Oral squamous cell carcinoma gene patterns connected with RNA methylation for prognostic prediction

Clinician’s Guide to Epitranscriptomics: An Example of N1-Methyladenosine (m1A) RNA Modification and Cancer

Epitranscriptomics is the study of modifications of RNA molecules by small molecular residues, such as the methyl (-CH3) group. These modifications are inheritable and reversible. A specific group of enzymes called “writers” introduces the change to the RNA; “erasers” delete it, while “readers” stimulate a downstream effect. Epitranscriptomic changes are present in every type of organism from single-celled ones to plants and animals and are a key to normal development as well as pathologic processes. Oncology is a fast-paced field, where a better understanding of tumor biology and (epi)genetics is necessary to provide new therapeutic targets and better clinical outcomes. Recently, changes to the epitranscriptome have been shown to be drivers of tumorigenesis, biomarkers, and means of predicting outcomes, as well as potential therapeutic targets. In this review, we aimed to give a concise overview of epitranscriptomics in the context of neoplastic disease with a focus on N1-methyladenosine (m1A) modification, in layman’s terms, to bring closer this omics to clinicians and their future clinical practice.

Identification and validation of key biomarkers based on RNA methylation genes in sepsis

Background

RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis.

Methods

Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to identify sepsis subgroups based on gene expression profile data obtained from the GEO database (GSE57065, GSE65682, and GSE95233). Unsupervised clustering analysis was used to identify distinct RNA modification subtypes. The CIBERSORT, WGCNA, GO and KEGG analysis were performed to explore immune infiltration pattern and biological function of each cluster. RF, SVM, XGB, and GLM algorithm were applied to identify the diagnostic RMGs in sepsis. Finally, the expression levels of the five key RMGs were verified by collecting PBMCs from septic patients using qRT-PCR, and their diagnostic efficacy for sepsis was verified in combination with clinical data using ROC analysis.

Results

Sepsis was divided into three subtypes (cluster 1 to 3). Cluster 1 highly expressed NSUN7 and TRMT6, with the characteristic of neutrophil activation and upregulation of MAPK signaling pathways. Cluster 2 highly expressed NSUN3, and was featured by the regulation of mRNA stability and amino acid metabolism. NSUN5 and NSUN6 were upregulated in cluster 3 which was involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism pathways. In addition, we identified that five RMGs (NSUN7, NOP2, PUS1, PUS3 and FTO) could function as biomarkers for clinic diagnose of sepsis. For validation, we determined that the relative expressions of NSUN7, NOP2, PUS1 and PUS3 were upregulated, while FTO was downregulated in septic patients. The area under the ROC curve (AUC) of NSUN7, NOP2, PUS1, PUS3 and FTO was 0.828, 0.707, 0.846, 0.834 and 0.976, respectively.

Conclusions

Our study uncovered that dysregulation of RNA methylation genes (m1A, m5C, m6Am, m7G and Ψ) was closely involved in the pathogenesis of sepsis, providing new insights into the classification of sepsis endotypes. We also revealed that five hub RMGs could function as novel diagnostic biomarkers and potential targets for treatment.

Splice site m6A methylation prevents binding of DGCR8 to suppress KRT4 pre-mRNA splicing in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the 11th most prevalent tumor worldwide. Despite advantages of therapeutic approaches, the 5-year survival rate of patients with OSCC is less than 50%. It is urgent to elucidate mechanisms underlying OSCC progression for developing novel treatment strategies. Our recent study has revealed that Keratin 4 (KRT4) suppresses OSCC development, which is downregulated in OSCC. Nevertheless, the mechanism downregulating KRT4 in OSCC remains unknown. In this study, touchdown PCR was utilized to detect KRT4 pre-mRNA splicing, while m⁶A RNA methylation was identified by methylated RNA immunoprecipitation (MeRIP). Besides, RNA immunoprecipitation (RIP) was used to determine RNA-protein interaction. Herein, this study indicated that intron splicing of KRT4 pre-mRNA was suppressed in OSCC. Mechanistically, m⁶A methylation of exon-intron boundaries prevented intron splicing of KRT4 pre-mRNA in OSCC. Besides, m⁶A methylation suppressed the binding of splice factor DGCR8 microprocessor complex subunit (DGCR8) to exon-intron boundaries in KRT4 pre-mRNA to prohibit intron splicing of KRT4 pre-mRNA in OSCC. These findings revealed the mechanism downregulating KRT4 in OSCC and provided potential therapeutic targets for OSCC.

