Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion.

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 st

SUBMITTER: Wang TH 

PROVIDER: S-EPMC8196729 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature