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.

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.

Pathogenic mechanism and therapeutic intervention of impaired N7-methylguanosine (m7G) tRNA modification.

Mutations modification enzymes including the tRNA N7-methylguanosine (m7G) methyltransferase complex component WDR4 were frequently found in patients with neural disorders, while the pathogenic mechanism and therapeutic intervention strategies are poorly explored. In this study, we revealed that patient-derived WDR4 mutation leads to temporal and cell-type-specific neural degeneration, and directly causes neural developmental disorders in mice. Mechanistically, WDR4 point mutation disrupts the interaction between WDR4 and METTL1 and accelerates METTL1 protein degradation. We further uncovered that impaired tRNA m7G modification caused by Wdr4 mutation decreases the mRNA translation of genes involved in mTOR pathway, leading to elevated endoplasmic reticulum stress markers, and increases neural cell apoptosis. Importantly, treatment with stress-attenuating drug Tauroursodeoxycholate (TUDCA) significantly decreases neural cell death and improves neural functions of the Wdr4 mutated mice. Moreover, adeno-associated virus mediated transduction of wild-type WDR4 restores METTL1 protein level and tRNA m7G modification in the mouse brain, and achieves long-lasting therapeutic effect in Wdr4 mutated mice. Most importantly, we further demonstrated that both TUDCA treatment and WDR4 restoration significantly improve the survival and functions of human iPSCs-derived neuron stem cells that harbor the patient's WDR4 mutation. Overall, our study uncovers molecular insights underlying WDR4 mutation in the pathogenesis of neural diseases and develops two promising therapeutic strategies for treatment of neural diseases caused by impaired tRNA modifications.

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.

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.

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.

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.

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.

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.

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.

A noval prognostic signature of the N7-methylguanosine (m7G)-related miRNA in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is characterized by high morbidity and mortality rates and poor prognosis. N7-methylguanosine play an increasingly vital role in lung adenocarcinoma. However, the prognostic value of N7-methylguanosine related-miRNAs in lung adenocarcinoma remains unclear. METHODS: In the study, the mRNA and miRNA expression profiles and corresponding clinical informations were downloaded from the public database. The prognostic signature was built using least absolute shrinkage and selection operator Cox analysis. The Kaplan-Meier method was used to compare survival outcomes between the high- and low-risk groups. Signatures for the development of lung adenocarcinoma were tested using univariate and multivariate Cox regression models. Single-sample gene set enrichment analysis was used to determine the immune cell infiltration score. First, we predicted METTL1 and WDR4 chemosensitivities based on a public pharmacogenomics database. The area under the receiver operating characteristic curve showed that the performance of signature in 1-,3-, and 5-year survival predictions were 0.68, 0.65, and 0.683, respectively. RESULTS: We established a novel prognostic signature consisting of 9 N7-Methylguanosine related miRNAs using least absolute shrinkage and selection operator Cox analysis. Patients in the high-risk group had shorter survival times than those in the low-risk group did. The calibration curves at 1, 3, and 5-year also illustrate the high predictive power of the structure. Signature was corrected using the Toumor stage. The expression levels of METTL1 and WDR4 significantly correlated with the sensitivity of cancer cells to antitumor drugs. CONCLUSIONS: A novel signature constructed using 9 N7-methylguanosine related-miRNAs can be used for prognostic prediction.

N7-methylguanosine modification of lncRNAs in a rat model of hypoxic pulmonary hypertension: a comprehensive analysis.

BACKGROUND: Long non-coding RNAs (lncRNAs) play a critical role in the pathogenesis of hypoxic pulmonary hypertension (HPH). The role of N7-methylguanosine (m7G) modification in lncRNAs has received increased attentions in recent years. However, the m7G-methylation of lncRNA in HPH has yet to be determined. We have therefore performed a transcriptome-wide analysis of m7G lncRNAs in HPH. RESULTS: Differentially-expressed m7Gs were detected in HPH, and m7G lncRNAs were significantly upregulated compared with non-m7G lncRNAs in HPH. Importantly, this was the first time that the upregulated m7G lncXR_591973 and m7G lncXR_592398 were identified in HPH. CONCLUSION: This study provides the first m7G transcriptome-wide analysis of HPH. Importantly, two HPH-associated m7G lncRNAs were identified, although their clinical significance requires further validation.

METTL1/WDR4-mediated m7G tRNA modifications and m7G codon usage promote mRNA translation and lung cancer progression.

Mis-regulated epigenetic modifications in RNAs are associated with human cancers. The transfer RNAs (tRNAs) are the most heavily modified RNA species in cells; however, little is known about the functions of tRNA modifications in cancers. In this study, we uncovered that the expression levels of tRNA N7-methylguanosine (m7G) methyltransferase complex components methyltransferase-like 1 (METTL1) and WD repeat domain 4 (WDR4) are significantly elevated in human lung cancer samples and negatively associated with patient prognosis. Impaired m7G tRNA modification upon METTL1/WDR4 depletion resulted in decreased cell proliferation, colony formation, cell invasion, and impaired tumorigenic capacities of lung cancer cells in vitro and in vivo. Moreover, gain-of-function and mutagenesis experiments revealed that METTL1 promoted lung cancer growth and invasion through regulation of m7G tRNA modifications. Profiling of tRNA methylation and mRNA translation revealed that highly translated mRNAs have higher frequencies of m7G tRNA-decoded codons, and knockdown of METTL1 resulted in decreased translation of mRNAs with higher frequencies of m7G tRNA codons, suggesting that tRNA modifications and codon usage play an essential function in mRNA translation regulation. Our data uncovered novel insights on mRNA translation regulation through tRNA modifications and the corresponding mRNA codon compositions in lung cancer, providing a new molecular basis underlying lung cancer progression.

RNA methylation and cancer treatment.

To this date, over 100 different types of RNA modification have been identified. Methylation of different RNA species has emerged as a critical regulator of transcript expression. RNA methylation and its related downstream signaling pathways are involved in plethora biological processes, including cell differentiation, sex determination and stress response, and others. It is catalyzed by the RNA methyltransferases, is demethylated by the demethylases (FTO and ALKBH5) and read by methylation binding protein (YTHDF1 and IGF2BP1). Increasing evidence indicates that this process closely connected to cancer cell proliferation, cellular stress, metastasis, immune response. And RNA methylation related protein has been becoming a promising targets of cancer therapy. This review outlines the relationship between different types of RNA methylation and cancer, and some FTO inhibitors in cancer treatment.

Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features.

N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.

m7GPredictor: An improved machine learning-based model for predicting internal m7G modifications using sequence properties.

As one of the most important post-transcriptional modifications, the N7-methylguanosine (m7G) plays a key role in many RNA processing events. The accurate identification of m7G is crucial for elucidating its biological significance and future application in the medical field. In this study, a machine learning-based model was developed for the prediction of internal m7G sites, and five different feature extraction methods (Pseudo dinucleotide composition, Pseudo k-tuple composition, K monomeric units, Ksnpf frequency, and Nucleotide chemical property) were used in the feature extraction. The Random Forest algorithm was used to find the optimized feature subset and the SVM-based predictor achieved the best performance by taking the top 240 features for model training. With different performance assessment methods, 10-fold cross validation, Jackknife test, and independent test, m7GPredictor achieved competitive performance compared with the state-of-the-art predictor iRNA-m7G. The predictor developed in this study can offer useful information for elucidating the mechanism of internal m7G sites and related experimental validations. The dataset used in this study and the source code of m7Gpredictor is all available at https://github.com/NWAFU-LiuLab/m7Gpredictor.