m5C RNA Research Articles

5-methylcytosine (m5C) RNA modification controls the innate immune response to virus infection by regulating type I interferons

5-methylcytosine (m5C) is one of the most prevalent modifications of RNA, playing important roles in RNA metabolism, nuclear export, and translation. However, the potential role of RNA m5C methylation in innate immunity remains elusive. Here, we show that depletion of NSUN2, an m5C methyltransferase, significantly inhibits the replication and gene expression of a wide range of RNA and DNA viruses. Notably, we found that this antiviral effect is largely driven by an enhanced type I interferon (IFN) response. The antiviral signaling pathway is dependent on the cytosolic RNA sensor RIG-I but not MDA5. Transcriptome-wide mapping of m5C following NSUN2 depletion in human A549 cells revealed a marked reduction in the m5C methylation of several abundant noncoding RNAs (ncRNAs). However, m5C methylation of viral RNA was not noticeably altered by NSUN2 depletion. In NSUN2-depleted cells, the host RNA polymerase (Pol) III transcribed ncRNAs, in particular RPPH1 and 7SL RNAs, were substantially up-regulated, leading to an increase of unshielded 7SL RNA in cytoplasm, which served as a direct ligand for the RIG-I-mediated IFN response. In NSUN2-depleted cells, inhibition of Pol III transcription or silencing of RPPH1 and 7SL RNA dampened IFN signaling, partially rescuing viral replication and gene expression. Finally, depletion of NSUN2 in an ex vivo human lung model and a mouse model inhibits viral replication and reduces pathogenesis, which is accompanied by enhanced type I IFN responses. Collectively, our data demonstrate that RNA m5C methylation controls antiviral innate immunity through modulating the m5C methylome of ncRNAs and their expression.

Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.

Background: 5-methylcytosine (m5C) RNA methylation plays a significant role in several human diseases. However, the functional role of m5C in type 2 diabetes (T2D) remains unclear. Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. After LASSO regression, we constructed a diagnostic model and validated its accuracy. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to confirm the biological functions of DEGs. Gene Set Enrichment Analysis (GSEA) was used to determine the functional enrichment of molecular subtypes. Weighted gene co-expression network analysis (WGCNA) was used to select the module that correlated with the most pyroptosis-related genes. Protein-protein interaction (PPI) network was established using the STRING database, and hub genes were identified using Cytoscape software. The competitive endogenous RNA (ceRNA) interaction network of the hub genes was obtained. The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration. Results: m5C-related genes were significantly differentially expressed in T2D and correlated with most T2D-related DEGs. LASSO regression showed that ZBTB4 could be a predictive gene for T2D. GO, KEGG, and GSEA indicated that the enriched modules and pathways were closely related to metabolism-related biological processes and cell death. The top five genes were identified as hub genes in the PPI network. In addition, a ceRNA interaction network of hub genes was obtained. Moreover, the expression levels of the hub genes were significantly correlated with the abundance of various immune cells. Conclusion: Our findings may provide insights into the molecular mechanisms underlying T2D based on its pathophysiology and suggest potential biomarkers and therapeutic targets for T2D.

5-Methylcytosine (m5C) modification in peripheral blood immune cells is a novel non-invasive biomarker for colorectal cancer diagnosis.

Current non-invasive tumor biomarkers failed to accurately identify patients with colorectal cancer (CRC), delaying CRC diagnosis and thus leading to poor prognosis. Dysregulation of 5-Methylcytosine (m5C) RNA has gradually been reported in various cancers, but their role in tumor diagnosis is rarely mentioned. Our study aimed to determine the role of m5C methylation modification in blood immune cells for the diagnosis of CRC. Peripheral blood samples were obtained from a total of 83 healthy controls and 196 CRC patients. We observed that m5C RNA contents in blood immune cells of CRC patients were markedly enhanced in both training set and validation set. Moreover, levels of m5C increased with CRC progression and metastasis but reduced after treatment. Compared with common blood tumor biomarkers, m5C levels in peripheral blood immune cells had superior discrimination and reclassification performance in diagnosing CRC. Besides, bioinformatics and qRT-PCR analysis identified increased expression of m5C-modified regulators NSUN5 and YBX1 in CRC patients’ blood. A series of animal models and cell co-culture models further demonstrated that CRC tumor cells could increase immune cells’ m5C levels and m5C-modified regulators. Monocyte was the predominant m5C-modified immune cell type in CRC patients’ blood by Gene set variation analysis (GSVA). Taken together, m5C methylation modification in peripheral blood immune cells was a promising biomarker for non-invasive diagnosis of CRC.

