PCa has become a focus of intense genomic research over the last decade, which has provided insights into its pathogenesis and avenues for future therapeutic interventions. Key findings have highlighted the roles of several important genes, including BRCA1, BRCA2, partner and localizer of BRCA2 (PALB2), DNA mismatch repair gene (ATM), checkpoint kinase 2 (CHEK2), MutS homolog 2 (MSH2), MSH6, nibrin (NBN), TMPRSS2-ERG, TP53 and phosphatase and tensin homolog deleted on chromosome ten (PTEN) (Table I). The findings deepen understanding of the biological behavior of PCa and pave the way for the development of targeted therapies and personalized treatment strategies. Future research is essential to explore the therapeutic potential of targeting these genetic alterations, with the goal of improving outcomes for patients with PCa.

Robinson et al (14) conducted a comprehensive genomic analysis of advanced PCa, which identified that alterations in BRCA2 and ATM are particularly relevant to the aggressiveness and therapeutic response of the disease. While BRCA1 and BRCA2 mutations are well-established as risk factors for hereditary breast and ovarian cancer, they are increasingly being recognized as being associated with an increased risk and poorer outcomes in patients with PCa (15). PALB2 a gene involved in DNA damage repair, has also been identified as a significant risk factor for PCa. A study found that mutations in PALB2 are associated with an elevated risk of developing PCa and may impact survival rates, underscoring its importance in hereditary cancer predisposition (16). ATM is a key gene in the cellular response to DNA double-strand breaks, and its mutation predisposes individuals to PCa, as discussed in the PRACTICAL Consortium study (17). This mutation is considered a key germline variant that is important to consider in the genetic counseling of patients at risk. Similarly, CHEK2, which is involved in the DNA damage response, has been shown to harbor germline variants that contribute to cancer susceptibility, albeit with varying penetrance (18).

Deficiency in the DNA mismatch repair system, involving genes such as MSH2 and MSH6, has been implicated in PCa. Sharma et al (19) highlighted that the loss of the proteins encoded by these genes is associated with a distinct subset of aggressive tumors, highlighting the importance of genomic stability in PCa. Hereditary genes have been identified as contributors to PCA, but somatic alterations are also crucial in PCa pathology. Inherited mutations in NBN have been identified as a risk factor, with carriers of NBN mutations being significantly more likely to develop PCa, which highlights the importance of DNA double-strand break repair mechanisms in cancer susceptibility (20). Furthermore, mutations in TP53 and PTEN are well-documented in advanced PCa. As noted by Maxwell et al (21), TP53 alterations are significantly associated with poorer clinical outcomes, while PTEN loss, associated with increased tumor aggressiveness, drives the progression of PCa (22). The interplay between these genetic alterations and their collective impact on tumor biology and therapeutic resistance is an active area of ongoing research.

The integration of genomic data into clinical practice has greatly improved risk stratification in PCa. Genomic tests, such as the Decipher biopsy and Oncotype DX, utilize gene expression profiles to improve the classification of patients into risk groups, extending beyond traditional clinicopathological parameters (23). This stratification enables clinicians to identify patients at a higher risk of aggressive disease and to tailor treatment approaches accordingly. For instance, genomic profiling has been instrumental in guiding decisions regarding active surveillance compared with intervention. It has enabled patients with low genomic risk scores to avoid overtreatment, and those with high risk scores to be directed toward earlier and potentially more aggressive treatment options, including intensity-modulated radiotherapy or radical prostatectomy (24). However, the application of genomic profiling to guide treatment decisions, such as choosing between active surveillance and intervention, has certain limitations. Patients classified at low genomic risk may have aggressive disease characteristics not accounted for by existing models, potentially leading to undertreatment. Conversely, patients with high-risk scores may respond favorably to conservative management, indicating that genomic scores do not capture the full clinical complexity of PCa.

Genomic alterations also influence systemic therapies. For example, BRCA1/BRCA2 mutations have been found to be associated with sensitivity to therapies targeting DNA repair mechanisms, including poly (ADP ribose) polymerase (PARP) inhibitors (25). Similarly, alterations in AR signaling pathways, such as mutations and amplifications of the AR gene, can guide the use of AR-targeted therapies such as abiraterone and enzalutamide (26). However, the presence of AR signaling pathway alterations further complicates treatment decisions, as their presence does not guarantee the enhanced efficacy of AR-targeted therapies. Therefore, while genomic research is propelling the evolution of personalized treatment paradigms in PCa, clinicians must address the challenges associated with variability in research outcomes and the limitations of existing genomic tools to ensure optimal patient management.

A notable case study demonstrates the clinical application of genomic findings in a patient with metastatic CRPC (mCRPC) harboring a BRCA2 mutation. Following genomic profiling, the patient was treated with the PARP inhibitor olaparib, and imaging revealed a significant reduction in tumor burden. This case exemplifies how genomic data not only informs treatment eligibility but also directly impacts clinical outcomes (27). While this case highlights the potential for genomic data to inform therapy decisions and improve clinical outcomes, questions remain concerning the generalizability of such results, as individual patient responses can vary widely due to genetic and phenotypic differences.

Another example comes from a cohort of patients undergoing active surveillance, where genomic profiling revealed the presence of the TMPRSS2-ERG fusion gene. Patients who tested positive for this fusion exhibited higher rates of progression, prompting a reconsideration of their management strategy. In patients undergoing active surveillance, the presence of TMPRSS2-ERG fusion and an increased genomic risk score were significantly associated with a higher likelihood of disease progression, demonstrating the potential for genomic data to alter clinical management plans (28). However, the reliability of the TMPRSS2-ERG fusion as a predictive biomarker remains inconsistent across studies. For instance, a meta-analysis found that the predictive significance of TMPRSS2-ERG fusion in prostate cancer is not universally supported, with a study showing no significant association between the fusion and disease progression (29). Additionally, a study by Álvarez-Garcia et al (30) highlighted the diversity of mechanisms leading to TMPRSS2-ERG fusion and questioned its clinical utility as a biomarker, emphasizing the need for further research to clarify its role. The risk of over-treatment based on genomic findings requires careful consideration.

Genomic mapping plays a critical role in the stratification of patients based on treatment response in PCa. For example, a study demonstrated that genomic alterations could guide therapy choices, thereby enhancing personalized treatment approaches (14). This stratification enabled clinicians to optimize therapeutic regimens and improve patient outcomes. However, the efficacy of such stratification relies on a thorough understanding of the underlying mechanisms and the potential impact of confounding factors on treatment responses. Therefore, while genomic mapping holds significant promise for improving PCa management, the ongoing evaluation of its clinical utility and the robustness of its predictive capabilities is essential to ensure that it translates into improved patient care.

Transcriptomics, the study of RNA transcripts produced by the genome under specific circumstances, has markedly improved the understanding of gene expression variability in PCa. By employing high-throughput sequencing and microarray technologies, the expression profiles of thousands of genes can be analyzed simultaneously, revealing the molecular complexities of PCa. This variability in gene expression is influenced by numerous factors, including genetic mutations, epigenetic changes and the tumor microenvironment (TME). Studies have demonstrated distinct transcriptional subtypes of PCa that are associated with clinical outcomes. For example, one landmark study comprehensively analyzed gene expression profiles associated with different grades of prostate tumors and discovered that specific gene sets associated with cell cycle regulation, androgen response and immune response significantly varied across these grades (31). In another study, the differential expression of the MYC oncogene and its downstream targets was implicated in PCa progression, which emphasizes the role of oncogenic signaling pathways in the etiology of the disease (32).

Transcriptomic analysis has led to the identification of alternative splicing events and non-coding RNAs that contribute to the complexity of gene regulation in PCa. For example, the long non-coding RNA (lncRNA) PCAT-1 has been shown to promote cell proliferation, and found to be upregulated in a subset of aggressive PCa cases, suggesting its potential as a therapeutic target (33). Thus, transcriptomics provides critical insights into the dynamic regulation of gene expression and its relevance to PCa pathogenesis.

Advances in transcriptomic research have provided valuable insights into the molecular landscape of PCa and revealed numerous novel therapeutic targets. Factors including AR variants (34), sex determining region Y-box 2 (SOX2) (35), lncRNAs, microRNAs (miRNAs), dysregulated pathways and immune modulation mechanisms, constitute promising areas for future drug development (Table II). Consideration of these findings together supports the clinical exploitation of these targets to provide more personalized and effective treatment strategies for patients with PCa. Furthermore, the continued integration of transcriptomic data with clinical outcomes will be essential for translating these discoveries into actionable therapies.

