Multivariate competing endogenous RNA network characterization for cancer MicroRNA biomarker discovery: a novel bioinformatics model with application to prostate cancer metastasis

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Plasma microRNA Signature as Companion Diagnostic for Abiraterone Acetate Treatment in Metastatic Castration-Resistant Prostate Cancer: A Pilot Study

Abiraterone acetate (AA) serves as a medication for managing persistent testosterone production in patients with metastatic castration-resistant prostate cancer (mCRPC). However, its efficacy varies among individuals; thus, the identification of biomarkers to predict and follow treatment response is required. In this pilot study, we explored the potential of circulating microRNAs (c-miRNAs) to stratify patients based on their responsiveness to AA. We conducted an analysis of plasma samples obtained from a cohort of 33 mCRPC patients before and after three, six, and nine months of AA treatment. Using miRNA RT-qPCR panels for candidate discovery and TaqMan RT-qPCR for validation, we identified promising miRNA signatures. Our investigation indicated that a signature based on miR-103a-3p and miR-378a-5p effectively discriminates between non-responder and responder patients, while also following the drug's efficacy over time. Additionally, through in silico analysis, we identified target genes and transcription factors of the two miRNAs, including PTEN and HOXB13, which are known to play roles in AA resistance in mCRPC. In summary, our study highlights two c-miRNAs as potential companion diagnostics of AA in mCRPC patients, offering novel insights for informed decision-making in the treatment of mCRPC.

Myocardial infarction unveiled: Key miRNA players screened by a novel lncRNA-miRNA-mRNA network model

BACKGROUND

Myocardial infarction (MI) is a major contributor to global mortality, and microRNAs (miRNAs) are important in its pathogenesis. Identifying blood miRNAs with clinical application potential for the early detection and treatment of MI is crucial.

METHODS

We obtained MI-related miRNA and miRNA microarray datasets from MI Knowledge Base (MIKB) and Gene Expression Omnibus (GEO), respectively. A new feature called target regulatory score (TRS) was proposed to characterize the RNA interaction network. MI-related miRNAs were characterized using TRS, transcription factor (TF) gene proportion (TFP), and ageing-related gene (AG) proportion (AGP) via the lncRNA-miRNA-mRNA network. A bioinformatics model was then developed to predict MI-related miRNAs, which were verified by literature and pathway enrichment analysis.

RESULTS

The TRS-characterized model outperformed previous methods in identifying MI-related miRNAs. MI-related miRNAs had high TRS, TFP, and AGP values, and combining the three features improved prediction accuracy to 0.743. With this method, 31 candidate MI-related miRNAs were screened from the specific-MI lncRNA-miRNA-mRNA network, associated with key MI pathways like circulatory system processes, inflammatory response, and oxygen level adaptation. Most candidate miRNAs were directly associated with MI according to literature evidence, except hsa-miR-520c-3p and hsa-miR-190b-5p. Furthermore, CAV1, PPARA and VEGFA were identified as MI key genes, and were targeted by most of the candidate miRNAs.

CONCLUSIONS

This study proposed a novel bioinformatics model based on multivariate biomolecular network analysis to identify putative key miRNAs of MI, which deserve further experimental and clinical validation for translational applications.