背景：胰腺癌（PC）是一种致命疾病。肿瘤微环境（TME）参与了PC的发生和发展。本研究重点评估TME相关基因在PC中的预后和治疗效用。方法：在获取差异表达的TME相关基因后，进行了单变量和多变量Cox分析以及最小绝对收缩和选择算子（LASSO）的研究，以识别与预后相关的基因，并建立风险模型来评估基于肿瘤基因组图谱（TCGA）数据集的风险分数，并通过来自基因表达清单（GEO）和临床蛋白质肿瘤分析联盟（CPTAC）的外部数据集进行验证。采用多组学分析来探索潜在机制，发现新的治疗靶点，并评估免疫治疗和化疗的敏感性。结果：鉴定出5个与TME相关的基因，分别是FERMT1，CARD9，IL20RB，MET和MMP3，并构建了风险评分公式。接下来，在癌细胞和正常胰腺细胞中验证了它们的mRNA表达情况。多种算法确认了风险模型具有可靠的预后预测能力，并且是一个独立的预后因素，表明高风险患者的预后较差。免疫细胞浸润、基因集富集分析（GSEA）和单细胞分析均显示免疫机制与低风险样本之间存在强烈关联。风险评分可以预测免疫治疗和一些化疗方案的敏感性，其中包括奥沙利铂和伊立替康。基于风险模型的突变谱图揭示了各种潜在治疗靶点（LAG3，TIGIT和ARID1A）。结论：基于TME相关基因的风险模型能够反映PC患者的预后，并成为一组新的PC治疗生物标志物。2023年版权所有Nie，Xu，Bai，Liu，Wang，Zeng，Gao，Xia和Chang。

Background: Pancreatic cancer (PC) is a deadly disease. The tumor microenvironment (TME) participates in PC oncogenesis. This study focuses on the assessment of the prognostic and treatment utility of TME-associated genes in PC. Methods: After obtaining the differentially expressed TME-related genes, univariate and multivariate Cox analyses and least absolute shrinkage and selection operator (LASSO) were performed to identify genes related to prognosis, and a risk model was established to evaluate risk scores, based on The Cancer Genome Atlas (TCGA) data set, and it was validated by external data sets from the Gene Expression Omnibus (GEO) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Multiomics analyses were adopted to explore the potential mechanisms, discover novel treatment targets, and assess the sensitivities of immunotherapy and chemotherapy. Results: Five TME-associated genes, namely, FERMT1, CARD9, IL20RB, MET, and MMP3, were identified and a risk score formula constructed. Next, their mRNA expressions were verified in cancer and normal pancreatic cells. Multiple algorithms confirmed that the risk model displayed a reliable ability of prognosis prediction and was an independent prognostic factor, indicating that high-risk patients had poor outcomes. Immunocyte infiltration, gene set enrichment analysis (GSEA), and single-cell analysis all showed a strong relationship between immune mechanism and low-risk samples. The risk score could predict the sensitivity of immunotherapy and some chemotherapy regimens, which included oxaliplatin and irinotecan. Various latent treatment targets (LAG3, TIGIT, and ARID1A) were addressed by mutation landscape based on the risk model. Conclusion: The risk model based on TME-related genes can reflect the prognosis of PC patients and functions as a novel set of biomarkers for PC therapy.Copyright © 2023 Nie, Xu, Bai, Liu, Wang, Zeng, Gao, Xia and Chang.