Prognostic-Related Biomarkers in Pancreatic Ductal Adenocarcinoma Correlating with Immune Infiltrates Based on Proteomics.

BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) accounts for 85% of pancreatic carcinoma cases. Patients with PDAC have a poor prognosis. The lack of reliable prognostic biomarkers makes treatment challenging for patients with PDAC. Using a bioinformatics database, we sought to identify prognostic biomarkers for PDAC. MATERIAL AND METHODS Using proteomic analysis of the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database, we were able to identify core differential proteins between early and advanced pancreatic ductal adenocarcinoma tissue, and then we used survival analysis, Cox regression analysis, and area under the ROC curves to screen for more significant differential proteins. Additionally, the Kaplan-Meier plotter database was utilized to determine the relationship between prognosis and immune infiltration in PDAC. RESULTS We identified 378 differential proteins in early (n=78) and advanced stages (n=47) of PDAC (P<0.05). PLG, COPS5, FYN, ITGB3, IRF3, and SPTA1 served as independent prognostic factors of patients with PDAC. Patients with higher COPS5 expression had shorter overall survival (OS) and recurrence-free survival, and those with higher PLG, ITGB3, and SPTA1, and lower FYN and IRF3 expression had shorter OS. More importantly, COPS5, IRF3 were negatively associated with macrophages and NK cells, but PLG, FYN, ITGB3, and SPTA1 were positively related to the expression of CD8+ T cells and B cells. COPS5 affected the prognosis of PDAC patients by acting on B cells, CD8+ T cells, macrophages, and NK cells immune infiltration, while PLG, FYN, ITGB3, IRF3, and SPTA1 affected PDAC patient prognosis through some immune cells. CONCLUSIONS PLG, COPS5, FYN, IRF3, ITGB3 and SPTA1 could be potential immunotherapeutic targets and valuable prognostic biomarkers of PDAC.

Perivascular epithelioid cell tumors of the liver misdiagnosed as hepatocellular carcinoma: Three case reports.

BACKGROUND: Hepatic perivascular epithelioid cell neoplasms (PEComas) are rare. Diagnostic and treatment experience with hepatic PEComa remains insufficient. CASE SUMMARY: Three hepatic PEComa cases are reported in this paper: One case of primary malignant hepatic PEComa, one case of benign hepatic PEComa, and one case of hepatic PEComa with an ovarian mature cystic teratoma. During preoperative imaging and pathological assessment of intraoperative frozen samples, patients were diagnosed with hepatocellular carcinoma (HCC), while postoperative pathology and immunohistochemistry subsequently revealed hepatic PEComa. Patients with hepatic PEComa which is misdiagnosed as HCC often require a wider surgical resection. It is easy to mistake them for distant metastases of hepatic PEComa and misdiagnosed as HCC, especially when it's combined with tumors in other organs. Three patients eventually underwent partial hepatectomy. After 1-4 years of follow-up, none of the patients experienced recurrence or metastases. CONCLUSION: A clear preoperative diagnosis of hepatic PEComa can reduce the scope of resection and prevent unnecessary injuries during surgery.

Risk factors for the development of sepsis in patients with cirrhosis in intensive care units.

Sepsis is a serious complication of liver cirrhosis. This study aimed to develop a risk prediction model for sepsis among patients with liver cirrhosis. A total of 3130 patients with liver cirrhosis were enrolled from the Medical Information Mart for Intensive Care IV database, and randomly assigned into training and validation cohorts in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was used to filter variables and select predictor variables. Multivariate logistic regression was used to establish the prediction model. Based on LASSO and multivariate logistic regression, gender, base excess, bicarbonate, white blood cells, potassium, fibrinogen, systolic blood pressure, mechanical ventilation, and vasopressor use were identified as independent risk variables, and then a nomogram was constructed and validated. The consistency index (C-index), receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram. As a result of the nomogram, good discrimination was achieved, with C-indexes of 0.814 and 0.828 for the training and validation cohorts, respectively, and an area under the curve of 0.849 in the training cohort and 0.821 in the validation cohort. The calibration curves demonstrated good agreement between the predictions and observations. The DCA curves showed the nomogram had significant clinical value. We developed and validated a risk-prediction model for sepsis in patients with liver cirrhosis. This model can assist clinicians in the early detection and prevention of sepsis in patients with liver cirrhosis.