Daily Sepsis Research Analysis
Analyzed 15 papers and selected 3 impactful papers.
Summary
Analyzed 15 papers and selected 3 impactful articles.
Selected Articles
1. Integrated immune and endothelial profiling predicts 90-day mortality in postoperative sepsis and septic shock.
In a prospective multicenter cohort of 219 postoperative patients, high-dimensional immune and endothelial profiling was used to derive a cellular risk score that predicted 90-day mortality in sepsis and septic shock. The score, built via Cox and LASSO modeling, improved prognostic stratification compared with conventional scores (SOFA, APACHE II) and was supported by validation using public single-cell RNA datasets.
Impact: This study delivers a data-driven cellular signature that enhances mortality prediction in postoperative sepsis, directly addressing a key gap in precision risk stratification. It integrates immune and endothelial biology, offering mechanistic and translational value.
Clinical Implications: The cellular risk score could inform early triage, monitoring intensity, and enrollment in targeted trials aimed at immune–endothelial homeostasis restoration. It may complement, and potentially improve upon, standard severity scores.
Key Findings
- High-dimensional spectral flow cytometry identified immune and endothelial subsets associated with 90-day mortality in postoperative sepsis/septic shock.
- A multivariable LASSO-Cox–derived cellular risk score improved prognostic stratification compared with SOFA and APACHE II (ROC and survival analyses).
- Public single-cell RNA datasets corroborated the identified cellular signatures.
Methodological Strengths
- Prospective multicenter cohort with high-dimensional spectral flow cytometry and both supervised and unsupervised analyses (UMAP, FlowSOM).
- Rigorous statistical modeling (multivariable Cox, LASSO-Cox), comparison against established scores, and external validation via public single-cell datasets.
Limitations
- Moderate sample size and focus on postoperative sepsis may limit generalizability.
- Lack of independent external clinical cohort validation and no interventional testing of biomarker-guided management.
Future Directions: Validate the cellular risk score in larger, diverse sepsis cohorts; assess clinical utility in decision-support; and test therapies targeting immune–endothelial dysregulation.
BACKGROUND: Sepsis and septic shock remain major causes of mortality in critically ill postoperative patients, largely because of the lack of reliable biomarkers for early risk stratification. The interplay between immune dysfunction and endothelial activation is key in the progression to multiorgan failure, however phenotypic characterisation of circulating endothelial subpopulations remains limited. METHODS: A Prospective multicentre study included 219 postoperative patients (Non-septic ICU patients, sepsis, septic shock). Peripheral Blood Mononuclear Cells were analysed using high-dimensional spectral flow cytometry. Both supervised gating strategies and high-dimensional unsupervised analyses (UMAP and FlowSOM) were applied to identify immune and endothelial cell subsets. Associations with 90-day mortality were assessed using univariate and multivariate Cox proportional hazards models, refined with LASSO-Cox regression, and integrated into a risk score. The predictive performance of this cellular risk score was compared with SOFA and APACHE II scores using ROC curves and survival analysis. Findings were further validated using publicly available single-cell RNA datasets. FINDINGS: Two B cell subsets (plasmablasts/IgG INTERPRETATION: The combination of immune and endothelial profiling provides a robust cellular signature that improves the prognostic stratification in postoperative sepsis. These biomarkers may support treatment and guide therapeutic strategies aimed at restoring immune-endothelial homoeostasis. FUNDING: This work was supported by the Instituto de Salud Carlos III [grant numbers: PI24/00754, FI25/00242 and CIBERINFEC CB21/13/00051], Junta de Castilla y León [GRS 2782/A2/2023, GRS 2804/A1/2023].
2. Glucagon-like peptide-1 receptor agonists and cardiovascular outcomes in dialysis patients with type 2 diabetes: a real-world propensity score-matched study.
In 1,688 matched pairs of dialysis patients with type 2 diabetes, GLP-1 receptor agonists were associated with lower risks of MACE (HR 0.88), all-cause mortality (HR 0.84), myocardial infarction (HR 0.84), heart failure (HR 0.87), and sepsis (HR 0.81) versus DPP-4 inhibitors. Hospitalization and emergency visits were also reduced, with consistent results across sensitivity and subgroup analyses.
Impact: Provides real-world evidence in a high-risk, understudied population that GLP-1 RAs may confer cardiovascular and infection risk benefits, informing therapeutic choices in dialysis patients.
Clinical Implications: Clinicians may consider GLP-1 receptor agonists for dialysis patients with T2DM to improve cardiovascular outcomes and reduce sepsis risk, while awaiting randomized trials to confirm causality and safety in ESKD.
Key Findings
- GLP-1 receptor agonists reduced MACE versus DPP-4 inhibitors (HR 0.88, 95% CI 0.78–0.99).
- Lower risks observed for all-cause mortality (HR 0.84), myocardial infarction (HR 0.84), heart failure (HR 0.87), and sepsis (HR 0.81).
- Healthcare utilization (hospitalizations and emergency visits) was reduced; findings were consistent across sensitivity and subgroup analyses.
Methodological Strengths
- Large real-world dataset with propensity score matching to balance baseline covariates.
- Multiple clinically relevant endpoints with consistent sensitivity and subgroup analyses.
Limitations
- Retrospective observational design with potential residual confounding and selection bias.
- Exposure misclassification and lack of detailed dosing/adherence data; causality cannot be inferred.
Future Directions: Randomized controlled trials in dialysis populations to confirm cardiovascular and infection benefits of GLP-1 RAs; mechanistic studies on infection risk reduction.
AIMS: Patients with type 2 diabetes mellitus (T2DM) undergoing dialysis have extremely high cardiovascular and mortality risks, yet evidence for effective therapies is limited. The benefits of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in this population remain unclear. METHODS: We conducted a retrospective, propensity score-matched cohort study using the TriNetX US Collaborative Network (2013-2022). Among 1688 matched pairs of new GLP-1 RA and dipeptidyl peptidase-4 inhibitor (DPP-4i) users, the primary outcome was major adverse cardiovascular events (MACE), with secondary outcomes of all-cause mortality, heart failure, sepsis, hospitalization, and emergency visits. RESULTS: GLP-1 RA users experienced significantly lower risk of MACE compared with DPP-4i users [hazard ratio (HR) 0.88, 95% confidence interval (CI) 0.78-0.99]. GLP-1 RAs were also associated with reduced all-cause mortality (HR 0.84, 95% CI 0.72-0.99), myocardial infarction (HR 0.84, 95% CI 0.70-0.99), heart failure (HR 0.87, 95% CI 0.78-0.98), and sepsis (HR 0.81, 95% CI 0.71-0.92). Healthcare utilization outcomes, such as hospitalization and emergency visits, were also reduced. Findings were consistent across sensitivity and subgroup analyses. CONCLUSIONS: In this large real-world dialysis cohort, GLP-1 RAs were associated with improved cardiovascular outcomes, survival, infection risk, and healthcare utilization, supporting their potential role in T2DM patients receiving dialysis.
3. LDHA, BIK, and CNIH4 Are Diagnostic Markers of Endoplasmic Reticulum Stress in Lung Cancer Comorbid With Sepsis: Integrating Machine Learning and Single-Cell Analysis of Immune Signaling.
Integrating GEO transcriptomics, machine learning, and single-cell analyses, the authors identified ER-stress markers LDHA, BIK, and CNIH4 as diagnostic biomarkers in lung cancer with sepsis (AUC ≥ 0.7). These proteins were upregulated in clinical tissues, further increased with sepsis, and their knockdown reduced PC9 cell migration/invasion. Molecular docking suggested tetrahydro-NAD and amikacin as candidate therapeutics.
