Abstract:
:This study proposed a deep learning (DL) algorithm to predict survival in patients with colon adenocarcinoma (COAD) based on multi-omics integration. The survival-sensitive model was constructed using an autoencoder for DL implementation based on The Cancer Genome Atlas (TCGA) data of patients with COAD. The autoencoder framework was compared to PCA, NMF, t-SNE, and univariable Cox-PH model for identifying survival-related features. The prognostic robustness of the inferred survival risk groups was validated using three independent confirmation cohorts. Differential expression analysis, Pearson's correlation analysis, construction of miRNA-target gene network, and function enrichment analysis were performed. Two risk groups with significant survival differences were identified in TCGA set using the autoencoder-based model (log-rank p-value = 5.51e-07). The autoencoder framework showed superior performance compared to PCA, NMF, t-SNE, and the univariable Cox-PH model based on the C-index, log-rank p-value, and Brier score. The robustness of the classification model was successfully verified in three independent validation sets. There were 1271 differentially expressed genes, 10 differentially expressed miRNAs, and 12 hypermethylated genes between the survival risk groups. Among these, miR-133b and its target genes (GNB4, PTPRZ1, RUNX1T1, EPHA7, GPM6A, BICC1, and ADAMTS5) were used to construct a network. These genes were significantly enriched in ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and glucose metabolism-related pathways. The risk subgroups obtained through a multi-omics data integration pipeline using the DL algorithm had good robustness. miR-133b and its target genes could be potential diagnostic markers. The results would assist in elucidating the possible pathogenesis of COAD.
journal_name
Biosci Repjournal_title
Bioscience reportsauthors
Lv J,Wang J,Shang X,Liu F,Guo Sdoi
10.1042/BSR20201482subject
Has Abstractpub_date
2020-12-01 00:00:00eissn
0144-8463issn
1573-4935pii
227089pub_type
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journal_title:Bioscience reports
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doi:10.1007/BF01119592
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doi:10.1042/BSR20200041
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journal_title:Bioscience reports
pub_type: 杂志文章
doi:10.1042/BSR20181320
更新日期:2018-09-07 00:00:00
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journal_title:Bioscience reports
pub_type: 杂志文章,meta分析
doi:10.1042/BSR20190845
更新日期:2019-12-20 00:00:00
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journal_title:Bioscience reports
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doi:10.1042/BSR20171062
更新日期:2017-10-27 00:00:00
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doi:10.1042/BSR20190482
更新日期:2019-10-30 00:00:00
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journal_title:Bioscience reports
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pub_type: 杂志文章,meta分析
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