m5C-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Lung Adenocarcinoma

5-Methylcytosine RNA modification and its roles in cancer and cancer chemotherapy resistance

Recent advancements in cancer therapies have improved clinical outcomes, yet therapeutic resistance remains a significant challenge because of its complex mechanisms. Among epigenetic factors, m5C RNA modification is emerging as a key player in cancer drug resistance, similar to the well-known m6A modification. m5C affects RNA metabolism processes, including splicing, export, translation, and stability, thereby influencing drug efficacy. This review highlights the critical roles of m5C in modulating resistance to chemotherapy, targeted therapy, radiotherapy, and immunotherapy. This review also discusses the functions of key regulators, including methyltransferases, demethylases, and m5C-binding proteins, as essential modulators of the m5C epigenetic landscape that contribute to its dynamic and complex regulatory network. Targeting these regulatory components offers a promising strategy to overcome resistance. We highlight the need for further research to elucidate the specific mechanisms by which m5C contributes to resistance and to develop precise m5C-targeted therapies, presenting m5C-focused strategies as potential novel anticancer treatments.

Integrative analysis of 5-methylcytosine associated signature in papillary thyroid cancer

Emerging evidence has indicated that m5C modification plays a vital role in cancer development. However, the function of m5C-lncRNAs in PTC has never been reported. This study aims to explore the regulation mechanism of m5C RNA methylation-related long noncoding RNAs (m5C-lncRNAs) in papillary thyroid cancer (PTC). Bioinformatics analysis was used to investigate the role of m5C-lncRNAs in the prognosis and tumor immune microenvironment of PTC. Subsequently, we preliminarily verified the regulation mechanisms of m5C-lncRNAs in vivo and in vitro experiments. A total of six m5C-lncRNAs and five immune cell types were selected to construct the risk score and immune risk score (IRS) model, respectively. Patients with a high-risk score had a worse prognosis and the ROC indicated a reliable prediction performance (AUC = 0.796). As expected, the ESTIMATE and immune scores were higher (P < 0.001) and the tumor purity (P < 0.05) was significantly lower in the low-risk subgroup. CIBERSORT analysis showed Tregs, M0 macrophages, dendritic cells resting, and eosinophils were positively correlated to the risk score. Moreover, the expression levels of PD-1, PD-L1, CTLA-4, TIM-3, LAG-3, and KLRB1 were lower in the high-risk subgroup. Importantly, patients in high-risk subgroup tended to have a better response to immunotherapy than those in low-risk subgroup (P = 0.022). Similar to the above risk score, the IRS model also showed favorable prognosis predictive performance (AUC = 0.764). An integrated nomogram combining risk score, IRS, and age exhibited good prognostic predictive performance. Additionally, we validate the downregulation of PPP1R12A-AS1 promotes proliferation and metastasis by activating the MAPK signaling pathway. Our research confirms that m5C-lncRNAs not only contribute to evaluating the prognosis of patients with PTC but also help predict immune cell infiltration and immunotherapy response.

The Quiet Giant: Identification, Effectors, Molecular Mechanism, Physiological and Pathological Function in mRNA 5-methylcytosine Modification

5-Methylcytosine (m5C) is a prevalent nucleotide alteration observed in transfer RNA (tRNA) and ribosomal RNA (rRNA), and it is also widely distributed in the transcriptome, serving as one of the internal modifications of messenger RNA (mRNA) in higher eukaryotes. Increasing evidence has substantiated the presence of m5C in mRNA. As research on m5C progresses, there is an initial comprehension of its molecular mechanisms and biological significance in mRNA. This work aims to provide a comprehensive summary of the most recent advancements in the identification and screening, distribution, molecular functions, and biological effects of m5C in mRNA. We outline the current status of research and provide prospects for potential future applications.

RNA modifications in long non-coding RNAs and their implications in cancer biology

Long non-coding RNAs (lncRNAs) represent the most diverse class of RNAs in cells and play crucial roles in maintaining cellular functions. RNA modifications, being a significant factor in regulating RNA biology, have been found to be extensively present in lncRNAs and exert regulatory effects on their behavior and biological functions. Most common types of RNA modifications in lncRNAs include N6-methyladenosine (m6A), 5-methylcytosine (m5C), and N1-methyladenosine (m1A). In this review, we summarize the major RNA modification types associated with lncRNAs, the regulatory roles of each modification, and the implications of modified lncRNAs in tumorigenesis and development. By examining these aspects, we aim to provide insights into the role of RNA modifications in lncRNAs and their potential impact on cancer biology.

NSUN2 regulates Wnt signaling pathway depending on the m5C RNA modification to promote the progression of hepatocellular carcinoma

5-Methylcytosine (m5C) RNA modification is a highly abundant and important epigenetic modification in mammals. As an important RNA m5C methyltransferase, NOP2/Sun-domain family member 2 (NSUN2)-mediated m5C RNA modification plays an important role in the regulation of the biological functions in many cancers. However, little is known about the biological role of NSUN2 in hepatocellular carcinoma (HCC). In this study, we found that the expression of NSUN2 was significantly upregulated in HCC, and the HCC patients with higher expression of NSUN2 had a poorer prognosis than those with lower expression of NSUN2. NSUN2 could affect the tumor immune regulation of HCC in several ways. In vitro and in vivo experiments confirmed that NSUN2 knockdown significantly decreased the abilities of proliferation, colony formation, migration and invasion of HCC cells. The methylated RNA immunoprecipitation-sequencing (MeRIP-seq) showed NSUN2 knockdown significantly affected the abundance, distribution, and composition of m5C RNA modification in HCC cells. Functional enrichment analyses and in vitro experiments suggested that NSUN2 could promote the HCC cells to proliferate, migrate and invade by regulating Wnt signaling pathway. SARS2 were identified via the RNA immunoprecipitation-sequencing (RIP-Seq) and MeRIP-seq as downstream target of NSUN2, which may play an important role in tumor-promoting effect of NSUN2-mediated m5C RNA modification in HCC. In conclusion, NSUN2 promotes HCC progression by regulating Wnt signaling pathway and SARS2 in an m5C-dependent manner.

Narrative review of research progress of RNA m5C methylation in head and neck malignancies

Background and Objective

Head and neck malignancies encompass a spectrum of malignant tumors occurring in the head and neck region, characterized by rapid progression, high recurrence rates, and dismal prognoses. Despite significant advancements in comprehensive surgery-based therapies, the 5-year survival rate for patients has not shown substantial improvement. There is an urgent need to investigate novel targeted therapies. With the advancements in epigenetics, RNA 5-methylcytosine (m5C) methylation, a prevalent form of RNA modification, has been identified by numerous studies as playing a pivotal role in the pathological processes of tumorigenesis and development. However, a comprehensive review within the realm of head and neck malignancies is currently lacking. This study aims to comprehensively review the biological implications of RNA m5C methylation regulators in the pathogenesis and progression of various systemic malignant tumors, with a specific focus on exploring the potential impact of RNA m5C methylation on head and neck malignancies.

Methods

A literature search on RNA m5C methylation and head and neck malignancies was conducted using PubMed, resulting in the inclusion of 46 relevant articles. The Cancer Genome Atlas (TCGA) database was utilized to analyze the correlation between m5C regulatory factors and clinicopathological features in patients with head and neck squamous cell carcinoma (HNSCC).

Key Content and Findings

Aberrant expression of RNA m5C methylation regulators is observed in head and neck malignancies, displaying a correlation with the clinicopathological grading of tumors.

Conclusions

RNA m5C methylation may contribute to the progression of head and neck malignancies and could be associated with an unfavorable prognosis for patients. These findings offer valuable insights for the development of targeted treatments for head and neck malignancies.

