Deng Zu-Liang, Zhou Ding-Zhong, Cao Su-Juan, Li Qing, Zhang Jian-Fang, Xie Hui
Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China.
Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China.
Inflammation. 2022 Aug;45(4):1732-1751. doi: 10.1007/s10753-022-01657-6. Epub 2022 Mar 23.
Pancreatic adenocarcinoma (PAAD) is a highly dangerous malignant tumor of the digestive tract, and difficult to diagnose, treat, and predict the prognosis. As we all know, tumor and inflammation can affect each other, and thus the inflammatory response in the microenvironment can be used to affect the prognosis. So far, the prognostic value of inflammatory response-related genes in PAAD is still unclear. Therefore, this study aimed to explore the inflammatory response-related genes for predicting the prognosis of PAAD. In this study, the mRNA expression profiles of PAAD patients and the corresponding clinical characteristics data of PAAD patients were downloaded from the public database. The least absolute shrinkage and selection operator (LASSO) Cox analysis model was used to identify and construct the prognostic gene signature in The Cancer Genome Atlas (TCGA) cohort. The PAAD patients used for verification are from the International Cancer Genome Consortium (ICGC) cohort. The Kaplan-Meier method was used to compare the overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were performed to identify the independent predictors of OS. Gene set enrichment analysis (GSEA) was performed to obtain gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the correlation between gene expression and immune infiltrates was investigated via single sample gene set enrichment analysis (ssGSEA). The GEPIA database was performed to examine prognostic genes in PAAD. LASSO Cox regression analysis was used to construct a model of inflammatory response-related gene signature. Compared with the low-risk group, patients in the high-risk group had significantly lower OS. The receiver operating characteristic curve (ROC) analysis confirmed the signature's predictive capacity. Multivariate Cox analysis showed that risk score is an independent predictor of OS. Functional analysis shows that the immune status between the two risk groups is significantly different, and the cancer-related pathways were abundant in the high-risk group. Moreover, the risk score is significantly related to tumor grade, stage, and immune infiltration types. It was also obtained that the expression level of prognostic genes was significantly correlated with the sensitivity of cancer cells to anti-tumor drugs. In addition, there are significant differences in the expression of PAAD tissues and adjacent non-tumor tissues. The novel signature constructed from five inflammatory response-related genes can be used to predict prognosis and affect the immune status of PAAD. In addition, suppressing these genes may be a treatment option.
胰腺腺癌(PAAD)是一种极具危险性的消化道恶性肿瘤,难以诊断、治疗及预测预后。众所周知,肿瘤与炎症会相互影响,因此微环境中的炎症反应可用于影响预后。目前,炎症反应相关基因在PAAD中的预后价值仍不明确。因此,本研究旨在探索用于预测PAAD预后的炎症反应相关基因。在本研究中,从公共数据库下载了PAAD患者的mRNA表达谱及PAAD患者相应的临床特征数据。使用最小绝对收缩和选择算子(LASSO)Cox分析模型在癌症基因组图谱(TCGA)队列中识别并构建预后基因特征。用于验证的PAAD患者来自国际癌症基因组联盟(ICGC)队列。采用Kaplan-Meier方法比较高风险组和低风险组之间的总生存期(OS)。进行单因素和多因素Cox分析以确定OS的独立预测因子。进行基因集富集分析(GSEA)以获得基因本体(GO)术语和京都基因与基因组百科全书(KEGG)通路,并通过单样本基因集富集分析(ssGSEA)研究基因表达与免疫浸润之间的相关性。利用GEPIA数据库检测PAAD中的预后基因。采用LASSO Cox回归分析构建炎症反应相关基因特征模型。与低风险组相比,高风险组患者的OS显著更低。受试者工作特征曲线(ROC)分析证实了该特征的预测能力。多因素Cox分析表明风险评分是OS的独立预测因子。功能分析表明,两个风险组之间的免疫状态存在显著差异,且高风险组中与癌症相关的通路丰富。此外,风险评分与肿瘤分级、分期及免疫浸润类型显著相关。还发现预后基因的表达水平与癌细胞对抗肿瘤药物的敏感性显著相关。此外,PAAD组织与相邻非肿瘤组织的表达存在显著差异。由五个炎症反应相关基因构建的新型特征可用于预测PAAD的预后并影响其免疫状态。此外,抑制这些基因可能是一种治疗选择。