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Updated: Feb 22, 2026

miRNA Expression Analyses in Prostate Cancer Clinical Tissues
Published on: September 8, 2015
Transcriptomic Insights into lncRNA-miRNA-mRNA Networks Regulating Angiogenesis and Metastasis in Prostate Cancer
Jonathan Puente-Rivera1,2, Stephanie I Nuñez Olvera3, Ameyatzin Ereth Robles-Chávez1
1Laboratorio de Patogénesis Celular Humana y Veterinaria, Posgrado en Ciencias Genómicas, Universidad Autónoma de la Ciudad de México (UACM), San Lorenzo 290, Col. Del Valle, Mexico City 09790, Mexico.
Abstract:
Prostate cancer (PCa) is a leading cause of cancer-related mortality in men and is often characterized by aggressive growth and bone metastasis. Angiogenesis plays a central role in tumor progression and dissemination. This study aimed to explore the regulatory roles of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in angiogenesis and metastasis during PCa progression. Publicly available RNA-seq datasets were analyzed to identify differentially expressed miRNAs between metastatic (N1) and nonmetastatic (N0) PCa. Bioinformatic tools were used to reconstruct co-regulatory networks involving miRNAs, lncRNAs, and angiogenesis-related mRNAs. RT-qPCR was performed on serum-derived liquid biopsies from N0 and N1 patients and healthy controls to validate the key regulatory axes. Transcriptomic analysis revealed that miRNAs such as hsa-miR-183-5p and hsa-miR-216a-5p were upregulated in N1 PCa and associated with pro-angiogenic signaling, whereas hsa-miR-206 and hsa-miR-184, known for their anti-angiogenic functions, were downregulated. Network analysis identified the LINC00261-miR-206-HIF1A axis as the central regulatory module. RT-qPCR validation confirmed the significant downregulation of LINC00261 and miR-206, along with HIF1A overexpression in N1 samples compared to N0 and controls (p < 0.001), supporting in silico predictions. These findings highlight the role of ncRNA-mediated regulation of PCa angiogenesis and metastasis. The LINC00261-miR-206-HIF1A axis may serve as a promising noninvasive biomarker and potential therapeutic target. The integration of computational and experimental data provides a strong rationale for the further functional validation of advanced PCa.
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