PMID- 33841512
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210413
IS  - 1664-8021 (Print)
IS  - 1664-8021 (Electronic)
IS  - 1664-8021 (Linking)
VI  - 12
DP  - 2021
TI  - Identification of miRNA-Mediated Subpathways as Prostate Cancer Biomarkers Based 
      on Topological Inference in a Machine Learning Process Using Integrated Gene and 
      miRNA Expression Data.
PG  - 656526
LID - 10.3389/fgene.2021.656526 [doi]
LID - 656526
AB  - Accurately identifying classification biomarkers for distinguishing between 
      normal and cancer samples is challenging. Additionally, the reproducibility of 
      single-molecule biomarkers is limited by the existence of heterogeneous patient 
      subgroups and differences in the sequencing techniques used to collect patient 
      data. In this study, we developed a method to identify robust biomarkers (i.e., 
      miRNA-mediated subpathways) associated with prostate cancer based on normal 
      prostate samples and cancer samples from a dataset from The Cancer Genome Atlas 
      (TCGA; n = 546) and datasets from the Gene Expression Omnibus (GEO) database (n = 
      139 and n = 90, with the latter being a cell line dataset). We also obtained 10 
      other cancer datasets to evaluate the performance of the method. We propose a 
      multi-omics data integration strategy for identifying classification biomarkers 
      using a machine learning method that involves reassigning topological weights to 
      the genes using a directed random walk (DRW)-based method. A global directed 
      pathway network (GDPN) was constructed based on the significantly differentially 
      expressed target genes of the significantly differentially expressed miRNAs, 
      which allowed us to identify the robust biomarkers in the form of miRNA-mediated 
      subpathways (miRNAs). The activity value of each miRNA-mediated subpathway was 
      calculated by integrating multiple types of data, which included the expression 
      of the miRNA and the miRNAs' target genes and GDPN topological information. 
      Finally, we identified the high-frequency miRNA-mediated subpathways involved in 
      prostate cancer using a support vector machine (SVM) model. The results 
      demonstrated that we obtained robust biomarkers of prostate cancer, which could 
      classify prostate cancer and normal samples. Our method outperformed seven other 
      methods, and many of the identified biomarkers were associated with known 
      clinical treatments.
CI  - Copyright (c) 2021 Ning, Yu, Zhao, Sun, Wu and Yu.
FAU - Ning, Ziyu
AU  - Ning Z
AD  - The Higher Educational Key Laboratory for Measuring and Control Technology and 
      Instrumentations of Heilongjiang Province, Harbin University of Science and 
      Technology, Harbin, China.
AD  - School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 
      China.
FAU - Yu, Shuang
AU  - Yu S
AD  - The Higher Educational Key Laboratory for Measuring and Control Technology and 
      Instrumentations of Heilongjiang Province, Harbin University of Science and 
      Technology, Harbin, China.
FAU - Zhao, Yanqiao
AU  - Zhao Y
AD  - The Higher Educational Key Laboratory for Measuring and Control Technology and 
      Instrumentations of Heilongjiang Province, Harbin University of Science and 
      Technology, Harbin, China.
FAU - Sun, Xiaoming
AU  - Sun X
AD  - The Higher Educational Key Laboratory for Measuring and Control Technology and 
      Instrumentations of Heilongjiang Province, Harbin University of Science and 
      Technology, Harbin, China.
FAU - Wu, Haibin
AU  - Wu H
AD  - The Higher Educational Key Laboratory for Measuring and Control Technology and 
      Instrumentations of Heilongjiang Province, Harbin University of Science and 
      Technology, Harbin, China.
FAU - Yu, Xiaoyang
AU  - Yu X
AD  - The Higher Educational Key Laboratory for Measuring and Control Technology and 
      Instrumentations of Heilongjiang Province, Harbin University of Science and 
      Technology, Harbin, China.
LA  - eng
PT  - Journal Article
DEP - 20210324
PL  - Switzerland
TA  - Front Genet
JT  - Frontiers in genetics
JID - 101560621
PMC - PMC8024646
OTO - NOTNLM
OT  - SVM
OT  - cancer
OT  - machine learning
OT  - miRNA-mediated subpathway
OT  - topological information
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2021/04/13 06:00
MHDA- 2021/04/13 06:01
PMCR- 2021/03/24
CRDT- 2021/04/12 06:18
PHST- 2021/01/21 00:00 [received]
PHST- 2021/03/02 00:00 [accepted]
PHST- 2021/04/12 06:18 [entrez]
PHST- 2021/04/13 06:00 [pubmed]
PHST- 2021/04/13 06:01 [medline]
PHST- 2021/03/24 00:00 [pmc-release]
AID - 10.3389/fgene.2021.656526 [doi]
PST - epublish
SO  - Front Genet. 2021 Mar 24;12:656526. doi: 10.3389/fgene.2021.656526. eCollection 
      2021.