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.
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.
Background and purposeArteriovenous malformations (AVMs) derived ophthalmic artery (OphA) branches are not common, however, their management is very challenging. We aimed at evaluating the safety and efficacy of Trans OphA ethanol embolotherapy for these lesions.Materials and methodsWe retrospectively reviewed 26 patients with AVMs fed by OphA, who underwent transOphA embolization using ethanol from February, 2015 to December, 2019. Sixty-six transOphA embolotherapy procedures (range, 1-4 procedures; mean, 2.5 procedures) were performed. Degree of devascularization, visual field, visual acuity, and quality-of-life outcomes were compared and analyzed at follow ups (mean, 32.6 months; month range 10-60). Complications were recorded.ResultsTwenty of the 26 patients (77%) reported complete or >90% AVM devascularization while six patients (23%) showed >70% devascularization. Eleven patients (42%) presented with visual acuity impairments with 4 complete relief, 6 improvements. Eight patients (42%) presented visual field defects with 4 complete relief, 3 with improvements. Ten patients (38.4%) presented with diplopia and exophthalmos with 2 complete relief, 6 major improvements. Bleeding was controlled in all cases (100%). All patients (100%) exhibited cosmetic deformities with 17 being completely relieved. Moreover, all patients (100%) exhibited impaired daily life, which was resolved in 21 patients with 5 patients reporting major improvements. No vision loss, death, or permanent disability in all patients.ConclusionsTransOphA ethanol embolotherapy was found to be efficacious, safe and it achieved symptomatic resolution or improvement of AVMs fed by OphA with acceptable complications without the risk of visual impairment.
Purpose: Exploring nonnegative matrix factorization (NMF) model-based clustering and prognostic modeling of head and neck squamous carcinoma (HNSCC). Methods: The transcriptome microarray data of HNSCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Shanghai Ninth People’s Hospital, and NMF clustering was constructed using the R software package. Relevant prognostic models were developed based on clustering. Results: Based on NMF, all samples were divided into 2 subgroups. Predictive models were constructed by analysing the differential gene between the two subgroups. Results of survival analysis in the current study revealed that the high-risk group had a poor prognosis. Further, results of multi-factor Cox regression analysis revealed that the predictive model was an independent predictor of prognosis. Conclusion: It was evident that the NMF-based prognostic model is a useful guide to the prognostic assessment of HNSCC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.