Zhu L, He Y, Feng G, Yu Y, Wang R, Chen N, Yuan H
Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, PR China.
Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, PR China; Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, PR China.
Int J Oral Maxillofac Surg. 2021 Sep;50(9):1131-1137. doi: 10.1016/j.ijom.2020.11.024. Epub 2020 Dec 29.
Oral squamous cell carcinoma (OSCC) is known for its high incidence, death rate, and relatively low 5-year survival. Long non-coding RNAs (lncRNAs) have been shown to play a significant role in cancerization and cancer progression. However, research on the association of polymorphisms in these lncRNAs with the prognosis of OSCC is lacking. Fifteen functional single-nucleotide polymorphisms (SNPs) in seven lncRNAs were selected to explore the relationship between these lncRNA SNPs and the prognosis among 209 OSCC patients. Kaplan-Meier analysis and Cox proportional hazards regression models were used to examine the associations. Further functional exploration of significant SNPs was done by eQTL analysis. Using multivariate Cox hazards regression analysis, a predictive role of NEAT1 rs3741384 GG and UCA1 rs7255437 TC+TT in a worse prognosis of OSCC was identified. In addition, a marked increased risk of death was observed with an increasing number of unfavourable genotypes (NUG). The NUG was then incorporated with clinical variables in the receiver operating characteristic curve, and the results indicated a potential role of the NUG in predicting OSCC patient risk of death (area under the curve increase from 0.616 to 0.703). In conclusion, the study findings indicate that genetic variants rs3741384 in NEAT and rs7255437 in UCA1 may influence the survival of OSCC patients.
口腔鳞状细胞癌(OSCC)以其高发病率、高死亡率和相对较低的5年生存率而闻名。长链非编码RNA(lncRNAs)已被证明在癌变和癌症进展中起重要作用。然而,关于这些lncRNAs多态性与OSCC预后的关联研究尚缺乏。我们选择了7种lncRNAs中的15个功能性单核苷酸多态性(SNPs),以探讨这些lncRNA SNPs与209例OSCC患者预后之间的关系。采用Kaplan-Meier分析和Cox比例风险回归模型来检验这种关联。通过eQTL分析对显著的SNPs进行了进一步的功能探索。使用多变量Cox风险回归分析,确定了NEAT1 rs3741384 GG和UCA1 rs7255437 TC+TT对OSCC预后较差的预测作用。此外,随着不利基因型数量的增加,观察到死亡风险显著增加。然后将不利基因型数量与临床变量纳入受试者工作特征曲线,结果表明不利基因型数量在预测OSCC患者死亡风险方面具有潜在作用(曲线下面积从0.616增加到0.703)。总之,研究结果表明,NEAT1中的基因变体rs3741384和UCA1中的rs7255437可能影响OSCC患者的生存。