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

AI Article Synopsis

  • This study aims to find multiomics biomarkers to detect early recurrence in prostate adenocarcinoma (PC) patients, using data from 494 cases from the Cancer Genome Atlas (TCGA).
  • Researchers utilized an autoencoder model and similarity network fusion to identify six key omics biomarkers, leading to the construction of a new multiomics panel.
  • The results demonstrated the panel's effectiveness in predicting recurrence risk, with promising statistical metrics that could aid clinical decisions post-surgery for PC patients.

Article 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, , cg00687383 (), and cg02318866 (; ), 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 -value = 5.33 × 10, which is better than the former study (-value = 5 × 10). 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, -value = 2.97 × 10, 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196729PMC
http://dx.doi.org/10.3390/cancers13112528DOI Listing

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