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An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

1
College of Pharmacy, Seoul National University, Seoul 08826, Korea
2
Department of Biomedical Sciences, College of Medicine, Inha University, 3-ga, Sinheung-dong, Jung-gu, Incheon 400-712, Korea
3
School of Medicine, Vietnam National University, Ho Chi Minh 70000, Vietnam
4
Department of Statistics, Seoul National University, Seoul 08826, Korea
5
Department of Medicine, College of Medicine, Inha University, 3-ga, Sinheung-dong, Jung-gu, Incheon 400-712, Korea
6
Division of Life and Pharmaceutical Sciences, Ewha Womans University, Seoul 120-750, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2019, 11(2), 155; https://doi.org/10.3390/cancers11020155
Received: 14 December 2018 / Revised: 18 January 2019 / Accepted: 21 January 2019 / Published: 29 January 2019
(This article belongs to the Special Issue Advances in Pancreatic Cancer Research)
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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 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 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. View Full-Text
Keywords: pancreatic ductal adenocarcinoma; systems biology; meta-analysis; machine learning; next-generation sequencing; transcriptomics; diagnostic biomarker; prognostic biomarker pancreatic ductal adenocarcinoma; systems biology; meta-analysis; machine learning; next-generation sequencing; transcriptomics; diagnostic biomarker; prognostic biomarker
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Long, N.P.; Jung, K.H.; Anh, N.H.; Yan, H.H.; Nghi, T.D.; Park, S.; Yoon, S.J.; Min, J.E.; Kim, H.M.; Lim, J.H.; Kim, J.M.; Lim, J.; Lee, S.; Hong, S.-S.; Kwon, S.W. An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer. Cancers 2019, 11, 155.

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