ALKBH1-Mediated tRNA Demethylation Regulates Translation.

tRNA is a central component of protein synthesis and the cell signaling network. One salient feature of tRNA is its heavily modified status, which can critically impact its function. Here, we show that mammalian ALKBH1 is a tRNA demethylase. It mediates the demethylation of N1-methyladenosine (m1A) in tRNAs. The ALKBH1-catalyzed demethylation of the target tRNAs results in attenuated translation initiation and decreased usage of tRNAs in protein synthesis. This process is dynamic and responds to glucose availability to affect translation. Our results uncover reversible methylation of tRNA as a new mechanism of post-transcriptional gene expression regulation.

Construction of a prognostic model for lung adenocarcinoma based on m6A/m5C/m1A genes.

BACKGROUND: Developing a prognostic model for lung adenocarcinoma (LUAD) that utilizes m6A/m5C/m1A genes holds immense importance in providing precise prognosis predictions for individuals. METHODS: This study mined m6A/m5C/m1A-related differential genes in LUAD based on public databases, identified LUAD tumor subtypes based on these genes, and further built a risk prognostic model grounded in differential genes between subtypes. The immune status between high- and low-risk groups was investigated, and the distribution of feature genes in tumor immune cells was analyzed using single-cell analysis. Based on the expression levels of feature genes, a projection of chemotherapeutic and targeted drugs was made for individuals identified as high-risk. Ultimately, cell experiments were further verified. RESULTS: The 6-gene risk prognosis model based on differential genes between tumor subtypes had good predictive performance. Individuals classified as low-risk exhibited a higher (P < 0.05) abundance of infiltrating immune cells. Feature genes were mainly distributed in tumor immune cells like CD4+T cells, CD8+T cells, and regulatory T cells. Four drugs with relatively low IC50 values were found in the high-risk group: Elesclomol, Pyrimethamine, Saracatinib, and Temsirolimus. In addition, four drugs with significant positive correlation (P < 0.001) between IC50 values and feature gene expression were found, including Alectinib, Estramustine, Brigatinib, and Elesclomol. The low expression of key gene NTSR1 reduced the IC50 value of irinotecan. CONCLUSION: Based on the m6A/m5C/m1A-related genes in LUAD, LUAD patients were divided into 2 subtypes, and a m6A/m5C/m1A-related LUAD prognostic model was constructed to provide a reference for the prognosis prediction of LUAD.

Quantification of tRNA m1A modification by templated-ligation qPCR.

N1-methyladenosine (m1A) is a widespread modification in all eukaryotic, many archaeal, and some bacterial tRNAs. m1A is generally located in the T loop of cytosolic tRNA and between the acceptor and D stems of mitochondrial tRNAs; it is involved in the tertiary interaction that stabilizes tRNA. Human tRNA m1A levels are dynamically regulated that fine-tune translation and can also serve as biomarkers for infectious disease. Although many methods have been used to measure m1A, a PCR method to assess m1A levels quantitatively in specific tRNAs has been lacking. Here we develop a templated-ligation followed by a qPCR method (TL-qPCR) that measures m1A levels in target tRNAs. Our method uses the SplintR ligase that efficiently ligates two tRNA complementary DNA oligonucleotides using tRNA as the template, followed by qPCR using the ligation product as the template. m1A interferes with the ligation in specific ways, allowing for the quantitative assessment of m1A levels using subnanogram amounts of total RNA. We identify the features of specificity and quantitation for m1A-modified model RNAs and apply these to total RNA samples from human cells. Our method enables easy access to study the dynamics and function of this pervasive tRNA modification.

Human T-cell leukemia virus type 1 uses a specific tRNAPro isodecoder to prime reverse transcription.

Human T-cell leukemia virus type 1 (HTLV-1) is the only oncogenic human retrovirus discovered to date. All retroviruses are believed to use a host cell tRNA to prime reverse transcription (RT). In HTLV-1, the primer-binding site (PBS) in the genomic RNA is complementary to the 3' 18 nucleotides (nt) of human tRNAPro The human genome encodes 20 cytoplasmic tRNAPro genes representing seven isodecoders, all of which share the same 3' 18 nt sequence but vary elsewhere. Whether all tRNAPro isodecoders are used to prime RT in cells is unknown. A previous study showed that a 3' 18 nt tRNAPro-derived fragment (tRFPro) is packaged into HTLV-1 particles and can serve as an RT primer in vitro. The role of this tRNA fragment in the viral life cycle is unclear. In retroviruses, N1-methylation of the tRNA primer at position A58 (m1A) is essential for successful plus-strand transfer. Using primer-extension assays performed in chronically HTLV-1-infected cells, we found that A58 of tRNAPro is m1A-modified, implying that full-length tRNAPro is capable of facilitating successful plus-strand transfer. Analysis of HTLV-1 RT primer extension products indicated that full-length tRNAPro is likely to be the primer. To determine which tRNAPro isodecoder is used as the RT primer, we sequenced the minus-strand strong-stop RT product containing the intact tRNA primer and established that HTLV-1 primes RT using a specific tRNAPro UGG isodecoder. Further studies are required to understand how this primer is annealed to the highly structured HTLV-1 PBS and to investigate the role of tRFPro in the viral life cycle.

Chemical manipulation of m1A mediates its detection in human tRNA.

N 1-methyl adenosine (m1A) is a widespread RNA modification present in tRNA, rRNA, and mRNA. m1A modification sites in tRNAs are evolutionarily conserved and its formation on tRNA is catalyzed by methyltransferase TRMT61A and TRMT6 complex. m1A promotes translation initiation and elongation. Due to its positive charge under physiological conditions, m1A can notably modulate RNA structure. It also blocks Watson-Crick-Franklin base-pairing and causes mutation and truncation during reverse transcription. Several misincorporation-based high-throughput sequencing methods have been developed to sequence m1A. In this study, we introduce a reduction-based m1A sequencing (red-m1A-seq). We report that NaBH4 reduction of m1A can improve the mutation and readthrough rates using commercially available RT enzymes to give a better positive signature, while alkaline-catalyzed Dimroth rearrangement can efficiently convert m1A to m6A to provide good controls, allowing the detection of m1A with higher sensitivity and accuracy. We applied red-m1A-seq to sequence human small RNA, and we not only detected all the previously reported tRNA m1A sites, but also new m1A sites in mt-tRNAAsn-GTT and 5.8S rRNA.

Differential m6A, m6Am, and m1A Demethylation Mediated by FTO in the Cell Nucleus and Cytoplasm.

FTO, the first RNA demethylase discovered, mediates the demethylation of internal N6-methyladenosine (m6A) and N6, 2-O-dimethyladenosine (m6Am) at the +1 position from the 5' cap in mRNA. Here we demonstrate that the cellular distribution of FTO is distinct among different cell lines, affecting the access of FTO to different RNA substrates. We find that FTO binds multiple RNA species, including mRNA, snRNA, and tRNA, and can demethylate internal m6A and cap m6Am in mRNA, internal m6A in U6 RNA, internal and cap m6Am in snRNAs, and N1-methyladenosine (m1A) in tRNA. FTO-mediated demethylation has a greater effect on the transcript levels of mRNAs possessing internal m6A than the ones with cap m6Am in the tested cells. We also show that FTO can directly repress translation by catalyzing m1A tRNA demethylation. Collectively, FTO-mediated RNA demethylation occurs to m6A and m6Am in mRNA and snRNA as well as m1A in tRNA.

Base-Resolution Mapping Reveals Distinct m1A Methylome in Nuclear- and Mitochondrial-Encoded Transcripts.

Gene expression can be post-transcriptionally regulated via dynamic and reversible RNA modifications. N1-methyladenosine (m1A) is a recently identified mRNA modification; however, little is known about its precise location and biogenesis. Here, we develop a base-resolution m1A profiling method, based on m1A-induced misincorporation during reverse transcription, and report distinct classes of m1A methylome in the human transcriptome. m1A in 5' UTR, particularly those at the mRNA cap, associate with increased translation efficiency. A different, small subset of m1A exhibit a GUUCRA tRNA-like motif, are evenly distributed in the transcriptome, and are dependent on the methyltransferase TRMT6/61A. Additionally, we show that m1A is prevalent in the mitochondrial-encoded transcripts. Manipulation of m1A level via TRMT61B, a mitochondria-localizing m1A methyltransferase, demonstrates that m1A in mitochondrial mRNA interferes with translation. Collectively, our approaches reveal distinct classes of m1A methylome and provide a resource for functional studies of m1A-mediated epitranscriptomic regulation.

RNA m1A methylation regulates glycolysis of cancer cells through modulating ATP5D.

Studies on biological functions of RNA modifications such as N6-methyladenosine (m6A) in mRNA have sprung up in recent years, while the roles of N1-methyladenosine (m1A) in cancer progression remain largely unknown. We find m1A demethylase ALKBH3 can regulate the glycolysis of cancer cells via a demethylation activity dependent manner. Specifically, sequencing and functional studies confirm that ATP5D, one of the most important subunit of adenosine 5'-triphosphate synthase, is involved in m1A demethylase ALKBH3-regulated glycolysis of cancer cells. The m1A modified A71 at the exon 1 of ATP5D negatively regulates its translation elongation via increasing the binding with YTHDF1/eRF1 complex, which facilitates the release of message RNA (mRNA) from ribosome complex. m1A also regulates mRNA stability of E2F1, which directly binds with ATP5D promoter to initiate its transcription. Targeted specific demethylation of ATP5D m1A by dm1ACRISPR system can significantly increase the expression of ATP5D and glycolysis of cancer cells. In vivo data confirm the roles of m1A/ATP5D in tumor growth and cancer progression. Our study reveals a crosstalk of mRNA m1A modification and cell metabolism, which expands the understanding of such interplays that are essential for cancer therapeutic application.

Identification of hepatoblastoma susceptibility loci in the TRMT6 gene from a seven-center case-control study.

Hepatoblastoma, the most frequently diagnosed primary paediatric liver tumour, bears the lowest somatic mutation burden among paediatric neoplasms. Therefore, it is essential to identify pathogenic germline genetic variants, especially those in oncogenic genes, for this disease. The tRNA methyltransferase 6 noncatalytic subunit (TRMT6) forms a tRNA methyltransferase complex with TRMT61A to catalyse adenosine methylation at position N1 of RNAs. TRMT6 has displayed tumour-promoting functions in several cancer types. However, the contribution of its genetic variants to hepatoblastoma remains unclear. In this study, we investigated the association between four TRMT6 polymorphisms (rs236170 A > G, rs451571 T > C, rs236188 G > A and rs236110 C > A) and the risk of hepatoblastoma in a cohort of 313 cases and 1446 healthy controls. Germline DNA was subjected to polymorphism genotyping via the TaqMan qPCR method. Odds ratio (OR) and 95% confidence interval (CI) were used to determine hepatoblastoma susceptibility variants. The rs236170 A > G, rs236188 G > A and rs236110 C > A polymorphisms were significantly associated with hepatoblastoma risk. Combination analysis of the four polymorphisms revealed that children bearing 1-4 risk genotypes were at significantly enhanced hepatoblastoma risk compared to those without risk genotype (adjusted OR = 1.52, 95% CI = 1.19-1.95, p = 0.0008). We also conducted stratification analyses by age, sex and clinical stage. Ultimately, we found that the rs236110 C > A was significantly associated with the downregulation of MCM8, a neighbouring gene of TRMT6. In conclusion, we identified three susceptibility loci in the TRMT6 gene for hepatoblastoma. Our findings warrant further validation by extensive case-control studies across different ethnicities.

Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD.

Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.

Characterization of the m6A/m1A/m5C/m7G-related regulators on the prognosis and immune microenvironment of glioma by integrated analysis of scRNA-seq and bulk RNA-seq data.

