Liu Beibei, Fan Yonghua, Zhang Xianjing, Li Huaqing, Gao Fei, Shang Wenli, Hu Juntao, Tang Zhanhong
Department of Intensive Care Unit, The Second Affiliated Hospital of Shandong First Medical University, Taian, China.
Department of Intensive Care Unit, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Int Arch Allergy Immunol. 2025;186(3):264-279. doi: 10.1159/000540949. Epub 2024 Sep 30.
Septic shock, a severe manifestation of infection-induced systemic immune response, poses a critical threat resulting in life-threatening multi-organ failure. Early diagnosis and intervention are imperative due to the potential for irreversible organ damage. However, specific and sensitive detection tools for the diagnosis of septic shock are still lacking.
Gene expression files of early septic shock were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT analysis was used to evaluate immune cell infiltration. Genes related to immunity and disease progression were identified using weighted gene co-expression network analysis (WGCNA), followed by enrichment analysis. CytoHubba was then employed to identify hub genes, and their relationships with immune cells were explored through correlation analysis. Blood samples from healthy controls and patients with early septic shock were collected to validate the expression of hub genes, and an external dataset was used to validate their diagnostic efficacy.
Twelve immune cells showed significant infiltration differences in early septic shock compared to control, such as neutrophils, M0 macrophages, and natural killer cells. The identified immune and disease-related genes were mainly enriched in immune, cell signaling, and metabolism pathways. In addition, six hub genes were identified (PECAM1, F11R, ITGAL, ICAM3, HK3, and MCEMP1), all significantly associated with M0 macrophages and exhibiting an area under curve of over 0.7. These genes exhibited abnormal expression in patients with early septic shock. External datasets and real-time qPCR validation supported the robustness of these findings.
Six immune-related hub genes may be potential biomarkers for early septic shock.
Septic shock, a severe manifestation of infection-induced systemic immune response, poses a critical threat resulting in life-threatening multi-organ failure. Early diagnosis and intervention are imperative due to the potential for irreversible organ damage. However, specific and sensitive detection tools for the diagnosis of septic shock are still lacking.
Gene expression files of early septic shock were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT analysis was used to evaluate immune cell infiltration. Genes related to immunity and disease progression were identified using weighted gene co-expression network analysis (WGCNA), followed by enrichment analysis. CytoHubba was then employed to identify hub genes, and their relationships with immune cells were explored through correlation analysis. Blood samples from healthy controls and patients with early septic shock were collected to validate the expression of hub genes, and an external dataset was used to validate their diagnostic efficacy.
Twelve immune cells showed significant infiltration differences in early septic shock compared to control, such as neutrophils, M0 macrophages, and natural killer cells. The identified immune and disease-related genes were mainly enriched in immune, cell signaling, and metabolism pathways. In addition, six hub genes were identified (PECAM1, F11R, ITGAL, ICAM3, HK3, and MCEMP1), all significantly associated with M0 macrophages and exhibiting an area under curve of over 0.7. These genes exhibited abnormal expression in patients with early septic shock. External datasets and real-time qPCR validation supported the robustness of these findings.
Six immune-related hub genes may be potential biomarkers for early septic shock.
感染性休克是感染诱导的全身免疫反应的严重表现，会造成危及生命的多器官功能衰竭，带来重大威胁。由于存在不可逆器官损伤的可能性，早期诊断和干预势在必行。然而，目前仍缺乏用于诊断感染性休克的特异性和敏感性检测工具。
从基因表达综合数据库（GEO）获取早期感染性休克的基因表达文件。使用CIBERSORT分析评估免疫细胞浸润情况。采用加权基因共表达网络分析（WGCNA）识别与免疫和疾病进展相关的基因，随后进行富集分析。接着利用CytoHubba识别枢纽基因，并通过相关性分析探究其与免疫细胞的关系。收集健康对照者和早期感染性休克患者的血样以验证枢纽基因的表达，并使用外部数据集验证其诊断效能。
与对照组相比，12种免疫细胞在早期感染性休克中表现出显著的浸润差异，如中性粒细胞、M0巨噬细胞和自然杀伤细胞。所识别的免疫和疾病相关基因主要富集于免疫、细胞信号传导和代谢途径。此外，识别出6个枢纽基因（PECAM1、F11R、ITGAL、ICAM3、HK3和MCEMP1），均与M0巨噬细胞显著相关，曲线下面积均超过0.7。这些基因在早期感染性休克患者中表现出异常表达。外部数据集和实时定量PCR验证支持了这些结果的可靠性。
6个免疫相关枢纽基因可能是早期感染性休克的潜在生物标志物。
感染性休克是感染诱导的全身免疫反应的严重表现，会造成危及生命的多器官功能衰竭，带来重大威胁。由于存在不可逆器官损伤的可能性，早期诊断和干预势在必行。然而，目前仍缺乏用于诊断感染性休克的特异性和敏感性检测工具。
从基因表达综合数据库（GEO）获取早期感染性休克的基因表达文件。使用CIBERSORT分析评估免疫细胞浸润情况。采用加权基因共表达网络分析（WGCNA）识别与免疫和疾病进展相关的基因，随后进行富集分析。接着利用CytoHubba识别枢纽基因，并通过相关性分析探究其与免疫细胞的关系。收集健康对照者和早期感染性休克患者的血样以验证枢纽基因的表达，并使用外部数据集验证其诊断效能。
与对照组相比，12种免疫细胞在早期感染性休克中表现出显著的浸润差异，如中性粒细胞、M0巨噬细胞和自然杀伤细胞。所识别的免疫和疾病相关基因主要富集于免疫、细胞信号传导和代谢途径。此外，识别出6个枢纽基因（PECAM1、F11R、ITGAL、ICAM3、HK3和MCEMP1），均与M0巨噬细胞显著相关，曲线下面积均超过0.7。这些基因在早期感染性休克患者中表现出异常表达。外部数据集和实时定量PCR验证支持了这些结果的可靠性。
6个免疫相关枢纽基因可能是早期感染性休克的潜在生物标志物。