Impact: Provides experimentally validated biomarkers linking ER stress to immune dysregulation in cancer–sepsis comorbidity, opening avenues for diagnostic development and therapeutic targeting.
Clinical Implications: These markers may enable earlier identification of lung cancer patients at risk when sepsis co-occurs and suggest ER-stress–focused interventions; however, clinical validation and interventional trials are needed.
Key Findings
- Machine learning identified LDHA, BIK, and CNIH4 (AUC ≥ 0.7) as ER-stress–related diagnostic biomarkers in lung cancer with sepsis.
- Protein expression of these markers was upregulated in lung cancer and further increased with sepsis; knockdown reduced PC9 cell migration and invasion.
- Single-cell analyses linked these markers to cell-type–specific expression and immune dysregulation; docking highlighted tetrahydro-NAD and amikacin as candidates.
Methodological Strengths
- Integration of DEG, WGCNA, PPI, exhaustive ML with ROC evaluation, and single-cell cross-validation.
- Experimental validation in clinical tissue cohorts and functional assays (wound healing, Transwell) in PC9 cells.
Limitations
- Reliance on public transcriptomic datasets with potential batch and selection biases; diagnostic performance needs external clinical validation.
- No in vivo validation; therapeutic candidates from docking lack pharmacologic confirmation.
Future Directions: Prospective clinical validation of biomarker panels, development of ER-stress–targeted diagnostics/therapeutics, and in vivo efficacy testing of candidate agents.
BACKGROUND: Lung cancer is intricately associated with the onset of sepsis. Endoplasmic reticulum (ER) stress (ERS) is a cellular stress response to aberrant protein folding in the ER, closely associated with the cellular immune response. Currently, numerous research have elucidated the correlation between ERS and lung cancer, as well as sepsis. The mechanism of ERS in lung cancer comorbid with sepsis requires more investigation. OBJECTIVES: This study aimed to investigate the interacting mechanisms between ERS and the immune response, explore prospective ERS-related diagnostic biomarkers for lung cancer comorbid with sepsis, and elucidate their underlying pathological roles. METHODS: Datasets for lung cancer and sepsis were sourced from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and weighted gene coexpression network analysis (WGCNA) modules were intersected with ERS-related genes. Protein-protein interaction (PPI) and enrichment analyses were conducted. A genetic diagnostic model was developed using exhaustive machine learning algorithms, with accuracy assessed by receiver operating characteristic (ROC) curves and confusion matrices. Hub genes (area under the curve [AUC] ≥ 0.7) were analyzed for immune cell infiltration and cross-validated using single-cell RNA sequencing datasets. Crucially, the expression and functional roles of the hub genes were experimentally validated by western blot in clinical tissue cohorts (adjacent normal, lung cancer, and lung cancer with sepsis) and via wound healing and Transwell assays in PC9 lung cancer cells. Finally, prospective therapeutic agents were identified through molecular docking. RESULTS: Machine learning identified lactate dehydrogenase A (LDHA), Bcl-2 interacting killer (BIK), and cornichon homolog 4 (CNIH4) as robust diagnostic biomarkers. Western blot analysis confirmed that the protein expression levels of LDHA, BIK, and CNIH4 were significantly upregulated in lung cancer and further elevated in the lung cancer comorbid with sepsis group. In vitro functional assays demonstrated that silencing these genes significantly inhibited the migration and invasion capabilities of PC9 cells. Single-cell analysis revealed that these markers exhibit cell-type-specific expression in malignant cells and regulate immune dysregulation, particularly correlating with the functions of plasma cells and monocytes. Molecular docking indicated that tetrahydro-NAD and amikacin are promising therapeutic candidates. CONCLUSIONS: We identified and experimentally validated LDHA, BIK, and CNIH4 as specific ERS-associated diagnostic biomarkers for lung cancer comorbid with sepsis. These markers drive tumor progression and modulate cellular immune responses, providing novel insights and therapeutic targets for this comorbidity.