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Lan Y, Guo W, Chen W, Chen M, Li S. Resistin as a potential diagnostic biomarker for sepsis: insights from DIA and ELISA analyses. Clin Proteomics 2024; 21:46. [PMID: 38951753 PMCID: PMC11218185 DOI: 10.1186/s12014-024-09498-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 06/21/2024] [Indexed: 07/03/2024] Open
Abstract
PURPOSE The primary objective of this investigation is to systematically screen and identify differentially expressed proteins (DEPs) within the plasma of individuals afflicted with sepsis. This endeavor employs both Data-Independent Acquisition (DIA) and enzyme-linked immunosorbent assay (ELISA) methodologies. The overarching goal is to furnish accessible and precise serum biomarkers conducive to the diagnostic discernment of sepsis. METHOD The study encompasses 53 sepsis patients admitted to the Affiliated Hospital of Southwest Medical University between January 2019 and December 2020, alongside a control cohort consisting of 16 individuals devoid of sepsis pathology. Subsequently, a subset comprising 10 randomly selected subjects from the control group and 22 from the sepsis group undergoes quantitative proteomic analysis via DIA. The acquired data undergoes Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analyses, facilitating the construction of a Protein-Protein Interaction (PPI) network to discern potential markers. Validation of core proteins is then accomplished through ELISA. Comparative analysis between the normal and sepsis groups ensues, characterized by Receiver Operating Characteristic (ROC) curve construction to evaluate diagnostic efficacy. RESULT A total of 187 DEPs were identified through bioinformatic methodologies. Examination reveals their predominant involvement in biological processes such as wound healing, coagulation, and blood coagulation. Functional pathway analysis further elucidates their engagement in the complement pathway and malaria. Resistin emerges as a candidate plasma biomarker, subsequently validated through ELISA. Notably, the protein exhibits significantly elevated levels in the serum of sepsis patients compared to the normal control group. ROC curve analysis underscores the robust diagnostic capacity of these biomarkers for sepsis. CONCLUSION Data-Independent Acquisition (DIA) and Enzyme-Linked Immunosorbent Assay (ELISA) show increased Resistin levels in sepsis patients, suggesting diagnostic potential, warranting further research.
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Affiliation(s)
- Youyu Lan
- Department of Rheumatology and Immunology, the Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Jiangyang District, Luzhou, 646000, Sichuan, China
| | - Wentao Guo
- Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Jiangyang District, Luzhou City, 646000, Sichuan Province, China
| | - Wenhao Chen
- Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Jiangyang District, Luzhou City, 646000, Sichuan Province, China
| | - Muhu Chen
- Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Jiangyang District, Luzhou City, 646000, Sichuan Province, China.
| | - Shaolan Li
- Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Jiangyang District, Luzhou City, 646000, Sichuan Province, China.
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Jiang J, Zhang J, Wang T, Yu D, Ren X. Prediction of Prognosis in Patients with Sepsis Based on Platelet-Related Genes. Horm Metab Res 2024. [PMID: 38870987 DOI: 10.1055/a-2331-1362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
The study aimed to develop a risk prognostic model using platelet-related genes (PRGs) to predict sepsis patient outcomes. Sepsis patient data from the Gene Expression Omnibus (GEO) database and PRGs from the Molecular Signatures Database (MSigDB) were analyzed. Differential analysis identified 1139 differentially expressed genes (DEGs) between sepsis and control groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed enrichment in functions related to immune cell regulation and pathways associated with immune response and infectious diseases. A risk prognostic model was established using LASSO and Cox regression analyses, incorporating 10 PRGs selected based on their association with sepsis prognosis. The model demonstrated good stratification and prognostic effects, confirmed by survival and receiver operating characteristic (ROC) curve analyses. It served as an independent prognostic factor in sepsis patients. Further analysis using the CIBERSORT algorithm showed higher infiltration of activated natural killer (NK) cells and lower infiltration of CD8 T cells and CD4 T cells naïve in the high-risk group compared to the low-risk group. Additionally, expression levels of human leukocyte antigen (HLA) genes were significantly lower in the high-risk group. In conclusion, the 10-gene risk model based on PRGs accurately predicted sepsis patient prognosis and immune infiltration levels. This study provides valuable insights into the role of platelets in sepsis prognosis and diagnosis, offering potential implications for personalized treatment strategies.
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Affiliation(s)
- Jing Jiang
- Intensive Care Unit, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, China
| | - Juan Zhang
- Cardiology, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, China
| | - Ting Wang
- Endocrinology, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, China
| | - Daihua Yu
- Intensive Care Unit, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, China
| | - Xiu Ren
- Intensive Care Unit, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, China
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Zeng N, Jian Z, Xu J, Peng T, Hong G, Xiao F. Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features. Heliyon 2024; 10:e29007. [PMID: 38628767 PMCID: PMC11019180 DOI: 10.1016/j.heliyon.2024.e29007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
Objective Sepsis is linked to high morbidity and mortality rates. Consequently, early diagnosis is crucial for proper treatment, reducing hospitalization, and mortality rates. Additionally, over one-fifth of sepsis patients still face a risk of death. Hence, early diagnosis, and effective treatment play pivotal roles in enhancing the prognosis of patients with sepsis. Method The study analyzed whole blood data obtained from patients with sepsis and control samples sourced from three datasets (GSE57065, GSE69528, and GSE28750). Commonly dysregulated immune-related genes (IRGs) among these three datasets were identified. The differential characteristics of these common IRGs in the sepsis and control samples were assessed using the REO-based algorithm. Based on these differential characteristics, samples from eight Gene Expression Omnibus (GEO) databases (GSE57065, GSE69528, GSE28750, GSE65682, GSE69063, GSE95233, GSE131761, and GSE154918), along with three ArrayExpress databases (E-MTAB-4421, E-MTAB-4451, and E-MTAB-7581), were categorized and scored. The effectiveness of these differential characteristics in distinguishing sepsis samples from control samples was evaluated using the AUC value derived from the receiver operating characteristic curve (ROC) curve. Furthermore, the expression of IRGs was validated in peripheral blood samples obtained from patients with sepsis through qRT-PCR. Results Among the three training datasets, a total of 84 common dysregulated immune-related genes (IRGs) were identified. Utilizing a within-sample relative expression ordering (REOs)-based algorithm to analyze these common IRGs, differential characteristics were observed in three reverse stable pairs (ELANE-RORA, IL18RAP-CD247, and IL1R1-CD28). In the eight GEO datasets, the expression of ELANE, IL18RAP, and IL1R1 demonstrated significant upregulation, while RORA, CD247, and CD28 expression exhibited notable downregulation during sepsis. These three pairs of immune-related marker genes displayed accuracies of 95.89% and 97.99% in distinguishing sepsis samples among the eight GEO datasets and the three independent ArrayExpress datasets, respectively. The area under the receiver operating characteristic curve ranged from 0.81 to 1.0. Additionally, among these three immune-related marker gene pairs, mRNA expression levels of ELANE and IL1R1 were upregulated, whereas the levels of CD247 and CD28 mRNA were downregulated in blood samples from patients with sepsis compared to normal controls. Conclusion These three immune-related marker gene pairs exhibit high predictive performance for blood samples from patients with sepsis. They hold potential as valuable auxiliary clinical blood screening tools for sepsis.
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Affiliation(s)
- Ni Zeng
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Zaijin Jian
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Junmei Xu
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Tian Peng
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Guiping Hong
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Feng Xiao
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
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Lin Q, Zeng R, Yang J, Xu Z, Jin S, Wei G. Prognostic stratification of sepsis through DNA damage response based RiskScore system: insights from single-cell RNA-sequencing and transcriptomic profiling. Front Immunol 2024; 15:1345321. [PMID: 38404591 PMCID: PMC10884272 DOI: 10.3389/fimmu.2024.1345321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 01/24/2024] [Indexed: 02/27/2024] Open
Abstract
