A Necroptosis-Related lncRNA-Based Signature to Predict Prognosis and Probe Molecular Characteristics of Stomach Adenocarcinoma.
A Necroptosis-Related lncRNA-Based Signature to Predict Prognosis and Probe Molecular Characteristics of Stomach Adenocarcinoma.
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基于坏死性凋亡相关 lncRNA 的特征来预测胃癌的预后并探测其分子特征
DOI：
10.3389/fgene.2022.833928
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发表时间：
2022
影响因子：
3.7
通讯作者：
Li Z
中科院分区：
文献类型：
--
作者：
Luo L;Li L;Liu L;Feng Z;Zeng Q;Shu X;Cao Y;Li Z
背景资料：坏死性凋亡作为一种非半胱天冬酶依赖的细胞死亡方式，在胃癌的发生、发展中起着重要作用。大量研究证实，长链非编码RNA（lncRNA）与GC患者的预后密切相关。然而，GC中坏死性凋亡与lncRNA之间的关系仍不清楚。 研究方法：从癌症基因组图谱（TCGA）数据库中检索胃腺癌（STAD）患者的分子谱数据（RNA测序和体细胞突变数据）和临床信息。采用Pearson相关分析鉴定坏死性凋亡相关lncRNA（NRL）。随后，进行单变量考克斯回归和LASSO-考克斯回归以在训练集中建立12-NRL特征并在测试集中验证它。最后，通过生存分析、列线图、考克斯回归、临床病理特征相关分析和受试者工作特征（ROC）曲线评估12-NRL标记的预后能力。此外，分析了特征风险评分（RS）与免疫细胞浸润、免疫检查点分子、体细胞基因突变和抗癌药物敏感性之间的相关性。 结果如下：在本研究中，开发了包括REPIN 1-AS 1、UBL 7-AS 1、LINC 00460、LINC 02773、CHROMR、LINC 01094、FLNB-AS 1、ITFG 1-AS 1、LASTR、PINK 1-AS、LINC 01638和PVT 1的12-NRL签名以改善STAD患者的预后预测。无监督的方法，包括主成分分析和t分布随机邻居嵌入，证实了本签名的能力，分离样本与RS。Kaplan-Meier和ROC曲线显示，该签名在TCGA训练集和测试集中具有可接受的预测效力。考克斯回归和分层生存分析表明，12-NRL签名是独立于各种临床参数的危险因素。此外，免疫细胞浸润、免疫检查点分子、体细胞基因突变和半抑制浓度在不同风险亚型之间存在显著差异，这意味着签名可以评估化疗和免疫治疗的临床疗效。 结论：这一12-NRL风险特征有助于评估STAD患者的预后和分子特征，并有助于改进治疗模式，从而可进一步应用于临床。
Background: As a caspase-independent type of cell death, necroptosis plays a significant role in the initiation, and progression of gastric cancer (GC). Numerous studies have confirmed that long non-coding RNAs (lncRNAs) are closely related to the prognosis of patients with GC. However, the relationship between necroptosis and lncRNAs in GC remains unclear. Methods: The molecular profiling data (RNA-sequencing and somatic mutation data) and clinical information of patients with stomach adenocarcinoma (STAD) were retrieved from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was conducted to identify the necroptosis-related lncRNAs (NRLs). Subsequently, univariate Cox regression and LASSO-Cox regression were conducted to establish a 12-NRLs signature in the training set and validate it in the testing set. Finally, the prognostic power of the 12-NRLs signature was appraised via survival analysis, nomogram, Cox regression, clinicopathological characteristics correlation analysis, and the receiver operating characteristic (ROC) curve. Furthermore, correlations between the signature risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analyzed. Results: In the present study, a 12-NRLs signature comprising REPIN1-AS1, UBL7-AS1, LINC00460, LINC02773, CHROMR, LINC01094, FLNB-AS1, ITFG1-AS1, LASTR, PINK1-AS, LINC01638, and PVT1 was developed to improve the prognosis prediction of STAD patients. Unsupervised methods, including principal component analysis and t-distributed stochastic neighbor embedding, confirmed the capability of the present signature to separate samples with RS. Kaplan-Meier and ROC curves revealed that the signature had an acceptable predictive potency in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 12-NRLs signature were risk factors independent of various clinical parameters. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and half-inhibitory concentration differed significantly among different risk subtypes, which implied that the signature could assess the clinical efficacy of chemotherapy and immunotherapy. Conclusion: This 12-NRLs risk signature may help assess the prognosis and molecular features of patients with STAD and improve treatment modalities, thus can be further applied clinically.
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