A Novel m7G-Related Gene Signature Predicts the Prognosis of Colon Cancer

A novel m7G-related miRNA prognostic signature for predicting clinical outcome and immune microenvironment in colon cancer

Background: Colon cancer (CC) is a highly prevalent malignancy worldwide, characterized by elevated mortality rates and poor prognosis. N7-methylguanosine (m7G) methylation is an emerging RNA modification type and involved in the development of many tumors. Despite this, the correlation between m7G-related miRNAs and CC remains to be elucidated. This research aimed to investigate the clinical significance of m7G-related miRNAs in predicting both the prognosis and tumor microenvironment (TME) of CC. Method: We retrieved transcriptome data and associated clinical information from a publicly accessible database. Using univariate Cox and LASSO regression analyses, we established a signature of m7G-related miRNAs. Additionally, we used CIBERSORT and ssGSEA algorithms to explore the association between the prognostic risk score and the TME in CC patients. By considering the risk signature and immune infiltration, we identified differentially expressed genes that contribute to the prognosis of CC. Finally, the expression patterns of prognostic miRNAs were verified using quantitative reverse transcriptase PCR (qRT-PCR) in cell lines. Results: We constructed a prognostic risk signature based on seven m7G-related miRNAs (miR-136-5p, miR-6887-3p, miR-195-5p, miR-149-3p, miR-4433a-5p, miR-31-5p, and miR-129-2-3p). Subsequently, we observed remarkable differences in patient outcomes between the high- and low-risk groups. The area under the curve (AUC) for 1-, 3-, and 5-year survivals in the ROC curve were 0.735, 0.707, and 0.632, respectively. Furthermore, our results showed that the risk score can serve as an independent prognostic biomarker for overall survival prediction. In terms of immune analysis, the results revealed a significant association between the risk signature and immune infiltration, as well as immune checkpoint expression. Finally, our study showed that CCDC160 and RLN3 is the gene most relevant to immune cells and function in CC. Conclusion: Our study conducted a comprehensive and systematic analysis of m7G-associated miRNAs to construct prognostic profiles of CC. We developed a prognostic risk model based on m7G-miRNAs, with the resulting risk scores demonstrating considerable potential as prognostic biomarkers. These findings provide substantial evidence for the critical role of m7G-related miRNAs in colon cancer and may offer new immunotherapeutic targets for patients with this disease.

RNA modifications in cancer immune therapy: regulators of immune cells and immune checkpoints

RNA modifications are epigenetic changes that alter the structure and function of RNA molecules, playing a crucial role in the onset, progression, and treatment of cancer. Immune checkpoint inhibitor (ICI) therapies, particularly PD-1 blockade and anti-CTLA-4 treatments, have changed the treatment landscape of virous cancers, showing great potential in the treatment of different cancer patients, but sensitivity to these therapies is limited to certain individuals. This review offers a comprehensive survey of the functions and therapeutic implications of the four principal RNA modifications, particularly highlighting the significance of m6A in the realms of immune cells in tumor and immunotherapy. This review starts by providing a foundational summary of the roles RNA modifications assume within the immune cell community, focusing on T cells, NK cells, macrophages, and dendritic cells. We then discuss how RNA modifications influence the intricate regulatory mechanisms governing immune checkpoint expression, modulation of ICI efficacy, and prediction of ICI treatment outcomes, and review drug therapies targeting genes regulated by RNA modifications. Finally, we explore the role of RNA modifications in gene editing, cancer vaccines, and adoptive T cell therapies, offering valuable insights into the use of RNA modifications in cancer immunotherapy.

Tumor-associated characteristics and immune dysregulation in nasopharyngeal carcinoma under the regulation of m7G-related tumor microenvironment cells

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a type of malignant tumor with high morbidity. Aberrant levels of N7-methylguanosine (m7G) are closely associated with tumor progression. However, the characteristics of the tumor microenvironment (TME) in NPC associated with m7G modification remain unclear.