Background A novel risk scoring system, predicated on DNA damage response (DDR), was developed to enhance prognostic predictions and potentially inform the creation of more effective therapeutic protocols for sepsis. Methods To thoroughly delineate the expression profiles of DDR markers within the context of sepsis, an analytical approach utilizing single-cell RNA-sequencing (scRNA-seq) was implemented. Our study utilized single-cell analysis techniques alongside weighted gene co-expression network analysis (WGCNA) to pinpoint the genes that exhibit the most substantial associations with DNA damage response (DDR). Through Cox proportional hazards LASSO regression, we distinguished DDR-associated genes and established a risk model, enabling the stratification of patients into high- and low-risk groups. Subsequently, we carried out an analysis to determine our model's predictive accuracy regarding patient survival. Moreover, we examined the distinct biological characteristics, various signal transduction routes, and immune system responses in sepsis patients, considering different risk categories and outcomes related to survival. Lastly, we conducted experimental validation of the identified genes through in vivo and in vitro assays, employing RT-PCR, ELISA, and flow cytometry. Results Both single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses have demonstrated a strong correlation between DNA damage response (DDR) levels and sepsis prognosis. Specific cell subtypes, including monocytes, megakaryocytes, CD4+ T cells, and neutrophils, have shown elevated DDR activity. Cells with increased DDR scores exhibited more robust and numerous interactions with other cell populations. The weighted gene co-expression network analysis (WGCNA) and single-cell analyses revealed 71 DDR-associated genes. We developed a four-gene risk scoring system using ARL4C, CD247, RPL7, and RPL31, identified through univariate COX, LASSO COX regression, and log-rank (Mantel-Cox) tests. Nomograms, calibration plots, and decision curve analyses (DCA) regarding these specific genes have provided significant clinical benefits for individuals diagnosed with sepsis. The study suggested that individuals categorized as lower-risk demonstrated enhanced infiltration of immune cells, upregulated expression of immune regulators, and a more prolific presence of immune-associated functionalities and pathways. RT-qPCR analyses on a sepsis rat model revealed differential gene expression predominantly in the four targeted genes. Furthermore, ARL4C knockdown in sepsis model in vivo and vitro caused increased inflammatory response and a worse prognosis. Conclusion The delineated DDR expression landscape offers insights into sepsis pathogenesis, whilst our riskScore model, based on a robust four-gene signature, could underpin personalized sepsis treatment strategies.
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Affiliation(s)
| | | | | | | | | | - Guan Wei
- Department of Emergency Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
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Guo W, Gou X, Yu L, Zhang Q, Yang P, Pang M, Pang X, Pang C, Wei Y, Zhang X. Exploring the interaction between T-cell antigen receptor-related genes and MAPT or ACHE using integrated bioinformatics analysis. Front Neurol 2023; 14:1129470. [PMID: 37056359 PMCID: PMC10086260 DOI: 10.3389/fneur.2023.1129470] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/10/2023] [Indexed: 03/30/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that primarily occurs in elderly individuals with cognitive impairment. Although extracellular β-amyloid (Aβ) accumulation and tau protein hyperphosphorylation are considered to be leading causes of AD, the molecular mechanism of AD remains unknown. Therefore, in this study, we aimed to explore potential biomarkers of AD. Next-generation sequencing (NGS) datasets, GSE173955 and GSE203206, were collected from the Gene Expression Omnibus (GEO) database. Analysis of differentially expressed genes (DEGs), gene ontology (GO) functional enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein networks were performed to identify genes that are potentially associated with AD. Analysis of the DEG based protein-protein interaction (PPI) network using Cytoscape indicated that neuroinflammation and T-cell antigen receptor (TCR)-associated genes (LCK, ZAP70, and CD44) were the top three hub genes. Next, we validated these three hub genes in the AD database and utilized two machine learning models from different AD datasets (GSE15222) to observe their general relationship with AD. Analysis using the random forest classifier indicated that accuracy (78%) observed using the top three genes as inputs differed only slightly from that (84%) observed using all genes as inputs. Furthermore, another data set, GSE97760, which was analyzed using our novel eigenvalue decomposition method, indicated that the top three hub genes may be involved in tauopathies associated with AD, rather than Aβ pathology. In addition, protein-protein docking simulation revealed that the top hub genes could form stable binding sites with acetylcholinesterase (ACHE). This suggests a potential interaction between hub genes and ACHE, which plays an essential role in the development of anti-AD drug design. Overall, the findings of this study, which systematically analyzed several AD datasets, illustrated that LCK, ZAP70, and CD44 may be used as AD biomarkers. We also established a robust prediction model for classifying patients with AD.
