An m6A/m5C/m1A/m7G-Related Long Non-coding RNA Signature to Predict Prognosis and Immune Features of Glioma

Comprehensive Analysis of RNA Modifications Related Genes in the Diagnosis and Subtype Classification of Dilated Cardiomyopathy

Background

RNA modifications are associated to various human diseases. However, the functions of RNA modification-related genes have yet to be thoroughly investigated in dilated cardiomyopathy (DCM). This study sought to conduct a comprehensive analysis of RNA modification-associated genes for the diagnosis and subtype classification of DCM.

Methods

We collected DCM and control sample RNA modification-related genes from Gene Expression Omnibus (GEO) microarray datasets. Differential expression analysis was performed on these using the "Limma" package in R. Univariate logistic regression, and the LASSO algorithm were used to identify optimal genes for diagnostic model establishment. Furthermore, ConsensusClusterPlus was used to identify RNA modification-molecular subtypes. Lastly, the expression of the hub RNA modification-related genes and their connection to DCM were confirmed using the clinical samples and mouse models.

Results

Twenty-six RNA modification-related genes were identified as dysregulated in DCM, with strong connections noted among these genes. A diagnostic model based on 13 genes (TRMT61B, MBD2, YTHDC2, NOP2, TRMT10C, WDR4, CPSF2, CSTF3, ZBTB4, UNG, NSUN6, TET1, and DNMT3B) with an AUC of 0.980 predicted DCM well. Infiltrating plasma B cells, eosinophils, CD8 T cells, and regulatory T cells correlated strongly with TRMT61B, MBD2, YTHDC2, and CPSF2. Two RNA modification-molecular subtypes (clusters 1 and 2) were identified. Cluster 1 had greater RNA modification scores, lower immune ratings, and lower HLA-DRB1 and HLA-DPB1 expression than Cluster 2. Cluster 2 engaged metabolism-related pathways, while Cluster 1 activated renin-angiotensin system pathways.We further found a substantial link between lower cardiac function and up-regulation of TET1, DNMT3B, and down-regulation of MBD2, TRMT61B in the 13 hub RNA modification-related genes.

Conclusion

In conclusion, our RNA modification-related diagnostic model predicts DCM well. The discovery of two RNA modification-molecular subgroups and four key pivotal genes may assist stratify DCM patients by risk.

Wilms' Tumor: A Review of Clinical Characteristics, Treatment Advances, and Research Opportunities

Nephroblastoma is a complex childhood cancer with a generally favorable prognosis, well-defined incidence, and demographic profile but with significant challenges in terms of recurrence and long-term health outcomes. Although the management of this pathology has evolved, leading to improved survival rates, continued research into the long-term effects of treatment and the genetic factors influencing its development is still required. The survival landscape for Wilms tumor is evolving, with emerging research focusing on therapeutic biomarkers and genetic predispositions that influence treatment efficacy and survival rates. Identifying predictors for treatment response, such as specific genetic markers and histologic features, emerges as a critical area of study that could refine future interventions. The management of Wilms tumor is complex, taking into account the stage of the disease, histological classification, and individual patient factors, including age and the presence of syndromic associations. As treatment paradigms evolve, the integration of precision medicine approaches may enhance the ability of clinicians to personalize treatment to improve long-term survival outcomes for a broader range of patients. Recent advances in technology, including machine-learning approaches, have facilitated the identification of therapeutic biomarkers that correlate with clinical outcomes. This innovative method enhances the ability to integrate clinical and genetic data to predict disease trajectory and therapeutic response.

Significant roles of RNA 5-methylcytosine methylation in cancer

Cancer stands as a leading cause of mortality and poses an escalating threat to global health. Epigenetic dysregulation is pivotal in the onset and advancement of cancer. Recent research on RNA 5-methylcytosine (m5C) methylation has underscored its significant role in cancer. RNA m5C methylation is a key component in gene expression regulation and is intricately linked to cancer development, offering valuable insights for cancer diagnosis, treatment, and prognosis. This review provides an in-depth examination of the three types of regulators associated with RNA m5C methylation and their biological functions. It further investigates the expression and impact of RNA m5C methylation and its regulators in cancer, focusing on their mechanisms in cancer progression and clinical relevance. The current research on inhibitors targeting RNA m5C methylation-related regulators remains underdeveloped, necessitating further exploration and discovery.

Long Non-Coding RNAs in Malignant Human Brain Tumors: Driving Forces Behind Progression and Therapy

Long non-coding RNAs (lncRNAs) play a pivotal role in regulating gene expression and are critically involved in the progression of malignant brain tumors, including glioblastoma, medulloblastoma, and meningioma. These lncRNAs interact with microRNAs (miRNAs), proteins, and DNA, influencing key processes such as cell proliferation, migration, and invasion. This review highlights the multifaceted impact of lncRNA dysregulation on tumor progression and underscores their potential as therapeutic targets to enhance the efficacy of chemotherapy, radiotherapy, and immunotherapy. The insights provided offer new directions for advancing basic research and clinical applications in malignant brain tumors.

Synergistic combination effect of the PCA-1/ALKBH3 inhibitor HUHS015 on prostate cancer drugs in vitro and in vivo

Prostate cancer antigen-1/ALKBH3, a DNA/RNA demethylase of 3-methylcytosine, 1-methyladenine (1-meA), and 6-meA, was found in prostate cancer as an important prognostic factor. Additionally, 1-meA has been associated with other cancers. The ALKBH3 inhibitor HUHS015 was found to be effective against prostate cancer both in vitro and in vivo . Herein, we investigated the effect of HUHS015 in combination with drugs for prostate cancer approved in Japan (including bicalutamide, cisplatin, mitoxantrone, prednisolone, ifosfamide, tegafur/uracil, docetaxel, dacarbazine, and estramustine) by treating DU145 cells with around IC 50 value concentrations of these drugs for 3 days. Additionally, the cells were observed for additional 9 days after drug removal. Combination treatment with dacarbazine, estramustine, tegafur/uracil, and HUHS015 showed a slight additive effect after 3 days. After drug washout of them and mitoxantrone, the combined effects and levels were enhanced and sustained, although the effects of each treatment alone declined. HUHS015 combined with cisplatin or docetaxel elicited synergistic and sustained effects. In vivo , combining HUHS015 and docetaxel, the first chemotherapeutic agent for castration-resistant prostate cancer, showed notable effects in the DU145 xenograft model. In conclusion, HUHS015 exhibited a synergistic effect with docetaxel and drugs acting on DNA in vitro , even after drug removal. Since cancer chemotherapy is typically administered during rest periods due to its high toxicity, combining it with an ALKBH3 inhibitor could be a promising strategy for enhancing cancer treatment, as it can elicit an additive effect during treatment, allowing dosage reduction, and synergistically sustain the effect after drug washout during rest periods.

Unveiling the role of RNA methylation in glioma: Mechanisms, prognostic biomarkers, and therapeutic targets

Gliomas, the most prevalent malignant brain tumors in the central nervous system, are marked by rapid growth, high recurrence rates, and poor prognosis. Glioblastoma (GBM) stands out as the most aggressive subtype, characterized by significant heterogeneity. The etiology of gliomas remains elusive. RNA modifications, particularly reversible methylation, play a crucial role in regulating transcription and translation throughout the RNA lifecycle. Increasing evidence highlights the prevalence of RNA methylation in primary central nervous system malignancies, underscoring its pivotal role in glioma pathogenesis. This review focuses on recent findings regarding changes in RNA methylation expression and their effects on glioma development and progression, including N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N7-methylguanosine (m7G). Given the extensive roles of RNA methylation in gliomas, the potential of RNA methylation-related regulators as prognostic markers and therapeutic targets was also explored, aiming to enhance clinical management and improve patient outcomes.

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.

Epigenetic frontiers: miRNAs, long non-coding RNAs and nanomaterials are pioneering to cancer therapy

Cancer has arisen from both genetic mutations and epigenetic changes, making epigenetics a crucial area of research for innovative cancer prevention and treatment strategies. This dual perspective has propelled epigenetics into the forefront of cancer research. This review highlights the important roles of DNA methylation, histone modifications and non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long non-coding RNAs, which are key regulators of cancer-related gene expression. It explores the potential of epigenetic-based therapies to revolutionize patient outcomes by selectively modulating specific epigenetic markers involved in tumorigenesis. The review examines promising epigenetic biomarkers for early cancer detection and prognosis. It also highlights recent progress in oligonucleotide-based therapies, including antisense oligonucleotides (ASOs) and antimiRs, to precisely modulate epigenetic processes. Furthermore, the concept of epigenetic editing is discussed, providing insight into the future role of precision medicine for cancer patients. The integration of nanomedicine into cancer therapy has been explored and offers innovative approaches to improve therapeutic efficacy. This comprehensive review of recent advances in epigenetic-based cancer therapy seeks to advance the field of precision oncology, ultimately culminating in improved patient outcomes in the fight against cancer.

The role of m5C RNA modification in cancer development and therapy

RNA modifications have been demonstrated to affect the function, stability, processing, and interactions of RNA, including pseudouridylation, acetylation and methylation. RNA methylation products, such as N6-methyladenosine (m6A), 5-methylcytidine (m5C), N7-methylguanosine (m7G), 2'-O-dimethyladenosine (m6Am), and N1-methyladenosine (m1A), have been reported to participate in tumorigenesis and tumor progression. The role of m6A in carcinogenesis has been well studied and summarized. In this review, we described the biological functions of m5C RNA modifications in tumorigenesis and tumor progression. Moreover, we highlighted the molecular mechanisms of m5C RNA modification in oncogenesis. Furthermore, we discussed whether targeting m5C regulator-associated genes could be a novel strategy for improving therapeutic outcomes in patients with cancer.

AC099850.3 promotes HBV-HCC cell proliferation and invasion through regulating CD276: a novel strategy for sorafenib and immune checkpoint combination therapy

BACKGROUND

This study investigates the molecular mechanisms of CC@AC&SF@PP NPs loaded with AC099850.3 siRNA and sorafenib (SF) for improving hepatitis B virus-related hepatocellular carcinoma (HBV-HCC).

