Chronic inflammation plays a positive role in the development and progression of colitis‐associated colorectal cancer (CAC). Medicinal plants and their extracts with anti‐inflammatory and immunoregulatory properties may be an effective treatment and prevention strategy for CAC. This research aimed to explore the potential chemoprevention of paeoniflorin (PF) for CAC by network pharmacology, molecular docking technology, and in vivo experiments. The results showed that interleukin‐6 (IL‐6) is a key target of PF against CAC. In the CAC mouse model, PF increased the survival rate of mice and decreased the number and size of colon tumors. Moreover, reduced histological score of colitis and expression of Ki‐67 and PCNA were observed in PF‐treated mice. In addition, the chemoprevention mechanisms of PF in CAC may be associated with suppression of the IL‐6/STAT3 signaling pathway and the IL‐17 level. This research provides experimental evidence of potential chemoprevention strategies for CAC treatment.

Background: Type 2 Diabetes Mellitus(T2DM) is an endocrine disease that caused mainly by insulin resistance (IR) and β cell dysfunction. The incidence of T2DM is quite high in the worldwide. To explore the molecular mechanism of Jinqi Jiangtang Tablet(JJT) in treating of T2DM based on Network Pharmacology. Methods: The active compounds, targets of three Traditional Chinese medicines in JJT were obtained by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform（TCMSP） database and Uniprot database; The targets of T2DM were screened through the Drugbank database; The compound-target network was constructed via the Cytoscape 3.7.2 software and used the built-in Network analyzer to analyze and select the key active compounds; The overlapping targets of drug and disease targets were gained by the VENNY online tool and the targets were built by STRING website to select the key genes; Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed on the potential targets using DAVID6.8 online tool to study the mechanism of overlapping targets. Via Systems Dock platform to validate the interaction between compound and targets Results: Twenty-five active compounds of JJT were screened, 101 drug targets, 142 disease targets and twenty-one overlapping targets. GO enrichment analysis showed that the biological processes (BP)mainly included the blood circulation ,etc. Cell composition(CC) mainly affected the integral component of plasma membrane, etc. Molecular functions(MF) mainly involved alpha-adrenergic receptor activity, etc. KEGG pathway analysis showed that there were twelve pathways related to T2DM, among which PPAR signaling pathway was related to T2DM mostly. RXRA is one of key targets of JJT and berberine performed well. Conclusions: This study revealed the mechanism of JJT in treatment of T2DM preliminarily and supplied a further foundation for studying its mechanism.

Background Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer. Methods Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient’s risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group. Results An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets. Conclusions The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.