构建和验证与脓毒症预后相关的新的炎症反应相关基因标记

Development and validation of novel inflammatory response-related gene signature for sepsis prognosis 构建和验证与脓毒症预后相关的新的炎症反应相关基因标记 1 2 equal first-author 3 equal 1 2 corresp Yuanqiang LU, luyuanqiang@zju.edu.cn Yuanqiang LU, luyuanqiang@zju.edu.cn luyuanqiang@zju.edu.cn https://orcid.org/0000-0002-9057-4344 Department of Emergency Medicine, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China Department of Emergency Medicine, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China 浙江大学医学院第一附属医院急诊科，中国杭州市，310003 浙江大学医学院第一附属医院急诊科，中国杭州市，310003 Zhejiang Provincial Key Laboratory for Diagnosis and Treatment of Aging and Physic-Chemical Injury Diseases, Hangzhou 310003, China Zhejiang Provincial Key Laboratory for Diagnosis and Treatment of Aging and Physic-Chemical Injury Diseases, Hangzhou 310003, China 浙江省增龄与理化损伤性疾病诊治研究重点实验室，中国杭州市，310003 浙江省增龄与理化损伤性疾病诊治研究重点实验室，中国杭州市，310003 Department of Anesthesiology and Intensive Care, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China Department of Anesthesiology and Intensive Care, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China 浙江大学医学院第一附属医院麻醉科与重症监护室，中国杭州市，310003 浙江大学医学院第一附属医院麻醉科与重症监护室，中国杭州市，310003 Due to the low specificity and sensitivity of biomarkers in sepsis diagnostics, the prognosis of sepsis patient outcomes still relies on the assessment of clinical symptoms. Inflammatory response is crucial to sepsis onset and progression; however, the significance of inflammatory response-related genes (IRRGs) in sepsis prognosis is uncertain. This study developed an IRRG-based signature for sepsis prognosis and immunological function. The Gene Expression Omnibus (GEO) database was retrieved for two sepsis microarray datasets, GSE64457 and GSE69528, followed by gene set enrichment analysis (GSEA) comparing sepsis and healthy samples. A predictive signature for IRRGs was created using least absolute shrinkage and selection operator (LASSO). To confirm the efficacy and reliability of the new prognostic signature, Cox regression, Kaplan-Meier (K-M) survival, and receiver operating characteristic (ROC) curve analyses were performed. Subsequently, we employed the GSE95233 dataset to independently validate the prognostic signature. A single-sample GSEA (ssGSEA) was conducted to quantify the immune cell enrichment score and immune-related pathway activity. We found that more gene sets were enriched in the inflammatory response in sepsis patient samples than in healthy patient samples, as determined by GSEA. The signature of nine IRRGs permitted the patients to be classified into two risk categories. Patients in the low-risk group showed significantly better 28-d survival than those in the high-risk group. ROC curve analysis corroborated the predictive capacity of the signature, with the area under the curve (AUC) for 28-d survival reaching 0.866. Meanwhile, the ssGSEA showed that the two risk groups had different immune states. The validation set and external dataset showed that the signature was clinically predictive. In conclusion, a signature consisting of nine IRRGs can be utilized to predict prognosis and influence the immunological status of sepsis patients. Thus, intervention based on these IRRGs may become a therapeutic option in the future. 目的： 2 探讨免疫反应相关的基因（inflammatory response-related genes，IRRGs）在预测脓毒症患者生存预后中的作用。 创新点： 2 鉴定与脓毒症生存预后密切相关的IRRGs，构建风险评分模型。 方法： 2 对Gene Expression Omnibus（GEO）数据库中478例脓毒症患者的微阵列芯片数据进行综合生物信息学分析，利用least absolute shrinkage and selectionoperator（LASSO）-Cox回归分析筛选与脓毒症28天生存率密切相关的IRRGs，并以此构建脓毒症预后风险评分模型。使用受试者工作特征曲线（ROC）及生存曲线（基于Kaplan-Meier法）评估预后风险评分模型的预测效能及区分度，并用GSE95233数据集进行验证。 结论： 2 利用9个IRRGs构建了与脓毒症28天生存预后有关的风险评估模型，且在GSE95233数据集中进行验证，证实这些IRRGs可作为脓毒症患者的预后生物标志物。 