Yan Xiaokai, Wan Haifeng, Hao Xiangyong, Lan Tian, Li Wei, Xu Lin, Yuan Kefei, Wu Hong
Department of Liver Surgery and Liver Transplantation, West China Hospital, Sichuan University, Chengdu, China,
Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China.
Cancer Manag Res. 2018 Dec 27;11:273-283. doi: 10.2147/CMAR.S185205. eCollection 2019.
Pancreatic cancer (PC) is one of the most common tumors with a poor prognosis. The current American Joint Committee on Cancer (AJCC) staging system, based on the anatomical features of tumors, is insufficient to predict PC outcomes. The current study is endeavored to identify important prognosis-related genes and build an effective predictive model.
Multiple public datasets were used to identify differentially expressed genes (DEGs) and survival-related genes (SRGs). Bioinformatics analysis of DEGs was used to identify the main biological processes and pathways involved in PC. A risk score based on SRGs was computed through a univariate Cox regression analysis. The performance of the risk score in predicting PC prognosis was evaluated with survival analysis, Harrell's concordance index (C-index), area under the curve (AUC), and calibration plots. A predictive nomogram was built through integrating the risk score with clinicopathological information.
A total of 945 DEGs were identified in five Gene Expression Omnibus datasets, and four SRGs (, , , and ) were significantly associated with PC progression and prognosis in four datasets. The risk score showed relatively good performance in predicting prognosis in multiple datasets. The predictive nomogram had greater C-index and AUC values, compared with those of the AJCC stage and risk score.
This study identified four new biomarkers that are significantly associated with the carcinogenesis, progression, and prognosis of PC, which may be helpful in studying the underlying mechanism of PC carcinogenesis. The predictive nomogram showed robust performance in predicting PC prognosis. Therefore, the current model may provide an effective and reliable guide for prognosis assessment and treatment decision-making in the clinic.
胰腺癌（PC）是最常见的肿瘤之一，预后较差。当前基于肿瘤解剖特征的美国癌症联合委员会（AJCC）分期系统不足以预测PC的预后。本研究致力于识别重要的预后相关基因并建立有效的预测模型。
使用多个公共数据集来识别差异表达基因（DEGs）和生存相关基因（SRGs）。对DEGs进行生物信息学分析，以识别PC中涉及的主要生物学过程和途径。通过单变量Cox回归分析计算基于SRGs的风险评分。通过生存分析、Harrell一致性指数（C指数）、曲线下面积（AUC）和校准图评估风险评分在预测PC预后方面的性能。通过将风险评分与临床病理信息整合建立预测列线图。
在五个基因表达综合数据库中总共识别出945个DEGs，在四个数据库中四个SRGs（，，，和）与PC进展和预后显著相关。风险评分在多个数据集的预后预测中表现出相对较好的性能。与AJCC分期和风险评分相比，预测列线图具有更高的C指数和AUC值。
本研究识别出四个与PC的发生、进展和预后显著相关的新生物标志物，这可能有助于研究PC致癌的潜在机制。预测列线图在预测PC预后方面表现出强大的性能。因此，当前模型可为临床预后评估和治疗决策提供有效且可靠的指导。
