Comprehensive Analysis of the Function, Immune Profiles, and Clinical Implication of m1A Regulators in Lung Adenocarcinoma

RNA modification regulators as promising biomarkers in gynecological cancers

This review explores the evolving landscape of gynecological oncology by focusing on emerging RNA modification signatures as promising biomarkers for assessing the risk and progression of ovarian, cervical, and uterine cancers. It provides a comprehensive overview of common RNA modifications, especially m6A, and their roles in cellular processes, emphasizing their implications in gynecological cancer development. The review meticulously examines specific m6A regulators including "writers", "readers", and "erasers" associated with three gynecological cancer types, discussing their involvement in initiation and progression. Methodologies for detecting RNA modifications are surveyed, highlighting advancements in high-throughput techniques with high sensitivity. A critical analysis of studies identifying m6A regulators as potential biomarkers is presented, addressing their diagnostic or prognostic significance. Mechanistic insights into RNA modification-mediated cancer progression are explored, shedding light on molecular pathways and potential therapeutic targets. Despite current challenges, the review discusses ongoing research efforts, future directions, and the transformative possibility of RNA modifications on early assessment and personalized therapy in gynecological oncology.

RNA modifications in cancer immune therapy: regulators of immune cells and immune checkpoints

RNA modifications are epigenetic changes that alter the structure and function of RNA molecules, playing a crucial role in the onset, progression, and treatment of cancer. Immune checkpoint inhibitor (ICI) therapies, particularly PD-1 blockade and anti-CTLA-4 treatments, have changed the treatment landscape of virous cancers, showing great potential in the treatment of different cancer patients, but sensitivity to these therapies is limited to certain individuals. This review offers a comprehensive survey of the functions and therapeutic implications of the four principal RNA modifications, particularly highlighting the significance of m6A in the realms of immune cells in tumor and immunotherapy. This review starts by providing a foundational summary of the roles RNA modifications assume within the immune cell community, focusing on T cells, NK cells, macrophages, and dendritic cells. We then discuss how RNA modifications influence the intricate regulatory mechanisms governing immune checkpoint expression, modulation of ICI efficacy, and prediction of ICI treatment outcomes, and review drug therapies targeting genes regulated by RNA modifications. Finally, we explore the role of RNA modifications in gene editing, cancer vaccines, and adoptive T cell therapies, offering valuable insights into the use of RNA modifications in cancer immunotherapy.

Bioinformatics analysis of the role of RNA modification regulators in polycystic ovary syndrome

Purpose

Polycystic ovary syndrome (PCOS) is the most common metabolic and endocrine disorder affecting women of reproductive age. The pathogenesis of PCOS is influenced by factors such as race, genetics, environment, hyperandrogenemia, hyperinsulinemia, and obesity. However, the molecular mechanisms linking RNA modification and PCOS remain underexplored. This study aims to investigate the potential genetic and molecular pathways connecting RNA modification with PCOS through bioinformatics analyses.

Methods

The GSE34526, GSE5850, and GSE98421 datasets were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. We identified intersecting differentially expressed genes (DEGs) and RNA modification-related genes within the GSE34526 dataset and visualized the overlaps using a Venn diagram. Subsequent analyses included Gene Ontology (GO), pathway enrichment (Kyoto Encyclopedia of Genes and Genomes), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis. Additionally, we constructed a protein-protein interaction network as well as mRNA-miRNA, mRNA-RNA binding protein, and mRNA-transcription factor (TF) regulatory networks. The expression and receiver operating characteristic curves of hub genes were also identified.

Results

The expression of several RNA modification-related DEGs (RMRDEGs) (ALYREF, NUDT1, AGO2, TET2, YTHDF2, and TRMT61B) showed significant differences in PCOS patients. GSEA and GSVA indicated that RMRDEGs were enriched in the hedgehog, MAPK, JAK STAT, and Notch pathways. Key transcription factors, including SP7, KLF8, HCFC1, IRF1, and MLLT1, were identified in the TF regulatory networks.

Conclusions

These findings suggest that there are gene and miRNA profile alterations exist in PCOS patients and highlight immune-related differences. This knowledge could pave the way for new research directions in the diagnosis and treatment of PCOS.

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.

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

BACKGROUND

Developing a prognostic model for lung adenocarcinoma (LUAD) that utilizes m6A/m5C/m1A genes holds immense importance in providing precise prognosis predictions for individuals.

METHODS

This study mined m6A/m5C/m1A-related differential genes in LUAD based on public databases, identified LUAD tumor subtypes based on these genes, and further built a risk prognostic model grounded in differential genes between subtypes. The immune status between high- and low-risk groups was investigated, and the distribution of feature genes in tumor immune cells was analyzed using single-cell analysis. Based on the expression levels of feature genes, a projection of chemotherapeutic and targeted drugs was made for individuals identified as high-risk. Ultimately, cell experiments were further verified.

RESULTS

The 6-gene risk prognosis model based on differential genes between tumor subtypes had good predictive performance. Individuals classified as low-risk exhibited a higher (P < 0.05) abundance of infiltrating immune cells. Feature genes were mainly distributed in tumor immune cells like CD4+T cells, CD8+T cells, and regulatory T cells. Four drugs with relatively low IC50 values were found in the high-risk group: Elesclomol, Pyrimethamine, Saracatinib, and Temsirolimus. In addition, four drugs with significant positive correlation (P < 0.001) between IC50 values and feature gene expression were found, including Alectinib, Estramustine, Brigatinib, and Elesclomol. The low expression of key gene NTSR1 reduced the IC50 value of irinotecan.

CONCLUSION

Based on the m6A/m5C/m1A-related genes in LUAD, LUAD patients were divided into 2 subtypes, and a m6A/m5C/m1A-related LUAD prognostic model was constructed to provide a reference for the prognosis prediction of LUAD.

m1A Regulatory gene signatures are associated with certain immune cell compositions of the tumor microenvironment and predict survival in kidney renal clear cell carcinoma

Adenosine N1 methylation (m1A) of RNA, a type of post-transcriptional modification, has been shown to play a significant role in the progression of cancer. The objective of the current research was to analyze the genetic alteration and prognostic significance of m1A regulators in kidney renal clear cell carcinoma (KIRC). Genomic and clinicopathological characteristics were obtained from 558 KIRC patients in the Cancer Genome Atlas (TCGA) and Gene Omnibus Expression (GEO) databases. Alterations in the gene expression of ten m1A-regulators were analyzed and survival analysis was performed using the Cox regression method. We also identified three clusters of patients based on their distinct m1A alteration patterns, using integrated analysis of the ten m1A-related regulators, which were significantly related to overall survival (OS), disease-free survival (DFS) and tumor microenvironment (TME) immune cell infiltration cells in KIRC. Our findings showed that m1A alteration patterns have critical roles in determining TME complexity and its immune cell composition. Furthermore, different m1A expression patterns were significantly associated with DFS and OS rates in KIRC patients. In conclusion, the identified m1A RNA modification patterns offer a potentially effective way to classify KIRC patients based on their TME immune cell infiltration, enabling the development of more personalized and successful treatment strategies for these patients.