Development of a prognostic risk model of uveal melanoma based on N7-methylguanosine-related regulators.

BACKGROUND: Uveal melanoma (UVM) stands as the predominant type of primary intraocular malignancy among adults. The clinical significance of N7-methylguanosine (m7G), a prevalent RNA modifications, in UVM remains unclear. METHODS: Primary information from 80 UVM patients were analyzed as the training set, incorporating clinical information, mutation annotations and mRNA expression obtained from The Cancer Genome Atlas (TCGA) website. The validation set was carried out using Gene Expression Omnibus (GEO) database GSE22138 and GSE84976. Kaplan-Meier and Cox regression of univariate analyses were subjected to identify m7G-related regulators as prognostic genes. RESULT: A prognostic risk model comprising EIF4E2, NUDT16, SNUPN and WDR4 was established through Cox regression of LASSO. Evaluation of the model's predictability for UVM patients' prognosis by Receiver Operating Characteristic (ROC) curves in the training set, demonstrated excellent performance Area Under the Curve (AUC) > 0.75. The high-risk prognosis within the TCGA cohort exhibit a notable worse outcome. Additionally, an independent correlation between the risk score and overall survival (OS) among UVM patients were identified. External validation of this model was carried out using the validation sets (GSE22138 and GSE84976). Immune-related analysis revealed that patients with high score of m7G-related risk model exhibited elevated level of immune infiltration and immune checkpoint gene expression. CONCLUSION: We have developed a risk prediction model based on four m7G-related regulators, facilitating effective estimate UVM patients' survival by clinicians. Our findings shed novel light on essential role of m7G-related regulators in UVM and suggest potential novel targets for the diagnosis, prognosis and therapy of UVM.

Identification and experimental validation of m7G-related molecular subtypes, immune signature, and feature genes in Alzheimer's disease.

Background: Studies has shown that N7-methylguanosine (m7G) modification plays a critical role in neurological diseases. However, the exact role and association of m7G with the immune microenvironment in Alzheimer's disease (AD) remain largely unknown and unexplored. Methods: The study datasets comprised 667 AD samples and 503 control samples selected from eight datasets in the Gene Expression Omnibus database; m7G regulator genes were obtained from previous literature. The AD subtypes were identified by consensus clustering analysis according to m7G regulator genes. The clinical characteristics, immune infiltration, and biological functions of the AD subgroups were evaluated. A combination of different types of machine-learning algorithms were used for the identification of AD genes. We also assessed and validated the diagnostic performance of the identified genes via qRT-PCR, immunofluorescence, and immunohistochemical analyses. Results: Two AD distinct subgroups, namely cluster A and cluster B, were identified. Cluster A had poor pathological progression and immune infiltration, representing a high-risk subgroup for AD. The differentially expressed genes of cluster A were enriched in immune and synapse-related pathways, suggesting that these genes probably contribute to AD progression by regulating immune-related pathways. Additionally, five feature genes (AEBP1, CARTPT, AK5, NPTX2, and COPG2IT1) were identified, which were used to construct a nomogram model with good ability to predict AD. The animal experiment analyses further confirmed that these feature genes were associated with AD development. Conclusion: To the best of our knowledge, this is the first study to reveal close correlations among m7G RNA modification, the immune microenvironment, and the pathogenesis of AD. We also identified five feature genes associated with AD, further contributing to our understanding of the underlying mechanisms and potential therapeutic targets for AD.

Unravelling the impact of RNA methylation genetic and epigenetic machinery in the treatment of cardiomyopathy.

Cardiomyopathy (CM) represents a heterogeneous group of diseases primarily affecting cardiac structure and function, with genetic and epigenetic dysregulation playing a pivotal role in its pathogenesis. Emerging evidence from the burgeoning field of epitranscriptomics has brought to light the significant impact of various RNA modifications, notably N6-methyladenosine (m6A), 5-methylcytosine (m5C), N7-methylguanosine (m7G), N1-methyladenosine (m1A), 2'-O-methylation (Nm), and 6,2'-O-dimethyladenosine (m6Am), on cardiomyocyte function and the broader processes of cardiac and vascular remodelling. These modifications have been shown to influence key pathological mechanisms including mitochondrial dysfunction, oxidative stress, cardiomyocyte apoptosis, inflammation, immune response, and myocardial fibrosis. Importantly, aberrations in the RNA methylation machinery have been observed in human CM cases and animal models, highlighting the critical role of RNA methylating enzymes and their potential as therapeutic targets or biomarkers for CM. This review underscores the necessity for a deeper understanding of RNA methylation processes in the context of CM, to illuminate novel therapeutic avenues and diagnostic tools, thereby addressing a significant gap in the current management strategies for this complex disease.

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.

Identification of N7-methylguanosine-related miRNAs as potential biomarkers for prognosis and drug response in breast cancer.

Objectives: The impact of N7-methylguanosine (m7G) on tumor progression and the regulatory role of microRNAs (miRNAs) in immune function significantly influence breast cancer (BC) prognosis. Investigating the interplay between m7G modification and miRNAs provides novel insights for assessing prognostics and drug responses in BC. Materials and methods: RNA sequences (miRNA and mRNA profiles) and clinical data for BC were acquired from the Cancer Genome Atlas (TCGA) database. A miRNA signature associated with 15 m7G in this cohort was identified using Cox regression and LASSO. The risk score model was evaluated using Kaplan-Meier and time-dependent ROC analysis, categorizing patients into high-risk and low-risk groups. Functional enrichment analyses were conducted to explore potential pathways. The immune system, including scores, cell infiltration, function, and drug sensitivity, was examined and compared between high-risk and low-risk groups. A nomogram that combines risk scores and clinical factors was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) was employed to explore m7G-related miRNA signatures and immune cell relationships in the tumor microenvironment. Additionally, drug susceptibility was compared between risk groups. Results: Fifteen m7G-related miRNAs were independently correlated with overall survival (OS) in BC patients. Time-dependent ROC analysis yielded area under the curve (AUC) values of 0.742, 0.726, and 0.712 for predicting 3-, 5-, and 10-year survival rates, respectively. The Kaplan-Meier analysis revealed a significant disparity in OS between the high-risk and low-risk groups (p = 1.3e-6). Multiple regression identified the risk score as a significant independent prognostic factor. An excellent calibration nomogram with a C-index of 0.785 (95 % CI: 0.728-0.843) was constructed. In immune analysis, low-risk patients exhibited heightened immune function and increased responsiveness to immunotherapy and chemotherapy compared to high-risk patients. Conclusion: This study systematically analyzed m7G-related miRNAs and revealed their regulatory mechanisms concerning the tumor microenvironment (TME), pathology, and the prognosis of BC patient. Based on these miRNAs, a prognostic model and nomogram were developed for BC patients, facilitating prognostic assessments. These findings can also assist in predicting treatment responses and guiding medication selection.

WDR4 promotes HCC pathogenesis through N7-methylguanosine by regulating and interacting with METTL1.

BACKGROUND: The N7-methylguanosine (m7G), a modification at defined internal positions within tRNAs and rRNAs, is correlated with tumor progression. Methyltransferase like 1 (METTL1)/ WD repeat domain 4 (WDR4) mediated tRNA m7G modification, which could alter many oncogenic mRNAs translation to promote progress of multiple cancer types. However, whether and how the internal mRNA m7G modification is involved in tumorigenesis remains unclear. METHODS: The immunohistochemistry assay was conducted to detect the expression of WDR4 and METTL1 in hepatocellular carcinoma (HCC) and the expression of both genes whether contributes to the prognosis of the survival rate of HCC patients. Then, CCK8, colony formation assays and tumor xenograft models were conducted to determine the effects of WDR4 on HCC cells in vitro and vivo. Besides, dot blot assay, m7G-MeRIP-seq and RNA-seq analysis were conducted to determine whether WDR4 contributes to m7G modification and underlying mechanism in HCC cells. Finally, rescue and CO-IP assay were conducted to explore whether WDR4 and METTL1 proteins form a complex in Huh7 cells. RESULTS: WDR4 modulates m7G modification at the internal sites of tumor-promoting mRNAs by forming the WDR4-METTL1 complex. WDR4 knockdown downregulated the expression of mRNA and protein levels of METTL1 gene and thus further modulate the formation of WDR4-METTL1 complex indirectly. METTL1 expression was markedly correlated with WDR4 expression in HCC tissues. HCC patients with high expression of both genes had a poor prognosis. CONCLUSIONS: WDR4 may contribute to HCC pathogenesis by interacting with and regulating the expression of METTL1 to synergistically modulate the m7G modification of target mRNAs in tumor cells.

Construction and validation of a prognostic signature for mucinous colonic adenocarcinoma based on N7-methylguanosine-related long non-coding RNAs.

Background: Mucinous colonic adenocarcinoma remains a challenging disease due to its high propensity for metastasis and recurrence. N7-methylguanosine (m7G) and long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on m7G-related lncRNA in mucinous colonic adenocarcinoma is lacking. Therefore, we sought to explore the prognostic impact of m7G-related lncRNAs in mucinous adenocarcinoma (MC) patients. Methods: In this study, Pearson analysis was used to identify m7G-related lncRNAs from transcriptome data in The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to further screen m7G-related lncRNAs and incorporate them into a prognostic signature. Based on the risk model, patients were divided into low- and high-risk groups and randomly assigned to the training set and test sets in a 6:4 ratio. Kaplan-Meier, receiver operating characteristic (ROC) curve, multivariate regression, and nomogram analyses were used to confirm the accuracy of the signature. The CIBERSORT algorithm was used to calculate the degree of immune cell infiltration (ICI). Finally, the correlation of the prognostic signature with tumor mutational burden (TMB) and immunophenotype score (IPS) was evaluated. Results: A total of 432 m7G-related lncRNAs were identified by Pearson analysis. Univariate Cox regression, LASSO regression and survival analysis were performed to further select six m7G-related lncRNAs (P<0.05): AC254629.1, LINC01133, LINC01134, MHENCR, SMIM2-AS1, and XACT. Based on the risk model, heat maps, Kaplan-Meier curves, and ROC curves were constructed, and the results showed that there were significant differences in expression levels and survival status between the two risk groups. The area under the ROC curve (AUC) values for 3-, 5-, and 10-year survival in the training set were 0.944, 0.957, and 1.000, respectively. And in the test set were 0.964, 1.000, and 1.000, respectively. Subsequently, univariate and multivariate regression analyses of clinical characteristics and risk score were performed. The results of risk score were [hazard ratio (HR): 6.458, 95% confidence interval (CI): 2.708-15.403, P<0.001; HR: 7.280, 95% CI: 2.500-21.203, P<0.001], respectively. Using the risk score as an independent prognostic factor, the AUC of it over 3, 5, and 10 years was 0.911, 0.955, and 0.961, respectively. Calibration plots for the nomogram show that the model calibration line is very close to the ideal calibration line, indicating good calibration. The level of ICI was significantly different in the different risk groups. Survival analysis showed that, regardless of TMB risk, patients with MC and a high-risk score consistently had a poor overall survival (OS). Conclusions: The m7G-related lncRNA prognostic signature has potential value for the prognosis of mucinous colonic adenocarcinoma.

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.

Identification and validation of N7 -methylguanosine-associated gene NCBP1 as prognostic and immune-associated biomarkers in breast cancer patients.

We intend to evaluate the importance of N7 -methylguanosine (m7G) for the prognosis of breast cancer (BC). We gained 29 m7G-related genes from the published literature and among them, 16 m7G-related genes were found to have differential expression. Five differentially expressed genes (CYFIP1, EIF4E, EIF4E3, NCBP1 and WDR4) were linked to overall survival. This suggests that m7G-related genes might be prognostic or therapeutic targets for BC patients. We put the five genes to LASSO regression analysis to create a four-gene signature, including EIF4E, EIF4E3, WDR4 and NCBP1, that divides samples into two risky groups. Survival was drastically worsened in a high-risk group (p < 0.001). The signature's predictive capacity was demonstrated using ROC (10-year AUC 0.689; 10-year AUC 0.615; 3-year AUC 0.602). We found that immune status was significantly different between the two risk groups. In particular, NCBP1 also has a poor prognosis, with higher diagnostic value in ROC. NCBP1 also has different immune states according to its high or low expression. Meanwhile, knockdown of NCBP1 suppresses BC malignancy in vitro. Therefore, m7G RNA regulators are crucial participants in BC and four-gene mRNA levels are important predictors of prognosis. NCBP1 plays a critical target of m7G mechanism in BC.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification.

BACKGROUND: The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC. METHODS: The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic. RESULTS: Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events. CONCLUSION: In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

N6-methyladenosine-induced METTL1 promotes tumor proliferation via CDK4.

N6-methyladenosine (m6A) and N7-methylguanosine (m7G) modification of RNA represent two major intracellular post-transcriptional regulation modes of gene expression. However, the crosstalk of these two epigenetic modifications in tumorigenesis remain poorly understood. Here, we show that m6A methyltransferase METTL3-mediated METTL1 promotes cell proliferation of head and neck squamous cell carcinoma (HNSC) through m7G modification of the cell-cycle regulator CDK4. By mining the database GEPIA, METTL1 was shown to be up-regulated in a broad spectrum of human cancers and correlated with patient clinical outcomes, particularly in HNSC. Mechanistically, METTL3 methylates METTL1 mRNA and mediates its elevation in HNSC via m6A. Functionally, over-expression of METTL1 enhances HNSC cell growth and facilitates cell-cycle progress, while METTL1 knockdown represses these biological behaviors. Moreover, METTL1 physically binds to CDK4 transcript and regulates its m7G modification level to stabilize CDK4. Importantly, the inhibitory effects of METTL1 knockdown on the proliferation of HNSC, esophageal cancer (ESCA), stomach adenocarcinoma (STAD), and colon adenocarcinoma (COAD) were significantly mitigated by over-expression of CDK4. Taken together, this study expands the understanding of epigenetic mechanisms involved in tumorigenesis and identifies the METTL1/CDK4 axis as a potential therapeutic target for digestive system tumors.

TMSC-m7G: A transformer architecture based on multi-sense-scaled embedding features and convolutional neural network to identify RNA N7-methylguanosine sites.

RNA N7-methylguanosine (m7G) is a crucial chemical modification of RNA molecules, whose principal duty is to maintain RNA function and protein translation. Studying and predicting RNA N7-methylguanosine sites aid in comprehending the biological function of RNA and the development of new drug therapy regimens. In the present scenario, the efficacy of techniques, specifically deep learning and machine learning, stands out in the prediction of RNA N7-methylguanosine sites, leading to improved accuracy and identification efficiency. In this study, we propose a model leveraging the transformer framework that integrates natural language processing and deep learning to predict m7G sites, called TMSC-m7G. In TMSC-m7G, a combination of multi-sense-scaled token embedding and fixed-position embedding is used to replace traditional word embedding for the extraction of contextual information from sequences. Moreover, a convolutional layer is added in the encoder to make up for the shortage of local information acquisition in transformer. The model's robustness and generalization are validated through 10-fold cross-validation and an independent dataset test. Results demonstrate outstanding performance in comparison to the most advanced models available. Among them, the Accuracy of TMSC-m7G reaches 98.70% and 92.92% on the benchmark dataset and independent dataset, respectively. To facilitate the popularization and use of the model, we have developed an intuitive online prediction tool, which is easily accessible for free at http://39.105.212.81/.

A signature of five 7-methylguanosine-related genes is a prognostic marker for lung squamous cell carcinoma.

Background: N7-methylguanosine (m7G) is an important posttranscriptional modification affecting mRNA and tRNA functions and stability. The genes regulating the m7G process have been previously found involved in the carcinogenesis process. We aimed to analyze the role of m7G-related genes as potential prognostic markers for lung squamous cell carcinoma (LSCC). Methods: Twenty-nine m7G-related genes were selected for the analysis in the LSCC cohort of the Cancer Genome Atlas (TCGA). Univariate, multivariate, and Kaplan-Meier analyses were used to evaluate the predictive value of risk model developed with m7G signature for overall survival (OS).. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of differentially expressed genes (DEGs) were performed for high- and low-risk LSCC groups. Results: We identified 17 differentially expressed m7G methylation-related genes in LSCC versus normal tissues. The expression of five m7G-related genes (EIF3D, LSM1, NCBP2, NUDT10, and NUDT11) was identified as an independent prognostic marker for OS in LSCC patients. A risk model with these five m7G-related genes predicted 2-, and 3-year survival rates of 0.623 and 0.626, respectively. The risk score significantly correlated with OS: LSCC patients with a higher risk score had shorter OS (P<0.01) and it was associated with lower immune response (P<0.01). Conclusions: We developed a novel m7G-related gene signature that can be of great utility to predict the prognosis for patients with LSCC.