Transcriptional expression of m6A and m5C RNA methyltransferase genes in the brain and fat body of honey bee adult workers.

Honey bee (Apis mellifera) adult workers change behaviors and nutrition according to age progression. Young workers, such as nurses, perform in-hive tasks and consume protein-rich pollen, while older workers (foragers) leave the colony to search for food, and consume carbohydrate-rich nectar. These environmentally stimulated events involve transcriptional and DNA epigenetic marks alterations in worker tissues. However, post-transcriptional RNA modifications (epitranscriptomics) are still poorly explored in bees. We investigated the transcriptional profiles of m6A and m5C RNA methyltransferases in the brain and fat body of adult workers of 1) different ages and performing different tasks [nurses of 8 days-old (N-8D) and foragers of 29 days-old (F-29D), sampled from wild-type colonies], and 2) same-aged young workers caged in an incubator and treated with a pollen-rich [PR] or a pollen-deprived [PD] diet for 8 days. In the brain, METTL3, DNMT2, NOP2, NSUN2, NSUN5, and NSUN7 genes increased expression during adulthood (from N-8D to F-29D), while the opposite pattern was observed in the fat body for METTL3, DNMT2, and NSUN2 genes. Regarding diet treatments, high expression levels were observed in the brains of the pollen-deprived group (DNMT2, NOP2, and NSUN2 genes) and the fat bodies of the pollen-rich group (NOP2, NSUN4, and NSUN5 genes) compared to the brains of the PR group and the fat bodies of the PD group, respectively. Our data indicate that RNA epigenetics may be an important regulatory layer in the development of adult workers, presenting tissue-specific signatures of RNA methyltransferases expression in response to age, behavior, and diet content.

Multi-omics analysis of expression and prognostic value of NSUN members in prostate cancer.

BackgroundProstate cancer is the most common tumor in men worldwide, seriously threatening the health of older men, and 5-methylcytosine (m5C) RNA modification has been shown to have a significant impact on the development and progression of various tumors. However, as the most critical methyltransferase for m5c RNA modification, the role of the NSUN members (NSUN1-7) in prostate cancer is unclear.MethodsWe obtained sequencing data of genes and related clinical data of prostate cancer from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database and analyzed the correlation between NSUN members’ expression and prognosis. we found that NSUN2 was closely implicated in the prognosis of prostate cancer, then verified the expression of NSUN2 in clinical samples, and obtained the correlation between NSUN2 and immune cell infiltration through CIBERSORT algorithm and ESTIMATE method. The relationship between NSUN2 copy number variation and immune cell infiltration was further analyzed in the TIMER database and identified signaling pathways associated with NSUN2 expression by GO, KEGG, and GSEA analysis. Finally, we verified the expression of NSUN2 in prostate cancer cell lines and confirmed the role of NSUN2 on the biological behavior of prostate cancer cells by proliferation and migration-related assays.ResultsNOP2 and NSUN2 were upregulated in prostate tumor tissues. NSUN2 expression is closely associated with tumor prognosis. NSUN2 high expression implies poor clinical features, and the NSUN family is significantly associated with tumor stromal score and immune score. Besides, NSUN2 is associated with a variety of immune infiltrating cells (B cells memory, T cells CD4 memory resting, T cells CD4 memory activated, NK cells resting, and so on). High NSUN2 expression lowers the sensitivity of many chemotherapy drugs, such as docetaxel, doxorubicin, fluorouracil, cisplatin, and etoposide. In prostate cancer, the most common type of mutation in NSUN2 is amplification, and NSUN2 copy number variation is closely associated with NSUN2 expression and immune cell infiltration. GSEA analysis showed that the related genes were mainly enriched in ubiquitin-mediated protein hydrolysis, cell cycle, RNA degradation, endometrial cancer, prostate cancer, p53 signaling pathway, and NSUN2 potentiated the proliferation and migration of prostate cancer cells.ConclusionsNSUN2 is highly expressed in prostate cancer, which contributes to the progression of prostate cancer, and is closely implicated in immune cell infiltration and chemotherapy drugs. NSUN2 is expected to be a prospective marker and a new treatment target for prostate cancer.