Identification and Biological Validation of a Chemokine/Chemokine Receptor-Based Risk Model for Predicting Immunotherapeutic Response and Prognosis in Head and Neck Squamous Cell Carcinoma

Over 80% of head and neck squamous cell carcinoma (HNSCC) patients failed to respond to immunotherapy, which can likely be attributed to the tumor microenvironment (TME) remolding mediated by chemokines/chemokine receptors (C/CR). This study aimed to establish a C/CR-based risk model for better immunotherapeutic responses and prognosis. After assessing the characteristic patterns of the C/CR cluster from the TCGA-HNSCC cohort, a six-gene C/CR-based risk model was developed to stratify patients by LASSO Cox analysis. The screened genes were multidimensionally validated by RT-qPCR, scRNA-seq, and protein data. A total of 30.4% of patients in the low-risk group had better responses to anti-PD-L1 immunotherapy. A Kaplan-Meier analysis showed that patients in the low-risk group had longer overall survival. A time-dependent receiver operating characteristic curve and Cox analyses indicated that risk score served as an independent predictive indicator. The robustness of the immunotherapy response and prognosis prediction was also validated in independent external datasets. Additionally, the TME landscape revealed that the low-risk group was immune activated. Furthermore, the cell communication analysis on the scRNA-seq dataset revealed that cancer-associated fibroblasts were the main communicators within the C/CR ligand-receptor network of TME. Collectively, The C/CR-based risk model simultaneously predicted immunotherapeutic response and prognosis, potentially optimizing personalized therapeutic strategies of HNSCC.

M7G-related molecular subtypes can predict the prognosis and correlate with immunotherapy and chemotherapy responses in bladder cancer patients

BACKGROUND

N7-methylguanosine (m7G) is closely associated with tumor prognosis and immune response in many cancer types. The correlation between m7G and bladder cancer (BC) needs further study. We aimed to orchestrate molecular subtypes and identify key genes for BC from the perspective of m7G.

METHODS

RNA-seq and clinical data of BC patients were extracted from TCGA and GSE13507 datasets. The patients were subtyped by "ConsensusClusterPlus" and "limma." The clusters were validated by the Kaplan‒Meier curves, univariable and multivariate Cox regression models, the concordance index, and calibration curves. The immunotherapy response was evaluated by immune checkpoints, immune infiltration, TIDE score, and IMvigor210 cohort. Genomics of Drug Sensitivity in Cancer was utilized to predict the chemotherapy response between the clusters.

RESULTS

The m7G-related cluster was ultimately established by EIF4G1, NUDT11, NUDT10, and CCNB1. The independent prognostic value of the m7G-related cluster was validated by the TCGA and GSE13507 datasets. The cluster was involved in immune-associated pathways, such as neutrophil degranulation, antigen processing cross-presentation, and signaling by interleukins pathways. Meanwhile, cluster 2 was positively correlated with many immune checkpoints, such as CD274, CTLA4, HAVCR2, LAG3, PDCD1, and PDCD1LG2. The cluster 2 was significantly correlated with a higher TIDE score than the cluster 1. Furthermore, in the IMvigor210 cohort, patients in the cluster 1 had a higher response rate than those in the cluster 2. Patients in the cluster 2 were sensitive to many chemotherapies.

CONCLUSIONS

We successfully determined molecular subtypes and identified key genes for BC from the perspective of m7G, thereby providing a roadmap for the evolution of immunotherapy and precision medicine.

Identification of a polyamine-related signature and six novel prognostic biomarkers in oral squamous cell carcinoma

Elevated polyamine levels are required for tumor transformation and development; however, expression patterns of polyamines and their diagnostic potential have not been investigated in oral squamous cell carcinoma (OSCC), and its impact on prognosis has yet to be determined. A total of 440 OSCC samples and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Consensus clustering was conducted to classify OSCC patients into two subgroups based on the expression of the 17 polyamine regulators. Polyamine-related differentially expressed genes (PARDEGs) among distinct polyamine clusters were determined. To create a prognostic model, PARDEGs were examined in the training cohorts using univariate-Lasso-multivariate Cox regression analyses. Six prognostic genes, namely, "CKS2," "RIMS3," "TRAC," "FMOD," CALML5," and "SPINK7," were identified and applied to develop a predictive model for OSCC. According to the median risk score, the patients were split into high-risk and low-risk groups. The predictive performance of the six gene models was proven by the ROC curve analysis of the training and validation cohorts. Kaplan-Meier curves revealed that the high-risk group had poorer prognosis. Furthermore, the low-risk group was more susceptible to four chemotherapy drugs according to the IC50 of the samples computed by the "pRRophetic" package. The correlation between the risk scores and the proportion of immune cells was calculated. Meanwhile, the tumor mutational burden (TMB) value of the high-risk group was higher. Real-time quantitative polymerase chain reaction was applied to verify the genes constructing the model. The possible connections of the six genes with various immune cell infiltration and therapeutic markers were anticipated. In conclusion, we identified a polyamine-related prognostic signature, and six novel biomarkers in OSCC, which may provide insights to identify new treatment targets for OSCC.

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.