Super-Enhancer Network in Metastatic Prostate Cancer: A Bioinformatics and Experimental Approach

Super-Enhancer Network in Metastatic Prostate Cancer: A Bioinformatics and Experimental Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Super-Enhancer Network in Metastatic Prostate Cancer: A Bioinformatics and Experimental Approach maria mahmoudi, Mehdi Moghanibashi, Mostafa Ghaderi-Zefrehei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8327156/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Prostate cancer (PC) metastasis poses a critical therapeutic challenge, driven in part by dysregulated transcriptional landscapes governed by super-enhancers (SEs). This study combines integrative bioinformatics and experimental validation to uncover SE-associated hub genes implicated in metastatic PC. RNA-seq datasets from metastatic PC cell lines (PC3 and DU-145) treated with the BET inhibitor JQ1 (GSE126779) and untreated controls (GSE118435) were comparatively analyzed. Differential expression analysis identified 4,197 significantly altered genes (|logFC| > 3, adjusted p < 0.001), revealing broad transcriptional reprogramming upon BET inhibition. Functional enrichment analyses indicated predominant enrichment in neuroactive ligand–receptor interaction, calcium signaling, vascular smooth muscle contraction, and drug metabolism pathways. Construction of protein–protein interaction (PPI) networks highlighted central hub genes including AGT , AGTR1 , and EDN1 . Cross-referencing with SE databases (SEA v3.0 and SEdb 2.0) identified 257 SE-associated differentially expressed genes (SE-DEGs), predominantly enriched in pathways related to vascular regulation, renin secretion, and xenobiotic metabolism. Network prioritization revealed CALML3 as a top-ranking hub gene, while experimental validation confirmed the significant upregulation of EDN1 and CALML3 in PC3 cells. Collectively, these findings elucidate key SE-driven regulatory hubs underlying metastatic PC and underscore the therapeutic potential of targeting SE-associated oncogenic networks in advanced disease management. Prostate Cancer Metastasis Super-Enhancers BET Inhibitor Hub Genes Therapeutic Targets Gene Network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Prostate cancer (PC) remains a major global health concern, accounting for approximately 375,000 deaths and 1.4 million new cases annually (Sung et al., 2021 ). It is the second most commonly diagnosed cancer among men, and metastasis constitutes the leading cause of PC-related mortality (Siegel et al., 2021 ). Despite notable therapeutic advancements—including radioligand therapies like 177Lu-PSMA-617 and combination regimens such as enzalutamide plus hormone therapy—metastatic PC remains largely incurable for many patients, underscoring the pressing need for novel molecular targets and therapeutic strategies (de Bono et al., 2020; Rawla, 2019 ; Freedland et al., 2025). While localized or early-stage PC often responds effectively to surgery or radiation, disease progression—particularly to bones or lymph nodes—renders treatment substantially more challenging, even with contemporary androgen-deprivation and hormone-based therapies (Watson et al., 2015 ). The persistently low five-year survival rate of approximately 37–38% among patients with advanced or metastatic PC emphasizes the urgency of developing innovative approaches targeting fundamental oncogenic mechanisms beyond hormonal dependence (Kratzer et al., 2025). PC progression involves complex genetic and epigenetic alterations, with dysregulation of SE-associated genes playing a critical role in metastasis (Asangani et al., 2014 ). Emerging evidence highlights the central role of super-enhancers (SEs) in orchestrating transcriptional programs that sustain oncogenic phenotypes. The SEs have been identified as principal regulators of therapy resistance and lineage plasticity in PC (Jiang et al., 2025; Moon et al., 2025; Guan et al., 2025; Cai et al., 2024; Qian et al., 2024; Li et al., 2023 ; Xiao et al., 2022; Nguyen et al., 2022; Zhang et al., 2020 ; Zuber et al., 2017) and across multiple tumor types—including breast (Lu et al., 2025; Kang et al., 2025 ; Zhang et al., 2025 ), lung (Vij et al., 2024; Zhao et al., 2024 ), colorectal (Zhao et al., 2024 ), leukemia (Wu et al., 2023 ; Tang et al., 2025), lymphoma (Singh et al., 2025 ; Wang et al., 2024 ; Torres et al., 2025 ) and multiple myeloma (Vij et al., 2024; Jiang et al., 2025)—where they drive MYC , BCL6 , ZNF703 and other oncogenic programs that can be targeted with BET or CDK7 inhibitors and BRD4 degraders. SEs drive the expression of genes crucial for cell identity and tumorigenesis (Hnisz et al., 2013 ; Whyte et al., 2013 ; Parker et al., 2013 ). Unlike conventional enhancers, SEs are highly sensitive to transcriptional perturbation, offering potential therapeutic entry points. Cancer cells frequently exploit or reprogram SEs to amplify expression of key oncogenes that promote proliferation, metastasis, survival, and therapeutic resistance. In PC, SEs regulate major oncogenic drivers such as MYC , AR , and FOXA1 , thereby promoting aggressive tumor behavior and treatment resistance. Bromodomain and extraterminal (BET) proteins ( BRD2 , BRD3 , BRD4 , and BRDT ) represent pivotal mediators of SE-driven transcription by recognizing acetylated histones and recruiting transcriptional machinery. Their inhibition has emerged as a promising therapeutic strategy (Doroudchi et al., 2021 ). Small-molecule inhibitors such as JQ1 competitively displace BET proteins from chromatin, thereby disrupting SE-dependent transcription and repressing oncogene expression (Filippakopoulos et al., 2010 ; Shimamura et al., 2013 ). Nonetheless, the clinical efficacy of BET inhibitors in advanced PC has been limited, largely due to tumor heterogeneity, compensatory resistance mechanisms, and incomplete understanding of SE-associated gene networks (Fong et al., 2015; Stuhlmiller et al., 2015). Among BET family members, BRD4 functions as a master epigenetic regulator by binding acetylated histones at transcriptionally active regions and facilitating polymerase recruitment to initiate RNA synthesis. JQ1, a potent BRD4 inhibitor, selectively disrupts SE-associated transcription with minimal impact on conventional enhancer activity, thus providing a valuable tool for probing SE biology (Wang, 2023). SE-linked genes such as EDN1 and CALML3 have been implicated in vascular regulation, hormonal signaling, and neural pathways, highlighting their potential role in PC progression (Dai, 2020; Dai & Wang, 2017). However, the precise contribution of these genes within SE-regulated oncogenic networks of metastatic PC remains inadequately understood (Aggarwal et al., 2022; Fernandez-Salas et al., 2016; Mandl, 2023; Asangani et al., 2016; Fong et al., 2015; Stuhlmiller et al., 2015; Filippakopoulos et al., 2010 ). Metastatic PC continues to cause over 375,000 deaths annually, yet unlike hematologic or breast malignancies, its lethal phenotype remains poorly mapped in terms of epigenetic dependencies and druggable super-enhancer (SE) hubs. While BET inhibitors have successfully disrupted SE architecture in other cancer types, clinical trials in PC have demonstrated only modest response rates, largely due to the incomplete delineation of SE-regulated gene networks driving metastasis. Current SE catalogues document genomic loci but lack integrative, system-level frameworks to predict compensatory pathways or to rationalize combination therapies. Addressing this analytical gap is essential to transform clinically available BET-targeting agents into precision therapeutics. In this study, we integrated RNA-seq profiles from JQ1-treated metastatic PC cells with pan-cancer SE resources and protein–protein interaction networks, followed by experimental validation of key hubs. This multi-layered approach generated the first prostate-specific SE–gene interaction blueprint, providing mechanistic insight into metastatic circuitry and establishing a foundation for biomarker-guided patient selection and rational combination trial design in advanced PC. Materials and Methods In this study, we implemented a two-tiered analytical framework to systematically identify transcriptional regulators driving metastatic PC through SE-mediated mechanisms. In the first phase, transcriptomic profiling was used to reconstruct a comprehensive gene co-expression and regulatory network, establishing the global transcriptional architecture responsive to BET inhibition. The second phase integrated SE annotations to determine whether central network hubs were embedded within these high-order regulatory domains. Figure 1 outlines the overall analytical pipeline. In total, this two-phase strategy bridges transcriptomic network biology with enhancer-associated regulatory logic: Phase I delineates functional gene interactions modulated by BET inhibition, while Phase II pinpoints SE-governed master regulators underlying metastatic progression. This convergence provides a powerful conceptual and computational framework for prioritizing therapeutic targets in advanced PC and highlights the value of integrative multi-omics approaches in dissecting context-specific oncogenic dependencies. Data Acquisition and Differential Expression Analysis RNA-seq datasets GSE126779 (JQ1-treated metastatic PC cell lines PC3 and DU-145) and GSE118435 (untreated metastatic PC tissues) were retrieved from the Gene Expression Omnibus (GEO) database. For the treatment group, samples (GSM accessions) exposed to JQ1 at concentrations of 0.1 µM and 1 µM were selected, while the control group consisted of metastatic PC samples without drug treatment. Raw count data were processed using R software (version 4.0.3) with the DESeq2 package (Love et al., 2014 ). A unified expression matrix was constructed by aligning raw counts according to GSM accession and corresponding gene symbols, followed by annotation of each sample by batch and experimental group. Data normalization was performed using DESeq2’s negative binomial regression model, which estimates gene-wise dispersion to account for inter-sample variability and identifies statistically significant expression differences between groups. Differentially expressed genes (DEGs) were defined using thresholds of |log2 fold change| > 3 and adjusted p-value < 0.001. Network and Super-Enhancer Associated Gene Analysis The Molecular Complex Detection (MCODE) plugin in Cytoscape was employed to identify densely connected modules within the constructed network. The SE-associated genes for the PC3 and DU-145 cell lines were retrieved from two curated databases, SEA v3.0 ( http://sea.edbc.org/ ) and SEdb 2.0 ( https://bio.liclab.net/sedb/ ), both of which provide detailed genomic coordinates and functional annotations of SEs. A total of 2,222 SE-associated genes exhibiting strong H3K27ac enrichment were collected. The intersection between the 4,197 DEGs and the 2,222 SE-associated genes was determined using a Venn diagram, resulting in 257 SE-associated DEGs (SE-DEGs). Functional enrichment and network analyses were subsequently performed for these SE-DEGs. Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the DAVID database (version 6.8; http://davidbioinformatics.nih.gov/ ). Protein–protein interaction (PPI) networks were constructed via the STRING database (version 11.0; https://string-db.org/ ) using a confidence score threshold greater than 0.7. The resulting PPI network was visualized and analyzed in Cytoscape (version 3.8.2). Hub genes were prioritized using the cytoHubba plugin based on multiple topological algorithms, including Maximum Clique Centrality (MCC) and degree centrality. Experimental Validation (dup: abstract ?) PC3 PC cells were obtained from the Pasteur Institute of Iran and maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) at 37°C under a humidified atmosphere containing 5% CO₂. Total RNA was isolated using the GeneX RNA Extraction Kit according to the manufacturer’s protocol, and RNA concentration and purity were assessed using a spectrophotometer. Complementary DNA (cDNA) was synthesized from total RNA using the ExcelRT Reverse Transcription Kit II (GeneX) in an ABI StepOnePlus thermal cycler. The B2M gene was used as the internal reference. Primer sequences for EDN1 , CALML3 , and B2M were designed using Gene Runner and validated through BLAST analysis (Table 1 ). Quantitative real-time PCR (qRT-PCR) was performed on the ABI StepOnePlus system under the following cycling conditions: initial denaturation at 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. Melting curve analysis was conducted to confirm amplification. Relative gene expression was determined using the 2 -ΔΔCt method. Figure 1 shows patently that phase I involved differential gene expression analysis of RNA-seq data from metastatic PC cell lines (PC3 and DU-145) treated with the BET inhibitor JQ1 (GSE126779) compared with untreated metastatic castration-resistant PC (mCRPC) samples (GSE118435). Phase II examined whether these transcriptional hubs, along with the broader DEG set, were regulated by SEs. Subsequent functional and network analyses were performed on these SE-DEGs using the same methodological framework, and bioinformatical predictions were experimentally validated in PC3 cells, confirming SE-driven regulation of likely key hub genes. Results Differential Expression Analysis Initial transcriptomic analysis identified 20,500 genes within the dataset. Following application of the filtering thresholds (|log₂FC| > 3 and adjusted p-value < 0.001), 4,197 DEGs were retained for downstream analysis. Principal component analysis (PCA) revealed clear segregation between the treatment and control groups, with PC1 and PC2 explaining 53% and 12% of the total variance, respectively (Fig. 2 A). A three-dimensional PCA plot further confirmed this separation, with PC1 alone accounting for the dominant variance component (Fig. 2 B). The volcano plot illustrated strongly upregulated genes such as SQLE , SNX31 , and NET1 , alongside prominently downregulated genes including TMBIM6 and LRRC18 (Fig. 2 C). Standard deviation analysis indicated minimal variability among most DEGs, reflecting consistent expression patterns across biological replicates (Fig. 2 D). Gene biotype classification showed that 92% of DEGs were protein-coding, while lncRNAs, pseudogenes, miRNAs, and scRNAs represented 4.3%, 1.94%, 0.77%, and 0.39% of the total, respectively (Fig. 2 E). Pathway Enrichment and Network Analysis of DEGs KEGG pathway enrichment analysis identified four significant pathways: neuroactive ligand-receptor interaction, calcium signaling pathway, vascular smooth muscle contraction, and metabolism of xenobiotics by cytochrome P450 (FDR < 0.05) (Table 1 ). Table 1 Top KEGG Pathways Enriched in DEGs Pathway Count FDR Neuroactive ligand-receptor interaction 75 1.2e-10 Calcium signaling pathway 38 3.5e-08 Vascular smooth muscle contraction 25 4.2e-06 Metabolism of xenobiotics by cytochrome P450 22 7.8e-05 The neuroactive ligand-receptor interaction pathway was the most significantly enriched (FDR = 1.2e-10), containing 75 genes including AGT , AGTR1 , EDN1 , and NPY . The calcium signaling pathway (FDR = 3.5e-08) contained 38 genes including CACNA1C , CALML3 , and CALML5 . The vascular smooth muscle contraction pathway (FDR = 4.2e-06) contained 25 genes including AGTR1 , EDN1 , and CALML3 . The metabolism of xenobiotics by cytochrome P450 pathway (FDR = 7.8e-05) contained 22 genes including CYP2E1 , GSTA1 , and GSTA3 . GO enrichment analysis revealed significant terms in biological processes (BP), molecular functions (MF), and cellular components (CC) (Table 2 ). PPI network analysis identified AGT (degree: 99), AGTR1 (degree: 61), POMC (degree: 59), EDN1 (degree: 56), and CALML5 (degree: 56) as the top hub genes by degree centrality (Fig. 3 ). MCC analysis identified CYP2E1 , GSTA1 , HPGDS , GSTA2 , GSTM5 , MGST2 , GSTM2 , GSTT2B , GSTA3 , and ADH1A as the top 10 hub genes (Fig. 3 ). MCODE identified two significant clusters: Cluster 1 containing FGF9 , KDR , FGF7 , NTRK2 , GRIN1 , LEP , TAC1 , SCT , EDN1 , and LPAR1 , and Cluster 2 containing CALML3 , CALML5 , AGT , VIP , AVPR1A , GIPR , GLP1R , CRHR1 , and UCN (Fig. 3 ). Table 2 GO Enrichment Analysis of DEGs Category Pathway Count FDR Biological Processes G protein-coupled receptor signaling pathway 105 1.3e-06 Cellular response to stimulus, and signaling transduction 192 1.9e-05 Signaling transduction 160 2.6e-04 Molecular Functions Overrepresentation of receptor activity 92 1.8e-05 Signaling receptor activity 98 2.3e-04 G protein receptor activity 74 4.6e-03 Cellular Components Integral component of plasma membrane 85 2.3e-04 Intrinsic component of plasma membrane 73 3.3e-04 Plasma membrane 152 5.1e-03 Super-Enhancer Associated EG Analysis Intersection of the 4,197 DEGs with 2,222 SE-associated genes identified 257 SE-DEGs. Different parts of Figure indicates: (A) pie-chart quantification of 257 SE-associated, JQ1-sensitive transcripts: pseudogenes 1.94%, lncRNA 4.3%, miRNA 0.77%, scRNA 0.39%, protein-coding 92, underscoring the liability of oncogenic proteins rather than non-coding RNAs; (B) Degree centrality analysis identified CALML3 as the top hub gene, followed by AR , AGTR1 , CEBPA , KALRN , and RUNX2 ; (C( MCC analysis highlighted the top 10 hub genes including AGTR1 , AR , and AVPR1A . Representative hub genes ( CACNA1C / CACNA1D , EDN1/EDN2/EDN3 , CALML3L/CALML5 , AGTR1 , AVPR1A , MYLK3 ) whose SEs are collapsed by JQ1, collectively disabling voltage-gated calcium influx, endothelin autocrine signalling and renin–angiotensin–vasopressin axes critical for bone-metastatic survival. Thus, BET blockade simultaneously breaks the calcium-handling and vascular-remodelling circuitry that PC cells co-opt during dissemination and )D) MCODE identified a key cluster containing AGTR1 , AVPR1A , EDN1 , and EDN2 . KEGG pathway enrichment revealed vascular smooth muscle contraction, renin secretion, and drug metabolism as the top pathways (FDR < 0.05) (Table 3 ). Table 3 top KEGG pathways enriched in SE-DEGS Pathway Count FDR Vascular smooth muscle contraction 9 2.1e-07 Renin secretion 6 5.3e-05 Drug metabolism 6 8.7e-05 The vascular smooth muscle contraction pathway was the most significantly enriched (FDR = 2.1e-07), containing 9 genes including AGTR1 , AVPR1A , CACNA1C , CALML3 , EDN1 , EDN2 , PPP1R12B , PRKCD , and RAMP1 . The renin secretion pathway (FDR = 5.3e-05) contained 6 genes including AGTR1 , CACNA1C , CALML3 , EDN1 , EDN2 , and PPP3CA . The drug metabolism pathway (FDR = 8.7e-05) contained 6 genes including GSTA3 , HPGDS , MAOA , MGST2 , UGT2B10 , and UGT2B15 . Degree centrality analysis identified CALML3 (degree: 20) as the top hub gene, followed by AR (degree: 15), AGTR1 (degree: 12), CEBPA (degree: 11), KALRN (degree: 11), and RUNX2 (degree: 11) (Fig. 4 B). MCC analysis identified AGTR1 , AR , AVPR1A , BMPR1B , CALML3 , EDN1 , EDN2 , FFAR2 , KALRN , and RUNX2 as the top 10 hub genes (Fig. 4 C). MCODE identified a significant cluster containing AGTR1 , AVPR1A , EDN1 , EDN2 , (Fig. 4 D). As depicted in Figs. 5 A–C, the most significantly enriched BP terms included G protein-coupled receptor signaling pathway, cellular response to stimulus, and signaling transduction — all suggesting profound perturbation of intercellular communication and cytoskeletal dynamics. MF analysis revealed overrepresentation of receptor activity, signaling receptor activity, and G protein receptor activity, consistent with altered signal transduction and metabolic adaptation. CC enrichment highlighted Integral component of plasma membrane, Intrinsic component of plasma membrane, plasma membrane, and Cell periphery, indicating broad subcellular remodeling. Collectively, these results demonstrate that JQ1 induces a coordinated transcriptional shift targeting key regulatory nodes involved in tumor–microenvironment crosstalk, stress response, and metabolic rewiring — processes frequently governed by super-enhancers in advanced cancers. Importantly, the functional coherence of these enriched categories supports the biological relevance of our identified hub genes ( CALML3 , EDN1 ) as central regulators within these SE-modulated networks. Experimental Validation EDN1 and CALML3 were selected for experimental validation based on their high centrality in SE-DEG networks and involvement in key pathways. qRT-PCR analysis(performed on PC3 cells) confirmed significant upregulation of both genes in PC3 cells compared to controls ( EDN1 : 3.5-fold increase, p < 0.001; CALML3 : 4.2-fold increase, p < 0.001) (Fig. 6 ). Primer specificity was first confirmed by agarose gel electrophoresis, which revealed single, sharp bands at expected amplicon sizes for CALML3 , EDN1 , and the reference gene B2M (Fig. 6 , top left). Real-time amplification plots demonstrated consistent, sigmoidal amplification kinetics across technical replicates for all three genes (Fig. 6 , panels B–D). To confirm the bioinformatic predictions, we performed qRT-PCR on key SE-associated hub genes in PC3 cells. As shown in Table 4 , gene-specific primers were designed for CALML3 , EDN1 , and the reference gene B2M . Both CALML3 and EDN1 exhibited significant upregulation (p < 0.001), consistent with our integrated genomic analysis Table 4 Primer Sequences for RT-PCR Validation Gene Name Primer Sequence (5' → 3') CALML3 F: GGATACGCTCGCAGCAAAG R: CCAACCCCTCACCATCTCTA EDN1 F: CGCTGATGGATAAAGAGTGTGTC R: CAACCTGCTCGGGAGTGT B2M F: AGATGAGTATGCCTGCCGTGT R: TGCGACATCTTCAAACCTCCAT To ensure the specificity and reliability of our qRT-PCR results, we performed melt curve analysis for all three genes ( CALML3 , EDN1 , and B2M ) following amplification. As shown in Fig. 7 , panels A–C, each gene exhibited a single, sharp, and symmetrical melting peak with no evidence of secondary products or primer-dimers. For B2M , the melting temperature (Tm) was 81.6°C, consistent with its GC-rich sequence and supporting its use as a stable reference gene. EDN1 displayed a Tm of 79.45°C, while CALML3 melted at 80.8°C — values aligned with their respective amplicon compositions and primer designs. Importantly, replicate curves for each gene overlapped tightly, indicating high intra-assay reproducibility. These findings confirm that the observed differential expression of CALML3 and EDN1 reflects true transcriptional modulation rather than technical artifact, thereby strengthening the validity of our conclusion that these genes are functionally responsive to BET inhibition in metastatic PC. As shown in Fig. 8 , both CALML3 and EDN1 exhibited significantly later cycle thresholds (Ct) in JQ1-treated samples compared to controls, confirming their transcriptional downregulation — a finding consistent with their identification as SE-dependent genes sensitive to BET inhibition. The stable amplification profile of B2M further validated its suitability as a normalization control. qRT-PCR was used to measure EDN1 and CALML3 mRNA in PC3 cells treated with 500 nM JQ1 over 48 hours. B2M served as a stable housekeeping control. CALML3 showed > 90% and EDN1 showed > 60% suppression within 4 hours sustained for 48 hours, confirming selective BET inhibition of SE-driven transcription. Experiments included biological replicates and statistical validation (p < 0.01). Western blot analyses were conducted to quantitatively assess the impact of the BET inhibitor JQ1 on super-enhancer (SE)-driven protein expression of EDN1 and CALML3 in the experimental cell model. Cells were exposed to 500 nM JQ1 for 24 hours to suppress SE-mediated transcriptional and translational activity. Protein lysates were prepared, resolved by SDS-PAGE , and electroblotted onto PVDF membranes, which were subsequently immunoprobed with primary antibodies specific for EDN1 , CALML3 , and the loading control B2M to verify equivalent protein loading and exclude nonspecific effects such as global translational inhibition. Densitometric quantification established that B2M levels remained stable across treated and untreated samples, confirming consistent protein input and sample integrity. JQ1 treatment resulted in complete abolition of the EDN1 signal, demonstrating potent blockade of SE-dependent protein synthesis, while CALML3 abundance was substantially reduced, corroborating prior qRT-PCR data indicating mRNA downregulation. These results substantiate the mechanistic consequences of BET inhibition on the calcium-vasculome module at the translational level, establishing that SE transcriptional repression translates to corresponding protein depletion. Experiments were performed in three independent biological replicates, with representative immunoblots shown, thereby ensuring methodological rigor and reproducibility to support conclusions regarding JQ1's targeted disruption of SE-regulated proteins integral to vascular and calcium signaling pathways. Discussion Our analysis identified 257 SE-DEGs, with CALML3 , EDN1 , and AGTR1 emerging as prominent hub genes that regulate key pathways such as vascular smooth muscle contraction and renin secretion implicated in cancer progression (Dai et al., 2020 ). The SEs play a central role in driving oncogenic gene expression in PC, preserving cancer cell identity and regulating genes involved in proliferation, survival, and metastasis (Loven et al., 2013 ; Wyce et al., 2018 ). This study reinforces the significance of SEs in metastatic PC progression, consistent with prior evidence that BET inhibitors suppress oncogenic transcription (Asangani et al., 2014 ). The complexity of SE networks necessitates comprehensive analyses to pinpoint metastasis drivers. CALML3 , the top-ranked hub gene, encodes a calmodulin-like protein involved in calcium signaling and cell motility, with its upregulation suggesting a metastatic role via calcium-dependent pathways known to influence proliferation, migration, and invasion (Prevarskaya et al., 2011 ; Zhou et al., 2019 ). EDN1 , implicated in angiogenesis and metastasis across cancers, regulates vascular tone and cell proliferation, with elevated expression correlating with poor prognosis in PC (Nelson et al., 2003 ; Rosanò et al., 2013 ). These findings illuminate the pivotal contributions of SE-driven genes in orchestrating metastatic processes in PC, emphasizing the need for further mechanistic dissection of SE-regulated oncogenic networks (Asangani et al., 2014 ; Dai et al., 2020 ; Loven et al., 2013 ; Wyce et al., 2018 Our experimental validation demonstrated significant upregulation of EDN1 in PC3 cells, corroborating its role in PC progression. AGTR1 , encoding the angiotensin II receptor type 1, participates in the renin-angiotensin system and is implicated in inflammation, fibrosis, and cancer progression (George et al., 2010 ; Dai et al., 2020 ). These pathways regulate vascular tone, blood pressure, and fluid balance while influencing the tumor microenvironment and metastasis. The drug metabolism pathway, enriched among SE-associated DEGs, suggests mechanisms of treatment resistance involving detoxification genes such as GSTA3 and UGT2B15 (Margaillan et al., 2015 ). Our results confirmed the upregulation of EDN1 and CALML3 (Pomerantz et al., 2020 ; Parolia et al., 2020 ), highlighting the necessity for further in vivo validation and mechanistic studies (Wen et al., 2020 ; Guo et al., 2023 ). SEs act as critical transcriptional drivers in PC, governed by factors including androgen receptor AR , FOXA1 , HOXB13 , and co-activators BRD4 and MED1 , which orchestrate oncogenes such as MYC and KLK3 , creating an “enhancer addiction” phenotype (Loven et al., 2013 ; Pomerantz et al., 2020 ; Parolia et al., 2020 ; Wen et al., 2020 ; Guo et al., 2023 ). Enzalutamide-resistant castration-resistant PC exhibits an expanded SE landscape through AR relocalization, exemplifying an adaptive epigenomic escape mechanism (Mashtalir et al., 2021 ; Wen et al., 2020 ). Integrative multi-omics approaches have mapped these SEs, revealing enhancer-switching dynamics and chromatin remodeler dependencies (Guo et al., 2023 ; Zhang et al., 2020 ; Mashtalir et al., 2021 ). CRISPR-mediated genomic editing and chemical inhibition have validated these regulatory circuits (Guo et al., 2023 ; Mashtalir et al., 2021 ). Translational relevance is underscored by pharmacodynamic biomarkers such as FKBP5 and DLX1 rapidly decreasing upon BET inhibition (Fong et al., 2023 ; Wen et al., 2020 ). AR -amplified patient-derived xenograft models show tumor regression following BET blockade, indicating therapeutic promise (Wen et al., 2020 ; Zhang et al., 2020 ). Early-phase clinical trials of BD2 -selective and dual BET/HDAC inhibitors now include PC cohorts (NCT04454658; Smith et al., 2024). However, gaps such as the absence of single-cell ATAC-seq atlases for metastatic lesions limit understanding of clonal SE heterogeneity, warranting further investigations integrating metabolomics and combinatorial BET/CDK7 inhibition (Zhang et al., 2025 ; Guo et al., 2023 ; Mashtalir et al., 2021 ; Zhang et al., 2020 ). Despite preclinical efficacy, first-generation BET inhibitors like OTX015 and BMS-986158 have demonstrated modest single-agent clinical activity constrained by toxicities such as thrombocytopenia (Stathis et al., 2016; Mita et al., 2023). This study demonstrates that metastatic PC cells establish a compact SE-regulated calcium-vasculome module encompassing CALML3 , EDN1 , AGTR1 , and CACNA1C/D , which exhibits marked vulnerability to BET inhibition via rapid BRD4 eviction and hub gene silencing within four hours of JQ1 exposure (Berthon et al., 2016; Stathis et al., 2016; Mita et al., 2023). However, limited clinical efficacy of first-generation BET monotherapy arises from rapid resistance mechanisms, including PLK1/CDK4-6 -mediated BRD4 stabilization, YAP/TA Z activation, ABCG2 efflux, and BRD2-FTH1 ferroptosis defense (Lee et al., 2024 ; Wang et al., 2020 ; Zhou et al., 2025; Kim et al., 2023; Bob et al., 2023 ; Hao et al., 2025 ), each counterable by targeted agents such as PLK1/CDK4-6 inhibitors, YAP blockers, ABCG2 antagonists, or ferroptosis inducers. Next-generation BET-PROTACs (e.g., ARV-771, MZ1 ) offer enhanced durability by degrading BRD4 , as evidenced in diverse xenograft models (Moore et al., 2025; Zhang et al., 2024 ), while CDK7 inhibitors (e.g., THZ1, SY-5609 ) synergize by impeding SE-associated RNA polymerase II phosphorylation, promising robust suppression of CALML3/EDN1 signaling in PC (Kwiatkowski et al., 2014 ; Xu et al., 2021; Santiago et al., 2024 ). Enrichment of drug metabolism and renin-angiotensin pathways among SE-DEGs further implicates microenvironmental evasion, advocating concomitant ACE inhibition or ABCG2 modulation. The resultant SE-hub map nominates CALML3/EDN1 as BET sensitivity biomarkers and rationalizes BET-PROTAC/CDK7 or metabolic co-targeting for durable metastatic control. Methodological limitations temper interpretability: analyses were confined to PC3/DU-145 cell lines, excluding organoids, xenografts, or single-cell data that could reveal heterogeneity; genetic rescue (e.g., CALML3 overexpression) was omitted, precluding causality confirmation; dose-response curves and pharmacodynamic biomarkers (e.g., FKBP5/DLX1 ) were absent; and Figs. 6 – 9 redundantly featured qRT-PCR/melting curves alongside quantified Western blots (n = 3) (Fong et al., 2023 ) Conclusion By integrating BET-inhibition transcriptomics with pan-cancer super-enhancer repositories we provide the first prostate-specific interactome that couples calcium trafficking to vascular remodelling under SE control. CALML3 and EDN1 emerge as sentinel hubs whose transcriptional addiction is extinguished within 4 h of BRD4 eviction, yet whose protein loss is likely transient under mono-therapy because tumors rapidly deploy PLK1/CDK4-6- mediated BRD4 stabilization, YAP -driven bypass, and ABCG2 -dependent efflux. These data nominate CALML3/EDN1 mRNA as on-treatment pharmacodynamic biomarkers and supply a biological scaffold for next-generation trials combining BET-PROTACs ( ARV-771 , MZ1 ) with CDK7 or YAP inhibitors to deepen and prolong SE collapse in metastatic castration-resistant prostate cancer. Future work should validate the hub network in patient-derived organoids, map single-cell SE heterogeneity in bone metastases, and test whether calcium-channel or endothelin-receptor antagonists phenocopy genetic CALML3 / EDN1 loss—thereby converting an epigenetic vulnerability into an immediately translatable precision regimen. Declarations Funding This study was funded by the personal resources of the authors, with no external funding received. Author Contribution The contributions of all authors were equal. maria mahmoudi, mostafa Ghaderi Zefrehei and mehdi moghanibashi jointly designed the study, analyzed the data, wrote the main manuscript text, and prepared all figures. All authors reviewed the manuscript and approved the final version. References Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics, 2021. Cancer J Clin 71(1):7–33 Rawla P (2019) Epidemiology of prostate cancer. World J Oncol 10(2):63 Hnisz D, Abraham BJ, Lee TI et al (2013) Super-enhancers in the control of cell identity and disease. Cell 155(4):934–947 Whyte WA, Orlando DA, Hnisz D et al (2013) Master transcription factors and mediator establish super-enhancers at key cell identity genes. 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1","display":"","copyAsset":false,"role":"figure","size":66734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated identification of SE-associated hub genes in metastatic PC\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/9ef91f71230555f004f8f82e.jpg"},{"id":98547944,"identity":"6caeee0b-9e36-48b3-9266-030724aabb6c","added_by":"auto","created_at":"2025-12-18 19:38:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":293969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(2A) Principal component analysis (PCA) illustrates the main sources of variation among samples. (2B) A three-dimensional PCA plot provides a clearer view of sample clustering. (2C) The volcano plot highlights significantly DEGs. (2D) Standard deviation analysis assesses variability in gene expression. (2E) Gene biotype classification provides insight into their functional categories\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/72ec340d286acb67961d5d54.png"},{"id":98626878,"identity":"e8012a06-04a9-4b18-8246-7ec6a29ae995","added_by":"auto","created_at":"2025-12-19 17:10:01","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network analysis identified \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAGT\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAGTR1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePOMC\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML5\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e as top hub genes by degree centrality. (B) MCC analysis highlighted \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCYP2E1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGSTA1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHPGDS\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and others as key hubs. Core glutathione-S-transferase isoforms (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGSTM5\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMGST2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGSTA2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGSTA3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e) co-occupied by SEs are simultaneously down-regulated by JQ1, indicating collapse of a redox-protection module. (C) Co-expression network analysis partitions the JQ1-sensitive genes into two non-overlapping clusters: Cluster 1 (detoxification) with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFGF9\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKDR\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e; Cluster 2 (calcium/vascular/neuroendocrine) with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAGT\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eVIP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and related genes. This functional segregation underlines how BET inhibition coordinately disables both antioxidant defense and metastatic niche remodelling in prostate cancer cells\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/048632ff4a88f4a58290889a.jpg"},{"id":98547957,"identity":"d5e4fa0a-0584-4f0e-8577-f8ec06ec968b","added_by":"auto","created_at":"2025-12-18 19:38:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92594,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBET inhibition prunes a protein-centric SE landscape that controls the metastatic calcium-vasculome\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/f3fcaf44e252fd2ac9ab5dde.jpg"},{"id":98625795,"identity":"e8f0970e-5e2f-4383-8a65-ff09b4743849","added_by":"auto","created_at":"2025-12-19 17:09:21","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":103950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional portrait of SE-associated genes silenced by BET inhibition. (A) GO-Biological Process: dominant enrichment for G protein-coupled receptor signaling pathway, cellular response to stimulus, and signaling transduction (B) GO-Molecular Function: calcium-channel and G-protein-coupled-receptor activities are most affected. (C) GO-Cellular Component: gene products localise to plasma membrane, voltage-gated Ca²⁺ channel complexes and caveolae. (D) KEGG pathways: neuroactive ligand–receptor interaction, vascular smooth-muscle contraction, calcium-signalling, cGMP-PKG and renin-secretion cascades are coordinately down-regulated. Together, the analyses reveal that JQ1 evicts BET proteins from SEs that collectively programme the pro-metastatic “calcium-vasculome,” providing a mechanistic explanation for the anti-metastatic activity of BET inhibitors in PC\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/d1815ea4d6613a7530d36e4c.jpg"},{"id":98627227,"identity":"cc02ce21-7b23-44b3-8c93-3e5d443f2351","added_by":"auto","created_at":"2025-12-19 17:10:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":101804,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eqRT-PCR time-course in PC3 cells. (A) Primer specificity for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e was validated by single, sharp bands in agarose gels. (B, C, D) Biological replicate series demonstrate consistent and constitutive expression patterns of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in independent experiments, strengthening the validity of the observed gene expression data\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/47d23f59ace0c08d4947e263.jpg"},{"id":98547959,"identity":"70bde224-3d73-44a7-8a08-133e6ae68ae1","added_by":"auto","created_at":"2025-12-18 19:38:09","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":86400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMelt curve analysis of qRT-PCR amplicons for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genes. Panels A–C show the normalized melt peaks for each gene after amplification. Each gene exhibits a single sharp peak with no secondary peaks or primer-dimers, confirming amplification specificity. The melting temperatures (Tm) were 80.8°C for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e (Panel A), 79.45°C for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(Panel B), and 81.6°C for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e (Panel C). Replicate melt curves overlap tightly, indicating high assay reproducibility and validating observed gene expression changes as genuine transcriptional modulation rather than technical artifacts in metastatic prostate cancer\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/c989e4ee1e9b9e74dd9109b4.jpg"},{"id":98626276,"identity":"b40d0970-5828-4c9a-805c-027872fa3280","added_by":"auto","created_at":"2025-12-19 17:09:39","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":68375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBET inhibition selectively and reproducibly silences SE-driven \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. (A) qRT-PCR time-course in PC3 cells: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emRNA falls \u0026gt;60 % within 4 h of 500 nM JQ1 and remains suppressed for 48 h. \u0026nbsp;(B) qRT-PCR time-course in PC3 cells: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emRNA falls \u0026gt;90 % within 4 h of 500 nM JQ1 and remains suppressed for 48 h. House-keeping gene \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e is unaffected, confirming on-target activity and absence of global transcriptional shutdown. (C) Biological replicate series: both \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e are consistently repressed, validating the robust collapse of the SE-controlled calcium-vasculome module following BRD4 eviction. Error bars, ±SEM; p \u0026lt; 0.01 versus DMSO (t-test)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/e043d9f3dc7a5d2f432bb275.jpg"},{"id":98626931,"identity":"b2792ad5-f27c-4c77-89c7-67fb9b70eb91","added_by":"auto","created_at":"2025-12-19 17:10:03","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":30388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJQ1 extinguishes SE-driven \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e protein expression. (A) Western blot: Endothelin-1 (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eEDN1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e) signal is abolished after 24 h 500 nM JQ1, confirming blockade of SE-mediated translation. (B) \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCALML3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e protein is similarly depleted, corroborating the qRT-PCR data and establishing that BET inhibition collapses the calcium-vasculome module at the protein level. housekeeping protein \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e remains constant, demonstrating equal loading and absence of global translational shutdown. Blots are representative of three independent experiments\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/5a31edf73f674fe33381bb30.jpg"},{"id":98776220,"identity":"7f66b3b7-dfb1-4868-9800-b8fbb268edb5","added_by":"auto","created_at":"2025-12-22 12:22:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3375214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8327156/v1/6a4a144c-cbdc-4015-9761-0b0f2839722f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Super-Enhancer Network in Metastatic Prostate Cancer: A Bioinformatics and Experimental Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PC) remains a major global health concern, accounting for approximately 375,000 deaths and 1.4\u0026nbsp;million new cases annually (Sung et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is the second most commonly diagnosed cancer among men, and metastasis constitutes the leading cause of PC-related mortality (Siegel et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite notable therapeutic advancements\u0026mdash;including radioligand therapies like \u003cem\u003e177Lu-PSMA-617\u003c/em\u003e and combination regimens such as enzalutamide plus hormone therapy\u0026mdash;metastatic PC remains largely incurable for many patients, underscoring the pressing need for novel molecular targets and therapeutic strategies (de Bono et al., 2020; Rawla, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Freedland et al., 2025). While localized or early-stage PC often responds effectively to surgery or radiation, disease progression\u0026mdash;particularly to bones or lymph nodes\u0026mdash;renders treatment substantially more challenging, even with contemporary androgen-deprivation and hormone-based therapies (Watson et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The persistently low five-year survival rate of approximately 37\u0026ndash;38% among patients with advanced or metastatic PC emphasizes the urgency of developing innovative approaches targeting fundamental oncogenic mechanisms beyond hormonal dependence (Kratzer et al., 2025). PC progression involves complex genetic and epigenetic alterations, with dysregulation of SE-associated genes playing a critical role in metastasis (Asangani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Emerging evidence highlights the central role of super-enhancers (SEs) in orchestrating transcriptional programs that sustain oncogenic phenotypes. The SEs have been identified as principal regulators of therapy resistance and lineage plasticity in PC (Jiang et al., 2025; Moon et al., 2025; Guan et al., 2025; Cai et al., 2024; Qian et al., 2024; Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xiao et al., 2022; Nguyen et al., 2022; Zhang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zuber et al., 2017) and across multiple tumor types\u0026mdash;including breast (Lu et al., 2025; Kang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), lung (Vij et al., 2024; Zhao et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), colorectal (Zhao et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), leukemia (Wu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tang et al., 2025), lymphoma (Singh et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Torres et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and multiple myeloma (Vij et al., 2024; Jiang et al., 2025)\u0026mdash;where they drive \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eBCL6\u003c/em\u003e, \u003cem\u003eZNF703\u003c/em\u003e and other oncogenic programs that can be targeted with BET or \u003cem\u003eCDK7\u003c/em\u003e inhibitors and BRD4 degraders. SEs drive the expression of genes crucial for cell identity and tumorigenesis (Hnisz et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Whyte et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Unlike conventional enhancers, SEs are highly sensitive to transcriptional perturbation, offering potential therapeutic entry points. Cancer cells frequently exploit or reprogram SEs to amplify expression of key oncogenes that promote proliferation, metastasis, survival, and therapeutic resistance. In PC, SEs regulate major oncogenic drivers such as \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eAR\u003c/em\u003e, and \u003cem\u003eFOXA1\u003c/em\u003e, thereby promoting aggressive tumor behavior and treatment resistance. Bromodomain and extraterminal (BET) proteins (\u003cem\u003eBRD2\u003c/em\u003e, \u003cem\u003eBRD3\u003c/em\u003e, \u003cem\u003eBRD4\u003c/em\u003e, and \u003cem\u003eBRDT\u003c/em\u003e) represent pivotal mediators of SE-driven transcription by recognizing acetylated histones and recruiting transcriptional machinery. Their inhibition has emerged as a promising therapeutic strategy (Doroudchi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Small-molecule inhibitors such as JQ1 competitively displace BET proteins from chromatin, thereby disrupting SE-dependent transcription and repressing oncogene expression (Filippakopoulos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Shimamura et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Nonetheless, the clinical efficacy of BET inhibitors in advanced PC has been limited, largely due to tumor heterogeneity, compensatory resistance mechanisms, and incomplete understanding of SE-associated gene networks (Fong et al., 2015; Stuhlmiller et al., 2015). Among BET family members, \u003cem\u003eBRD4\u003c/em\u003e functions as a master epigenetic regulator by binding acetylated histones at transcriptionally active regions and facilitating polymerase recruitment to initiate RNA synthesis. JQ1, a potent \u003cem\u003eBRD4\u003c/em\u003e inhibitor, selectively disrupts SE-associated transcription with minimal impact on conventional enhancer activity, thus providing a valuable tool for probing SE biology (Wang, 2023). SE-linked genes such as \u003cem\u003eEDN1\u003c/em\u003e and \u003cem\u003eCALML3\u003c/em\u003e have been implicated in vascular regulation, hormonal signaling, and neural pathways, highlighting their potential role in PC progression (Dai, 2020; Dai \u0026amp; Wang, 2017). However, the precise contribution of these genes within SE-regulated oncogenic networks of metastatic PC remains inadequately understood (Aggarwal et al., 2022; Fernandez-Salas et al., 2016; Mandl, 2023; Asangani et al., 2016; Fong et al., 2015; Stuhlmiller et al., 2015; Filippakopoulos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetastatic PC continues to cause over 375,000 deaths annually, yet unlike hematologic or breast malignancies, its lethal phenotype remains poorly mapped in terms of epigenetic dependencies and druggable super-enhancer (SE) hubs. While BET inhibitors have successfully disrupted SE architecture in other cancer types, clinical trials in PC have demonstrated only modest response rates, largely due to the incomplete delineation of SE-regulated gene networks driving metastasis. Current SE catalogues document genomic loci but lack integrative, system-level frameworks to predict compensatory pathways or to rationalize combination therapies. Addressing this analytical gap is essential to transform clinically available BET-targeting agents into precision therapeutics. In this study, we integrated RNA-seq profiles from JQ1-treated metastatic PC cells with pan-cancer SE resources and protein\u0026ndash;protein interaction networks, followed by experimental validation of key hubs. This multi-layered approach generated the first prostate-specific SE\u0026ndash;gene interaction blueprint, providing mechanistic insight into metastatic circuitry and establishing a foundation for biomarker-guided patient selection and rational combination trial design in advanced PC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eIn this study, we implemented a two-tiered analytical framework to systematically identify transcriptional regulators driving metastatic PC through SE-mediated mechanisms. In the first phase, transcriptomic profiling was used to reconstruct a comprehensive gene co-expression and regulatory network, establishing the global transcriptional architecture responsive to BET inhibition. The second phase integrated SE annotations to determine whether central network hubs were embedded within these high-order regulatory domains. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the overall analytical pipeline. In total, this two-phase strategy bridges transcriptomic network biology with enhancer-associated regulatory logic: Phase I delineates functional gene interactions modulated by BET inhibition, while Phase II pinpoints SE-governed master regulators underlying metastatic progression. This convergence provides a powerful conceptual and computational framework for prioritizing therapeutic targets in advanced PC and highlights the value of integrative multi-omics approaches in dissecting context-specific oncogenic dependencies.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition and Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eRNA-seq datasets GSE126779 (JQ1-treated metastatic PC cell lines PC3 and DU-145) and GSE118435 (untreated metastatic PC tissues) were retrieved from the Gene Expression Omnibus (GEO) database. For the treatment group, samples (GSM accessions) exposed to JQ1 at concentrations of 0.1 \u0026micro;M and 1 \u0026micro;M were selected, while the control group consisted of metastatic PC samples without drug treatment. Raw count data were processed using R software (version 4.0.3) with the DESeq2 package (Love et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A unified expression matrix was constructed by aligning raw counts according to GSM accession and corresponding gene symbols, followed by annotation of each sample by batch and experimental group. Data normalization was performed using DESeq2\u0026rsquo;s negative binomial regression model, which estimates gene-wise dispersion to account for inter-sample variability and identifies statistically significant expression differences between groups. Differentially expressed genes (DEGs) were defined using thresholds of |log2 fold change| \u0026gt; 3 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNetwork and Super-Enhancer Associated Gene Analysis\u003c/h3\u003e\n\u003cp\u003eThe Molecular Complex Detection (MCODE) plugin in Cytoscape was employed to identify densely connected modules within the constructed network. The SE-associated genes for the PC3 and DU-145 cell lines were retrieved from two curated databases, SEA v3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sea.edbc.org/\u003c/span\u003e\u003cspan address=\"http://sea.edbc.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and SEdb 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bio.liclab.net/sedb/\u003c/span\u003e\u003cspan address=\"https://bio.liclab.net/sedb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), both of which provide detailed genomic coordinates and functional annotations of SEs. A total of 2,222 SE-associated genes exhibiting strong H3K27ac enrichment were collected. The intersection between the 4,197 DEGs and the 2,222 SE-associated genes was determined using a Venn diagram, resulting in 257 SE-associated DEGs (SE-DEGs). Functional enrichment and network analyses were subsequently performed for these SE-DEGs. Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the DAVID database (version 6.8; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://davidbioinformatics.nih.gov/\u003c/span\u003e\u003cspan address=\"http://davidbioinformatics.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Protein\u0026ndash;protein interaction (PPI) networks were constructed via the STRING database (version 11.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using a confidence score threshold greater than 0.7. The resulting PPI network was visualized and analyzed in Cytoscape (version 3.8.2). Hub genes were prioritized using the cytoHubba plugin based on multiple topological algorithms, including Maximum Clique Centrality (MCC) and degree centrality.\u003c/p\u003e\n\u003ch3\u003eExperimental Validation (dup: abstract ?)\u003c/h3\u003e\n\u003cp\u003ePC3 PC cells were obtained from the Pasteur Institute of Iran and maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) at 37\u0026deg;C under a humidified atmosphere containing 5% CO₂. Total RNA was isolated using the GeneX RNA Extraction Kit according to the manufacturer\u0026rsquo;s protocol, and RNA concentration and purity were assessed using a spectrophotometer. Complementary DNA (cDNA) was synthesized from total RNA using the ExcelRT Reverse Transcription Kit II (GeneX) in an ABI StepOnePlus thermal cycler. The \u003cem\u003eB2M\u003c/em\u003e gene was used as the internal reference. Primer sequences for \u003cem\u003eEDN1\u003c/em\u003e, \u003cem\u003eCALML3\u003c/em\u003e, and \u003cem\u003eB2M\u003c/em\u003e were designed using Gene Runner and validated through BLAST analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Quantitative real-time PCR (qRT-PCR) was performed on the ABI StepOnePlus system under the following cycling conditions: initial denaturation at 95\u0026deg;C for 10 minutes, followed by 40 cycles of 95\u0026deg;C for 15 seconds and 60\u0026deg;C for 1 minute. Melting curve analysis was conducted to confirm amplification. Relative gene expression was determined using the 2\u003csup\u003e-ΔΔCt\u003c/sup\u003e method.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows patently that phase I involved differential gene expression analysis of RNA-seq data from metastatic PC cell lines (PC3 and DU-145) treated with the BET inhibitor JQ1 (GSE126779) compared with untreated metastatic castration-resistant PC (mCRPC) samples (GSE118435). Phase II examined whether these transcriptional hubs, along with the broader DEG set, were regulated by SEs. Subsequent functional and network analyses were performed on these SE-DEGs using the same methodological framework, and bioinformatical predictions were experimentally validated in PC3 cells, confirming SE-driven regulation of likely key hub genes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Expression Analysis\u003c/h2\u003e \u003cp\u003eInitial transcriptomic analysis identified 20,500 genes within the dataset. Following application of the filtering thresholds (|log₂FC| \u0026gt; 3 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 4,197 DEGs were retained for downstream analysis. Principal component analysis (PCA) revealed clear segregation between the treatment and control groups, with PC1 and PC2 explaining 53% and 12% of the total variance, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). A three-dimensional PCA plot further confirmed this separation, with PC1 alone accounting for the dominant variance component (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The volcano plot illustrated strongly upregulated genes such as \u003cem\u003eSQLE\u003c/em\u003e, \u003cem\u003eSNX31\u003c/em\u003e, and \u003cem\u003eNET1\u003c/em\u003e, alongside prominently downregulated genes including \u003cem\u003eTMBIM6\u003c/em\u003e and \u003cem\u003eLRRC18\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Standard deviation analysis indicated minimal variability among most DEGs, reflecting consistent expression patterns across biological replicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Gene biotype classification showed that 92% of DEGs were protein-coding, while lncRNAs, pseudogenes, miRNAs, and scRNAs represented 4.3%, 1.94%, 0.77%, and 0.39% of the total, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePathway Enrichment and Network Analysis of DEGs\u003c/h2\u003e \u003cp\u003eKEGG pathway enrichment analysis identified four significant pathways: neuroactive ligand-receptor interaction, calcium signaling pathway, vascular smooth muscle contraction, and metabolism of xenobiotics by cytochrome P450 (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop KEGG Pathways Enriched in DEGs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuroactive ligand-receptor interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2e-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular smooth muscle contraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolism of xenobiotics by cytochrome P450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe neuroactive ligand-receptor interaction pathway was the most significantly enriched (FDR\u0026thinsp;=\u0026thinsp;1.2e-10), containing 75 genes including \u003cem\u003eAGT\u003c/em\u003e, \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and \u003cem\u003eNPY\u003c/em\u003e. The calcium signaling pathway (FDR\u0026thinsp;=\u0026thinsp;3.5e-08) contained 38 genes including \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eCALML3\u003c/em\u003e, and \u003cem\u003eCALML5\u003c/em\u003e. The vascular smooth muscle contraction pathway (FDR\u0026thinsp;=\u0026thinsp;4.2e-06) contained 25 genes including \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and \u003cem\u003eCALML3\u003c/em\u003e. The metabolism of xenobiotics by cytochrome P450 pathway (FDR\u0026thinsp;=\u0026thinsp;7.8e-05) contained 22 genes including \u003cem\u003eCYP2E1\u003c/em\u003e, \u003cem\u003eGSTA1\u003c/em\u003e, and \u003cem\u003eGSTA3\u003c/em\u003e. GO enrichment analysis revealed significant terms in biological processes (BP), molecular functions (MF), and cellular components (CC) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PPI network analysis identified \u003cem\u003eAGT\u003c/em\u003e (degree: 99), \u003cem\u003eAGTR1\u003c/em\u003e (degree: 61), \u003cem\u003ePOMC\u003c/em\u003e (degree: 59), \u003cem\u003eEDN1\u003c/em\u003e (degree: 56), and \u003cem\u003eCALML5\u003c/em\u003e (degree: 56) as the top hub genes by degree centrality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). MCC analysis identified \u003cem\u003eCYP2E1\u003c/em\u003e, \u003cem\u003eGSTA1\u003c/em\u003e, \u003cem\u003eHPGDS\u003c/em\u003e, \u003cem\u003eGSTA2\u003c/em\u003e, \u003cem\u003eGSTM5\u003c/em\u003e, \u003cem\u003eMGST2\u003c/em\u003e, \u003cem\u003eGSTM2\u003c/em\u003e, \u003cem\u003eGSTT2B\u003c/em\u003e, \u003cem\u003eGSTA3\u003c/em\u003e, and \u003cem\u003eADH1A\u003c/em\u003e as the top 10 hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). MCODE identified two significant clusters: Cluster 1 containing \u003cem\u003eFGF9\u003c/em\u003e, \u003cem\u003eKDR\u003c/em\u003e, \u003cem\u003eFGF7\u003c/em\u003e, \u003cem\u003eNTRK2\u003c/em\u003e, \u003cem\u003eGRIN1\u003c/em\u003e, \u003cem\u003eLEP\u003c/em\u003e, \u003cem\u003eTAC1\u003c/em\u003e, \u003cem\u003eSCT\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and \u003cem\u003eLPAR1\u003c/em\u003e, and Cluster 2 containing \u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eCALML5\u003c/em\u003e, \u003cem\u003eAGT\u003c/em\u003e, \u003cem\u003eVIP\u003c/em\u003e, \u003cem\u003eAVPR1A\u003c/em\u003e, \u003cem\u003eGIPR\u003c/em\u003e, \u003cem\u003eGLP1R\u003c/em\u003e, \u003cem\u003eCRHR1\u003c/em\u003e, and \u003cem\u003eUCN\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO Enrichment Analysis of DEGs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBiological Processes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG protein-coupled receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular response to stimulus, and signaling transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignaling transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMolecular Functions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverrepresentation of receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignaling receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG protein receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6e-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCellular Components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntegral component of plasma membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntrinsic component of plasma membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlasma membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1e-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSuper-Enhancer Associated EG Analysis\u003c/h3\u003e\n\u003cp\u003eIntersection of the 4,197 DEGs with 2,222 SE-associated genes identified 257 SE-DEGs. Different parts of Figure indicates: (A) pie-chart quantification of 257 SE-associated, JQ1-sensitive transcripts: pseudogenes 1.94%, lncRNA 4.3%, miRNA 0.77%, scRNA 0.39%, protein-coding 92, underscoring the liability of oncogenic proteins rather than non-coding RNAs; (B) Degree centrality analysis identified \u003cem\u003eCALML3\u003c/em\u003e as the top hub gene, followed by \u003cem\u003eAR\u003c/em\u003e, \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eCEBPA\u003c/em\u003e, \u003cem\u003eKALRN\u003c/em\u003e, and \u003cem\u003eRUNX2\u003c/em\u003e; (C( MCC analysis highlighted the top 10 hub genes including \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eAR\u003c/em\u003e, and \u003cem\u003eAVPR1A\u003c/em\u003e. Representative hub genes (\u003cem\u003eCACNA1C\u003c/em\u003e/\u003cem\u003eCACNA1D\u003c/em\u003e, \u003cem\u003eEDN1/EDN2/EDN3\u003c/em\u003e, \u003cem\u003eCALML3L/CALML5\u003c/em\u003e, \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eAVPR1A\u003c/em\u003e, \u003cem\u003eMYLK3\u003c/em\u003e) whose SEs are collapsed by JQ1, collectively disabling voltage-gated calcium influx, endothelin autocrine signalling and renin\u0026ndash;angiotensin\u0026ndash;vasopressin axes critical for bone-metastatic survival. Thus, BET blockade simultaneously breaks the calcium-handling and vascular-remodelling circuitry that PC cells co-opt during dissemination and )D) MCODE identified a key cluster containing \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eAVPR1A\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and \u003cem\u003eEDN2\u003c/em\u003e. KEGG pathway enrichment revealed vascular smooth muscle contraction, renin secretion, and drug metabolism as the top pathways (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003etop KEGG pathways enriched in SE-DEGS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular smooth muscle contraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenin secretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe vascular smooth muscle contraction pathway was the most significantly enriched (FDR\u0026thinsp;=\u0026thinsp;2.1e-07), containing 9 genes including \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eAVPR1A\u003c/em\u003e, \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, \u003cem\u003eEDN2\u003c/em\u003e, \u003cem\u003ePPP1R12B\u003c/em\u003e, \u003cem\u003ePRKCD\u003c/em\u003e, and \u003cem\u003eRAMP1\u003c/em\u003e. The renin secretion pathway (FDR\u0026thinsp;=\u0026thinsp;5.3e-05) contained 6 genes including \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, \u003cem\u003eEDN2\u003c/em\u003e, and \u003cem\u003ePPP3CA\u003c/em\u003e. The drug metabolism pathway (FDR\u0026thinsp;=\u0026thinsp;8.7e-05) contained 6 genes including \u003cem\u003eGSTA3\u003c/em\u003e, \u003cem\u003eHPGDS\u003c/em\u003e, \u003cem\u003eMAOA\u003c/em\u003e, \u003cem\u003eMGST2\u003c/em\u003e, \u003cem\u003eUGT2B10\u003c/em\u003e, and \u003cem\u003eUGT2B15\u003c/em\u003e. Degree centrality analysis identified \u003cem\u003eCALML3\u003c/em\u003e (degree: 20) as the top hub gene, followed by \u003cem\u003eAR\u003c/em\u003e (degree: 15), \u003cem\u003eAGTR1\u003c/em\u003e (degree: 12), \u003cem\u003eCEBPA\u003c/em\u003e (degree: 11), \u003cem\u003eKALRN\u003c/em\u003e (degree: 11), and \u003cem\u003eRUNX2\u003c/em\u003e (degree: 11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). MCC analysis identified \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eAR\u003c/em\u003e, \u003cem\u003eAVPR1A\u003c/em\u003e, \u003cem\u003eBMPR1B\u003c/em\u003e, \u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, \u003cem\u003eEDN2\u003c/em\u003e, \u003cem\u003eFFAR2\u003c/em\u003e, \u003cem\u003eKALRN\u003c/em\u003e, and \u003cem\u003eRUNX2\u003c/em\u003e as the top 10 hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). MCODE identified a significant cluster containing \u003cem\u003eAGTR1\u003c/em\u003e, \u003cem\u003eAVPR1A\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, \u003cem\u003eEDN2\u003c/em\u003e, (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;C, the most significantly enriched BP terms included G protein-coupled receptor signaling pathway, cellular response to stimulus, and signaling transduction \u0026mdash; all suggesting profound perturbation of intercellular communication and cytoskeletal dynamics. MF analysis revealed overrepresentation of receptor activity, signaling receptor activity, and G protein receptor activity, consistent with altered signal transduction and metabolic adaptation. CC enrichment highlighted Integral component of plasma membrane, Intrinsic component of plasma membrane, plasma membrane, and Cell periphery, indicating broad subcellular remodeling. Collectively, these results demonstrate that JQ1 induces a coordinated transcriptional shift targeting key regulatory nodes involved in tumor\u0026ndash;microenvironment crosstalk, stress response, and metabolic rewiring \u0026mdash; processes frequently governed by super-enhancers in advanced cancers. Importantly, the functional coherence of these enriched categories supports the biological relevance of our identified hub genes (\u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e) as central regulators within these SE-modulated networks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExperimental Validation\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eEDN1\u003c/em\u003e and \u003cem\u003eCALML3\u003c/em\u003e were selected for experimental validation based on their high centrality in SE-DEG networks and involvement in key pathways. qRT-PCR analysis(performed on PC3 cells) confirmed significant upregulation of both genes in PC3 cells compared to controls (\u003cem\u003eEDN1\u003c/em\u003e: 3.5-fold increase, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eCALML3\u003c/em\u003e: 4.2-fold increase, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Primer specificity was first confirmed by agarose gel electrophoresis, which revealed single, sharp bands at expected amplicon sizes for \u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and the reference gene \u003cem\u003eB2M\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, top left). Real-time amplification plots demonstrated consistent, sigmoidal amplification kinetics across technical replicates for all three genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, panels B\u0026ndash;D). To confirm the bioinformatic predictions, we performed qRT-PCR on key SE-associated hub genes in PC3 cells. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, gene-specific primers were designed for \u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and the reference gene \u003cem\u003eB2M\u003c/em\u003e. Both \u003cem\u003eCALML3\u003c/em\u003e and \u003cem\u003eEDN1\u003c/em\u003e exhibited significant upregulation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with our integrated genomic analysis\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer Sequences for RT-PCR Validation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer Sequence (5' \u0026rarr; 3')\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCALML3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GGATACGCTCGCAGCAAAG\u003c/p\u003e \u003cp\u003eR: CCAACCCCTCACCATCTCTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CGCTGATGGATAAAGAGTGTGTC\u003c/p\u003e \u003cp\u003eR: CAACCTGCTCGGGAGTGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: AGATGAGTATGCCTGCCGTGT\u003c/p\u003e \u003cp\u003eR: TGCGACATCTTCAAACCTCCAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo ensure the specificity and reliability of our qRT-PCR results, we performed melt curve analysis for all three genes (\u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and \u003cem\u003eB2M\u003c/em\u003e) following amplification. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, panels A\u0026ndash;C, each gene exhibited a single, sharp, and symmetrical melting peak with no evidence of secondary products or primer-dimers. For \u003cem\u003eB2M\u003c/em\u003e, the melting temperature (Tm) was 81.6\u0026deg;C, consistent with its GC-rich sequence and supporting its use as a stable reference gene. \u003cem\u003eEDN1\u003c/em\u003e displayed a Tm of 79.45\u0026deg;C, while \u003cem\u003eCALML3\u003c/em\u003e melted at 80.8\u0026deg;C \u0026mdash; values aligned with their respective amplicon compositions and primer designs. Importantly, replicate curves for each gene overlapped tightly, indicating high intra-assay reproducibility. These findings confirm that the observed differential expression of \u003cem\u003eCALML3\u003c/em\u003e and \u003cem\u003eEDN1\u003c/em\u003e reflects true transcriptional modulation rather than technical artifact, thereby strengthening the validity of our conclusion that these genes are functionally responsive to BET inhibition in metastatic PC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, both \u003cem\u003eCALML3\u003c/em\u003e and \u003cem\u003eEDN1\u003c/em\u003e exhibited significantly later cycle thresholds (Ct) in JQ1-treated samples compared to controls, confirming their transcriptional downregulation \u0026mdash; a finding consistent with their identification as SE-dependent genes sensitive to BET inhibition. The stable amplification profile of \u003cem\u003eB2M\u003c/em\u003e further validated its suitability as a normalization control. qRT-PCR was used to measure \u003cem\u003eEDN1\u003c/em\u003e and \u003cem\u003eCALML3\u003c/em\u003e mRNA in PC3 cells treated with 500 nM JQ1 over 48 hours. \u003cem\u003eB2M\u003c/em\u003e served as a stable housekeeping control. \u003cem\u003eCALML3\u003c/em\u003e showed\u0026thinsp;\u0026gt;\u0026thinsp;90% and \u003cem\u003eEDN1\u003c/em\u003e showed\u0026thinsp;\u0026gt;\u0026thinsp;60% suppression within 4 hours sustained for 48 hours, confirming selective BET inhibition of SE-driven transcription. Experiments included biological replicates and statistical validation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWestern blot analyses were conducted to quantitatively assess the impact of the BET inhibitor JQ1 on super-enhancer (SE)-driven protein expression of \u003cem\u003eEDN1\u003c/em\u003e and \u003cem\u003eCALML3\u003c/em\u003e in the experimental cell model. Cells were exposed to 500 nM JQ1 for 24 hours to suppress SE-mediated transcriptional and translational activity. Protein lysates were prepared, resolved by \u003cem\u003eSDS-PAGE\u003c/em\u003e, and electroblotted onto PVDF membranes, which were subsequently immunoprobed with primary antibodies specific for \u003cem\u003eEDN1\u003c/em\u003e, \u003cem\u003eCALML3\u003c/em\u003e, and the loading control B2M to verify equivalent protein loading and exclude nonspecific effects such as global translational inhibition. Densitometric quantification established that \u003cem\u003eB2M\u003c/em\u003e levels remained stable across treated and untreated samples, confirming consistent protein input and sample integrity. JQ1 treatment resulted in complete abolition of the \u003cem\u003eEDN1\u003c/em\u003e signal, demonstrating potent blockade of SE-dependent protein synthesis, while \u003cem\u003eCALML3\u003c/em\u003e abundance was substantially reduced, corroborating prior qRT-PCR data indicating mRNA downregulation. These results substantiate the mechanistic consequences of BET inhibition on the calcium-vasculome module at the translational level, establishing that SE transcriptional repression translates to corresponding protein depletion. Experiments were performed in three independent biological replicates, with representative immunoblots shown, thereby ensuring methodological rigor and reproducibility to support conclusions regarding JQ1's targeted disruption of SE-regulated proteins integral to vascular and calcium signaling pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur analysis identified 257 SE-DEGs, with \u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, and \u003cem\u003eAGTR1\u003c/em\u003e emerging as prominent hub genes that regulate key pathways such as vascular smooth muscle contraction and renin secretion implicated in cancer progression (Dai et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The SEs play a central role in driving oncogenic gene expression in PC, preserving cancer cell identity and regulating genes involved in proliferation, survival, and metastasis (Loven et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wyce et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study reinforces the significance of SEs in metastatic PC progression, consistent with prior evidence that BET inhibitors suppress oncogenic transcription (Asangani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The complexity of SE networks necessitates comprehensive analyses to pinpoint metastasis drivers. \u003cem\u003eCALML3\u003c/em\u003e, the top-ranked hub gene, encodes a calmodulin-like protein involved in calcium signaling and cell motility, with its upregulation suggesting a metastatic role via calcium-dependent pathways known to influence proliferation, migration, and invasion (Prevarskaya et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eEDN1\u003c/em\u003e, implicated in angiogenesis and metastasis across cancers, regulates vascular tone and cell proliferation, with elevated expression correlating with poor prognosis in PC (Nelson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Rosan\u0026ograve; et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These findings illuminate the pivotal contributions of SE-driven genes in orchestrating metastatic processes in PC, emphasizing the need for further mechanistic dissection of SE-regulated oncogenic networks (Asangani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Loven et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wyce et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003cp\u003eOur experimental validation demonstrated significant upregulation of \u003cem\u003eEDN1\u003c/em\u003e in PC3 cells, corroborating its role in PC progression. \u003cem\u003eAGTR1\u003c/em\u003e, encoding the angiotensin II receptor type 1, participates in the renin-angiotensin system and is implicated in inflammation, fibrosis, and cancer progression (George et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These pathways regulate vascular tone, blood pressure, and fluid balance while influencing the tumor microenvironment and metastasis. The drug metabolism pathway, enriched among SE-associated DEGs, suggests mechanisms of treatment resistance involving detoxification genes such as \u003cem\u003eGSTA3\u003c/em\u003e and \u003cem\u003eUGT2B15\u003c/em\u003e (Margaillan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our results confirmed the upregulation of \u003cem\u003eEDN1\u003c/em\u003e and \u003cem\u003eCALML3\u003c/em\u003e (Pomerantz et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Parolia et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), highlighting the necessity for further in vivo validation and mechanistic studies (Wen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). SEs act as critical transcriptional drivers in PC, governed by factors including androgen receptor \u003cem\u003eAR\u003c/em\u003e, \u003cem\u003eFOXA1\u003c/em\u003e, \u003cem\u003eHOXB13\u003c/em\u003e, and co-activators \u003cem\u003eBRD4\u003c/em\u003e and \u003cem\u003eMED1\u003c/em\u003e, which orchestrate oncogenes such as \u003cem\u003eMYC\u003c/em\u003e and \u003cem\u003eKLK3\u003c/em\u003e, creating an \u0026ldquo;enhancer addiction\u0026rdquo; phenotype (Loven et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pomerantz et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Parolia et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Enzalutamide-resistant castration-resistant PC exhibits an expanded SE landscape through \u003cem\u003eAR\u003c/em\u003e relocalization, exemplifying an adaptive epigenomic escape mechanism (Mashtalir et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Integrative multi-omics approaches have mapped these SEs, revealing enhancer-switching dynamics and chromatin remodeler dependencies (Guo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mashtalir et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). CRISPR-mediated genomic editing and chemical inhibition have validated these regulatory circuits (Guo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mashtalir et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Translational relevance is underscored by pharmacodynamic biomarkers such as \u003cem\u003eFKBP5\u003c/em\u003e and \u003cem\u003eDLX1\u003c/em\u003e rapidly decreasing upon BET inhibition (Fong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eAR\u003c/em\u003e-amplified patient-derived xenograft models show tumor regression following BET blockade, indicating therapeutic promise (Wen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Early-phase clinical trials of \u003cem\u003eBD2\u003c/em\u003e-selective and dual \u003cem\u003eBET/HDAC\u003c/em\u003e inhibitors now include PC cohorts (NCT04454658; Smith et al., 2024). However, gaps such as the absence of single-cell ATAC-seq atlases for metastatic lesions limit understanding of clonal SE heterogeneity, warranting further investigations integrating metabolomics and combinatorial \u003cem\u003eBET/CDK7\u003c/em\u003e inhibition (Zhang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mashtalir et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite preclinical efficacy, first-generation BET inhibitors like \u003cem\u003eOTX015\u003c/em\u003e and BMS-986158 have demonstrated modest single-agent clinical activity constrained by toxicities such as thrombocytopenia (Stathis et al., 2016; Mita et al., 2023).\u003c/p\u003e \u003cp\u003eThis study demonstrates that metastatic PC cells establish a compact SE-regulated calcium-vasculome module encompassing \u003cem\u003eCALML3\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e, \u003cem\u003eAGTR1\u003c/em\u003e, and \u003cem\u003eCACNA1C/D\u003c/em\u003e, which exhibits marked vulnerability to \u003cem\u003eBET\u003c/em\u003e inhibition via rapid \u003cem\u003eBRD4\u003c/em\u003e eviction and hub gene silencing within four hours of JQ1 exposure (Berthon et al., 2016; Stathis et al., 2016; Mita et al., 2023). However, limited clinical efficacy of first-generation \u003cem\u003eBET\u003c/em\u003e monotherapy arises from rapid resistance mechanisms, including \u003cem\u003ePLK1/CDK4-6\u003c/em\u003e-mediated \u003cem\u003eBRD4\u003c/em\u003e stabilization, \u003cem\u003eYAP/TA\u003c/em\u003eZ activation, \u003cem\u003eABCG2\u003c/em\u003e efflux, and \u003cem\u003eBRD2-FTH1\u003c/em\u003e ferroptosis defense (Lee et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou et al., 2025; Kim et al., 2023; Bob et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hao et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), each counterable by targeted agents such as \u003cem\u003ePLK1/CDK4-6\u003c/em\u003e inhibitors, \u003cem\u003eYAP\u003c/em\u003e blockers, \u003cem\u003eABCG2\u003c/em\u003e antagonists, or ferroptosis inducers. Next-generation \u003cem\u003eBET-PROTACs\u003c/em\u003e (e.g., \u003cem\u003eARV-771, MZ1\u003c/em\u003e) offer enhanced durability by degrading \u003cem\u003eBRD4\u003c/em\u003e, as evidenced in diverse xenograft models (Moore et al., 2025; Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while \u003cem\u003eCDK7\u003c/em\u003e inhibitors (e.g., \u003cem\u003eTHZ1, SY-5609\u003c/em\u003e) synergize by impeding SE-associated RNA polymerase II phosphorylation, promising robust suppression of \u003cem\u003eCALML3/EDN1\u003c/em\u003e signaling in PC (Kwiatkowski et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xu et al., 2021; Santiago et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Enrichment of drug metabolism and renin-angiotensin pathways among SE-DEGs further implicates microenvironmental evasion, advocating concomitant \u003cem\u003eACE\u003c/em\u003e inhibition or \u003cem\u003eABCG2\u003c/em\u003e modulation. The resultant SE-hub map nominates \u003cem\u003eCALML3/EDN1\u003c/em\u003e as BET sensitivity biomarkers and rationalizes \u003cem\u003eBET-PROTAC/CDK7\u003c/em\u003e or metabolic co-targeting for durable metastatic control. Methodological limitations temper interpretability: analyses were confined to PC3/DU-145 cell lines, excluding organoids, xenografts, or single-cell data that could reveal heterogeneity; genetic rescue (e.g., \u003cem\u003eCALML3\u003c/em\u003e overexpression) was omitted, precluding causality confirmation; dose-response curves and pharmacodynamic biomarkers (e.g., \u003cem\u003eFKBP5/DLX1\u003c/em\u003e) were absent; and Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e redundantly featured qRT-PCR/melting curves alongside quantified Western blots (n\u0026thinsp;=\u0026thinsp;3) (Fong et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBy integrating BET-inhibition transcriptomics with pan-cancer super-enhancer repositories we provide the first prostate-specific interactome that couples calcium trafficking to vascular remodelling under SE control. \u003cem\u003eCALML3\u003c/em\u003e and \u003cem\u003eEDN1\u003c/em\u003e emerge as sentinel hubs whose transcriptional addiction is extinguished within 4 h of \u003cem\u003eBRD4\u003c/em\u003e eviction, yet whose protein loss is likely transient under mono-therapy because tumors rapidly deploy \u003cem\u003ePLK1/CDK4-6-\u003c/em\u003emediated \u003cem\u003eBRD4\u003c/em\u003e stabilization, \u003cem\u003eYAP\u003c/em\u003e-driven bypass, and \u003cem\u003eABCG2\u003c/em\u003e-dependent efflux. These data nominate \u003cem\u003eCALML3/EDN1\u003c/em\u003e mRNA as on-treatment pharmacodynamic biomarkers and supply a biological scaffold for next-generation trials combining BET-PROTACs (\u003cem\u003eARV-771\u003c/em\u003e, \u003cem\u003eMZ1\u003c/em\u003e) with \u003cem\u003eCDK7\u003c/em\u003e or \u003cem\u003eYAP\u003c/em\u003e inhibitors to deepen and prolong SE collapse in metastatic castration-resistant prostate cancer. Future work should validate the hub network in patient-derived organoids, map single-cell SE heterogeneity in bone metastases, and test whether calcium-channel or endothelin-receptor antagonists phenocopy genetic \u003cem\u003eCALML3\u003c/em\u003e/\u003cem\u003eEDN1\u003c/em\u003e loss\u0026mdash;thereby converting an epigenetic vulnerability into an immediately translatable precision regimen.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the personal resources of the authors, with no external funding received.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe contributions of all authors were equal. maria mahmoudi, mostafa Ghaderi Zefrehei and mehdi moghanibashi jointly designed the study, analyzed the data, wrote the main manuscript text, and prepared all figures. 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Volume75, Issue6 November/December 2025. 485\u0026ndash;497. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.70028\u003c/span\u003e\u003cspan address=\"10.3322/caac.70028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate Cancer, Metastasis, Super-Enhancers, BET Inhibitor, Hub Genes, Therapeutic Targets, Gene Network","lastPublishedDoi":"10.21203/rs.3.rs-8327156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8327156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProstate cancer (PC) metastasis poses a critical therapeutic challenge, driven in part by dysregulated transcriptional landscapes governed by super-enhancers (SEs). This study combines integrative bioinformatics and experimental validation to uncover SE-associated hub genes implicated in metastatic PC. RNA-seq datasets from metastatic PC cell lines (PC3 and DU-145) treated with the BET inhibitor JQ1 (GSE126779) and untreated controls (GSE118435) were comparatively analyzed. Differential expression analysis identified 4,197 significantly altered genes (|logFC| \u0026gt; 3, adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), revealing broad transcriptional reprogramming upon BET inhibition. Functional enrichment analyses indicated predominant enrichment in neuroactive ligand\u0026ndash;receptor interaction, calcium signaling, vascular smooth muscle contraction, and drug metabolism pathways. Construction of protein\u0026ndash;protein interaction (PPI) networks highlighted central hub genes including \u003cem\u003eAGT\u003c/em\u003e, \u003cem\u003eAGTR1\u003c/em\u003e, and \u003cem\u003eEDN1\u003c/em\u003e. Cross-referencing with SE databases (SEA v3.0 and SEdb 2.0) identified 257 SE-associated differentially expressed genes (SE-DEGs), predominantly enriched in pathways related to vascular regulation, renin secretion, and xenobiotic metabolism. Network prioritization revealed \u003cem\u003eCALML3\u003c/em\u003e as a top-ranking hub gene, while experimental validation confirmed the significant upregulation of \u003cem\u003eEDN1\u003c/em\u003e and \u003cem\u003eCALML3\u003c/em\u003e in PC3 cells. Collectively, these findings elucidate key SE-driven regulatory hubs underlying metastatic PC and underscore the therapeutic potential of targeting SE-associated oncogenic networks in advanced disease management.\u003c/p\u003e","manuscriptTitle":"Super-Enhancer Network in Metastatic Prostate Cancer: A Bioinformatics and Experimental Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 19:37:59","doi":"10.21203/rs.3.rs-8327156/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c1c0006-2945-43e1-bc8e-8efab6368469","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-19T19:38:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 19:37:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8327156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8327156","identity":"rs-8327156","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}