Gene signature Inflammatory response-related gene (IRRG) Prognosis Immune function Sepsis 基因特征 炎症反应相关基因（IRRG） 预后 免疫功能 脓毒症 1 Introduction 1 Sepsis comprises a series of aberrant body reactions induced by infection, resulting in life-threatening organ damage with a high fatality and disability rate ( Rhodes et al., 2017 Rhodes et al., 2017 ). Due to its quick progression and grave prognosis, sepsis has become one of the most prevalent acute and critical diseases among patients undergoing major surgery or suffering burns ( Fleischmann-Struzek et al., 2020 Fleischmann-Struzek et al., 2020 ; Evans et al., 2021 Evans et al., 2021 ). Despite the ongoing advancement of comprehensive treatment methods such as early fluid resuscitation and organ function support therapy, sepsis remains one of the leading causes of death in intensive care units (ICUs) ( Gotts and Matthay, 2016 Gotts and Matthay, 2016 ). It is predicted to affect at least 48.9 million people annually, with a death rate of up to 30% ( Fleischmann et al., 2016 Fleischmann et al., 2016 ; Rhee et al., 2017 Rhee et al., 2017 ; Markwart et al., 2020 Markwart et al., 2020 ). The Surviving Sepsis Campaign (SSC) has emphasized that the early diagnosis and prompt treatment of sepsis are crucial for improving prognosis ( Levy et al., 2018 Levy et al., 2018 ). In light of these facts, there is a significant need in clinical practice for an efficient prognostic biomarker that is able to reliably predict the clinical prognosis of sepsis patients. The onset and progression of sepsis are complicated processes that frequently involve the pathophysiological alterations of a number of organs and body systems. It is believed that sepsis is a sickness that goes through two phases and has a close connection with the inflammatory response. The early phase of hyperinflammation, also called “cytokine storm,” is characterized by the excessive production of inflammatory chemicals by the innate immune system, which may or may not result in tissue necrosis ( Bosmann and Ward, 2013 Bosmann and Ward, 2013 ; Delano and Ward, 2016 Delano and Ward, 2016 ). Shortly after this exaggerated inflammation response, the immune system dampens, resulting in a state of hypoinflammation. In this condition, both lymphoid and myeloid cells of the immune system become exhausted and go through apoptosis, leaving individuals immunocompromised (van der Poll et al., 2017; Silwal et al., 2020 Silwal et al., 2020 ). Multiple studies have sought to integrate pro-inflammatory and anti-inflammatory markers, which is the most likely strategy to succeed in predicting sepsis patients’ disease progression ( Andaluz-Ojeda et al., 2012 Andaluz-Ojeda et al., 2012 ; Reinhart et al., 2012 Reinhart et al., 2012 ). However, these indicators are not reliable as predictive tools for sepsis patients because they lack both specificity and sensitivity. With the rapid advancement of high-throughput sequencing and gene chip technology, genomics, transcriptomics, and proteomics have been progressively used for illness research ( Bauer et al., 2009 Bauer et al., 2009 ). Similarly, bioinformatics and machine learning analysis approaches have been widely employed in the analysis of illness gene expression profiles, the identification of specific molecular markers, and the development of clinical prognostic prediction models ( Cuocolo et al., 2020 Cuocolo et al., 2020 ; Zhong et al., 2021 Zhong et al., 2021 ). In light of these methodologies, certain inflammatory response-related genes, also known as IRRGs, have been utilized to predict the prognosis of metastatic disease ( Ju et al., 2021 Ju et al., 2021 ; Xiao et al., 2021 Xiao et al., 2021 ). On the other hand, the IRRG signature has not been used to date to predict the consequences of sepsis. As a result, we set out to create and validate an IRRG signature to provide a reliable prognosis prediction for sepsis patients, as well as to describe the immunological status of sepsis patients characterized by a varying prognostic risk. For the purpose of this investigation, we firstly accessed the Gene Expression Omnibus (GEO) database to obtain the gene expression profile and accompanying clinical data of patients suffering from sepsis. Then, we created a prognostic signature using the IRRGs from the training cohort, and validated the stability and reliability of the model using the validation cohort. Finally, we looked at the disparities in immunological state that existed between the two different risk groups. 