METHODS

A total of 68,795 single cells from single-cell RNA sequencing data derived from 11 NPC tumor samples and 3 nasopharyngeal lymphatic hyperplasia (NLH) samples were clustered using a nonnegative matrix factorization algorithm according to 61 m7G RNA modification regulators.

RESULTS

The m7G regulators were found differential expression in the TME cells of NPC, and most m7G-related immune cell clusters in NPC tissues had a higher abundance compared to non-NPC tissues. Specifically, m7G scores in the CD4+ and CD8+ T cell clusters were significantly lower in NPC than in NLH. T cell clusters differentially expressed immune co-stimulators and co-inhibitors. Macrophage clusters differentially expressed EIF4A1, and high EIF4A1 expression was associated with poor survival in patients with head and neck squamous carcinoma. EIF4A1 was upregulated in NPC tissues compared to the non-NPC tissues and mainly expressed in CD86+ macrophages. Moreover, B cell clusters exhibited tumor biological characteristics under the regulation of m7G-related genes in NPC. The fibroblast clusters interacted with the above immune cell clusters and enriched tumor biological pathways, such as FGER2 signaling pathway. Importantly, there were correlations and interactions through various ligand-receptor links among epithelial cells and m7G-related TME cell clusters.

CONCLUSION

Our study revealed tumor-associated characteristics and immune dysregulation in the NPC microenvironment under the regulation of m7G-related TME cells. These results demonstrated the underlying regulatory roles of m7G in NPC.

CD39 identifies a specific CD8 + T cell population in lung adenocarcinoma-related metastatic pleural effusion

Malignant pleural effusion (MPE), which is a complex microenvironment that contains numerous immune and tumour signals, is common in lung cancer. Gene alterations, such as driver gene mutations, are believed to affect the components of tumour immunity in the microenvironment (TIME) of non-small-cell lung cancer. In this study, we have shown that pleural CD39 + CD8 + T cells are selectively elevated in lung adenocarcinoma (LUAD) with wild-type epidermal growth factor receptor (EGFRwt) compared to those with newly diagnosed mutant EGFR (EGFRmu). Furthermore, these CD39 + CD8 + T cells are more prevalent in MPE with acquired resistance to EGFR-tyrosine kinase inhibitors (AR-EGFR-TKIs). Our analysis reveals that pleural CD39 + CD8 + T cells exhibit an exhausted phenotype while still retaining cytolytic function. Additionally, they have a higher T cell receptor (TCR) repertoire clonality compared to CD39-CD8 + T cells, which is a unique characteristic of LUAD-related MPE. Further investigation has shown that TCR-Vβ clonality tends to be more enhanced in pleural CD39 + CD8 + T cells from MPE with AR-EGFR-TKIs. In summary, we have identified a subset of CD8 + T cells expressing CD39 in MPE, which may potentially be tumour-reactive CD8 + T cells. This study provides new insights into the dynamic immune composition of the EGFRmu tumour microenvironment.

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.

Integrative analyses reveal biological function and prognostic role of m7G methylation regulators in high-grade glioma

Based on 29 m7G regulators, glioma patients were categorized into three groups using data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets. Distinct characteristics were observed in immune cell infiltration, functional enrichment, and clinical prognosis for every glioma subtype. Analyzing the differentially expressed genes (DEGs) confirmed the distinction among the three m7G clusters. A predictive tool for overall survival (OS) in high-grade glioma patients was developed and confirmed, consisting of 13 m7G regulators forming a prognostic signature. Elevated m7G levels were found to be associated with increased tumor mutation burden and immune activation, indicating a tumor microenvironment characterized by inflammation and a lower overall survival rate. In contrast, reduced m7G scores were linked to a deficiency in immune infiltration, a low burden of mutations, and a non-inflamed phenotype, suggesting a more positive clinical outlook. Additionally, the m7G risk scores were found to impact chemotherapy sensitivity. The m7G predictive pattern shows potential as a marker for the overall survival of patients with high-grade glioma. By significantly improving our comprehension of the functional role of m7G regulators in the advancement of glioma and their impact on clinical results, this study offers valuable perspectives for precision therapy in the management of high-grade 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.