PDF(Pubmed)
Abstract：
UNASSIGNED: Thyroid cancer is the most common malignancy of the endocrine system. PANoptosis is a specific form of inflammatory cell death. It mainly includes pyroptosis, apoptosis and necrotic apoptosis. There is increasing evidence that PANoptosis plays a crucial role in tumour development. However, no pathogenic mechanism associated with PANoptosis in thyroid cancer has been identified.
UNASSIGNED: Based on the currently identified PANoptosis genes, a dataset of thyroid cancer patients from the GEO database was analysed. To screen the common differentially expressed genes of thyroid cancer and PANoptosis. To analyse the functional characteristics of PANoptosis-related genes (PRGs) and screen key expression pathways. The prognostic model was established by LASSO regression and key genes were identified. The association between hub genes and immune cells was evaluated based on the CIBERSORT algorithm. Predictive models were validated by validation datasets, immunohistochemistry as well as drug-gene interactions were explored.
UNASSIGNED: The results showed that eight key genes (NUAK2, TNFRSF10B, TNFRSF10C, TNFRSF12A, UNC5B, and PMAIP1) exhibited good diagnostic performance in differentiating between thyroid cancer patients and controls. These key genes were associated with macrophages, CD4+ T cells and neutrophils. In addition, PRGs were mainly enriched in the immunomodulatory pathway and TNF signalling pathway. The predictive performance of the model was confirmed in the validation dataset. The DGIdb database reveals 36 potential therapeutic target drugs for thyroid cancer.
UNASSIGNED: Our study suggests that PANoptosis may be involved in immune dysregulation in thyroid cancer by regulating macrophages, CD4+ T cells and activated T and B cells and TNF signalling pathways. This study suggests potential targets and mechanisms for thyroid cancer development.
UNASSIGNED: Based on the currently identified PANoptosis genes, a dataset of thyroid cancer patients from the GEO database was analysed. To screen the common differentially expressed genes of thyroid cancer and PANoptosis. To analyse the functional characteristics of PANoptosis-related genes (PRGs) and screen key expression pathways. The prognostic model was established by LASSO regression and key genes were identified. The association between hub genes and immune cells was evaluated based on the CIBERSORT algorithm. Predictive models were validated by validation datasets, immunohistochemistry as well as drug-gene interactions were explored.
UNASSIGNED: The results showed that eight key genes (NUAK2, TNFRSF10B, TNFRSF10C, TNFRSF12A, UNC5B, and PMAIP1) exhibited good diagnostic performance in differentiating between thyroid cancer patients and controls. These key genes were associated with macrophages, CD4+ T cells and neutrophils. In addition, PRGs were mainly enriched in the immunomodulatory pathway and TNF signalling pathway. The predictive performance of the model was confirmed in the validation dataset. The DGIdb database reveals 36 potential therapeutic target drugs for thyroid cancer.
UNASSIGNED: Our study suggests that PANoptosis may be involved in immune dysregulation in thyroid cancer by regulating macrophages, CD4+ T cells and activated T and B cells and TNF signalling pathways. This study suggests potential targets and mechanisms for thyroid cancer development.
摘要：
■甲状腺癌是内分泌系统中最常见的恶性肿瘤。PANoptosis是一种特定形式的炎性细胞死亡。它主要包括焦亡，细胞凋亡和坏死细胞凋亡。越来越多的证据表明，PANoptosis在肿瘤发展中起着至关重要的作用。然而,在甲状腺癌中尚未发现与PANoptosis相关的致病机制.
■根据目前鉴定的PANoptosis基因，对GEO数据库中甲状腺癌患者的数据集进行了分析.目的筛选甲状腺癌和PANoptosis常见的差异表达基因。分析PANoptosis相关基因(PRGs)的功能特点，筛选关键表达通路。通过LASSO回归建立预后模型并鉴定关键基因。基于CIBERSORT算法评估了hub基因与免疫细胞之间的关联。预测模型通过验证数据集进行了验证，研究了免疫组织化学以及药物-基因相互作用。
■结果显示8个关键基因(NUAK2,TNFRSF10B,TNFRSF10C,TNFRSF12A,UNC5B,和PMAIP1）在区分甲状腺癌患者和对照组方面表现出良好的诊断性能。这些关键基因与巨噬细胞有关，CD4+T细胞和中性粒细胞。此外,PRGs主要富集在免疫调节通路和TNF信号通路中。模型的预测性能在验证数据集中得到证实。DGIdb数据库揭示了36种潜在的甲状腺癌治疗靶点药物。
■我们的研究表明，PANoptosis可能通过调节巨噬细胞参与甲状腺癌的免疫失调，CD4+T细胞和活化的T和B细胞以及TNF信号通路。这项研究提出了甲状腺癌发展的潜在目标和机制。
■根据目前鉴定的PANoptosis基因，对GEO数据库中甲状腺癌患者的数据集进行了分析.目的筛选甲状腺癌和PANoptosis常见的差异表达基因。分析PANoptosis相关基因(PRGs)的功能特点，筛选关键表达通路。通过LASSO回归建立预后模型并鉴定关键基因。基于CIBERSORT算法评估了hub基因与免疫细胞之间的关联。预测模型通过验证数据集进行了验证，研究了免疫组织化学以及药物-基因相互作用。
■结果显示8个关键基因(NUAK2,TNFRSF10B,TNFRSF10C,TNFRSF12A,UNC5B,和PMAIP1）在区分甲状腺癌患者和对照组方面表现出良好的诊断性能。这些关键基因与巨噬细胞有关，CD4+T细胞和中性粒细胞。此外,PRGs主要富集在免疫调节通路和TNF信号通路中。模型的预测性能在验证数据集中得到证实。DGIdb数据库揭示了36种潜在的甲状腺癌治疗靶点药物。
■我们的研究表明，PANoptosis可能通过调节巨噬细胞参与甲状腺癌的免疫失调，CD4+T细胞和活化的T和B细胞以及TNF信号通路。这项研究提出了甲状腺癌发展的潜在目标和机制。
