Identification of angiogenesis-related genes in the diagnosis of benign prostatic hyperplasia using bioinformatics analysis

Introduction

Benign prostatic hyperplasia (BPH) is a prevalent, chronic progressive disorder among elderly males. It has an age-standardized incidence of 5,531.88 per 100,000 people and an age-standardized prevalence of 2,782.59 per 100,000 people, making it the most common urological disease. Its incidence continues to rise with advancing age (1,2). The pathogenesis of BPH involves continuous proliferation of prostatic epithelium and stroma, ultimately resulting in benign prostatic enlargement. It primarily manifests as lower urinary tract symptoms (LUTS) clinically like urgency, nocturia, frequency, dysuria, difficulty in voiding, hesitancy, weak urinary stream, or interrupted urinary flow (3). In addition to aging and androgen levels as the principal contributing factors, obesity, dietary factors, inflammatory gene polymorphisms, and oxidative stress have also been implicated in exacerbating the condition (4). Obesity alters metabolic homeostasis in men and enhances inflammatory responses (5), thereby exerting unfavorable effects on BPH progression. Current therapeutic approaches to BPH are primarily classified into two categories: 5-α-reductase inhibitors (e.g., finasteride) and α-adrenergic receptor antagonists (e.g., terazosin). The former inhibits testosterone-to-dihydrotestosterone (DHT) conversion, thereby attenuating androgen-driven prostatic growth, whereas the latter relaxes the smooth muscle of the prostate and bladder neck by suppressing sympathetic activity. However, prolonged use of these agents is related to adverse effects, like erectile dysfunction, lowered libido, and ejaculatory disorders (6), and fatigue, orthostatic hypotension, and dizziness (7).

Angiogenesis, the formation of new blood vessels, is critical in tissue repair, regeneration, and cancer development. It is a multistep and tightly regulated process whereby new capillaries sprout from pre-existing vasculature. Under physiological conditions, angiogenesis is beneficial and indispensable in embryonic development and the female menstrual cycle (8,9). By contrast, under pathological conditions, aberrant angiogenesis is implicated in the pathogenesis of malignant tumors, ocular disorders, cardiovascular disease, and chronic inflammatory conditions (10-13). During inflammatory responses, angiogenesis facilitates the infiltration of inflammatory mediators, growth factors, and immune cells into sites of injury or stress (14,15). Furthermore, the inflammatory microenvironment and hypoxia related to chronic inflammation further promote angiogenesis, thereby aggravating inflammatory processes (16,17). To date, most investigations have concentrated on the role of angiogenesis in cancer pathogenesis and therapy (18,19), while comparatively less attention has been directed toward its relation to chronic inflammatory disorders.

At present, how angiogenesis-related genes influence BPH and its progression remains incompletely elucidated. Bioinformatics approaches have been widely applied to explore disease mechanisms and represent a powerful strategy for unveiling the role of angiogenesis-linked genes in BPH and targeted therapy. In this study, datasets GSE132714, GSE7307, GSE119195, GSE3868, and GSE101486 came from the Gene Expression Omnibus (GEO). Training and validation modules were established, and key module genes were found via weighted gene co-expression network analysis (WGCNA). Candidate hub genes were subsequently screened using functional enrichment analysis (FEA), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF). Receiver operating characteristic (ROC) curves were plotted, and the areas under the curves (AUCs) were assessed. Ultimately, three hub genes, CARTPT, TOB1, and GLO1, were identified as closely related to both BPH pathogenesis and angiogenesis. This study may provide valuable insights for identifying biomarkers linked to BPH and angiogenesis. We present this article in accordance with the TRIPOD reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0385/rc).

Methods

Data collection

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Gene expression profiles of BPH were downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo) to serve as training and validation cohorts. Three BPH datasets were selected as the training set: GSE132714 (GPL16791) (18 BPH patients vs. 4 controls), GSE7307 (GPL570) (7 BPH patients vs. 7 controls), and GSE101486 (GPL10558) (22 BPH patients). Additionally, two datasets were selected as external validation cohorts: GSE3868 (GPL96) (2 BPH patients vs. 2 controls) and GSE119195 (GPL6244) (5 BPH patients vs. 3 controls). 6,073 angiogenesis-related genes were from GeneCards (https://www.genecards.org/; version 6.1, June 17, 2026). Data sources are summarized in Table 1, and the workflow is provided in Figure 1.

Figure 1 Workflow of data collection. DEG, differentially expressed gene; GEO, Gene Expression Omnibus; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; ROC, receiver operating characteristic; SVM-RFE, support vector machine-recursive feature elimination; WGCNA, weighted gene co-expression network analysis.

Data processing and identification of differentially expressed genes (DEGs)

GSE132714, GSE7307, and GSE101486 were merged, and batch effects were corrected utilizing the Bioconductor “SVA” in R (20). DEGs were identified using “Limma” in R with the thresholds |log₂fold change (FC)| >0.5 and adjusted P value (adj. P) <0.05. Genes with log₂FC >1 and P < 0.05 were upregulated, while those with log₂FC <−1 and adj. P<0.05 were downregulated. Heatmaps and volcano plots of DEGs were generated via “Pheatmap” and “ggplot2” in R.

WGCNA and module gene selection

WGCNA groups genes into modules based on high correlation. These modules are then characterized by their eigengenes or key hub genes, and their associations with clinical traits are examined using eigengene network analysis. It also enables the computation of module membership (MM) measures (21). WGCNA was enabled by “WGCNA” in R to identify gene modules most strongly related to BPH. After preprocessing to detect missing values and outliers, hierarchical clustering was performed to remove highly dispersed samples. The correlation matrix was transformed into an adjacency matrix using a selected optimal soft-thresholding power. This adjacency matrix was subsequently converted into a topological overlap matrix (TOM). Gene modules were then found after dynamic tree cutting, summarized into eigengenes, and correlated with external clinical traits through heatmaps. To further identify the most biologically relevant hub genes within the significant modules, we calculated MM and gene significance (GS). Genes with MM >0.8 and GS >0.2 were defined as hub genes and selected for subsequent analysis. Finally, correlations between module genes and DEGs were further assessed, with scatter plots constructed to visualize relationships.

Candidate gene selection via machine learning (ML)

Diagnostic candidate genes for BPH were identified utilizing RF and SVM-RFE. The RF algorithm is widely applicable, robust to variable conditions, and demonstrates high accuracy, sensitivity, and specificity, making it suitable for predicting continuous variables with stable results (22). SVM-RFE is a supervised ML approach that iteratively eliminates features generated by the SVM model to optimize gene selection (23). The intersection of genes identified by both algorithms was considered the set of candidate genes.

FEA of significant co-expression modules

The biological functions of significant modules were assessed via FEA. DAVID (http://david.abcc.ncifcrf.gov/) provides annotation across biological processes (BPs), molecular functions (MFs), and cellular components (CCs). Using “clusterProfiler” in R (24), Gene Ontology (GO) (25) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (26) pathway enrichment analyses were carried out. Results were visualized as bubble plots generated by “ggplot2” in R, and genes with a P value <0.05 were ranked by count. The top three enriched genes were regarded as the key co-expressed module genes.

Construction of the nomogram model

To provide an effective diagnostic tool for BPH, a nomogram was derived based on the candidate genes through “rms” in R (27). The nomogram illustrated the contribution of candidate genes to BPH risk. In addition, boxplots and heatmaps were generated (28) to visualize the associations between candidate genes and BPH.

Validation of candidate gene expression

Two datasets were selected for external validation: GSE3868 (GPL96) (2 BPH patients vs. 2 controls) and GSE119195 (GPL6244) (5 BPH patients vs. 3 controls). ROC curve was analyzed to rate the diagnostic performance of candidate genes, with AUC computed.

Statistical analysis

All statistical analyses were enabled by R 4.3.3. Continuous variables were compared via the Wilcoxon or Kruskal-Wallis test, with a P value <0.05 denoting statistical significance.

Results

Identification of DEGs

To explore DEGs related to the diagnosis of BPH, three gene expression datasets, GSE132714, GSE7307, and GSE119195, were retrieved from GEO. “SVA” in R was applied to eliminate batch effects across the datasets, thereby constructing a unified expression matrix. Principal component analysis (PCA) plots and boxplots before surrogate variable analysis (SVA) adjustment are shown in Figure 2A,2B, while those after adjustment are displayed in Figure 2C,2D. Ultimately, 459 DEGs were identified: 353 were upregulated, and 106 were downregulated. The volcano plot of these DEGs is presented in Figure 3. In this plot, the horizontal and vertical axes represent log₂FC and −log₁₀(adj. P), respectively. Red, blue, and black nodes denote downregulated DEGs, upregulated DEGs, and non-significant genes.

Figure 2 Effect of SVA batch effect correction on gene expression data. (A) Boxplots and (B) PCA plots before SVA adjustment for the combined training cohort (n=47 BPH, n=11 controls). (C) Boxplots and (D) PCA plots after SVA adjustment. BPH, benign prostatic hyperplasia; PCA, principal component analysis; SVA, surrogate variable analysis.

Figure 3 The volcano plot of these DEGs. In this plot, the horizontal and vertical axes represent log₂FC and −log₁₀(adj. P), respectively. Red, blue, and black nodes denote downregulated DEGs, upregulated DEGs, and non-significant genes. Adj. P, adjusted P value; DEG, differentially expressed gene; FC, fold change.

GO and KEGG pathway analyses

Subsequently, FEA of DEGs was carried out using the DAVID database, encompassing GO terms and KEGG pathways. GO enrichment included three categories: CC, BP, and MF. DEGs were primarily enriched in the negative and positive regulation of apoptotic processes and cell migration within BP; protein binding, RNA binding, and protein homodimerization within MF; and the nucleus, cytoplasm, and cytosol within CC. KEGG pathway analysis revealed the most significantly enriched pathways of “neurodegeneration, multiple diseases”, amyotrophic lateral sclerosis, and the Rap1 signaling pathway (Figure 4A-4D).

Figure 4 GO and KEGG pathway enrichment analysis of DEGs. (A) DEGs were primarily enriched in the negative and positive regulation of apoptotic processes and cell migration within BP; (B) protein binding, RNA binding, and protein homodimerization within MF; and (C) the nucleus, cytoplasm, and cytosol within CC. (D) KEGG pathway analysis revealed the most significantly enriched pathways of “neurodegeneration, multiple diseases”, amyotrophic lateral sclerosis, and the Rap1 signaling pathway. BP, biological process; CC, cellular component; DEG, differentially expressed gene; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

Construction of WGCNA

Samples were clustered based on the constructed matrix, with the largest cluster retained for analysis (dataExpr). 1,955 messenger RNAs (mRNAs) from 58 samples were included for network construction. WGCNA was then applied to identify co-expression modules from these mRNAs. Hierarchical clustering indicated that all samples fell within the clustering range, with no outliers requiring exclusion (Figure 5A). When the soft-thresholding power β was set to 19, the network exhibited high independence and low mean connectivity (Figure 5B). Therefore, β=19 was selected for subsequent analysis, generating a hierarchical clustering tree. WGCNA identified two modules: the turquoise module (498 genes) and the gray module (1,457 genes). Among these, the turquoise module demonstrated the strongest relation to BPH, showing a positive correlation with normal samples (r=0.58, P=1.469×10⁻⁶) and a negative link to BPH samples (r=−0.58, P=1.469×10⁻⁶). This module was therefore the key module for further investigation (Figure 5C,5D). Clinical relevance analysis confirmed that the turquoise module exhibited a strong relation to BPH in both MM and GS scatter plots (Figure 5E).

