Construction of Sepsis Diagnostic Models and Identification of Macrophage Subpopulations Based on Pyroptosis-Related Genes

Sun, Zhaoqing; Tao, Zhen; Ning, Caihong; Shen, Dingcheng; Pei, Wei; Zhou, Ruihao; Zhu, Sipin; Huang, Gengwen

doi:10.1097/shk.0000000000002137

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…Several studies have shown that these genes are closely associated with sepsis. For example, ELANE has been identified as a component of the diagnostic gene signatures in several studies [ [39] , [40] , [41] ]. In addition, ELANE serves as a target of miR-608 in human monocytes and participates in the process of inflammation; therefore, it is a novel target for the diagnosis or treatment of sepsis [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

Zeng,

Jian,

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

Zeng,

Jian,

et al. 2024

Heliyon

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A machine learning model derived from analysis of time-course gene-expression datasets reveal temporally stable gene markers predictive of sepsis mortality

Huang,

Atreya,

Holder

et al. 2023

Shock

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Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units (ICU) and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor outcomes remains a critical challenge. Here, we performed secondary analysis of publicly available time-series gene-expression datasets from peripheral blood of patients admitted to the ICU to elucidate temporally stable gene expression markers between sepsis survivors and non-survivors. Using a limited set of genes that were determined to be temporally stable, we derived a dynamical model using a Support Vector Machine (SVM) classifier to accurately predict the mortality of sepsis patients. Our model had robust performance in a test dataset, where patients’ transcriptome was sampled at alternate time points, with an area under the curve (AUC) of 0.89 (95% CI: 0.82-0.96) upon 5-fold cross-validation. We also identified 7 potential biomarkers of sepsis mortality (STAT5A, CX3CR1, LCP1, SNRPG, RPS27L, LSM5, SHCBP1 that require future validation. Pending prospective testing, our model may be used to identify sepsis patients with high risk of mortality accounting for the dynamic nature of the disease and with potential therapeutic implications.

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Single-Cell Transcriptome Analysis in Health and Disease

Bhattachan,

Jeschke

2023

Shock

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The analysis of the single-cell transcriptome has emerged as a powerful tool to gain insights of the basic mechanisms of health and disease. It is widely used to reveal the cellular diversity and complexity of tissues at cellular resolution by RNA sequencing of the whole transcriptome from a single cell. Equally, it is applied to discover an unknown, rare population of cells in the tissue. The prime advantage of single-cell transcriptome analysis is the detection of stochastic nature of gene expression of the cell in tissue. Moreover, the availability of multiple platforms for the single-cell transcriptome has broadened its approaches to using cells of different sizes and shapes, including the capture of short or full-length transcripts, which is helpful in the analysis of challenging biological samples. And with the development of numerous packages in R and Python, new directions in the computational analysis of single-cell transcriptomes can be taken to characterize healthy versus diseased tissues to obtain novel pathological insights. To further examine the biology of different cell types, downstream analysis such as differential gene expression analysis, GO term analysis, KEGG pathway analysis, cell-cell interaction analysis, and trajectory analysis has become standard practice in the workflow of single-cell transcriptome analysis. Here, we provide a broad overview of single-cell transcriptome analysis in health and disease conditions currently applied in various studies.

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Construction of Sepsis Diagnostic Models and Identification of Macrophage Subpopulations Based on Pyroptosis-Related Genes

Cited by 4 publications

References 40 publications

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

A machine learning model derived from analysis of time-course gene-expression datasets reveal temporally stable gene markers predictive of sepsis mortality

Single-Cell Transcriptome Analysis in Health and Disease

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