2 Materials and methods 1 2.1 Data collection 2 The RNA-sequencing (RNA-seq) data were acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo https://www.ncbi.nlm.nih.gov/geo ) ( Barrett et al., 2013 Barrett et al., 2013 ). Two sepsis microarray datasets, GSE64457 ( Demaret et al., 2015 Demaret et al., 2015 ) and GSE69528 ( Pankla et al., 2009 Pankla et al., 2009 ), were acquired from GEO. Another two datasets, GSE65682 ( Scicluna et al., 2015 Scicluna et al., 2015 ) and GSE95233 ( Venet et al., 2017 Venet et al., 2017 ), were also retrieved from GEO. GSE64457 contained 23 samples including healthy controls ( n =8) and sepsis patients ( n =15). GSE69528 contained 138 samples including healthy controls ( n =55) and sepsis patients ( n =83). After excluding 324 healthy controls and sepsis samples that lacked information on 28-d survival, GSE65682 was chosen for further investigation to screen 478 sepsis patients. GSE95233 was used as an external validation set with 28-d survival information of 51 patients with sepsis. All the above sepsis patients met the diagnostic criteria ( Singer et al., 2016 Singer et al., 2016 ). 2.2 Gene set enrichment analysis (GSEA) 2 GSEA ( http://software.broadinstitute.org/gsea http://software.broadinstitute.org/gsea ) was used to compare molecular pathways and processes between healthy controls and sepsis patients ( Subramanian et al., 2005 Subramanian et al., 2005 ). With an adjusted P value less than 0.05, enriched gene sets were considered statistically significant. 2.3 Identification of IRRGs 2 The “genes defining inflammatory response” gene sets in the Molecular Signatures Database (MSigDB) ( http://www. http://www. ‍gsea-msigdb.‍org) were scoured for the IRRGs, which were then extracted for analysis ( Liberzon et al., 2015 Liberzon et al., 2015 ). 2.4 Development and validation of a prognostic IRRG signature 2 Patients suffering from sepsis were randomly divided into two groups: training group ( n =240) and validation group ( n =238). First, univariate Cox regressions were used to investigate the relationships between IRRGs and 28-d survival in the training set. This was performed to determine whether or not there was a significant relationship between the two variables. Second, prognostic IRRGs were discovered by applying least absolute shrinkage and selection operator (LASSO) Cox regression, which eliminated multicollinearity and decreased the number of prognostic IRRGs ( Scicluna et al., 2015 Scicluna et al., 2015 ). LASSO was then used to exclude any variables from the model that had a regression coefficient equal to zero after the shrinkage phase had been completed. A tuning parameter known as lambda ( λ ) is responsible for determining the amount of shrinkage; for this parameter, higher values indicate a greater amount of shrinkage. Using 10-fold cross-validation, an ideal was chosen when the partial likelihood deviation was the smallest. Third, we utilized multivariate Cox regression analysis to find predictive IRRGs and generated an IRRG signature for sepsis prognosis by utilizing the Akaike information criterion (AIC) with the lowest value. The following equation was used to obtain the risk score: risk score= β gene (1)×EXP gene (1)+ β gene (2)×EXP gene (2)+…+ β gene ( n )×EXP gene ( n ), with EXP gene reflecting the expression level of the discovered IRRGs adjusted by the Z -score, and β representing the coefficient of that specific IRRG in the prior multivariate Cox regression analysis. Based on their risk score, patients with sepsis in the training and validation sets were classified as low-risk or high-risk. Using the “survival” R package, Kaplan-Meier (K-M) analysis was employed to compare 28-d survival between the low- and high-risk groups ( Rich et al., 2010 Rich et al., 2010 ). The “pROC” R package was utilized to evaluate the reliability of the signature ( Robin et al., 2011 Robin et al., 2011 ). 2.5 ‍ Comparison of immune status of sepsis patients from different risk groups 2 Using single-sample GSEA (ssGSEA), we compared the enrichment scores of immune cells and the activity of immune-related pathways across the low- and high-risk groups. The “gsva” R package ( Hänzelmann et al., 2013 Hänzelmann et al., 2013 ) was used for this task, and P < 0.05 was considered statistically significant. 