Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation

作者全名："Huang, Xu; Tan, Jixiang; Chen, Xiaoying; Zhao, Lin"

作者地址："[Huang, Xu; Tan, Jixiang; Chen, Xiaoying; Zhao, Lin] Chongqing Med Univ, Affiliated Hosp 1, Dept Intens Care Unit, Chongqing, Peoples R China; [Zhao, Lin] Chongqing Med Univ, Affiliated Hosp 1, Dept Intens Care Unit, 1 Youyi Rd, Chongqing, Peoples R China"

通信作者："Zhao, L (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Intens Care Unit, 1 Youyi Rd, Chongqing, Peoples R China."

来源：INTERNATIONAL JOURNAL OF GENERAL MEDICINE

ESI学科分类：　

WOS号：WOS:000826220300001

JCR分区：Q2

影响因子：2.3

年份：2022

卷号：15

期号：　

开始页：6055

结束页：6071

文献类型：Article

关键词：sepsis; diagnostic biomarker; prognostic biomarkers

摘要："Purpose: Sepsis is a serious life-threatening condition characterised by multi-organ failure due to a disturbed immune response caused by severe infection. The pathogenesis of sepsis is unclear. The aim of this article is to identify potential diagnostic and prognostic biomarkers of sepsis to improve the survival of patients with sepsis. Methods: We downloaded 7 datasets from Gene Expression Omnibus database and screened the immune-related differential genes (IRDEGs). The related functions of IRDEGs were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). CIBERSORT was used to evaluate the infiltration of the immune cells, and Pearson algorithm of R software was used to calculate the correlation between the immune cell content and gene expression to screen the genes most related to immune cells in sepsis group, which were intersected with IRDEGs to obtain common genes. Key genes were identified from common genes based on the area under the receiver operating characteristic curve (AUC) greater than 0.8 in the 6 datasets. We then analyzed the predictive value of key genes in sepsis survival. Finally, we verified the expression of key genes in patients with sepsis by PCR analysis. Results: A total of 164 IRDEGs were obtained, which were associated mainly with inflammatory and immunometabolic responses. Ten key genes (IL1R2, LTB4R, S100A11, S100A12, SORT1, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) were identified with high diagnostic efficacy. Logistic regression analysis revealed that six of the key genes (LTB4R, S100A11, SORT1, RASGRP1, CD3G and CD8A) may affect the survival prognosis of sepsis. PCR analysis confirmed that the expression of seven key genes (IL1R2, S100A12, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) was consistent with microarray outcome. Conclusion: This study explored the immune and metabolic response mechanisms associated with sepsis, and identified ten potential diagnostic and six prognostic genes."

基金机构："Basic Science and Cutting -edge Technology Research Projects of Chongqing Science and Technology Commission [cstc2019jcyj-msxmX0848, cstc2020jcyj-msxmX0336]"

基金资助正文：Funding This study was supported by the Basic Science and Cutting -edge Technology Research Projects of Chongqing Science and Technology Commission (N.O. cstc2019jcyj-msxmX0848 and N.O. cstc2020jcyj-msxmX0336) .