An integrated model of FTO and METTL3 expression that predicts prognosis in lung squamous cell carcinoma patients

FTO和METTL3表达的整合模型预测肺鳞状细胞癌患者的预后

阅读:1

Abstract

BACKGROUND: Lung squamous cell carcinoma (LUSC) approximately accounts for a third of lung cancers. However, the role of N6-methyladenosine (m6A) in LUSC remains largely unknown according to previous studies. METHODS: In this study, we investigated the mutations, copy number variants (CNVs), expression of 20 m6A RNA methylation regulators, and clinical data from The Cancer Genome Atlas-LUSC (TCGA-LUSC). These data were used for the training cohort of screening potential biomarkers. The prognostic model of m6A RNA methylation regulators was constructed. A receiver operating characteristic (ROC) analysis was undertaken to determine the area under the curves (AUCs) (for 3- and 5-year survival) for the model. Additionally, the accuracy of the two-gene model was confirmed with external data verifications. Combined two-gene model and clinincal information were performed to construct a nomogram to predict patient's prognostic risk assessment. RESULTS: Fat mass- and obesity-associated protein (FTO) and methyltransferase-like 3 (METTL3) were identified as potential prognostic biomarkers to evaluate benign and malignant tumors and prognosticate. The following prognostic model of m6A RNA methylation regulators was constructed: risk score = 0.162 × FTO - 0.069 × METTL3. Patients in low-risk group [median overall survival (mOS), 43.4 months] had longer survival than those with high-risk (mOS, 67.3 months) with P=0.0023. The smoking grade and risk score could be independent prognostic factors (P=0.00098 and P=0.0014, respectively). Ultimately, a nomogram was developed to assist clinicians to predict clinical outcomes. CONCLUSIONS: FTO and METTL3 are potential prognostic biomarkers of LUSC. The two-gene model's use of prognostic risk scores may provide guidance in the selection of therapeutic strategies.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。