Metastatic Metabolic Plasticity Is Shaped by RNA Methylation Modifications.

The m5C and f5C RNA modifications support mitochondrial mRNA translation promoting metastasis.

Overall Survival Signature of 5-Methylcytosine Regulators Related Long Non-Coding RNA in Hepatocellular Carcinoma.

PurposeStudies reported that 5-methylcytosine (m5C) RNA transferase alters tumor progression; however, studies of m5C-related lncRNA remain lacking. This article intends to study the lncRNA modified by m5C RNA transferase in hepatocellular carcinoma using a combination of computational biology and basic experiments.MethodWe identified 13 m5C RNA transferase-related genes and selected long non-coding RNAs with a Pearson correlation coefficient greater than 0.4. Univariate Cox regression analysis was used to screen m5C RNA transferase lncRNA related to survival phenotype. We divided TCGA-LIHC into two types of m5C RNA using non-negative matrix decomposition. According to WGCNA, the co-expression models of two lncRNA regulation modes were constructed to analyze the characteristic biological processes of the two m5C RNA transferase-related lncRNA gene models. Then, a predictive model of m5C RNA transferase lncRNA was using LASSO regression. Finally, we used cell experiments, transwell experiments, and clone formation experiments to test the relationship between SNHG4 and tumor cell proliferation in Hep-G2 and Hep-3b cells line.ResultsWe identified 436 m5C RNA transferase-related lncRNAs. Using univariate Cox regression analysis, 43 prognostic-related lncRNAs were determined according to P < 0.001. We divided TCGA-LIHC into two regulation modes of m5C RNA transferase using non-negative matrix factorization. The two regulation modes showed significant differences in overall and disease-free survival. We used LASSO to construct m5c-related lncRNA prognostic signature. Thus, a predictive m5C-lncRNA model was established using four lncRNAs: AC026412.3, AC010969.2, SNHG4, and AP003392.5. The score calculated by the m5C-lncRNA model significantly correlated with the overall survival of hepatocellular carcinoma. The receiver operating characteristic curve and decision curve analysis verified the accuracy of the predictive model. We observed a more robust immune response in the high-risk score group. The transwell experiments and clone formation experiments suggested that m5C RNA transferase-related lncRNA SNHG4 promotes the proliferation and migration of Hep-G2 and Hep-3b cells line.ConclusionTwo lncRNA expression patterns regulated by m5C RNA transferase were identified. The difference between the two expression patterns and the survival phenotype in the biological process was pointed out. A 5-methylcytosine RNA methyltransferases-related lncRNA overall survival signature was constructed. These results provide some understanding of the influence of m5C transferase on hepatocellular carcinoma. The prediction model of m5C transferase lncRNA has potential clinical value in managing hepatocellular carcinoma.

Roles of m5C RNA Modification Patterns in Biochemical Recurrence and Tumor Microenvironment Characterization of Prostate Adenocarcinoma.

BackgroundProstate cancer is the second most common cancer with a high risk of biochemical recurrence (BCR) among men. Recently, 5-methylcytosine (m5C) modification has attracted more attention as a new layer of RNA post-transcriptional regulation. Hence, we aimed at investigating the potential roles of m5C modification regulators in the BCR of prostate adenocarcinoma (PRAD).MethodsCNV data, mutation annotation data, mRNA expression profiles, and clinical data were downloaded from TCGA and GEO databases. Kaplan-Meier curves analysis, log-rank test, univariate and multivariate Cox regression, and time-dependent ROC curves analysis were performed to evaluate the prognostic factors. Principal components analysis (PCA) was applied to validate the distinction between subgroups. Gene set variation analysis (GSVA) was used to investigate the underlying pathways associated with m5C modification patterns. Single sample gene set enrichment analysis (ssGSEA) was utilized to assess the infiltration of distinct immune cells. Tumor Immune Dysfunction and Exclusion (TIDE) prediction was carried out to assess the potential response to immune checkpoint blockade (ICB) therapy. The m5C modification signature was constructed via LASSO Cox’s proportional hazards regression method.ResultsAfter comprehensively analyzing various types of data from TCGA dataset, and exploring the differential expression and prognostic value of each m5C regulator, we identified m5C modification patterns based on 17 m5C regulators. Two patterns presented a significant difference in the risk of BCR, the tumor microenvironment (TME), and immunotherapy response in PRAD. We found that TET2, which was highly expressed in adjacent normal tissues compared to tumor tissues, was closely associated with many infiltrating immune cells. The m5C modification signature was constructed for the clinical application. Risk score calculated by m5C signature was associated with T stage, N stage, Gleason score, and the possibility of BCR (HR, 4.197; 95% CI, 3.016-5.842; p < 0.001). A higher risk score also represented the possibility of immunotherapy response. Finally, the potential roles of m5C modification signature were validated in the testing dataset.ConclusionsOur study revealed the potential roles of m5C modification in the PRAD BCR and TME diversity, which may provide new insight into the field of prostate cancer in future research.

M5C-Related lncRNA Predicts Lung Adenocarcinoma and Tumor Microenvironment Remodeling: Computational Biology and Basic Science.

Purpose: Epigenetic RNA modification regulates gene expression post-transcriptionally. The aim of this study was to construct a prognostic risk model for lung adenocarcinoma (LUAD) using long non-coding RNAs (lncRNAs) related to m5C RNA methylation. Method: The lncRNAs regulated by m5C methyltransferase were identified in TCGA-LUAD dataset using Pearson correlation analysis (coefficient > 0.4), and clustered using non-negative matrix decomposition. The co-expressing gene modules were identified by WGCNA and functionally annotated. The prognostically relevant lncRNAs were screened by LASSO regression and a risk model was constructed. LINC00628 was silenced in the NCI-H460 and NCI-H1299 cell lines using siRNA constructs, and migration and invasion were assessed by the Transwell and wound healing assays respectively. Results: We identified 185 m5C methyltransferase-related lncRNAs in LUAD, of which 16 were significantly associated with overall survival. The lncRNAs were grouped into two clusters on the basis of m5C pattern, and were associated with significant differences in overall and disease-free survival. GSVA revealed a close relationship among m5C score, ribosomes, endolysosomes and lymphocyte migration. Using LASSO regression, we constructed a prognostic signature consisting of LINC00628, LINC02147, and MIR34AHG. The m5C-lncRNA signature score was closely related to overall survival, and the accuracy of the predictive model was verified by the receiver operating characteristic curve and decision curve analysis. Knocking down LINC00628 in NCI-H460 and NCI-H1299 cells significantly reduced their migration and invasion compared to that of control cells. Conclusion: We constructed a prognostic risk model of LUAD using three lncRNAs regulated by m5C methyltransferase, which has potential clinical value.

M5C RNA methyltransferase-related gene NSUN4 stimulates malignant progression of hepatocellular carcinoma and can be a prognostic marker.

Hepatocellular carcinoma (HCC) is a cancer with relatively high mortality, yet little attention has been devoted for related prognostic biomarkers. This study analyzed differential expression of m5C RNA methyltransferase-related genes in normal samples and tumors samples in TCGA-LIHC using Wilcoxon test. K-means consensus clustering analysis was implemented to subdivide samples. Independent prognostic factors were screened by univariate and multivariate Cox regression analyses. KEGG pathway enrichment analysis was performed on the screened independent prognostic factor using GSEA tools. qPCR was conducted to test mRNA expression of key m5C RNA methyltransferase-related genes in tissues and cells. There were 7 m5C RNA methyltransferase-related genes (NOP2, NSUN4, etc.) differentially expressed in HCC tumor tissues. HCC samples were classified into 3 subgroups through clustering analysis according to the expression mode of m5C RNA methyltransferase-related genes. It was also discovered that patients in different subgroups presented significant differences in survival rate and distribution of grade. Additionally, NOP2, NSUN4 and NSUN5 expression notable varied in different grades. Through regression analyses combined with various clinical pathological factors, it was displayed that NSUN4 could work as an independent prognostic factor. KEGG analysis showed that NSUN4 mainly enriched in signaling pathways involved in ADHERENS JUNCTION, RNA DEGRADATION, MTOR SIGNALING PATHWAY, COMPLEMENT and COAGULATION CASCADES. As examined by qPCR, NSUN4 was conspicuously upregulated in HCC patient's tissues and cells. Altogether, our study preliminarily developed a novel biomarker that could be independently used in prognosis of HCC, which may provide a new direction for the study of related molecular mechanism or treatment regimen.

