Jingang Jian1,2†
Xin’an Wang3†
Jun Zhang1Chenchao Zhou1Xiaorui Hou1,2
Yuhua Huang1
Jianquan Hou1,2*
Yuxin Lin1,4*
Xuedong Wei1*- 1Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- 2Department of Urology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
- 3Department of Urology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- 4Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
Prostate cancer (PCa) is commonly occurred with high incidence in men worldwide, and many patients will be eventually suffered from the dilemma of castration-resistance with the time of disease progression. Castration-resistant PCa (CRPC) is an advanced subtype of PCa with heterogeneous carcinogenesis, resulting in poor prognosis and difficulties in therapy. Currently, disorders in androgen receptor (AR)-related signaling are widely acknowledged as the leading cause of CRPC development, and some non-AR-based strategies are also proposed for CRPC clinical analyses. The initiation of CRPC is a consequence of abnormal interaction and regulation among molecules and pathways at multi-biological levels. In this study, CRPC-associated genes, RNAs, proteins, and metabolites were manually collected and integrated by a comprehensive literature review, and they were functionally classified and compared based on the role during CRPC evolution, i.e., drivers, suppressors, and biomarkers, etc. Finally, translational perspectives for data-driven and artificial intelligence-powered CRPC systems biology analysis were discussed to highlight the significance of novel molecule-based approaches for CRPC precision medicine and holistic healthcare.
Introduction
Prostate cancer (PCa) is the most frequently diagnosed cancer in men, representing 29% of all male cancer cases and ranking second only to lung cancer in terms of fatalities (1). The incidence and mortality of PCa in Asia are much lower than those in Europe and in the United States, but the increasing trend is much higher. The incidence of PCa is influenced by multiple factors such as age, race, and genetics, etc., and biological characteristics of the tumor, as well as the prognosis, can vary significantly among different individuals and populations. In 1941, Huggins and Hodges discovered that PCa could be treated by castration. In the early stage of the tumor, almost all PCa patients are responsive to androgen deprivation therapy (ADT). However, after a median of 18 to 24 months of treatment, nearly all patients progressed to castration-resistant prostate cancer (CRPC) (2). CRPC is a heterogeneous status with complex molecular characteristics, and its poor prognosis and high mortality rate remain to be a significant clinical challenge.
The occurrence and development of CRPC result from interactions among various carcinogenic mechanisms, which are not fully deciphered. Currently, chemotherapy, novel endocrine therapy, and immunotherapy have been used for CRPC clinical treatment, and these methods may be effective during the initial stages. However, drug resistance typically develops soon. CRPC is generally a fatal condition, with a median time to death of 1–2 years after entering this stage (3). To fight against this dilemma, biomarkers across different biological levels, e.g., genes, RNAs, proteins, and metabolites, were identified by both computational and experimental techniques for early prediction, precision prognosis and personalized therapy of CRPC, and this has increased the flourishing of molecule-based approaches for CRPC application (4).
Due to the high heterogeneity in CRPC evolution, the reliability and efficacy of current therapeutic strategies for CRPC clinical practice are still unsatisfactory. Two important issues are widely concerned across CRPC studies, i.e., what are the key signatures that could be used for indicating the development of CRPC, and what therapeutic schedules should be applied when a patient has been diagnosed with CRPC. With the accumulation of multi-omics biomedical data and technologies, a great number of biological molecules have been identified for CRPC risk prediction and personalized therapeutics.
In this study, a systematic literature search was conducted to collect reported CRPC-associated molecules, e.g., genes, RNAs, proteins and metabolites etc., using the NCBI PubMed up to September 2023. The search formula was defined as “prostate cancer[tiab] AND [CRPC(tiab) OR castration-resistant (tiab)] AND [gene*(tiab) OR pathway*(tiab) OR signaling*(tiab)].” As shown in Figure 1, a total of 4940 articles were obtained from NCBI PubMed using the above search criteria. Among them, 417 articles that were not indexed in Science Citation Index Expanded or not written in English were excluded. After reviewing the titles and abstracts, 3935 articles that were not focused on CRPC studies, i.e., unrelated to the pathogenesis or clinical prevention and therapeutic strategies of CRPC, were excluded. Based on a detailed review of the remaining 588 articles, a total of 233 articles with clear description on the associations between identified molecules and CRPC genesis were included and analyzed from three perspectives: First, introducing the carcinogenesis and clinical strategies for CRPC prevention and treatment based both on androgen receptor (AR)-related and non-AR-based mechanisms. Then, conducting a comprehensive functional characterization from single molecules to integrated pathways at three aspects, i.e, drivers promoting CRPC occurrence and progression, suppressors inhibiting CRPC development, and biomarkers indicating the state transition into CRPC. Finally, discussing future directions for CRPC precision medicine and personalized therapy to indicate novel approaches and opportunities for data-driven translational CRPC studies.
