Introduction

Prostate cancer (PCa) is a widespread malignancy affecting men, contributing significantly to global mortality rates. It ranks as the most common cancer among men, excluding skin cancer, with an estimated 1,414,259 people diagnosed worldwide in 2020, making it the fourth most frequently diagnosed cancer on a global scale1. MicroRNAs (miRNAs) play a pivotal role in the transformation of cancer cells. They can function either as tumour-suppressor genes or oncogenes, targeting genes involved in tumour development and progression, or inhibiting the cell cycle, respectively2. Since the discovery of miRNAs, they have held great promise for cancer diagnosis, prognosis, and therapeutic interventions. Distinct miRNA profiles specific to different tumour types can serve as phenotypic signatures, aiding in cancer diagnosis, prognosis, and treatment. Accurate malignancy prediction through miRNA profiles could revolutionize diagnostics3.

In the context of prostate cancer, miRNAs have been instrumental. A study conducted by Brase and colleagues highlighted miR-141 and miR-375 as the most distinctive markers for assessing tumour progression, based on an examination of serum samples from individuals with both metastatic prostate cancer and localized tumours4,5. Another study by Stuopelyte and colleagues revealed the diagnostic and discriminatory potential of miR-21 in urinary samples from prostate cancer patients compared to those with benign prostatic hyperplasia (BPH)6.This research also identified a diagnostic panel comprising miR-21, miR-19a, and miR-19b, demonstrating superior diagnostic capabilities compared to the traditional PSA test7. As miRNAs continue to unveil their diagnostic and prognostic prowess, their integration into clinical practice could usher in a new era of personalized and more effective strategies for managing prostate cancer, ultimately improving patient outcomes on a global scale.

Furthermore, a unique molecular signature involving miR-20a, along with miR-17, miR-20b, and miR-106a, effectively differentiated high and low-risk prostate cancer. Elevated levels of this miRNA panel were associated with advanced tumour stages and a shorter time to biochemical recurrence (BCR) in post-radical prostatectomy patients8.Notably, the clinical significance of microRNA-153 expression in prostate cancer has been relatively scarce in the literature. However, it has been observed that heightened microRNA-153 expression in PCa tissues correlates closely with aggressive clinical and pathological parameters, such as lymph node and bone metastasis, high Gleason scores, and advanced TNM stages. Patients with elevated microRNA-153 expression exhibited significantly lower 5-year overall survival rates compared to those with lower expression levels. Importantly, Cox’s multivariate regression analysis demonstrated that microRNA-153 expression independently predicted 5-year overall survival in PCa patients9.

Comprehensive analysis to characterize the proteomic impact of a specific panel of 12 microRNAs that exhibit potent suppression in metastatic PC.These microRNAs, collectively referred to as SiM-miRNAs, include miR-1, miR-133a, miR-133b, miR-135a, miR-143-3p, miR-145-3p, miR-205, miR-221-3p, miR-221-5p, miR-222-3p, miR-24-1-5p, and miR-31.Using reverse-phase proteomic arrays, systematically examined the proteomic alterations induced by the re-expression of these SiM-miRNAs in prostate cancer cells. The results revealed that the reintroduction of these SiM-miRNAs into PC cells led to the suppression of cell proliferation and the targeting of critical oncogenic pathways. These pathways encompassed cell cycle regulation, apoptosis, Akt/mammalian target of rapamycin signalling, metastasis, and modulation of the androgen receptor (AR) axis. This study sheds light on the potential therapeutic significance of these SiM-miRNAs in mitigating the progression of metastatic prostate cancer10,11.These findings collectively contribute to advancing our understanding of the molecular intricacies of prostate cancer, paving the way for potential targeted therapeutic interventions in the management of metastatic disease.

Analysing the impact of differentially expressed miRNAs on transcriptomic signatures in prostate using computational methods is a complex yet valuable approach to understanding regulatory mechanisms and potential biomarkers in prostate cancer and related diseases. Identifying and characterizing miRNA-mRNA interactions allows for the unravelling of complex regulatory networks governing gene expression. By leveraging computational approaches, researchers can systematically analyse large-scale transcriptomic datasets to identify differentially expressed miRNAs in prostate cancer tissues compared to normal tissues. Subsequently, these miRNAs can be integrated into network models, allowing for the elucidation of complex regulatory networks governing gene expression in prostate cancer cells. Moreover, the identification and characterization of miRNA-mRNA interactions enable researchers to pinpoint specific genes and pathways affected by dysregulated miRNAs. This information is crucial for understanding the molecular mechanisms driving prostate cancer development and progression. Additionally, these interactions serve as a foundation for the identification of potential biomarkers that may have diagnostic, prognostic, or therapeutic implications.