Immune Infiltrates of m5C RNA Methylation-Related LncRNAs in Uterine Corpus Endometrial Carcinoma.

Aberrant 5-methylcytidine (m5C) modification plays an essential role in the progression of different cancers. More and more researchers are focusing on developing a lncRNA-based risk model to assess the clinical prognosis of cancer patients. However, the impact of m5C-related lncRNAs on the prognosis of patients with uterine corpus endometrial carcinoma (UCEC), as well as the immune microenvironment of UCEC, remains unclear. Here, we comprehensively analyzed the predictive value of m5C-associated lncRNAs in UCEC and their association with the tumor immune microenvironment, according to the information extracted from the TCGA-UCEC dataset. We identified a total of 32 m5C-associated lncRNAs that were significantly correlated with the prognosis of UCEC patients. Two molecular subtypes were determined by consensus clustering analysis of these 32 m5C-associated prognostic lncRNAs. Further data showed that cluster 1 was associated with poor clinical prognosis, advanced tumor grade, higher PD-L1 expression levels, higher ESTIMATEScore, and higher immuneScore, as well as the immune cell infiltration. Then, 17 m5C-associated lncRNAs with prognostic values were obtained using LASSO regression analysis. And a risk model was constructed based on these 17 lncRNAs. It was revealed that the risk model could be used as an independent factor for UCEC prognosis. In addition, patients with UCEC in the high-risk group had higher tumor grades and immune scores. The risk model based on m5C-related lncRNAs was also closely associated with infiltrating immune cells. In conclusion, our study elucidated the crucial roles of the identified m5C-related lncRNAs in the UCEC patients' prognoses, as well as in the immune microenvironment in UCEC. The results suggest that the components of risk models based on the m5C-related lncRNAs may serve as important mediators of the immune microenvironment in UCEC.

FMRP promotes transcription-coupled homologous recombination via facilitating TET1-mediated m5C RNA modification demethylation

RNA modifications regulate a variety of cellular processes including DNA repair.The RNA methyltransferase TRDMT1 generates methyl-5-cytosine (m5C) on messen-ger RNA (mRNA) at DNA double-strand breaks (DSBs) in transcribed regions, pro-moting transcription-coupled homologous recombination (HR). Here, we identifiedthat Fragile X mental retardation protein (FMRP) promotes transcription-coupled HRvia its interaction with both the m5C writer TRDMT1 and the m5C eraser ten-eleventranslocation protein 1 (TET1). TRDMT1, FMRP, and TET1 function in a temporalorder at the transcriptionally active sites of DSBs. FMRP displays a higher affinity forDNA:RNA hybrids containing m5C-modified RNA than for hybrids without modifica-tion and facilitates demethylation of m5C by TET1 in vitro. Loss of either the chroma-tin- or RNA-binding domain of FMRP compromises demethylation of damage-inducedm5C in cells. Importantly, FMRP is required for R-loop resolving in cells. Due to unre-solved R-loop and m5C preventing completion of DSB repair, FMRP depletion or lowexpression leads to delayed repair of DSBs at transcriptionally active sites and sensitizescancer cells to radiation in a BRCA-independent manner. Together, ourfindings presentan m5C reader, FMRP, which acts as a coordinator between the m5C writer and eraserto promote mRNA-dependent repair and cell survival in cancer.

Exploring m&lt;sup&gt;6&lt;/sup&gt;A and m&lt;sup&gt;5&lt;/sup&gt;C Epitranscriptomes upon Viral Infection: an Example with HIV

The role of RNA modifications in biological processes has been the focus of an increasing number of studies in the last few years and is known nowadays as epitranscriptomics. Among others, N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have been described on mRNA molecules and may have a role in modulating cellular processes. Epitranscriptomics is thus a new layer of regulation that must be considered in addition to transcriptomic analyses, as it can also be altered or modulated by exposure to any chemical or biological agent, including viral infections. Here, we present a workflow that allows analysis of the joint cellular and viral epitranscriptomic landscape of the m6A and m5C marks simultaneously, in cells infected or not with the human immunodeficiency virus (HIV). Upon mRNA isolation and fragmentation from HIV- infected and non-infected cells, we used two different procedures: MeRIP-Seq, an RNA immunoprecipitation-based technique, to enrich for RNA fragments containing the m6A mark and BS-Seq, a bisulfite conversion-based technique, to identify the m5C mark at a single nucleotide resolution. Upon methylation-specific capture, RNA libraries are prepared for high-throughput sequencing. We also developed a dedicated bioinformatics pipeline to identify differentially methylated (DM) transcripts independently from their basal expression profile. Overall, the methodology allows exploration of multiple epitranscriptomic marks simultaneously and provides an atlas of DM transcripts upon viral infection or any other cell perturbation. This approach offers new opportunities to identify novel players and novel mechanisms of cell response, such as cellular factors promoting or restricting viral replication.

Exploring m6A and m5C Epitranscriptomes upon Viral Infection: an Example with HIV.

The role of RNA modifications in biological processes has been the focus of an increasing number of studies in the last few years and is known nowadays as epitranscriptomics. Among others, N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have been described on mRNA molecules and may have a role in modulating cellular processes. Epitranscriptomics is thus a new layer of regulation that must be considered in addition to transcriptomic analyses, as it can also be altered or modulated by exposure to any chemical or biological agent, including viral infections. Here, we present a workflow that allows analysis of the joint cellular and viral epitranscriptomic landscape of the m6A and m5C marks simultaneously, in cells infected or not with the human immunodeficiency virus (HIV). Upon mRNA isolation and fragmentation from HIV- infected and non-infected cells, we used two different procedures: MeRIP-Seq, an RNA immunoprecipitation-based technique, to enrich for RNA fragments containing the m6A mark and BS-Seq, a bisulfite conversion-based technique, to identify the m5C mark at a single nucleotide resolution. Upon methylation-specific capture, RNA libraries are prepared for high-throughput sequencing. We also developed a dedicated bioinformatics pipeline to identify differentially methylated (DM) transcripts independently from their basal expression profile. Overall, the methodology allows exploration of multiple epitranscriptomic marks simultaneously and provides an atlas of DM transcripts upon viral infection or any other cell perturbation. This approach offers new opportunities to identify novel players and novel mechanisms of cell response, such as cellular factors promoting or restricting viral replication.

5-methylcytosine RNA methylation regulators affect prognosis and tumor microenvironment in lung adenocarcinoma.

BackgroundAccumulating evidence has shown that 5-methylcytosinec (m5C) RNA methylation plays an essential role in tumorigenesis. However, the roles of m5C regulators in the prognosis, tumor microenvironment (TME), and immunotherapy responses of lung adenocarcinoma (LUAD) have not been fully analyzed.MethodsBased on 14 m5C RNA regulators, we evaluated the m5C RNA modification patterns in patients with LUAD (n=594) in The Cancer Genome Atlas (TCGA). Unsupervised clustering analysis was performed to confirm distinct m5C modification patterns. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the biological functions of differentially expressed genes (DEGs) among different m5C RNA modification patterns. An m5C signature (m5Csig) was constructed using least absolute shrinkage and selection operator (LASSO) algorithms. The GSE72094 cohort (n=442) from the Gene Expression Omnibus (GEO) was used to validate m5Csig. A receiver operating characteristic (ROC) model was constructed to evaluate the sensitivity and specificity of m5Csig. Tumor-infiltrating immune cells (TIICs) between the high- and low-risk groups were estimated using the Cell Type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm.ResultsWe identified 3 m5C RNA modification clusters. Overall survival (OS) differed among the 3 clusters. The m5Csig, including TRDMT1, NSUN1, NSUN4, NSUN7, and ALYREF, was constructed to classify patients with LUAD into high- and low-risk groups. The high-risk group, with more immune cell infiltration, had a significantly poorer OS than that the low-risk group, which was associated with better response to immune checkpoint blockade therapy.ConclusionsThe present study revealed that m5C RNA regulators play a significant role in TME regulation in LUAD. The m5Csig can predict the prognosis of patients with LUAD and might provide novel strategies for tumor immunotherapy.

Distinct Roles of m5C RNA Methyltransferase NSUN2 in Major Gynecologic Cancers.

