胰腺癌（PAAD）通常表现出“冷”或免疫抑制的肿瘤环境，这与免疫检查点阻断疗法的抵抗有关；然而，其根本机制尚不完全清楚。在这里，我们的目的是提高对肿瘤微环境中发生的分子机制的理解，并确定生物标志物、治疗靶点和改善 PAAD 治疗的潜在药物。根据具有不同疾病结果的免疫热或冷 PAAD 亚型对患者进行分类。进行 Cox 回归和加权相关网络分析来构建一个新的基因特征，称为“热肿瘤、预后和免疫相关基因下调”(DPIRG)，用于通过机器学习开发 PAAD 的预后模型。毫升）。全面分析了DPIRGs在PAAD中的作用，并通过ML识别了能够区分PAAD免疫亚型和预测预后的生物标志物基因。使用公共单细胞转录组和蛋白质组资源验证生物标志物的表达。通过分子对接研究鉴定出使冷肿瘤变热的候选药物和相应的靶蛋白。使用DPIRG签名作为输入数据，从137个ML组合中选择生存随机森林和偏最小二乘回归Cox的组合来构建优化的PAAD预后模型。通过分析遗传/表观遗传改变、免疫浸润、通路富集和 miRNA 调控，研究了 DPIRG 的作用和分子机制。鉴定了生物标志物和潜在的治疗靶点，包括 PLEC、TRPV1 和 ITGB4 等，并验证了生物标志物的细胞类型特异性表达。候选药物包括沙利度胺、SB-431542 和博来霉素 A2，是根据其有利地调节 DPIRG 表达的能力而确定的。通过结合多种 ML 算法，我们开发了一种在 PAAD 队列中具有出色性能的新型预后模型。事实证明，ML 对于识别生物标志物和改善 PAAD 患者分层和免疫治疗的潜在靶点也具有强大的作用。© 2024。作者。

Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, we aimed to improve our understanding of the molecular mechanisms occurring in the tumor microenvironment and to identify biomarkers, therapeutic targets, and potential drugs to improve PAAD treatment.Patients were categorized according to immunologically hot or cold PAAD subtypes with distinct disease outcomes. Cox regression and weighted correlation network analysis were performed to construct a novel gene signature, referred to as 'Downregulated in hot tumors, Prognostic, and Immune-Related Genes' (DPIRGs), which was used to develop prognostic models for PAAD via machine learning (ML). The role of DPIRGs in PAAD was comprehensively analyzed, and biomarker genes able to distinguish PAAD immune subtypes and predict prognosis were identified by ML. The expression of biomarkers was verified using public single-cell transcriptomic and proteomic resources. Drug candidates for turning cold tumors hot and corresponding target proteins were identified via molecular docking studies.Using the DPIRG signature as input data, a combination of survival random forest and partial least squares regression Cox was selected from 137 ML combinations to construct an optimized PAAD prognostic model. The effects and molecular mechanisms of DPIRGs were investigated by analysis of genetic/epigenetic alterations, immune infiltration, pathway enrichment, and miRNA regulation. Biomarkers and potential therapeutic targets, including PLEC, TRPV1, and ITGB4, among others, were identified, and the cell type-specific expression of the biomarkers was validated. Drug candidates, including thalidomide, SB-431542, and bleomycin A2, were identified based on their ability to modulate DPIRG expression favorably.By combining multiple ML algorithms, we developed a novel prognostic model with excellent performance in PAAD cohorts. ML also proved to be powerful for identifying biomarkers and potential targets for improved PAAD patient stratification and immunotherapy.© 2024. The Author(s).