RNA epigenetics in pulmonary diseases: Insights into methylation modification of lncRNAs in lung cancer

Long non-coding RNAs (lncRNAs) are pivotal controllers of gene expression through epigenetic mechanisms, Methylation, a prominent area of study in epigenetics, significantly impacts cellular processes. Various RNA base methylations, including m6A, m5C, m1A, and 2'-O-methylation, profoundly influence lncRNA folding, interactions, and stability, thereby shaping their functionality. LncRNAs and methylation significantly contribute to tumor development, especially in lung cancer. Their roles encompass cell differentiation, proliferation, the generation of cancer stem cells, and modulation of immune responses. Recent studies have suggested that dysregulation of lncRNA methylation can contribute to lung cancer development. Furthermore, methylation modifications of lncRNAs hold potential for clinical application in lung cancer. Dysregulated lncRNA methylation can promote lung cancer progression and may offer insights into potential biomarker or therapeutic target. This review summarizes the current knowledge of lncRNA methylation in lung cancer and its implications for RNA epigenetics and pulmonary diseases.

Emerging role of RNA modification and long noncoding RNA interaction in cancer

RNA modification, especially N6-methyladenosine, 5-methylcytosine, and N7-methylguanosine methylation, participates in the occurrence and progression of cancer through multiple pathways. The function and expression of these epigenetic regulators have gradually become a hot topic in cancer research. Mutation and regulation of noncoding RNA, especially lncRNA, play a major role in cancer. Generally, lncRNAs exert tumor-suppressive or oncogenic functions and its dysregulation can promote tumor occurrence and metastasis. In this review, we summarize N6-methyladenosine, 5-methylcytosine, and N7-methylguanosine modifications in lncRNAs. Furthermore, we discuss the relationship between epigenetic RNA modification and lncRNA interaction and cancer progression in various cancers. Therefore, this review gives a comprehensive understanding of the mechanisms by which RNA modification affects the progression of various cancers by regulating lncRNAs, which may shed new light on cancer research and provide new insights into cancer therapy.

NSUN2 promotes lung adenocarcinoma progression through stabilizing PIK3R2 mRNA in an m5C-dependent manner

It is well known that 5-methylcytosine (m5C) is involved in variety of crucial biological processes in cancers. However, its biological roles in lung adenocarcinoma (LAUD) remain to be determined. The LUAD samples were used to assess the clinical value of NOP2/Sun RNA Methyltransferase 2 (NSUN2). Dot blot was used to determine global m5C levels. ChIP and dual-luciferase assays were performed to investigate the MYC-associated zinc finger protein (MAZ)-binding sites in NSUN2 promoter. RNA-seq was used to explore the downstream molecular mechanisms of NSUN2. Dual luciferase reporter assay, m5C-RIP-qPCR, and mRNA stability assay were conducted to explore the effect of NSUN2-depletion on target genes. Cell viability, transwell, and xenograft mouse model were designed to demonstrate the characteristic of NSUN2 in promoting LUAD progression. The m5C methyltransferase NSUN2 was highly expressed and caused elevated m5C methylation in LUAD samples. Mechanistically, MAZ positively regulated the transcription of NSUN2 and was related to poor survival of LUAD patients. Silencing NSUN2 decreased the global m5C levels, suppressed proliferation, migration and invasion, and inhibited activation of PI3K-AKT signaling in A549 and SPAC-1 cells. Phosphoinositide-3-Kinase Regulatory Subunit 2 (PIK3R2) was upregulated by NSUN2-mediated m5C methylation by enhancing its mRNA stabilization and activated the phosphorylation of the PI3K-AKT signaling. The present study explored the underlying mechanism and biological function of NSUN2-meditated m5C RNA methylation in LUAD. NSUN2 was discovered to facilitate the malignancy progression of LUAD through regulating m5C modifications to stabilize PIK3R2 activating the PI3K-AKT signaling, suggesting that NSUN2 could be a novel biomarker and promising therapeutic target for LUAD patients.

Deciphering disulfidptosis: Uncovering a lncRNA-based signature for prognostic assessment, personalized immunotherapy, and therapeutic agent selection in lung adenocarcinoma patients

BACKGROUND

Disulfidptosis, a recently identified type of regulated cell death, plays critical roles in various biological processes of cancer; however, whether they can impact the prognosis of lung adenocarcinoma (LUAD) remains to be fully elucidated. We aimed to adopt this concept to develop and validate a lncRNA signature for LUAD prognostic prediction.

METHODS

For this study, the TCGA-LUAD dataset was used as the training cohort, and multiple datasets from the GEO database were pooled as the validation cohort. Disulfidptosis regulated genes were obtained from published studies, and various statistical methods, including Kaplan-Meier (KM), Cox, and LASSO, were used to train our gene signature DISULncSig. We utilized KM analysis, COX analysis, receiver operating characteristic analysis, time-dependent AUC analysis, principal component analysis, nomogram predictor analysis, and functional assays in our validation process. We also compared DISULncSig with previous studies. We performed analyses to evaluate DISULncSig's immunotherapeutic ability, focusing on eight immune algorithms, TMB, and TIDE. Additionally, we investigated potential drugs that could be effective in treating patients with high-risk scores. Additionally qRT-PCR examined the expression patterns of DISULncSig lncRNAs, and the ability of DISULncSig in pan-cancer was also assessed.

RESULTS

DISULncSig containing twelve lncRNAs was trained and showed strong predictive ability in the validation cohort. Compared with previous similar studies, DISULncSig had more prognostic ability advantages. DISULncSig was closely related to the immune status of LUAD, and its tight relationship with checkpoints KIR2DL3, IL10, IL2, CD40LG, SELP, BTLA, and CD28 may be the key to its potential immunotherapeutic ability. For the high DISULncSig score population, we found ten drug candidates, among which epothilone-b may have the most potential. The pan-cancer analysis found that DISULncSig was a risk factor in multiple cancers. Additionally, we discovered that some of the DISULncSig lncRNAs could play crucial roles in specific cancer types.

CONCLUSION

The current study established a powerful prognostic DISULncSig signature for LUAD that was also valid for most pan-cancers. This signature could serve as a potential target for immunotherapy and might help the more efficient application of drugs to specific populations.

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.

Exploring the importance of m5c in the diagnosis and subtype classification of COPD using the GEO database

BACKGROUND

5-Methylcytosine (m5C) is an mRNA modifier that is associated with the occurrence and development of viral infection, pulmonary fibrosis, lung cancer, and other diseases. However, the role of m5C regulators in chronic obstructive pulmonary disease (COPD) remains unknown.

METHODS

In this study, by analysing the GSE42057 dataset, the differential expression of m5c regulators in the COPD group and control group was obtained, and a correlation analysis was conducted. The random forest model and support vector machine model were used to predict the occurrence of COPD. A nomogram model was also constructed to predict the prevalence of COPD. The COPD patients were divided into subtypes by consistent cluster analysis based on m5c methylation regulators. Immune cell infiltration was performed on the m5c methylation subtypes. Differentially expressed genes (DEGs) between m5c methylation subtypes were screened, and the DEGs were analysed by Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we verified the expression of several m5C regulators and related pathways using a COPD cell model.

RESULTS

Seven m5c methylation regulators were differentially expressed. The random forest model based on the above genes was the most accurate for predicting the occurrence of COPD. A nomogram model based on the above genes could also accurately predict the prevalence of COPD, and the implementation of these models could benefit COPD patients. The consistent cluster analysis divided the COPD patients into two subtypes (Cluster A and Cluster B). The main component analysis algorithm determined the m5c methylation subtypes and found that patients in Cluster A had a higher m5c score than those in Cluster B. GO analysis of the DEGs between the m5c methylation COPD patient subtypes revealed that DEGS were mainly enriched in leukocyte-mediated immunity and regulation of T-cell activation. KEGG analysis revealed that DEGS were mainly enriched in Th1 and Th2 cell differentiation, neutrophil extracellular trap formation, and the NF-κB signalling pathway. Immunocyte correlation analysis revealed that Cluster B was associated with neutrophil- and macrophage-mediated immunity, while Cluster A was associated with CD4 + T-cell- and CD8 + T-cell-mediated immunity. Cell experiments have also verified some of the above research results.

CONCLUSION

The diagnosis and subtype classification of COPD patients based on m5c regulators may provide a new strategy for the diagnosis and treatment of COPD.

