TY - JOUR
T1 - An integrative data mining and omics-based translational model for the identification and validation of oncogenic biomarkers of pancreatic cancer
AU - Long, Nguyen Phuoc
AU - Jung, Kyung Hee
AU - Anh, Nguyen Hoang
AU - Yan, Hong Hua
AU - Nghi, Tran Diem
AU - Park, Seongoh
AU - Yoon, Sang Jun
AU - Min, Jung Eun
AU - Kim, Hyung Min
AU - Lim, Joo Han
AU - Kim, Joon Mee
AU - Lim, Johan
AU - Lee, Sanghyuk
AU - Hong, Soon Sun
AU - Kwon, Sung Won
N1 - Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Diagnostic biomarker
KW - Machine learning
KW - Meta-analysis
KW - Next-generation sequencing
KW - Pancreatic ductal adenocarcinoma
KW - Prognostic biomarker
KW - Systems biology
KW - Transcriptomics
UR - http://www.scopus.com/inward/record.url?scp=85062356774&partnerID=8YFLogxK
U2 - 10.3390/cancers11020155
DO - 10.3390/cancers11020155
M3 - Article
AN - SCOPUS:85062356774
SN - 2072-6694
VL - 11
JO - Cancers
JF - Cancers
IS - 2
M1 - 155
ER -