Materials and methods

Retrieval of miRNA expression data associated with prostate cancer

Relevant miRNA datasets for prostate cancer were retrieved from the Gene Expression Omnibus (GEO) database12. Searches were conducted using specific keywords related to prostate cancer. Inclusion criteria for dataset selection included the presence of prostate cancer-related samples, availability of clinical information (e.g., tumor stage or metastasis status), adequate sample size (≥ 30 samples), and high data quality, including pre-normalized datasets or compatibility with standard normalization pipelines. Selected datasets were downloaded in a standardized format and prepared for analysis using bioinformatics tools, ensuring compatibility with downstream workflows12.

Identification of differentially expressed MicroRNAs (DEMs) in patients with prostate disease

Differentially expressed miRNAs (DEMs) in prostate disease were identified using the ‘limma’ package in R13. miRNA expression data underwent preprocessing and normalization, followed by the construction of a design matrix to represent the experimental conditions. Linear models were applied to calculate fold changes and p-values for each miRNA. Multiple testing correction was performed using the Benjamini-Hochberg method to control the false discovery rate (FDR). DEMs were filtered based on an FDR threshold of ≤ 0.05 and a fold change ≥ 2 or  ≤ −2. Significant DEMs were visualized and annotated to ensure clarity in interpretation. Functional analysis of the DEMs was performed to assess their biological relevance.

Integration of DEMs and mRNA interactions in the context of prostate cancer

We integrated DEMs with mRNA interactions to explore the gene regulatory network mediated by miRNAs. This analysis highlighted the impact of miRNA expression changes on mRNA targets and their associated biological processes, aiding in the identification of key regulatory mechanisms and potential therapeutic targets. miRNA-target interactions (MTIs) were sourced from MiRTarBase14, which aggregates experimentally validated MTIs through rigorous literature retrieval, natural language processing (NLP), and stringent filtering. Manual curation and quality control ensured the inclusion of reliable, high-confidence MTIs. This curated resource enabled the construction of a robust regulatory network for advancing the understanding of miRNA roles in prostate cancer and other biological contexts14.

Construction of an mRNA-miRNA Target Network and Identification of hub nodes

We constructed a Protein-Protein Interaction (PPI) network by integrating Reactome pathway data with protein interaction information and visualizing it using Cytoscape15. This approach facilitated the analysis of protein interactions within the context of prostate cancer pathways. The network was visualized to identify key proteins and their associations, and functional and enrichment analyses were performed using the ReactomeFI plugin within Cytoscape. The PPI network was filtered to include only interactions with a confidence score ≥ 0.7, ensuring high-confidence associations. To identify significant genes and miRNAs, we applied the CytoHubba plugin within Cytoscape16. Two metrics were used: Degree Centrality and Maximal Clique Centrality (MCC). Degree Centrality was calculated to rank nodes based on the number of direct connections, with a cutoff value of ≥ 10 interactions used to highlight highly connected nodes. MCC was employed to identify nodes within densely connected subgroups, representing elements critical to network integrity. Genes and miRNAs scoring in the top 10% for both Degree Centrality and MCC were prioritized as potential regulatory hubs. Default parameters of the CytoHubba plugin were used unless otherwise specified. These analyses provided a ranked list of significant genes and miRNAs for further validation and interpretation15,16.

Functional enrichment analysis of differentially expressed miRNAs (DEmiRNA) utilizing gene ontology (GO) and associated pathways

Enrichment analysis was performed using the ClueGO plugin in Cytoscape17. The analysis identified enriched functional terms and pathways within the dataset, linking them visually in a network format. Gene Ontology (GO) categories for Biological Process and KEGG pathways were selected, with a two-sided hypergeometric test and a significance threshold of p ≤ 0.05. Benjamini-Hochberg correction was applied to control the false discovery rate. Default parameters for network connectivity and term grouping were used unless specified. This analysis provided clusters of interconnected biological functions, offering insights into the functional roles of genes or proteins in the dataset17.

Connectivity map analysis

We utilized the Connectivity Map (CMap) database via the CLUE platform (https://clue.io/) to identify potential therapeutics for prostate cancer. Gene signatures from our previous analyses were submitted, and compounds were identified based on their ability to either mimic or reverse these expression patterns. Compounds were ranked by their connectivity scores, with a threshold of ≤-90 or ≥ 90 used to prioritize strong reversers or mimickers, respectively. Mechanism of action (MoA) and molecular targets of each compound were analyzed using CMap annotations. We focused on compounds with established biological activity, particularly those modulating cancer-relevant pathways. Priority was given to compounds interacting with key pathways implicated in prostate cancer progression, providing insights into potential therapeutic strategies.

Results

Retrieval of miRNA expression data pertaining to prostate cancer

Four datasets were utilized in this study to analyze microRNA (miRNA) expression profiles in prostate cancer. Dataset 1, obtained from The Cancer Genome Atlas (TCGA), included miRNA sequencing data comparing Prostate Adenocarcinoma (PRAD) and normal prostate tissues, aiming to identify molecular differences between cancerous and healthy tissues (Fig. 1). Dataset 2, retrieved from the Gene Expression Omnibus (GEO) with accession number GSE6636 and platform GPL3238, examined miRNA expression in normal prostate tissues and prostatic tumors. Normal samples were derived from young individuals who died of trauma or areas adjacent to tumors, while tumor samples originated from older prostate cancer patients. Dataset 3, sourced from ArrayExpress with identifier E_MTAB_408, focused on comparing miRNA expression in prostate tumors and benign prostatic hyperplasia (BPH), highlighting molecular differences between malignant and benign prostate growth. Dataset 4 included miRNA expression data from human primary and metastatic prostate cancer samples, as well as normal adjacent benign tissues, and was retrieved from GEO with the identifier GSE21036. Together, these datasets provided a comprehensive basis for comparative analyses of miRNA expression patterns in prostate cancer and related conditions, as summarized in Table 1.

Fig. 1
figure 1

Protein-Protein Interaction (PPI) Network of Differentially Expressed miRNA Targets Identified Utilizing the Reactome Database

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Table 1 Prostrate dataset features
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Screening and analysis of differentially expressed MicroRNAs (DEMs) associated with prostate cancer and their mRNA targets

Seven differentially expressed miRNAs (DEmiRNAs) were identified and analyzed in this study: hsa-miR-185-5p, hsa-miR-153-3p, hsa-miR-198, hsa-miR-182-5p, hsa-miR-223-3p, hsa-miR-372-3p, and hsa-miR-188-5p. Detailed information on these DEmiRNAs is presented in Table 2. Among these, miR-185-5p exhibited a dual role in prostate cancer, with evidence supporting both tumor-suppressive and oncogenic functions, depending on the biological context.

Table 2 Upregulated miRNA common among the four different prostate cancer subtypes.
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miRNA-target interactions (MTIs) were curated using MiRTarBase in order to construct the network. Six miRNA were analyzed and their mRNA targets screened for redundancy and 97 non redundant mRNA targets were chosen. We identified these targets via experimentally validated methods including reporter assays, Western blotting, qPCR, microarray studies and next generation sequencing (NGS). The curated targets were used to build regulatory networks that were screened for hubs using hub screening and then analyzed for functional enrichment to identify key regulatory elements as well as their associated biological pathways.

Construction of mRNA target-based networks and hub identification

The mRNA target-based network was constructed using the ReactomeFIViz application within Cytoscape. This network was based on pathway enrichment analysis performed using the Reactome database. The analysis revealed a total of 1849 nodes and 3604 edges, representing the intricate interactions within the biological pathways related to prostate cancer. The network visualization, highlights the functional relationships among genes within these pathways, providing insights into the regulatory mechanisms associated with cancer. This network serves as a basis for further hub identification and pathway-specific analyses.

Hub nodes within the constructed network were identified using the CytoHubba plugin in Cytoscape. CytoHubba offers eleven topological analysis methods, including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, and centrality metrics such as Bottleneck, Eccentricity, Closeness, Radiality, Betweenness, and Stress. For this analysis, the Maximal Clique Centrality (MCC) metric was employed, as it effectively identifies critical proteins irrespective of their connectivity levels within the network. Using MCC, key hub nodes were identified, representing central genes or proteins within the network. These hubs, which play significant roles in the network structure and function, are visualized in Fig. 2.

Fig. 2
figure 2

Protein-Protein Interaction (PPI) Network of Differentially Expressed miRNA Targets Identified Utilizing the Reactome Database

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Functional enrichment analysis utilizing gene ontology (GO)

ClueGO analysis identified enriched Gene Ontology (GO) terms associated with the molecular functions and biological processes of miRNA targets. A total of 74 enriched terms were identified, providing insights into the functional roles of these targets. The analysis revealed that the most prominent GO term was “regulation of vascular-associated smooth muscle cell proliferation” (40.54%), followed by “cell junction assembly” (16.22%), and “peptidyl-serine modification” (12.16%). Other significant terms included “stress-activated MAPK cascade” (8.11%), “positive regulation of cell adhesion” (5.41%), and “cyclin-dependent protein kinase activity” (2.7%). The distribution of these terms is shown in Fig. 3, highlighting the most enriched terms and their relative contributions to the dataset. Statistical significance of the enriched terms is denoted by asterisks (*p ≤ 0.05, **p ≤ 0.01). These results provide a detailed overview of the biological processes and molecular functions relevant to the miRNA targets analyzed.

Fig. 3
figure 3

ClueGO-Based Pie Chart Depicting the Molecular Functions and Biological Processes of miRNA Targets

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CMap analysis

The Table 3 summarizes the key genes identified in the study, along with their associated perturbagens and mechanisms of action derived from the Connectivity Map (CMap) analysis. FOXO3 was modulated through gene overexpression and knockdown perturbations, though no specific mechanism of action was identified. RHOA was linked to narciclasine and other perturbagens targeting its role in cofilin signaling, LIM kinase activation, and Rho kinase activation. VEGFA was associated with carvedilol and midostaurin, which act as adrenergic receptor antagonists and FLT3/KIT inhibitors, highlighting their involvement in angiogenesis pathways. PTEN was influenced by temsirolimus, an MTOR inhibitor that regulates cell growth and survival pathways. SMAD7 was modulated by gene overexpression and knockdown but lacked a clearly defined mechanism of action. CDK2 emerged as a target for several inhibitors, including arcyriaflavin-A, indirubin, purvalanol-A/B, and Tyrphostin AG-555, with mechanisms involving CDK inhibition, glycogen synthase kinase inhibition, tyrosine kinase inhibition, and EGFR inhibition. NFAT3, while included in the analysis, had no available data on perturbagens or mechanisms of action. These results provide a comprehensive view of critical genes and their modulators, emphasizing their roles in cell cycle regulation, angiogenesis, and signalling pathways in prostate cancer.

Table 3 Summarizes the significant genes identified in the study and their associated perturbagens retrieved from Connectivity Map (CMap) analysis.
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Discussion

This study provides a computational analysis of differentially expressed miRNAs and their regulatory roles in prostate cancer progression, offering insights into molecular mechanisms and potential therapeutic targets.

In this study, we highlight the critical roles of certain miRNAs in prostate cancer progression and demonstrate their regulatory complexity and potential therapeutic applications. MiR-185-5p showed dual activity in prostate cancer being either a tumor suppressor or an oncogene based on the biology18. The ECM receptor interaction pathway mediated by miR-153-3p, which also associates with advanced tumor stages in prostate cancer19, regulates the ECM receptor interaction pathway, a major mediator of invasion and metastasis in prostate cancer. In prostate cancer, miR-198 acts as a tumor suppressor in cell lines and reduces proliferation and tumor growth20,21. Additionally, miR-182-5p promotes metastasis in prostate cancer by instigating reduced FOXO1 and FOXO3 activity and increased matrix metalloproteinase (MMP) activity, thereby increasing cancer cell invasive potential22,23. MiR-372 mediates its effects in prostate cancer via inhibition of Wnt/β-Catenin and mTOR, and further inhibition of FGF9 acts to support tumorigenesis23,24,25,26. Furthermore, MiR-182 promotes metastasis by down regulation of FOXO1 and FOXO3 and facilitating migration22,27. Furthermore, miR 188 5p promotes prostate cancer progression by down regulating PTEN, a well-established tumor suppressor that is known to regulate cell proliferation and invasion25. These results underscore the various, context dependent roles of miRNAs in prostate cancer biology and as biomarkers and therapeutic targets for future studies of probes to improve prostate cancer diagnosis and treatment.