2.6 Statistical analysis 2 In order to distinguish IRRGs linked with prognosis, LASSO-Cox regression analysis was utilized. K-M analysis and the log-rank test were used to compare survival between the low- and high-risk groups. The Mann-Whitney test was used to compare ssGSEA scores of immune cells or pathways between the low-and high-risk groups, while adjusting the P values with the Benjamini-Hochberg (B-H) procedure. The predictive accuracy of the prognostic prediction signature was assessed by receiver operating characteristic (ROC) curve analysis. All of these tasks necessitated using version 4.1 of the R programming language. A double-tailed P value less than 0.05 was considered statistically significant. 3 Results 1 Fig. 1 depicted the flowchart of the investigation. The relevant clinical data of the modeling dataset were presented in Table S1. 3.1 GSEA 2 The inflammatory response (normalized enrichment score (NES)=1.928, adjusted P < 0.05) and regulation of inflammatory response (NES=1.822, adjusted P < 0.05) were considerably enriched in sepsis patients (Fig. 2). 3.2 Identification of prognostic IRRGs 2 The MSigDB had 200 IRRGs, which were listed in Table S2. First, we identified 44 IRRGs that were associated with 28-d survival and had prognostic significance ( P < 0.05; Fig. 3, Table S3). Second, LASSO-Cox regression analysis was employed to reduce multicollinearity and limit the number of IRRGs to 19 ( P < 0.05; Figs. 4a and 4b, Table S4). Fig. 4c is a visual representation of the correlation between these IRRGs. 3.3 Development of prognostic IRRG signature 2 The clinical prognosis prediction model of sepsis was created using nine IRRGs ( CCL22 , CX3CL1 , CXCR6 , FFAR2 , FPR1 , HBEGF , ITGA5 , RGS16 , and SELL ) by multivariate Cox regression analysis and the principle of minimum AIC (Fig. 5, Table 1). The following risk scoring formula was obtained through gene expression and regression coefficient in the model: risk score=1.059× CCL22 +1.227× CX3CL1 +0.460× CXCR6‍ - ‍ 0.789× FFAR2 +2.743× FPR1 +0.411× HBEGF‍ - ‍ 0.915× ITGA5 +0.565× RGS16 ‍- ‍ ‍1.733× SELL (AIC=581.29, consistency index=0.73; Table S5). Patients in the training set were separated into low-risk ( n =120) and high-risk ( n =120) groups based on the median patient risk score critical value (Figs. 6a and 6b). The heatmap depicted the nine IRRG expression profiles of each sepsis patient (Fig. 6c). The K-M curve analysis revealed that the low-risk group had considerably higher 28-d survival than the high-risk group ( P < 0.05; Fig. 6d). The area under curve (AUC) of the ROC of the risk score signature to predict 28-d survival in sepsis patients was 0.866 ( P < 0.05; Fig. 6e). Signature Combination of prognostic signature Value of Akaike information criterion 1 CXCR6 + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 607.33 2 CXCR6 + FPR1 + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 588.36 3 CXCR6 + FFAR2 + FPR1 + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 587.88 4 CXCR6 + FFAR2 + FPR1 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 585.93 5 CXCR6 + FFAR2 + FPR1 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 585.90 6 CCL22 + FFAR2 + FPR1 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 584.54 7 CCL22 + CXCR6 + FFAR2 + FPR1 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 584.33 8 CCL22 + CXCR6 + FFAR2 + FPR1 + HBEGF + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 583.61 9 CCL22 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 + IRF7 583.29 10 CCL22 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 + CD14 583.26 11 CCL22 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 + IRF1 583.16 12 CCL22 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 + CD70 583.14 13 CCL22 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 582.54 14 CCL22 + CX3CL1 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP + STAB1 582.36 15 CCL22 + CX3CL1 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 + TAPBP 582.32 16 CCL22 + CX3CL1 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 + CSF3 582.24 17 CCL22 + CX3CL1 + CXCR6 +FFAR2+ FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA + PROK2 581.82 18 CCL22 + CX3CL1 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B + INHBA 581.60 19 CCL22 + CX3CL1 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL + ADORA2B 581.48 20 CCL22 + CX3CL1 + CXCR6 + FFAR2 + FPR1 + HBEGF + ITGA5 + RGS16 + SELL 581.29 3.4 Validation of prognostic signature of IRRGs 2 Patients in the validation set were divided into low-risk ( n =117) and high-risk ( n =121) groups based on the same risk score as in the training set (Figs. 7a and 7b). The heatmap showed nine IRRG expression profiles of each patient with sepsis in the validation set (Fig. 7c). K-M curve analysis also indicated that the 28-d survival of the low-risk group was significantly higher than that of the high-risk group in the validation set ( P < 0.05; Fig. 7d). The AUC of signature to predict 28-d survival in sepsis patients by risk score was 0.858 in the validation set ( P < 0.05; Fig. 7e). The verification of the external dataset showed that the 28-d survival of the low-risk group was significantly higher than that of the high-risk group ( P < 0.05; Fig. 8a), and the AUC of signature to predict 28-d survival in sepsis patients by risk score was 0.813 ( P < 0.05; Fig. 8b). 3.5 ‍ Comparison of immune status of sepsis patients from different risk groups 2 We next investigated the immune cell enrichment scores using the ssGSEA results. Patients with sepsis in the high-risk group had higher enrichment scores of dendritic cells (DCs) and immature DCs (iDCs) than those in the low-risk group, while they had lower enrichment scores of neutrophils, natural killer (NK) cells, plasmacytoid DCs (pDCs), and tumor infiltrating lymphocytes (TILs) than those in the low-risk group ( P < 0.05; Fig. 9a). As for immune functions, patients with sepsis in the high-risk group had higher enrichment scores of type Ⅱ inborn errors of interferon (IFN) response than those in the low-risk group, and they had lower enrichment scores of C-C motif chemokine receptor (CCR), checkpoint, human leukocyte antigen (HLA), inflammation-promoting, major histocompatibility complex (MHC) class Ⅰ, parainflammation, T cell co-inhibition, T cell co-stimulation,and Type Ⅰ IFN response than those in the low-risk group ( P < 0.05; Fig. 9b). Fig. 9c presents a comparison of the immunological checkpoints of sepsis patients that fell into one of several different risk categories. 4 Discussion 1 Sepsis involves the body’s dysregulated immune reaction to infection, which causes life-threatening organ dysfunction. The situation rapidly deteriorates and may develop to septic shock and multiple organ failure, resulting in persistent tissue hypoperfusion, cell malfunction, and abnormal body metabolism, all of which represent a significant death risk ( Winters et al., 2010 Winters et al., 2010 ; Delano and Ward, 2016 Delano and Ward, 2016 ; Rhee et al., 2017 Rhee et al., 2017 ). Because there are few reliable markers, it is not always possible to detect and predict the consequences of sepsis treatment. The genetic signatures of numerous solid tumors have been correlated with a variety of clinical outcomes to improve clinical care through the creation of personalized medicine ( Zou et al., 2021 Zou et al., 2021 ; Deng et al., 2022 Deng et al., 2022 ). As a result, it is imperative to carry out extensive research on impact biomarkers to improve the clinical care and prognosis of sepsis patients. Serum indicators associated with the inflammatory response, such as the neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, lymphocyte-monocyte ratio, procalcitonin, and C-reactive protein, have been linked to the prognosis of sepsis ( Ruan et al., 2018 Ruan et al., 2018 ; Kriplani et al., 2022 Kriplani et al., 2022 ). In terms of detail and sensitivity, however, their performances fall short. Unfortunately, no research has been performed on the IRRG signature as a predictor of sepsis. Numerous studies have showed that S1PR3 gene signatures closely predict the prognosis of sepsis ( Feng et al., 2021 Feng et al., 2021 ). In addition to its superior effectiveness in sepsis prognosis, the IRRG signature developed in this study provides other advantages over the previously described gene signature. For instance, it may define IRRGs as low- or high-expression, and establishes correlations between risk ratings and 28-d survival. In this study, the expression levels of prognostic signature genes were determined using high-throughput sequencing, which