the intestinal cytokines-JAKs-STAT3 and 5 axes with RNA N6-methyladenosine to promote chronic inflammation-induced colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers, with a high mortality rate worldwide. Mounting evidence indicates that mRNA modifications are crucial in RNA metabolism, transcription, processing, splicing, degradation, and translation. Studies show that N6-methyladenosine (m6A) is mammalians' most common epi-transcriptomic modification. It has been demonstrated that m6A is involved in cancer formation, progression, invasion, and metastasis, suggesting it could be a potential biomarker for CRC diagnosis and developing therapeutics. Cytokines, growth factors, and hormones function in JAK/STAT3/5 signaling pathway, and they could regulate the intestinal response to infection, inflammation, and tumorigenesis. Reports show that the JAK/STAT3/5 pathway is involved in CRC development. However, the underlying mechanism is still unclear. Signal Transducer and Activator of Transcription 3/5 (STAT3, STAT5) can act as oncogenes or tumor suppressors in the context of tissue types. Also, epigenetic modifications and mutations could alter the balance between pro-oncogenic and tumor suppressor activities of the STAT3/5 signaling pathway. Thus, exploring the interaction of cytokines-JAKs-STAT3 and/or STAT5 with mRNA m6A is of great interest. This review provides a comprehensive overview of the characteristics and functions of m6A and JAKs-STAT3/5 and their relationship with gastrointestinal (GI) cancers.

Variable calling of m6A and associated features in databases: a guide for end-users

N6-methyladenosine (m$^{6}$A) is a widely-studied methylation to messenger RNAs, which has been linked to diverse cellular processes and human diseases. Numerous databases that collate m$^{6}$A profiles of distinct cell types have been created to facilitate quick and easy mining of m$^{6}$A signatures associated with cell-specific phenotypes. However, these databases contain inherent complexities that have not been explicitly reported, which may lead to inaccurate identification and interpretation of m$^{6}$A-associated biology by end-users who are unaware of them. Here, we review various m$^{6}$A-related databases, and highlight several critical matters. In particular, differences in peak-calling pipelines across databases drive substantial variability in both peak number and coordinates with only moderate reproducibility, and the inclusion of peak calls from early m$^{6}$A sequencing protocols may lead to the reporting of false positives or negatives. The awareness of these matters will help end-users avoid the inclusion of potentially unreliable data in their studies and better utilize m$^{6}$A databases to derive biologically meaningful results.

Simultaneous Detection of Adenosine-to-Inosine Editing and N6-Methyladenosine at Identical RNA Sites through Deamination-Assisted Reverse Transcription Stalling

Adenosine-to-inosine (A-to-I) editing and N6-methyladenosine (m6A) modifications are pivotal RNA modifications with widespread functional significance in physiological and pathological processes. Although significant effort has been dedicated to developing methodologies for identifying and quantifying these modifications, traditional approaches have often focused on each modification independently, neglecting the potential co-occurrence of A-to-I editing and m6A modifications at the same adenosine residues. This limitation has constrained our understanding of the intricate regulatory mechanisms governing RNA function and the interplay between different types of RNA modifications. To address this gap, we introduced an innovative technique called deamination-assisted reverse transcription stalling (DARTS), specifically designed for the simultaneous quantification of A-to-I editing and m6A at the same RNA sites. DARTS leverages the selective deamination activity of the engineered TadA-TadA8e protein, which converts adenosine residues to inosine, in combination with the unique property of Bst 2.0 DNA polymerase, which stalls when encountering inosine during reverse transcription. This approach enables the accurate quantification of A-to-I editing, m6A, and unmodified adenosine at identical RNA sites. The DARTS method is remarkable for its ability to directly quantify two distinct types of RNA modifications simultaneously, a capability that has remained largely unexplored in the field of RNA biology. By facilitating a comprehensive analysis of the co-occurrence and interaction between A-to-I editing and m6A modifications, DARTS opens new avenues for exploring the complex regulatory networks modulated by different RNA modifications.

m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq

We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a genomic feature-based classifier that refines the identification of m6A sites, distinguishing those genuinely present from those that can be detected in in-vitro transcribed (IVT) control experiments. We find that m6ACali effectively identifies non-specific binding peaks reported by exomePeak2 and MACS2 in novel MeRIP-Seq datasets without the need for paired IVT controls. The model interpretation revealed that off-target antibody binding sites commonly occur at short exons and short mRNAs, originating from high read coverage regions that share the motif sequence with true m6A sites. We also reveal that the ML strategy can efficiently adjust differentially methylated peaks and other antibody-dependent, base-resolution m6A detection techniques. As a result, m6ACali offers a promising method for the universal enhancement of m6A profiles generated by MeRIP-Seq experiments, elevating the benchmark for omics-level m6A data integration.

Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead

RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.

m6A-TCPred: a web server to predict tissue-conserved human m6A sites using machine learning approach

BACKGROUND

N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level.

RESULTS

We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively.

CONCLUSION

Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .

Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment

Nanopore signal analysis enables detection of nucleotide modifications from native DNA and RNA sequencing, providing both accurate genetic/transcriptomic and epigenetic information without additional library preparation. Presently, only a limited set of modifications can be directly basecalled (e.g. 5-methylcytosine), while most others require exploratory methods that often begin with alignment of nanopore signal to a nucleotide reference. We present Uncalled4, a toolkit for nanopore signal alignment, analysis, and visualization. Uncalled4 features an efficient banded signal alignment algorithm, BAM signal alignment file format, statistics for comparing signal alignment methods, and a reproducible de novo training method for k-mer-based pore models, revealing potential errors in ONT's state-of-the-art DNA model. We apply Uncalled4 to RNA 6-methyladenine (m6A) detection in seven human cell lines, identifying 26% more modifications than Nanopolish using m6Anet, including in several genes where m6A has known implications in cancer. Uncalled4 is available open-source at github.com/skovaka/uncalled4.