Figure 5 Construction of gene co-expression network based on WGCNA and screening of BPH-related modules. (A) Sample clustering dendrogram of the 58 samples (n=47 BPH, n=11 controls) included in the WGCNA analysis, showing the outcome of hierarchical clustering. (B) The soft-thresholding power β was set to 19. The network exhibited high independence and low mean connectivity. (C,D) WGCNA identified two modules: the turquoise module (498 genes) and the gray module (1,457 genes). Among these, the turquoise module demonstrated the strongest relation to BPH, showing a positive correlation with normal samples (r=0.58, P=1.469×10⁻⁶) and a negative link to BPH samples (r=−0.58, P=1.469×10⁻⁶). WGCNA module clustering and module-trait association analysis with BPH. (E) Validation of clinical relevance for the turquoise module, the MM and GS scatter plots. BPH, benign prostatic hyperplasia; GS, gene significance; MM, module membership; WGCNA, weighted gene co-expression network analysis.

Hub gene identification via ml and protein-protein interaction (PPI) network analysis

To further refine BPH-related DEGs, overlapping gene sets were derived from (I) preprocessed DEGs, (II) angiogenesis-related gene sets, and (III) module genes with significant MM and GS values obtained from WGCNA. The intersection of these sets, visualized via a Venn diagram, yielded 71 candidate genes (Figure 6A). To identify potential hub genes from this pool, two ML approaches were employed. RF analysis identified six candidate genes (Figure 6B,6C), while SVM-RFE identified twenty-four candidate genes (Figure 6D). The intersection of these results, visualized through a Venn diagram, revealed three genes that were ultimately designated as hub genes (Figure 6E). Furthermore, correlation analysis demonstrated a significant association between these identified hub genes and key angiogenesis markers (e.g., VEGFA). The detailed results are provided in Figure S1 and Table S1.

Figure 6 Identification of candidate biomarkers via machine learning algorithms. (A) Venn diagram showing the intersection of three gene sets (71 candidate genes). (B) Results from the SVM-RFE algorithm. (C) Feature importance derived from RF. (D) Overlapping features or final model performance. (E) Intersection analysis and identification of hub genes. DEG, differentially expressed gene; RF, random forest; SVM, support vector machine; SVM-RFE, support vector machine-recursive feature elimination; WGCNA, weighted gene co-expression network analysis.

Evaluation of diagnostic value

To achieve improved performance in diagnosis and prediction, a nomogram was derived based on three hub genes through logistic regression analysis (Figure 7A). The detailed logistic regression coefficients and intercept for the model are provided in Table S2. The AUC values for each gene were subsequently computed: CARTPT [AUC =0.956; 95% confidence interval (CI): 0.87, 1.00], TOB1 (AUC =0.939; 95% CI: 0.88, 1.00), and GLO1 (AUC =0.933; 95% CI: 0.90, 1.00) (Figure 7B-7D). To further assess their diagnostic value, GSE3868 (GPL96) and GSE119195 (GPL6244) were selected as validation cohorts. In the training cohort, boxplot analysis demonstrated that the expression levels of CARTPT, TOB1, and GLO1 were significantly downregulated in the BPH group compared with the healthy control group (P<0.001) (Figure 7E). The results of the gene heatmap were consistent with those of the boxplot (Figure 7F). In contrast, validation cohort analysis revealed that CARTPT and GLO1 expression levels were significantly upregulated in the BPH group (P<0.05) (Figure 8A). These findings indicated inconsistent gene expression trends between the training and validation cohorts. Nevertheless, in the nomogram analysis, CARTPT, TOB1, and GLO1 exhibited favorable diagnostic potential. The nomogram demonstrates that the three genes can predict the individual risk of disease occurrence, with a higher total score corresponding to a higher risk. The AUC values for each gene were subsequently computed: CARTPT (AUC =1.000; 95% CI: 1.00, 1.00), TOB1 (AUC =0.889; 95% CI: 0.75, 1.00), and GLO1 (AUC =0.986; 95% CI: 0.95, 1.00) (Figure 8B-8D). Furthermore, to assess the agreement between the predicted probabilities and the observed outcomes, calibration plots were generated. The results demonstrated good calibration for the diagnostic models based on CARTPT, TOB1, and GLO1, indicating that the predicted risks were consistent with the actual risks (Figure 9). Furthermore, decision curve analysis (DCA) was performed to evaluate the clinical utility of the CARTPT, TOB1, and GLO1 models. The results demonstrated that all three models yielded a higher net benefit than either the ‘treat all’ or ‘treat none’ strategies across a wide range of risk thresholds (approximately 0 to 0.95), indicating their potential value in guiding clinical decision-making (Figure 10A-10C).

