Background: Pancreatic stellate cells (PSCs) is a highly heterogeneic stroma cell population in pancreatic cancer tissue. Interaction between PSCs and pancreatic cancer cells has not been well elucidated. This research was aimed to study the relationship between fibroblast activation protein α (FAPα)-positive (FAPα+) PSCs and the pathological features and prognosis of pancreatic cancer. The effects and mechanisms of FAPα + PSCs in pancreatic cancer were also explored.Methods: Tissue microarray analysis was used to detect FAPα expression in tumor and adjacent tissues.The relationship between FAPα expression and pancreatic pathological features and prognosis were analyzed.The effects of FAPα+ PSCs on the proliferation, migration and invasion of pancreatic cancer were detected in vitro and in vivo. A cytokine chip was used to detect the differential expression of cytokines in FAPαpositive (FAPα+) and FAPα-negative (FAPα−) PSCs. Phosphorylated tyrosine kinase receptors were detected by a human phosphotyrosine kinase receptor protein chip. The interaction between differential cytokine and tyrosine kinase receptors was detected by immunoprecipitation.
Results: Compared with the adjacent tissues, pancreatic cancer stromal tissues showed high FAPα expression. FAPα was mainly expressed in the PSCs. FAPα+ PSCs were associated with lymph node metastasis. Higher numbers of FAPα+ PSCs predicted shorter survival. Pancreatic cancer cells released TGFβ1 and induced PSCs to express FAPα. FAPα+ PSCs released the chemokine CXCL1 and promoted the phosphorylation of the tyrosine kinase receptors EphB1 and EphB3 in pancreatic cancer cells. CXCL1, EphrinB1, and EphrinB3 worked together to promote the migration and invasion of pancreatic cancer cells by Akt phosphorylation. Talabostat (PT100), an FAPα inhibitor, inhibited the roles of FAPα+ PSCs. Conclusions: FAPα+ PSCs can promote the migration, invasion, and metastasis of pancreatic cancer by the Akt signaling pathway. This interaction of FAPα+ PSCs with pancreatic cancer cells may become a new strategy for the comprehensive treatment of pancreatic cancer.
Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies. Recent studies reveal that tumor microenvironment (TME) components significantly affect HCC growth and progression, particularly the infiltrating stromal and immune cells. Thus, mining of TME-related biomarkers is crucial to improve the survival of patients with HCC. Public access of The Cancer Genome Atlas (TCGA) database allows convenient performance of gene expression-based analysis of big data, which contributes to the exploration of potential association between genes and prognosis of a variety of malignancies, including HCC. The “Estimation of STromal and Immune cells in MAlignant Tumors using Expression data” algorithm renders the quantification of the stromal and immune components in TME possible by calculating the stromal and immune scores. Differentially expressed genes (DEGs) were screened by dividing the HCC cohort of TCGA database into high- and low-score groups according to stromal and immune scores. Further analyses of functional enrichment and protein-protein interaction networks show that the DEGs are mainly involved in immune response, cell adhesion, and extracellular matrix. Finally, seven DEGs have significant association with HCC poor outcomes. These genes contain FABP3, GALNT5, GPR84, ITGB6, MYEOV, PLEKHS1, and STRA6 and may be candidate biomarkers for HCC prognosis.
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