ArticlePloS one2021
Prediction of potential prognostic biomarkers in metastatic prostate cancer based on a circular RNA-mediated competing endogenous RNA regulatory network.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Integrative Single-Cell and Machine Learning Analysis Identifies an EMT-Associated Prognostic Signature for Papillary Thyroid Cancer.Cancer medicine · 2026Article
- Exploring TGFBR3 in disease pathogenesis: Mechanisms, clinical implications, and pharmacological modulation.Journal of pharmaceutical analysis · 2026Review
- Identification of a Prognostic ceRNA Network Regulating TMBIM6 in Prostate Adenocarcinoma via Integrated Bioinformatic Analysis.International journal of molecular sciences · 2025Article
- Deep learning assisted identification of SCUBE2 and SLC16 A5 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer.Medical & biological engineering & computing · 2025Article
- Exploring markers in nursing care of prostate cancer.Medicine · 2025Article
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- The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery.Biomedicines · 2023Review
- The Circular RNA Circ_0085494 Regulates Prostate Cancer Progression Through NRBP1/miR-497-5p Axis.Biochemical genetics · 2023Article
- Contactin 1: An Important and Emerging Oncogenic Protein Promoting Cancer Progression and Metastasis.Genes · 2020Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, studies on competing endogenous RNA (ceRNA) networks have become prevalent, and circular RNAs (circRNAs) have crucial implications for the development and progression of carcinoma. However, studies relevant to metastatic prostate cancer (mPCa) are scant. This study aims to discover potential ceRNAs that may be related to the prognosis of mPCa. RNA-Seq data were obtained from the MiOncoCirc database and Gene Expression Omnibus (GEO). Differential expression patterns of RNAs were examined using R packages. Circular RNA Interactome, miRTarBase, miRDB and TargetScan were applied to predict the corresponding relation between circRNAs, miRNAs and mRNAs. The Gene Ontology (GO) annotations were performed to present related GO terms, and Gene Set Enrichment Analysis (GSEA) tools were applied for pathway annotations. Moreover, survival analysis was conducted for the hub genes. We found 820 circRNAs, 81 miRNAs and 179 mRNAs that were distinguishingly expressed between primary prostate cancer (PCa) and mPCa samples. A ceRNA network including 45 circRNAs, 24 miRNAs and 56 mRNAs was constructed. In addition, the protein-protein interaction (PPI) network was built, and 10 hub genes were selected by using the CytoHubba application. Among the 10 hub genes, survival analysis showed that ITGA1, LMOD1, MYH11, MYLK, SORBS1 and TGFBR3 were significantly connected with disease-free survival (DFS). The circRNA-mediated ceRNA network provides potential prognostic biomarkers for metastatic prostate cancer.
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