Central Laboratory, Peking University School and Hospital of Stomatology& National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
Department of Dentistry, Xuanwu Hospital Capital Medical University, Beijing, China.
BMC Genom Data. 2022 Nov 16;23(1):80. doi: 10.1186/s12863-022-01097-z.
To investigate the prognostic value of ferroptosis-related long noncoding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC) and to construct a prognostic risk and immune activity model.
We obtained clinical and RNA-seq information on OSCC patient data in The Cancer Genome Atlas (TCGA) Genome Data Sharing (GDC) portal. Through a combination of a differential analysis, Pearson correlation analysis and Cox regression analysis, ferroptosis-related lncRNAs were identified, and a prognostic model was established based on these ferroptosis-related lncRNAs. The accuracy of the model was evaluated via analyses based on survival curves, receiver operating characteristic (ROC) curves, and clinical decision curve analysis (DCA). Univariate Cox and multivariate Cox regression analyses were performed to evaluate independent prognostic factors. Then, the infiltration and functional enrichment of immune cells in high- and low-risk groups were compared. Finally, certain small-molecule drugs that potentially target OSCC were predicted via use of the L1000FWD database.
The prognostic model included 8 ferroptosis-related lncRNAs (FIRRE, LINC01305, AC099850.3, AL512274.1, AC090246.1, MIAT, AC079921.2 and LINC00524). The area under the ROC curve (AUC) was 0.726. The DCA revealed that the risk score based on the prognostic model was a better prognostic indicator than other clinical indicators. The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for OSCC. There were differences in immune cell infiltration, immune functions, m6A-related gene expression levels, and signal pathway enrichment between the high- and low-risk groups. Subsequently, several small-molecule drugs were predicted for use against differentially expressed ferroptosis-related genes in OSCC.
We constructed a new prognostic model of OSCC based on ferroptosis-related lncRNAs. The model is valuable for prognostic prediction and immune evaluation, laying a foundation for the study of ferroptosis-related lncRNAs in OSCC.
本研究旨在探讨铁死亡相关长链非编码 RNA（lncRNA）在口腔鳞状细胞癌（OSCC）中的预后价值，并构建预后风险和免疫活性模型。
本研究从癌症基因组图谱（TCGA）基因组数据共享（GDC）门户中获取了 OSCC 患者的临床和 RNA-seq 信息。通过差异分析、Pearson 相关性分析和 Cox 回归分析相结合，确定了铁死亡相关 lncRNA，并基于这些铁死亡相关 lncRNA 构建了预后模型。通过生存曲线、接收者操作特征（ROC）曲线和临床决策曲线分析（DCA）评估模型的准确性。采用单因素 Cox 和多因素 Cox 回归分析评估独立预后因素。然后，比较高风险组和低风险组之间免疫细胞的浸润和功能富集情况。最后，通过使用 L1000FWD 数据库预测潜在针对 OSCC 的小分子药物。
该预后模型包含 8 个铁死亡相关 lncRNA（FIRRE、LINC01305、AC099850.3、AL512274.1、AC090246.1、MIAT、AC079921.2 和 LINC00524）。ROC 曲线下面积（AUC）为 0.726。DCA 显示，基于预后模型的风险评分是比其他临床指标更好的预后指标。多因素 Cox 回归分析表明，风险评分是 OSCC 的独立预后因素。高风险组和低风险组之间的免疫细胞浸润、免疫功能、m6A 相关基因表达水平和信号通路富集存在差异。随后，预测了几种针对 OSCC 差异表达铁死亡相关基因的小分子药物。
本研究构建了基于铁死亡相关 lncRNA 的 OSCC 新预后模型。该模型对预后预测和免疫评估具有价值，为 OSCC 中铁死亡相关 lncRNA 的研究奠定了基础。