BACKGROUND: Proliferation, metabolism, tumor occurrence and development in gliomas are greatly influenced by RNA modifications. However, no research has integrated the four RNA methylation regulators of m6A, m1A, m5C and m7G in gliomas to analyze their relationship with glioma prognosis and intratumoral heterogeneity. METHODS: Based on three in-house single-cell RNA-sequencing (scRNA-seq) data, the glioma heterogeneity and characteristics of m6A/m1A/m5C/m7G-related regulators were elucidated. Based on publicly available bulk RNA-sequencing (RNA-seq) data, a risk-score system for predicting the overall survival (OS) for gliomas was established by three machine learning methods and multivariate Cox regression analysis, and validated in an independent cohort. RESULTS: Seven cell types were identified in gliomas by three scRNA-seq data, and 22 m6A/m1A/m5C/m7G-related regulators among the marker genes of different cell subtypes were discovered. Three m6A/m1A/m5C/m7G-related regulators were selected to construct prognostic risk-score model, including EIFA, NSUN6 and TET1. The high-risk patients showed higher immune checkpoint expression, higher tumor microenvironment scores, as well as higher tumor mutation burden and poorer prognosis compared with low-risk patients. Additionally, the area under the curve values of the risk score and nomogram were 0.833 and 0.922 for 3 year survival and 0.759 and 0.885 for 5 year survival for gliomas. EIF3A was significantly highly expressed in glioma tissues in our in-house RNA-sequencing data (p < 0.05). CONCLUSION: These findings may contribute to further understanding of the role of m6A/m1A/m5C/m7G-related regulators in gliomas, and provide novel and reliable biomarkers for gliomas prognosis and treatment.

m1A regulator-mediated methylation modification patterns correlated with autophagy to predict the prognosis of hepatocellular carcinoma.

BACKGROUND: N1-methyladenosine (m1A), among the most common internal modifications on RNAs, has a crucial role to play in cancer development. The purpose of this study were systematically investigate the modification characteristics of m1A in hepatocellular carcinoma (HCC) to unveil its potential as an anticancer target and to develop a model related to m1A modification characteristics with biological functions. This model could predict the prognosis for patients with HCC. METHODS: An integrated analysis of the TCGA-LIHC database was performed to explore the gene signatures and clinical relevance of 10 m1A regulators. Furthermore, the biological pathways regulated by m1A modification patterns were investigated. The risk model was established using the genes that showed differential expression (DEGs) between various m1A modification patterns and autophagy clusters. These in vitro experiments were subsequently designed to validate the role of m1A in HCC cell growth and autophagy. Immunohistochemistry was employed to assess m1A levels and the expression of DEGs from the risk model in HCC tissues and paracancer tissues using tissue microarray. RESULTS: The risk model, constructed from five DEGs (CDK5R2, TRIM36, DCAF8L, CYP26B, and PAGE1), exhibited significant prognostic value in predicting survival rates among individuals with HCC. Moreover, HCC tissues showed decreased levels of m1A compared to paracancer tissues. Furthermore, the low m1A level group indicated a poorer clinical outcome for patients with HCC. Additionally, m1A modification may positively influence autophagy regulation, thereby inhibiting HCC cells proliferation under nutrient deficiency conditions. CONCLUSIONS: The risk model, comprising m1A regulators correlated with autophagy and constructed from five DEGs, could be instrumental in predicting HCC prognosis. The reduced level of m1A may represent a potential target for anti-HCC strategies.

Surgery for M1A Non-Small-Cell Lung Cancer With Additional Pulmonary Nodules in a Contralateral Lobe.

