QKI shuttles internal m7G-modified transcripts into stress granules and modulates mRNA metabolism.

N7-methylguanosine (m7G) modification, routinely occurring at mRNA 5' cap or within tRNAs/rRNAs, also exists internally in messenger RNAs (mRNAs). Although m7G-cap is essential for pre-mRNA processing and protein synthesis, the exact role of mRNA internal m7G modification remains elusive. Here, we report that mRNA internal m7G is selectively recognized by Quaking proteins (QKIs). By transcriptome-wide profiling/mapping of internal m7G methylome and QKI-binding sites, we identified more than 1,000 high-confidence m7G-modified and QKI-bound mRNA targets with a conserved "GANGAN (N = A/C/U/G)" motif. Strikingly, QKI7 interacts (via C terminus) with the stress granule (SG) core protein G3BP1 and shuttles internal m7G-modified transcripts into SGs to regulate mRNA stability and translation under stress conditions. Specifically, QKI7 attenuates the translation efficiency of essential genes in Hippo signaling pathways to sensitize cancer cells to chemotherapy. Collectively, we characterized QKIs as mRNA internal m7G-binding proteins that modulate target mRNA metabolism and cellular drug resistance.

METTL1-mediated m7G modification of Arg-TCT tRNA drives oncogenic transformation.

The emerging "epitranscriptomics" field is providing insights into the biological and pathological roles of different RNA modifications. The RNA methyltransferase METTL1 catalyzes N7-methylguanosine (m7G) modification of tRNAs. Here we find METTL1 is frequently amplified and overexpressed in cancers and is associated with poor patient survival. METTL1 depletion causes decreased abundance of m7G-modified tRNAs and altered cell cycle and inhibits oncogenicity. Conversely, METTL1 overexpression induces oncogenic cell transformation and cancer. Mechanistically, we find increased abundance of m7G-modified tRNAs, in particular Arg-TCT-4-1, and increased translation of mRNAs, including cell cycle regulators that are enriched in the corresponding AGA codon. Accordingly, Arg-TCT expression is elevated in many tumor types and is associated with patient survival, and strikingly, overexpression of this individual tRNA induces oncogenic transformation. Thus, METTL1-mediated tRNA modification drives oncogenic transformation through a remodeling of the mRNA "translatome" to increase expression of growth-promoting proteins and represents a promising anti-cancer target.

METTL1 Promotes let-7 MicroRNA Processing via m7G Methylation.

7-methylguanosine (m7G) is present at mRNA caps and at defined internal positions within tRNAs and rRNAs. However, its detection within low-abundance mRNAs and microRNAs (miRNAs) has been hampered by a lack of sensitive detection strategies. Here, we adapt a chemical reactivity assay to detect internal m7G in miRNAs. Using this technique (Borohydride Reduction sequencing [BoRed-seq]) alongside RNA immunoprecipitation, we identify m7G within a subset of miRNAs that inhibit cell migration. We show that the METTL1 methyltransferase mediates m7G methylation within miRNAs and that this enzyme regulates cell migration via its catalytic activity. Using refined mass spectrometry methods, we map m7G to a single guanosine within the let-7e-5p miRNA. We show that METTL1-mediated methylation augments let-7 miRNA processing by disrupting an inhibitory secondary structure within the primary miRNA transcript (pri-miRNA). These results identify METTL1-dependent N7-methylation of guanosine as a new RNA modification pathway that regulates miRNA structure, biogenesis, and cell migration.

Transcriptome-wide Mapping of Internal N7-Methylguanosine Methylome in Mammalian mRNA.

N7-methylguanosine (m7G) is a positively charged, essential modification at the 5' cap of eukaryotic mRNA, regulating mRNA export, translation, and splicing. m7G also occurs internally within tRNA and rRNA, but its existence and distribution within eukaryotic mRNA remain to be investigated. Here, we show the presence of internal m7G sites within mammalian mRNA. We then performed transcriptome-wide profiling of internal m7G methylome using m7G-MeRIP sequencing (MeRIP-seq). To map this modification at base resolution, we developed a chemical-assisted sequencing approach that selectively converts internal m7G sites into abasic sites, inducing misincorporation at these sites during reverse transcription. This base-resolution m7G-seq enabled transcriptome-wide mapping of m7G in human tRNA and mRNA, revealing distribution features of the internal m7G methylome in human cells. We also identified METTL1 as a methyltransferase that installs a subset of m7G within mRNA and showed that internal m7G methylation could affect mRNA translation.