AR signaling is a cornerstone in the progression of PCa. Transcriptomic studies have identified AR splice variants, particularly AR splice variant 7 (AR-V7), which lack the ligand-binding domain and are associated with resistance to ADT. Notably, these studies indicate that targeting AR-V7 with small molecule inhibitors or RNA-targeting therapies may improve treatment outcomes in patients with CRPC (34,36). SOX2 has also been implicated in promoting PCa stem cell properties and tumor growth (37). High SOX2 expression is associated with a poor prognosis, suggesting the potential of SOX2 as a therapeutic target. Therefore, inhibitors targeting SOX2 signaling pathways may serve as novel strategies for the treatment of advanced PCa (38).

Advances in transcriptomics have underscored the role of lncRNAs in PCa pathogenesis. For example, lncRNA PCa gene expression marker 1 (PCGEM1) is upregulated in prostate tumors and is associated with cancer cell proliferation and migration (39). In addition, silencing PCGEM1 expression has been shown to reduce tumor growth in vivo, supporting its potential as a therapeutic target (39). Another lncRNA, metastasis associated lung adenocarcinoma transcript 1 (MALAT1), has been found to play an important role in the epithelial-mesenchymal transition (EMT) of PCa cells (40). By regulating the splicing of its target genes, MALAT1 attenuates EMT and potentially inhibits PCa metastasis (41). miRNAs are key regulatory molecules in gene expression that have been found to contribute to tumorigenesis in PCa. For example, miR-145 suppresses PCa cell proliferation by targeting multiple oncogenes (42). In addition, the loss of miR-145 expression is associated with advanced disease stages, suggesting the potential of miR-145 as a therapeutic agent that could be restored using miRNA mimic strategies. Furthermore, the miR-221/222 cluster is implicated in androgen resistance (43). Targeting this miRNA cluster has demonstrated promising results in re-sensitizing CRPC cells to androgen therapies (44), highlighting its potential as an adjunct treatment option.

Transcriptomic analyses have revealed dysregulation of the phosphoinositide 3-kinase-AKT-mTOR pathway in PCa, which is frequently associated with poor prognosis, tumor growth and metastasis (45). Therapeutic strategies targeting this pathway, such as AKT inhibitors, have shown some effectiveness and are being explored in clinical trials (46), suggesting that this pathway could be a viable target for novel PCa therapies. The Wnt/β-catenin signaling pathway is another critical area of interest identified through transcriptomic analysis. Aberrant activation of this pathway is associated with the progression of PCa to an aggressive phenotype (47). Targeting Wnt/β-catenin signaling with small molecule inhibitors has been suggested to represent a prospective therapeutic strategy, particularly in patients exhibiting dysregulation of this pathway (48).

A number of transcriptomic studies have focused on the interaction between PCa cells and the TME, particularly the immune landscape. These studies reveal that high expression levels of immune checkpoint molecules, along with T cell exhaustion markers, are associated with poor outcomes (49). Therefore, combining immune checkpoint inhibitors with therapies that modulate the TME offers the potential for new therapeutic approaches. In addition, the transcriptomic identification of cytokines and chemokines that facilitate tumor-immune cell interactions has uncovered promising targets for therapeutic intervention (50); specifically, therapeutic agents disrupting the chemokine C-X-C motif ligand 12/chemokine receptor 4 axis have been identified for further investigation, as they showed promise in the reversal of immune suppression in PCa (51).

Insights gained from transcriptomic studies have been pivotal in the development of targeted therapies for PCa. As the molecular signatures of the disease are elucidated, it becomes possible to explore and refine therapeutic strategies targeting specific dysregulated pathways. A notable example is the AR, a critical driver of PCa. Transcriptomic analyses have identified mutations and splice variants of the AR gene that confer resistance to standard ADTs (14). These findings have facilitated the development of novel therapeutic agents that target the wild-type receptor and its variants. Drugs such as enzalutamide and abiraterone are now utilized for the treatment of advanced PCa and have significantly improved patient outcomes (52).

Transcriptomic profiling has identified pathways involved in cell survival, apoptosis and proliferation that are dysregulated in PCa, leading to the discovery of novel intervention targets. For example, inhibitors targeting the cell cycle regulatory protein CDK4/6 have shown promise in clinical trials, particularly in tumors expressing high levels of the relevant transcripts (53,54). However, the heterogeneity of transcriptomic profiles across different tumor stages and patient demographics may affect the reliability of these targets. In addition, transcriptomic analyses of the TME have opened new avenues for immunotherapy in PCa (55). By identifying expression profiles associated with immune evasion, combinatorial therapeutic strategies are being explored that integrate immune checkpoint inhibitors with conventional treatments to harness the immune system in PCa (56).

