构建和验证铜死亡相关lncRNA风险模型预测胃腺癌的预后

Construction and Validation of a Cuproptosis-Related lncRNA Risk Model to Predict the Prognosis of Stomach Adenocarcinoma

摘要: 背景：铜死亡是一种新的程序性细胞死亡方式，且被报道在肿瘤中起作用。越来越多的研究表明，长链非编码RNA (lncRNA)在胃腺癌(Stomach adenocarcinoma, STAD)的发生和发展中起重要作用。然而，铜死亡相关lncRNA在胃腺癌中的作用和预后价值仍然未知。目的：构建并验证铜死亡相关lncRNA风险模型来预测STAD患者的预后。方法：从The Cancer Genome Atlas (TCGA)数据库下载了STAD的转录谱和临床信息。然后通过共表达网络、最小绝对收缩和选择算子(LASSO)算法以及COX回归模型构建了8个铜死亡相关lncRNA预后模型，并将患者分为高风险组和低风险组。此外，通过综合方法评估模型的预测能力。然后，构建了一个列线图来预测STAD患者的预后。通过GO、KEGG分析研究了高低风险组患者之间差异表达基因的生物学功能。使用免疫相关功能分析及肿瘤突变负荷(TMB)来研究高低风险组之间的免疫功能的差异。结果：构建了一个由8个铜死亡相关lncRNA组成的风险模型来预测STAD患者的预后。与低风险组STAD患者相比，高风险组的患者总生存期较短(P < 0.001)。ROC曲线分析验证了模型的有效性。与其他临床特征相比，铜死亡相关的lncRNA模型对患者预后的预测更为准确。GO和KEGG富集分析表明，高低风险组之间差异表达的基因与代谢高度相关。此外，高风险与低TMB的叠加会导致STAD患者的生存期进一步缩短。结论：8个与铜死亡相关的lncRNA构建的风险模型有助于评估STAD患者的预后。

Abstract: Background: Cuproptosis is a new type of programmed cell death and has been reported to play a role in tumors. More and more studies have shown that long non-coding RNA (lncRNA) plays an im-portant role in the occurrence and development of stomach adenocarcinoma (STAD). However, the role and prognostic value of cuproptosis related lncRNAs in stomach adenocarcinoma are still un-known. Objective: To construct and validate a cuproptosis related lncRNA risk model to predict the prognosis of patients with STAD. Methods: The transcriptional profile and clinical information of STAD were downloaded from The Cancer Genome Atlas (TCGA) database. Then eight prognosis models of cuproptosis related lncRNA were constructed through co-expression network, LASSO re-gression analysis and COX regression analysis, and patients were divided into high-risk group and low-risk group. In addition, the predictive power of the model was assessed through a comprehen-sive approach. Then, a nomogram was constructed to predict the prognosis of patients with STAD. The biological functions of differentially expressed genes between patients in high and low risk groups were studied by GO and KEGG analysis. Immunological function analysis and tumor muta-tion burden (TMB) were used to study the difference of immune function between high-risk group and low-risk group. Results: A risk model consisting of 8 cuproptosis related lncRNAs was con-structed to predict the prognosis of patients with STAD. Compared with STAD patients in low-risk group, the total survival time of patients in high-risk group was shorter (P < 0.001). ROC curve analysis verifies the effectiveness of the model. Compared with other clinical features, the cupropto-sis related lncRNA model is more accurate in predicting the prognosis of patients. GO and KEGG en-richment analysis showed that genes differentially expressed between high and low risk groups were highly correlated with metabolism. In addition, the superposition of high risk and low TMB will further shorten the survival time of patients with STAD. Conclusion: The risk model constructed by 8 lncRNA related to cuproptosis is helpful to evaluate the prognosis of patients with STAD.