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients. METHODS: Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples. RESULTS: By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints. CONCLUSION: In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

Combined signature of N7-methylguanosine regulators with their related genes and the tumor microenvironment: a prognostic and therapeutic biomarker for breast cancer.

Background: Identifying predictive markers for breast cancer (BC) prognosis and immunotherapeutic responses remains challenging. Recent findings indicate that N7-methylguanosine (m7G) modification and the tumor microenvironment (TME) are critical for BC tumorigenesis and metastasis, suggesting that integrating m7G modifications and TME cell characteristics could improve the predictive accuracy for prognosis and immunotherapeutic responses. Methods: We utilized bulk RNA-sequencing data from The Cancer Genome Atlas Breast Cancer Cohort and the GSE42568 and GSE146558 datasets to identify BC-specific m7G-modification regulators and associated genes. We used multiple m7G databases and RNA interference to validate the relationships between BC-specific m7G-modification regulators (METTL1 and WDR4) and related genes. Single-cell RNA-sequencing data from GSE176078 confirmed the association between m7G modifications and TME cells. We constructed an m7G-TME classifier, validated the results using an independent BC cohort (GSE20685; n = 327), investigated the clinical significance of BC-specific m7G-modifying regulators by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, and performed tissue-microarray assays on 192 BC samples. Results: Immunohistochemistry and RT-qPCR results indicated that METTL1 and WDR4 overexpression in BC correlated with poor patient prognosis. Moreover, single-cell analysis revealed relationships between m7G modification and TME cells, indicating their potential as indicators of BC prognosis and treatment responses. The m7G-TME classifier enabled patient subgrouping and revealed significantly better survival and treatment responses in the m7Glow+TMEhigh group. Significant differences in tumor biological functions and immunophenotypes occurred among the different subgroups. Conclusions: The m7G-TME classifier offers a promising tool for predicting prognosis and immunotherapeutic responses in BC, which could support personalized therapeutic strategies.

Biological roles of RNA m7G modification and its implications in cancer.

M7G modification, known as one of the common post-transcriptional modifications of RNA, is present in many different types of RNAs. With the accurate identification of m7G modifications within RNAs, their functional roles in the regulation of gene expression and different physiological functions have been revealed. In addition, there is growing evidence that m7G modifications are crucial in the emergence of cancer. Here, we review the most recent findings regarding the detection techniques, distribution, biological functions and Regulators of m7G. We also summarize the connections between m7G modifications and cancer development, drug resistance, and tumor microenvironment as well as we discuss the research's future directions and trends.

The functions and mechanisms of post-translational modification in protein regulators of RNA methylation: Current status and future perspectives.

RNA methylation, an epigenetic modification that does not alter gene sequence, may be important to diverse biological processes. Protein regulators of RNA methylation include "writers," "erasers," and "readers," which respectively deposit, remove, and recognize methylated RNA. RNA methylation, particularly N6-methyladenosine (m6A), 5-methylcytosine (m5C), N3-methylcytosine (m3C), N1-methyladenosine (m1A) and N7-methylguanosine (m7G), has been suggested as disease therapeutic targets. Despite advances in the structure and pharmacology of RNA methylation regulators that have improved drug discovery, regulating these proteins by various post-translational modifications (PTMs) has received little attention. PTM modifies protein structure and function, affecting all aspects of normal biology and pathogenesis, including immunology, cell differentiation, DNA damage repair, and tumors. It is becoming evident that RNA methylation regulators are also regulated by diverse PTMs. PTM of RNA methylation regulators induces their covalent linkage to new functional groups, hence modifying their activity and function. Mass spectrometry has identified many PTMs on protein regulators of RNA methylation. In this review, we describe the functions and PTM of protein regulators of RNA methylation and summarize the recent advances in the regulatory mode of human disease and its underlying mechanisms.

Comprehensive analysis of circRNAs for N7-methylguanosine methylation modification in human oral squamous cell carcinoma.