Deciphering the vital roles and mechanism of m5C modification on RNA in cancers

5-methylcytosine (m5C modification) plays an essential role in tumors, which affects different types of RNA, the expression of downstream target genes, and downstream pathways, thus participating in the tumor process. However, the effect of m5C modification on RNA in tumors and the exact mechanism have not been systematically reviewed. Therefore, we reviewed the status and sites of m5C modification, as well as the expression pattern and biological functions of m5C regulators in tumors, and further summarized the effects and regulation mechanism of m5C modification on messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), long non-coding RNA (lncRNA) and other RNA in tumors. Finally, we summed up the interaction network, potential application, and value in clinical diagnosis and treatment of tumors. Taken together, this review benefits revealing the mechanism of m5C modification in tumor progression and provides new strategies for tumor diagnosis and treatment.

The complex nature of lncRNA-mediated chromatin dynamics in multiple myeloma

Extensive genome-wide sequencing efforts have unveiled the intricate regulatory potential of long non-protein coding RNAs (lncRNAs) within the domain of haematological malignancies. Notably, lncRNAs have been found to directly modulate chromatin architecture, thereby impacting gene expression and disease progression by interacting with DNA, RNA, and proteins in a tissue- or condition-specific manner. Furthermore, recent studies have highlighted the intricate epigenetic control of lncRNAs in cancer. Consequently, this provides a rationale to explore the possibility of therapeutically targeting lncRNAs themselves or the epigenetic mechanisms that govern their activity. Within the scope of this review, we will assess the current state of knowledge regarding the epigenetic regulation of lncRNAs and how, in turn, lncRNAs contribute to chromatin remodelling in the context of multiple myeloma.

NOP2/Sun RNA methyltransferase 2 is a potential pan-cancer prognostic biomarker and is related to immunity

BACKGROUND

NOP2/Sun RNA methyltransferase 2 (NSUN2), an important methyltransferase of m5C, has been poorly studied in cancers, and the relationship between NSUN2 and immunity remains largely unclear. Therefore, the purpose of this study was to explore the expression and prognostic value of NSUN2 and the role of NSUN2 in immunity in cancers.

METHODS

The TIMER, CPTAC and other databases were used to analyze the expression of NSUN2, its correlation with clinical stage and its prognostic value across cancers. Moreover, the TISIDB, TIMER2.0 and Sangerbox platform were used to depict the relationships between NSUN2 and immune molecular subtypes, tumor-infiltrating lymphocytes (TILs), immune checkpoints (ICPs) and immunoregulatory genes. Furthermore, the NSUN2-interacting proteins and related genes as well as the coexpression networks of NSUN2 in LIHC, LUAD and HNSC were explored with the STRING, DAVID, GEPIA2 and LinkedOmics databases. Finally, the subcellular location and function of NSUN2 in HepG2, A549 and 5-8F cells were investigated by performing immunofluorescence, CCK-8 and wound healing assays.

RESULTS

Overall, NSUN2 was highly expressed and related to a poor prognosis in most types of cancers and was also significantly associated with immune molecular subtypes in some cancer types. Furthermore, NSUN2 was significantly associated with the levels of ICPs and immunoregulatory genes. In addition, NSUN2 was found to be involved in a series of immune-related biological processes, such as the humoral immune response in LIHC and LUAD and T-cell activation and B-cell activation in HNSC. Immunofluorescence and CCK-8 assays also confirmed that NSUN2 was widely expressed in the nucleus and cytoplasm, and overexpression of NSUN2 promoted the proliferation and migration of HepG2, A549 and 5-8F cells. NSUN2 was also confirmed to positively regulate the expression of PD-L1.

CONCLUSION

NSUN2 serves as a pan-cancer prognostic biomarker and is correlated with the immune infiltration of tumors.

m5C methylation modification guides the prognostic value and immune landscapes in acute myeloid leukemia

The development, incidence, and metastasis of tumors are all intimately correlated with 5-methylcytosine (m5C). However, uncertainty surrounds the function of m5C in acute myeloid leukemia (AML). In this study, multicenter AML data were collected and analyzed comprehensively to grasp the gene expression level, clinicopathological characteristics, prognostic significance of m5C in AML and its relationship with the tumor microenvironment (TME). The m5C gene-mediated scoring system (m5CSS) was created using principal component analysis, and multiple cox regression analyses were utilized to determine the prognostic relevance of the m5C score. The investigation of the correlation among m5C, immune characteristics, clinical characteristics, immune infiltration level, as well as drug reaction at immune checkpoints, and immunotherapy efficacy confirmed that the change of the characteristics of immune cell infiltration and patient prognosis are linked with the m5C gene. Moreover, the m5CSS was employed to assess the pattern of m5C modification. Further analyses showed that the m5C score can served as a reliable indicator of AML prognosis. Crucially, the prognostic value of the m5C score was validated in terms of drug resistance and immunotherapy. This work reveals that AML diversity and the generation of complex TMEs are both impacted by m5C modifications. Therefore, understanding the m5C modification pattern will improve grasp of TME infiltration characteristics and assist exploring more efficient immunotherapeutic approaches.

Identification of m5C-related lncRNAs signature to predict prognosis and therapeutic responses in esophageal squamous cell carcinoma patients

Esophageal squamous cell carcinoma (ESCC) has a dismal prognosis because of atypical early symptoms and heterogeneous therapeutic responses. 5-methylcytosine (m5C) modification plays an important role in the onset and development of many tumors and is widespread in long non-coding RNA (lncRNA) transcripts. However, the functions of m5C and lncRNAs in ESCC have not been completely elucidated. Herein, this study aimed to explore the role of m5C-related lncRNAs in ESCC. The RNA-seq transcriptome profiles and clinical information were downloaded from the TCGA-ESCC database. Pearson analysis was used to identify m5C-related lncRNAs. Then we established the m5C-related lncRNAs prognostic signature (m5C-LPS) using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Then, the prognostic value of m5C-LPS was evaluated internally and externally using the TCGA-ESCC and GSE53622 databases through multiple methods. We also detected the expression of these lncRNAs in ESCC cell lines and patient tissues. Fluorescence in situ hybridization (FISH) was used to detect the prognostic value of specific lncRNA. In addition, clinical parameters, immune status, genomic variants, oncogenic pathways, enrichment pathways, and therapeutic response features associated with m5C-LPS were explored using bioinformatics methods. We constructed and validated a prognostic signature based on 9 m5C-related lncRNAs (AC002091.2, AC009275.1, CAHM, LINC02057.1, AC0006329.1, AC037459.3, AC064807.1, ATP2B1-AS1, and UBAC2-AS1). The quantitative real-time polymerase chain reaction (qRT-PCR) revealed that most lncRNAs were upregulated in ESCC cell lines and patient tissues. And AC002091.2 was validated to have significant prognostic value in ESCC patients. A composite nomogram was generated to facilitate clinical practice by integrating this signature with the N stage. Besides, patients in the low-risk group were characterized by good clinical outcomes, favorable immune status, and low oncogenic alteration. Function enrichment analysis indicated that the risk score was associated with mRNA splicing, ncRNA processing, and DNA damage repair response. At the same time, we found significant differences in the responses to chemoradiotherapy between the two groups, proving the value of m5C-LPS in treatment decision-making in ESCC. This study established a novel prognostic signature based on 9 m5C-related lncRNAs, which is a promising biomarker for predicting clinical outcomes and therapeutic response in ESCC.

The Role of RNA Methylation Modification Related Genes in Prognosis and Immunotherapy of Colorectal Cancer

Background

Researches showed RNA methylation genes can affect the prognosis of tumors. Thus, the study aimed to comprehensively analyze the effects of RNA methylation regulatory genes in prognosis and treatment of colorectal cancer (CRC).

Methods

Prognostic signature associated with CRCs were constructed by differential expression analysis, Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Receiver operating characteristic (ROC) and Kaplan-Meier survival analyses were used to validate the reliability of the developed model. Gene Ontology (GO), Gene set variation analysis (GSVA), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for functional annotation. Finally, normal and cancerous tissue were collected to validate gene by quantitative real-time PCR (qRT-PCR).

Results

A prognostic risk model based on leucine rich pentatricopeptide repeat containing (LRPPRC) and ubiquitin-like with PHD and ring finger domains 2 (UHRF2) was constructed and relevant to the overall survival (OS) of CRC. Functional enrichment analysis revealed that collagen fibrous tissue, ion channel complex and other pathways were significantly enriched, which might help explain the underlying molecular mechanisms. There were significant differences in ImmuneScore, StromalScore, ESTIMATEScore between high- and low-risk groups (p < 0.05). Ultimately, qRT-PCR validation showed that a significant upregulation in the expression of LRPPRC and UHRF2 in cancerous tissue, which verified the effectiveness of our signature.

Conclusion

In conclusion, 2 prognostic genes (LRPPRC and UHRF2) related to RNA methylation were identified by bioinformatics analysis, which might supply a new insight into the treatment and evaluation of CRC.

Vital roles of m5C RNA modification in cancer and immune cell biology

RNA modification plays an important role in epigenetics at the posttranscriptional level, and 5-methylcytosine (m5C) has attracted increasing attention in recent years due to the improvement in RNA m5C site detection methods. By influencing transcription, transportation and translation, m5C modification of mRNA, tRNA, rRNA, lncRNA and other RNAs has been proven to affect gene expression and metabolism and is associated with a wide range of diseases, including malignant cancers. RNA m5C modifications also substantially impact the tumor microenvironment (TME) by targeting different groups of immune cells, including B cells, T cells, macrophages, granulocytes, NK cells, dendritic cells and mast cells. Alterations in immune cell expression, infiltration and activation are highly linked to tumor malignancy and patient prognosis. This review provides a novel and holistic examination of m5C-mediated cancer development by examining the exact mechanisms underlying the oncogenicity of m5C RNA modification and summarizing the biological effects of m5C RNA modification on tumor cells as well as immune cells. Understanding methylation-related tumorigenesis can provide useful insights for the diagnosis as well as the treatment of cancer.

The lncRNA epigenetics: The significance of m6A and m5C lncRNA modifications in cancer

Most of our transcribed RNAs are represented by non-coding sequences. Long non-coding RNAs (lncRNAs) are transcripts with no or very limited protein coding ability and a length &gt;200nt. They can be epigenetically modified. N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), 7-methylguanosine (m7G) and 2’-O-methylation (Nm) are some of the lncRNAs epigenetic modifications. The epigenetic modifications of RNA are controlled by three classes of enzymes, each playing a role in a specific phase of the modification. These enzymes are defined as “writers”, “readers” and “erasers”. m6A and m5C are the most studied epigenetic modifications in RNA. These modifications alter the structure and properties, thus modulating the functions and interactions of lncRNAs. The aberrant expression of several lncRNAs is linked to the development of a variety of cancers and the epigenetic signatures of m6A- or m5C-related lncRNAs are increasingly recognized as potential biomarkers of prognosis, predictors of disease stage and overall survival. In the present manuscript, the most up to date literature is reviewed with the focus on m6A and m5C modifications of lncRNAs and their significance in cancer.

Costimulatory molecule-related lncRNA model as a potential prognostic biomarker in non-small cell lung cancer

OBJECTIVE

Costimulatory molecules have been demonstrated to exert essential roles in multiple cancers. However, their role in lung cancer remains elusive. Here, we sought to identify costimulatory molecule-related lncRNAs in non-small cell lung cancer (NSCLC) and establish a prognostic signature to predict the prognosis of patients with NSCLC.

METHODS

A total of 535 lung adenocarcinoma (LUAD) and 502 lung squamous cell carcinoma (LUSC) patients from the cancer genome atlas (TCGA) database were recruited. A novel costimulatory molecule-based lncRNA prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival. The Homo_sapiens.GRCh38 data set was set as a reference file for probe annotation.

RESULTS

A total of 593 costimulatory molecule-related lncRNAs were extracted. After analysis, six costimulatory molecule-related lncRNAs (AC084859.1, AC079949.2, HSPC324, LINC01150, LINC01150, and AC090617.5) were screened. A prognostic model based on the six lncRNAs was established using systematic bioinformatics analyses. The prognostic model had a prognostic value in NSCLC patients. Furthermore, a prognostic nomogram was established based on clinical parameters and a risk-score model. Patients with different risk scores had considerably different tumor-infiltrating immune cells, somatic mutational loading, clinical outcomes, signaling pathways, and immunotherapy efficacy. In addition, LINC01137 was associated with unfavorable disease outcomes and fueled tumor progression in NSCLC.

CONCLUSION

Taken together, our study demonstrated that a costimulatory molecule-related lncRNA model could be a potential prognostic biomarker in NSCLC. Moreover, LINC01137 could facilitate the proliferation and invasion of lung cancer.

Construction of a m5C-related long non-coding RNA signature for the prognosis of hepatocellular carcinoma

RNA modification serves as a kind of posttranscriptional modification. Besides N6-methyladenosine (m6A), 5-methylcytosine(m5C) is also an important RNA modification. Long non-coding RNAs (lncRNAs) play an important role in tumor progression. Thus, we performed bioinformatic analysis to establish a m5C-related lncRNA signature(m5ClncSig) for hepatocellular carcinoma (HCC). The RNA sequencing data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation coefficient analysis was applied to conduct m5C-related genes and m5C-related lncRNAs co-expressing network. Univariate Cox regression was used to screen the m5C-related lncRNAs with prognosis value. LASSO regression was applied to establish m5ClncSig. Functional analysis including KEGG and GO were performed. The relation between m5ClncSig and immunity was assessed by CIBERSORT and ESTIMATE. RP11-498C9.15 was selected for in vitro validation. A m5ClncSig was established containing 8 lncRNAs with significantly prognosis value. According to risk score calculated by m5ClncSig, high-risk group had worse clinical outcomes than low-risk group. The risk score was validated as an independent prognosis factor. Moreover, the abundances of 11 types of immune cells were significantly different between high-risk group and low-risk group while 8 immune-related genes expressed differently between these two groups. RP11-498C9.15 was validated as a risk factor in HCC progression.

NSUN2 promotes osteosarcoma progression by enhancing the stability of FABP5 mRNA via m5C methylation

5-methylcytosine (m⁵C) modification, which is mainly induced by the RNA methyltransferase NSUN2 (NOP2/Sun domain family, member 2), is an important chemical posttranscriptional modification in mRNA and has been proven to play important roles in the progression of many cancers. However, the functions and underlying molecular mechanisms of NSUN2-mediated m⁵C in osteosarcoma (OS) remain unclear. In this study, we found NSUN2 was highly expressed in OS tissues and cells. We also discovered that higher expression of NSUN2 predicted poorer prognosis of OS patients. Our study showed that NSUN2 could promote the progression of OS cells. Moreover, we employed RNA sequencing, RNA immunoprecipitation (RIP), and methylated RIP to screen and validate the candidate targets of NSUN2 and identified FABP5 as the target. We observed that NSUN2 stabilized FABP5 mRNA by inducing m⁵C modification and further promoted fatty acid metabolism in OS cells. Moreover, both knocking down the expression of FABP5 and adding fatty acid oxidation inhibitor could counterbalance the promoting effect of NSUN2 on the progression of OS. Our study confirms that NSUN2 can up-regulate the expression of FABP5 by improving the stability of FABP5 mRNA via m⁵C, so as to promote fatty acid metabolism in OS cells, and finally plays the role in promoting the progression of OS. Our findings suggest that NSUN2 is a promising prognostic marker for OS patients and may serve as a potential therapeutic target for OS treatment. A schematic illustration was proposed to summarize our findings.

Modification of m5C regulators in sarcoma can guide different immune infiltrations as well as immunotherapy

Background

Recent studies have found that 5-methylcytosine (m5C) modulators are associated with the prognosis and treatment of cancer. However, the relevance of m5C modulators in sarcoma prognosis and the tumour microenvironment is unclear.

Methods

We selected 15 m5C regulators and performed unsupervised clustering to identify m5C modification patterns and differentially expressed genes associated with the m5C phenotype in The Cancer Genome Atlas (TCGA) sarcomas. The extent of immune cell infiltration in different clustering groups was explored using single-sample gene set enrichment analysis and estimation algorithms. A principal component analysis algorithm-based m5C scoring protocol was performed to assess the m5C modification patterns of individual tumors.

Results

We identified two distinct m5C modification patterns in the TCGA sarcoma cohort, which possess different clinical outcomes and biological processes. Tumour microenvironment analysis revealed two groups of immune infiltration patterns highly consistent with m5C modification patterns, classified as immune inflammatory and immune desert types. We constructed m5C scores and found that high m5C scores were closely associated with leiomyosarcoma and other subtypes, and were associated with poorer prognosis, lower PD-L1 expression, and poorer immunotherapy outcomes. The best application was validated against the m5C database.

