Frontiers in Oncology (Nov 2019)

A Long Non-coding RNA Signature to Improve Prognostic Prediction of Pancreatic Ductal Adenocarcinoma

  • Chenhao Zhou,
  • Shun Wang,
  • Qiang Zhou,
  • Jin Zhao,
  • Xianghou Xia,
  • Wanyong Chen,
  • Wanyong Chen,
  • Yan Zheng,
  • Min Xue,
  • Feng Yang,
  • Deliang Fu,
  • Yirui Yin,
  • Manar Atyah,
  • Lunxiu Qin,
  • Lunxiu Qin,
  • Yue Zhao,
  • Christiane Bruns,
  • Huliang Jia,
  • Ning Ren,
  • Ning Ren,
  • Qiongzhu Dong,
  • Qiongzhu Dong

Journal volume & issue
Vol. 9


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Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive solid malignant tumors worldwide. Increasing investigations demonstrate that long non-coding RNAs (lncRNAs) expression is abnormally dysregulated in cancers. It is crucial to identify and predict the prognosis of patients for the selection of further therapeutic treatment.Methods: PDAC lncRNAs abundance profiles were used to establish a signature that could better predict the prognosis of PDAC patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a multi-lncRNA signature in the TCGA training cohort (N = 107). The signature was then validated in a TCGA validation cohort (N = 70) and another independent Fudan cohort (N = 46).Results: A five-lncRNA signature was constructed and it was significantly related to the overall survival (OS), either in the training or validation cohorts. Through the subgroup and Cox regression analyses, the signature was proven to be independent of other clinic-pathologic parameters. Receiver operating characteristic curve (ROC) analysis also indicated that our signature had a better predictive capacity of PDAC prognosis. Furthermore, ClueGO and CluePedia analyses showed that a number of cancer-related and drug response pathways were enriched in high risk groups.Conclusions: Identifying the five-lncRNA signature (RP11-159F24.5, RP11-744N12.2, RP11-388M20.1, RP11-356C4.5, CTC-459F4.9) may provide insight into personalized prognosis prediction and new therapies for PDAC patients.