Mettl1/Wdr4-Mediated m7G tRNA Methylome Is Required for Normal mRNA Translation and Embryonic Stem Cell Self-Renewal and Differentiation.

tRNAs are subject to numerous modifications, including methylation. Mutations in the human N7-methylguanosine (m7G) methyltransferase complex METTL1/WDR4 cause primordial dwarfism and brain malformation, yet the molecular and cellular function in mammals is not well understood. We developed m7G methylated tRNA immunoprecipitation sequencing (MeRIP-seq) and tRNA reduction and cleavage sequencing (TRAC-seq) to reveal the m7G tRNA methylome in mouse embryonic stem cells (mESCs). A subset of 22 tRNAs is modified at a "RAGGU" motif within the variable loop. We observe increased ribosome occupancy at the corresponding codons in Mettl1 knockout mESCs, implying widespread effects on tRNA function, ribosome pausing, and mRNA translation. Translation of cell cycle genes and those associated with brain abnormalities is particularly affected. Mettl1 or Wdr4 knockout mESCs display defective self-renewal and neural differentiation. Our study uncovers the complexity of the mammalian m7G tRNA methylome and highlights its essential role in ESCs with links to human disease.

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.

Molecular mechanism of tRNA binding by the Escherichia coli N7 guanosine methyltransferase TrmB.

Among the large and diverse collection of tRNA modifications, 7-methylguanosine (m7G) is frequently found in the tRNA variable loop at position 46. This modification is introduced by the TrmB enzyme, which is conserved in bacteria and eukaryotes. However, the molecular determinants and the mechanism for tRNA recognition by TrmB are not well understood. Complementing the report of various phenotypes for different organisms lacking TrmB homologs, we report here hydrogen peroxide sensitivity for the Escherichia coli ΔtrmB knockout strain. To gain insight into the molecular mechanism of tRNA binding by E. coli TrmB in real time, we developed a new assay based on introducing a 4-thiouridine modification at position 8 of in vitro transcribed tRNAPhe enabling us to fluorescently label this unmodified tRNA. Using rapid kinetic stopped-flow measurements with this fluorescent tRNA, we examined the interaction of WT and single substitution variants of TrmB with tRNA. Our results reveal the role of S-adenosylmethionine for rapid and stable tRNA binding, the rate-limiting nature of m7G46 catalysis for tRNA release, and the importance of residues R26, T127, and R155 across the entire surface of TrmB for tRNA binding.

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.

RNA epigenetic modifications in digestive tract cancers: Friends or foes.

Digestive tract cancers are among the most common malignancies worldwide and have high incidence and mortality rates. Thus, the discovery of more effective diagnostic and therapeutic targets is urgently required. The development of technologies to accurately detect RNA modification has led to the identification of numerous RNA chemical modifications in humans (epitranscriptomics) that are involved in the occurrence and development of digestive tract cancers. RNA modifications can cooperatively regulate gene expression to facilitate normal physiological functions of the digestive system. However, the dysfunction of relevant RNA-modifying enzymes ("writers," "erasers," and "readers") can lead to the development of digestive tract cancers. Consequently, targeting dysregulated enzyme activity could represent a potent therapeutic strategy for the treatment of digestive tract cancers. In this review, we summarize the most widely studied roles and mechanisms of RNA modifications (m6A, m1A, m5C, m7G, A-to-I editing, pseudouridine [Ψ]) in relation to digestive tract cancers, highlight the crosstalk between RNA modifications, and discuss their roles in the interactions between the digestive system and microbiota during carcinogenesis. The clinical significance of novel therapeutic methods based on RNA-modifying enzymes is also discussed. This review will help guide future research into digestive tract cancers that are resistant to current therapeutics.

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.

Global and single-nucleotide resolution detection of 7-methylguanosine in RNA.

RNA modifications, including N-7-methylguanosine (m7G), are pivotal in governing RNA stability and gene expression regulation. The accurate detection of internal m7G modifications is of paramount significance, given recent associations between altered m7G deposition and elevated expression of the methyltransferase METTL1 in various human cancers. The development of robust m7G detection techniques has posed a significant challenge in the field of epitranscriptomics. In this study, we introduce two methodologies for the global and accurate identification of m7G modifications in human RNA. We introduce borohydride reduction sequencing (Bo-Seq), which provides base resolution mapping of m7G modifications. Bo-Seq achieves exceptional performance through the optimization of RNA depurination and scission, involving the strategic use of high concentrations of NaBH4, neutral pH and the addition of 7-methylguanosine monophosphate (m7GMP) during the reducing reaction. Notably, compared to NaBH4-based methods, Bo-Seq enhances the m7G detection performance, and simplifies the detection process, eliminating the necessity for intricate chemical steps and reducing the protocol duration. In addition, we present an antibody-based approach, which enables the assessment of m7G relative levels across RNA molecules and biological samples, however it should be used with caution due to limitations associated with variations in antibody quality between batches. In summary, our novel approaches address the pressing need for reliable and accessible methods to detect RNA m7G methylation in human cells. These advancements hold the potential to catalyse future investigations in the critical field of epitranscriptomics, shedding light on the complex regulatory roles of m7G in gene expression and its implications in cancer biology.