In summary, transcriptomics not only provides information about the molecular mechanisms associated with PCa but also impacts therapeutic approaches. By identifying novel biomarkers, predicting treatment responses and uncovering novel therapeutic targets, transcriptomic research lays the groundwork for the continued advancement of targeted and personalized therapies in PCa management.

Proteomics is an expansive field that focuses on the large-scale study of proteins, particularly in terms of their functions and structures. In the context of PCa, one of the most critical proteomics techniques is MS. This technique sensitively detects proteins and is key in the identification and quantification of protein expression across different phases of PCa progression.

The most frequently used form of MS is liquid chromatography-tandem MS, which facilitates the separation and identification of complex protein mixtures. Notably, it is able to analyze protein post-translational modifications, which are crucial in cancer biology (57). Advancements in MS technology, including improved resolution, sensitivity and speed, have greatly enhanced its ability to identify low-abundance proteins that may serve as potential biomarkers for the early diagnosis and monitoring of PCa (58).

Techniques such as two-dimensional gel electrophoresis (2-DE) and affinity-based methods can be employed alongside MS. 2-DE separates proteins based on their isoelectric point and molecular weight, while affinity-based methods target specific proteins or post-translational modifications (59). Furthermore, high-throughput proteomic platforms enable the large-scale screening of tissue samples, yielding valuable insights into the protein landscapes associated with PCa pathology (60).

The exploration of proteomic alterations in PCa has unveiled a plethora of potential biomarkers and therapeutic targets. Elucidation of the specific roles of these proteins in tumor biology offers opportunities for the development of novel clinical applications, such as improved diagnostics or targeted therapies (Table III). As research advances, the integration of proteomic data into clinical practice holds promise for personalized treatment strategies in PCa management. Continuous collaboration between basic research and clinical application is essential to translate these findings into therapeutic interventions that improve patient outcomes.

The AR plays a pivotal role in PCa progression. Alterations in protein expression, mutations and splice variants such as AR-V7 are implicated in the development of CRPC. These changes confer resistance to ADTs by promoting AR signaling, even in low-androgen environments (61). Targeting AR signaling remains a cornerstone of PCa treatment, with second-generation anti-androgens, including enzalutamide and abiraterone, showing efficacy against CRPC. Furthermore, circulating AR-V7 has been identified in patients with PCa, and thus has emerged as a crucial biomarker for predicting resistance to AR-targeted therapies, thereby helping to guide treatment decisions towards alternative therapies such as taxanes (62).

Ki-67 is a nuclear protein associated with cellular proliferation that serves as a proliferation marker in various types of cancer, including PCa. High Ki-67 expression is associated with a poor prognosis and aggressive tumor behavior, indicating that tumors with elevated Ki-67 are more likely to grow and evade treatment (63). Ki-67 is used clinically as a prognostic marker to determine the aggressiveness of prostate tumors. Testing for Ki-67 levels, in conjunction with other clinical parameters, can help to guide treatment strategies, particularly in patients with intermediate-risk disease who may benefit from more aggressive therapies (63).

Glutaminase (GLS) plays a critical role in glutamine metabolism, which is essential for cellular bioenergetics and anabolic processes in rapidly proliferating cancer cells. Elevated GLS activity is associated with increased cancer cell proliferation and survival, particularly under the nutrient deprivation conditions common in solid tumors (64). Inhibitors of GLS, such as CB-839, are being investigated in clinical trials for their potential in treating advanced PCa, particularly in combination with conventional therapies (65). Targeting metabolic pathways offers a novel strategy, potentially overcoming resistance mechanisms associated with traditional hormonal therapies.

Nutrient transporters, such as L-type amino acid transporter 1 (LAT1), have been demonstrated to serve an important role in PCa cells (66). Other notable nutrient transporters include the solute carrier family 1 member 5, which is involved in glutamine uptake and has been shown to be upregulated in various cancers, including PCa (67). Additionally, the solute carrier family 7 member 5 transporter, which facilitates the uptake of large neutral amino acids, has also been identified as a key player in cancer cell metabolism and proliferation. The activity of these transporters is often influenced by the composition of the microenvironment and can be targeted for therapeutic intervention (67). These transporters are pivotal for the uptake of amino acids and other metabolites essential for tumor growth and metabolism, and the dysregulated expression of these transporters contributes to the aggressive behavior of PCa cells (68). The inhibition of nutrient transporters appears to be a promising therapeutic strategy; LAT1 inhibitors such as JPH203 have shown the potential to disrupt amino acid supply and diminish tumor growth (69). These strategies are being explored in basic research and may provide a complementary treatment method to existing therapies in the future.

Heat shock proteins (HSPs) are critical in protein folding, which protects cells from stress-induced apoptosis (70). In PCa, elevated levels of HSPs, including HSP70 and HSP90, are associated with increased tumor survival and resistance to chemotherapy (70). Their ability to stabilize oncogenic proteins further increases cancer cell viability. Targeting HSPs is being investigated as a therapeutic strategy for PCa. HSP90 inhibitors, such as tanespimycin, have been shown to disrupt the stability of client proteins, such as mutant p53 and AR, and to promote apoptosis in cancer cells (71). Clinical trials evaluating HSP inhibitors, such as tanespimycin, are underway and aim to assess their efficacy in conjunction with standard PCa treatments (72). Other HSPs, such as HSP20, HSP40, HSP60 and HSP80, are also being investigated for their roles in cancer and potential therapeutic applications (73).

Galectin-3 is a member of the galectin family of lectins, which is involved in various biological processes, including cell proliferation, apoptosis and inflammation (74). Galectin-3 has been shown to promote tumor growth and metastasis through its effects on the TME (74). In addition, elevated serum levels of galectin-3 have been linked to a poor prognosis in patients with PCa (75). Exploration of the potential of galectin-3 as a therapeutic target indicates that galectin-3 inhibitors have the ability to reduce tumor growth and improve treatment outcomes (75).

Prostate-specific membrane antigen (PSMA) is a transmembrane protein that is expressed in higher levels in PCa cells than in normal prostate tissue. It plays a role in the regulation of AR activity and may also contribute to tumor growth and progression via its enzymatic activity (76). PSMA-targeted therapies, such as PSMA-617, a radiolabeled small molecule, have shown promise in the imaging and treatment of advanced PCa. PSMA-targeted radioligand therapy has demonstrated significant clinical efficacy, and PSMA-617 has been approved by the FDA as a new radiotherapeutic option for patients with mCRPC (77). Furthermore, PSMA can also be used as a clinical biomarker for patient stratification (78).

α-Methylacyl-CoA racemase (AMACR) is an enzyme implicated in fatty acid metabolism that has been found to be upregulated in PCa cells. It facilitates the conversion of α-methylacyl-CoA to its racemic forms, contributing to altered lipid metabolism in tumor cells (79). AMACR has been explored as a diagnostic marker in PCa, particularly for use in histopathological assessments. This has revealed that elevated levels of AMACR in prostate biopsy specimens are associated with the presence and aggressiveness of cancer, making it a useful biomarker to assist pathologists in distinguishing between benign and malignant lesions (80).

The complexity of the TME presents considerable challenges in PCa research. Proteomics has been vital in characterizing the TME, which consists of cancer cells, stromal cells and extracellular matrix components, all of which interact dynamically to promote tumor growth and metastasis (81). A proteomic study has highlighted the shifts in the protein composition of the TME in response to oncogenic signaling and therapeutic interventions (82). Tumor-associated macrophages and their secretory profiles have been found to be associated with changes in the extracellular matrix composition, thereby influencing tumor progression (82). These intricate interactions underscore the potential of proteomic methodologies to elucidate the cross-talk between PCa cells and the surrounding stromal components. Spatial proteomics has been used to investigate the heterogeneity of protein expression within the TME. Techniques such as imaging mass cytometry have enabled the protein distributions in tissue sections to be mapped, thereby revealing the spatial organization of proteins involved in immune evasion and treatment resistance (83). Such insights are crucial for the development of novel therapeutic targets aimed at remodeling the TME to inhibit tumor growth and improve patient outcomes. While proteomics has been instrumental in elucidating interactions within the TME in PCa, careful consideration of the methodologies employed and the variability of findings is essential. Understanding these nuances will be key in the development of novel therapeutic targets aimed at modulating the TME to inhibit tumor growth and, ultimately, improve patient outcomes.