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Affiliation(s)
- Wenbo Guo
- College of Computer Science, Sichuan Normal University, Chengdu, China
| | - Xun Gou
- College of Life Science, Sichuan Normal University, Chengdu, China
| | - Lei Yu
- College of Computer Science, Sichuan Normal University, Chengdu, China
| | - Qi Zhang
- College of Computer Science, Sichuan Normal University, Chengdu, China
| | - Ping Yang
- College of Computer Science, Sichuan Normal University, Chengdu, China
| | - Minghui Pang
- College of Mathematics and Physics, Chengdu University of Technology, Chengdu, China
| | - Xinping Pang
- West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, China
| | - Chaoyang Pang
- College of Computer Science, Sichuan Normal University, Chengdu, China
- *Correspondence: Chaoyang Pang
| | - Yanyun Wei
- National Key Laboratory of Science and Technology on Vacuum Electronics, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Yanyun Wei
| | - XiaoYu Zhang
- College of Life Science, Sichuan Normal University, Chengdu, China
- XiaoYu Zhang
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Screening of potential immune-related genes expressed during sepsis using gene sequencing technology. Sci Rep 2023; 13:4258. [PMID: 36918563 PMCID: PMC10014830 DOI: 10.1038/s41598-022-23062-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 10/25/2022] [Indexed: 03/16/2023] Open
Abstract
To screen potential pivotal targets in sepsis through peripheral blood. Septic patients (n = 23) and healthy volunteers (n = 10) were enrolled according to SEPSIS 3.0. Peripheral blood was collected within 24 h of enrollment, RNA-seq was performed on the peripheral blood. The sequencing data was screened for DEGs (p < 0.01; logFC ≥ 2). PPI, WGCNA and survival curve analysis were used to identify potential targets. Then, 5 PBMC samples were conducted by single-cell sequencing for cell lineage location. Finally, mouse sepsis model and clinic samples were performed to verify the targets gene using RNA-seq and RT-PCR, respectively. Compared to the control group, 1007 DEGs were found in septic group. BCL9L, BCL11B, CD247, CD96, MAFG and SAMD3 were in the core of network. These six genes correlated to the survival rate of septic patients and they were mainly expressed in T cells, except that MAFG was located in monocyte cell. The expression levels of six key genes were confirmed by animal and clinical samples. BCL9L, BCL11B, CD247, CD96 and SAMD3 were decreased in sepsis and mainly expressed in the T cell; while MAFG increased in sepsis and localizes to monocytes. These genes may be therapeutic targets for sepsis.
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Liao S, Lin Y, Liu L, Yang S, Lin Y, He J, Shao Y. ADAM10-a "multitasker" in sepsis: focus on its posttranslational target. Inflamm Res 2023; 72:395-423. [PMID: 36565333 PMCID: PMC9789377 DOI: 10.1007/s00011-022-01673-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 07/25/2022] [Accepted: 11/30/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Sepsis has a complex pathogenesis in which the uncontrolled systemic inflammatory response triggered by infection leads to vascular barrier disruption, microcirculation dysfunction and multiple organ dysfunction syndrome. Numerous recent studies reveal that a disintegrin and metalloproteinase 10 (ADAM10) acts as a "molecular scissor" playing a pivotal role in the inflammatory response during sepsis by regulating proteolysis by cleaving various membrane protein substrates, including proinflammatory cytokines, cadherins and Notch, which are involved in intercellular communication. ADAM10 can also act as the cellular receptor for Staphylococcus aureus α-toxin, leading to lethal sepsis. However, its substrate-specific modulation and precise targets in sepsis have not yet to be elucidated. METHODS We performed a computer-based online search using PubMed and Google Scholar for published articles concerning ADAM10 and sepsis. CONCLUSIONS In this review, we focus on the functions of ADAM10 in sepsis-related complex endothelium-immune cell interactions and microcirculation dysfunction through the diversity of its substrates and its enzymatic activity. In addition, we highlight the posttranslational mechanisms of ADAM10 at specific subcellular sites, or in multimolecular complexes, which will provide the insight to intervene in the pathophysiological process of sepsis caused by ADAM10 dysregulation.