INTRODUCTION: Limited consensus exists on the optimal treatment strategy for clinical M1a non-small-cell lung cancer (NSCLC) presenting as a primary tumor with additional intrapulmonary nodules in a contralateral lobe ("M1a-Contra"). This study sought to compare long-term survival of patients with M1a-Contra tumors receiving multimodal therapy with versus without thoracic surgery. METHODS: Overall survival of patients with cT1-4, N0-3, M1a NSCLC with contralateral intrapulmonary nodules who received surgery as part of multimodal therapy ("Thoracic Surgery") versus systemic therapy with or without radiation ("No Thoracic Surgery") in the National Cancer Database from 2010 to 2015 was evaluated using Kaplan-Meier analysis, Cox proportional hazards modeling, and propensity score matching. RESULTS: Of the 5042 patients who satisfied study inclusion criteria, 357 (7.1%) received multimodal therapy including surgery. In multivariable-adjusted analysis, the Thoracic Surgery cohort had better overall survival than the No Thoracic Surgery cohort (HR: 0.66, 95% CI: 0.56-0.79, P < 0.001). In a propensity score-matched analysis of 386 patients, well-balanced on 12 common prognostic covariates, the Thoracic Surgery group had better 5-year overall survival than the No Thoracic Surgery group (P = 0.020). In propensity score-matched analyses stratified by clinical N status, Thoracic Surgery was associated with better overall survival than No Thoracic Surgery for patients with cN0 disease and cN1-2 disease. CONCLUSIONS: In this national analysis, multimodal treatment including surgery was associated with better overall survival than systemic therapy with or without radiation without surgery for patients with M1a-Contra tumors. These preliminary findings highlight the importance of further evaluation of surgery in a multidisciplinary treatment setting for M1a-Contra tumors.

An Overview of Current Detection Methods for RNA Methylation.

Epitranscriptomic mechanisms, which constitute an important layer in post-transcriptional gene regulation, are involved in numerous cellular processes under health and disease such as stem cell development or cancer. Among various such mechanisms, RNA methylation is considered to have vital roles in eukaryotes primarily due to its dynamic and reversible nature. There are numerous RNA methylations that include, but are not limited to, 2'-O-dimethyladenosine (m6Am), N7-methylguanosine (m7G), N6-methyladenosine (m6A) and N1-methyladenosine (m1A). These biochemical modifications modulate the fate of RNA by affecting the processes such as translation, target site determination, RNA processing, polyadenylation, splicing, structure, editing and stability. Thus, it is highly important to quantitatively measure the changes in RNA methylation marks to gain insight into cellular processes under health and disease. Although there are complicating challenges in identifying certain methylation marks genome wide, various methods have been developed recently to facilitate the quantitative measurement of methylated RNAs. To this end, the detection methods for RNA methylation can be classified in five categories such as antibody-based, digestion-based, ligation-based, hybridization-based or direct RNA-based methods. In this review, we have aimed to summarize our current understanding of the detection methods for RNA methylation, highlighting their advantages and disadvantages, along with the current challenges in the field.

Small Molecule-Inducible and Photoactivatable Cellular RNA N1-Methyladenosine Editing.

N1-methyladenosine (m1A) modification is one of the most prevalent epigenetic modifications on RNA. Given the vital role of m1A modification in RNA processing such as splicing, stability and translation, developing a precise and controllable m1A editing tool is pivotal for in-depth investigating the biological functions of m1A. In this study, we developed an abscisic acid (ABA)-inducible and reversible m1A demethylation tool (termed AI-dm1A), which targets specific transcripts by combining the chemical proximity-induction techniques with the CRISPR/dCas13b system and ALKBH3. We successfully employed AI-dm1A to selectively demethylate the m1A modifications at A8422 of MALAT1 RNA, and this demethylation process could be reversed by removing ABA. Furthermore, we validated its demethylation function on various types of cellular RNAs including mRNA, rRNA and lncRNA. Additionally, we used AI-dm1A to specifically demethylate m1A on ATP5D mRNA, which promoted ATP5D expression and enhanced the glycolysis activity of tumor cells. Conversely, by replacing the demethylase ALKBH3 with methyltransferase TRMT61A, we also developed a controllable m1A methylation tool, namely AI-m1A. Finally, we caged ABA by 4,5-dimethoxy-2-nitrobenzyl (DMNB) to achieve light-inducible m1A methylation or demethylation on specific transcripts. Collectively, our m1A editing tool enables us to flexibly study how m1A modifications on specific transcript influence biological functions and phenotypes.

The Molecular Basis of Human ALKBH3 Mediated RNA N1 -methyladenosine (m1 A) Demethylation.