RNA methylation has recently emerged as an important category of epigenetic modifications, which plays diverse physiopathological roles in various cancers. Recent studies have confirmed the presence of 5-methylcytosine (m5C) modification on mammalian mRNAs, mainly modified by NOP2/Sun RNA methyltransferase family member 2 (NSUN2), but little is known about the underlying functions of m5C. Gynecologic cancers are malignancies starting from women’s reproductive organs. The prevalence of gynecologic cancers leads to a massive economic burden and public health concern. In this study, we investigated the potential biological functions of NSUN2 in common gynecologic cancers including cervical cancer, ovarian cancer, and endometrial cancer. Remarkably, distinct scenarios were found. The levels of NSUN2 did not show alteration in endometrial cancer, and in ovarian cancer, depletion of upregulated NSUN2 did not reduce carcinogenesis in cancer cells, suggesting that the upregulated NSUN2 might be an incidental effect. On the contrary, NSUN2 played a role in tumorigenesis of cervical cancer; depletion of upregulated NSUN2 notably inhibited migration and invasion of cancer cells, and only wild-type but not catalytically inactive NSUN2 rescued these malignant phenotypes of cancer cells. Mechanistically, NSUN2 promoted migration and invasion by leading to m5C methylation on keratin 13 (KRT13) transcripts, and methylated KRT13 transcripts would be recognized and stabilized by an m5C reader, Y-box binding protein 1 (YBX1). Collectively, these results not only displayed the nature of diversity among human malignancies, but also demonstrated a novel NSUN2-dependent m5C-YBX1-KRT13 oncogenic regulatory pathway.

Exploration of Potential Roles of m5C-Related Regulators in Colon Adenocarcinoma Prognosis.

Objectives: The purpose of this study was to investigate the role of 13 m5C-related regulators in colon adenocarcinoma (COAD) and determine their prognostic value. Methods: Gene expression and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) datasets. The expression of m5C-related regulators was analyzed with clinicopathological characteristics and alterations within m5C-related regulators. Subsequently, different subtypes of patients with COAD were identified. Then, the prognostic value of m5C-related regulators in COAD was confirmed via univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The prognostic value of risk scores was evaluated using the Kaplan-Meier method, receiver operating characteristic (ROC) curve. The correlation between the two m5C-related regulators, risk score, and clinicopathological characteristics were explored. Additionally, Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and Gene Ontology (GO) analysis were performed for biological functional analysis. Finally, the expression level of two m5C-related regulators in clinical samples and cell lines was detected by quantitative reverse transcription-polymerase chain reaction and through the Human Protein Atlas database. Results: m5C-related regulators were found to be differentially expressed in COAD with different clinicopathological features. We observed a high alteration frequency in these genes, which were significantly correlated with their mRNA expression levels. Two clusters with different prognostic features were identified. Based on two independent prognostic m5C-related regulators (NSUN6 and ALYREF), a risk signature with good predictive significance was constructed. Univariate and multivariate Cox regression analyses suggested that the risk score was an independent prognostic factor. Furthermore, this risk signature could serve as a prognostic indicator for overall survival in subgroups of patients with different clinical characteristics. Biological processes and pathways associated with cancer, immune response, and RNA processing were identified. Conclusion: We revealed the genetic signatures and prognostic values of m5C-related regulators in COAD. Together, this has improved our understanding of m5C RNA modification and provided novel insights to identify predictive biomarkers and develop molecular targeted therapy for COAD.

Machine-Learning-Based m5C Score for the Prognosis Diagnosis of Osteosarcoma.

Background Osteosarcoma is a common and highly metastatic malignant tumor, and m5C RNA methylation regulates various biological processes. The purpose of this study was to explore the prognostic role of m5C in osteosarcoma using machine learning. Methods Osteosarcoma gene data and the corresponding clinical information were downloaded from the GEO database. Machine learning methods were used to screen m5C-related genes and construct m5C scores. In addition, the clusterProfiler package was used to predict the m5C-related functional pathways. xCell and CIBERSORT were used to calculate the immune microenvironment cells. GSVA was applied to analyze different categories of m5C genes, and the correlation between the GSVA and m5C scores was evaluated. Results Twenty m5C genes were identified, and 54 related genes were screened. The m5C score was constructed based on the PCA score. With an increase in the m5C score, the expression of m5C genes and their related genes changed. Functional analysis indicated that the focal adhesion, cell-substrate adherens junction, cell adhesion molecule binding, and E2F targets might change with the m5C score. The naive B cells and CD4+ memory T cell also changed with the m5C score. The results of the correlation analysis showed that the m5C score was significantly correlated with the reader and eraser genes. Conclusion The m5C score might be a prognostic index for osteosarcoma.