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Affiliation(s)
- Shuanglin Liao
- grid.410560.60000 0004 1760 3078The Intensive Care Unit, The First Dongguan Affiliated Hospital, Guangdong Medical University, Jiaoping Road 42, Tangxia Town, Dongguan, 523710 Guangdong China
| | - Yao Lin
- The Key Laboratory of Organ Dysfunction and Protection Translational Medicine, Jieyang Medical Research Center, Jieyang People’s Hospital, Tianfu Road 107, Rongcheng District, Jieyang, 522000 Guangdong China
| | - Lizhen Liu
- grid.410560.60000 0004 1760 3078The Intensive Care Unit, The First Dongguan Affiliated Hospital, Guangdong Medical University, Jiaoping Road 42, Tangxia Town, Dongguan, 523710 Guangdong China
| | - Shuai Yang
- grid.410560.60000 0004 1760 3078The Intensive Care Unit, The First Dongguan Affiliated Hospital, Guangdong Medical University, Jiaoping Road 42, Tangxia Town, Dongguan, 523710 Guangdong China
| | - YingYing Lin
- The Key Laboratory of Organ Dysfunction and Protection Translational Medicine, Jieyang Medical Research Center, Jieyang People’s Hospital, Tianfu Road 107, Rongcheng District, Jieyang, 522000 Guangdong China
| | - Junbing He
- The Key Laboratory of Organ Dysfunction and Protection Translational Medicine, Jieyang Medical Research Center, Jieyang People’s Hospital, Tianfu Road 107, Rongcheng District, Jieyang, 522000 Guangdong China
| | - Yiming Shao
- grid.410560.60000 0004 1760 3078The Intensive Care Unit, The First Dongguan Affiliated Hospital, Guangdong Medical University, Jiaoping Road 42, Tangxia Town, Dongguan, 523710 Guangdong China
- grid.410560.60000 0004 1760 3078The Key Laboratory of Sepsis Translational Medicine, Guangdong Medical University, Zhanjiang, Guangdong China
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Bai Y, Zhao N, Zhang Z, Jia Y, Zhang G, Dong G. Identification and validation of a novel four-gene diagnostic model for neonatal early-onset sepsis with bacterial infection. Eur J Pediatr 2023; 182:977-985. [PMID: 36527479 PMCID: PMC10023633 DOI: 10.1007/s00431-022-04753-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/25/2022] [Accepted: 12/03/2022] [Indexed: 12/23/2022]
Abstract
Neonatal early-onset sepsis (EOS) has unfortunately been the third leading cause of neonatal death worldwide. The current study is aimed at discovering reliable biomarkers for the diagnosis of neonatal EOS through transcriptomic analysis of publicly available datasets. Whole blood mRNA expression profiling of neonatal EOS patients in the GSE25504 dataset was downloaded and analyzed. The binomial LASSO model was constructed to select genes that most accurately predicted neonatal EOS. Then, ROC curves were generated to assess the performance of the predictive features in differentiating between neonatal EOS and normal infants. Finally, the miRNA-mRNA network was established to explore the potential biological mechanisms of genes within the model. Four genes (CST7, CD3G, CD247, and ANKRD22) were identified that most accurately predicted neonatal EOS and were subsequently used to construct a diagnostic model. ROC analysis revealed that this diagnostic model performed well in differentiating between neonatal EOS and normal infants in both the GSE25504 dataset and our clinical cohort. Finally, the miRNA-mRNA network consisting of the four genes and potential target miRNAs was constructed. Through bioinformatics analysis, a diagnostic four-gene model that can accurately distinguish neonatal EOS in newborns with bacterial infection was constructed, which can be used as an auxiliary test for diagnosing neonatal EOS with bacterial infection in the future. CONCLUSION In the current study, we analyzed gene expression profiles of neonatal EOS patients from public databases to develop a genetic model for predicting sepsis, which could provide insight into early molecular changes and biological mechanisms of neonatal EOS. WHAT IS KNOWN • Infants with suspected EOS usually receive empiric antibiotic therapy directly after birth. • When blood cultures are negative after 48 to 72 hours, empirical antibiotic treatment is often halted. Needless to say, this is not a short time. Additionally, because of the concern for inadequate clinical sepsis production and the limited sensitivity of blood cultures, the duration of antibiotic therapy for the kid is typically extended. WHAT IS NEW • We established a 4-gene diagnostic model of neonatal EOS with bacterial infection by bioinformatics analysis method. The model has better diagnostic performance compared with conventional inflammatory indicators such as CRP, Hb, NEU%, and PCT.
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Affiliation(s)
- Yong Bai
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children's Infection and Immunity, Zhengzhou, China
| | - Na Zhao
- Department of Pathology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Zhenhua Zhang
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children's Infection and Immunity, Zhengzhou, China
| | - Yangjie Jia
- Department of Pathology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Genhao Zhang
- Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Geng Dong
- Children's Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children's Infection and Immunity, Zhengzhou, China.
