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Background: Compound kushen injection (CKI), a Chinese patent drug, is widely used in the treatment of various cancers, especially neoplasms of the digestive system. However, the underlying mechanism of CKI in pancreatic cancer (PC) treatment has not been totally elucidated. Methods: Here, to overcome the limitation of conventional network pharmacology methods with a weak combination with clinical information, this study proposes a network pharmacology approach of integrated bioinformatics that applies a weighted gene co-expression network analysis (WGCNA) to conventional network pharmacology, and then integrates molecular docking technology and biological experiments to verify the results of this network pharmacology analysis. Results: The WGCNA analysis revealed 2 gene modules closely associated with classification, staging and survival status of PC. Further CytoHubba analysis revealed 10 hub genes (NCAPG, BUB1, CDK1, TPX2, DLGAP5, INAVA, MST1R, TMPRSS4, TMEM92 and SFN) associated with the development of PC, and survival analysis found 5 genes (TSPOAP1, ADGRG6, GPR87, FAM111B and MMP28) associated with the prognosis and survival of PC. By integrating these results into the conventional network pharmacology study of CKI treating PC, we found that the mechanism of CKI for PC treatment was related to cell cycle, JAK-STAT, ErbB, PI3K-Akt and mTOR signalling pathways. Finally, we found that CDK1, JAK1, EGFR, MAPK1 and MAPK3 served as core genes regulated by CKI in PC treatment, and were further verified by molecular docking, cell proliferation assay, RT-qPCR and western blot analysis. Conclusions: Overall, this study suggests that the optimized network pharmacology approach is suitable to explore the molecular mechanism of CKI in the treatment of PC, which provides a reference for further investigating biomarkers for diagnosis and prognosis of PC and even the clinical rational application of CKI. [ABSTRACT FROM AUTHOR]

Background: This study profiled the somatic genes mutations and the copy number variations (CNVs) in cerebrospinal fluid (CSF)-circulating tumor DNA (ctDNA) from patients with neoplastic meningitis (NM).Methods: A total of 62 CSF ctDNA samples were collected from 58 NM patients for the next generation sequencing. The data were bioinformatically analyzed by (Database for Annotation, Visualization and Integrated Discovery) DAVID software.Results: The most common mutated gene was TP53 (54/62; 87.10%), followed by EGFR (44/62; 70.97%), PTEN (39/62; 62.90%), CDKN2A (32/62; 51.61%), APC (27/62: 43.55%), TET2 (27/62; 43.55%), GNAQ (18/62; 29.03%), NOTCH1 (17/62; 27.42%), VHL (17/62; 27.42%), FLT3 (16/62; 25.81%), PTCH1 (15/62; 24.19%), BRCA2 (13/62; 20.97%), KDR (10/62; 16.13%), KIT (9/62; 14.52%), MLH1 (9/62; 14.52%), ATM (8/62; 12.90%), CBL (8/62; 12.90%), and DNMT3A (7/62; 11.29%). The mutated genes were enriched in the PI3K-Akt signaling pathway by the KEGG pathway analysis. Furthermore, the CNVs of these genes were also identified in these 62 samples. The mutated genes in CSF samples receiving intrathecal chemotherapy and systemic therapy were enriched in the ERK1/2 signaling pathway.Conclusions: This study identified genes mutations in all CSF ctDNA samples, indicating that these mutated genes may be acted as a kind of biomarker for diagnosis of NM, and these mutated genes may affect meningeal metastasis through PI3K-Akt signaling pathway. [ABSTRACT FROM AUTHOR]