Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis

Identification of amino acid metabolism‑related genes as diagnostic and prognostic biomarkers in sepsis through machine learning

Previous research has highlighted the critical role of amino acid metabolism (AAM) in the pathophysiology of sepsis. The present study aimed to explore the potential diagnostic and prognostic value of AAM-related genes (AAMGs) in sepsis, as well as their underlying molecular mechanisms. Gene expression profiles from the Gene Expression Omnibus (GSE65682, GSE185263 and GSE154918 datasets) were analyzed. Based on weighted gene co-expression network analysis and machine learning algorithms, hub AAMGs were identified in the GSE65682 database. Subsequently, hub AAMGs were evaluated for their expression levels and diagnostic and prognostic significance in sepsis, as well as their interactions with regulatory pathways and role in immune cell infiltration. Additionally, trends in AAMG expression were validated using clinical samples, and their functions in sepsis were confirmed through an in vitro model. In total, four AAMGs were identified, two of which, methionine synthase (MTR) and methionine-R-isomerase 1 (MRI1), demonstrated significant differential expression in the GSE65682, GSE185263 and GSE154918 datasets, which was further validated using clinical samples. A diagnostic nomogram based on MTR and MRI1 expression demonstrated strong diagnostic effectiveness across the three aforementioned databases. Moreover, the expression of both genes were negatively correlated with sepsis prognosis and showed stratified prognostic capabilities. Newly identified pathways included KRAS and IL-2/STAT5 signaling. MTR and MRI1 negatively correlated with the infiltration of inflammatory cells, such as M1 macrophages and neutrophils, and positively correlated with anti-inflammatory cells, such as CD8+ T and dendritic cells. In vitro experiments further demonstrated that overexpression of MTR could mitigate the inhibition of cloning and proliferation induced by LPS and ATP in RAW 264.7 cells. These findings highlighted the potential of MTR and MRI1 as biomarkers for diagnosing and prognosticating sepsis, potentially acting through the regulation of methionine in the pathophysiology of this disease. The present study provided new insights into the role of AAM in the mechanisms underlying sepsis and in the potential development of future targeted therapies.

The Diagnostic Utility of Host RNA Biosignatures in Adult Patients With Sepsis: A Systematic Review and Meta-Analysis

OBJECTIVES

Sepsis is a life-threatening medical emergency, with a profound healthcare burden globally. Its pathophysiology is complex, heterogeneous and temporally dynamic, making diagnosis challenging. Medical management is predicated on early diagnosis and timely intervention. Transcriptomics is one of the novel "-omics" technologies being evaluated for recognition of sepsis. Our objective was to evaluate the performance of host gene expression biosignatures for the diagnosis of all-cause sepsis in adults.

DATA SOURCES

PubMed/Ovid Medline, Ovid Embase, and Cochrane databases from inception to June 2023.

STUDY SELECTION

We included studies evaluating the performance of host gene expression biosignatures in adults who were diagnosed with sepsis using existing clinical definitions. Controls where applicable were patients without clinical sepsis.

DATA EXTRACTION

Data including population demographics, sample size, study design, tissue specimen, type of transcriptome, health status of comparator group, and performance of transcriptomic biomarkers were independently extracted by at least two reviewers.

DATA SYNTHESIS

Meta-analysis to describe the performance of host gene expression biosignatures for the diagnosis of sepsis in adult patients was performed using the random-effects model. Risk of bias was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A total of 117 studies (n = 17,469), comprising 132 separate patient datasets, were included in our final analysis. Performance of transcriptomics for the diagnosis of sepsis against pooled controls showed area under the receiver operating characteristic curve (AUC, 0.86; 95% CI, 0.84-0.88). Studies using healthy controls showed AUC 0.87 (95% CI, 0.84-0.89), while studies using controls with systemic inflammatory response syndrome (SIRS) had AUC 0.84 (95% CI, 0.78-0.90). Transcripts with excellent discrimination against SIRS controls include UrSepsisModel, a 210 differentially expressed genes biosignature, microRNA-143, and Septicyte laboratory.

