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

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 influen

SUBMITTER: Long NP 

PROVIDER: S-EPMC6407035 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature