Prognostic Evaluation for Oral Squamous Cell Carcinoma: A Novel Method Based on m 6 A Methylation Regulators

Current Medical Science(2022)

引用 0|浏览5
暂无评分
摘要
Objective This study aimed to examine a novel method for prognostic evaluation of patients with oral squamous cell carcinoma (OSCC) based on the expression of heterogeneous nuclear ribonucleoprotein C (HNRNPC), YTH domain-binding protein 2 (YTHDF2), and methyltransferase 14 (METTL14). Methods We obtained the RNA sequence and clinical information of OSCC patients from The Cancer Genome Atlas database. An optical method was established by the least absolute shrinkage and selection operator Cox regression algorithm, which was used to calculate the risk score of every sample. In addition, all samples ( n =239) were classified into high-risk ( n =119) and low-risk ( n =120) groups, and the overall survival (OS) time and clinical characteristics were compared between groups. Moreover, bioinformatics analysis was carried out. Gene set enrichment analysis was performed to investigate the signaling pathways of HNRNPC, YTHDF2, and METTL14. Results The two groups showed significantly different OS time, tumor grades, tumor stages, and pathologic T stages ( P <0.05). The receiver operating characteristic analysis identified that our method was effective and it was more accurate than use of age, gender, tumor grade, tumor stage, pathologic T stage, and pathologic N stage in OSCC prognostic prediction. Gene set enrichment analysis revealed that HNRNPC, YTHDF2, and METTL14 were mainly associated with ubiquitin-mediated proteolysis, cell cycle, RNA degradation, and spliceosome signaling pathways. Conclusion The method based on the expression of HNRNPC, YTHDF2, and METTL14 can predict the prognosis of patients with OSCC independently, and its prognostic value is better than that of clinicopathological characteristic indicators.
更多
查看译文
关键词
oral squamous cell carcinoma, m6A methylation regulators, RNA modification, prognostic prediction, The Cancer Genome Atlas
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要