CONCLUSIONS

Transcriptomics is a promising approach for the accurate diagnosis of sepsis in adults and demonstrates good discriminatory ability against both healthy and SIRS control subjects.

Upregulation of CRISP3 and its clinical values in adult sepsis: a comprehensive analysis based on microarrays and a two-retrospective-cohort study

Background

Current lines of evidence indicate that cysteine-rich secretory protein 3 (CRISP3) is an immunoregulatory factor. Nevertheless, no study has explored the relationships between the values of CRISP3 and sepsis.

Methods

We conducted a comprehensive literature search and meta-analysis from the Gene Expression Omnibus (GEO) and ArrayExpress to determine the expression of CRISP3 in sepsis patients. Then, we explored whether plasma CRISP3 could serve as a potential biomarker to predict the risk of sepsis via two retrospective trauma cohorts. We evaluated the prediction power using the area under the curve (AUC).

Results

A total of 23 datasets were recruited for the comprehensive meta-analysis, and the combined standardized mean difference (SMD) of CRISP3 was 0.90 (0.50-1.30) (p < 0.001), suggesting that CRISP3 was overexpressed in sepsis patients. Meanwhile, sepsis patients had higher CRISP3 concentrations than non-sepsis patients in 54 trauma patients (p < 0.001). Plasma CRISP3 on admission was significantly associated with the incidence of sepsis [OR = 1.004 (1.002-1.006), p < 0.001]. As a predictive biomarker, CRISP3 obtained a better AUC [0.811 (0.681-0.905)] than C-reactive protein (CRP) [0.605 (0.463-0.735)], procalcitonin (PCT) [0.554 (0.412-0.689)], and Sequential Organ Failure Assessment (SOFA) [0.754 (0.618-0.861)]. Additionally, the clinical relationships between plasma CRISP3 and sepsis were verified in another trauma cohort with 166 patients [OR = 1.002 (1.001-1.003), p < 0.001]. The AUC of CRISP3 was 0.772 (0.701-0.834), which was better than that of CRP [0.521 (0.442-0.599)] and PCT [0.531 (0.452-0.609)], but not SOFA [0.791 (0.717-0.853)].

Conclusion

Our study indicated and validated that CRISP3 was highly expressed in sepsis. More importantly, CRISP3 may serve as a latent biomarker to predict the risk of sepsis.

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study

This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for sepsis diagnosis. Methods include differential expression analysis, weighted gene co-expression network analysis (WGCNA) for identifying sepsis-related modules and hub genes, and functional enrichment analyses to explore the roles of hub genes. Machine learning algorithms identify 3 diagnostic genes - CD177, LDHA, and MCEMP1 - consistently highly expressed in sepsis patients. The nomogram model effectively predicts sepsis risk, supported by receiver operator characteristic (ROC) curves. Correlations between diagnostic genes and immune cell infiltration are observed. MR analysis reveals a positive causal relationship between MCEMP1 and sepsis risk. In conclusion, this study presents potential sepsis diagnostic biomarkers, highlighting the genetic association of MCEMP1 with sepsis for insights into early diagnosis.

From Molecular Mechanisms to Clinical Therapy: Understanding Sepsis-Induced Multiple Organ Dysfunction

Sepsis-induced multiple organ dysfunction arises from the highly complex pathophysiology encompassing the interplay of inflammation, oxidative stress, endothelial dysfunction, mitochondrial damage, cellular energy failure, and dysbiosis. Over the past decades, numerous studies have been dedicated to elucidating the underlying molecular mechanisms of sepsis in order to develop effective treatments. Current research underscores liver and cardiac dysfunction, along with acute lung and kidney injuries, as predominant causes of mortality in sepsis patients. This understanding of sepsis-induced organ failure unveils potential therapeutic targets for sepsis treatment. Various novel therapeutics, including melatonin, metformin, palmitoylethanolamide (PEA), certain herbal extracts, and gut microbiota modulators, have demonstrated efficacy in different sepsis models. In recent years, the research focus has shifted from anti-inflammatory and antioxidative agents to exploring the modulation of energy metabolism and gut microbiota in sepsis. These approaches have shown a significant impact in preventing multiple organ damage and mortality in various animal sepsis models but require further clinical investigation. The accumulation of this knowledge enriches our understanding of sepsis and is anticipated to facilitate the development of effective therapeutic strategies in the future.

