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A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis.

ABSTRACT: Sepsis is a life-threatening syndrome resulting from immune system dysfunction that is caused by infection. It is of great importance to analyze the immune characteristics of sepsis, identify the key immune system related genes, and construct diagnostic models for sepsis. In this study, the sepsis transcriptome and expression profiling data were merged into an integrated dataset containing 277 sepsis samples and 117 non-sepsis control samples. Single-sample gene set enrichment analysis (ssGSEA) was used to assess the immune cell infiltration. Two sepsis immune subtypes were identified based on the 22 differential immune cells between the sepsis and the healthy control groups. Weighted gene co-expression network analysis (WCGNA) was used to identify the key module genes. Then, 36 differentially expressed immune-related genes were identified, based on which a robust diagnostic model was constructed with 11 diagnostic genes. The expression of 11 diagnostic genes was finally assessed in the training and validation datasets respectively. In this study, we provide comprehensive insight into the immune features of sepsis and establish a robust diagnostic model for sepsis. These findings may provide new strategies for the early diagnosis of sepsis in the future.

SUBMITTER: Xue H

PROVIDER: S-EPMC10734174 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis.

Xue Haiyan H Xiao Ziyan Z Zhao Xiujuan X Li Shu S Wang Zhenzhou Z Zhao Jie J Zhu Fengxue F

BMC genomics 20231220 1

Sepsis is a life-threatening syndrome resulting from immune system dysfunction that is caused by infection. It is of great importance to analyze the immune characteristics of sepsis, identify the key immune system related genes, and construct diagnostic models for sepsis. In this study, the sepsis transcriptome and expression profiling data were merged into an integrated dataset containing 277 sepsis samples and 117 non-sepsis control samples. Single-sample gene set enrichment analysis (ssGSEA) ...[more]

PMID: 38124071

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Project description:PurposeSepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.Materials and methodsThe differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.ResultsA total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002).ConclusionsOur study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.

Project description:BackgroundThe immune system plays a fundamental role in the pathophysiology of sepsis, and autophagy and autophagy-related molecules are crucial in innate and adaptive immune responses; however, the potential roles of autophagy-related genes (ARGs) in sepsis are not comprehensively understood.MethodsA systematic search was conducted in ArrayExpress and Gene Expression Omnibus (GEO) cohorts from July 2005 to May 2022. Machine learning approaches, including modified Lasso penalized regression, support vector machine, and artificial neural network, were applied to identify hub ARGs, thereby developing a prediction model termed ARG classifier. Diagnostic and prognostic performance of the model was comprehensively analyzed using multi-transcriptome data. Subsequently, we systematically correlated the ARG classifier/hub ARGs with immunological characteristics of multiple aspects, including immune cell infiltration, immune and molecular pathways, cytokine levels, and immune-related genes. Further, we collected clinical specimens to preliminarily investigate ARG expression levels and to assess the diagnostic performance of ARG classifier.ResultsA total of ten GEO and three ArrayExpress datasets were included in this study. Based on machine learning algorithms, eight key ARGs (ATG4C, BAX, BIRC5, ERBB2, FKBP1B, HIF1A, NCKAP1, and NFKB1) were integrated to establish ARG classifier. The model exhibited excellent diagnostic values (AUC > 0.85) in multiple datasets and multiple points in time and superiorly distinguished sepsis from other critical illnesses. ARG classifier showed significant correlations with clinical characteristics or endotypes and performed better in predicting mortality (AUC = 0.70) than other clinical characteristics. Additionally, the identified hub ARGs were significantly associated with immune cell infiltration (B, T, NK, dendritic, T regulatory, and myeloid-derived suppressor cells), immune and molecular pathways (inflammation-promoting pathways, HLA, cytolytic activity, apoptosis, type-II IFN response, complement and coagulation cascades), levels of several cytokines (PDGFRB, IL-10, IFNG, and TNF), which indicated that ARG classifier/hub ARGs adequately reflected the immune microenvironment during sepsis. Finally, using clinical specimens, the expression levels of key ARGs in patients with sepsis were found to differ significantly from those of control patients, and ARG classifier exhibited superior diagnostic performance, compared to procalcitonin and C-reactive protein.ConclusionCollectively, a diagnostic and prognostic model (ARG classifier) based on eight ARGs was developed which may assist clinicians in diagnosis of sepsis and recognizing patient at high risk to guide personalized treatment. Additionally, the ARG classifier effectively reflected the immune microenvironment diversity of sepsis and may facilitate personalized counseling for specific therapy.

Project description:PurposeSepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis.Patients and methodsThe GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein-protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR).ResultsTotally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway.ConclusionThe 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis.

Dataset Information

A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis.

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A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis.

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