Figure 7 Construction and validation of the diagnostic model. (A) Nomogram constructed based on the three hub genes. ROC curve analysis for (B) CARTPT, (C) TOB1, and (D) GLO1. (E) Expression levels of the three hub genes in the training cohort (n=47 BPH, n=11 controls) are presented as boxplots. Data are shown as mean ± SD, and statistical significance was determined by the Wilcoxon test (****, P<0.0001). (F) Heatmap visualizing the expression patterns of these genes in the training cohort. AUC, area under the curve; BPH, benign prostatic hyperplasia; CI, confidence interval; ROC, receiver operating characteristic; SD, standard deviation.

Figure 8 External validation of hub genes. (A) Expression levels of the three genes in the external validation cohort (GSE3868: BPH, n=2; control, n=2; GSE119195: BPH, n=5; control, n=3) are presented as boxplots. ROC curve analysis for (B) CARTPT, (C) TOB1, and (D) GLO1. Statistical significance was determined by the Wilcoxon test (****, P<0.0001); ns, not significant. AUC, area under the curve; BPH, benign prostatic hyperplasia; CI, confidence interval; ROC, receiver operating characteristic.

Figure 9 Calibration curve of the angiogenesis-related gene signature for predicting BPH. BPH, benign prostatic hyperplasia; CI, confidence interval.

Figure 10 DCA of the candidate genes in the external validation cohort. (A) CARTPT. (B) TOB1. (C) GLO1. DCA, decision curve analysis.

Discussion

BPH is among the most prevalent urological health problems among males. Due to its slow clinical progression, complex and incompletely understood etiological factors, and lack of effective biomarkers, BPH patients frequently miss the optimal window for diagnosis and treatment. BPH typically arises in the transition zone (TZ) and central zone of the prostate. When stromal and epithelial cells proliferate under the influence of inflammation and sex hormones, prostatic enlargement ensues, leading to the development of BPH (29). Its occurrence is often accompanied by prostatic inflammation and LUTS (30), and the impact of chronic inflammation on the disease course can not be neglected.

Angiogenesis is the formation of new vascular networks from preexisting vessels through the proliferation, migration, and maturation of endothelial cells. During inflammation, blood vessels exhibit elevated permeability, enabling inflammatory mediators and immune cells to infiltrate sites of injury or stress (31). Under pathological conditions, a reciprocal relationship exists between inflammation and angiogenesis. Proliferative tissue in the setting of inflammation is enriched with inflammatory cells, growth factors, macrophages, and other immune cells that secrete a range of pro-angiogenic molecules in response to hypoxia (11). Conversely, angiogenesis sustains inflammation by supplying oxygen and nutrients to inflamed tissues, thereby facilitating the production of cytokines, adhesion molecules, and other inflammatory mediators (32). To further elucidate the association between angiogenesis and BPH, commonly used ML algorithms and possible biomarkers and therapeutic targets were identified via WGCNA.

In this study, BPH and normal prostate samples from public databases were integrated and analyzed. A total of 459 DEGs were identified through weighted co-expression network construction and ML approaches. Subsequent analyses included GO and pathway enrichment, PPI network construction, and nomogram modeling. After external dataset validation, three hub genes related to BPH and angiogenesis, CARTPT, TOB1, and GLO1, were ultimately identified. ROC curve analysis demonstrated that these hub genes possessed excellent diagnostic efficacy.

