Department of Anesthesiology, China Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China.
Department of Vascular Surgery, China Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China.
Oncol Rep. 2019 Mar;41(3):1521-1530. doi: 10.3892/or.2019.6979. Epub 2019 Jan 22.
Pancreatic adenocarcinoma (PAC) is the most common type of pancreatic cancer, which commonly has an unfavorable prognosis. The present study aimed to develop a novel prognostic prediction strategy for PAC patients. mRNA sequencing data of PAC (the training dataset) were extracted from The Cancer Genome Atlas database, and the validation datasets (GSE62452 and GSE79668) were acquired from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between good and poor prognosis groups were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Subsequently, the risk score system was constructed and confirmed using Kaplan‑Meier (KM) survival analysis. After the survival associated‑clinical factors were screened using Cox regression analysis, they were performed with stratified analysis. Using DAVID tool, the DEGs correlated with risk scores were conducted with enrichment analysis. The results revealed that there were a total of 242 DEGs between the poor and good prognosis groups. Afterwards, a risk score system was constructed based on 6 prognosis‑associated genes (CXCL11, FSTL4, SEZ6L, SPRR1B, SSTR2 and TINAG), which was confirmed in both the training and validation datasets. Cox regression analysis showed that risk score, targeted molecular therapy, and new tumor (the new tumor event days after the initial treatment according to the TCGA database) were significantly related to clinical prognosis. Under the same clinical condition, 6 clinical factors (age, history of chronic pancreatitis, alcohol consumption, radiation therapy, targeted molecular therapy and new tumor (event days) had significant associations with clinical prognosis. Under the same risk condition, only targeted molecular therapy was significantly correlated with clinical prognosis. In conclusion, the 6‑gene risk score system may be a promising strategy for predicting the outcome of PAC patients.
胰腺导管腺癌（PAC）是最常见的胰腺癌类型，通常预后不良。本研究旨在为 PAC 患者开发一种新的预后预测策略。从癌症基因组图谱数据库中提取 PAC 的 mRNA 测序数据（训练数据集），并从基因表达综合数据库中获取验证数据集（GSE62452 和 GSE79668）。使用 limma 包分析好预后组和差预后组之间的差异表达基因（DEGs），然后使用 Cox 回归分析筛选预后相关基因。随后，使用 Kaplan-Meier（KM）生存分析构建和验证风险评分系统。使用 Cox 回归分析筛选与生存相关的临床因素后，进行分层分析。使用 DAVID 工具对与风险评分相关的 DEGs 进行富集分析。结果显示，在预后良好组和预后不良组之间共有 242 个 DEGs。随后，基于 6 个预后相关基因（CXCL11、FSTL4、SEZ6L、SPRR1B、SSTR2 和 TINAG）构建了一个风险评分系统，在训练集和验证集中均得到了验证。Cox 回归分析表明，风险评分、靶向分子治疗和新肿瘤（根据 TCGA 数据库，初始治疗后新肿瘤的发生天数）与临床预后显著相关。在相同的临床条件下，6 个临床因素（年龄、慢性胰腺炎病史、饮酒、放射治疗、靶向分子治疗和新肿瘤（事件天数）与临床预后有显著关联。在相同的风险条件下，只有靶向分子治疗与临床预后显著相关。总之，该 6 基因风险评分系统可能是预测 PAC 患者预后的一种有前途的策略。