Metabolomics is a powerful analytical approach designed to characterize all the metabolites present in biological samples, offering insights into metabolic changes associated with various diseases, including cancer. In PCa research, several methodologies are being employed to analyze metabolomic profiles and elucidate the underlying biochemical pathways that contribute to tumorigenesis and progression. The primary techniques utilized in metabolomics include MS and nuclear magnetic resonance (NMR) spectroscopy. MS, particularly when combined with chromatography methods such as gas or liquid chromatography, allows for the sensitive detection, identification and quantification of metabolites in complex biological matrices. The high sensitivity and resolution of modern mass spectrometers allow low-abundance metabolites to be detected, which can be crucial in cancer (84).

NMR spectroscopy is a non-destructive technique for analyzing metabolites in a sample and providing structural information. Although it is less sensitive than MS, NMR is valuable for quantifying metabolites in intact tissues, preserving the biological context of the sample (85). Furthermore, advanced bioinformatics tools are essential in metabolomics research. These tools are used for data processing, statistical analysis and the interpretation of complex metabolomic datasets. Computational techniques such as multivariate analysis, machine learning (ML) and pathway enrichment analysis contribute to the identification of key metabolic pathways disrupted in PCa (86).

Metabolomics has emerged as a powerful tool in cancer research, offering insights into the biochemical mechanisms of tumor biology. Key findings highlight alterations in energy, amino acid, lipid and nucleotide metabolism, all of which contribute to the aggressive nature of this malignancy (Table IV). In future research, a focus on the integration of metabolomic data with genomic and proteomic information is necessary to fully understand the complexities of PCa biology and to refine therapeutic strategies.

One of the hallmarks of cancer is the switch from oxidative phosphorylation to aerobic glycolysis, known as the Warburg effect (87). In PCa tissues, altered levels of key metabolites associated with energy metabolism have been detected, including increased lactate and decreased citric acid cycle intermediates (88). In addition to supporting rapid proliferation, this shift influences the TME by promoting acidotic conditions, facilitating invasion and metastasis (89).

PCa cells exhibit distinct patterns of amino acid metabolism. Notably, increased levels of branched-chain amino acids (BCAAs), including leucine and valine, have been identified in patients with PCa compared with healthy controls (90). In addition, tryptophan metabolism, particularly its kynurenine pathway, has been implicated in immune evasion mechanisms employed by tumors (90). These alterations indicate potential biomarkers and therapeutic targets, as the availability of amino acids can influence tumor growth.

Lipid metabolism is notably altered in PCa, with increased levels of specific fatty acids, sterols and phospholipids. A previous review of metabolomic studies highlighted that levels of unsaturated fatty acids, notably arachidonic acid, are elevated in PCa, which suggests increased membrane fluidity and a potential role in cancer progression (91). Furthermore, sterol metabolism, particularly the accumulation of cholesterol and its metabolites, contributes to membrane biogenesis and the activation of signaling pathways that promote oncogenesis (92).

PCa cells exhibit heightened nucleotide synthesis, as evidenced by elevated levels of purines and pyrimidines, which supports the rapid proliferation of cancer cells (93). Targeting nucleotide metabolism pathways is a novel therapeutic strategy that may hinder cancer cell growth by disrupting the synthesis of nucleotides.

Metabolomic analyses have revealed alterations in carbohydrate metabolism pathways, including increased glucose uptake and metabolism. Elevated hexosamine biosynthesis in PCa has been shown to contribute to glycosylation changes in proteins associated with oncogenic signaling (94). Furthermore, changes in glycogen metabolism have been documented, further emphasizing the shifts in energy substrates utilized by cancer cells (94).

The identification of these metabolic alterations has implications for the management of PCa. In particular, metabolomic profiling may aid in risk stratification and the development of personalized medicine approaches. For instance, elevated BCAAs and lipid alterations may serve as potential biomarkers for the early detection and monitoring of disease progression (95). In addition, targeting these metabolic pathways provides opportunities for innovative treatment options, such as metabolic inhibitors that disrupt cancer cell energetics and proliferation.

The integration of metabolomic data with clinical parameters has the potential to enhance diagnosis and prognostication in PCa. By identifying associations between metabolomic findings and clinical outcomes, it may be possible to develop a more comprehensive understanding of the disease and identify patterns that could inform personalized treatment strategies. For example, studies have demonstrated that panels of metabolites combined with clinical data, such as PSA levels, Gleason scores and imaging findings, can be used to improve diagnostic accuracy (96,97). In addition, a multi-analyte metabolomic assay incorporating metabolomic signatures with conventional markers was shown to provide improved results in the prediction of malignancy in patients with elevated PSA levels (98). Nevertheless, variability in study design, sample sizes and metabolic profiling techniques may affect the reliability and generalizability of these findings.

