Inhibition of phosphodiesterase 4D suppresses mTORC1 signaling and pancreatic cancer growth

The impact of platelets on the metastatic potential of tumour cells

In cancer, activation of platelets by tumor cells is critical to disease progression. Development of precise antiplatelet targeting may improve outcomes from anticancer therapy. Alongside a distinct shift in functionality such as pro-metastatic and pro-coagulant properties, platelet production is often accelerated significantly early in carcinogenesis and the cancer-associated thrombocytosis increases the risk of metastasis formation and thromboembolic events. Tumor-activated platelets facilitate the proliferation of migrating tumor cells and shield them from immune surveillance and physical stress during circulation. Additionally, platelet-tumor cell interactions promote tumor cell intravasation, intravascular arrest, and extravasation through a repertoire of adhesion molecules, growth factors and angiogenic factors. Particularly, the presence of circulating tumor cell (CTC) clusters in association with platelets is a negative prognostic indicator. The contribution of platelets to the metastatic process is an area of intense investigation and this review provides an overview of the advances in understanding platelet-tumor cell interactions and their contribution to disease progression. Also, we review the potential of targeting platelets to interfere with the metastatic process.

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

Background

Pancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models.

Methods

To identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models.

Results

We identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154).

Conclusion

The five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups.

Roflumilast inhibits tumor growth and migration in STK11/LKB1 deficient pancreatic cancer

Pancreatic cancer is a malignant tumor of the digestive system. It is highly aggressive, easily metastasizes, and extremely difficult to treat. This study aimed to analyze the genes that might regulate pancreatic cancer migration to provide an essential basis for the prognostic assessment of pancreatic cancer and individualized treatment. A CRISPR knockout library directed against 915 murine genes was transfected into TB 32047 cell line to screen which gene loss promoted cell migration. Next-generation sequencing and PinAPL.py- analysis was performed to identify candidate genes. We then assessed the effect of serine/threonine kinase 11 (STK11) knockout on pancreatic cancer by wound-healing assay, chick agnosia (CAM) assay, and orthotopic mouse pancreatic cancer model. We performed RNA sequence and Western blotting for mechanistic studies to identify and verify the pathways. After accelerated Transwell migration screening, STK11 was identified as one of the top candidate genes. Further experiments showed that targeted knockout of STK11 promoted the cell migration and increased liver metastasis in mice. Mechanistic analyses revealed that STK11 knockout influences blood vessel morphogenesis and is closely associated with the enhanced expression of phosphodiesterases (PDEs), especially PDE4D, PDE4B, and PDE10A. PDE4 inhibitor Roflumilast inhibited STK11-KO cell migration and tumor size, further demonstrating that PDEs are essential for STK11-deficient cell migration. Our findings support the adoption of therapeutic strategies, including Roflumilast, for patients with STK11-mutated pancreatic cancer in order to improve treatment efficacy and ultimately prolong survival.

cAMP-PKA/EPAC signaling and cancer: the interplay in tumor microenvironment

Cancer is a complex disease resulting from abnormal cell growth that is induced by a number of genetic and environmental factors. The tumor microenvironment (TME), which involves extracellular matrix, cancer-associated fibroblasts (CAF), tumor-infiltrating immune cells and angiogenesis, plays a critical role in tumor progression. Cyclic adenosine monophosphate (cAMP) is a second messenger that has pleiotropic effects on the TME. The downstream effectors of cAMP include cAMP-dependent protein kinase (PKA), exchange protein activated by cAMP (EPAC) and ion channels. While cAMP can activate PKA or EPAC and promote cancer cell growth, it can also inhibit cell proliferation and survival in context- and cancer type-dependent manner. Tumor-associated stromal cells, such as CAF and immune cells, can release cytokines and growth factors that either stimulate or inhibit cAMP production within the TME. Recent studies have shown that targeting cAMP signaling in the TME has therapeutic benefits in cancer. Small-molecule agents that inhibit adenylate cyclase and PKA have been shown to inhibit tumor growth. In addition, cAMP-elevating agents, such as forskolin, can not only induce cancer cell death, but also directly inhibit cell proliferation in some cancer types. In this review, we summarize current understanding of cAMP signaling in cancer biology and immunology and discuss the basis for its context-dependent dual role in oncogenesis. Understanding the precise mechanisms by which cAMP and the TME interact in cancer will be critical for the development of effective therapies. Future studies aimed at investigating the cAMP-cancer axis and its regulation in the TME may provide new insights into the underlying mechanisms of tumorigenesis and lead to the development of novel therapeutic strategies.

Overexpression of Pde4d in rat granulosa cells inhibits maturation and atresia of antral follicles to induce polycystic ovary

BACKGROUND

Follicle dysplasia can cause polycystic ovary syndrome, which can lead to anovulatory infertility. This study explored gene(s) that may contribute to polycystic ovary syndrome.

METHODS

Three animal models of polycystic ovary syndrome were created by treating 3-week-old rats respectively with estradiol valerate, testosterone propionate, or constant illumination for 8 weeks. Granulosa cells from the three disease groups and from healthy controls were transcriptionally profiled to identify differentially expressed genes. The phosphodiesterase-4d (Pde4d) was screened as the most promising candidate pathogenic gene. The Pde4d was overexpressed in rats via intrabursal infection with recombinant lentivirus to see the effect of Pde4d on ovarian morphology. The potential roles of the candidate gene and interactors of the encoded protein were explored using polymerase chain reaction, western blotting, transfection and co-immunoprecipitation.

RESULTS

All three rat models of polycystic ovary syndrome showed polycystic ovary phenotype. Seven promising candidate genes were obtained by transcriptomics and verifications. Pde4d was further investigated because it could trigger downstream signaling pathways. The Pde4d overexpression in rat ovary induced cystic follicles. It inhibited follicle maturation through a mechanism involving inhibition of cAMP-PKA-CREB signaling. The Pde4d also inhibited phosphorylation of c-Jun N-terminal kinase to reduce apoptosis in the ovary, through a mechanism involving interaction of its poly-proline domain with the protein POSH.

CONCLUSION

Upregulation of Pde4d may contribute to polycystic ovary syndrome by impeding follicle maturation and preventing apoptotic atresia.