Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify Gene Signatures Related to Prognosis and Immunotherapy Response

SIMPLE SUMMARY: Pancreatic ductal adenocarcinoma (PDAC) has an insidious onset and rapid progression, and its morbidity and mortality are increasing year by year. Currently, there are limited therapeutic methods and no effective therapeutic guidance. Tumor microenvironments (TME) of PDAC are highly specific and associated with the failure of chemotherapy, radiotherapy, and immunotherapy. Different TMEs have different sensitivities to treatment modalities. Therefore, constructing a prediction model based on TME classification and giving corresponding treatment measures according to the classification results will provide a new idea for clinical precision diagnosis and treatment. Further verification of gene function related to TME will greatly provide effective potential clinical treatment targets for personalized therapy. ABSTRACT: The hallmark of pancreatic ductal adenocarcinoma (PDAC) is an exuberant tumor microenvironment (TME) comprised of diverse cell types that play key roles in carcinogenesis, chemo-resistance, and immune evasion. Here, we propose a gene signature score through the characterization of cell components in TME for promoting personalized treatments and further identifying effective therapeutic targets. We identified three TME subtypes based on cell components quantified by single sample gene set enrichment analysis. A prognostic risk score model (TMEscore) was established based on TME-associated genes using a random forest algorithm and unsupervised clustering, followed by validation in immunotherapy cohorts from the GEO dataset for its performance in predicting prognosis. Importantly, TMEscore positively correlated with the expression of immunosuppressive checkpoints and negatively with the gene signature of T cells’ responses to IL2, IL15, and IL21. Subsequently, we further screened and verified F2R-like Trypsin Receptor1 (F2RL1) among the core genes related to TME, which promoted the malignant progression of PDAC and has been confirmed as a good biomarker with therapeutic potential in vitro and in vivo experiments. Taken together, we proposed a novel TMEscore for risk stratification and selection of PDAC patients in immunotherapy trials and validated effective pharmacological targets.