Importance of gene expression signatures in pancreatic cancer prognosis and the establishment of a prediction model
Received 24 August 2018
Accepted for publication 9 November 2018
Published 27 December 2018 Volume 2019:11 Pages 273—283
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Professor Nakshatri
Xiaokai Yan,1,* Haifeng Wan,1 Xiangyong Hao,2 Tian Lan,1 Wei Li,1 Lin Xu,1,3 Kefei Yuan,1,3,* Hong Wu1,3
1Department of Liver Surgery and Liver Transplantation, West China Hospital, Sichuan University, Chengdu, China; 2Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, Gansu Province, China; 3Laboratory of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
*These authors contributed equally to this work
Background and aim: 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.
Methods: 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.
Results: A total of 945 DEGs were identified in five Gene Expression Omnibus datasets, and four SRGs (LYRM1, KNTC1, IGF2BP2, and CDC6) 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.
Conclusion: 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.
Keywords: risk score, nomogram, TCGA, GEO
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