Survival prediction in patients with colon adenocarcinoma via multi-omics data integration using a deep learning algorithm
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...
Uloženo v:
| Vydáno v: | Bioscience reports |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
01.12.2020
|
| Témata: | |
| ISSN: | 1573-4935, 1573-4935 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| 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. |
|---|---|
| AbstractList | 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. 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.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. |
| Author | Lv, Jiudi Liu, Fangfang Guo, Shixun Shang, Xiujuan Wang, Junjie |
| Author_xml | – sequence: 1 givenname: Jiudi surname: Lv fullname: Lv, Jiudi organization: Xinxiang Central Hospital, Xinxiang, China – sequence: 2 givenname: Junjie surname: Wang fullname: Wang, Junjie organization: Xinxiang Central Hospital, Xinxiang, China – sequence: 3 givenname: Xiujuan surname: Shang fullname: Shang, Xiujuan organization: Xinxiang Central Hospital, Xinxiang, China – sequence: 4 givenname: Fangfang surname: Liu fullname: Liu, Fangfang organization: Xinxiang Central Hospital, Xinxiang, China – sequence: 5 givenname: Shixun surname: Guo fullname: Guo, Shixun organization: Xinxiang Central Hospital, Xinxiang, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33258470$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkEtPwzAQhC1URB9w4o585BJw_EiTI1S8pEpItPdo42yLUWIH2ymCX08oReK0M6OZ77BTMrLOIiHnKbtKmeTXt6sXzjhLZc6PyCRVc5HIQqjRPz0m0xDeGGODkSdkLARXuZyzCfla9X5ndtDQzmNtdDTOUmNpB9GgjYF-mPhKtWuGGGq0ToPXxroW6M4AbfsmmsS1RgdaQ4RhGnHrYY_pg7FbCrRG7GiD4O3eN1vnB2h7So430AQ8O9wZWd_frRePyfL54Wlxs0w6lbFEg8qZqFhW55UslC5SVfFMzXXOK6mFyAWi3swBCkzrFAsGqGUOkKWbLM-04DNy-YvtvHvvMcSyNUFj04BF14eSyyxjQgn5U704VPuqxbrsvGnBf5Z_7-LfSr9wFw |
| ContentType | Journal Article |
| Copyright | Copyright 2020 The Author(s). |
| Copyright_xml | – notice: Copyright 2020 The Author(s). |
| DBID | NPM 7X8 |
| DOI | 10.1042/BSR20201482 |
| DatabaseName | PubMed MEDLINE - Academic |
| DatabaseTitle | PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Chemistry Biology |
| EISSN | 1573-4935 |
| ExternalDocumentID | 33258470 |
| Genre | Journal Article |
| GroupedDBID | --- .86 0R~ 23N 4.4 5GY 5RE 5VS 6J9 78A 7X7 AAHRG ABJNI ABUWG ACGFS ACIWK ACPRK ADBBV ADIMF AEGXH AENEX AFBBN AFRAH AGIHE AHBYD ALMA_UNASSIGNED_HOLDINGS AOIJS ATCPS BAWUL BCNDV BENPR BGNMA CS3 DIK DL5 DU5 E3Z EBD EBS EMOBN EPAXT F5P FRP GX1 H13 HCIFZ HYE HZ~ I09 IZQ KDC KQ8 LAK M2O M2P M48 M4Y MV1 NPM NU0 O9- OK1 OVD QOK QOS R4E RHI RNS RPM RPO RPX RRX SBL SDH SDM SOJ SV3 TR2 TSK U2A VC2 ~EX 7X8 PYCSY |
| ID | FETCH-LOGICAL-p560-ca5803b06d8b495c915b2657c82b4c3383eecf7aa9e1d1e90aec48aa61f686c32 |
| IEDL.DBID | 7X8 |
| ISSN | 1573-4935 |
| IngestDate | Thu Jul 10 23:42:13 EDT 2025 Sun Nov 09 08:39:27 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | deep learning multi-omics data miRNA methylation |
| Language | English |
| License | Copyright 2020 The Author(s). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-p560-ca5803b06d8b495c915b2657c82b4c3383eecf7aa9e1d1e90aec48aa61f686c32 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 33258470 |
| PQID | 2466035343 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2466035343 pubmed_primary_33258470 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-Dec-01 20201201 |
| PublicationDateYYYYMMDD | 2020-12-01 |
| PublicationDate_xml | – month: 12 year: 2020 text: 2020-Dec-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Bioscience reports |
| PublicationTitleAlternate | Biosci Rep |
| PublicationYear | 2020 |
| SSID | ssj0004934 |
| Score | 2.2597075 |
| Snippet | This study proposed a deep learning (DL) algorithm to predict survival in patients with colon adenocarcinoma (COAD) based on multi-omics integration. The... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| Title | Survival prediction in patients with colon adenocarcinoma via multi-omics data integration using a deep learning algorithm |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/33258470 https://www.proquest.com/docview/2466035343 |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEF7UKnrxUV_1xQpel6bZzeskWiwetBTbQ29lsrutAZvGpi3or3emSdGLIHgJySHLsjM7882bsZvYBa0iCARC0VAoVCkCpGfEUMcACDc8VRQKPwXtdtjvR53S4ZaXaZUrmbgU1GaiyUded5XvO9KTSt5m74KmRlF0tRyhsc4qEqEMXcyg_6NbeFRElb1ACnz3yvo85NP6ffcFzX5yp7m_Y8uljmnt_Xd3-2y3RJf8rmCHA7Zm0yrbKuZNflTZdnM13u2QfXbnKCWQz3g2pWANEYgnKS8breacPLScelqnHFA4oc6b6iSdjIEvEuDLRERBJc05pyxTvuo7QctQMv2IAzfWZrycS4HfbyPc8ux1fMR6rYde81GUgxhEhoBIaPBCR8aOb8IY7SkdNbzY9b1Ah26sNNm41uphABDZhmnYyAGrVQjgN4Z-6GvpHrONdJLaU8aNBkcGOvbBARVoA5bsHVzQGCXR1Kyx69X5DvA8KHgBqZ3M88H3CdfYSUGkQVY05BhI6VK01zn7w9_nbIcIX2SkXLDKEG-5vWSbejFL8unVkoHw2e48fwFNVtLA |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Survival+prediction+in+patients+with+colon+adenocarcinoma+via+multi-omics+data+integration+using+a+deep+learning+algorithm&rft.jtitle=Bioscience+reports&rft.au=Lv%2C+Jiudi&rft.au=Wang%2C+Junjie&rft.au=Shang%2C+Xiujuan&rft.au=Liu%2C+Fangfang&rft.date=2020-12-01&rft.issn=1573-4935&rft.eissn=1573-4935&rft_id=info:doi/10.1042%2FBSR20201482&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1573-4935&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1573-4935&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1573-4935&client=summon |