MicroRNAs (miRNAs) are key post transcriptional regulators of gene expression serving to regulate gene expression by binding to target mRNAs resulting in degradation or translational repression. Specific miRNAs have been dysregulated in prostate cancer in tumorigenesis and metastasis. A MiRTarBase based comprehensive analysis was performed and 97 non-redundant mRNA targets of six miRNAs were found: such as FOXO3, NFAT3, PTEN, RHOA, VEGFA, SMAD7 and CDK228,29,30,31,32,33. An mRNA target-based network was constructed using the ReactomeFIViz application within Cytoscape with 1,849 nodes and 3,604 edges. The multiple crosstalks between genes of prostate cancer pathways in this intricate network are emphasized. Central nodes involved in network stability were identified using Hub analysis performed by the Cytohubba plugin using the Maximal Clique Centrality (MCC) metric. In addition, we further elucidated the biological processes associated with these miRNA targets using functional enrichment analysis using ClueGO and found 74 enriched GO terms34,35,36. Furthermore, processes including regulation of vascular associated smooth muscle cell proliferation (40.54%), cell junction assembly (16.22%) and peptidyl-serine modification (12.16%) were most prominent. These results indicate that miRNA regulation in prostate cancer is a major determinant of cell proliferation, adhesion and signal transduction pathways.

FOXO3 is a forkhead box O transcription factor that negatively regulates genes required for cell cycle arrest and apoptosis to function as a tumor suppressor22,23. Theby inhibiting FOXO1 and FOXO3, miR182 promotes metastasis in prostate cancer37. Like NFAT3, an oncosomes member of the nuclear factor of activated T cells family regulates cell cycleregulation by regulation of cyclin-dependent kinases38. Signaling through integrins results in activation of NFAT and COX2, PGE2 production to promote angiogenesis by endothelial cell proliferation. The down regulation of PTEN, a well known tumour suppressor gene, is common in prostate promoting uncontrolled cell proliferation and survival39. Downregulation of PTEN by MiR-188-5p leads to the increase of proliferation and invasion in LNCaP cells. RHOA is a member of Rho GTPase family contributes to cytoskeletal dynamics and cell motility. Rho family GTPases, such as Cdc42, are dysregulated in metastasis and this leads to guanine nucleotide exchange factor (GEF) mediated promotion of metastasis. VEGFA is an important angiogenesis regulator, and up regulation of the gene is involved in the formation of blood vessels thereby aiding in tumor growth. We find that SMAD7 plays an inhibitory role in the TGF-β signaling pathway and that inappropriate dysregulation may promote tumor progression40. CDK2 is critically required for cell cycle progression, and mutations in its activity can result in uncontrolled cell division, a defining feature of cancer.

Prostate cancer progression is intricately linked to the dysregulation of key genes such as CDK2, VEGFA, and PTEN and recent potential therapeutic compounds targeting these genes are being listed offering promising avenues for treatment. In the current study, our CMap analysis identified possible therapeutic compounds against these key genes. An example of success was that several inhibitors of CDK2 were identified: arcyriaflavin-A, indirubin, purvalanol-A/B, as well as Tyrphostin AG-555, with mechanisms of inhibition involving CDK inhibition and tyrosine kinase inhibition41. Their role in angiogenesis pathways was highlighted by an association of carvedilol, which is an adrenergic receptor antagonist, and midostaurin, which is an inhibitor of FLT3/KIT, with VEGFA42. Temsirolimus, an mTOR inhibitor that mediates cell growth and survival pathways, influenced PTEN43. These findings signal that modulation of critical pathways in prostate cancer progression may be possible by targeting these miRNAs regulated genes with specific compounds.

Conclusion

The results of this study emphasize that miRNAs are essential mediators in gene expression and cancer pathway regulation in prostate cancer. This research lays the groundwork for future work that will discover key miRNAs and targeted genes with the corresponding therapeutic compounds from CMap analysis that will help develop targeted therapies for prostate cancer. The results suggest miRNAs could become a diagnostic biomarker, and a therapeutic target for this complex disease.