The application of ML approaches to metabolomic datasets is useful in the identification of novel metabolites as potential prognostic markers. The classification of patients with PCa based on their metabolomic profiles can contribute to the prediction of disease progression, response to therapy and overall survival (99). ML models can also help to refine treatment decisions, allowing clinicians to tailor interventions based on the metabolic signature of each individual patient (100). However, while ML models have the capacity to refine treatment decisions tailored to specific metabolic signatures, concerns about overfitting and the requirement for external validation should not be overlooked (100).

As metabolomics continues to evolve, its integration with other omics technologies, such as genomics, proteomics and transcriptomics, may provide a holistic view of the tumor landscape. This multi-omics approach is expected to yield deeper insights into the molecular mechanisms associated with PCa, but the complexities associated with data integration and interpretation present ongoing challenges. Ultimately, addressing these issues will be crucial in facilitating the development of more effective diagnostic and therapeutic strategies.

Single-cell sequencing technology has emerged as a groundbreaking tool for understanding the intratumoral heterogeneity and cellular complexity of PCa (102). This technology enables individual cells to be analyzed, thereby providing insights that are not possible in bulk tissue analyses. Advancements in single-cell RNA sequencing, single-cell DNA sequencing and single-cell assay for transposase-accessible chromatin sequencing have markedly improved the general understanding of PCa biology (102).

Single-cell RNA sequencing allows the transcriptomes of thousands of individual cells to be profiled, leading to the identification of distinct cellular populations within tumor (103). For example, a recent study has revealed the presence of previously unrecognized subpopulations of tumor cells that exhibit unique gene expression profiles associated with differential responses to therapy (104). Furthermore, spatial transcriptomics, which combines single-cell RNA sequencing with spatial information, is a novel technique that can characterize the spatial organization of tumor cells within the TME (105), which is critical for understanding cellular interactions and signaling pathways in PCa.

Advancements in droplet-based and microfluidic technologies have enabled the high-throughput analysis of single cells, which has major implications for large-scale studies of PCa (106). Techniques such as 10× Genomics Chromium and Seq-Well have made it feasible to examine thousands of individual cells simultaneously, which greatly increases throughput and reduces costs (105). These methods facilitate the identification of rare cell populations, which may have critical implications for tumor progression and treatment response.

A critical finding from single-cell sequencing in PCa is the extensive intratumoral heterogeneity that exists among cancer cells. Different clones within a single tumor can exhibit divergent genetic and phenotypic characteristics, contributing to variable responses to therapy and disease progression (104). For instance, single-cell RNA sequencing has identified distinct basal and luminal PCa subtypes, with variations in metabolic and signaling pathways that can influence outcomes following therapies such as ADT (105).

Single-cell technologies have been instrumental in elucidating the role of the TME in PCa. The TME comprises not only tumor cells but also stromal cells, immune cells and extracellular matrix components, all of which profoundly influence cancer progression (107). Single cell RNA sequencing has facilitated the characterization of immune cell populations infiltrating prostate tumors, providing insights into how these cells may facilitate or inhibit tumor growth. For example, a recent study identified a subset of immunosuppressive macrophages that contribute to tumor immune evasion (108).

The identification of specific cell types that contribute to cancer progression and treatment resistance is crucial. Cancer stem cells (CSCs) have the ability to drive tumor growth and metastasis (109). Single-cell technologies have identified markers associated with CSCs, highlighting their potential as targets for therapy (110). The insights obtained from single-cell studies have profound implications for understanding cancer cell plasticity. For example, the identification of hybrid states, where tumor cells adopt features of different lineages, helps to explain the emergence of treatment resistance and metastasis (111).

Single-cell sequencing has revolutionized PCa research by enabling the exploration of cellular heterogeneity within tumors (112). This approach facilitates the identification of distinct cell populations, including tumor-initiating cells and immune subsets, thereby deepening understanding of the TME and cancer progression (112). However, discrepancies in findings across studies raise concerns about reproducibility. For example, differences in sequencing techniques and data analysis pipelines can lead to inconsistent interpretations of cellular states and their functional roles (113). Furthermore, the resolution of single-cell data may overlook the influence of cell-cell interactions, limiting the scope of insights into tumor biology (114). Thus, while single-cell sequencing provides invaluable data for the advancement of PCa research, careful consideration of methodological variations is crucial for drawing robust and reliable conclusions.