is a commonly utilized technology that yields accurate results. In this work, we evaluated the expression of 200 genes associated with the inflammatory response and 28-d survival in sepsis patients. The univariate Cox analysis of the training cohort revealed that 44 IRRGs were linked with 28-d survival. Using LASSO and multivariate Cox regression approaches, a prognostic signature was established. The nine IRRGs in the signature were validated against the validation set and external dataset. Based on the median risk score, participants were categorized as low- or high-risk groups. The results suggested that the high-risk group had a lower 28-d survival rate. In both the training and validation sets, a distinct IRRG prognostic signature revealed small predictive value for the survival of sepsis patients based on ROC analysis. All of these nine IRRG signatures that might predict the prognosis of sepsis patients, CCL22 , CX3CL1 , CXCR6 , FFAR2 , FPR1 , HBEGF , ITGA5 , RGS16 , and SELL had been studied extensively in prior research. It is usually considered that CCL22 , a kind of anti-inflammatory (M2) chemokine, plays a role in the transition of monocytes and macrophages to the M2 phenotype, which may contribute to sepsis-induced immunosuppression ( Watanabe et al., 2016 Watanabe et al., 2016 ). However, no study has established a link between CCL22 and sepsis prognosis. CX3CL1 , also known as fractalkine, is essential for chemokine cell directionality, immune response, inflammatory response, and tissue healing among other tasks ( Helmke et al., 2019 Helmke et al., 2019 ; Korbecki et al., 2020 Korbecki et al., 2020 ; Ni et al., 2020 Ni et al., 2020 ). Basic experiments showed that the CX3CL1 / CX3CR1 axis may reduce the release of proinflammatory cytokines in sepsis mice through the downregulation of CX3CR1 mediated by transcription factor nuclear factor-κB (NF-‍κB) ( Raspé et al., 2013 Raspé et al., 2013 ). According to one study, blocking FFAR2 may play an important role in sepsis and contribute to the pathophysiology of the inflammatory response it causes ( Schlatterer et al., 2021a Schlatterer et al., 2021a ). The induction of chemotaxis towards local areas of infection or inflammation is the most well-described and established effector function of FFAR2 activation in neutrophils, although the effect on other neutrophil functions, such as the release of cytokines, is less evident. In addition, contradictory evidence has been found on FFAR2 -dependent cytokine and antimicrobial peptide production with either stimulatory or inhibitory effects ( Schlatterer et al., 2021b Schlatterer et al., 2021b ). FPR1 may limit neutrophil antimicrobial action, hence alleviating the systemic inflammatory response syndrome and defending the body against subsequent infection following tissue trauma or initial infection ( Itagaki et al., 2020 Itagaki et al., 2020 ). A previous study has suggested that RGS16 overexpression may also be linked to the early onset of cardiac failure in sepsis ( Patten et al., 2002 Patten et al., 2002 ). CXCR6 may serve as a marker for malignancies that emerge in an inflammatory environment, and mediate the pro-tumorigenic effects of inflammation through direct effects on cancer cell growth and by stimulating the migration and proliferation of tumor-associated leukocytes ( Darash-Yahana et al., 2009 Darash-Yahana et al., 2009 ). HBEGF has the potential to activate NF-κB, which would result in increased levels of interleukin-6 (IL-6) and IL-8 expression ( Miyata et al., 2012 Miyata et al., 2012 ). ITGA5 is a microRNA-92a (miR-92a) target gene, and miR-92a suppression alleviates the inflammatory response by reducing the release of proinflammatory cytokines IL-6 and tumor necrosis factor-α (TNF-α) ( Xu and Zhou, 2020 Xu and Zhou, 2020 ). L-Selectin, as a cell surface adhesion molecule and a member of the adhesion/homing receptor family, promotes leukocyte migration to lymphoid organs, which is related to the severity of inflammation, and is encoded by the SELL gene ( Wedepohl et al., 2012 Wedepohl et al., 2012 ). In sepsis, the underlying mechanism of CXCR6 , HBEGF , ITGA5 , or SELL IRRG has not been fully elucidated. The presence of inflammation results