Conclusion

We constructed an m5C score for sarcoma based on the TCGA database and identified a poorer prognosis in the high m5c score group. The stability and good prognostic predictive power of the m5C score was verified by an external database. We found that sarcomas in the low m5C score group may have a better response to immunotherapy.

Genetic and epigenetic defects of the RNA modification machinery in cancer

Cancer was initially considered to be an exclusively genetic disease, but an interplay of dysregulated genetic and epigenetic mechanisms is now known to contribute to the cancer phenotype. More recently, chemical modifications of RNA molecules - the so-called epitranscriptome - have been found to regulate various aspects of RNA function and homeostasis. Specific enzymes, known as RNA-modifying proteins (RMPs), are responsible for depositing, removing, and reading chemical modifications in RNA. Intensive investigations in the epitranscriptomic field in recent years, in conjunction with great technological advances, have revealed the critical role of RNA modifications in regulating numerous cellular pathways. Furthermore, growing evidence has revealed that RNA modification machinery is often altered in human cancers, highlighting the enormous potential of RMPs as pharmacological targets or diagnostic markers.

Identification and Validation of a m5C RNA Modification-Related Gene Signature for Predicting Prognosis and Immunotherapeutic Efficiency of Gastric Cancer

Background

5-methylcytosine (m5C) is a major site of RNA methylation modification, and its abnormal modification is associated with the development of gastric cancer (GC). This study aimed to explore the value of m5C-related genes on the prognosis of GC patients through bioinformatics.

Methods

First, m5C-related genes were obtained by nonnegative matrix factorization (NMF) analysis and differentially expressed analysis. The m5C-related model was established and validated in distinct datasets. Moreover, a differential analysis of risk scores according to clinical characteristics was performed. The enrichment analysis was carried out to elucidate the underlying molecular mechanisms. Furthermore, we calculated the differences in immunotherapy and chemotherapy sensitivity between the high- and low-risk groups. Finally, we validated the expression levels of identified model genes by quantitative real-time polymerase chain reaction (qRT-PCR).

Results

A total of five m5C-related subtypes of GC patients in the TCGA database were identified. The m5C-related model was constructed based on APOD, ASCL2, MFAP2, and CREB3L3. Functional enrichment revealed that the m5C-related model might involve in the cell cycle and cell adhesion. Moreover, the high-risk group had a higher abundance of stromal and immune cells in malignant tumor tissues and a lower tumor purity than the low-risk group. The patients in the high-risk group were more sensitive to chemotherapy and had better sensitivity to CTLA4 inhibitors. Furthermore, qRT-PCR results from our specimens verified an over-expression of ASCL2, CREB3L3, and MFAP2 in the cancer cells compared with the normal cells.

Conclusion

A total of five GC subtypes were identified, and a risk model was constructed based on m5C modification.

Prognosis and Novel Drug Targets for Key lncRNAs of Epigenetic Modification in Colorectal Cancer

Background

Colorectal cancer (CRC) has been the 3rd most commonly malignant tumor of the gastrointestinal tract in the world. 5-Methylcytosine (m⁵C) and long noncoding RNAs (lncRNAs) have an essential role in predicting the prognosis and immune response for CRC patients. Therefore, we built a m⁵C-related lncRNA (m⁵CRlncRNA) model to investigate the prognosis and treatment methods for CRC patients.

Methods

Firstly, we secured the transcriptome and clinical data for CRC from The Cancer Genome Atlas (TCGA). Then, m⁵CRlncRNAs were recognized by coexpression analysis. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were utilized to build m⁵C-related prognostic characteristics. Besides, Kaplan-Meier analysis, ROC, PCA, C-index, enrichment analysis, and nomogram were performed to investigate the model. Additionally, immunotherapy responses and antitumor medicines were explored for CRC patients.

Results

A total of 8 m⁵C-related lncRNAs (AC093157.1, LINC00513, AC025171.4, AC090948.2, ZEB1-AS1, AC109449.1, AC009041.3, and LINC02516) were adopted to construct a risk model to investigate survival and prognosis for CRC patients. CRC samples were separated into low- and high-risk groups, with the latter having a worse prognosis. The m⁵C-related lncRNA model helps us to better distinguish immunotherapy responses and IC50 of antitumor medicines in different groups of CRC patients.

Conclusion

The research may give new perspectives on tailored therapy approaches as well as novel theories for forecasting the prognosis of CRC patients.

RNA Modifications Meet Tumors

RNA modifications occur through the whole process of gene expression regulation, including transcription, translation, and post-translational processes. They are closely associated with gene expression, RNA stability, and cell cycle. RNA modifications in tumor cells play a vital role in tumor development and metastasis, changes in the tumor microenvironment, drug resistance in tumors, construction of tumor cell-cell "internet", etc. Several types of RNA modifications have been identified to date and have various effects on the biological characteristics of different tumors. In this review, we discussed the function of RNA modifications, including N 6-methyladenine (m6A), 5-methylcytosine (m5C), N 7-methyladenosine (m7G), N 1-methyladenosine (m1A), pseudouridine (Ψ), and adenosine-to-inosine (A-to-I), in the microenvironment and therapy of solid and liquid tumors.

Pan-Cancer Gene Analysis of m6A Modification and Immune Infiltration in Uterine Corpus Endometrial Carcinoma

Objective

This investigation was to test the potential role of m6A-related long non-coding RNAs (lncRNAs) and immune infiltration as crucial factors in the diagnosis and treatment of uterine corpus endometrial cancer (UCEC).

Method

The UCEC RNA-seq data were downloaded in the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). There were 587 samples totally, containing 543 UCEC cases and 35 healthy cases. The clinical information of UCEC cases included survival time, survival status, gender, age, stage, and TMN stage. Twenty-three m6A-related genes were found in published journals. The RNA-seq documents of UCEC were downloaded in the Cancer Genome Atlas (TCGA). The hub gene data of UCEC were downloaded from GEPIA2 database. The different packages of R language were applied to calculate and analyze in this research.

Results

Among 587 cases in our study, we discovered 3039 lncRNAs in the TCGA-UCEC database. After the differential analysis, 23 m6A-associated genetics were screened and twenty-one m6A-associated differential genetics were found. In the end, we obtained 20 m6A-related lncRNAs. LNCTAM34A was considered as a predictive gene through univariate and multivariate Cox regression analysis. In addition to the above, patients with high LNCTAM34A expression had better outcomes than those with low LNCTAM34A expression. The high-risk cohort had greater scores of activated dendritic cells (aDCs), B cells, and T cell regulatory (Tregs) than low-risk cohort; in the meanwhile, high-risk cohort had lower scores of DCs and iDCs. Then, the high-risk cohort displayed greater scores in the immune functions of MHC class I, para-inflammation, and type I IFN response than those of low-risk cohort. Among 27 immune-inducible genes, the level of CD244, KIR3DLI, NRP1, PDCD1LG2, and TNFRSF8 was reduced in UCEC samples and the level of CD27, CD28, CD70, CD80, CD86, HAVCR2, ICOS, IDO1, LAIR1, PDCD1, TIGIT, TNFRSF18, -25, -9, -14, and VTCN1 was increased in UCEC samples.

Conclusion

The key role of M6A-related lncRNAs in immune microenvironment in high-risk patients of UCEC. The patients with strong expression of LNCTAM34A have a good prognosis, and LNCTAM34A can be used as a prognostic gene for UCEC. m6A-related lncRNAs can be used as a potential treatment for UCEC. Our observations can be used as a hypothetical basis for future in vitro and animal experiments.

