An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer
- 주제(키워드) pancreatic ductal adenocarcinoma , systems biology , meta-analysis , machine learning , next-generation sequencing , transcriptomics , diagnostic biomarker , prognostic biomarker
- 주제(기타) Oncology
- 설명문(일반) [Nguyen Phuoc Long; Nguyen Hoang Anh; Yoon, Sang Jun; Min, Jung Eun; Kim, Hyung Min; Kwon, Sung Won] Seoul Natl Univ, Coll Pharm, Seoul 08826, South Korea; [Jung, Kyung Hee; Yan, Hong Hua; Hong, Soon-Sun] Inha Univ, Coll Med, Dept Biomed Sci, 3 Ga, Incheon 400712, South Korea; [Tran Diem Nghi] Vietnam Natl Univ, Sch Med, Ho Chi Minh 70000, Vietnam; [Park, Seongoh; Lim, Johan] Seoul Natl Univ, Dept Stat, Seoul 08826, South Korea; [Lim, Joo Han; Kim, Joon Mee] Inha Univ, Coll Med, Dept Med, 3 Ga, Incheon 400712, South Korea; [Lee, Sanghyuk] Ewha Womans Univ, Div Life & Pharmaceut Sci, Seoul 120750, South Korea
- 등재 SCIE, SCOPUS
- OA유형 Green Published, Green Submitted, gold
- 발행기관 MDPI
- 발행년도 2019
- URI http://www.dcollection.net/handler/ewha/000000159705
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/cancers11020155
- PubMed https://pubmed.ncbi.nlm.nih.gov/30700038
초록/요약
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.
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