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MicroRNA-regulated transcriptome analysis identifies four major subtypes with prognostic and therapeutic implications in prostate cancer
- Source :
- Computational and Structural Biotechnology Journal, Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 4941-4953 (2021)
- Publication Year :
- 2021
- Publisher :
- Research Network of Computational and Structural Biotechnology, 2021.
-
Abstract
- Graphical abstract<br />MicroRNA (miRNA) deregulation plays a critical role in the heterogeneous development of prostate cancer (PCa) by tuning mRNA levels. Herein, we aimed to characterize the molecular features of PCa by clustering the miRNA-regulated transcriptome with non-negative matrix factorization. Using 478 PCa samples from The Cancer Genome Atlas, four molecular subtypes (S-I, S-II, S-III, and S-IV) were identified and validated in two merged microarray and RNAseq datasets with 656 and 252 samples, respectively. Interestingly, the four subtypes showed distinct clinical and biological features after comprehensive analyses of clinical features, multiomic profiles, immune infiltration, and drug sensitivity. S-I is basal/stem/mesenchymal-like and immune-excluded with marked transforming growth factor β, epithelial-mesenchymal transition and hypoxia signals, increased sensitivity to olaparib, and intermediate prognosis. S-II is luminal/metabolism-active and responsive to androgen deprivation therapy with frequent TMPRSS2-ERG fusion and a good prognosis. S-III is characterized by moderate proliferative and metabolic activity, sensitivity to taxane-based chemotherapy, and intermediate prognosis. S-IV is highly proliferative with moderate EMT and stemness, frequent deletions of TP53, PTEN and RB, and the poorest prognosis; it is also immune-inflamed and sensitive to anti-PD-L1 therapy. Overall, based on miRNA-regulated gene profiles, this study identified four distinct PCa subtypes that could improve risk stratification at diagnosis and provide therapeutic guidance.
- Subjects :
- Microarray
DFS, disease-free survival
MDSCs, myeloid-derived suppressor cells
AUC, Area under the dose-response curve
Biochemistry
k-NN, K-nearest neighbor
GEP, gene expression profile
Androgen deprivation therapy
Transcriptome
CTLA-4, cytotoxic T-lymphocyte associated protein-4
chemistry.chemical_compound
Prostate cancer
DEmiRs, differentially expressed miRNAs
SCNAs, somatic copy number alterations
Structural Biology
TGFβ, transforming growth factor β
BCR, biochemical recurrence
ICB, immune checkpoint blockade
GEO, Gene Expression Omnibus
NTP, Nearest template prediction
TMB, tumor mutation burden
PD-1, programmed cell death protein-1
NEPC, neuroendocrine prostate cancer
Computer Science Applications
NMF, non-negative matrix factorization
CCLs, cancer cell lines
Tregs, regulatory T cells
miRNAs
EMT, epithelial-mesenchymal transition
mCRPC, metastatic castration-resistant prostate cancer
Biotechnology
Research Article
FDR, false discovery rate
SubMap, Subclass mapping
Molecular subtypes
Biophysics
PCa, prostate cancer
Biology
KEGG, Kyoto Encyclopedia of Genes and Genomes
Olaparib
OS, overall survival
GSEA, Gene Set Enrichment Analysis
microRNA
GO, Gene Ontology
ssGSEA, single-sample gene set enrichment analysis
Genetics
medicine
TNAs, tumor neoantigens
PTEN
miRNAs, microRNAs
IFN, interferon
ADT, androgen deprivation therapy
ComputingMethodologies_COMPUTERGRAPHICS
Taxane
medicine.disease
PD-L1, programmed death-ligand 1
chemistry
Cancer research
biology.protein
TCGA, The Cancer Genome Atlas
MIRcor, miRNA-correlated
AR, androgen receptor
Heterogeneity
TP248.13-248.65
CAFs, cancer-associated fibroblasts
Subjects
Details
- Language :
- English
- ISSN :
- 20010370
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- Computational and Structural Biotechnology Journal
- Accession number :
- edsair.doi.dedup.....f138460d0ba30547d9145ec00105384e