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.

Establishment of a prognostic model toward lung squamous cell carcinoma based on m7G-related genes in the cancer genome atlas.

Lung squamous cell carcinoma (LUSC) is a non-small cell lung cancer with a poor prognosis owing to late diagnosis. New molecular markers are urgently needed to improve the diagnosis and prognosis of LUSC. 7-Methylguanosine (m7G) modifications, a tRNA modification, are common in eubacteria, eukaryotes, and a few archaea. These modifications promote the turnover and stability of some mRNAs to prevent mRNA decay, improve translation efficiency, and reduce ribosomal pausing but are associated with poor survival in human cancer cells. However, expression of m7G-related genes in LUSC and their association with prognosis remain unclear. In the present study, we identified nine differentially expressed genes related to prognosis by comparing the expression profiles of tumor tissues (502 LUSC reports) with normal tissues (49 adjacent nontumor lung tissue reports). The genes included six upregulated genes (KLK7, LCE3E, AREG, KLK6, ZBED2, and MAPK4) and three downregulated genes (ADH1C, NTS, and ERLIN2). Based on these nine genes, patients with LUSC were classified into low- and high-risk groups to analyze the trends in prognosis. We found that the nine m7G-related genes play important roles in immune regulation, hormone regulation, and drug sensitivity through pathways including antigen processing and presentation, adherent plaques, extracellular matrix receptor interactions, drug metabolism of cytochrome P-450, and metabolism of cytochrome P-450 to xenobiotics; the functions of these genes are likely accomplished in part by m6A modifications. The effect of m7G-related genes on the diagnosis and prognosis of LUSC was further indicated by population analysis.NEW & NOTEWORTHY Based on the differential expression of 7-methylguanosine (m7G) modification-associated genes between normal and lung squamous cell carcinoma (LUSC) tissues, and considering the performance of our m7G-related gene risk profiles as independent risk factors in predicting overall survival, we conclude that m7G modification is closely linked to the development of LUSC. In addition, this study offers a new genetic marker for predicting the prognosis of patients with LUSC and presents a crucial theoretical foundation for future investigations on the relationship between m7G modification-related genes, immunity, and drug sensitivity in LUSC.

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.

METTL1 promotes tumorigenesis through tRNA-derived fragment biogenesis in prostate cancer.

Newly growing evidence highlights the essential role that epitranscriptomic marks play in the development of many cancers; however, little is known about the role and implications of altered epitranscriptome deposition in prostate cancer. Here, we show that the transfer RNA N7-methylguanosine (m7G) transferase METTL1 is highly expressed in primary and advanced prostate tumours. Mechanistically, we find that METTL1 depletion causes the loss of m7G tRNA methylation and promotes the biogenesis of a novel class of small non-coding RNAs derived from 5'tRNA fragments. 5'tRNA-derived small RNAs steer translation control to favour the synthesis of key regulators of tumour growth suppression, interferon pathway, and immune effectors. Knockdown of Mettl1 in prostate cancer preclinical models increases intratumoural infiltration of pro-inflammatory immune cells and enhances responses to immunotherapy. Collectively, our findings reveal a therapeutically actionable role of METTL1-directed m7G tRNA methylation in cancer cell translation control and tumour biology.

An innovative model based on N7-methylguanosine-related lncRNAs for forecasting prognosis and tumor immune landscape in bladder cancer.

BACKGROUND: As a novel type of the prevalent post-transcriptional modifications, N7-methylguanosine (m7G) modification is essential in the tumorigenesis, progression, and invasion of many cancers, including bladder cancer (BCa). However, the integrated roles of m7G-related lncRNAs in BCa remain undiscovered. This study aims to develop a prognostic model based on the m7G-related lncRNAs and explore its predictive value of the prognosis and anti-cancer treatment sensitivity. METHODS: We obtained RNA-seq data and corresponding clinicopathological information from the TCGA database and collected m7G-related genes from previous studies and GSEA. Based on LASSO and Cox regression analysis, we developed a m7G prognostic model. The Kaplan-Meier (K-M) survival analysis and ROC curves were performed to evaluate the predictive power of the model. Gene set enrichment analysis (GSEA) was conducted to explore the molecular mechanisms behind apparent discrepancies between the low- and high-risk groups. We also investigated immune cell infiltration, TIDE score, TMB, the sensitivity of common chemotherapy drugs, and the response to immunotherapy between the two risk groups. Finally, we validated the expression levels of these ten m7G-related lncRNAs in BCa cell lines by qRT-PCR. RESULTS: We developed a m7G prognostic model (risk score) composed of 10 m7G-related lncRNAs that are significantly associated with the OS of BCa patients. The K-M survival curves revealed that the high-risk group patients had significantly worse OS than those in the low-risk group. The Cox regression analysis confirmed that the risk score was a significant independent prognostic factor for BCa patients. We found that the high-risk group had higher the immune scores and immune cell infiltration. Furthermore, the results of the sensitivity of common anti-BCa drugs showed that the high-risk group was more sensitive to neoadjuvant cisplatin-based chemotherapy and anti-PD1 immunotherapy. Finally, qRT-PCR revealed that AC006058.1, AC073133.2, LINC00677, and LINC01338 were significantly downregulated in BCa cell lines, while the expression levels of AC124312.2 and AL158209.1 were significantly upregulated in BCa cell lines compared with normal cell lines. CONCLUSION: The m7G prognostic model can be applied to accurately predict the prognosis and provide robust directions for clinicians to develop better individual-based and precise treatment strategies for BCa patients.

Analysis of m7G-Related signatures in the tumour immune microenvironment and identification of clinical prognostic regulators in breast cancer.

BACKGROUND: Breast cancer is a malignant tumour that seriously threatens women's life and health and exhibits high inter-individual heterogeneity, emphasising the need for more in-depth research on its pathogenesis. While internal 7-methylguanosine (m7G) modifications affect RNA processing and function and are believed to be involved in human diseases, little is currently known about the role of m7G modification in breast cancer. METHODS AND RESULTS: We elucidated the expression, copy number variation incidence and prognostic value of 24 m7G-related genes (m7GRGs) in breast cancer. Subsequently, based on the expression of these 24 m7GRGs, consensus clustering was used to divide tumour samples from the TCGA-BRCA dataset into four subtypes based on significant differences in their immune cell infiltration and stromal scores. Differentially expressed genes between subtypes were mainly enriched in immune-related pathways such as 'Ribosome', 'TNF signalling pathway' and 'Salmonella infection'. Support vector machines and multivariate Cox regression analysis were applied based on these 24 m7GRGs, and four m7GRGs-AGO2, EIF4E3, DPCS and EIF4E-were identified for constructing the prediction model. An ROC curve indicated that a nomogram model based on the risk model and clinical factors had strong ability to predict the prognosis of breast cancer. The prognoses of patients in the high- and low-TMB groups were significantly different (p = 0.03). Moreover, the four-gene signature was able to predict the response to chemotherapy. CONCLUSIONS: In conclusion, we identified four different subtypes of breast cancer with significant differences in the immune microenvironment and pathways. We elucidated prognostic biomarkers associated with breast cancer and constructed a prognostic model involving four m7GRGs. In addition, we predicted the candidate drugs related to breast cancer based on the prognosis model.

A new prediction model of hepatocellular carcinoma based on N7-methylguanosine modification.

PURPOSE: Hepatocellular carcinoma (HCC) is a kind of primary liver cancer. It is a common malignant tumor of digestive system that is difficult to predict the prognosis of patients. As an important epigenetic modification, N7 methyl guanosine (m7G) is indispensable in gene regulation. This regulation may affect the development and occurrence of cancer. However, the prognosis of long non coding RNAs (lncRNAs) in HCC is limited, especially how m7G-related lncRNAs regulate the development of HCC has not been reported. METHODS: The Cancer Genome Atlas (TCGA) provides us with the expression data and corresponding clinical information of HCC patients we need. We used a series of statistical methods to screen four kinds of m7G-related lncRNAs related to HCC prognosis and through a series of verifications, the results were in line with our expectations. Finally, we also explored the IC50 difference and correlation analysis of various common chemotherapy drugs. RESULT: Our study identified four differentially expressed m7g-related lncRNAs associated with HCC prognosis. Survival curve analysis showed that high risk lncRNAs would lead to poor prognosis of HCC patients. M7G signature's AUC was 0.789, which shows that the prognosis model we studied has certain significance in predicting the prognosis of HCC patients. Moreover, our study found that different risk groups have different immune and tumor related pathways through gene set enrichment analysis. In addition, many immune cell functions are significantly different among different risk groups, such as T cell functions, including coordination of type I INF response and coordination of type II INF response. The expression of PDCD1, HHLA2, CTLA-4 and many other immune checkpoints in different risk groups is also different. Additionally, we analyzed the differences of IC50 and risk correlation of 15 chemotherapeutic drugs among different risk groups. CONCLUSION: A novel lncRNAs associated with m7G predicts the prognosis of HCC.

RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features.

N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.