Wang Xiaoguang, Ni Man, Han Daxiong
School of Pharmaceutical Sciences, Xiamen University, Xiamen 361102, China.
Evid Based Complement Alternat Med. 2022 Jul 8;2022:3660110. doi: 10.1155/2022/3660110. eCollection 2022.
Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis.
Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan-Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases.
Univariate Cox regression and Kaplan-Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan-Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( < 0.05). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells.
The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.
胰腺癌（PCa）预后生物标志物为改善该疾病的生存结局提供依据。本研究旨在基于微阵列生物信息学分析的基因表达谱鉴定一种预后风险模型。
使用单变量Cox回归和Kaplan-Meier生存分析在TCGA-PAAD队列中鉴定预后免疫基因。多变量Cox回归（stepAIC）用于从蛋白质-蛋白质相互作用（PPI）网络中的前20个枢纽基因中鉴定预后基因。建立了预后风险模型，并在GSE62452中验证了其预测PCa总生存的性能。使用在线数据库分析PCa肿瘤中的基因突变和浸润免疫细胞。
单变量Cox回归和Kaplan-Meier生存分析鉴定出128个预后基因。多变量Cox回归（stepAIC）从PPI网络中的20个枢纽基因中鉴定出5个预后基因（PLCG1、MET、TNFSF10、CXCL9和TLR3）。使用这5个基因的特征建立了预后风险模型。该模型在预测TCGA和GSE62452队列中的3年和5年总生存方面具有中等到高的准确性（AUC＞0.700）。Kaplan-Meier生存分析表明，高风险评分与PCa的不良生存结局相关（＜0.05）。此外，这5个基因的突变与不良生存相关。这5个基因与多种免疫细胞相关。
预后风险模型与PCa患者的生存显著相关。该模型通过调节免疫细胞浸润来调节PCa肿瘤进展和预后。
