Biomarker identification through multiomics data analysis of prostate cancer prognostication using a deep learning model and similarity network fusion

Tzu Hao Wang, Cheng Yang Lee, Tzong Yi Lee, Hsien Da Huang, Justin Bo Kai Hsu, Tzu Hao Chang

研究成果: 雜誌貢獻文章同行評審

摘要

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 autoen-coder 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.
原文英語
文章編號2528
期刊Cancers
13
發行號11
DOIs
出版狀態已發佈 - 六月 2021

ASJC Scopus subject areas

  • 腫瘤科
  • 癌症研究

指紋

深入研究「Biomarker identification through multiomics data analysis of prostate cancer prognostication using a deep learning model and similarity network fusion」主題。共同形成了獨特的指紋。

引用此