Yang Li, Zhou Lin, Li Fangyi, Chen Xiaotong, Li Ting, Zou Zijun, Zhi Yaowei, He Zhijie
Department of Critical Care Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Department of Health Management Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Front Cell Dev Biol. 2023 Aug 28;11:1218379. doi: 10.3389/fcell.2023.1218379. eCollection 2023.
Autophagy is involved in the pathophysiological process of sepsis. This study was designed to identify autophagy-related key genes in sepsis, analyze their correlation with immune cell signatures, and search for new diagnostic and prognostic biomarkers. Whole blood RNA datasets GSE65682, GSE134347, and GSE134358 were downloaded and processed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify autophagy-related key genes in sepsis. Then, key genes were analyzed by functional enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene and competing endogenous RNA (ceRNA) network analysis. Subsequently, key genes with diagnostic efficiency and prognostic value were identified by receiver operating characteristic (ROC) curves and survival analysis respectively. The signatures of immune cells were estimated using CIBERSORT algorithm. The correlation between significantly different immune cell signatures and key genes was assessed by correlation analysis. Finally, key genes with both diagnostic and prognostic value were verified by RT-qPCR. 14 autophagy-related key genes were identified and their TF-gene and ceRNA regulatory networks were constructed. Among the key genes, 11 genes (ATIC, BCL2, EEF2, EIF2AK3, HSPA8, IKBKB, NLRC4, PARP1, PRKCQ, SH3GLB1, and WIPI1) had diagnostic efficiency (AUC > 0.90) and 5 genes (CAPN2, IKBKB, PRKCQ, SH3GLB1 and WIPI1) were associated with survival prognosis (-value < 0.05). IKBKB, PRKCQ, SH3GLB1 and WIPI1 had both diagnostic and prognostic value, and their expression were verified by RT-qPCR. Analysis of immune cell signatures showed that the abundance of neutrophil, monocyte, M0 macrophage, gamma delta T cell, activated mast cell and M1 macrophage subtypes increased in the sepsis group, while the abundance of resting NK cell, resting memory CD4 T cell, CD8 T cell, naive B cell and resting dendritic cell subtypes decreased. Most of the key genes correlated with the predicted frequencies of CD8 T cells, resting memory CD4 T cells, M1 macrophages and naive B cells. We identified autophagy-related key genes with diagnostic and prognostic value in sepsis and discovered associations between key genes and immune cell signatures. This work may provide new directions for the discovery of promising biomarkers for sepsis.
自噬参与脓毒症的病理生理过程。本研究旨在鉴定脓毒症中自噬相关关键基因,分析其与免疫细胞特征的相关性,并寻找新的诊断和预后生物标志物。下载并处理了全血RNA数据集GSE65682、GSE134347和GSE134358。采用差异表达分析和加权基因共表达网络分析(WGCNA)来鉴定脓毒症中自噬相关关键基因。然后,通过功能富集、蛋白质-蛋白质相互作用(PPI)、转录因子(TF)-基因和竞争性内源性RNA(ceRNA)网络分析对关键基因进行分析。随后,分别通过受试者工作特征(ROC)曲线和生存分析鉴定具有诊断效率和预后价值的关键基因。使用CIBERSORT算法估计免疫细胞的特征。通过相关性分析评估显著不同的免疫细胞特征与关键基因之间的相关性。最后,通过RT-qPCR验证具有诊断和预后价值双功能的关键基因。鉴定出14个自噬相关关键基因并构建了它们的TF-基因和ceRNA调控网络。在这些关键基因中,11个基因(ATIC、BCL2、EEF2、EIF2AK3、HSPA8、IKBKB、NLRC4、PARP1、PRKCQ、SH3GLB1和WIPI1)具有诊断效率(AUC>0.90),5个基因(CAPN2、IKBKB、PRKCQ、SH3GLB1和WIPI1)与生存预后相关(P值<0.05)。IKBKB、PRKCQ、SH3GLB1和WIPI1具有诊断和预后双功能,其表达通过RT-qPCR验证。免疫细胞特征分析表明,脓毒症组中性粒细胞、单核细胞、M0巨噬细胞、γδT细胞、活化肥大细胞和M1巨噬细胞亚型的丰度增加,而静息NK细胞、静息记忆CD4 T细胞、CD8 T细胞、幼稚B细胞和静息树突状细胞亚型的丰度降低。大多数关键基因与CD8 T细胞、静息记忆CD4 T细胞、M1巨噬细胞和幼稚B细胞的预测频率相关。我们鉴定了脓毒症中具有诊断和预后价值的自噬相关关键基因,并发现了关键基因与免疫细胞特征之间的关联。这项工作可能为发现有前景的脓毒症生物标志物提供新方向。