PMID- 34904882
OWN - NLM
STAT- MEDLINE
DCOM- 20220610
LR  - 20221202
IS  - 1535-3699 (Electronic)
IS  - 1535-3702 (Print)
IS  - 1535-3699 (Linking)
VI  - 247
IP  - 11
DP  - 2022 Jun
TI  - Survival stratification for colorectal cancer via multi-omics integration using 
      an autoencoder-based model.
PG  - 898-909
LID - 10.1177/15353702211065010 [doi]
AB  - Prognosis stratification in colorectal cancer helps to address cancer 
      heterogeneity and contributes to the improvement of tailored treatments for 
      colorectal cancer patients. In this study, an autoencoder-based model was 
      implemented to predict the prognosis of colorectal cancer via the integration of 
      multi-omics data. DNA methylation, RNA-seq, and miRNA-seq data from The Cancer 
      Genome Atlas (TCGA) database were integrated as input for the autoencoder, and 
      175 transformed features were produced. The survival-related features were used 
      to cluster the samples using k-means clustering. The autoencoder-based strategy 
      was compared to the principal component analysis (PCA)-, t-distributed random 
      neighbor embedded (t-SNE)-, non-negative matrix factorization (NMF)-, or 
      individual Cox proportional hazards (Cox-PH)-based strategies. Using the 175 
      transformed features, tumor samples were clustered into two groups (G1 and G2) 
      with significantly different survival rates. The autoencoder-based strategy 
      performed better at identifying survival-related features than the other 
      transformation strategies. Further, the two survival groups were robustly 
      validated using "hold-out" validation and five validation cohorts. Gene 
      expression profiles, miRNA profiles, DNA methylation, and signaling pathway 
      profiles varied from the poor prognosis group (G2) to the good prognosis group 
      (G1). miRNA-mRNA networks were constructed using six differentially expressed 
      miRNAs (let-7c, mir-34c, mir-133b, let-7e, mir-144, and mir-106a) and 19 
      predicted target genes. The autoencoder-based computational framework could 
      distinguish good prognosis samples from bad prognosis samples and facilitate a 
      better understanding of the molecular biology of colorectal cancer.
FAU - Song, Hu
AU  - Song H
AD  - Department of Gastrointestinal Surgery, the Affiliated Hospital of Xuzhou Medical 
      University, Xuzhou, Jiangsu 221002, PR China.
FAU - Ruan, Chengwei
AU  - Ruan C
AD  - Department of Anorectal Surgery, the Affiliated Hospital of Xuzhou Medical 
      University, Xuzhou, Jiangsu 221002, PR China.
FAU - Xu, Yixin
AU  - Xu Y
AD  - Department of Gastrointestinal Surgery, the Affiliated Hospital of Xuzhou Medical 
      University, Xuzhou, Jiangsu 221002, PR China.
FAU - Xu, Teng
AU  - Xu T
AD  - Department of Gastrointestinal Surgery, the Affiliated Hospital of Xuzhou Medical 
      University, Xuzhou, Jiangsu 221002, PR China.
FAU - Fan, Ruizhi
AU  - Fan R
AD  - Department of Gastrointestinal Surgery, the Affiliated Hospital of Xuzhou Medical 
      University, Xuzhou, Jiangsu 221002, PR China.
FAU - Jiang, Tao
AU  - Jiang T
AD  - Department of Gastrointestinal Surgery, the Affiliated Hospital of Xuzhou Medical 
      University, Xuzhou, Jiangsu 221002, PR China.
FAU - Cao, Meng
AU  - Cao M
AD  - Department of Gastrointestinal Surgery, the Affiliated Hospital of Xuzhou Medical 
      University, Xuzhou, Jiangsu 221002, PR China.
FAU - Song, Jun
AU  - Song J
AUID- ORCID: 0000-0003-4588-6522
AD  - Department of Gastrointestinal Surgery, the Affiliated Hospital of Xuzhou Medical 
      University, Xuzhou, Jiangsu 221002, PR China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20211214
PL  - Switzerland
TA  - Exp Biol Med (Maywood)
JT  - Experimental biology and medicine (Maywood, N.J.)
JID - 100973463
RN  - 0 (MicroRNAs)
SB  - IM
MH  - Cluster Analysis
MH  - *Colorectal Neoplasms/genetics
MH  - DNA Methylation/genetics
MH  - Humans
MH  - *MicroRNAs/genetics
MH  - Transcriptome/genetics
PMC - PMC9189567
OTO - NOTNLM
OT  - Autoencoder
OT  - K-means clustering
OT  - deep learning
OT  - multi-omics
OT  - survival
COIS- DECLARATION OF CONFLICTING INTERESTS: The author(s) declare no potential 
      conflicts of interest with respect to the research, authorship, and/or 
      publication of this article.
EDAT- 2021/12/15 06:00
MHDA- 2022/06/11 06:00
PMCR- 2022/12/01
CRDT- 2021/12/14 12:13
PHST- 2021/12/15 06:00 [pubmed]
PHST- 2022/06/11 06:00 [medline]
PHST- 2021/12/14 12:13 [entrez]
PHST- 2022/12/01 00:00 [pmc-release]
AID - 10.1177_15353702211065010 [pii]
AID - 10.1177/15353702211065010 [doi]
PST - ppublish
SO  - Exp Biol Med (Maywood). 2022 Jun;247(11):898-909. doi: 10.1177/15353702211065010. 
      Epub 2021 Dec 14.