Background RNA methylation is one of the most common RNA modifications and is dynamic and reversible. The enzymes and downstream effectors associated with RNA methylation modifications can be targeted to regulate RNA methylation levels. This mechanism can affect RNA processing, metabolism, cell proliferation and migration, and regulation of physiological or pathological processes. The aim of this study was to investigate the role of RNA methylation-related genes in hepatocellular carcinoma (HCC). Methods Baseline RNA methylation data were extracted from The Cancer Genome Atlas database. The expression pattern, predictive value, mutational profile, and interaction network of RNA methylation genes in pancancer were examined. Then, the association between the expression of RNA methylation genes and immune infiltration was investigated. In addition, a risk score model for HCC was developed and analyzed. Results Cancer cells had a higher expression of RNA methylation genes than normal cells in some cancer cells, and a higher expression of RNA methylation genes could negatively affect patient prognosis. Enrichment analysis revealed that RNA methylation genes are involved in the mRNA surveillance pathway and RNA degradation and transport. A 4-gene ( ALYREF, NSUN4, TRMT6, YTHDF1 ) prognostic signature was established to predict HCC prognosis based on RNA methylation-related genes. Finally, the role of prognostic models in HCC was validated. Conclusion RNA methylation genes can be an indicator of oncogenicity in relation to HCC prognosis and are associated with immune infiltration in the tumour microenvironment. This finding could provide clinicians with the opportunity to explore new strategic approaches.

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Aim: A glioblastoma (GBM) prognostic model was developed with GBM -related alternative splicing (AS) data and prognostic markers were identified. Methods: AS data and clinical data of GBM patients were retrieved from The Cancer Genome Atlas (TCGA) SpliceSeq database and TCGA database, respectively. The data from these two databases were intersected to screen the prognosis-associated AS events, which was subsequently examined in Univariate Cox regression models. To avoid model overfitting, LASSO regression analysis was conducted. On the basis of these AS events, we established a prognostic model of GBM with the use of multivariate Cox regression analysis. On the strength of this model, the patients were assigned into high-risk and low-risk groups with a median risk score as the threshold. Kaplan-Meier survival, receiver operating characteristic (ROC), and calibration curves were applied to evaluate the performance of this model. Finally, combined with the risk model and clinicopathological characteristics, Cox regression analysis was utilized to identify the independent prognostic markers of GBM, and a nomogram was constructed. Results: The AS and clinical data of 169 GBM patients from the TCGA SpliceSeq and TCGA databases were collected. Univariate Cox regression analysis identified 1000 prognosis-related AS events in GBM, and then Lasso regression analysis identified 16 AS events. A GBM prognostic risk model was constructed based on AS events of 7 genes (FAM86B1, ZNF302, C19orf57, RPL39L, CBLL1, RWDD1, IGF2BP2). Through this model, we found lower overall survival (OS) rates of the high-risk population versus the low-risk population (p < 0.05). ROC and calibration curve analyses demonstrated the good ability of this model to predict the OS of GBM patients. Cox regression analysis suggested risk score as an independent prognostic factor for GBM. We also found that IGF2BP2 is associated with patient prognosis and have a strong relationship with immunotherapy response. Conclusion: The prognostic model based on AS events can significantly distinguish the survival rate of high-risk and low-risk GBM patients and IGF2BP2 were identified as a novel prognostic biomarker and immunotherapeutic target.

Background: In this study, a prognostic model based on pyroptosis-related genes was established to predict overall survival (OS) in patients with hepatocellular carcinoma(HCC).Methods: The gene expression data and clinical information of HCC patients were acquired from The Cancer Genome Atlas (TCGA). Using bioinformatics analysis, this predictive signature was constructed and validated. The performance of predictive signature was assessed by the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). Results: A total of 3 pyroptosis-related genes (BAK1, GSDME, and NOD2) were used to construct a survival prognostic model, and experimental validation performed using an experimental cohort. The prognosis model exhibited good performance based on the AUC (AUC: 0.826 at 1 years, 0.796 at 3 years, 0.867 at 5 years). The calibration plots showed excellent calibration.Conclusion: In this study, a novel prognostic model based on three pyroptosis-related genes is constructed and used to predict the prognosis of HCC patients. The model can accurately and conveniently predict the 1- 3-and 5-year OS of HCC patients.