in an immunosuppressive environment, which speeds up the course of sepsis ( Hotchkiss et al., 2013 Hotchkiss et al., 2013 ; Venet and Monneret, 2018 Venet and Monneret, 2018 ). We revealed that patients in the high-risk group had higher enrichment scores of DCs and iDCs than those in the low-risk group, and they had lower enrichment scores of neutrophils, NK cells, pDCs, and TIL than those in the low-risk group. Bouras et al. (2018) Bouras et al. (2018) found that the presence of DCs in the blood was linked to increased chances of survival in sepsis patients. Neutrophils, which are the most common type of leukocytes found in the bloodstream, play roles in both inflammatory and immune responses ( Kolaczkowska and Kubes, 2013 Kolaczkowska and Kubes, 2013 ). Hanna et al. (2019) Hanna et al. (2019) found that decreased neutrophil CD16 expression was associated with a higher risk of death in severely ill sepsis patients. According to previous research, morphological and functional changes in neutrophils during sepsis decreased bacterial clearance and predispose sepsis patients to nosocomial infections, resulting in poor prognosis ( He et al., 2021 He et al., 2021 ; Lu et al., 2022 Lu et al., 2022 ). NK cells are also a subtype of lymphocytes known as innate lymphoid cells, which are essential for initiating host defenses and coordinating innate and adaptive immune responses. During sepsis, NK cells undergo quantitative, morphological, and functional alterations. A considerable decline in the number of circulating NK cells is associated with an increased risk of death in sepsis patients, probably due to increased apoptosis ( Jensen et al., 2018 Jensen et al., 2018 ). Our investigation has some constraints, inevitably. First, we began by evaluating the clinical efficacy of our prognostic signature without using additional prospective real-world data. Second, due to a paucity of relevant clinical data, we were unable to incorporate all critical clinical data into our predictive model. Finally, it was acknowledged that associations between the risk score and immunological condition must be empirically validated. 5 Conclusions 1 Our research established a novel predictive signature of nine IRRGs for the prognosis of sepsis. This signature was found to be independently associated with 28-d survival, providing a valuable tool for the prediction of sepsis prognosis. 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J Zhejiang Univ-Sci B (Biomed & Biotechnol) , 22 ( 8 ): 664 - 681 . https://doi.org/10.1631/jzus.B2000494 https://doi.org/10.1631/jzus.B2000494 Integrative analysis of prognostic long non-coding RNAs with copy number variation in bladder cancer Zou WJ , Chen L , Mao WW , et al. , 2021 . Identification of inflammatory response-related gene signature associated with immune status and prognosis of lung adenocarcinoma . Front Bioeng Biotechnol , 9 : 772206 . https://doi.org/10.3389/fbioe.2021.772206 https://doi.org/10.3389/fbioe.2021.772206 Identification of inflammatory response-related gene signature associated with immune status and prognosis of lung adenocarcinoma Shuai JIANG and Wenyuan ZHANG conceived the idea and performed data analyses, wrote and edited the manuscript. Yuanqiang LU contributed to the study design, data analysis, and writing and editing of the manuscript. All authors have read and approved the final manuscript, and therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data. This work was supported by the Key Research and Development Program of Zhejiang Province (No. 2019C03076) and the Opening Foundation of the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases (No. 2018KF02), China. 10.1631/jzus.B2200285 1673-1581（2022）12-1028-14 This work was supported by the Key Research and Development Program of Zhejiang Province (No. 2019C03076) and the Opening Foundation of the State Key Laboratory for Diagnosis and Treatment of Infectious Diseases (No. 2018KF02), China. 2022-05-18 2022-10-20 2022-12-15 2022-12-27 2022-11-21 Copyright © Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2022 2022 浙江大学出版社 Shuai JIANG, Wenyuan ZHANG, and Yuanqiang LU declare that they have no conflict of interest. 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