Platelets are a class of pluripotent cells that, in addition to hemostasis and maintaining vascular endothelial integrity, are also involved in tumor growth and distant metastasis. The tumor microenvironment is a complex and comprehensive system composed of tumor cells and their surrounding immune and inflammatory cells, tumor‐related fibroblasts, nearby interstitial tissues, microvessels, and various cytokines and chemokines. As an important member of the tumor microenvironment, platelets can promote tumor invasion and metastasis through various mechanisms. Understanding the role of platelets in tumor metastasis is important for diagnosing the risk of metastasis and prolonging survival. In this study, we more fully elucidate the underlying mechanisms by which platelets promote tumor growth and metastasis by modulating processes, such as immune escape, angiogenesis, tumor cell homing, and tumor cell exudation, and further summarize the effects of platelet−tumor cell interactions in the tumor microenvironment and possible tumor treatment strategies based on platelet studies. Our summary will more comprehensively and clearly demonstrate the role of platelets in tumor metastasis, so as to help clinical judgment of the potential risk of metastasis in cancer patients, with a view to improving the prognosis of patients.

This article aims to explore the underlying molecular mechanisms and prognosis‐related genes in pancreatic cancer metastasis. Pancreatic cancer metastasis‐related gene chip data were downloaded from GENE EXPRESSION OMNIBUS(GEO)database. Differentially expressed genes were screened after R‐package pre‐treatment. Functional annotations and related signalling pathways were analysed using DAVID software. GEPIA (Gene Expression Profiling Interactive Analysis) was used to perform prognostic analysis, and differential genes associated with prognosis were screened and validated using data from GEO. We screened 40 healthy patients, 40 primary pancreatic cancer and 40 metastatic pancreatic cancer patients, collected serum, designed primers and used qPCR to test the expression of prognosis‐related genes in each group. 109 differentially expressed genes related with pancreatic cancer metastasis were screened, of which 49 were up‐regulated and 60 were down‐regulated. Functional annotation and pathway analysis revealed differentially expressed genes were mainly concentrated in protein activation cascade, extracellular matrix construction, decomposition, etc In the biological process, it is mainly involved in signalling pathways such as PPAR, PI3K‐Akt and ECM receptor interaction. Prognostic analysis showed the expression levels of four genes were significantly correlated with the overall survival time of patients with pancreatic cancer, namely SCG5, CRYBA2, CPE and CHGB. qPCR experiments showed the expression of these four genes was decreased in both the primary pancreatic cancer group and the metastatic pancreatic cancer group, and the latter was more significantly reduced. Pancreatic cancer metastasis is closely related to the activation of PPAR pathway, PI3K‐Akt pathway and ECM receptor interaction. SCG5, CRYBA2, CPE and CHGB genes are associated with the prognosis of pancreatic cancer, and their low expression suggests a poor prognosis.

Aim: To investigate diagnostic value of preoperative inflammatory biomarkers in pancreatic cancer (PCC). Materials & methods: Preoperative circulating Alb/Fib ratio, neutrophil/lymphocyte ratio (NLR), derived NLR (dNLR), platelet/lymphocyte ratio and lymphocyte/monocyte ratio were detected and calculated in 226 PCC individuals, 232 healthy controls and 142 additional cancer controls. Receiver-operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic efficacy of PCC. Results: Combined circulating dNLR and Alb could effectively improve the diagnosis of PCC (AUC = 0.931), single dNLR could distinguish early-stage PCC and the disease from healthy controls (AUC = 0.895) and additional cancer controls (AUC = 0.794). Conclusion: Circulating dNLR was an effective biomarker for diagnosis and identification of early-stage PCC. Combined dNLR and Alb could improve the diagnostic efficacy of the disease.