N7-methylguanosin regulators-mediated methylation modification patterns and characterization of the immune microenvironment in lower-grade glioma

Predicting the risk of primary Sjögren's syndrome with key N7-methylguanosine-related genes: A novel XGBoost model

Objectives

N7-methylguanosine (m7G) plays a crucial role in mRNA metabolism and other biological processes. However, its regulators' function in Primary Sjögren's Syndrome (PSS) remains enigmatic.

Methods

We screened five key m7G-related genes across multiple datasets, leveraging statistical and machine learning computations. Based on these genes, we developed a prediction model employing the extreme gradient boosting decision tree (XGBoost) method to assess PSS risk. Immune infiltration in PSS samples was analyzed using the ssGSEA method, revealing the immune landscape of PSS patients.

Results

The XGBoost model exhibited high accuracy, AUC, sensitivity, and specificity in both training, test sets and extra-test set. The decision curve confirmed its clinical utility. Our findings suggest that m7G methylation might contribute to PSS pathogenesis through immune modulation.

Conclusions

m7G regulators play an important role in the development of PSS. Our study of m7G-realted genes may inform future immunotherapy strategies for PSS.

M7G-related tumor immunity: novel insights of RNA modification and potential therapeutic targets

RNA modifications play a pivotal role in regulating cellular biology by exerting influence over distribution features and molecular functions at the post-transcriptional level. Among these modifications, N7-methylguanosine (m7G) stands out as one of the most prevalent. Over recent years, significant attention has been directed towards understanding the implications of m7G modification. This modification is present in diverse RNA molecules, including transfer RNAs, messenger RNAs, ribosomal RNAs, and other noncoding RNAs. Its regulation occurs through a series of specific methyltransferases and m7G-binding proteins. Notably, m7G modification has been implicated in various diseases, prominently across multiple cancer types. Earlier studies have elucidated the significance of m7G modification in the context of immune biology regulation within the tumor microenvironment. This comprehensive review culminates in a synthesis of findings related to the modulation of immune cells infiltration, encompassing T cells, B cells, and various innate immune cells, all orchestrated by m7G modification. Furthermore, the interplay between m7G modification and its regulatory proteins can profoundly affect the efficacy of diverse adjuvant therapeutics, thereby potentially serving as a pivotal biomarker and therapeutic target for combinatory interventions in diverse cancer types.

A Novel RNA Methylation-Related Prognostic Signature and its Tumor Microenvironment Characterization in Hepatocellular Carcinoma

INTRODUCTION

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system. RNA methylation plays an important role in tumorigenesis and metastasis, which could alter gene expression and even function at multiple levels, such as RNA splicing, stability, translocation, and translation. In this study, we aimed to conduct a comprehensive analysis of RNA methylation-related genes (RMGs) in HCC and their relationship with survival and clinical features.

METHODS

A retrospective analysis was performed using publicly available HCC-related datasets. The differentially expressed genes (DEGs) between HCC and controls were identified from TCGA-LlHC and intersected with RMGs to obtain differentially expressed RNA methylation-related genes (DERMGs). Regression analysis was used to screen for prognostic genes and construct risk models. Simultaneously, clinical, immune infiltration and therapeutic efficacy analyses were performed. Finally, multivariate cox regression was used to identify independent risk factors, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the expression levels of the core genes of the model.

RESULTS

A 21-gene risk model for HCC was established with excellent performance based on ROC curves and survival analysis. Risk scores correlated with tumor grade, pathologic T, and TNM stage. Immune infiltration analysis showed correlations with immune scores, 11 immune cells, and 30 immune checkpoints. Low-risk patients showed a higher susceptibility to immunotherapy. The risk score and TNM stage were independent prognostic factors. qRT-PCR confirmed higher expression of PRDM9, ALPP, and GAD1 in HCC.

CONCLUSIONS

This study identified RNA methylation-related signature genes in HCC and constructed a risk model that predicts patient outcomes and reflects the immune microenvironment. Prognostic genes are involved in complex regulatory mechanisms, which may be useful for cancer diagnosis, prognosis, and therapy.

Insights into the regulatory role of RNA methylation modifications in glioma

Epitranscriptomic abnormalities, which are highly prevalent in primary central nervous system malignancies, have been identified as crucial contributors to the development and progression of gliomas. RNA epitranscriptomic modifications, particularly the reversible modification methylation, have been observed throughout the RNA cycle. Epitranscriptomic modifications, which regulate RNA transcription and translation, have profound biological implications. These modifications are associated with the development of several cancer types. Notably, three main protein types-writers, erasers, and readers, in conjunction with other related proteins, mediate these epitranscriptomic changes. This review primarily focuses on the role of recently identified RNA methylation modifications in gliomas, such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), N7-methylguanosine (m7G), and N1-methyladenosine (m1A). We delved into their corresponding writers, erasers, readers, and related binding proteins to propose new approaches and prognostic indicators for patients with glioma.

Identification and prediction of m7G-related Alzheimer's disease subtypes: insights from immune infiltration and machine learning models

Introduction

Alzheimer's disease (AD) is a complex and progressive neurodegenerative disorder that primarily affects older individuals. N7-methylguanosine (m7G) is a common RNA chemical modification that impacts the development of numerous diseases. Thus, our work investigated m7G-related AD subtypes and established a predictive model.

Methods

The datasets for AD patients, including GSE33000 and GSE44770, were obtained from the Gene Expression Omnibus (GEO) database, which were derived from the prefrontal cortex of the brain. We performed differential analysis of m7G regulators and examined the immune signatures differences between AD and matched-normal samples. Consensus clustering was employed to identify AD subtypes based on m7G-related differentially expressed genes (DEGs), and immune signatures were explored among different clusters. Furthermore, we developed four machine learning models based on the expression profiles of m7G-related DEGs and identified five important genes from the optimal model. We evaluated the predictive power of the 5-gene-based model using an external AD dataset (GSE44770).

Results

A total of 15 genes related to m7G were found to be dysregulated in patients with AD compared to non-AD patients. This finding suggests that there are differences in immune characteristics between these two groups. Based on the differentially expressed m7G regulators, we categorized AD patients into two clusters and calculated the ESTIMATE score for each cluster. Cluster 2 exhibited a higher ImmuneScore than Cluster 1. We performed the receiver operating characteristic (ROC) analysis to compare the performance of four models, and we found that the Random Forest (RF) model had the highest AUC value of 1.000. Furthermore, we tested the predictive efficacy of a 5-gene-based RF model on an external AD dataset and obtained an AUC value of 0.968. The nomogram, calibration curve, and decision curve analysis (DCA) confirmed the accuracy of our model in predicting AD subtypes.

Conclusion

The present study systematically examines the biological significance of m7G methylation modification in AD and investigates its association with immune infiltration characteristics. Furthermore, the study develops potential predictive models to assess the risk of m7G subtypes and the pathological outcomes of patients with AD, which can facilitate risk classification and clinical management of AD patients.