Systematic Analysis of Tumor Microenvironment Patterns and Oxidative Stress Characteristics of Endometrial Carcinoma Mediated by 5-Methylcytosine Regulators

As a widely distributed RNA methylation modification, m5C is involved in the regulation of tumorigenesis. Nevertheless, its fundamental process is not clear. This research sought to examine the genetic properties of the 5-methylcytosine (m5C) regulator in endometrial carcinoma, as well as the prognostic significance and impact of m5C regulators on oxidative stress. Therefore, the TCGA-UCEC data set was used to explore the characteristics of 17 RNAm5C-related genes in the transcriptome, genome, and regulatory network. The subtypes of RNAm5C in UCEC were identified based on the expression levels of 17 RNAm5C-related genes. The prognosis of RNAm5C-2 was significantly better than that of RNAm5C-1. Then, we examined the differences (variations) across various subtypes in terms of immune cell infiltration (ICI) as well as the expression of immune-related signal markers. The findings demonstrated that there were distinct variations in the infiltration level of immune cells in each subtype, which may be the reason for the differences in the prognosis of each subtype. In addition, the differentially expressed genes (DEGs) among RNAm5C subtypes of different UCEC tumors were identified, and the DEGs significant for survival were screened. After obtaining 34 prognostic genes, the dimensionality was reduced to construct an RNA methylation score (RS). As per the findings, RS is a more accurate marker for determining the prognosis for patients with endometrial cancer. The RS was used to categorize UCEC tumor samples, and these results led to the formation of high-score and low-score groups. The patients in the group with a high-RNA methylation score exhibited a survival time that was considerably longer in contrast with those in the group with a low-RNA methylation score. The capacity of RS to predict whether or not immunotherapy would be beneficial was explored further. In the group with a high-RNA methylation score, the objective response rate to the anti-PD-L1 therapy was substantially greater compared to that observed in the subgroup with a low-RNA methylation score. Additionally, there were variations across various RS groups in terms of clinical features, tumor mutation burden, and the infiltration level of immune cells. After binary tree analysis and PCR verification of 34 prognostic genes, it is finally found that the six genes of MAGOH3P, TRBJ2_3, YTHDF1P1, RP11_323D18.5, RP11_405M12.2, and ADAM30 are significantly overexpressed in cancer tissues. These genes can be used as potential biomarkers of endometrial cancer and provide data support for precise immunotherapy in UCEC tumors.

m6A-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Osteosarcoma

Background

m6A-related lncRNAs have demonstrated great potential tumor diagnostic and therapeutic targets. The goal of this work was to find m6A-regulated lncRNAs in osteosarcoma patients.

Method

The Cancer Genome Atlas (TCGA) database was used to retrieve RNA sequencing and medical information from osteosarcoma sufferers. The Pearson's correlation test was used to identify the m6A-related lncRNAs. A risk model was built using univariate and multivariable Cox regression analysis. Kaplan–Meier survival analysis and receiver functional requirements were used to assess the risk model's performance (ROC). By using the CIBERSORT method, the associations between the relative risks and different immune cell infiltration were investigated. Lastly, the bioactivities of high-risk and low-risk subgroups were investigated using Gene Set Enrichment Analysis (GSEA).

Result

A total of 531 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs have demonstrated prognostic values. A total of 88 OS patients were separated into cluster 1, cluster 2, and cluster 3. The overall survival rate of OS patients in cluster 3 was more favorable than that of those in cluster 1 and cluster 2. The average Stromal score was much higher in cluster 1 than in cluster 2 and cluster 3 (P < 0.05). The expression levels of lncRNAs used in the construction of the risk prediction model in the high-risk group were generally lower than those in the low-risk group. Analysis of patient survival indicated that the survival of the low-risk group was higher than that of the high-risk group (P < 0.0001) and the area under the curve (AUC) of the ROC curve was 0.719. Using the CIBERSORT algorithm, the results revealed that Macrophages M0, Macrophages M2, and T cells CD4 memory resting accounted for a large proportion of immune cell infiltration. By GSEA analysis, our results implied that the high-risk group was mainly involved in unfolded protein response, DNA repair signaling, and epithelial-mesenchymal transition signaling pathway and glycolysis pathway; meanwhile, the low-risk group was mainly involved in estrogen response early and KRAS signaling pathway.

Conclusion

Our investigation showed that m6A-related lncRNAs remained tightly connected to the immunological microenvironment of osteosarcoma tumors, potentially influencing carcinogenesis and development. The immune microenvironment and immune-related biochemical pathways can be changed by regulating the transcription of M6A modulators or lncRNAs. In addition, we looked for risk-related signaling of m6A-related lncRNAs in osteosarcomas and built and validated the risk prediction system. The findings of our current analysis will facilitate the assessment of outcomes and the development of immunotherapies for sufferers of osteosarcomas.

Prognostic Value and Immune Landscapes of m5C-Related lncRNAs in Lung Squamous Cell Carcinoma

5-methylcytosine (m5C) modification is involved in tumor progression. However, the lncRNAs associated with m5C in lung squamous cell carcinoma (LUSC) have not been elucidated. The Cancer Genome Atlas database was used to get the open-accessed transcriptional profiling and clinical information of LUSC patients. All the statistical analyses were performed based on R software v 4.0.0 and SPSS13.0. First, there were 614 m5C-related lncRNAs identified under the criterion of |R|>0.4 and p < 0.001 with m5C genes. Next, a prognosis model based on ERICD, AL021068.1, LINC01341, AC254562.3, and AP002360.1 was established, which showed good prediction efficiency in both the training and validation cohorts. Next, a nomogram plot was established by combining the risk score and clinical features for a better application in clinical settings. Pathway enrichment analysis showed that the pathways of angiogenesis, TGF-β signaling, IL6-JAK-STAT3 signaling, protein secretion, androgen response, interferon-α response, and unfolded protein response were significantly enriched in the high-risk patients. Immune infiltration analysis showed that the risk score was positively correlated with neutrophils, resting CD4+ memory T cells, and M2 macrophages, yet negatively correlated with follicular helper T cells, CD8+ T cells, and activated NK cells. Moreover, we found that high-risk patients might be more sensitive to immunotherapy, imatinib, yet resistant to erlotinib, gefitinib, and vinorelbine. In summary, our prognosis model is an effective tool that could robustly predict LUSC patient prognosis, which had the potential for clinical guidance.

The Interferon Gamma-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common clinically malignant tumours of the digestive system, with high incidence and mortality and poor prognosis. Interferon-gamma (IFN-γ) and long noncoding RNAs (lncRNAs) have prognostic values and were closely associated with immune microenvironment in COAD. Thus, identifying IFN-γ-related lncRNAs may be valuable in predicting the survival of patients with COAD. In this study, we identified IFN-γ-related lncRNAs and divided COAD patients from the Cancer Genome Atlas (TCGA) database into training and validation sets. Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were performed to select IFN-γ-related lncRNA-associated prognoses. Thirteen lncRNAs (AC025165.8, AC091633.3, FENDRR, LINC00882, LINC01828, LINC01829, MYOSLID, RP11-154H23.4, RP11-20J15.3, RP11-324L17.1, RP11-342A23.2, RP11-805I24.3, SERTAD4-AS1) were identified to construct an IFN-γ-related lncRNA prognostic signature in TCGA training (n =213) and validation (n =213) cohorts. COAD patient risk scores were calculated and classified into high- and low-risk groups based on the median value of the risk scores in each dataset. We compared the overall survival (OS) of patients stratified by age, gender, and stage. The OS in the high-risk group was significantly shorter than that in the low-risk group. In addition, the clinical nomogram incorporating the prognostic signature and clinical features showed a high concordance index of 0.78 and accurately predicted 1-, 3-, and 5-year survival times among COAD patients in the high- and low-risk groups. Based on the risk model, the high- and low-risk groups exhibited distinct differences in the immune system by gene set enrichment analysis (GSEA) functional annotation, and differentially expressed genes (DEGs) between the high- and low-risk groups were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We investigated the expression of multiple immune checkpoint genes in the high- and low-risk groups and plotted Kaplan-Meier survival curves, indicating that immune checkpoint genes, such as LAG3 and PD. L1, STING and TIM 3, were also expressed differently between the two risk groups. Subsequently, there were dramatic differences in mutated genes, SNV (single nucleotide variants) classes, variant types and variant allele frequencies between low- and high-risk patients with COAD. Patients stratified by risk scores had different sensitivities to common chemotherapeutic agents. Finally, we used quantitative real-time polymerase chain reaction (qRT-PCR) assays to demonstrate that three lncRNAs were significantly differentially expressed in COAD tissues and adjacent normal tissues. Considered together, a thirteen-lncRNA prognostic signature has great potential to be a prognostic biomarker and could play an essential role in the immune microenvironment of COAD.

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.

Role of main RNA modifications in cancer: N6-methyladenosine, 5-methylcytosine, and pseudouridine

Cancer is one of the major diseases threatening human life and health worldwide. Epigenetic modification refers to heritable changes in the genetic material without any changes in the nucleic acid sequence and results in heritable phenotypic changes. Epigenetic modifications regulate many biological processes, such as growth, aging, and various diseases, including cancer. With the advancement of next-generation sequencing technology, the role of RNA modifications in cancer progression has become increasingly prominent and is a hot spot in scientific research. This review studied several common RNA modifications, such as N⁶-methyladenosine, 5-methylcytosine, and pseudouridine. The deposition and roles of these modifications in coding and noncoding RNAs are summarized in detail. Based on the RNA modification background, this review summarized the expression, function, and underlying molecular mechanism of these modifications and their regulators in cancer and further discussed the role of some existing small-molecule inhibitors. More in-depth studies on RNA modification and cancer are needed to broaden the understanding of epigenetics and cancer diagnosis, treatment, and prognosis.

Comprehensive Analysis of 5-Methylcytosine (m5C) Regulators and the Immune Microenvironment in Pancreatic Adenocarcinoma to Aid Immunotherapy

Background

Pancreatic adenocarcinoma (PAAD) is one of the most malignant cancers and has a poor prognosis. As a critical RNA modification, 5-methylcytosine (m⁵C) has been reported to regulate tumor progression, including PAAD progression. However, a comprehensive analysis of m⁵C regulators in PAAD is lacking.

Methods

In the present study, PAAD datasets were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and ArrayExpress databases. The expression pattern of m⁵C regulators were analyzed and patients were divided into different m⁵C clusters according to consensus clustering based on m⁵C regulators. Additionally, m⁵C differentially expressed genes (DEGs) were determined using Limma package. Based on m⁵C DEGs, patients were divided into m⁵C gene clusters. Moreover, m⁵C gene signatures were derived from m⁵C DEGs and a quantitative indicator, the m⁵C score, was developed from the m⁵C gene signatures.

Results

Our study showed that m⁵C regulators were differentially expressed in patients with PAAD. The m⁵C clusters and gene clusters based on m⁵C regulators and m⁵C DEGs were related to immune cell infiltration, immune-related genes and patient survival status, indicating that m⁵C modification play a central role in regulating PAAD development partly by modulating immune microenvironment. Additionally, a quantitative indicator, the m⁵C score, was also developed and was related to a series of immune-related indicators. Moreover, the m⁵C score precisely predicted the immunotherapy response and prognosis of patients with PAAD.

Conclusion

In summary, we confirmed that m⁵C regulators regulate PAAD development by modulating the immune microenvironment. In addition, a quantitative indicator, the m⁵C score, was developed to predict immunotherapy response and prognosis and assisted in identifying PAAD patients suitable for tailored immunotherapy strategies.

Biological roles of RNA m5C modification and its implications in Cancer immunotherapy

Epigenetics including DNA and RNA modifications have always been the hotspot field of life sciences in the post-genome era. Since the first mapping of N6-methyladenosine (m6A) and the discovery of its widespread presence in mRNA, there are at least 160-170 RNA modifications have been discovered. These methylations occur in different RNA types, and their distribution is species-specific. 5-methylcytosine (m5C) has been found in mRNA, rRNA and tRNA of representative organisms from all kinds of species. As reversible epigenetic modifications, m5C modifications of RNA affect the fate of the modified RNA molecules and play important roles in various biological processes including RNA stability control, protein synthesis, and transcriptional regulation. Furthermore, accumulative evidence also implicates the role of RNA m5C in tumorigenesis. Here, we review the latest progresses in the biological roles of m5C modifications and how it is regulated by corresponding "writers", "readers" and "erasers" proteins, as well as the potential molecular mechanism in tumorigenesis and cancer immunotherapy.

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.

The Role of m5C-Related lncRNAs in Predicting Overall Prognosis and Regulating the Lower Grade Glioma Microenvironment

Glioma is the most lethal primary brain tumor with a poor prognosis and high recurrence rate. Enormous efforts have been made to find therapeutic targets for gliomas. In the current study, we identified m5C-related lncRNAs through Pearson correlation analysis by the criteria |R|>0.5 and p<0.001 in TCGA LGG and CGGA325 datasets. We then established an eight-lncRNA m5C-related prognostic signature (m5C LPS) through lasso cox regression analysis and multivariate analysis. The performance of the signature was confirmed in the CGGA325 dataset and evaluated in differential subgroups divided by relevant clinicopathological characteristics. Patients were then divided into high and low risk groups using risk scores calculated with the signature. Next, we performed GO, KEGG and gene set enrichment analysis (GSEA) and identified the m5C LPS to be related with glioma microenvironment, immune response, EMT, cell cycle, and hypoxia. Correlation of the risk groups with immune cell infiltration, somatic mutation, and CNVs was then explored. Responses to immuno- and chemotherapies in different risk groups were evaluated using submap and pRRophetic R packages respectively. The high-risk group was more sensitive to anti-CTLA4 therapy and to compounds including Temozolomide, Bleomycin, Cisplatin, Cyclopamine, A.443654 (Akt inhibitor), AZD6482 (PI3K inhibitor), GDC0941(PI3K inhibitor), and metformin. We present for the first time a m5C-related lncRNA signature for lower grade glioma patient prognosis and therapy response prediction with validated performance, providing a promising target for future research.

Molecular Characterization Clinical and Immunotherapeutic Characteristics of m5C Regulator NOP2 Across 33 Cancer Types

Background: Recent studies have identified that RNA 5-methylcytosine (m5C) is a wide-spread epigenetic modification in tumorigenesis. However, the clinical and immunotherapeutic values of m5C regulator NOP2 in 33 cancers remain unclear.

Methods: The mRNA expression data and clinical data of 33 cancers were downloaded from The Cancer Genome Atlas (TCGA) database. The immunotherapy data including GSE67501, GSE78220, GSE35640, and IMvigor210 were downloaded from the Gene Expression Omnibus (GEO) database and the website based on the Creative Commons 3.0 license (http://research-pub.Gene.com/imvigor210corebiologies). The expression, survival, clinical parameters, tumor mutation burden (TMB), microsatellite instability (MSI), and tumor microenvironment (TME) were evaluated. Finally, the relationship between NOP2 and immunotherapy response was further explored.

Results: NOP2 was significantly upregulated in most cancers, and high NOP2 expression was associated with poor prognosis. TMB, MSI, and NOP2 activities were involved in the dysregulation of NOP2. NOP2 was closely associated with immune cell infiltration, immune modulators, and immunotherapeutic inactivation.

Conclusions: We comprehensively explored the clinical and immunotherapeutic values of NOP2 in cancers, providing evidence regarding the function of NOP2 and its role in clinical treatment.

Identification of the Expression Patterns and Potential Prognostic Role of 5-Methylcytosine Regulators in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer with a poor prognosis. 5-methylcytosine (m5C) modification plays a nonnegligible role in tumor pathogenesis and progression. However, little is known about the role of m5C regulators in HCC.

Methods: Based on 9 m5C regulators, the m5C modification patterns of HCC samples extracted from public databases were systematically evaluated and correlated with tumor immune and prognosis characteristics. An integrated model called the “m5Cscore” was constructed using principal component analysis, and its prognostic value was evaluated.

Results: Almost all m5C regulators were differentially expressed between HCC and normal tissues. Through unsupervised clustering, three different m5Cclusters were ultimately uncovered; these clusters were characterized by differences in prognosis, immune cell infiltration, and pathway signatures. The m5Cscore was constructed to quantify the m5C modifications of individual patients. Subsequent analysis revealed that the m5Cscore was an independent prognostic factor of HCC and could be a novel indicator to predict the prognosis of HCC.