N1 -methyladenosine (m1 A) is a prevalent post-transcriptional RNA modification, and the distribution and dynamics of the modification play key epitranscriptomic roles in cell development. At present, the human AlkB Fe(II)/α-ketoglutarate-dependent dioxygenase family member ALKBH3 is the only known mRNA m1 A demethylase, but its catalytic mechanism remains unclear. Here, we present the structures of ALKBH3-oligo crosslinked complexes obtained with the assistance of a synthetic antibody crystallization chaperone. Structural and biochemical results showed that ALKBH3 utilized two ß-hairpins (ß4-loop-ß5 and ß'-loop-ß'') and the α2 helix to facilitate single-stranded substrate binding. Moreover, a bubble-like region around Asp194 and a key residue inside the active pocket (Thr133) enabled specific recognition and demethylation of m1 A- and 3-methylcytidine (m3 C)-modified substrates. Mutation of Thr133 to the corresponding residue in the AlkB Fe(II)/α-ketoglutarate-dependent dioxygenase family members FTO or ALKBH5 converted ALKBH3 substrate selectivity from m1 A to N6 -methyladenosine (m6 A), as did Asp194 deletion. Our findings provide a molecular basis for understanding the mechanisms of substrate recognition and m1 A demethylation by ALKBH3. This study is expected to aid structure-guided design of chemical probes for further functional studies and therapeutic applications.

Exploration and validation of a combined Hypoxia and m6A/m5C/m1A regulated gene signature for prognosis prediction of liver cancer.

BACKGROUND: It is widely acknowledged that hypoxia and m6A/m5C/m1A RNA modifications promote the occurrence and development of tumors by regulating the tumor microenvironment. This study aimed to establish a novel liver cancer risk signature based on hypoxia and m6A/m5C/m1A modifications. METHODS: We collected data from The Cancer Genome Atlas (TCGA-LIHC), the National Omics Data Encyclopedia (NODE-HCC), the International Cancer Genome Consortium (ICGC), and the Gene Expression Omnibus (GEO) databases for our study (GSE59729, GSE41666). Using Cox regression and least absolute shrinkage and selection operator (LASSO) method, we developed a risk signature for liver cancer based on differentially expressed genes related to hypoxia and genes regulated by m6A/m5C/m1A modifications. We stratified patients into high- and low-risk groups and assessed differences between these groups in terms of gene mutations, copy number variations, pathway enrichment, stemness scores, immune infiltration, and predictive capabilities of the model for immunotherapy and chemotherapy efficacy. RESULTS: Our analysis revealed a significantly correlated between hypoxia and methylation as well as m6A/m5C/m1A RNA methylation. The three-gene prognosis signature (CEP55, DPH2, SMS) combining hypoxia and m6A/m5C/m1A regulated genes exhibited strong predictive performance in TCGA-LIHC, NODE-HCC, and ICGC-LIHC-JP cohorts. The low-risk group demonstrated a significantly better overall survival compared to the high-risk group (p < 0.0001 in TCGA, p = 0.0043 in NODE, p = 0.0015 in ICGC). The area under the curve (AUC) values for survival at 1, 2, and 3 years are all greater than 0.65 in the three cohorts. Univariate and Multivariate Cox regression analyses of the three datasets indicated that the signature could serve as an independent prognostic predictor (p < 0.001 in the three cohorts). The high-risk group exhibited more genome changes and higher homologous recombination deficiency scores and stemness scores. Analysis of immune infiltration and immune activation confirmed that the signature was associated with various immune microenvironment characteristics. Finally, patients in the high-risk group experienced a more favorable response to immunotherapy, and various common chemotherapy drugs. CONCLUSION: Our prognostic signature which integrates hypoxia and m6A/m5C/m1A-regulated genes, provides valuable insights for clinical prediction and treatment guidance for liver cancer patients.

Breast cancer prognosis and immunological characteristics are predicted using the m6A/m5C/m1A/m7G-related long noncoding RNA signature.

