Objective: Gastric carcinoma (GC) is the fifth most commonly diagnosed cancer and the third leading cause of cancer-related deaths globally. The tumor microenvironment plays a significant role in the pathogenesis, prognosis, and response to immunotherapy. However, the immune-related molecular mechanisms underlying GC remain elusive. Bioinformatics analysis of the gene expression of GC and paracancerous healthy tissues from the same patient was performed to identify the key genes and signaling pathways, as well as their correlation to the infiltration of the tumor microenvironment (TME) by various immune cells related to GC development. Methods: We employed GSE19826, a gene expression profile from the Gene Expression Omnibus (GEO), for our analysis. Functional enrichment analysis of Differentially Expressed Genes (DEGs) was conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database. Results: Cytoscape software facilitated the identification of nine hub DEGs, namely, FN1, COL1A1, COL1A2, THBS2, COL3A1, COL5A1, APOE, SPP1, and BGN. Various network analysis algorithms were applied to determine their high connectivity. Among these hub genes, FN1, COL1A2, THBS2, COL3A1, COL5A1, and BGN were found to be associated with a poor prognosis for GC patients. Subsequent analysis using the TIMER database revealed the infiltration status of the TME concerning the overexpression of these six genes. Specifically, the abovementioned genes demonstrated direct correlations with cancer-associated fibroblasts, M1 and M2 macrophages, myeloid-derived suppressor cells, and activated dendritic cells. Conclusion: Our findings suggest that the identified hub genes, particularly BGN, FN1, COL1A2, THBS2, COL3A1, and COL5A1, play crucial roles in GC prognosis and TME cell infiltration. This comprehensive analysis enhances our understanding of the molecular mechanisms underlying GC development and may contribute to the identification of potential therapeutic targets and prognostic markers for GC patients.

Liver cirrhosis is a chronically progressive disease often leading to severe complications such as acute decompensation (AD) and acute-on-chronic liver failure (ACLF). Both conditions are frequently triggered by bacterial infections. However, conventional biomarkers such as procalcitonin (PCT) and C-reactive protein (CRP) have limited diagnostic accuracy for detecting infections in patients with cirrhosis. The aim of this study was to better characterize the inflammatory response in AD and ACLF and to identify reliable biomarkers for the early detection of bacterial infections. In this prospective study, 74 consecutive patients with liver cirrhosis and either AD or ACLF were enrolled between October 2022 and October 2023 during hospitalization at the University Hospital Essen. Bacterial infection was identified as a precipitating event in 33 patients, whereas other causes were found in the remaining 41 patients. In addition to CRP and PCT, several proinflammatory cytokines and interleukin receptor antagonists were analyzed, including IL-1β, IL-1RA, IL-17E, IL-17F, IL-17A, IL-6 and IL-23, which play central roles in type I and type III immune responses. Univariable logistic regression analyses were conducted to identify biomarkers significantly associated with bacterial infections. Significant biomarkers were then incorporated into multivariable models alongside predefined clinical parameters. Model performance and generalizability were evaluated through internal validation using 1,000-fold bootstrap resampling with 74 patients per sample. Key biomarkers identified included CRP, PCT, IL-1β, and particularly IL-1RA. After dichotomization, the following combinations achieved the highest diagnostic accuracy for bacterial infection: IL-1RA alone (AUC: 0.762; 95% confidence interval [CI]: 0.652–0.872), IL-1RA combined with PCT (AUC: 0.750; 95% CI 0.635–0.866), and IL-1RA combined with CRP (AUC: 0.744; 95% CI 0.625–0.863). To minimize overfitting and improve model calibration, ridge regression was applied. The final model, which included IL-1RA and selected clinical parameters, achieved an AUC of 0.764 (95% CI 0.654–0.874), with an accuracy of 73%, sensitivity of 61%, specificity of 83%, and precision of 74%. Based on this model, a risk stratification tool was developed that defined thresholds to achieve 80% sensitivity and specificity, respectively. A freely available online application was created to facilitate clinical implementation. In summary, the model developed in this study enables improved identification of bacterial infections in patients with liver cirrhosis. This could support earlier and more targeted therapy in this highly vulnerable high-risk population. External validation and application in larger cohorts are necessary to further confirm the clinical utility of the model.

Objective Secondary sclerosing cholangitis (SSC) represents a disease with a poor prognosis increasingly diagnosed in clinical settings. Notably, SSC in critically ill patients (SSC-CIP) is the most frequent cause. Variables associated with worse prognosis remain unclear. The primary aim of this study was to identify factors associated with transplant-free survival in SSC-CIP patients using readily available data. Methods A cohort of 47 patients diagnosed with SSC-CIP was retrospectively analysed for clinical, biochemical and endoscopic variables. Kaplan-Meier survival curves, log-rank tests and univariate Cox proportional hazards models were used to assess associations with transplant-free survival. A multivariable Cox regression model was constructed using Lasso regularisation and validated with Bootstrap resampling. Model performance was assessed using the C-statistic for discrimination. Results Kaplan-Meier analysis identified bile duct obstruction requiring stent placement, and cholangitis episodes, as significant prognostic factors. In univariable analysis, age over 47 years (HR 2.61 (95% CI 1.02, 7.06), p=0.04), at least one cholangitis episode (HR 2.46 (95% CI 1.005, 6.06), p=0.04), stent placement (HR 2.89 (95% CI 1.13, 7.38), p=0.03), lower albumin levels (HR 0.52 (95% CI 0.28, 0.97), p=0.04) and higher international normalised ratio (INR) (HR 3.22 (95% CI 1.09, 9.53), p=0.03) were significant. Multivariable analysis showed that age at diagnosis, albumin and INR were significant independent predictors. The C-index was 0.78 (95% CI 0.65, 0.91), surpassing the model of end-stage liver disease score’s prognostic accuracy (Concordance Index at 3 years: 66.2% vs 74.9%). Conclusion These findings provide valuable insights for establishing standard exception criteria for this rare liver disease, which could lead to improved organ allocation. Further prospective multicentre studies are necessary to validate our findings.