Meaningful nomograms based on systemic immune inflammation index predicted survival in metastatic pancreatic cancer patients receiving chemotherapy.

OBJECTIVE: The purpose of the study is to construct meaningful nomogram models according to the independent prognostic factor for metastatic pancreatic cancer receiving chemotherapy. METHODS: This study is retrospective and consecutively included 143 patients from January 2013 to June 2021. The receiver operating characteristic (ROC) curve with the area under the curve (AUC) is utilized to determine the optimal cut-off value. The Kaplan-Meier survival analysis, univariate and multivariable Cox regression analysis are exploited to identify the correlation of inflammatory biomarkers and clinicopathological features with survival. R software are run to construct nomograms based on independent risk factors to visualize survival. Nomogram model is examined using calibration curve and decision curve analysis (DCA). RESULTS: The best cut-off values of 966.71, 0.257, and 2.54 for the systemic immunological inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) were obtained by ROC analysis. Cox proportional-hazards model revealed that baseline SII, history of drinking and metastasis sites were independent prognostic indices for survival. We established prognostic nomograms for primary endpoints of this study. The nomograms' predictive potential and clinical efficacy have been evaluated by calibration curves and DCA. CONCLUSION: We constructed nomograms based on independent prognostic factors, these models have promising applications in clinical practice to assist clinicians in personalizing the management of patients.

Network Pharmacological Screening of Herbal Monomers that Regulate Apoptosis-Associated Genes in Acute Pancreatitis.

OBJECTIVES: In this study, we screened for differentially expressed genes in acute pancreatitis and the herbal monomers that regulate these genes. METHODS: Gene expression profile data were downloaded from the Gene Expression Omnibus database (GSE3644). We used the Human Protein Reference Database to determine the protein-protein interaction network and CFinder software (Department of Biological Physics of Eötvös University, Budapest, Hungary) to identify several functional modules. Then, we used Database for Annotation, Visualization and Integrated Discovery software (Frederick, Md) to perform a gene ontology-biological process functional enrichment analysis. Based on a database of herbal monomers and a literature search, we constructed a gene-herbal monomer regulatory network using Cytoscape software (San Diego, Calif), and we analyzed the relationships between apoptosis, genes, and herbal monomers. RESULTS: A total of 1745 differentially expressed genes were identified. Nine modules were identified, and the main function of module 3 was closely related to apoptosis. Within module 3, we selected 13 genes that were closely related to apoptosis for further analysis. In the gene-herbal monomer regulatory network, 18 herbal monomers that regulate multiple target genes were selected as the focus of this study. CONCLUSIONS: These herbal monomers regulate multiple target genes to induce apoptosis and may potentially be used as new drugs for acute pancreatitis treatment in the future.