The advent of multi-omics approaches, encompassing genomics, transcriptomics, proteomics, metabolomics and epigenomics, has provided substantial insights into cancer biology, including PCa. By integrating diverse datasets from different omics technologies, a holistic view of the TME can be gained, which facilitates a deeper understanding of the molecular mechanisms driving disease progression, metastasis and therapeutic resistance.

One of the critical advantages of multi-omics integration is its ability to elucidate the complex interplay between genetic alterations, gene expression profiles, post-transcriptional modifications, protein interactions and metabolic changes within tumor cells. For example, the integration of genomic and transcriptomic data can be used to identify key driver mutations and their downstream effects on gene expression, providing a more accurate delineation of cancer subtypes (115). This integrative approach also allows specific pathways that might be targeted for therapeutic intervention to be identified. Furthermore, the combination of different omics data facilitates the discovery and validation of biomarkers (Fig. 2). Biomarkers derived through multi-omics approaches have the potential to be more specific and sensitive, thereby improving the screening, diagnosis and prognosis of PCa. For example, the integration of metabolomic profiles with genomic data has revealed distinctive metabolic signatures associated with tumor aggressiveness, which improves the predictive capability of biomarkers beyond that of individual omics datasets (116).

Multi-omics integration facilitates the identification of biomarkers that reflect the complexity of interactions between tumor heterogeneity and the TME (Fig. 2). By analyzing data from various sources, it is possible to identify tumor cell-intrinsic markers and markers associated with the TME, including immune cell infiltration and stromal interactions. This comprehensive perspective is critical for the development of targeted therapies and personalized treatment strategies, offering insights that may help to address variations in patient responses to therapies (117).

While the potential for multi-omics integration is immense, there are notable challenges to overcome, particularly in data standardization, integration methodologies and bioinformatics analysis tools (11). The inherent heterogeneity of omics data presents a major barrier; for example, discrepancies in measurement techniques and data formats can complicate integration efforts (118). The establishment of effective standardization protocols is necessary to ensure comparability across studies (119). Various integration methods, including statistical learning techniques such as multi-factor analysis and ML algorithms, and computational frameworks such as Multi-Omics Factor Analysis, are crucial for managing these data types effectively (119). However, the application of advanced bioinformatics tools, including network-based approaches, is able to elucidate biologically meaningful interactions despite data complexity (86).

A primary obstacle to multi-omics integration is the inherent complexity and heterogeneity of the generated data (Fig. 2). Each omics technique produces vast amounts of information, often with varying scales, measures and corresponding analysis methods. This creates substantial analytical and interpretative challenges, necessitating sophisticated strategies to integrate and interpret the diverse datasets (117). In addition, the integration of multi-omics data requires robust bioinformatics tools and expertise. Advanced statistical methods and ML algorithms are essential for effective data processing, normalization and integration. However, the development and implementation of these tools has not kept up with the pace of data generation. Consequently, it is urgently necessary for ongoing research to focus on the improvement of computational methodologies for handling multi-omics data, to enable the extraction of meaningful biological insights (120).

Another challenge is the lack of standardized protocols for multi-omics studies, leading to inconsistencies in data collection, processing and analysis across different studies. Standardization is required not only for methodological procedures but also for data representation, which is essential for accurate comparisons and meta-analyses in PCa research (121). Collaborative initiatives and consortia focused on the establishment of standardized practices may help to address these issues and promote the wider adoption of multi-omics approaches in clinical settings. For example, the Multi-Omics for Health and Disease consortium of the NIH National Human Genome Research Institute is working to validate and enhance generalizable multi-omics approaches to identify meaningful biological changes related to health and disease in ancestrally diverse populations (122). Additionally, the National Microbiology Data Collaborative EDGE initiative aims to develop data harmonization, integration, visualization and analysis methods for multi-omics datasets (122).

The integration of multi-omics data in clinical settings poses ethical considerations and privacy concerns for patients (123) (Fig. 2). A major challenge is ensuring the anonymity and confidentiality of patient information, as genomic data can be uniquely identifying (123). The implementation of stringent data protection measures and adherence to regulatory guidelines is essential for safeguarding sensitive information (124). Furthermore, discrepancies in data integration methodologies can lead to inconsistent results, complicating interpretations and clinical applications (125). For example, variations in study cohorts and analytical frameworks may yield conflicting biological insights, hindering reproducibility and eroding trust in multi-omics approaches (126). Addressing these challenges is essential for the ethical advancement of PCa research.