Department of Respiratory, Jinhua Guangfu Cancer Hospital, Jinhua, Zhejiang 321200, China.
Aging (Albany NY). 2023 Aug 7;15(15):7741-7759. doi: 10.18632/aging.204938.
Disulfidptosis is a new cell death model caused by accumulating intracellular disulfides bonding to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic value of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD). The data of expression profiles and scRNA-seq were collected from TCGA and GEO databases. The different expressions of DRGs between normal and LUAD tissues were compared. The LASSO analysis and multivariate Cox regression analysis were utilized to develop a DRGs model for the prognosis evaluation in LUAD. The model's predictive accuracy was evaluated with the area under the receiver operating characteristic curve (AUC) and C-index. Survival analysis, univariate and multivariate Cox regression analysis were used to assessing the predictive value of the DRGs model. ScRNA-seq data were analyzed with "Seurat" and "Monocle 2" packages. There were significant differences in 22 DRGs between normal and tumor tissues. A model with five DRGs (ACTB, FLNB, NCKAP1, SLC3A2, SLC7A11) was constructed. The AUC and C-index of the model were significantly higher than that based on clinical parameters. Survival analysis, univariate and multivariate Cox regression analysis demonstrated risk score was an independent prognostic predictor. In the scRNA-seq study, we identified 14 clusters and 11 cell types. Clusters 2, 8, and 13 were annotated into Epithelial cells. SLC7A11 and SLC3A2, NCKAP1 and FLNB, ACTB expressed most abundantly in Epithelial cells, Endothelial cells, Naive CD4 T, respectively. We explored the expression of DRGs in LUAD and constructed a predictive DRGs model, which was stable and reliable for predicting LUAD prognosis.
二硫键化细胞死亡是一种新的细胞死亡模型，由细胞内二硫键积累与肌动蛋白细胞骨架蛋白结合引起。本研究旨在探讨肺腺癌（LUAD）中与二硫键化细胞死亡相关基因（DRGs）的表达及其预后价值。从 TCGA 和 GEO 数据库中收集了表达谱和 scRNA-seq 数据。比较了正常组织和 LUAD 组织中 DRGs 的不同表达。利用 LASSO 分析和多变量 Cox 回归分析建立了用于 LUAD 预后评估的 DRGs 模型。通过接受者操作特征曲线（AUC）和 C 指数评估模型的预测准确性。生存分析、单因素和多因素 Cox 回归分析用于评估 DRGs 模型的预测价值。利用 "Seurat" 和 "Monocle 2" 包对 scRNA-seq 数据进行分析。正常组织和肿瘤组织之间有 22 个 DRGs 存在显著差异。构建了一个包含 5 个 DRGs（ACTB、FLNB、NCKAP1、SLC3A2、SLC7A11）的模型。该模型的 AUC 和 C 指数显著高于基于临床参数的模型。生存分析、单因素和多因素 Cox 回归分析表明，风险评分是独立的预后预测因子。在 scRNA-seq 研究中，我们鉴定了 14 个簇和 11 种细胞类型。簇 2、8 和 13 注释为上皮细胞。SLC7A11 和 SLC3A2、NCKAP1 和 FLNB、ACTB 在 Epithelial cells、Endothelial cells、Naive CD4 T 细胞中表达最为丰富。我们探讨了 LUAD 中 DRGs 的表达情况，并构建了一个预测性的 DRGs 模型，该模型对预测 LUAD 预后具有稳定性和可靠性。
