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Wang TH, Lee CY, Lee TY, Huang HD, Hsu JBK, Chang TH. Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion. Cancers (Basel) 2021; 13:cancers13112528. [PMID: 34064004 PMCID: PMC8196729 DOI: 10.3390/cancers13112528] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/18/2021] [Accepted: 05/18/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Around 30% of men treated with adjuvant therapy experience recurrences of prostate cancer (PC). Current monitoring of the relapse of PC requires regular postoperative prostate-specific antigen (PSA) value follow-up. Our study aims to identify potential multiomics biomarkers using modern computational analytic methods, deep learning (DL), similarity network fusion (SNF), and the Cancer Genome Atlas (TCGA) prostate adenocarcinoma (PRAD) dataset. Six significantly intersected omics biomarkers from the two models, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23) were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panels and clinical information achieve p-value = 2.97 × 10−15 and C-index = 0.713, and the prediction performance of five-year recurrence achieves AUC = 0.789. The results show that the multiomics panel provided valuable biomarkers for the early detection of high-risk recurrent patients, and integrating multiomics data gave us the power to detect the complex mechanisms of cancer among the interactions of different genetic and epigenetic factors. Abstract This study is to identify potential multiomics biomarkers for the early detection of the prognostic recurrence of PC patients. A total of 494 prostate adenocarcinoma (PRAD) patients (60-recurrent included) from the Cancer Genome Atlas (TCGA) portal were analyzed using the autoencoder model and similarity network fusion. Then, multiomics panels were constructed according to the intersected omics biomarkers identified from the two models. Six intersected omics biomarkers, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23), were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panel achieved p-value = 5.33 × 10−9, which is better than the former study (p-value = 5 × 10−7). Additionally, when evaluating the selected multiomics biomarkers with clinical information (Gleason score, age, and cancer stage), a high-performance prediction model was generated with C-index = 0.713, p-value = 2.97 × 10−15, and AUC = 0.789. The risk score generated from the selected multiomics biomarkers worked as an effective indicator for the prediction of PRAD recurrence. This study helps us to understand the etiology and pathways of PRAD and further benefits both patients and physicians with potential prognostic biomarkers when making clinical decisions after surgical treatment.
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Affiliation(s)
- Tzu-Hao Wang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (T.-H.W.); (C.-Y.L.)
- School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Cheng-Yang Lee
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (T.-H.W.); (C.-Y.L.)
- Office of Information Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China; (T.-Y.L.); (H.-D.H.)
- School of Life and Health Science, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Hsien-Da Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China; (T.-Y.L.); (H.-D.H.)
- School of Life and Health Science, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Justin Bo-Kai Hsu
- Department of Medical Research, Taipei Medical University Hospital, Taipei 110, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Correspondence: (J.B.-K.H.); (T.-H.C.)
| | - Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (T.-H.W.); (C.-Y.L.)
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Correspondence: (J.B.-K.H.); (T.-H.C.)
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Abstract
Antibodies targeting the extracellular domains of ErbB receptors have been extensively studied for cancer drug development. This work has led to clinical approval of monoclonal antibodies against the well-known oncogenes EGFR and ErbB2. Here we discuss the biological activities of ErbB4, a less-studied member of the EGFR/ErbB growth factor receptor family and speculate on the potential clinical relevance of antibodies targeting ErbB4. In addition to their significance as therapeutics, the role of ErbB4 antibodies in prognostic and predictive applications is surveyed.
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Affiliation(s)
- Maija Hollmén
- Department of Medical Biochemistry and Genetics, and Medicity Research Laboratory, University of Turku, and Turku Graduate School of Biomedical Sciences, Turku, Finland
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