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

Title
An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer
Authors
Nguyen Phuoc LongJung, Kyung HeeNguyen Hoang AnhYan, Hong HuaTran Diem NghiPark, SeongohYoon, Sang JunMin, Jung EunKim, Hyung MinLim, Joo HanKim, Joon MeeLim, JohanLee, SanghyukHong, Soon-SunKwon, Sung Won
Ewha Authors
이상혁
SCOPUS Author ID
이상혁scopus
Issue Date
2019
Journal Title
CANCERS
ISSN
2072-6694JCR Link
Citation
CANCERS vol. 11, no. 2
Keywords
pancreatic ductal adenocarcinomasystems biologymeta-analysismachine learningnext-generation sequencingtranscriptomicsdiagnostic biomarkerprognostic biomarker
Publisher
MDPI
Indexed
SCIE; SCOPUS WOS
Document Type
Article
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.
DOI
10.3390/cancers11020155
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자연과학대학 > 생명과학전공 > Journal papers
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