An integrative data mining and omics-based translational model for the identification and validation of oncogenic biomarkers of pancreatic cancer

Nguyen Phuoc Long, Kyung Hee Jung, Nguyen Hoang Anh, Hong Hua Yan, Tran Diem Nghi, Seongoh Park, Sang Jun Yoon, Jung Eun Min, Hyung Min Kim, Joo Han Lim, Joon Mee Kim, Johan Lim, Sanghyuk Lee, Soon Sun Hong, Sung Won Kwon

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR) OS = 2.2, p-value < 0.001), ANXA2 (HR OS = 2.1, p-value < 0.001), and LAMC2 (HR DFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

Original languageEnglish
Article number155
JournalCancers
Volume11
Issue number2
DOIs
StatePublished - 2019

Keywords

  • Diagnostic biomarker
  • Machine learning
  • Meta-analysis
  • Next-generation sequencing
  • Pancreatic ductal adenocarcinoma
  • Prognostic biomarker
  • Systems biology
  • Transcriptomics

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