Xie Haoran, Xu Jingxian, Xie Zhiwen, Xie Ni, Lu Jiawei, Yu Lanting, Li Baiwen, Cheng Li
Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Key Laboratory of Pancreatic Diseases, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Genet. 2022 Jun 16;13:919638. doi: 10.3389/fgene.2022.919638. eCollection 2022.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors with a poor prognosis. Recently, necroptosis has been reported to participate in the progression of multiple tumors. However, few studies have revealed the relationship between necroptosis and PDAC, and the role of necroptosis in PDAC has not yet been clarified. The mRNA expression data and corresponding clinical information of PDAC patients were downloaded from the TCGA and GEO databases. The necroptosis-related genes (NRGs) were obtained from the CUSABIO website. Consensus clustering was performed to divide PDAC patients into two clusters. Univariate and LASSO Cox regression analyses were applied to screen the NRGs related to prognosis to construct the prognostic model. The predictive value of the prognostic model was evaluated by Kaplan-Meier survival analysis and ROC curve. Univariate and multivariate Cox regression analyses were used to evaluate whether the risk score could be used as an independent predictor of PDAC prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA) were used for functional enrichment analysis. Finally, using qRT-PCR examined NRGs mRNA expression . Based on the TCGA database, a total of 22 differential expressed NRGs were identified, among which eight NRGs (CAPN2, CHMP4C, PLA2G4F, PYGB, BCL2, JAK3, PLA2G4C and STAT4) that may be related to prognosis were screened by univariate Cox regression analysis. And CAPN2, CHMP4C, PLA2G4C and STAT4 were further selected to construct the prognostic model. Kaplan-Meier survival analysis and ROC curve showed that there was a significant correlation between the risk model and prognosis. Univariate and multivariate Cox regression analyses showed that the risk score of the prognostic model could be used as an independent predictor. The model efficacy was further demonstrated in the GEO cohort. Functional analysis revealed that there were significant differences in immune status between high and low-risk groups. Finally, the qRT-PCR results revealed a similar dysregulation of NRGs in PDAC cell lines. This study successfully constructed and verified a prognostic model based on NRGs, which has a good predictive value for the prognosis of PDAC patients.
胰腺导管腺癌（PDAC）是预后最差的恶性肿瘤之一。近来，有报道称坏死性凋亡参与多种肿瘤的进展。然而，很少有研究揭示坏死性凋亡与PDAC之间的关系，坏死性凋亡在PDAC中的作用尚未阐明。从TCGA和GEO数据库下载PDAC患者的mRNA表达数据及相应临床信息。从CUSABIO网站获取坏死性凋亡相关基因（NRGs）。进行一致性聚类将PDAC患者分为两个簇。应用单因素和LASSO Cox回归分析筛选与预后相关的NRGs以构建预后模型。通过Kaplan-Meier生存分析和ROC曲线评估预后模型的预测价值。使用单因素和多因素Cox回归分析评估风险评分是否可作为PDAC预后的独立预测指标。采用基因本体论（GO）、京都基因与基因组百科全书（KEGG）和单样本基因集富集分析（ssGSEA）进行功能富集分析。最后，采用qRT-PCR检测NRGs的mRNA表达。基于TCGA数据库，共鉴定出22个差异表达的NRGs，其中通过单因素Cox回归分析筛选出8个可能与预后相关的NRGs（钙蛋白酶2、染色质修饰蛋白4C、磷脂酶A2G4F、糖原磷酸化酶B、B细胞淋巴瘤2、Janus激酶3、磷脂酶A2G4C和信号转导和转录激活因子4）。进一步选择钙蛋白酶2、染色质修饰蛋白4C、磷脂酶A2G4C和信号转导和转录激活因子4构建预后模型。Kaplan-Meier生存分析和ROC曲线显示风险模型与预后之间存在显著相关性。单因素和多因素Cox回归分析表明预后模型的风险评分可作为独立预测指标。该模型的有效性在GEO队列中得到进一步验证。功能分析显示高风险组和低风险组之间免疫状态存在显著差异。最后，qRT-PCR结果显示PDAC细胞系中NRGs存在类似的失调。本研究成功构建并验证了基于NRGs的预后模型，对PDAC患者的预后具有良好的预测价值。