Figure 1 The flowchart and standards used for literature selection in this study.
CRPC carcinogenesis and clinical intervention strategies
AR-related mechanisms and therapeutic schemes
As shown in Figure 2, the carcinogenesis of CRPC could be divided into two primary aspects, i.e., the AR-related mechanisms, and the non-AR-based mechanisms. Among them, AR-related mechanisms have been widely concerned by researchers and clinical practitioners, including AR overexpression, mutations, and splice variants, abnormal AR transcription and modifications, AR-related alternative pathway activation, and abnormal androgen synthesis (5).
Figure 2 The mechanisms of PCa developed into CRPC.
As a famous star in CRPC development, targeting AR signaling axis has already been the first-line approach for CRPC therapy. As illustrated in Figure 3, most of the studies focus on the upstream regulation of AR pathway during CRPC evolution. For example, USP16, KDM4B, and RNF8 could regulate AR signaling by mediating the expression of c-myc (6–8). Interestingly, Larsson et al. found that FcγRIIIa receptors could interact with AR receptors and affect the progression of CRPC in xenograft mouse models (9). COP1 promoted GATA2 degradation to inhibit AR expression and activation (10), however, Shen et al. found that MAPK4 activated GATA2 to regulate AR transcription in mice (11).
Figure 3 AR and ARv7-related pathways in CRPC occurrence and development.
CRPC exhibits substantial heterogeneity in terms of its sensitivity to ADT, tissue histopathological types, and genetic profiles. In patients with metastatic CRPC (mCRPC), the occurrence rate of SPOP mutations is relatively low, however, patients carrying SPOP mutations have a relatively better prognosis and are more sensitive to treatment with novel anti-androgen drugs (12, 13). In addition, Taplin et al. found that the detection rate of ARv7 in mCRPC patients was less than 10% in a randomized trial (14), but ARv7 positive patients had poor response to treatment with novel anti-androgen drugs (15). Such variances directly impacted the responsiveness (or resistance) of patients with the same histopathological type to medications. In clinical practice, corresponding theoretical support for a uniform treatment approach to CRPC is still limited, making it challenging to achieve the desired therapeutic outcomes. Deeper exploration of the heterogeneity of CRPC among patients, identifying relevant molecular targets, understanding how these targets vary among different patient subgroups or racial populations, and how this affects treatment outcomes are significant for the personalized management of CRPC patients. With the rapid advance of sequencing techniques, the next-generation sequencing (NGS) is increasingly being widely utilized in clinical diagnosis and treatment. Therefore, it has become essential to analyze the mechanisms and pathological characteristics of CRPC, to categorize CRPC patients accordingly, and to develop personalized drug dosing plans to achieve optimal treatment outcomes.
Non-AR-based mechanisms for CRPC management
The understanding of non-AR-based mechanisms of CRPC opens new avenues for the development of novel therapies against resistance. As shown in Figure 2, the non-AR-based mechanisms are scattered but could be summarized as the following aspects according to recent literature reports.
Neuroendocrine cell-related mechanisms: Neuroendocrine CRPC could be induced by treatments such as ADT, radiotherapy, and chemotherapy, where neuroendocrine differentiation of PCa cells is the main driving force of disease development. Neuroendocrine CRPC exhibits resistance to hormone therapy with rapid progresses but does not reveal an elevation in PSA levels (16). Previous studies indicated that neuroendocrine cells were negative for PSA, and were more abundant in CRPC tumors (17). Moreover, neuroendocrine cells expressed IL-8, and CXCR2, and IL-8/CXCR2 had a significant role in benign and malignant neuroendocrine cells by interacting with p53 signaling (18). Li et al. demonstrated that CXCR2 expression could alter the phenotype of PCa cells, and the inhibition of CXCR2 expression in neuroendocrine PCa cells had the significance to re-sensitized enzalutamide-resistant PCa to enzalutamide (19).
Prostate stem cell-related mechanisms: It mainly includes the transformation of normal stem cells into malignant cells and the activation of tumor stem cells from differentiated tumor cells in response to external stimuli. Here a small subset of cells expressing CD44+/α2β1/CD133+ and lacking of AR expression are identified as prostate cancer stem cells (PCSC), and they hold the ability of proliferating even in androgen-depleted environments or under ADT (20). The research progress in targeted therapy for PCSC includes approaches targeting the prostate CSC microenvironment, targeted nanoparticles, and CAR-T cells targeting the CSC marker epithelial cell adhesion molecule (EpCAM), and some of them have already been entered into clinical trials (21, 22).
The molecular heterogeneity and variability of cellular populations within tumor microenvironment (TME): Chen et al. performed single-cell sequencing and discovered the activated endothelial cells, KLK3-high T-cell clusters, and KLK3-positive T cells in TME for CRPC progression to elucidate the significant variability presented in PCa and offered insights for pinpointing therapeutic targets and developing robust tumor biomarkers (23). In recent years, cancer immunotherapy has garnered increasing attention in cancer therapeutics. Some small-molecule tyrosine kinase inhibitors, whether used as single agents or in combination with other immunotherapies, may potentially improve clinical outcomes (24).
Deregulations in pathways including PI3K-Akt-mTOR, Wnt, Hippo, Hedgehog, and Notch etc: The PI3K-Akt-mTOR signaling pathway played a crucial role in regulating cell survival, proliferation, differentiation, and angiogenesis. It is recognized as one of the important pathway implicated in driving the progression of CRPC (25). In a randomized study conducted on mCRPC patients who had undergone prior docetaxel chemotherapy, the combination of the Akt inhibitor Ipatasertib with abiraterone was compared to abiraterone alone. It was observed that patients with PTEN loss derived a radiographic progression-free survival (rPFS) benefit from varying doses of Ipatasertib in conjunction with abiraterone (26). Robinson et al. identified abnormalities within the Wnt pathway in 18% of mCRPC patients. These abnormalities encompassed periodic alterations in adenomatous polyposis coli, β-catenin, and R-spondins within the pathway, implying a potential pivotal role of this pathway in CRPC progression (27). Currently, small molecule drugs and biological agents directed at the Wnt pathway remain in early stages of research, thus further exploration of the potential anti-tumor mechanisms induced by Wnt pathway inhibition needs to be conducted.
Mutations in the genetic architecture: In addition to epigenetic changes, molecular mutations in specific genes were found to be associated with the prognosis of patients with CRPC, which could guide clinical treatment for patients. In CRPC, inherited or systemic mutations, particularly alterations in the BRCA1 and BRCA2 genes, were linked to an unfavorable prognosis (28). In patients with metastatic hormone-sensitive PCa and mCRPC, TP53 mutations (32%) and PTEN mutations or copy number variations (20%), along with RB1 copy number variations (6%), were commonly observed (29). These genetic alterations were significantly correlated with increased tumor burden and a less favorable clinical prognosis (30, 31).
Functional classification of CRPC-related signatures: from single molecules to integrated pathways
Drivers promoting CRPC occurrence and progression
Genes positively associated with CRPC progression
Mutation and abnormal expression of genes enriched in AR regulation play a pivotal role in CRPC progression. As shown in Table 1, more than half of the retrieved genes were involved in the regulation of AR signaling. For example, YB-1, KIF4A, KIF20A and PRPF6 have been found to regulate AR and ARv7 transcription and splicing (32–35). The progression of CRPC is a process of cross-talk among multiple signaling pathways, and some genes have been shown to regulate multiple signaling pathways. For example, Choi et al. found that the knockdown of ISL1 inhibited AR signaling and AKT/NF-κB signaling and promoted enzalutamide resistance in CRPC through epithelial to mesenchymal transition (36). PROS, PKIB and PCDH7 regulated the progression of CRPC by mediating the PI3K/AKT signaling pathway (37). In addition to mRNA transcript changes, the alternations in protein abundance, e.g., TXNDC5, SREBP-1, OCT1, β-arrestin2, and p66Shc, would also contribute to the development of CRPC (38–41). It should be noticed that AR mutations are seldom occurred in the early stages of PCa, whereas aberrant AR signal transduction and alterations in AR-related pathways are prevalently observed in advanced PCa (42). Thus, early detection of AR-related molecular alterations could offer insightful opportunities for CRPC precision diagnosis and prevention.
Table 1 Drivers promoting CRPC occurrence and progression.
RNAs involved in promoting the development of CRPC
As shown in Table 1, numerous studies demonstrated the role of RNAs in CRPC development. For instance, certain specific RNA molecules could modulate the proliferation and invasive capabilities of CRPC cells, consequently influencing tumor progression. In addition, RNA could serve as a molecular marker to predict the occurrence and prognosis of CRPC. In particular, several studies showed that the elevated expression of long non-coding RNAs (lncRNAs) in CRPC was related to the degree of malignancy and drug resistance of tumors. For example, HOXD-AS1 facilitated PCa progression and chemo-resistance by recruiting WDR5 (43). HOTAIR promoted neuroendocrine differentiation in CRPC (44). CCAT1 was an oncogenic factor for CRPC progression and was highly up-regulated in CRPC, and elevated CCAT1 was associated with poor prognosis (45). SOCS2-AS1 promoted the growth of castration-resistant and androgen-dependent cells and inhibited apoptosis in PCa (46). In addition, microRNAs (miRNAs) also play an important roles in CRPC, such as miR-221 and miR-302/367, and they promoted the development of CRPC by inhibiting the expression of targeted anti-tumor proteins (47, 48).
Enzymes that regulate CRPC progression
As shown in Figure 4, enzymes play an important role in the progression of CRPC, and many studies have been conducted on enzymes related to histone modification. For example, histone demethylases LSD1, JMJD1A, KDM3B, KDM4B, KDM5B and KDM5C were found to be involved in the regulation of AR, c-Myc or PTEN signaling pathways to affect the development of CRPC (49–54). KDM8 could double activate AR and JMJD5, participating in the regulation of androgen response and the regulation of PCa metabolism genes (55). Over-expression of HAT1 increased AR expression and was associated with the resistance of CRPC cells to enzalutamide (56). Methylation-modifying enzymes, kinases and oxidorereductases also played significant functions in CRPC development and progression. For example, INMT promoted the production or release of methylation of anticancer metabolites, and PRMT5 and EZH2 regulated the transcription of AR through methylation (57–59). Kinases such as PKA, Lyn, TNK2, MAPK4 and Etk are involved in CRPC progression by regulating AR signaling pathway (11, 60–62). PKC, LIMK2, IKKα, and GCN2 have been reported to be involved in CRPC regulation through various mechanisms, as listed in Table 1. Oxidoreductase such as GPX2, AKR1C3, HO-1 and SQLE have been reported to be elevated in CRPC and to contribute to CRPC progression and prognosis (63–65). Ubiquitination is a regulator in CRPC progression. For example, Siah2 regulated the transcriptional activity of AR, and USP16 promoted CRPC proliferation through deubiquitination and stabilization of c-Myc (6, 66). Other enzymes such as UGT2B17, PADI2 and V-ATPase could facilitate CRPC progression by regulating AR signaling (67–69). ACSL3 contributed to the growth of CRPC through intratumoral steroidogenesis (70). CTSK promoted the tumor growth and metastasis by IL-17/CTSK/EMT axis and mediates M2 macrophage polarization in CRPC (71).
Figure 4 Enzymes regulating CRPC progression.
Receptor molecules involved in CRPC progression
AR is reported to be functional in CRPC progression by mediating the effects of androgens. As shown in Table 1, it is widely acknowledged that resistance to ADT is often a result from aberrations within the AR signaling, such as mutations in AR gene or heightened expression of the AR protein. Accumulating evidence confirmed that the aberrant cross-talk between AR expression and other oncogenic pathways could promote CRPC progression (72). Some receptor molecules regulate the progression of CRPC by regulating AR directly or indirectly. For example, the overexpression of RON could activate multiple transcription factors, and it promoted AR activation of AR response genes and nuclear localization (73). FcγRIIIa facilitated the growth and metastasis of PCa by regulating the AR and PIP5K1α pathways (9). TLX plays an oncogenic role in prostate carcinogenesis by suppressing oncogene-induced senescence, and it could confer resistance to androgen deprivation and anti-androgen (74). ErbB2 stabilized AR protein, and the expression of ERBB2 was increased in some abiraterone-resistant PCa patients (75). ARv7 is considered as a key driver of ENZR in CRPC (76). Ectopic overexpression of EP4 drived PCa cells proliferation and PSA production via regulating ARv7 signaling pathway (77). Ubiquitination is an intracellular protein regulatory mechanism, which is closely related to the occurrence and progression of CRPC. It has been reported that the high expression of the ubiquitination modifying enzyme Siah2 could promote the transcriptional activity of AR and deubiquitinate the enzyme USP16 could regulate the proliferation of CRPC cells through deubiquitinating and stabilizing c-Myc. Other receptors that have been implicated in CRPC include cell surface molecular receptor and tumor immunotherapy receptor. LRH-1 and ERRα facilitate CRPC progression via promoting intratumoral androgen biosynthesis (78, 79). Interplay among EGFR and signal transducer and STAT3 could mediate the progression of PCa (80). Co-expression of AVPR1A with AVPR2 was highly correlated with the development of PCa (81). The expression of CXCR7 was elevated after ADT, and it could facilitate the growth and metastasis of CRPC via MIF/CXCR7/AKT signaling pathway (82). PCa patients with high expression of CHRM1 and CHRM3 were more likely to progress to CRPC (83). The expression of FGFR1 and Notch1 were all elevated in CRPC and they regulated the proliferation and progression of CRPC through different mechanisms (84, 85).
Other molecules
The transition from HSPC to the castration-resistant stage is also encompassed by hormones, cytokines, and cellular components. As described in Table 1, androgens are signaling molecules that are necessary for the growth and maintenance of PCa cell survival. 5alphaDH-DOC within CRPC tissues might activate the AR pathway for proliferation and survival of CRPC cells under an extremely low level of DHT (86). 11KT is a potent AR agonist and is the major active androgen in PCa patients after castration (87). Other hormones are also functional in PCa progression, tumor growth, and invasion (88–90). Cytokines are a class of secreted proteins or molecules that can regulate and influence cell-to-cell interactions and communication. In the context of CRPC, cytokines and factors mediating the interaction between tumor cells and immune cells to promote the proliferation, invasion, and metastasis of PCa cells. As stated in Table 1, IL-6 promoted the progression from PCa to castration resistance through multiple signaling pathways (91). In androgen-deprived conditions, IL-23 promoted PCa cell proliferation by activating the AR pathway signaling (92). Recent studies suggested a close relationship between abnormal fatty acid metabolism and CRPC progression. Lactate regulated the metabolic-epigenetic axis to foster metastatic potential in PCa (93). Some cells have also been reported to be functional in the advancement of CRPC. CD4lowHLA-G+ T cells may drive androgen-independent PCa progression by mediating the migration and activity of CD11blowF4/80hi macrophages (94). Platelets could synthesize testosterone in a novel mechanism, and might sustain CRPC state (95).
Suppressors inhibiting CRPC evolution
Genes that inhibit the growth of CRPC
As illustrated in Table 2, several genes were found to be negatively associated with CRPC progression. For example, the expression of RB1 was negatively correlated with the prognosis of CRPC patients (96). In addition, the knockdown of PLZF promoted the CRPC phenotype and facilitated the proliferation of CRPC cells in a xenograft model (97). On the other hand, several genes, i.e., PTEN, LRIG1, PAGE4, NKX3–1, ZBTB7A, and PDCD4, could regulate the AR signaling pathway through various ways to inhibit the progression of CRPC (98–103). Furthermore, DAB2IP knockdown cells showed drug resistance, and increasing DAB2IP enhanced drug sensitivity. Besides, a study also found that it could regulate the Wnt/β-catenin and IGF-I signaling pathways (104). KLF5 downregulation increased the expression of BECN1 and induced cell autophagy in PCa. It could also desensitize CRPC cells to docetaxel through the AMPK/mTOR/p70S6K signaling pathway (105).
Table 2 Suppressors inhibiting CRPC evolution.
RNAs involved in inhibiting CRPC progression
MicroRNAs (miRNAs) are small non-coding RNA molecules that can regulate gene expression post-transcriptionally by binding to target mRNAs and inhibiting their translation or promoting their degradation. As shown in Table 2, many studies have focused on the correlation between miRNA and PCa progression. The AR signaling pathway represents the classical route of progression in CRPC. In this study, miR-205, and miR-200a were found to regulate AR signaling through different pathways to inhibit the progression of CRPC. Among them, the high expression of miR-200a indicated good prognosis (106, 107). In addition, miR-452 suppressed PCa cells migration and invasion by modulating WWP1 (108). MiR-200b-3p/200c-3p inhibited the PCa progression by mediating transcriptional regulation of PRKAR2B (109). MiR-644a mediated tumorigenesis in CRPC patients via disrupting the Warburg effect (110). In addition to miRNAs, lncRNAs are also a class of non-coding RNA molecules in CRPC development. For example, DRAIC could inhibit the growth of PCa by suppressing NF-κB activation via interacting with IκB kinase (111).
Enzymes that inhibit the development and progression of CRPC
As shown in Table 2, there is a paucity of studies investigating the inhibitory effects of enzymes on CRPC occurrence and progression. Rasool et al. discovered and demonstrated in murine models that the loss of heterologous LCMT1, along with biased protein phosphatase 2A activity, drived the progression of PCa and confers resistance to treatment (112). Depletion of DPP4 enhanced growth factor activity, and inhibition of DPP4 accelerated the emergence of PCa resistance. Kashiwagi et al. discovered that depletion of DPP4 augments growth factor activity, while inhibition of DPP4 expedited the emergence of PCa resistance (113). The study conducted by Ko et al. revealed that the emergence of CRPC was facilitated by the loss of a specific splice form of HSD17B4, which was responsible for inactivating androgen hormones (114). In addition, CRPC-like cells with loss of GNPNAT1 function exhibited augmented proliferation and invasion (115).
Biomarkers indicating the state transition into CRPC
Biomarkers are important predictors indicating the state change for CRPC management. Previous studies identified plenty of potential biomarkers that may help the early detection, prognosis, and treatment response prediction of CRPC patients. As listed in Table 3, HSD3B1 is a biomarker that enhanced dihydrotestosterone synthesis from extra-gonadal precursors and has been shown to predict castration resistance in PCa in two retrospective studies (116, 117). PHF8 could promote CRPC progression through the HIF/PHF8/AR axis (118). The expression of Gal-1 in CRPC cells was significantly higher than that in hormone-sensitive PCa cells (119). MiR-32, FOXC2, and miRNA-221/222 have been found to be potential biomarkers for the progression and malignant invasion of CRPC (120–123). LIF was associated with CRPC neuroendocrine and could be used as a serum biomarker for the diagnosis of advanced PCa (124).
Table 3 Biomarkers indicating the state transition into CRPC.
Bone metastasis has a negative effect on patient quality of life and contributes to fatal outcomes. Hence, timely intervention holds immense significance. As shown in Table 3, studies have shown that STAT3 and MUC1 were closely related to bone metastasis in CRPC patients (125, 126). The expressions of LY6D, RGS2, CPT1B, miR-1290, and miR-375 were related to the prognosis of CRPC patients and could be used as potential indicators for predicting prognosis (127–129). In addition, MED15 and stanniocalcin 2 were found to be overexpressed in CRPC and aggressive PCa, while they were expressed at lower levels in benign prostate tissue (130, 131). Furthermore, SLPI was a potential biomarker in the cell proliferation of CRPC under androgen deprivation conditions and its levels were observed to be increased significantly in mCRPC (132). SOX7 and SOX9 belong to the same SOX gene family, however, during castration resistance, SOX9 was found to be significantly increased, while SOX7 was observed to decrease significantly (133).
The identification of biomarkers holds both theoretical and clinical significance for CRPC risk prediction and personalized therapy. For example, the integration of biomarkers including HSD3B1, PHF8, Gal-1, and the SOX gene family facilitated the construction of computational models for CRPC early diagnosis (116, 119). The utilization of factors including LIF, NR6A1, miR-32, FOXC2, and miRNA-221/222 could improve the stratification of patients for applying personalized clinical therapeutics (122–124). Moreover, STAT3, and MUC1 indicated the possibility of bone metastasis, which would be helpful of monitoring the prognosis of CRPC patients into metastatic status (125, 126).
Translational perspectives toward CRPC holistic healthcare
Perspective 1: improving both AR and non-AR-targeted precision molecular therapy
AR plays a pivotal role in PCa, particularly in cases of clinical CRPC. In PCa cell models, AR overexpression has been frequently observed and established as a primary driving factor for PCa progression (134). Over the past few decades, numerous anti-AR drugs have been developed and approved for use across different stages of PCa. In the 1980s and 1990s, the FDA approved the first-generation AR antagonists, including flutamide, nilutamide, and bicalutamide, which had efficacy in the early stages of the disease but ultimately led to the development of resistance and progression to CRPC. With the in-depth research into the AR, second-generation AR antagonists that target the ligand-binding domain (LBD), such as Enzalutamide, Apalutamide, and Darolutamide, have been developed and applied. These agents possess higher AR binding affinity, allowing for more effective suppression of AR expression, and have led to significant improvements in patient survival rates (135–137). However, with the rapid development of resistance, these drugs only provide short-term effects and may potentially give rise to central nervous system toxicity and cardiovascular toxicity (138, 139). It is acknowledged that PCa demonstrates significant inter- and intra-tumor heterogeneity. Targeting a single molecule (e.g., AR) does not benefit all patients, and does not affect all tumor cells equally. Recent studies indicated that many non-AR-based mechanisms were involved in CRPC development, including neuroendocrine cell-related mechanisms, prostate stem cell-related mechanisms, alternations in TME, and deregulations in non-AR genes and pathways. Understanding the role of non-AR-based mechanisms in the development of castration resistance in PCa is also important for identifying new therapeutic targets or strategies against castration resistance. Moreover, as shown in Figure 5 the integration of both AR and non-AR strategies, e.g., inhibiting of AR and neuroendocrine cell-expressed CXCR2 simultaneously, may achieve a better therapeutic effect on CRPC (19).
Figure 5 Translational perspectives for CRPC precision medicine and personalized therapy.
Perspective 2: identifying molecular mechanisms based on novel programmed cell death types for CRPC personalized medicine
Tumor cells demonstrate the ability to evade apoptosis, which is an important cause of drug resistance and recurrence in cancer therapy. In recent years, novel regulated cell death pathways such as ferroptosis and pyroptosis have gained increasing attention as representatives in cancer drug discovery and application (140). In PCa studies, Wang et al. triggered ferroptosis in mice using a stable GPX4 inhibitor in a genetically engineered model, and it inhibited the growth and spread of RB-deficient PCa tumors. This finding offered promising prospects for the treatment of RB1-deficient malignant PCa (141). Wu et al. confirmed that inhibiting CDC20 could promote pyroptosis in PCa cells and boost tumor immunity in a mouse model of PCa (142). Wang et al. synthesized a series of aggregation-induced emission materials to mediate the process of ferroptosis and pyroptosis for enhancing PCa immunotherapy (143, 144). As shown in Figure 5, the identification and application of novel programmed cell death-based approaches would be an emerging direction for CRPC treatment, especially for patients with failure under traditional CRPC therapeutics.