Integrated multi-omics and artificial intelligence to explore new neutrophils clusters and potential biomarkers in sepsis with experimental validation

Background

Sepsis, causing serious organ and tissue damage and even death, has not been fully elucidated. Therefore, understanding the key mechanisms underlying sepsis-associated immune responses would lead to more potential therapeutic strategies.

Methods

Single-cell RNA data of 4 sepsis patients and 2 healthy controls in the GSE167363 data set were studied. The pseudotemporal trajectory analyzed neutrophil clusters under sepsis. Using the hdWGCNA method, key gene modules of neutrophils were explored. Multiple machine learning methods were used to screen and validate hub genes for neutrophils. SCENIC was then used to explore transcription factors regulating hub genes. Finally, quantitative reverse transcription-polymerase chain reaction was to validate mRNA expression of hub genes in peripheral blood neutrophils of two mice sepsis models.

Results

We discovered two novel neutrophil subtypes with a significant increase under sepsis. These two neutrophil subtypes were enriched in the late state during neutrophils differentiation. The hdWGCNA analysis of neutrophils unveiled that 3 distinct modules (Turquoise, brown, and blue modules) were closely correlated with two neutrophil subtypes. 8 machine learning methods revealed 8 hub genes with high accuracy and robustness (ALPL, ACTB, CD177, GAPDH, SLC25A37, S100A8, S100A9, and STXBP2). The SCENIC analysis revealed that APLP, CD177, GAPDH, S100A9, and STXBP2 were significant associated with various transcriptional factors. Finally, ALPL, CD177, S100A8, S100A9, and STXBP2 significantly up regulated in peripheral blood neutrophils of CLP and LPS-induced sepsis mice models.

Conclusions

Our research discovered new clusters of neutrophils in sepsis. These five hub genes provide novel biomarkers targeting neutrophils for the treatment of sepsis.

Comprehensive modular analyses of scar subtypes illuminate underlying molecular mechanisms and potential therapeutic targets

Pathological scarring resulting from traumas and wounds, such as hypertrophic scars and keloids, pose significant aesthetic, functional and psychological challenges. This study provides a comprehensive transcriptomic analysis of these conditions, aiming to illuminate underlying molecular mechanisms and potential therapeutic targets. We employed a co-expression and module analysis tool to identify significant gene clusters associated with distinct pathophysiological processes and mechanisms, notably lipid metabolism, sebum production, cellular energy metabolism and skin barrier function. This examination yielded critical insights into several skin conditions including folliculitis, skin fibrosis, fibrosarcoma and congenital ichthyosis. Particular attention was paid to Module Cluster (MCluster) 3, encompassing genes like BLK, TRPV1 and GABRD, all displaying high expression and potential implications in immune modulation. Preliminary immunohistochemistry validation supported these findings, showing elevated expression of these genes in non-fibrotic samples rich in immune activity. The complex interplay of different cell types in scar formation, such as fibroblasts, myofibroblasts, keratinocytes and mast cells, was also explored, revealing promising therapeutic strategies. This study underscores the promise of targeted gene therapy for pathological scars, paving the way for more personalised therapeutic approaches. The results necessitate further research to fully ascertain the roles of these identified genes and pathways in skin disease pathogenesis and potential therapeutics. Nonetheless, our work forms a strong foundation for a new era of personalised medicine for patients suffering from pathological scarring.