Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer
Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer
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DOI：
10.1007/s13402-023-00836-3
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发表时间：
2023-07-11
影响因子：
6.6
通讯作者：
Zhao，Yupei
中科院分区：
文献类型：
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作者：
Chen，Guangyu;Liu，Yueze;Zhao，Yupei
目的胰腺癌(PC)患者可分为不同的分子亚型，并可从精确的治疗中获益。然而，肿瘤微环境(TME)中代谢和免疫亚型之间的相互作用仍不清楚。方法采用非监督共识聚类和ssGSEA分析方法构建与代谢和免疫相关的分子亚型。不同的代谢和免疫亚型具有不同的预后和TME。然后，根据代谢亚型和免疫亚型的差异表达基因(Deg)，通过Lasso回归和Cox回归筛选出重叠的基因，并利用它们构建风险评分特征，从而将PC患者分为高危和低危两组。建立诺模图来预测每个PC患者的存活率。采用RT-PCR、体外细胞增殖实验、PC类化合物、免疫组织化学染色等方法确定与PCc相关的关键癌基因。结果在癌症药物敏感性基因组学数据库中，高危患者对多种化疗药物的疗效较好。我们建立了一个包含风险组、年龄和阳性淋巴结数的诺模图，以预测平均1年、2年和3年曲线下面积(AUC)等于0.792、0.752和0.751的每个PC患者的存活率。FAM83A、KLF5、LIPH、MYEOV在PC细胞系和PC组织中表达上调。FAM83A、KLF5、LIPH、MYEOV基因敲除可抑制PC细胞和PC有机物的增殖。结论基于代谢和免疫分子亚型的风险评分特征能准确预测PC的预后和指导治疗，代谢免疫生物标志物可能为PC提供新的靶向治疗。
PurposePatients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancerMethodsUnsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PCResultsHigh-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoidsConclusionThe risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC