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作为脓毒症预后生物标志物的杯状相关基因:对免疫功能和个性化免疫治疗的见解
Authors Zhang J, Wu Y , Du Y, Du Y, Bao D, Lu H, Zhou X , Li R , Pei H, She H , Mao Q
Received 29 January 2024
Accepted for publication 25 June 2024
Published 2 July 2024 Volume 2024:17 Pages 4229—4245
DOI https://doi.org/10.2147/JIR.S461766
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Ning Quan
Jun Zhang,1,* Yinyu Wu,1,* Yuanlin Du,1,* Yunxia Du,1 Daiqin Bao,1 Haibin Lu,2 Xiaoqiong Zhou,1 Rui Li,1 Haoyu Pei,1 Han She,1 Qingxiang Mao1
1Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China; 2Department of Intensive Care Unit, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Han She; Qingxiang Mao, Email mazuishehan@163.com; qxmao@tmmu.edu.cn
Background: This study aimed to discover diagnostic and prognostic biomarkers for sepsis immunotherapy through analyzing the novel cellular death process, cuproptosis.
Methods: We used transcriptome data from sepsis patients to identify key cuproptosis-related genes (CuRGs). We created a predictive model and used the CIBERSORT algorithm to observe the link between these genes and the septic immune microenvironment. We segregated sepsis patients into three subgroups, comparing immune function, immune cell infiltration, and differential analysis. Single-cell sequencing and real-time quantitative PCR were used to view the regulatory effect of CuRGs on the immune microenvironment and compare the mRNA levels of these genes in sepsis patients and healthy controls. We established a sepsis forecast model adapted to heart rate, body temperature, white blood cell count, and cuproptosis key genes. This was followed by a drug sensitivity analysis of cuproptosis key genes.
Results: Our results filtered three key genes (LIAS, PDHB, PDHA1) that impact sepsis prognosis. We noticed that the high-risk group had poorer immune cell function and lesser immune cell infiltration. We also discovered a significant connection between CuRGs and immune cell infiltration in sepsis. Through consensus clustering, sepsis patients were classified into three subgroups. The best immune functionality and prognosis was observed in subgroup B. Single-cell sequencing exposed that the key genes manage the immune microenvironment by affecting T cell activation. The qPCR results highlighted substantial mRNA level reduction of the three key genes in the SP compared to the HC. The prediction model, which combines CuRGs and traditional diagnostic indicators, performed better in accuracy than the other markers. The drug sensitivity analysis listed bisphenol A as highly sensitive to all the key genes.
Conclusion: Our study suggests these CuRGs may offer substantial potential for sepsis prognosis prediction and personalized immunotherapy.
Keywords: sepsis, cuproptosis, prognosis, immune cell infiltration, nomogram