Identification of Key MicroRNAs and Mechanisms in Prostate Cancer Evolution Based on Biomarker Prioritization Model and Carcinogenic Survey

Abstract

Background: Prostate cancer (PCa) is occurred with increasing incidence and heterogeneous pathogenesis. Although clinical strategies are accumulated for PCa prevention, there is still a lack of sensitive biomarkers for the holistic management in PCa occurrence and progression. Based on systems biology and artificial intelligence, translational informatics provides new perspectives for PCa biomarker prioritization and carcinogenic survey.Methods: In this study, gene expression and miRNA-mRNA association data were integrated to construct conditional networks specific to PCa occurrence and progression, respectively. Based on network modeling, hub miRNAs with significantly strong single-line regulatory power were topologically identified and those shared by the condition-specific network systems were chosen as candidate biomarkers for computational validation and functional enrichment analysis.Results: Nine miRNAs, i.e., hsa-miR-1-3p, hsa-miR-125b-5p, hsa-miR-145-5p, hsa-miR-182-5p, hsa-miR-198, hsa-miR-22-3p, hsa-miR-24-3p, hsa-miR-34a-5p, and hsa-miR-499a-5p, were prioritized as key players for PCa management. Most of these miRNAs achieved high AUC values (AUC > 0.70) in differentiating different prostate samples. Among them, seven of the miRNAs have been previously reported as PCa biomarkers, which indicated the performance of the proposed model. The remaining hsa-miR-22-3p and hsa-miR-499a-5p could serve as novel candidates for PCa predicting and monitoring. In particular, key miRNA-mRNA regulations were extracted for pathogenetic understanding. Here hsa-miR-145-5p was selected as the case and hsa-miR-145-5p/NDRG2/AR and hsa-miR-145-5p/KLF5/AR axis were found to be putative mechanisms during PCa evolution. In addition, Wnt signaling, prostate cancer, microRNAs in cancer etc. were significantly enriched by the identified miRNAs-mRNAs, demonstrating the functional role of the identified miRNAs in PCa genesis.Conclusion: Biomarker miRNAs together with the associated miRNA-mRNA relations were computationally identified and analyzed for PCa management and carcinogenic deciphering. Further experimental and clinical validations using low-throughput techniques and human samples are expected for future translational studies.

Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection

“…Then KTR-specific mRNAs were extracted to investigate their interactions based on PPI data, and a cross-level miRNA-mRNA-PPI network (miR-PPIN) was finally constructed to identify miRNAs and infer key miRNA-mRNA regulations in KTR. Compared with traditional approaches equally treating the contribution of different miRNA targets in the network for model training [ 11 ], in this pipeline the structural importance of target genes was mainly prioritized based on their topological activities in PPIN, and the targets in the centre of PPIN, i.e., high degree, closeness, and betweenness, were classified as CORE factors for miRNA regulatory power measurement.

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Cancer Detection Using an Artificial Secretable MicroRNA Found in Blood and Urine

“…For instance, identification of miR-33a-5p, miR-128-3p [36], and Sec-miR levels could be applied for lung cancer detection. Prostate cancer may be found when simultaneously detecting has-miR12-5p, has-miR-198 [37] and Sec-miR.…”

Natural Product-Based Studies for the Management of Castration-Resistant Prostate Cancer: Computational to Clinical Studies

“…The higher rates may indicate a greater disease incidence and higher rates of PCa than other places across the globe ( Wild et al, 2020 ; Sung et al, 2021 ). For a prevalent illness like PCa, little is known about its genesis, and only a few risk factors have been discovered ( Lin et al, 2021 ). Several factors are responsible for changes in its prevalence at the regional level due to changes in the susceptibility of different population groups to environmental risk factors, including racial/ethnic backgrounds, geographical heterogeneity, advancing age and an intact hypothalamic-pituitary-gonadal axis, family history, genetic mutations (e.g., BRCA1 and BRCA2), and diagnosis and access to good quality treatment ( Hoimes and Kelly, 2009 ; Rebbeck et al, 2013 ; Shackleton et al, 2021 ).…”