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Updated: Jul 28, 2025
Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients
Published on: November 19, 2019
An in-silico Approach for Recognition of Long non-coding RNA-Associated Competing Endogenous RNA Axes in Prostate
Mohammad Taheri1, Arash Safarzadeh2, Soudeh Ghafouri-Fard3
1Institute of Human Genetics, Jena University Hospital, Jena, Germany. mohammad_823@yahoo.com.
Purpose:
Prostate cancer is among the most central sources of cancer-related mortalities. In order to find novel candidates for therapeutic strategies in this kind of cancer, we developed an in-silico method for identification of competing endogenous RNA network.
Methods:
According to the microarray data analyses between prostate tumor and normal specimens, we attained 1312 differentially expressed (DE)mRNAs, including 778 down-regulated DEmRNAs (such as CXCL13 and BMP5) and 584 up-regulated DEmRNAs (such as OR51E2 and LUZP2), 39 DElncRNAs, including 10 down-regulated DElncRNAs (such as UBXN10-AS1 and FENDRR) and 29 up-regulated DElncRNAs (such as PCA3 and LINC00992) and 10 DEmiRNAs, including 2 down-regulated DEmiRNAs (such as MIR675 and MIR1908) and 8 up-regulated DEmiRNAs (such as MIR6773 and MIR4683).
Results:
We constructed the ceRNA network between these transcripts. We also evaluated the related signaling pathways and the significance of these RNAs in prediction of survival of patients with prostate cancer.
Conclusion:
This study provides novel candidates for construction of specific treatment routes for prostate cancer.
Insights
This study identifies potential therapeutic targets for prostate cancer by analyzing gene expression data to build a competing endogenous RNA network. These findings offer new avenues for developing targeted prostate cancer treatments.
Area of Science:
- Oncology
- Bioinformatics
- Molecular Biology
Background:
- Prostate cancer is a leading cause of cancer-related deaths globally.
- Identifying novel therapeutic targets is crucial for improving patient outcomes.
Purpose of the Study:
- To develop an in-silico method for identifying competing endogenous RNA (ceRNA) networks in prostate cancer.
- To discover novel RNA candidates for targeted prostate cancer therapies.
Main Methods:
- Analysis of microarray data from prostate tumor and normal tissues.
- Identification of differentially expressed mRNAs, lncRNAs, and miRNAs.
- Construction of a ceRNA network using identified transcripts.
Main Results:
- 1312 differentially expressed mRNAs, 39 DElncRNAs, and 10 DEmiRNAs were identified.
- A ceRNA network was constructed from these differentially expressed transcripts.
- The study evaluated the significance of these RNAs in predicting patient survival and related signaling pathways.
Conclusions:
- The study provides novel RNA candidates for developing targeted prostate cancer treatments.
- The identified ceRNA network offers potential therapeutic strategies for prostate cancer.
