Identification of Potential Early Diagnostic Biomarkers of Sepsis

被引:28
|
作者
Li, Zhenhua [1 ,2 ]
Huang, Bin [2 ]
Yi, Wenfeng [2 ]
Wang, Fei [1 ]
Wei, Shizhuang [1 ]
Yan, Huaixing [1 ]
Qin, Pan [1 ]
Zou, Donghua [1 ]
Wei, Rongguo [3 ]
Chen, Nian [4 ]
机构
[1] Guangxi Med Univ, Dept Emergency Med, Affiliated Hosp 5, Nanning 530022, Peoples R China
[2] Guangxi Med Univ, Intens Care Unit, Affiliated Hosp 5, Nanning 530022, Peoples R China
[3] Guangxi Med Univ, Dept Clin Lab, Affiliated Hosp 5, Nanning 530022, Peoples R China
[4] Guangxi Med Univ, Dept Infect Dis, Affiliated Hosp 5, Nanning 530022, Peoples R China
关键词
sepsis; early diagnosis; LASSO model; SLC2A6; WGCNA; diagnostic biomarker; MORTALITY; LANDSCAPE; CELLS;
D O I
10.2147/JIR.S298604
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Objective: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival. Methods: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples. Results: A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells. Conclusion: In summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis.
引用
收藏
页码:621 / 631
页数:11
相关论文
共 50 条
  • [1] Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning
    Ke, Li
    Gao, Han
    Hu, Chang
    Zhang, Jiahao
    Zhao, Qiuyue
    Sun, Zhongyi
    Peng, Zhiyong
    [J]. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2023, 21 : 2316 - 2331
  • [2] Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis
    Lin, Guoxin
    Li, Nannan
    Liu, Jishi
    Sun, Jian
    Zhang, Hao
    Gui, Ming
    Zeng, Youjie
    Tang, Juan
    [J]. PEERJ, 2024, 12
  • [3] Identification of Immune-Related Key Genes as Potential Diagnostic Biomarkers of Sepsis in Children
    Wang, Huabin
    Huang, Junbin
    Yi, Wenfang
    Li, Jiahong
    He, Nannan
    Kang, Liangliang
    He, Zhijie
    Chen, Chun
    [J]. JOURNAL OF INFLAMMATION RESEARCH, 2022, 15 : 2441 - 2459
  • [4] Identification of potential biomarkers of sepsis using bioinformatics analysis
    Yang, Yu-Xia
    Li, Li
    [J]. EXPERIMENTAL AND THERAPEUTIC MEDICINE, 2017, 13 (05) : 1689 - 1696
  • [5] Identification of biomarkers for early diagnosis of sepsis associated AKI
    Bertocchi, Cristina
    Hasslacher, Julia
    Praxmarer, Verena
    Schmid, Stefan
    Dunzendorfer, Stefan
    Bellmann, Romuald
    Joannidis, Michael
    [J]. WIENER KLINISCHE WOCHENSCHRIFT, 2009, 121 (15-16) : A38 - A38
  • [6] Identification of potential early biomarkers of preeclampsia
    Timofeeva, Angelika V.
    Gusar, Vladyslava A.
    Kan, Nataliya E.
    Prozorovskaya, Kseniya N.
    Karapetyan, Anna O.
    Bayev, Oleg R.
    Chagovets, Vitaliy V.
    Kliver, Sergei F.
    Iakovishina, Daria Yu.
    Frankevich, Vladimir E.
    Sukhikh, Gennadiy T.
    [J]. PLACENTA, 2018, 61 : 61 - 71
  • [7] Immunologic biomarkers for diagnostic of early-onset neonatal sepsis
    Memar, Mohammad Yousef
    Alizadeh, Naser
    Varshochi, Mojtaba
    Kafil, Hossein Samadi
    [J]. JOURNAL OF MATERNAL-FETAL & NEONATAL MEDICINE, 2019, 32 (01): : 143 - 153
  • [8] Identification and diagnostic potential of serum microRNAs as biomarkers for early detection of Alzheimer's disease
    Han, Ying-Hao
    Xiang, Hong-Yi
    Lee, Dong Hun
    Feng, Lin
    Sun, Hu -Nan
    Jin, Mei-Hua
    Kwon, Taeho
    [J]. AGING-US, 2023, 15 (21): : 12085 - 12103
  • [9] Early-Onset Neonatal Sepsis: Inflammatory Biomarkers and MicroRNA as Potential Diagnostic Tools in Preterm Newborns
    Janec, Petr
    Mojzisek, Marek
    Panek, Martin
    Haluzik, Martin
    Zivny, Jan
    Janota, Jan
    [J]. FOLIA BIOLOGICA, 2023, 69 (5-6) : 173 - 180
  • [10] IDENTIFICATION OF POTENTIAL BIOMARKERS BY SERUM PROTEOMICS ANALYSIS IN RATS WITH SEPSIS
    Jiao, Jing
    Gao, Min
    Zhang, Huali
    Wang, Nian
    Xiao, Zihui
    Liu, Ke
    Yang, Mingshi
    Wang, Kangkai
    Xiao, Xianzhong
    [J]. SHOCK, 2014, 42 (01): : 75 - 81