Cai Weiyang, Bao Wenming, Chen Shengwei, Yang Yan, Li Yanyan
Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Cancer Cell Int. 2021 Dec 20;21(1):698. doi: 10.1186/s12935-021-02378-w.
Pancreatic cancer is one of the most common malignancies worldwide. In recent years, specific metabolic activities, which involves the development of tumor, caused wide public concern. In this study, we wish to explore the correlation between metabolism and progression of tumor.
A retrospective analysis including 95 patients with pancreatic ductal adenocarcinoma (PDAC) and PDAC patients from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and The Gene Expression Omnibus (GEO) database were involved in our study. Multivariate Cox regression analysis was used to construct the prognosis model. The potential connection between metabolism and immunity of PDAC was investigated through a weighted gene co-expression network analysis (WGCNA). 22 types of Tumor-infiltrating immune cells (TIICs) between high-risk and low-risk groups were estimated through CIBERSORT. Moreover, the potential immune-related signaling pathways between high-risk and low-risk groups were explored through the gene set enrichment analysis (GSEA). The role of key gene GMPS in developing pancreatic tumor was further investigated through CCK-8, colony-information, and Transwell.
The prognostic value of the MetS factors was analyzed using the Cox regression model, and a clinical MetS-based nomogram was established. Then, we established a metabolism-related signature to predict the prognosis of PDAC patients based on the TCGA databases and was validated in the ICGC database and the GEO database to find the distinct molecular mechanism of MetS genes in PDAC. The result of WGCNA showed that the blue module was associated with risk score, and genes in the blue module were found to be enriched in the immune-related signaling pathway. Furthermore, the result of CIBERSORT demonstrated that proportions of T cells CD8, T cells Regulatory, Tregs NK cells Activated, Dendritic cells Activated, and Mast cells Resting were different between high-risk and low-risk groups. These differences are potential causes of different prognoses of PDAC patients. GSEA and the protein-protein interaction network (PPI) further revealed that our metabolism-related signature was significantly enriched in immune-related biological processes. Moreover, knockdown of GMPS in PDAC cells suppressed proliferation, migration, and invasion of tumor cells, whereas overexpression of GMPS performed oppositely.
The results shine light on fundamental mechanisms of metabolic genes on PDAC and establish a reliable and referable signature to evaluate the prognosis of PDAC. GMPS was identified as a potential candidate oncogene with in PDAC, which can be a novel biomarker and therapeutic target for PDAC treatment.
胰腺癌是全球最常见的恶性肿瘤之一。近年来，涉及肿瘤发生发展的特定代谢活动引起了广泛关注。在本研究中，我们希望探讨代谢与肿瘤进展之间的相关性。
本研究纳入了95例胰腺导管腺癌（PDAC）患者，并回顾性分析了来自癌症基因组图谱（TCGA）、国际癌症基因组联盟（ICGC）和基因表达综合数据库（GEO）的PDAC患者数据。采用多因素Cox回归分析构建预后模型。通过加权基因共表达网络分析（WGCNA）研究PDAC代谢与免疫之间的潜在联系。通过CIBERSORT评估高危组和低危组之间22种肿瘤浸润免疫细胞（TIIC）的比例。此外，通过基因集富集分析（GSEA）探索高危组和低危组之间潜在的免疫相关信号通路。通过CCK-8、集落形成实验和Transwell实验进一步研究关键基因GMPS在胰腺肿瘤发生发展中的作用。
使用Cox回归模型分析代谢综合征（MetS）因素的预后价值，并建立了基于临床MetS的列线图。然后，我们基于TCGA数据库建立了一个与代谢相关的特征来预测PDAC患者的预后，并在ICGC数据库和GEO数据库中进行了验证，以发现MetS基因在PDAC中的独特分子机制。WGCNA结果显示蓝色模块与风险评分相关，且蓝色模块中的基因在免疫相关信号通路中富集。此外，CIBERSORT结果表明，高危组和低危组之间CD8+T细胞、调节性T细胞、活化的自然杀伤细胞、活化的树突状细胞和静止的肥大细胞的比例存在差异。这些差异是PDAC患者预后不同的潜在原因。GSEA和蛋白质-蛋白质相互作用网络（PPI）进一步揭示，我们的代谢相关特征在免疫相关生物学过程中显著富集。此外，敲低PDAC细胞中的GMPS可抑制肿瘤细胞的增殖、迁移和侵袭，而GMPS的过表达则产生相反的效果。
本研究结果揭示了代谢基因在PDAC中的基本机制，并建立了一个可靠且可参考的特征来评估PDAC的预后。GMPS被确定为PDAC中一种潜在的候选癌基因，可为PDAC治疗提供新的生物标志物和治疗靶点。
