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An integrated approach for mining precise RNA-based cervical cancer staging biomarkers.
- Source :
-
Gene [Gene] 2019 Sep 05; Vol. 712, pp. 143961. Date of Electronic Publication: 2019 Jul 04. - Publication Year :
- 2019
-
Abstract
- Since international federation of gynecology and obstetrics (FIGO) staging is mainly based on clinical assessment, an integrated approach for mining RNA based biomarkers for understanding the molecular deregulation of signaling pathways and RNAs in cervical cancer was proposed in this study. Publicly available data were mined for identifying significant RNAs after patient staging. Significant miRNA families were identified from mRNA-miRNA and lncRNA-miRNA interaction network analyses followed by stage specific mRNA-miRNA-lncRNA association network generation. Integrated bioinformatic analyses of selected mRNAs and lncRNAs were performed. Results suggest that HBA1, HBA2, HBB, SLC2A1, CXCL10 (stage I), PKIA (stage III) and S100A7 (stage IV) were important. miRNA family enrichment of interacting miRNA partners of selected RNAs indicated the enrichment of let-7 family. Assembly of collagen fibrils and other multimeric structures&#95;Homosapiens&#95;R-HSA-2022090 in pathway analysis and progesterone&#95;CTD&#95;00006624 in DSigDB analysis were the most significant and SLC2A1, hsa-miR-188-3p, hsa-miR-378a-3p and hsa-miR-150-5p were selected as survival markers.<br /> (Copyright © 2019 Elsevier B.V. All rights reserved.)
- Subjects :
- Collagen chemistry
DNA Methylation
Disease Progression
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
MicroRNAs metabolism
Oligonucleotide Array Sequence Analysis
Papillomaviridae metabolism
Papillomavirus Infections complications
Uterine Cervical Neoplasms virology
Biomarkers, Tumor metabolism
Computational Biology methods
Data Mining methods
RNA, Neoplasm metabolism
Uterine Cervical Neoplasms genetics
Uterine Cervical Neoplasms metabolism
Subjects
Details
- Language :
- English
- ISSN :
- 1879-0038
- Volume :
- 712
- Database :
- MEDLINE
- Journal :
- Gene
- Publication Type :
- Academic Journal
- Accession number :
- 31279709
- Full Text :
- https://doi.org/10.1016/j.gene.2019.143961