Genomics-based Exploration Of The Impact Of Hypoxia-inflammatory Microenvironment On Treatment Strategies For Pancreatic Cancer

Objective: The development and progression of pancreatic cancer（PAC）depends on its local tumor microenvironment（TME）.Hypoxia and inflammation are two important factors shaping the TME in PAC.However,in this case the interaction between these two factors and the regulation of the TME remains poorly understood.The aim of this study was to develop a hypoxia-inflammation-related risk score model to predict the prognosis and tumor immune microenvironment of PAC patients and further explore the significance of response to immunotherapy and chemotherapy.Methods: First,tumor subtypes associated with hypoxia-inflammation were established in the TCGA cohort by NMF clustering analysis and the immune status of the different subtypes was further identified using the ss GSEA algorithm.In addition,lasso-cox regression strategy was applied to identify prognosis-related genes and develop a hypoxia-inflammation-based prognostic model.GEO datasets as separate cohort for external validation.Finally,CCK-8,wound healing,and transwell assay were used to assess the effects of the prognosis-related gene matrix metallopeptidase 28（MMP28）on PAC cell proliferation,migration and invasion.Results: Low hypoxia（P<0.001）and low inflammatory status（P<0.001）were identified as favorable factors for patient’ overall survival（OS）.In addition,patients with low hypoxia ＆ low inflammatory status showed high immunity and good prognosis while patients with high hypoxia ＆ high inflammatory status had low immunity and poor prognosis（P<0.05）.Lasso-cox regression method identified 9prognosis-related genes（GPR162,ZMAT1,MET,GJB5,FAM83 A,ANLN,SLC16A11,MMP28 and SNPH）to construct a risk prognosis model.The OS of low-risk patients was better than that of high-risk patients（P<0.05）.Multivariate Cox regression analysis indicated that the hypoxia-inflammation model could be used as an independent prognostic factor in patients with PAC.The calibration curve analysis produced by the nomogram integrating risk score and associated clinical risk factors showed good predictive performance.The CIBERSORT and ss GSEA algorithms indicated the presence of an immunosuppressive TME in the high-risk group through the analysis of immune cell infiltration.However,although we found that low-risk patients were more likely to benefit from immunotherapy,immunotherapy response rates were lower and not significantly different in both high-and low-risk groups.Meanwhile,our study showed that patients with high-risk score were less sensitive to most chemotherapeutic drugs,and further identified eight candidate small molecule drugs that may improve the prognosis of patients with high-risk score.In addition,the results of this study suggest that the study of the relationship between high levels of parainflammation and TP53 mutation rates in high-risk patients and targeted hyaluronic acid in combination with gemcitabine and paclitaxel therapy are the focus of their future treatment options.Finally,knockdown of MMP28 significantly reduced the ability of PAC cells to proliferate,migrate and invade.Conclusions: The hypoxia-inflammation-based risk model represents a promising instrumental approach to PAC risk stratification.It can be used as a prognostic classifier for clinical decision-making and may hold promise for the development of novel hypoxia-inflammation-immune biomarkers and individualized patient treatment.