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整合多组学分析揭示了Toll样受体信号通路在胰腺癌中的作用。

Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer.

作者信息

Peng Jie, Sun Jiaao, Yu Youfeng, Yuan Qihang, Zhang Yong

机构信息

Ningde Clinical Medical College of Fujian Medical University, Fujian, China.

Ningde Municipal Hospital of Ningde Normal University, Fujian, China.

出版信息

Sci Rep. 2025 Jan 2;15(1):52. doi: 10.1038/s41598-024-84062-3.

Abstract

As one of the most destructive and invasive cancers, pancreatic cancer exhibits complex tumor heterogeneity, which has been a major challenge for clinicians in terms of patient treatment and prognosis. The toll-like receptor (TLR) pathway is closely related to the immune microenvironment within various cancer tissues. To explore the development pattern of pancreatic cancer and find an ideal biomarker, our research has explored the mechanism of the TLR pathway in pancreatic cancer. We collected single-cell expression data from 57,024 cells and transcriptomic data from 945 pancreatic cancer patients, and conducted a series of analyses at both the single-cell and transcriptomic levels. By calculating the TLR pathway score, we clustered pancreatic cancer patients and conducted a series of analyses including metabolic pathways, immune microenvironment, drug sensitivity and so on. In the process of building prognostic models, we screened 33 core genes related to the prognosis of pancreatic cancer, and combined a series of machine learning algorithms to build the prognosis model of pancreatic cancer. We used single cell sequencing to clarify the complex intrinsic relationship between TLR pathway and pancreatic cancer. The strongest TLR signals were observed in macrophages and endothelial cells. With the occurrence of pancreatic cancer, the TLR signal of various cell types gradually increased, but with the increase of the malignant degree of ductal epithelial cells, the TLR signal gradually weakened. Cluster analysis showed that patients with the most active TLR pathway had severe dysregulation of immune microenvironment and the worst prognosis. Finally, we combined a series of machine learning algorithms to build a pancreatic cancer prognosis model that includes four genes (NT5E, TGFBI, ANLN, and FAM83A). The model showed strong performance in predicting the survival state of pancreatic cancer samples. We explored the important role of TLR pathway in pancreatic cancer and established and validated a new prognosis model for pancreatic cancer based on TLR-related genes.

摘要

作为最具破坏性和侵袭性的癌症之一,胰腺癌表现出复杂的肿瘤异质性,这在患者治疗和预后方面一直是临床医生面临的重大挑战。Toll样受体(TLR)通路与各种癌症组织内的免疫微环境密切相关。为了探索胰腺癌的发展模式并找到理想的生物标志物,我们的研究探讨了TLR通路在胰腺癌中的作用机制。我们收集了来自57024个细胞的单细胞表达数据和来自945例胰腺癌患者的转录组数据,并在单细胞和转录组水平上进行了一系列分析。通过计算TLR通路评分,我们对胰腺癌患者进行了聚类,并进行了包括代谢途径、免疫微环境、药物敏感性等在内的一系列分析。在构建预后模型的过程中,我们筛选了33个与胰腺癌预后相关的核心基因,并结合一系列机器学习算法构建了胰腺癌的预后模型。我们使用单细胞测序来阐明TLR通路与胰腺癌之间复杂的内在关系。在巨噬细胞和内皮细胞中观察到最强的TLR信号。随着胰腺癌的发生,各种细胞类型的TLR信号逐渐增加,但随着导管上皮细胞恶性程度的增加,TLR信号逐渐减弱。聚类分析表明,TLR通路最活跃的患者免疫微环境严重失调,预后最差。最后,我们结合一系列机器学习算法构建了一个包含四个基因(NT5E、TGFBI、ANLN和FAM83A)的胰腺癌预后模型。该模型在预测胰腺癌样本的生存状态方面表现出强大的性能。我们探讨了TLR通路在胰腺癌中的重要作用,并基于TLR相关基因建立并验证了一种新的胰腺癌预后模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2772/11696379/04fde8798951/41598_2024_84062_Fig1_HTML.jpg

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