METHODS

A dataset of 44 HBV-HCC patients and their survival information was selected from the TCGA database. Immune genes related to survival status were identified using the ImmPort database and WGCNA analysis. A prognostic risk model was constructed and analyzed using Lasso regression. Differential analysis was performed to screen key genes, and their significance and predictive accuracy for HBV-HCC were validated using Kaplan-Meier survival curves, ROC analysis, CIBERSORT analysis, and correlation analysis. The correlation between AC099850.3 and the gene expression matrix was calculated, followed by GO and KEGG enrichment analysis using AC099850.3 and its co-expressed genes. HepG2.2.15 cells were selected for in vitro validation, and lentivirus interference, cell cycle determination, CCK-8 experiments, colony formation assays, Transwell experiments, scratch experiments, and flow cytometry were performed to investigate the effects of key genes on HepG2.2.15 cells. A subcutaneous transplanted tumor model in mice was constructed to verify the inhibitory effect of key genes on HBV-HCC tumors. Subsequently, pH-triggered drug release NPs (CC@AC&SF@PP) were prepared, and their therapeutic effects on HBV-HCC in situ tumor mice were studied.

RESULTS

A prognostic risk model (AC012313.9, MIR210HG, AC099850.3, AL645933.2, C6orf223, GDF10) was constructed through bioinformatics analysis, showing good sensitivity and specificity in diagnostic prediction. AC099850.3 was identified as a key gene, and enrichment analysis revealed its impact on cell cycle pathways. In vitro cell experiments demonstrated that AC099850.3 promotes HepG2.2.15 cell proliferation and invasion by regulating immune checkpoint CD276 expression and cell cycle progression. In vivo, subcutaneously transplanted tumor experiments showed that AC099850.3 promotes the growth of HBV-HCC tumors in nude mice. Furthermore, pH-triggered drug release NPs (CC@AC&SF@PP) loaded with AC099850.3 siRNA and SF were successfully prepared and delivered to the in situ HBV-HCC, enhancing the effectiveness of combined therapy for HBV-HCC.

CONCLUSIONS

AC099850.3 accelerates the cell cycle progression and promotes the occurrence and development of HBV-HCC by upregulating immune checkpoint CD276 expression. CC@AC&SF@PP NPs loaded with AC099850.3 siRNA and SF improve the effectiveness of combined therapy for HBV-HCC.

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data

With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we conceived this study. We have developed an integrated computational workflow designed for the detection of m1A modifications from direct RNA sequencing data. This workflow comprises a feature extractor responsible for capturing signal characteristics (such as mean, standard deviations, and length of electric signals), a single molecule-level m1A predictor trained with features extracted from the IVT dataset using classical machine learning algorithms, a confident m1A site selector employing the binomial test to identify statistically significant m1A sites, and an m1A modification rate estimator. Our model achieved accurate molecule-level prediction (Average AUC = 0.9689) and reliable m1A site detection and quantification. To show the feasibility of our workflow, we conducted a study on in vivo transcribed human HEK293 cell line, and the results were carefully annotated and compared with other techniques (i.e., Illumina sequencing-based techniques). We believed that this tool will enabling a comprehensive understanding of the m1A modification and its functional mechanisms within cells and organisms.

Molecular insights into regulatory RNAs in the cellular machinery

It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.

New insights into immune cells in cancer immunotherapy: from epigenetic modification, metabolic modulation to cell communication

Cancer is one of the leading causes of death worldwide, and more effective ways of attacking cancer are being sought. Cancer immunotherapy is a new and effective therapeutic method after surgery, radiotherapy, chemotherapy, and targeted therapy. Cancer immunotherapy aims to kill tumor cells by stimulating or rebuilding the body's immune system, with specific efficiency and high safety. However, only few tumor patients respond to immunotherapy and due to the complex and variable characters of cancer immune escape, the behavior and regulatory mechanisms of immune cells need to be deeply explored from more dimensions. Epigenetic modifications, metabolic modulation, and cell-to-cell communication are key factors in immune cell adaptation and response to the complex tumor microenvironment. They collectively determine the state and function of immune cells through modulating gene expression, changing in energy and nutrient demands. In addition, immune cells engage in complex communication networks with other immune components, which are mediated by exosomes, cytokines, and chemokines, and are pivotal in shaping the tumor progression and therapeutic response. Understanding the interactions and combined effects of such multidimensions mechanisms in immune cell modulation is important for revealing the mechanisms of immunotherapy failure and developing new therapeutic targets and strategies.

Identification of RNA methylation-related lncRNAs for prognostic assessment and immunotherapy in bladder cancer-based on single cell/Bulk RNA sequencing data

Bladder cancer is a malignancy characterized by significant heterogeneity. RNA methylation has received an increasing amount of attention in recent years. RNA data were collected from the GEO database, and cell subsets were classified according to specific cell markers. Epithelial, immunological, and fibroblast cells were clustered individually to explore the tumor heterogeneity. To distinguish between malignant and benign cells, the InferCNV R package was employed. The monocle2 R package was used for pseudotime analysis. The Decouple R package was used for transcription factor analysis of each cell subgroup, and PROGENy was used to predict the activity of pathways related to tumors. The target lncRNA was screened for model construction. In addition, the qPCR experiment was used to detect the transcription level of lncRNA. Epithelial cells, fibroblasts, and T cells significantly differ in tumor and normal tissues. The lncRNAs related to m6A/m5C/m1A were intersected to construct the model. Finally, six model lncRNAs (PSMB8-AS1, THUMPD3-AS1, U47924.27, XXbac-B135H6.15, MIR99AHG, and C14orf132) were screened. High-risk individuals were shown to have a better prognosis. qPCR experiments showed that the model lncRNA was differentially expressed between normal and tumor cells. Immunotherapy will be more effective in treating individuals with lower risk than those with higher risk using 4 candidate drugs. The prognostic m6A/m5C/m1A-related lncRNA model was constructed for evaluating the clinical outcomes of bladder cancer patients and guiding clinical medication.