The 2024 Nucleic Acids Research database issue and the online molecular biology database collection

The 2024 Nucleic Acids Research database issue contains 180 papers from across biology and neighbouring disciplines. There are 90 papers reporting on new databases and 83 updates from resources previously published in the Issue. Updates from databases most recently published elsewhere account for a further seven. Nucleic acid databases include the new NAKB for structural information and updates from Genbank, ENA, GEO, Tarbase and JASPAR. The Issue's Breakthrough Article concerns NMPFamsDB for novel prokaryotic protein families and the AlphaFold Protein Structure Database has an important update. Metabolism is covered by updates from Reactome, Wikipathways and Metabolights. Microbes are covered by RefSeq, UNITE, SPIRE and P10K; viruses by ViralZone and PhageScope. Medically-oriented databases include the familiar COSMIC, Drugbank and TTD. Genomics-related resources include Ensembl, UCSC Genome Browser and Monarch. New arrivals cover plant imaging (OPIA and PlantPAD) and crop plants (SoyMD, TCOD and CropGS-Hub). The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Over the last year the NAR online Molecular Biology Database Collection has been updated, reviewing 1060 entries, adding 97 new resources and eliminating 388 discontinued URLs bringing the current total to 1959 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

PRMD: an integrated database for plant RNA modifications

The scope and function of RNA modifications in model plant systems have been extensively studied, resulting in the identification of an increasing number of novel RNA modifications in recent years. Researchers have gradually revealed that RNA modifications, especially N6-methyladenosine (m6A), which is one of the most abundant and commonly studied RNA modifications in plants, have important roles in physiological and pathological processes. These modifications alter the structure of RNA, which affects its molecular complementarity and binding to specific proteins, thereby resulting in various of physiological effects. The increasing interest in plant RNA modifications has necessitated research into RNA modifications and associated datasets. However, there is a lack of a convenient and integrated database with comprehensive annotations and intuitive visualization of plant RNA modifications. Here, we developed the Plant RNA Modification Database (PRMD; http://bioinformatics.sc.cn/PRMD and http://rnainformatics.org.cn/PRMD) to facilitate RNA modification research. This database contains information regarding 20 plant species and provides an intuitive interface for displaying information. Moreover, PRMD offers multiple tools, including RMlevelDiff, RMplantVar, RNAmodNet and Blast (for functional analyses), and mRNAbrowse, RNAlollipop, JBrowse and Integrative Genomics Viewer (for displaying data). Furthermore, PRMD is freely available, making it useful for the rapid development and promotion of research on plant RNA modifications.

Global RNA modifications to the MALAT1 triple helix differentially affect thermostability and weaken binding to METTL16

Therapeutic mRNAs are generated using modified nucleotides, namely N1-methylpseudouridine (m1Ψ) triphosphate, so that the mRNA evades detection by the immune system. RNA modifications, even at a single-nucleotide position, perturb RNA structure, although it is not well understood how structure and function is impacted by globally modified RNAs. Therefore, we examined the metastasis-associated lung adenocarcinoma transcript 1 triple helix, a highly structured stability element that includes single-, double-, and triple-stranded RNA, globally modified with N6-methyladenosine (m6A), pseudouridine (Ψ), or m1Ψ. UV thermal denaturation assays showed that m6A destabilizes both the Hoogsteen and Watson-Crick faces of the RNA by ∼20 °C, Ψ stabilizes the Hoogsteen and Watson-Crick faces of the RNA by ∼12 °C, and m1Ψ has minimal effect on the stability of the Hoogsteen face of the RNA but increases the stability of the Watson-Crick face by ∼9 °C. Native gel-shift assays revealed that binding of the methyltransferase-like protein 16 to the metastasis-associated lung adenocarcinoma transcript 1 triple helix was weakened by at least 8-, 99-, and 23-fold, respectively, when RNA is globally modified with m6A, Ψ, or m1Ψ. These results demonstrate that a more thermostable RNA structure does not lead to tighter RNA-protein interactions, thereby highlighting the regulatory power of RNA modifications by multiple means.

Molecular Characterization and Establishment of a Prognostic Model Based on Primary Immunodeficiency Features in Association with RNA Modifications in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Although immunotherapy is effective for some patients, most find it difficult to benefit from it. This study aims to explore the impact of specific immune pathways and their regulated molecular mechanisms in TNBC. The gene expression data of breast cancer patients were obtained from the TCGA and METABRIC databases. Gene set variation analysis (GSVA) revealed specific upregulation or abnormal expression of immunodeficiency pathways in TNBC patients. Multi-omics data showed significant differential expression of Primary Immunodeficiency Genes (PIDGs) in TNBC patients, who are prone to genomic-level variations. Consensus clustering was used in two datasets to classify patients into two distinct molecular subtypes based on PIDGs expression patterns, with each displaying different biological features and immune landscapes. To further explore the prognostic characteristics of PIDGs-regulated molecules, we constructed a four-gene prognostic PIDG score model and a nomogram using least absolute shrinkage and selection operator (LASSO) regression analysis in combination with clinicopathological parameters. The PIDG score was closely associated with the immune therapy and drug sensitivity of TNBC patients, providing potential guidance for clinical treatment. Particularly noteworthy is the close association of this scoring with RNA modifications; patients with different scores also exhibited different mutation landscapes. This study offers new insights for the clinical treatment of TNBC and for identifying novel prognostic markers and therapeutic targets in TNBC.

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.