Conclusion: This study comprehensively explored and systematically profiled the features of m5C modification in HCC. m5C modification patterns play a crucial role in the tumor immune microenvironment (TIME) and prognosis of HCC. The m5Cscore provides a more holistic understanding of m5C modification in HCC and provides a practical tool for predicting the prognosis of HCC. This study will help clinicians identify effective indicators of HCC to improve the poor prognosis of this disease.

5-Methylcytosine-Related Long Noncoding RNAs Are Potential Biomarkers to Predict Overall Survival and Regulate Tumor-Immune Environment in Patients with Bladder Cancer

The role of 5-methylcytosine-related long noncoding RNAs (m5C-lncRNAs) in bladder cancer (BLCA) remains unclear. Here, we aim to study the prognostic value, gene expression characteristics, and correlation between the m5C-lncRNA risk model and the tumor microenvironment, immune infiltration, and tumor mutations in BLCA. After collecting BLCA patient RNA sequence transcriptome data, clinical information and mutation data from the Cancer Genome Atlas (TCGA) database, 17 m5C-related lncRNAs independently correlated with OS were obtained by Lasso and multivariate Cox regression analysis, and a risk model was constructed. Univariate Cox, multivariate Cox regression analysis, and the C-index curve proved that the risk model was a significant independent prognostic indicator for patients with BLCA. ESTIMATE and CIBERSORT indicated that the higher the number of immune cells and stromal cells in TME, the higher the prognostic risk. We found that in the low-risk group, the expression levels of immune cells that predicted a good prognosis were higher, including plasma cells, regulatory T cells, and CD 8 T cells. There is a negative correlation between TMB and risk score. The TMB of the low-risk group is significantly higher than that of the high-risk group. In conclusion, the m5C-related risk model is crucial to predict the prognosis of patients with BLCA.

5-Methylcytosine Related LncRNAs Reveal Immune Characteristics, Predict Prognosis and Oncology Treatment Outcome in Lower-Grade Gliomas

5-Methylcytosine (m5C) methylation is an important RNA modification pattern that can participate in oncogenesis and progression of cancers by affecting RNA stability, expression of oncogenes, and the activity of cancer signaling pathways. Alterations in the expression pattern of long non-coding RNAs (lncRNAs) are potentially correlated with abnormalities in the m5C regulation features of cancers. Our aim was to reveal the mechanisms by which lncRNAs regulated the m5C process, to explore the impact of aberrant regulation of m5C on the biological properties of lower-grade gliomas (LGG), and to optimize current therapeutic. By searching 1017 LGG samples from the Cancer Genome Atlas and Chinese Glioma Genome Atlas, we first clarified the potential impact of m5C regulators on LGG prognosis in this study and used univariate Cox analysis and least absolute shrinkage and selection operator regression to explore clinically meaningful lncRNAs. Consequently, we identified four lncRNAs, including LINC00265, CIRBP-AS1, GDNF-AS1, and ZBTB20-AS4, and established a novel m5C-related lncRNAs signature (m5CrLS) that was effective in predicting prognosis. Notably, mutation rate, WHO class II, IDH mutation, 1p/19q co-deletion and MGMT promoter methylation were increased in the low m5CrLS score group. Patients with increased m5CrLS scores mostly showed activation of tumor malignancy-related pathways, increased immune infiltrating cells, and decreased anti-tumor immune function. Besides, the relatively high expression of immune checkpoints also revealed the immunosuppressed state of patients with high m5CrLS scores. In particular, m5CrLS stratification was sensitive to assess the efficacy of LGG to temozolomide and the responsiveness of immune checkpoint blockade. In conclusion, our results revealed the molecular basis of LGG, provided valuable clues for our understanding of m5C-related lncRNAs, and filled a gap between epigenetics and tumor microenvironment.

Comprehensive Analysis of Immune Implication and Prognostic Value of IFI44L in Non-Small Cell Lung Cancer

Interferon-induced protein 44-like (IFI44L), a type I interferon-stimulated gene (ISG), has been reported to be involved in innate immune processes and to act as a tumor suppressor in several cancers. However, its immune implication on lung cancer remains unclear. Here, we systemically analyzed the immune association of IFI44L with multiple tumor-infiltrating immune cells (TIICs) and immunomodulators through bioinformatics methods in The Cancer Genome Atlas (TCGA) lung cancer cohorts. Then, the IFI44L-related immunomodulators were selected to construct the prognostic signatures in the lung adenocarcinoma (LUAD) cohort and the lung squamous cell carcinoma (LUSC) cohort, respectively. Concordance index and time-dependent receiver operating characteristics (ROC) curves were applied to evaluate the prognostic signatures. GSE72094 and GSE50081 were used to validate the TCGA-LUAD signature and TCGA-LUSC signature, respectively. A nomogram was established by risk score and clinical features in the LUAD cohort. Finally, the prognostic value and biological function of IFI44L were verified in a real-world cohort and in vitro experiments. The results indicated that IFI44L showed significant correlation with TIICs in LUAD and LUSC samples. Functional enrichment analysis showed that IFI44L may participate in various cancer/immune-related pathways, including JAK/STAT signaling pathway and NF-κB signaling pathway. A total of 44 immunomodulators presented obvious association with IFI44L in the TCGA-LUAD cohort and a robust 10-immunomodulator signature was constructed. Patients in the higher-risk group presented worse prognosis than those in the lower-risk group. Notably, the risk signature was successfully validated in GSE72094. Multivariate Cox regression suggested that the risk signature could act as independent prognostic factors in both TCGA-LUAD and GSE72094 cohorts. Besides, a 17-immunomodulator signature was established in the TCGA-LUSC cohort and similar results were presented through analysis. The nomogram exhibited good accuracy in predicting overall survival (OS) outcome among TCGA-LUAD patients than the risk signature and other clinical features, with the area under curve values being 0.782 at 1 year, 0.825 at 3 years, and 0.792 at 5 years. Finally, tissue microarray analysis indicated that higher expression of IFI44L presented opposite relationship with pathological stage (p = 0.016) and a better outcome among lung cancer patients (p = 0.024). Functional experiments found that IFI44L overexpression significantly inhibited the proliferation, migration, and invasion in LUAD and LUSC cells; RT-qPCR experiments verified the correlation between the expression level of IFI44L with multiple immunomodulators in SPC-A-1 and NCI-H520 cells. In conclusion, our research highlighted that IFI44L is associated with tumor immune infiltration and provided information on IFI44L's immune implication, which indicates that IFI44L has potential clinical immunotherapeutic value and the proposed nomogram is a promising biomarker for non-small cell lung cancer patients.

5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

Prognostic Risk Model and Tumor Immune Environment Modulation of m5C-Related LncRNAs in Pancreatic Ductal Adenocarcinoma

RNA methylation modification is a key process in epigenetics that regulates posttranscriptional gene expression. With advances in next-generation sequencing technology, 5-methylcytosine (m5C) modification has also been found in multiple RNAs. Long non-coding RNAs (lncRNAs) were proved to have a key role in cancer progression and closely related to the tumor immune microenvironment. Thus, based on the PDAC patients' clinical information and genetic transcriptome data from the TCGA database, we performed a detailed bioinformatic analysis to establish a m5C-related lncRNA prognostic risk model for PDAC patients and discovered the relationship between the risk model and PDAC immune microenvironment. Pearson correlation coefficient analysis was applied to conduct a m5C regulatory gene and m5C-related lncRNA co-expression network. Expression of m5C-related lncRNAs screened by univariate regression analysis with prognostic value showed a significant difference between pancreatic cancer and normal tissues. The least absolute shrinkage and selection operator (LASSO) Cox regression method was applied to determine an 8-m5C-related lncRNA prognostic risk model. We used principal component analysis to indicate that the risk model could distinguish all the samples clearly. The clinical nomogram also accurately predicted 1-, 1.5-, 2-, and 3-year survival time among PDAC patients. Additionally, this risk model was validated in the entire group and sub-test groups using KM analysis and ROC analysis. Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the survival of PDAC patients. Furthermore, the association between the risk model and tumor immune microenvironment was evaluated via the ESTIMATE R package and CIBERSORT method. Consequently, the results indicated that immune cells were associated with m5C-related lncRNA risk model scores and had different distribution in the high- and low-risk groups. Based on all these analyses, the m5C-related lncRNA risk model could be a reliable prognostic tool and therapeutic target for PDAC patients.