RNA sequencing reveals differential long noncoding RNA expression profiles in bacterial and viral meningitis in children

BACKGROUND

We aimed to investigate the involvement of long non-coding RNA (lncRNA) in bacterial and viral meningitis in children.

METHODS

The peripheral blood of five bacterial meningitis patients, five viral meningitis samples, and five healthy individuals were collected for RNA sequencing. Then, the differentially expressed lncRNA and mRNA were detected in bacterial meningitis vs. controls, viral meningitis vs. healthy samples, and bacterial vs. viral meningitis patients. Besides, co-expression and the competing endogenous RNA (ceRNA) networks were constructed. Receiver operating characteristic curve (ROC) analysis was performed.

RESULTS

Compared with the control group, 2 lncRNAs and 32 mRNAs were identified in bacterial meningitis patients, and 115 lncRNAs and 54 mRNAs were detected in viral meningitis. Compared with bacterial meningitis, 165 lncRNAs and 765 mRNAs were identified in viral meningitis. 2 lncRNAs and 31 mRNAs were specific to bacterial meningitis, and 115 lncRNAs and 53 mRNAs were specific to viral meningitis. The function enrichment results indicated that these mRNAs were involved in innate immune response, inflammatory response, and immune system process. A total of 8 and 1401 co-expression relationships were respectively found in bacterial and viral meningitis groups. The ceRNA networks contained 1 lncRNA-mRNA pair and 4 miRNA-mRNA pairs in viral meningitis group. GPR68 and KIF5C, identified in bacterial meningitis co-expression analysis, had an area under the curve (AUC) of 1.00, while the AUC of OR52K2 and CCR5 is 0.883 and 0.698, respectively.

CONCLUSIONS

Our research is the first to profile the lncRNAs in bacterial and viral meningitis in children and may provide new insight into understanding meningitis regulatory mechanisms.

Comprehensive analysis of N1-methylandenosine regulators and m1A-related mRNAs and lncRNAs as prognostic factors in bladder cancer

Bladder cancer (BC) is a lethal malignancy and recurs frequently. m1A plays a vital role in maintaining the biological functions of non-coding RNAs. The Cancer Genome Atlas (TCGA) is a free website from where transcriptome data of BC were obtained. We chose m1A methylation regulators for this study. Six m1A methylation regulator genes have a higher expression in BC tissue compared to normal tissue. The aberrant expression of those m1A regulator genes was remarkably related to BC prognosis and clinicopathological features. First, m1A-related mRNAs and long noncoding RNAs (lncRNAs) were identified. Next, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were performed to get the optimum RNAs for the development of prognostic signatures. Also, a nomogram with T status, lncRNA risk scores and mRNA risk scores was constructed. It revealed an adequate capacity to predict the overall survival of BC cases in the training set as well as in the testing set and in the total TCGA cohort. In conclusion, m1A methylation regulator genes played an important role in predicting the overall survival of BC patients. In addition, m1A-related lncRNAs and mRNAs illustrated underlying mechanisms of tumorigenesis and development of BC.

SRY-box transcription factor 21 antisense divergent transcript 1: Regulatory roles and clinical significance in neoplastic conditions and Alzheimer's Disease

SRY-box transcription factor 21 antisense divergent transcript 1 (SOX21-AS1) is a multifaceted long non-coding RNA (lncRNA) that plays diverse roles in both neoplastic conditions and Alzheimer's disease. Its aberrant expression intricately regulates a wide spectrum of cellular processes, spanning from epithelial-mesenchymal transition (EMT), apoptosis, migration, metastasis, and stemness to drug resistance. SOX21-AS1 achieves these effects through its involvement in the competitive endogenous RNA (ceRNA) network, modulation of downstream genes, and regulation of critical pathways, including PI3K/AKT, Hippo, Wnt/β-catenin, and ERK signaling. Of significant clinical relevance, SOX21-AS1 expression has shown robust correlations with various clinical-pathological features. Moreover, it has demonstrated promising prognostic and diagnostic potential across a spectrum of tumors, as evidenced by existing literature and TCGA pan-cancer analyses. In Alzheimer's disease, SOX21-AS1 assumes a distinctive role. It influences neuronal viability, apoptosis, and oxidative stress by interacting with miR-107 and miR-132, and affecting the PI3K/AKT and Wnt signaling pathways. This comprehensive review sheds light on the functions of SOX21-AS1 and the regulated mechanisms underpinning its impact on neoplastic conditions and Alzheimer's disease. It underscores the clinical significance of SOX21-AS1 and positions it as a promising therapeutic target in both the oncological and neurodegenerative domains.

Construction and validation of a cuproptosis-related lncRNA prognosis signature in bladder carcinoma

BACKGROUND

Bladder cancer (BLCA) is a prevalent urological tumor with high morbidity and mortality. However, BLCA treatment remains challenging due to a lack of effective biomarkers. Long non-coding RNAs (lncRNAs), as active participants in tumor progression are involved in multiple biological regulatory mechanisms, and cuproptosis-related genes participate in the development of cancer. It is important to discover cuproptosis- related lncRNAs for BLCA diagnosis and treatment.

