Daily Sepsis Research Analysis
Analyzed 15 papers and selected 3 impactful papers.
Summary
Analyzed 15 papers and selected 3 impactful articles.
Selected Articles
1. Systematic characterization of pyroptosis-related gene patterns identifies potential prognostic inflammatory phenotypes in sepsis.
Using large public cohorts, a 7-gene pyroptosis-related score stratified sepsis mortality with strong discrimination (AUCs >0.75; external AUC 0.984) and captured immune remodeling signatures (M0/M2 macrophages↑, CD8+ T cells↓, checkpoints↑). Single-cell data showed declining scores in survivors, supporting biological relevance and potential use for immunomodulatory trial stratification.
Impact: Links pyroptosis biology to clinically useful prognostication and immune phenotyping with multi-cohort and single-cell support.
Clinical Implications: The 7-gene PRG-score could enable early risk stratification and identify candidates for immunomodulatory interventions (e.g., checkpoint or macrophage-targeted strategies), pending prospective validation.
Key Findings
- Two molecular sepsis subtypes identified by PRG patterns; poor-prognosis subtype enriched for inflammatory pathways.
- A 7-gene PRG-score (TUBG2, TNFAIP3, CXCL8, WFDC1, DEFA4, CX3CR1, ZBP1) predicted short-term mortality (AUCs >0.75); external validation AUC 0.984.
- Single-cell analysis showed PRG-scores decreased over time in survivors but remained elevated in non-survivors.
- High PRG-scores associated with increased M0/M2 macrophages, reduced CD8+ T cells, and higher IL-10 and immune checkpoint expression.
- A nomogram combining PRG-score and age improved individualized survival estimation.
Methodological Strengths
- Multi-dataset development with external validation and single-cell temporal analyses
- Robust modeling using LASSO and Cox regression with immune infiltration profiling
Limitations
- Retrospective analyses of public datasets may be subject to confounding and batch effects
- Lack of prospective, interventional validation linking PRG-score to treatment decisions
Future Directions: Prospective multicenter validation and integration of PRG-score into adaptive immunotherapy trials targeting checkpoints or macrophage polarization.
Sepsis is a life-threatening syndrome driven by a dysregulated host response to infection, yet reliable prognostic biomarkers remain limited. Pyroptosis has emerged as an important contributor to immune dysregulation in sepsis. Here, we systematically characterized pyroptosis-related gene (PRG) patterns using public transcriptomic cohorts (GSE65682). Consensus clustering identified two distinct molecular subtypes, with the poorer-prognosis subtype enriched in inflammatory pathways. Based on differentially expressed genes, we constructed a 7-gene PRG-score (TUBG2, TNFAIP3, CXCL8, WFDC1, DEFA4, CX3CR1, and ZBP1) via LASSO and Cox regression. This model demonstrated good predictive performance for short-term mortality (AUCs > 0.75), which was further evaluated in an independent septic shock cohort (GSE95233, AUC = 0.984). Single-cell RNA sequencing analysis (GSE167363) showed that PRG-scores tended to remain elevated in non-survivors but decreased over time in survivors. Immune infiltration analysis indicated that higher PRG-scores were associated with features of immune remodeling, including increased M0/M2 macrophage proportions, reduced CD8 + T cells, and higher expression of immune checkpoints (e.g., CTLA4, TIGIT) and IL-10. In addition, a prognostic nomogram integrating the PRG-score and age improved individualized survival estimation. Overall, these findings suggest that the 7-gene PRG-score may reflect immune status and is associated with prognosis, providing insights into molecular subtyping and potential immunomodulatory strategies in sepsis.
2. Lactate-Associated Gene Signatures as Predictors: A Comprehensive Analysis of Immune Profiles in Sepsis.
Integrating sepsis transcriptomes with lactylation biology, the study identified 138 lactylation-related DE genes and, via Mendelian randomization, nominated LYRM4 and MDC1 as protective factors. Machine learning and immune infiltration analyses, plus dataset and hospital patient validation, support their relevance and link these genes to CD8+ T-cell features.
Impact: Introduces lactylation-linked genomic predictors in sepsis and applies causal inference to nominate protective genes, expanding epigenetic-immunologic mechanisms.
Clinical Implications: MDC1 and LYRM4 may serve as biomarkers for stratification and could inform immunometabolic intervention strategies once validated in prospective cohorts.
Key Findings
- Identified 1,356 differentially expressed genes between sepsis and healthy controls.
- Defined 138 lactylation-related differentially expressed genes (Sepsis-DELRGs).
- Mendelian randomization suggested LYRM4 and MDC1 as protective factors against sepsis.
- Machine learning predicted key Sepsis-DELRGs and immune infiltration analyses linked targets to T-cell features (including CD8+).
- Expression of MDC1 and LYRM4 examined in GSE131761, GSE80496, and in hospital sepsis patients.
Methodological Strengths
- Integration of transcriptomics with lactylation biology and Mendelian randomization for causal inference
- Use of machine learning and multi-source validation (public datasets and hospital patients)
Limitations
- Observational, retrospective analyses; MR suggests but does not prove causal biology
- Size and characteristics of the hospital validation cohort are not detailed, limiting generalizability
Future Directions: Prospective validation across diverse sepsis populations and mechanistic studies to delineate how lactylation modulates MDC1/LYRM4 pathways and T-cell function.
BackgroundSepsis is a complex disorder characterized by a dysregulated immune response to infection. Elevated lactic acid levels and lactylation modification may induce changes in gene expression and immune cell infiltration in sepsis.MethodsRNA-seq data and clinical information related to sepsis were obtained from GEO datasets. Differential expression analysis identified genes that are differentially expressed between sepsis patients and healthy controls. The lactylation-related genes were then integrated with the differential expressed genes to classify genes as Sepsis-related differentially expressed lactylation-related genes (Sepsis-DELRGs). Mendelian randomization analysis was performed to assess the causal relationship between Sepsis-DELRGs and the two groups. Machine learning algorithms predicted potential Sepsis-DELRGs, and immune infiltration analysis examined the relationships between these genes and immune cell types. Finally, we examined the expression of MDC1 and LYRM4 within the GSE131761 and GSE80496 datasets and in sepsis patients from our hospital.ResultsA total of 1356 differentially expressed genes were identified between sepsis patients and healthy controls. From these, 138 Sepsis-DELRGs associated with sepsis were isolated. Mendelian randomization identified LYRM4 and MDC1 as protective factors against sepsis. Both genes positively influence CD8
3. Development and Validation of a Risk Stratification Model Incorporating Serum Lactate and Albumin for Predicting Enteral Nutrition Feeding Intolerance in Patients With Sepsis.
In 230 sepsis patients starting enteral nutrition, higher lactate and lower albumin were strongly associated with feeding intolerance. A 4-variable model (albumin, APACHE II, lactate, mechanical ventilation) achieved AUC 0.854 with good calibration and net benefit, outperforming single predictors.
Impact: Delivers a pragmatic, internally validated tool using widely available variables to anticipate EN feeding intolerance and tailor nutrition care.
Clinical Implications: Supports early risk stratification to adjust feeding strategies (e.g., slow titration, prokinetics, closer monitoring) and resource allocation in sepsis ICUs.
Key Findings
- ENFI group had higher lactate (median 2.70 vs 1.82 mmol/L; P<0.001) and lower albumin (27.83 vs 32.82 g/L; P<0.001).
- LASSO selected four predictors: albumin, APACHE II, lactate, and mechanical ventilation.
- Multivariable ORs: albumin 0.80 per g/L, APACHE II 1.09 per point, lactate 2.03 per mmol/L, mechanical ventilation 2.52 (all P<0.05).
- Model discrimination AUC 0.854; bootstrap-corrected AUC 0.845; good calibration (Hosmer-Lemeshow P=0.299); decision curve showed net benefit from 10–70% thresholds.
Methodological Strengths
- Predictor selection via LASSO with multivariable modeling, calibration, and decision curve analysis
- Internal bootstrap validation with strong discrimination
Limitations
- Single-cohort development without external validation limits generalizability
- Potential residual confounding and center-specific practices may affect model transportability
Future Directions: External, multicenter validation and impact analysis to test whether model-guided nutrition strategies improve outcomes.
PURPOSE: Enteral nutrition feeding intolerance (ENFI) is a common complication in patients with sepsis and may compromise nutritional support and clinical recovery. This study aimed to develop and validate a risk stratification model incorporating serum lactate (LAC) and albumin (ALB) for predicting ENFI in patients with sepsis. METHODS: A total of 258 patients were screened for eligibility according to predefined inclusion and exclusion criteria. The final complete-case cohort comprised 230 patients with sepsis who received enteral nutrition between December 2021 and June 2025, including 88 patients in the ENFI group and 142 in the non-ENFI group. Clinical variables and laboratory indicators obtained within 24 hours before enteral nutrition initiation were analyzed. Predictors were screened using least absolute shrinkage and selection operator regression and entered into multivariable logistic regression for model construction. Model performance was evaluated by receiver operating characteristic analysis, calibration analysis, bootstrap internal validation, and decision curve analysis. FINDINGS: Lactate levels were significantly higher in the ENFI group than in the non-ENFI group (median [interquartile range], 2.70 [2.00-3.86] vs 1.82 [1.19-2.48] mmol/L; P < 0.001), whereas ALB levels were significantly lower (27.83 ± 5.02 vs 32.82 ± 4.33 g/L; P < 0.001). Least absolute shrinkage and selection operator regression identified 4 predictors: ALB, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, LAC, and mechanical ventilation. Multivariable analysis showed that decreased ALB (odds ratio [OR] = 0.80; 95% CI, 0.74-0.86), increased APACHE II score (OR = 1.09; 95% CI, 1.01-1.17), increased LAC (OR = 2.03; 95% CI, 1.50-2.85), and mechanical ventilation (OR = 2.52; 95% CI, 1.28-5.10) were independent predictors of ENFI (all P < 0.05). The 4-variable model achieved an area under the curve of 0.854 (95% CI, 0.803-0.905), outperforming single indicators. Bootstrap internal validation yielded a corrected area under the curve of 0.845, and the Hosmer-Lemeshow test indicated good fit (P = 0.299). Decision curve analysis reported a positive net clinical benefit across a threshold probability range of 10% to 70%. IMPLICATIONS: A 4-variable prediction model anchored by LAC and ALB and further incorporating APACHE II score and mechanical ventilation showed good discrimination for predicting ENFI in patients with sepsis. This model may provide a practical tool for early risk stratification and may help support individualized enteral nutrition monitoring in clinical practice.