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Xiu CD, Ying LX, Chun HY, Fu LJ. Advances in CD247. Scand J Immunol 2022; 96:e13170. [PMID: 35388926 DOI: 10.1111/sji.13170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/27/2022] [Accepted: 04/04/2022] [Indexed: 11/27/2022]
Abstract
CD247, which is also known as CD3ζ, CD3H, CD3Q, CD3Z, IMD25, T3Z, and TCRZ, encodes CD3ζ protein, which is expressed primarily in natural killer (NK) and T cells. Since the discovery of the ζ peptide in 1986, it has been continuously investigated. In this paper, we review the composition, molecular mechanisms and regulatory factors of CD247 expression in T cells; and review the autoimmune diseases, tumors and inflammatory diseases associated with CD247, providing a detailed and comprehensive reference for further research on the mechanism of CD247 and related diseases.
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Affiliation(s)
- Chen De Xiu
- Department of Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Lei Xian Ying
- Department of Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Hu Ying Chun
- Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Li Jia Fu
- Department of Cardiology, The Affiliated Hospital of Southwest Medical University, Key Laboratory of Medical Electrophysiology, Ministry of Education, Luzhou, Sichuan, China
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10
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Jiang Y, Miao Q, Hu L, Zhou T, Hu Y, Tian Y. FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis. Comb Chem High Throughput Screen 2021; 25:1722-1730. [PMID: 34397323 DOI: 10.2174/1386207324666210816123508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 06/11/2021] [Accepted: 06/27/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. MATERIAL AND METHODS GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. RESULTS A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. CONCLUSION FYN and CD247 are expected to become new biomarkers of septic shock.
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Affiliation(s)
- Yue Jiang
- Department of Clinical Medicine, Affiliated of Southwest Medical University, Luzhou, 646000, China
| | - Qian Miao
- Department of Clinical Medicine, Affiliated of Southwest Medical University, Luzhou, 646000, China
| | - Lin Hu
- Department of Pediatrics, people's Hospital of Lushan County, Ya'an, 625600. 0
| | - Tingyan Zhou
- Department of Clinical Medicine, Affiliated of Southwest Medical University, Luzhou, 646000, China
| | - Yingchun Hu
- Department of Emergency, Affiliated of Southwest Medical University, 646000, China
| | - Ye Tian
- Department of Emergency, Affiliated of Southwest Medical University, 646000, China
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Zeng X, Feng J, Yang Y, Zhao R, Yu Q, Qin H, Wei L, Ji P, Li H, Wu Z, Zhang J. Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis. J Inflamm Res 2021; 14:829-841. [PMID: 33737824 PMCID: PMC7962593 DOI: 10.2147/jir.s301663] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 02/26/2021] [Indexed: 12/20/2022] Open
Abstract
Objective Sepsis is a disease associated with high mortality. We performed bioinformatic analysis to identify key biomarkers associated with sepsis and septic shock. Methods The top 20% of genes showing the greatest variance between sepsis and controls in the GSE13904 dataset (children) were screened by co-expression network analysis. The differentially expressed genes (DEGs) were identified through analyzing differential gene expression between sepsis patients and control in the GSE13904 (children) and GSE154918 (adult) data sets. Intersection analysis of module genes and DEGs was performed to identify common DEGs for enrichment analysis, protein-protein interaction network (PPI network) analysis, and Short Time-series Expression Miner (STEM) analysis. The PPI network genes were ranked by degree of connectivity, and the top 100 sepsis-associated genes were identified based on the area under the receiver operating characteristic curve (AUC). In addition, we evaluated differences in immune cell infiltration between sepsis patients and controls in children (GSE13904, GSE25504) and adults (GSE9960, GSE154918). Finally, we analyzed differences in DNA methylation levels between sepsis patients and controls in GSE138074 (adults). Results The common genes were associated mainly with up-regulated inflammatory and metabolic responses, as well as down-regulated immune responses. Sepsis patients showed lower infiltration by most types of immune cells. Genes in the PPI network with AUC values greater than 0.9 in both GSE13904 (children) and GSE154918 (adults) were screened as key genes for diagnosis. These key genes (MAPK14, FGR, RHOG, LAT, PRKACB, UBE2Q2, ITK, IL2RB, and CD247) were also identified in STEM analysis to be progressively dysregulated across controls, sepsis patients and patients with septic shock. In addition, the expression of MAPK14, FGR, and CD247 was modified by methylation. Conclusion This study identified several potential diagnostic genes and inflammatory and metabolic responses mechanisms associated with the development of sepsis.
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Affiliation(s)
- Xiaoliang Zeng
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Jihua Feng
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Yanli Yang
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Ruzhi Zhao
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Qiao Yu
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Han Qin
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Lile Wei
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Pan Ji
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Hongyuan Li
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Zimeng Wu
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
| | - Jianfeng Zhang
- Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China
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