Comprehensive Analysis of the Prognostic Value and Immune Infiltrates of the Three-m5C Signature in Colon Carcinoma.

BackgroundThe 5-methylcytosine (m5C) is one of the important forms of RNA post modification, and its regulatory mechanism in tumors has received increasing attention. However, its potential role in colorectal cancer remains unclear.Materials and MethodsHere, we systematically investigated the genetic variation and prognostic value of the 14 m5c RNA methylation regulators in colon cancer. The prognostic risk score was constructed using three m5C regulators, which was verified in the GSE17536 (N=177), GSE41258 (N=248) and GSE38832 (N=122) datasets.ResultsThe risk score developed from the three-m5C signature represents an independent prognostic factor, which can accurately predict the prognosis of patients with colon cancer in multiple datasets. The cytokine–cytokine receptor interaction and chemokine signaling pathway were significantly enriched in the low-risk score group. Further analysis showed that the three-m5C signature was related to tumor immune microenvironment (TIME), affecting the abundance of tumor-infiltrating immune cells. Especially, patients with low risk score had higher immune score than those with high risk score. In addition, gene set enrichment analysis (GSEA) confirmed that all three regulatory factors are associated with the MAPK/p38 signaling pathway.ConclusionIn conclusion, our study illustrates that the three-m5C signature may be involved in the regulation of colon cancer immune microenvironment in synergy with the MAPK signaling pathway. Therefore, further studying the three-m5C signature regulatory mechanisms might provide promising targets for improving the responsiveness of colon cancer to immunotherapy.

Comprehensive Analysis of m5C RNA Methylation Regulator Genes in Clear Cell Renal Cell Carcinoma.

Background Recent research found that N5-methylcytosine (m5C) was involved in the development and occurrence of numerous cancers. However, the function and mechanism of m5C RNA methylation regulators in clear cell renal cell carcinoma (ccRCC) remains undiscovered. This study is aimed at investigating the predictive and clinical value of these m5C-related genes in ccRCC. Methods Based on The Cancer Genome Atlas (TCGA) database, the expression patterns of twelve m5C regulators and matched clinicopathological characteristics were downloaded and analyzed. To reveal the relationships between the expression levels of m5C-related genes and the prognosis value in ccRCC, consensus clustering analysis was carried out. By univariate Cox analysis and last absolute shrinkage and selection operator (LASSO) Cox regression algorithm, a m5C-related risk signature was constructed in the training group and further validated in the testing group and the entire cohort. Then, the predictive ability of survival of this m5C-related risk signature was analyzed by Cox regression analysis and nomogram. Functional annotation and single-sample Gene Set Enrichment Analysis (ssGSEA) were applied to further explore the biological function and potential signaling pathways. Furthermore, we performed qRT-PCR experiments and measured global m5C RNA methylation level to validate this signature in vitro and tissue samples. Results In the TCGA-KIRC cohort, we found significant differences in the expression of m5C RNA methylation-related genes between ccRCC tissues and normal kidney tissues. Consensus cluster analysis was conducted to separate patients into two m5C RNA methylation subtypes. Significantly better outcomes were observed in ccRCC patients in cluster 1 than in cluster 2. m5C RNA methylation-related risk score was calculated to evaluate the prognosis of ccRCC patients by seven screened m5C RNA methylation regulators (NOP2, NSUN2, NSUN3, NSUN4, NSUN5, TET2, and DNMT3B) in the training cohort. The AUC for the 1-, 2-, and 3-year survival in the training cohort were 0.792, 0.675, and 0.709, respectively, indicating that the risk signature had an excellent prognosis prediction in ccRCC. Additionally, univariate and multivariate Cox regression analyses revealed that the risk signature could be an independent prognostic factor in ccRCC. The results of ssGSEA suggested that the immune cells with different infiltration degrees between the high-risk and low-risk groups were T cells including follicular helper T cells, Th1_cells, Th2_cells, and CD8+_T_cells, and the main differences in immune-related functions between the two groups were the interferon response and T cell costimulation. In addition, qRT-PCR experiments confirmed our results in renal cell lines and tissue samples. Conclusions According to the seven selected regulatory factors of m5C RNA methylation, a risk signature associated with m5C methylation that can independently predict prognosis in patients with ccRCC was developed and further verified the predictive efficiency.