Table4_A Necroptosis-Related lncRNA-Based Signature to Predict Prognosis and Probe Molecular Characteristics of Stomach Adenocarcinoma.XLSX

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.

背景：作为一种半胱天冬酶非依赖性细胞死亡方式，坏死性凋亡（necroptosis）在胃癌（GC）的发生与进展中发挥关键作用。诸多研究已证实，长链非编码RNA（lncRNAs）与胃癌患者的预后密切相关。然而，胃癌中坏死性凋亡与长链非编码RNA之间的关联仍有待阐明。
方法：从癌症基因组图谱（TCGA）数据库中获取胃腺癌（STAD）患者的分子谱数据（RNA测序（RNA-sequencing）与体细胞突变数据）及临床信息。通过皮尔逊相关分析筛选坏死性凋亡相关长链非编码RNA（NRLs）。随后，在训练集中采用单变量Cox回归与套索Cox（LASSO-Cox）回归构建12个坏死性凋亡相关长链非编码RNA特征，并在测试集中对该特征进行验证。最终，通过生存分析、列线图（nomogram）、Cox回归、临床病理特征相关性分析及受试者工作特征（ROC）曲线评估该12-NRLs特征的预后效能。此外，还分析了特征风险评分（RS）与免疫细胞浸润、免疫检查点分子、体细胞基因突变及抗肿瘤药物敏感性之间的相关性。
结果：本研究构建了包含REPIN1-AS1、UBL7-AS1、LINC00460、LINC02773、CHROMR、LINC01094、FLNB-AS1、ITFG1-AS1、LASTR、PINK1-AS、LINC01638及PVT1的12-NRLs风险特征，用于改善胃腺癌患者的预后预测效果。采用主成分分析（principal component analysis）与t分布邻域嵌入（t-distributed stochastic neighbor embedding）等无监督方法，证实该特征可有效区分不同风险评分的样本。卡普兰-迈耶（Kaplan-Meier）曲线与ROC曲线显示，该特征在TCGA训练集与测试集中均具备良好的预测效能。Cox回归与分层生存分析表明，该12-NRLs特征是独立于多项临床参数的风险因素。此外，不同风险亚型间的免疫细胞浸润、免疫检查点分子表达、体细胞基因突变情况及半抑制浓度均存在显著差异，提示该特征可用于评估化疗与免疫治疗的临床疗效。
结论：该12-NRLs风险特征有助于评估胃腺癌患者的预后与分子特征，优化治疗方案，具备进一步临床转化应用的潜力。