Department of Anesthesiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen 518071, Guangdong, China.
Department of Anesthesiology, The Fifth People's Hospital of Ningxia, Shizuishan 753000, Ningxia, China.
Transpl Immunol. 2022 Oct;74:101660. doi: 10.1016/j.trim.2022.101660. Epub 2022 Jul 2.
Sepsis in patients is a great threat to human health due to its high incidence rate, its rapid and unpredictable progression, as well as it is difficult to treat, and it has poor prognosis. Ferroptosis is a newly discovered type of cell death characterized by the iron-dependent peroxide aggregation. Furthermore, ferroptosis is different from other forms of cell death, namely apoptosis, necrosis, pyroptosis and autophagy. Our study investigated the role of ferroptosis-related genes in sepsis.
The GSE65682 dataset from the Gene Expression Omnibus (GEO) database was used to screen ferroptosis-related genes associated with sepsis, and the GSE134347 dataset for the external validation of selected hub genes. The univariate Cox regression analysis, Kaplan-Meier (K-M) survival analysis and weighted gene co-expression network analysis (WGCNA) were used to identify hub genes. Evaluation of the immune cell infiltration in sepsis was used to explain the immune heterogeneity among the cell subtypes. Gene set variation analysis (GSVA) and transcriptional regulatory analysis of selected hub genes further elucidated the probable mechanism of ferroptosis-related genes associated with prognosis in sepsis. Finally, we constructed a competing endogenous RNA (ceRNA) network model.
A total of 479 RNA-seq data points were used for analysis, including 365 samples from patients who survived sepsis and 114 samples from patients who succumbed to sepsis from the available GSE65682 dataset. Consequently, the univariate Cox regression analysis and consensus clustering analysis divide all 479 sepsis samples into two clusters of "survivals" vs. "non-survivals". Following complex analysis were identified as the most important ferroptosis-related genes. Indeed, the WGCNA and K-M analyses associated the expression patterns of NEDD4L and SIAH2 hub genes as the best prognosis for the survival of sepsis (p < 0.05). The expression trend was also consistent with the survival trend of the NEDD4L and SIAH2 hub genes by the external validation of GSE134347 (p < 0.05). Immune cell infiltration analysis indicated that the types and numbers of different immune cells vary among different subtypes and NEDD4L and SIAH2 hub genes. For example, NEDD4L and SIAH2 gene expression had a positive correlation with M0 macrophages and a negative correlation with neutrophils (p > 0.05). Finally, analysis of two hub genes and transcription factors (TFs) showed that 71 TFs were predicted to be related to NEDD4L while 64 TFs to SIAH2 by the Cistrome DB online database.
We suggest that NEDD4L and SIAH2 hub genes are involved in the ferroptosis-associated sepsis. The pattern of NEDD4L and SIAH2 expression in patients undergoing sepsis may have prognostic potential for the severity of sepsis and eventually for patients' survival.
脓毒症患者的发病率高，病情进展迅速且难以预测，预后较差，因此对人类健康构成了巨大威胁。铁死亡是一种新发现的细胞死亡方式，其特征是铁依赖性过氧化物聚集。此外，铁死亡不同于其他形式的细胞死亡，如细胞凋亡、坏死、细胞焦亡和自噬。本研究探讨了铁死亡相关基因在脓毒症中的作用。
从基因表达综合数据库（GEO）的 GSE65682 数据集筛选与脓毒症相关的铁死亡相关基因，并使用 GSE134347 数据集对选定的枢纽基因进行外部验证。单变量 Cox 回归分析、Kaplan-Meier（K-M）生存分析和加权基因共表达网络分析（WGCNA）用于鉴定枢纽基因。对脓毒症中免疫细胞浸润的评估用于解释细胞亚型之间的免疫异质性。基因集变异分析（GSVA）和选定枢纽基因的转录调控分析进一步阐明了与脓毒症预后相关的铁死亡相关基因的可能机制。最后，我们构建了竞争性内源 RNA（ceRNA）网络模型。
共分析了 479 个 RNA-seq 数据点，包括来自 GSE65682 数据集中幸存的 365 例脓毒症患者和死亡的 114 例脓毒症患者的样本。因此，单变量 Cox 回归分析和共识聚类分析将所有 479 例脓毒症样本分为“存活”与“非存活”两组。随后的复杂分析确定了最重要的铁死亡相关基因。事实上，WGCNA 和 K-M 分析将 NEDD4L 和 SIAH2 枢纽基因的表达模式与脓毒症存活的最佳预后相关（p<0.05）。通过 GSE134347 的外部验证，NEDD4L 和 SIAH2 枢纽基因的表达趋势也与生存趋势一致（p<0.05）。免疫细胞浸润分析表明，不同亚型和 NEDD4L 和 SIAH2 枢纽基因之间不同免疫细胞的类型和数量存在差异。例如，NEDD4L 和 SIAH2 基因的表达与 M0 巨噬细胞呈正相关，与中性粒细胞呈负相关（p>0.05）。最后，通过 Cistrome DB 在线数据库分析两个枢纽基因和转录因子（TFs），发现 71 个 TFs 被预测与 NEDD4L 相关，而 64 个 TFs 与 SIAH2 相关。
本研究表明，NEDD4L 和 SIAH2 枢纽基因参与了铁死亡相关的脓毒症。脓毒症患者中 NEDD4L 和 SIAH2 的表达模式可能对脓毒症的严重程度具有预后潜力，最终对患者的生存具有预后潜力。