According to statistics, breast cancer (BC) has replaced lung cancer as the most common cancer in the world. Therefore, specific detection markers and therapeutic targets need to be explored as a way to improve the survival rate of BC patients. We first identified m6A/m5C/m1A/m7G-related long noncoding RNAs (MRlncRNAs) and developed a model of 16 MRlncRNAs. Kaplan-Meier survival analysis was applied to assess the prognostic power of the model, while univariate Cox analysis and multivariate Cox analysis were used to assess the prognostic value of the constructed model. Then, we constructed a nomogram to illustrate whether the predicted results were in good agreement with the actual outcomes. We tried to use the model to distinguish the difference in sensitivity to immunotherapy between the two groups and performed some analyses such as immune infiltration analysis, ssGSEA and IC50 prediction. To explore the novel anti-tumor drug response, we reclassified the patients into two clusters. Next, we assessed their response to clinical treatment by the R package pRRophetic, which is determined by the IC50 of each BC patient. We finally identified 11 MRlncRNAs and based on them, a risk model was constructed. In this model, we found good agreement between calibration plots and prognosis prediction. The AUC of ROC curves was 0.751, 0.734, and 0.769 for 1-year, 2-year, and 3-year overall survival (OS), respectively. The results showed that the IC50 was significantly different between the risk groups, suggesting that the risk groups can be used as a guide for systemic treatment. We regrouped patients into two clusters based on 11 MRlncRNAs expression. Next, we conducted immune scores for 2 clusters, which showed that cluster 1 had higher stromal scores, immune scores and higher estimated (microenvironment) scores, demonstrating that TME of cluster 1 was different from cluster 2. The results of this study support that MRlncRNAs can predict tumor prognosis and help differentiate patients with different sensitivities to immunotherapy as a basis for individualized treatment for BC patients.

Non-coding RNA methylation modifications in hepatocellular carcinoma: interactions and potential implications.

RNA methylation modification plays a crucial role as an epigenetic regulator in the oncogenesis of hepatocellular carcinoma (HCC). Numerous studies have investigated the molecular mechanisms underlying the methylation of protein-coding RNAs in the progression of HCC. Beyond their impact on mRNA, methylation modifications also influence the biological functions of non-coding RNAs (ncRNAs). Here, we present an advanced and comprehensive overview of the interplay between methylation modifications and ncRNAs in HCC, with a specific focus on their potential implications for the tumor immune microenvironment. Moreover, we summarize promising therapeutic targets for HCC based on methylation-related proteins. In the future, a more profound investigation is warranted to elucidate the effects of ncRNA methylation modifications on HCC pathogenesis and devise valuable intervention strategies. Video Abstract.

Identification of methyltransferase modification genes associated with prognosis and immune features of pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) is a malignant tumor with a high mortality rate. Methylation modifications acted a crucial role to affect cancer progression. The current study aimed to explore the potential role of methylase regulators in PAAD prognosis and immune microenvironment. METHODS: PubMed and TCGA databases were used to systematically analyze methylase regulators in PAAD. We identified three methylase clusters based on RNA methylase transcriptome data and obtained three gene clusters based on methylase modification-related differently expressed genes using principal component analysis (PCA) analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) biological processes were performed to explore the processes enriched in the different subgroups and single sample gene-set enrichment analysis (ssGSEA) was used to analyze the relationship between subgroups and immune infiltration in PAAD. RESULTS: We systematically screened 43 methylase regulators in PAAD samples and identified three methylase clusters with different clinical outcomes, as well as detected a significant relationship between methylase clusters and tumor immune infiltration. The top ten mutated genes include TP53, Kirsten rat sarcoma viral oncogene homolog (KRAS), titin gene (TTN), mucin 16 (MUC16), SMAD4, cyclin-dependent kinase inhibitor 2a (CDKN2A), Ryanodine receptor isoform-1 (RYR1), ring finger 43 (RNF43), protocadherin-15 (PCDH15), and AT-rich interacting domain-containing protein 1 A gene (ARID1A). CONCLUSION: The current study constructed an m6A/m5C/m1A/m7G modulator genes and explored methylase modification-related genes, which were related to the prognosis of PAAD patients and the immune checkpoint point cytotoxic T-lymphocyte associated protein 4 (CTLA4). These findings may provide prognostic predictors and direction for immunotherapy strategies for the treatment of PAAD.