Literature DB >> 32119844

A qualitative transcriptional prognostic signature for patients with stage I-II pancreatic ductal adenocarcinoma.

Haiyan Huang1, Yi Zou2, Huarong Zhang1, Xiang Li1, Yawei Li1, Xusheng Deng1, Huaqin Sun1, Zheng Guo3, Lu Ao4.   

Abstract

Accurately prognostic evaluation of patients with stage I-II pancreatic ductal adenocarcinoma (PDAC) is of importance to treatment decision and patient management. Most previously reported prognostic signatures were based on risk scores summarized from quantitative expression measurements of signature genes, which are susceptible to experimental batch effects and impractical for clinical applications. Based on the within-sample relative expression orderings of genes, we developed a robust qualitative transcriptional prognostic signature, consisting of 64 gene pairs (64-GPS), to predict the overall survival (OS) of 161 stage I-II PDAC patients in the training dataset who were treated with surgery only. Samples were classified into the high-risk group when at least 25 of 64 gene pairs suggested it was at high risk. The signature was successfully validated in 324 samples from 6 independent datasets produced by different laboratories. All samples in the low-risk group had significantly better OS than samples in the high-risk group. Multivariate Cox regression analyses showed that the 64-GPS remained significantly associated with the OS of patients after adjusting available clinical factors. Transcriptomic analysis of the 2 prognostic subgroups showed that the differential expression signals were highly reproducible in all datasets, whereas the differences between samples grouped by the TNM staging system were weak and irreproducible. The epigenomic analysis showed that the epigenetic alternations may cause consistently transcriptional changes between the 2 different prognostic groups. The genomic analysis revealed that mutation‑induced disturbances in several key genes, such as LRMDA, MAPK10, and CREBBP, might lead to poor prognosis for PDAC patients. Conclusively, the 64-GPS can robustly predict the prognosis of patients with stage I-II PDAC, which provides theoretical basis for clinical individualized treatment.
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.

Entities:  

Year:  2020        PMID: 32119844     DOI: 10.1016/j.trsl.2020.02.004

Source DB:  PubMed          Journal:  Transl Res        ISSN: 1878-1810            Impact factor:   7.012


  2 in total

1.  A novel qualitative signature based on lncRNA pairs for prognosis prediction in hepatocellular carcinoma.

Authors:  Xiaoyun Bu; Luyao Ma; Shuang Liu; Dongsheng Wen; Anna Kan; Yujie Xu; Xuanjia Lin; Ming Shi
Journal:  Cancer Cell Int       Date:  2022-02-22       Impact factor: 5.722

2.  Metabolism-Related Gene Pairs to Predict the Clinical Outcome and Molecular Characteristics of Early Hepatocellular Carcinoma.

Authors:  Junling Wu; Zeman Lin; Daihan Ji; Zhenli Li; Huarong Zhang; Shuting Lu; Shenglin Wang; Xiaolong Liu; Lu Ao
Journal:  Cancers (Basel)       Date:  2022-08-16       Impact factor: 6.575

  2 in total

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