背景:胰腺腺癌(PAAD)通常表现出“冷”或免疫抑制性肿瘤环境,这与对免疫检查点阻断治疗的抗性有关;然而,潜在的机制还没有完全理解。这里,我们旨在提高我们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,治疗目标,以及改善PAAD治疗的潜在药物。
方法:根据具有不同疾病结局的免疫热或冷PAAD亚型对患者进行分类。进行Cox回归和加权相关网络分析以构建新的基因标签,在热瘤中被称为下调,预后,和免疫相关基因(DPIRGs),用于通过机器学习(ML)开发PAAD的预后模型。综合分析了DPIRGs在PAAD中的作用,和能够区分PAAD免疫亚型和预测预后的生物标志物基因通过ML鉴定。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了冷肿瘤变热的候选药物和相应的靶蛋白。
结果:使用DPIRG签名作为输入数据,从137ML组合中选择生存随机森林和偏最小二乘回归Cox的组合,构建优化的PAAD预后模型.通过分析遗传/表观遗传改变,研究了DPIRGs的作用和分子机制。免疫浸润,途径富集,和miRNA调控。生物标志物和潜在的治疗靶点,包括PLEC,TRPV1和ITGB4等,被确认,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基于它们有利地调节DPIRG表达的能力进行鉴定。
结论:通过组合多种ML算法,我们开发了一种在PAAD队列中表现优异的新型预后模型.ML还被证明对于识别生物标志物和改善PAAD患者分层和免疫疗法的潜在靶标是强大的。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.