N7-methylguanosine (m7G) modification is closely related to the occurrence of tumors. However, the m7G modification of circRNAs in oral squamous cell carcinoma (OSCC) remains to be investigated. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) was used to measure the methylation levels of m7G and identify m7G sites in circRNAs in human OSCC and normal tissues. The host genes of differentially methylated and differentially expressed circRNAs were analyzed by Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, and circRNA-miRNA-mRNA networks were predicted using the miRanda and miRDB databases. The analysis identified 2348 m7G peaks in 624 circRNAs in OSCC tissues. In addition, the source of m7G-methylated circRNAs in OSCC was mainly the sense overlap region compared with normal tissues. The most conserved m7G motif in OSCC tissues was CCUGU, whereas the most conserved motif in normal tissues was RCCUG (R = G/A). Importantly, GO enrichment and KEGG pathway analysis showed that the host genes of differentially methylated and differentially expressed circRNAs were involved in many cellular biological functions. Furthermore, the significantly differentially expressed circRNAs were analyzed to predict the circRNA-miRNA-mRNA networks. This study revealed the whole profile of circRNAs of differential m7G methylation in OSCC and suggests that m7G-modified circRNAs may impact the development of OSCC.

Landscape of internal N7-methylguanosine of long non-coding RNA modifications in resistant acute myeloid leukemia.

BACKGROUND: Growing evidence indicates that RNA methylation plays a fundamental role in epigenetic regulation, which is associated with the tumorigenesis and drug resistance. Among them, acute myeloid leukemia (AML), as the top acute leukemia for adults, is a deadly disease threatening human health. Although N7-methylguanosine (m7G) has been identified as an important regulatory modification, its distribution has still remained elusive. METHODS: The present study aimed to explore the long non-coding RNA (lncRNA) functional profile of m7G in AML and drug-resistant AML cells. The transcriptome-wide m7G methylation of lncRNA was analyzed in AML and drug-resistant AML cells. RNA MeRIP-seq was performed to identify m7G peaks on lncRNA and differences in m7G distribution between AML and drug-resistant AML cells. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to predict the possible roles and m7G-associated pathway. RESULTS: Using m7G peak sequencing, it was found that a sequence motif was necessary for m7G methylation in drug-resistant AML lncRNA. Unsupervised hierarchical cluster analysis confirmed that lncRNA m7G methylation occurred more frequently in drug-resistant AML cells than in AML cells. RNA sequencing demonstrated that more genes were upregulated by methylation in drug-resistant AML cells, while methylation downregulated more genes in AML cells. The GO and KEGG pathway enrichment analyses revealed that genes having a significant correlation with m7G sites in lncRNA were involved in drug-resistant AML signaling pathways. CONCLUSION: Significant differences in the levels and patterns of m7G methylation between drug-resistant AML cells and AML cells were revealed. Furthermore, the cellular functions potentially influenced by m7G in drug-resistant AML cells were predicted, providing evidence implicating m7G-mediated lncRNA epigenetic regulation in the progression of drug resistance in AML. These findings highlight the involvement of m7G in the development of drug resistance in AML.

Exploration and validation of m7G-related genes as signatures in the immune microenvironment and prognostic indicators in low-grade glioma.

OBJECTIVES: Currently, an increasing number of studies are focusing on the impact of m7G modification in cancer. This study aims to investigate the prognostic value of m7G-related genes in low-grade glioma (LGG). METHODS: LGG samples were obtained from the CGGA database, and normal samples were obtained from GTEx. Differentially expressed m7G-related genes were identified, and genes highly associated with macrophage M2 in LGG patients were identified by immuno-infiltration and WGCNA analysis. The intersection of differentially expressed m7G-related genes and macrophage M2-associated genes yielded candidate genes, and hub genes were identified using 5 algorithms in CytoHubba. Enrichment analysis verified the relevant pathways of hub genes, and their performance in tumor classification was evaluated. RESULTS: A total of 3329 differentially expressed m7G-related genes were identified. 1289 genes were highly associated with macrophage M2 in LGG patients. The intersection of m7G-related genes and results in WGCNA yielded 840 candidate genes, and six hub genes (STXBP1, CPLX1, PAB3A, APBA1, RIMS1, and GRIN2B) were identified. Hub genes were enriched in synaptic transmission-related pathways and showed good performance for tumor classification. There were significant differences in survival levels between clusters. CONCLUSIONS: The identified m7G-related genes may provide new insight into the treatment and prognosis of LGG.