Daily Sepsis Research Analysis
Analyzed 15 papers and selected 3 impactful papers.
Summary
Integrated immune–endothelial cellular profiling improved 90-day mortality prediction in postoperative sepsis beyond SOFA and APACHE II. In a real-world dialysis cohort, GLP-1 receptor agonists were associated with lower risks of MACE, mortality, heart failure, and sepsis versus DPP-4 inhibitors. ER stress–linked markers (LDHA, BIK, CNIH4) were identified and experimentally validated as diagnostic biomarkers for lung cancer comorbid with sepsis.
Research Themes
- Immune–endothelial prognostic signatures in sepsis
- Antidiabetic therapy and infection risk in dialysis
- ER stress biomarkers at the cancer–sepsis interface
Selected Articles
1. Integrated immune and endothelial profiling predicts 90-day mortality in postoperative sepsis and septic shock.
In a prospective multicentre cohort of postoperative ICU patients, high-dimensional spectral flow cytometry identified immune and endothelial cell subsets associated with 90-day mortality. A LASSO-Cox–derived cellular risk score outperformed SOFA and APACHE II and was supported by validation using public single-cell RNA datasets.
Impact: It proposes a mechanistically anchored prognostic signature that surpasses standard clinical scores, enabling precision risk stratification in postoperative sepsis.
Clinical Implications: Early incorporation of immune–endothelial cytometry could refine triage and inform therapies targeting immune–endothelial homeostasis, pending prospective external clinical validation and workflow integration.
Key Findings
- Integrated immune and endothelial profiling identified cellular subsets associated with 90-day mortality in postoperative sepsis/septic shock.
- A LASSO-Cox–derived cellular risk score outperformed SOFA and APACHE II by ROC and survival analyses.
- Findings were corroborated using publicly available single-cell RNA sequencing datasets.
Methodological Strengths
- Prospective multicentre design with high-dimensional spectral flow cytometry and both supervised and unsupervised analyses (UMAP, FlowSOM).
- Rigorous prognostic modeling (multivariable and LASSO-Cox) benchmarked against SOFA/APACHE II and supported by single-cell RNA validation.
Limitations
- Moderate sample size (n=219) may limit generalizability across sepsis phenotypes.
- External validation relied on public scRNA datasets rather than an independent prospective clinical cohort.
Future Directions: Prospective external validation, development of streamlined cytometry or transcriptomic surrogates for bedside use, and interventional trials testing immune–endothelial–guided therapies.
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 propensity-matched pairs of dialysis patients with T2DM, 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) compared with DPP-4 inhibitors. Hospitalizations and emergency visits were also reduced, with consistent findings across sensitivity and subgroup analyses.
Impact: It fills a critical evidence gap in dialysis, suggesting GLP-1 RAs may improve cardiovascular outcomes and reduce sepsis risk where therapeutic options are limited.
Clinical Implications: For dialysis patients with T2DM, GLP-1 RAs may be preferred over DPP-4 inhibitors when feasible, given associations with lower cardiovascular and sepsis risks; randomized trials in ESKD are needed to confirm causality and optimize dosing.
Key Findings
- GLP-1 RA use was associated with reduced MACE compared with DPP-4 inhibitors (HR 0.88, 95% CI 0.78–0.99).
- Lower risks were also 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, including hospitalizations and emergency visits, was reduced; results were consistent across sensitivity and subgroup analyses.
Methodological Strengths
- Large real-world dataset (TriNetX) with propensity score matching to balance baseline characteristics.
- Robustness demonstrated by consistent sensitivity and subgroup analyses across outcomes.
Limitations
- Retrospective observational design is subject to residual confounding and confounding by indication.
- Medication exposure, adherence, and dialysis-related factors (e.g., vintage, modality) may be imperfectly captured.
Future Directions: Conduct randomized controlled trials in ESKD to establish causality, assess safety and dosing, and explore infection-related mechanisms of GLP-1 RAs.
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 datasets with ER stress pathways and machine learning identified LDHA, BIK, and CNIH4 as diagnostic biomarkers for lung cancer with sepsis (AUC ≥ 0.7). Protein overexpression was confirmed in clinical tissues, gene silencing reduced PC9 cell migration and invasion, and single-cell analyses linked these markers to immune dysregulation.
Impact: It provides mechanistically grounded, experimentally validated biomarkers at the intersection of oncology and sepsis, with potential diagnostic utility and therapeutic targeting opportunities.
Clinical Implications: LDHA, BIK, and CNIH4 could aid early recognition and risk stratification of lung cancer patients developing sepsis and represent putative therapeutic targets; prospective clinical validation is required before adoption.
Key Findings
- Machine learning identified LDHA, BIK, and CNIH4 as ER stress–related diagnostic biomarkers for lung cancer with sepsis (AUC ≥ 0.7).
- Western blot confirmed increased protein expression in lung cancer and further elevation in lung cancer with sepsis; silencing reduced PC9 cell migration and invasion.
- Single-cell analyses showed cell-type–specific expression patterns linked to immune dysregulation (notably plasma cells and monocytes), and docking suggested tetrahydro-NAD and amikacin as candidates.
Methodological Strengths
- Multi-omic integration of GEO datasets with exhaustive machine learning, ROC-based evaluation, and single-cell cross-validation.
- Experimental validation via Western blot in clinical tissues and functional assays (wound healing, Transwell) in PC9 cells.
Limitations
- Reliance on retrospective public datasets and a limited institutional tissue cohort may limit generalizability.
- In vitro validation used a single lung cancer cell line (PC9), and docking predictions lack in vivo confirmation.
Future Directions: Prospective clinical validation of diagnostic performance, multi-center tissue cohorts, in vivo functional studies, and translational trials targeting ER stress pathways.
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.