METHODS

A predictive signature was constructed based on least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses of the 9 cuproptosis-related lncRNAs. Samples were divided into high-risk group and low-risk group based on their median risk scores to explore their prognosis.

RESULTS

This signature is well predictive, as evidenced by the receiver operating characteristic curves (ROC curves) and K-M curves. Based on the nomogram, we were able to visually forecast the survival rates of patients with BLCA at 1-, 3-, and 5-year, and the calibration plots displayed that the actual results were well matched with the predicted 1-, 3-, and 5-year survival rates. Furthermore, BLCA patients in the high-risk group had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and lower TMB. Finally, we investigated the response of antitumor drugs for BLCA patients in different risk groups, and a statistically significant difference was observed in the sensitivity of those drugs between low- and the high-risk groups.

CONCLUSION

According to the 9 cuproptosis-related lncRNAs, we constructed a signature which can be served as a promising prognostic biomarker for BLCA patients.

A methylation- and immune-related lncRNA signature to predict ovarian cancer outcome and uncover mechanisms of chemoresistance

Tumor-associated lncRNAs regulated by epigenetic modification switches mediate immune escape and chemoresistance in ovarian cancer (OC). However, the underlying mechanisms and concrete targets have not been systematically elucidated. Here, we discovered that methylation modifications played a significant role in regulating immune cell infiltration and sensitizing OC to chemotherapy by modulating immune-related lncRNAs (irlncRNAs), which represent tumor immune status. Through deep analysis of the TCGA database, a prognostic risk model incorporating four methylation-related lncRNAs (mrlncRNAs) and irlncRNAs was constructed. Twenty-one mrlncRNA/irlncRNA pairs were identified that were significantly related to the overall survival (OS) of OC patients. Subsequently, we selected four lncRNAs to construct a risk signature predictive of OS and indicative of OC immune infiltration, and verified the robustness of the risk signature in an internal validation set. The risk score was an independent prognostic factor for OC prognosis, which was demonstrated via multifactorial Cox regression analysis and nomogram. Moreover, risk scores were negatively related to the expression of CD274, CTLA4, ICOS, LAG3, PDCD1, and PDCD1LG2 and negatively correlated with CD8+, CD4+, and Treg tumor-infiltrating immune cells. In addition, a high-risk score was associated with a higher IC50 value for cisplatin, which was associated with a significantly worse clinical outcome. Next, a competing endogenous RNA (ceRNA) network and a signaling pathway controlling the infiltration of CD8+ T cells were explored based on the lncRNA model, which suggested a potential therapeutic target for immunotherapy. Overall, this study constructed a prognostic model by pairing mrlncRNAs and irlncRNAs and revealed the critical role of the FTO/RP5-991G20.1/hsa-miR-1976/MEIS1 signaling pathway in regulating immune function and enhancing anticancer therapy.

Using integrated analysis from multicentre studies to identify RNA methylation-related lncRNA risk stratification systems for glioma

BACKGROUND

N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A) are the main RNA methylation modifications involved in the progression of cancer. However, it is still unclear whether RNA methylation-related long noncoding RNAs (lncRNAs) affect the prognosis of glioma.

METHODS

We summarized 32 m6A/m5C/m1A-related genes and downloaded RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify differentially expressed (DE-) RNA methylation-related lncRNAs in order to construct a prognostic signature of glioma and in order to determine their correlation with immune function, immune therapy and drug sensitivity. In vitro and in vivo assays were performed to elucidate the effects of RNA methylation-related lncRNAs on glioma.

RESULTS

A total of ten RNA methylation-related lncRNAs were used to construct a survival and prognosis signature, which had good independent prediction ability for patients. It was found that the high-risk group had worse overall survival (OS) than the low-risk group in all cohorts. In addition, the risk group informed the immune function, immunotherapy response and drug sensitivity of patients with glioma in different subgroups. Knockdown of RP11-98I9.4 and RP11-752G15.8 induced a more invasive phenotype, accelerated cell growth and apparent resistance to temozolomide (TMZ) both in vitro and in vivo. We observed significantly elevated global RNA m5C and m6A levels in glioma cells.

CONCLUSION

Our study determined the prognostic implication of RNA methylation-related lncRNAs in gliomas, established an RNA methylation-related lncRNA prognostic model, and elucidated that RP11-98I9.4 and RP11-752G15.8 could suppress glioma proliferation, migration and TMZ resistance. In the future, these RNA methylation-related lncRNAs may become a new choice for immunotherapy of glioma.

Identification and validation of a m7G-related lncRNA signature for predicting the prognosis and therapy response in hepatocellular carcinoma

BACKGROUND

N7-methylguanosine (m7G) is one of the most common RNA posttranscriptional modifications; however, its potential role in hepatocellular carcinoma (HCC) remains unknown. We developed a prediction signature based on m7G-related long noncoding RNAs (lncRNAs) to predict HCC prognosis and provide a reference for immunotherapy and chemotherapy.

METHODS

RNA-seq data from The Cancer Genome Atlas (TCGA) database and relevant clinical data were used. Univariate and multivariate Cox regression analyses were conducted to identify m7G-related lncRNAs with prognostic value to build a predictive signature. We evaluated the prognostic value and clinical relevance of this signature and explored the correlation between the predictive signature and the chemotherapy treatment response of HCC. Moreover, an in vitro study to validate the function of CASC19 was performed.

RESULTS

Six m7G-related lncRNAs were identified to create a signature. This signature was considered an independent risk factor for the prognosis of patients with HCC. TIDE analyses showed that the high-risk group might be more sensitive to immunotherapy. ssGSEA indicated that the predictive signature was strongly related to the immune activities of HCC. HCC in high-risk patients was more sensitive to the common chemotherapy drugs bleomycin, doxorubicin, gemcitabine, and lenalidomide. In vitro knockdown of CASC19 inhibited the proliferation, migration and invasion of HCC cells.

CONCLUSION

We established a 6 m7G-related lncRNA signature that may assist in predicting the prognosis and response to chemotherapy and immunotherapy of HCC.

The joint role of methylation and immune-related lncRNAs in ovarian cancer: Defining molecular subtypes and developing prognostic signature

INTRODUCTION

Complex outcome of ovarian cancer (OC) stems from the tumor immune microenvironment (TIME) influenced by genetic and epigenetic factors. This study aimed to comprehensively explored the subclasses of OC through lncRNAs related to both N6-methyladenosine (m6A)/N1-methyladenosine (m1A)/N7-methylguanosine (m7G)/5-methylcytosine (m5C) in terms of epigenetic variability and immune molecules and develop a new set of risk predictive systems.

MATERIAL AND METHODS

The lncRNA data of OC were collected from TCGA. Spearman correlation analysis on lncRNA data of OC with immune-related gene expression and with m6A/m5C/m1A/m7G were respectively conducted. The m6A/m5C/m1A/m7G-related m6A/m5C/m1A/m7G related immune lncRNA subtypes were identified on the basis of the prognostic lncRNAs. Heterogeneity among subtypes was evaluated by tumor mutation analysis, tumor microenvironment (TME) component analysis, response to immune checkpoint blocked (ICB) and chemotherapeutic drugs. A risk predictive system was developed based on the results of Cox regression analysis and random survival forest analysis of the differences between each specific cluster and other clusters.

RESULTS

Three m6A/m5C/m1A/m7G-related immune lncRNA subtypes of OC showing distinct differences in prognosis, mutation pattern, TIME components, immunotherapy and chemotherapy response were identified. A set of risk predictive system consisting of 10 lncRNA for OC was developed, according to which the risk score of samples in each OC dataset was calculated and risk type was defined.

CONCLUSIONS

This study classified three m6A/m5C/m1A/m7G-related immune lncRNA subtypes with distinct heterogeneous mutation patterns, TME components, ICB therapy and immune response, and provided a set of risk predictive system consisted of 10 lncRNA for OC.

Significance of methylation-related genes in diagnosis and subtype classification of renal interstitial fibrosis

BACKGROUND

RNA methylation modifications, such as N1-methyladenosine/N6-methyladenosine /N5-methylcytosine (m¹A/m⁶A/m⁵C), are the most common RNA modifications and are crucial for a number of biological processes. Nonetheless, the role of RNA methylation modifications of m¹A/m⁶A/m⁵C in the pathogenesis of renal interstitial fibrosis (RIF) remains incompletely understood.

METHODS

Firstly, we downloaded 2 expression datasets from the GEO database, namely GSE22459 and GSE76882. In a differential analysis of these datasets between patients with and without RIF, we selected 33 methylation-related genes (MRGs). We then applied a PPI network, LASSO analysis, SVM-RFE algorithm, and RF algorithm to identify key MRGs.

RESULTS

We eventually obtained five candidate MRGs (WTAP, ALKBH5, YTHDF2, RBMX, and ELAVL1) to forecast the risk of RIF. We created a nomogram model derived from five key MRGs, which revealed that the nomogram model may be advantageous to patients. Based on the selected five significant MRGs, patients with RIF were classified into two MRG patterns using consensus clustering, and the correlation between the five MRGs, the two MRG patterns, and the genetic pattern with immune cell infiltration was shown. Moreover, we conducted GO and KEGG analyses on 768 DEGs between MRG clusters A and B to look into their different involvement in RIF. To measure the MRG patterns, a PCA algorithm was developed to determine MRG scores for each sample. The MRG scores of the patients in cluster B were higher than those in cluster A.

CONCLUSIONS

Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m¹A/m⁶A/m⁵C regulators may be associated with RIF.

Deciphering glioma epitranscriptome: focus on RNA modifications

Gliomas are highly malignant tumors accounting for the majority of brain neoplasms. They are characterized by nuclear atypia, high mitotic rate and cellular polymorphism that often contributes to aggressiveness and resistance to standard therapy. They often associate with challenging treatment approaches and poor outcomes. New treatment strategies or regimens to improve the efficacy of glioma treatment require a deeper understanding of glioma occurrence and development as well as elucidation of their molecular biological characteristics. Recent studies have revealed RNA modifications as a key regulatory mechanism involved in tumorigenesis, tumor progression, immune regulation, and response to therapy. The present review discusses research advances on several RNA modifications involved in glioma progression and tumor microenvironment (TME) immunoregulation as well as in the development of adaptive drug resistance, summarizing current progress on major RNA modification targeting strategies.

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

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

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 >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.

The abdominal aortic aneurysm-related disease model based on machine learning predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation

Introduction: Abdominal aortic aneurysms (AAA) are among the most lethal non-cancerous diseases. A comprehensive analysis of the AAA-related disease model has yet to be conducted. Methods: Weighted correlation network analysis (WGCNA) was performed for the AAA-related genes. Machine learning random forest and LASSO regression analysis were performed to develop the AAA-related score. Immune characteristics and epigenetic characteristics of the AAA-related score were explored. Results: Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation. Discussion: The pathogenic roles of four model genes, UBE2K, TMEM230, VAMP7, and PUM2, in AAA, need further validation by in vitro and in vivo experiments.

Understanding the Epitranscriptome for Avant-Garde Brain Tumour Diagnostics

RNA modifications are diverse, dynamic, and reversible transcript alterations rapidly gaining attention due to their newly defined RNA regulatory roles in cellular pathways and pathogenic mechanisms. The exciting emerging field of 'epitranscriptomics' is predominantly centred on studying the most abundant mRNA modification, N6-methyladenine (m⁶A). The m⁶A mark, similar to many other RNA modifications, is strictly regulated by so-called 'writer', 'reader', and 'eraser' protein species. The abundance of genes coding for the expression of these regulator proteins and m⁶A levels shows great potential as diagnostic and predictive tools across several cancer fields. This review explores our current understanding of RNA modifications in glioma biology and the potential of epitranscriptomics to develop new diagnostic and predictive classification tools that can stratify these highly complex and heterogeneous brain tumours.

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

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is a malignant tumor with a high mortality rate. Methylation modifications acted a crucial role to affect cancer progression. The current study aimed to explore the potential role of methylase regulators in PAAD prognosis and immune microenvironment.

METHODS

PubMed and TCGA databases were used to systematically analyze methylase regulators in PAAD. We identified three methylase clusters based on RNA methylase transcriptome data and obtained three gene clusters based on methylase modification-related differently expressed genes using principal component analysis (PCA) analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) biological processes were performed to explore the processes enriched in the different subgroups and single sample gene-set enrichment analysis (ssGSEA) was used to analyze the relationship between subgroups and immune infiltration in PAAD.

RESULTS

We systematically screened 43 methylase regulators in PAAD samples and identified three methylase clusters with different clinical outcomes, as well as detected a significant relationship between methylase clusters and tumor immune infiltration. The top ten mutated genes include TP53, Kirsten rat sarcoma viral oncogene homolog (KRAS), titin gene (TTN), mucin 16 (MUC16), SMAD4, cyclin-dependent kinase inhibitor 2a (CDKN2A), Ryanodine receptor isoform-1 (RYR1), ring finger 43 (RNF43), protocadherin-15 (PCDH15), and AT-rich interacting domain-containing protein 1 A gene (ARID1A).

CONCLUSION

The current study constructed an m6A/m5C/m1A/m7G modulator genes and explored methylase modification-related genes, which were related to the prognosis of PAAD patients and the immune checkpoint point cytotoxic T-lymphocyte associated protein 4 (CTLA4). These findings may provide prognostic predictors and direction for immunotherapy strategies for the treatment of PAAD.

Internal m7G methylation: A novel epitranscriptomic contributor in brain development and diseases

In recent years, N7-methylguanosine (m7G) methylation, originally considered as messenger RNA (mRNA) 5' caps modifications, has been identified at defined internal positions within multiple types of RNAs, including transfer RNAs, ribosomal RNAs, miRNA, and mRNAs. Scientists have put substantial efforts to discover m7G methyltransferases and methylated sites in RNAs to unveil the essential roles of m7G modifications in the regulation of gene expression and determine the association of m7G dysregulation in various diseases, including neurological disorders. Here, we review recent findings regarding the distribution, abundance, biogenesis, modifiers, and functions of m7G modifications. We also provide an up-to-date summary of m7G detection and profile mapping techniques, databases for validated and predicted m7G RNA sites, and web servers for m7G methylation prediction. Furthermore, we discuss the pathological roles of METTL1/WDR-driven m7G methylation in neurological disorders. Last, we outline a roadmap for future directions and trends of m7G modification research, particularly in the central nervous system.

Multiple epigenetic modification profiles reveal the tumor immune microenvironment and clinical outcomes of uveal melanoma

Uveal melanoma (UM) is an aggressive intraocular cancer that, in 50% of cases, spreads to the patient's other systems. The exact cause of the increased metastatic rate is still unknown. Methylation and immune response, metastasis, and the expansion of cancer cells are closely related. Additionally, proteins linked to RNA methylation have come to light as possible cancer treatment targets. However, the relationship between methylation-related genes (MRGs) and the tumor microenvironment (TME) is still not understood. The goal of this work was to discover important MRGs and create a signature for UM patients' prognosis prediction. Using two different data sets, we examined the MRG expression patterns in the transcriptional and genomic regions of 106 UM samples. We discovered a connection between the clinicopathological traits of the patients, their prognosis, the capability of TME cells to infiltrate, and various MRG changes. Following that, we developed an MRGs signature to forecast prognosis, and we evaluated the model's precision in patients with UM. We grouped the patients into multiple categories based on their clinical traits, looked at the survival rates for various groups within various groupings, and tested their accuracy. Additionally, to increase the practical usability of the MRGs model, we created a very accurate nomogram. TIDE scores were higher in the low-risk group. We go over how MGRs could impact UM's TME, immunotherapy responsiveness, prognosis, and clinically significant features. We looked for different chemotherapeutic drugs and cutting-edge targeted agents for patients in diverse subgroups in order to better understand MRGs in UM. This helped in the creation of customized therapy to open new doors. We could also further research the prognosis and develop more efficient immunotherapy regimens.