Perspective 3: integrating multi-omics data and artificial intelligence for CRPC systems modeling and clinical application
Identifying molecular targets and understanding how these targets vary among different patient subgroups or racial groups and how this affects treatment outcomes are of clinical interest for CRPC personalized management. It is reported that there was a higher similarity at pathway level than that at single gene level in the expression of genes across different PCa datasets, which could partly explain why the single-gene based approaches cannot benefit all patient cohorts and indicate the significance for the development of network medicine-based strategies to fight against therapeutic heterogeneity in cancers (145). In the era of big data and artificial intelligence (AI), computer-aided modeling has now become an emerging approach for translational cancer researches. Compared with traditional experimental methods, computational algorithms simulate the diversity and dynamicity of disease occurrence and progression under a systems biology framework, which would promote the identification and characterization of key signatures for disease early diagnosis and personalized therapy (146, 147). The development of CRPC is a heterogenous process in which genetic, epigenetic, and environmental factors generate large-scale biological networks and contribute to the complexity in PCa phenotype from androgen dependence to castration resistance, thus it is of great significance to integrate multi-omics molecular data with image and clinical information as prior knowledge for multi-step AI model training (148). Here the AI models could be simply divided as two sub-categories based on the methods of feature selection, i.e., traditional models that manually characterize features for training, and deep learning-based techniques automatically extracting features for optimization. The typical applications of AI models for PCa studies are pathological evaluation and classification of multiple PCa status such as benign and malignant lesion identification, PCa grading and molecular subtyping, prognosis and risk stratification, prediction of time to CRPC (149–151), etc. Although there is a promising perspective of AI modeling in PCa and CRPC, the limitations and challenges are still worthy to be concerned. First, the clinical data have the characteristics of small sample size but high dimension and heterogeneity, hence how to reduce the overfitting results and address difficulties in model generalization are the leading issues to be considered. Second, the quality of datasets will directly affect the accuracy of model output. Currently, there is still a lack of in-depth research on clinical data standardization and privacy protection. Construction of PCa-related ontologies would be a possible and feasible way to provide a systematical framework for decoding the large amounts of PCa data and knowledge, and this will contribute to the development of data sharing and integration for model analyses (152). Finally, the interpretability of AI needs to be improved continually, and clinical urologists and pathologists should strengthen their professional behaviors to avoid the biases of missed diagnosis caused by AI models (153).
Conclusions
Although there has been a notable advancement in the field of CRPC research, the current clinical management of CRPC remains a challenge. The emergence of CRPC tumors is predominantly propelled by genetic and molecular events. For instance, accumulating evidence confirmed the role of AR signaling in the progression of PCa to castration resistance. However, the evolution of CRPC is a complex and dynamic process, and AR signaling is not the only clue for CRPC understanding. Hence, it is urgently needed for further elucidating the pathogenesis of CRPC by integrating molecular signatures at muti-omics levels. This review provides an updated landscape of literature-reported molecules for CRPC, which may offer novel insights and targets for translational CRPC research to facilitate the early diagnosis and personalized therapeutics of CRPC.
Author contributions
JJ: Writing – review & editing, Writing – original draft, Validation, Formal analysis, Data curation. XW: Writing – review & editing, Writing – original draft, Validation, Formal analysis, Data curation. JZ: Writing – original draft, Formal analysis, Data curation. CZ: Writing – original draft, Formal analysis. XH: Writing – original draft, Formal analysis. YH: Writing – review & editing, Validation, Funding acquisition. JH: Writing – review & editing, Validation, Supervision, Conceptualization. YL: Writing – review & editing, Writing – original draft, Validation, Supervision, Funding acquisition, Conceptualization. XW: Writing – review & editing, Writing – original draft, Validation, Supervision, Conceptualization.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (grant number 32200533), the General Program of Jiangsu Health Commission (grant number H2019040), and the Suzhou Science and Technology Plan Project (grant number SLJ2022008).
Acknowledgments
The authors gratefully thank the academic editor and reviewers for their constructive suggestions to help improve this manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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Keywords: castration-resistant prostate cancer, molecular signatures, carcinogenic mechanisms, personalized medicine, medical systems biology
Citation: Jian J, Wang X, Zhang J, Zhou C, Hou X, Huang Y, Hou J, Lin Y and Wei X (2024) Molecular landscape for risk prediction and personalized therapeutics of castration-resistant prostate cancer: at a glance. Front. Endocrinol. 15:1360430. doi: 10.3389/fendo.2024.1360430
Received: 23 December 2023; Accepted: 20 May 2024;
Published: 03 June 2024.
Edited by:
Fred Sinowatz, Ludwig Maximilian University of Munich, GermanyCopyright © 2024 Jian, Wang, Zhang, Zhou, Hou, Huang, Hou, Lin and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xuedong Wei, d3hkMDQyMkAxNjMuY29t; Yuxin Lin, bGlueXV4aW5Ac3VkYS5lZHUuY24=; Jianquan Hou, eGYxOTJAMTYzLmNvbQ==
†These authors have contributed equally to this work