基于生物信息学构建铁死亡相关lncRNA模型以改善胃癌患者的预后评估

Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics.

作者信息

Pan Jiahui, Zhang Xinyue, Fang Xuedong, Xin Zhuoyuan

机构信息

The Key Laboratory of Zoonosis Research, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China.

Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China.

出版信息

Front Genet. 2021 Aug 23;12:739470. doi: 10.3389/fgene.2021.739470. eCollection 2021.

DOI:10.3389/fgene.2021.739470

PMID:34497636

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8419360/

Abstract

BACKGROUND

Gastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.

METHODS

Ferroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan-Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).

RESULTS

A ferroptosis-related "lncRNA-mRNA" co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.

CONCLUSION

Our prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

摘要

背景

胃癌是最严重的胃肠道恶性肿瘤之一，预后较差。铁死亡是一种铁依赖性的程序性细胞死亡形式，可能影响胃癌患者的预后。长链非编码RNA（lncRNA）可通过调节铁死亡过程影响癌症预后，这可能是胃癌潜在的总生存期（OS）预测因子。

方法

从癌症基因组图谱（TCGA）和FerrDb数据库收集铁死亡相关lncRNA表达谱以及临床病理和OS信息。用DESeq2方法筛选差异表达的铁死亡相关lncRNA。通过共表达分析和功能注释，我们确定了铁死亡相关lncRNA与胃癌患者OS率之间的关联。使用带有最小绝对收缩和选择算子（LASSO）算法的Cox回归分析，我们基于17个铁死亡相关lncRNA构建了一个预后模型。我们还使用Kaplan-Meier（K-M）生存曲线分析、受试者工作特征（ROC）曲线分析和决策曲线分析（DCA）评估了该模型的预后能力。

结果

构建了一个铁死亡相关的“lncRNA-mRNA”共表达网络。功能注释显示FOXO和HIF-1信号通路失调，这可能控制胃癌患者的预后。然后，构建了一个包含17个lncRNA的铁死亡相关胃癌预后特征模型。根据使用该模型计算的风险评分，将患者分为高风险组和低风险组。K-M生存曲线分析显示，风险评分越高，预后越差。ROC曲线分析表明，我们模型的ROC曲线下面积（AUC）为0.751，优于其他已发表的模型。多变量Cox回归分析结果表明，lncRNA特征是OS率的独立危险因素。最后，使用列线图和DCA，我们还观察到该模型在胃癌患者预后预测方面具有较好的临床实用性潜力。