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.

Identification and validation of RNA methylation-related alternative splicing gene signature for low-grade glioma to predict survival and immune landscapes

BACKGROUND

Low-grade glioma (LGG) is a crucial pathological type of glioma. The present study aimed to explore multiple RNA methylation regulator-related AS events and investigate their prognostic values in LGG.

METHODS

The prognostic model for low-grade glioma was established using the LASSO regression analysis. To validate prognostic value, we performed Kaplan-Maier survival analysis, ROC curves and nomograms. The ESTIMATE algorithm, the CIBERSORT algorithm and the ssGSEA algorithm were utilized to explore the role of the immune microenvironment in LGG. Subsequently, we then used GO, KEGG and GSEA enrichment analysis to explore the functional roles of these genes. In addition, we employed the GDSC database to screen potential chemotherapeutic agents.

RESULTS

Eight RNA methylation related AS events were involved in construct a survival and prognosis model, which had good ability of independent prediction for patients with LGG. Patients in the high-risk group had shorter life expectancy and higher mortality, while patients in the low-risk group had a better prognosis. We constructed a nomogram which showed an excellent predictive performance for individual OS. The risk score exhibited a close correlation with some immune cells and expression of immune checkpoints. Patients in high-risk group were characterized by immunosuppressive microenvironment and poor response to immunotherapy, and were sensitive to more chemotherapeutic drugs. Pathway and functional enrichment analyses further confirmed that significant differences existed in immune landscape between the two subgroups.

CONCLUSION

The prognostic RNA methylation-related alternative splicing signature constructed could constitute a promising prognostic biomarker, which could serve to optimize treatment regimens.

Comprehensive analysis of cuproptosis-related lncRNAs model in tumor immune microenvironment and prognostic value of cervical cancer

Cervical cancer (CC) is the fourth leading gynecological malignancy in females worldwide. Cuproptosis, a form of cell death induced by copper, elicits a novel therapeutic strategy in anticancer therapy. Nonetheless, the effects of cuproptosis-related lncRNAs in CC remain unclear. Therefore, we aim to investigate cuproptosis-related lncRNAs, develop a risk model for prognostic prediction, and elucidate the immunological profile of CC. Transcription profiles and clinical follow-up data of CC were retrieved from The Cancer Genome Atlas (TCGA) database. Afterward, the risk model was built by distinguishing prognostic cuproptosis-related lncRNAs using the least absolute shrinkage and selection operator (LASSO) Cox regression. The correctness of the risk model was validated, and a nomogram was established followed by tumor immune microenvironment analysis. Tumor immune dysfunction and exclusion (TIDE) scores were used to assess immunotherapy response, and anticancer pharmaceutical half-maximal inhibitory concentration (IC50) prediction was performed for potential chemotherapy medicines. Finally, through coexpression analysis, 199 cuproptosis-related lncRNAs were collected. A unique risk model was generated using 6 selected prognostic cuproptosis-related lncRNAs. The risk score performed a reliable independent prediction of CC survival with higher diagnostic effectiveness compared to generic clinical characteristics. Immunological cell infiltration investigation indicated that the risk model was substantially linked with CC patients' immunology, and the low-risk patients had lower TIDE scores and increased checkpoint expression, suggesting a stronger immunotherapy response. Besides, the high-risk group exhibited distinct sensitivity to anticancer medications. The immune-related progression was connected to the differentially expressed genes (DEGs) between risk groups. Generally, the risk model comprised 6 cuproptosis-related lncRNAs that may help predict CC patients' overall survival, indicate immunocyte infiltration, and identify individualized treatment.

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.

The role of RNA modification in the generation of acquired drug resistance in glioma

Glioma is the most common malignant tumor in the central nervous system. The clinical treatment strategy is mainly surgery combined with concurrent temozolomide chemotherapy, but patients can develop drug resistance during treatment, which severely limits its therapeutic efficacy. Epigenetic regulation at the RNA level is plastic and adaptable, and it can induce a variety of tumor responses to drugs. The regulators of RNA modification include methyltransferases, demethylases, and methylation binding proteins; these are also considered to play an important role in the development, prognosis, and therapeutic response of gliomas, which provides a basis for finding new targets of epigenetic drugs and resetting the sensitivity of tumor cells to temozolomide. This review discusses the relationship between the development of adaptive drug resistance and RNA modification in glioma and summarizes the progress of several major RNA modification strategies in this field, especially RNA m6A modification, m5C modification, and adenosine-to-inosine editing.

Post-Transcriptional Modifications of RNA as Regulators of Apoptosis in Glioblastoma

This review is devoted to changes in the post-transcriptional maturation of RNA in human glioblastoma cells, which leads to disruption of the normal course of apoptosis in them. The review thoroughly highlights the latest information on both post-transcriptional modifications of certain regulatory RNAs, associated with the process of apoptosis, presents data on the features of apoptosis in glioblastoma cells, and shows the relationship between regulatory RNAs and the apoptosis in tumor cells. In conclusion, potential target candidates are presented that are necessary for the development of new drugs for the treatment of glioblastoma.