The CARTPT gene is located on chromosome 5q13-14 and encodes the cocaine- and amphetamine-regulated transcript (CART) peptide. The pathogenesis of BPH is not limited to simple cellular proliferation but is also characterized by aberrant angiogenesis and endothelial dysfunction (33). Persistent chronic inflammation and oxidative stress damage the local prostatic microvascular endothelium, leading to tissue ischemia and hypoxia, which subsequently stimulate the release of pro-angiogenic factors such as vascular endothelial growth factor (VEGF) (34). During this pathological process, CARTPT is considered to function as a vasculoprotective factor. By attenuating chronic inflammation and oxidative stress, CARTPT protects the microvascular endothelium from injury. Preservation of endothelial integrity helps improve prostatic microcirculation and alleviate tissue hypoxia, thereby ultimately ameliorating the clinical manifestations of BPH.

Mechanistically, CART has been demonstrated to exert potent anti-inflammatory effects, including the modulation of immune cell activity through the vagus nerve and the attenuation of lipopolysaccharide (LPS)-induced inflammation (35). In addition, endogenous CART peptides promote insulin secretion while suppressing glucagon release (36). Angiotensin II (Ang II), a key mediator of angiogenesis, is also an important regulator of insulin signaling in the cardiovascular system and metabolic tissues, where it induces insulin resistance through activation of intracellular protein kinase C (PKC) (37). Although Ang II-mediated vascular injury contributes to endothelial dysfunction, the anti-inflammatory and antioxidant properties of CART may effectively counteract this damage. Notably, CARTPT is upregulated in BPH but downregulated or even completely absent in prostate cancer. This distinct expression pattern highlights its potential as a valuable biomarker for distinguishing benign from malignant prostatic lesions (38).

TOB1, also known as transducer of ErbB2, 1, is a tumor suppressor protein originally identified for its ability to bind the receptor tyrosine kinase ErbB-2 (39). It belongs to the Tob/BTG family of antiproliferative proteins. The soybean peptide lunasin exhibits marked chemopreventive activity in prostate epithelial cells, primarily through the selective regulation of tumor suppressor gene expression. Previous studies have shown that lunasin treatment significantly upregulates TOB1 expression in non-tumorigenic prostate epithelial cells (RWPE-1). As a key component of the lunasin-mediated antitumor signaling network, TOB1 acts synergistically with genes such as HIF1A and THBS1, forming an important molecular basis for the tumor-suppressive effects of lunasin within the non-tumorigenic cellular microenvironment (40).

In addition, TOB1, together with GALNT7, INAFM1, and APELA, has been identified as a potential prognostic biomarker for patients with lymph node-metastatic prostate cancer (41). Overexpression of TOB1 not only increases the expression of the tumor suppressor phosphatase and tensin homolog (PTEN) in lung cancer cells but also modulates downstream effectors of the PI3K/PTEN signaling pathway, including Akt and ERK1/2 (42). In prostate tissue, testosterone is converted into DHT, which subsequently upregulates insulin-like growth factor-1 (IGF-1) expression (43). Binding of IGF-1 to its receptor activates the PI3K/AKT signaling pathway, thereby promoting the proliferation of both prostate epithelial and stromal cells (44). Meanwhile, immune cell infiltration and the release of multiple cytokines during chronic prostatic inflammation can likewise activate downstream signaling pathways such as PI3K/AKT, thereby driving the progression of hyperplastic lesions. Therefore, TOB1 may also represent an important participant in the inflammatory microenvironment associated with BPH.

GLO1 is a key intracellular metabolic detoxification enzyme whose primary physiological function is to eliminate methylglyoxal (MG), a highly reactive byproduct of glucose metabolism (45). In the prostatic tissue microenvironment, GLO1 activity is frequently suppressed with aging or under conditions of persistent chronic inflammation, resulting in the abnormal accumulation of MG and its derived advanced glycation end products (AGEs) within stromal and epithelial cells (46), thereby triggering pronounced oxidative stress (47). The accumulation of AGEs not only directly damages cellular structures but also exacerbates microenvironmental dysregulation through the activation of inflammatory signaling pathways. This persistent imbalance between tissue injury and repair is considered one of the key mechanisms driving abnormal prostatic hyperplasia and pathological tissue remodeling (48).

Notably, the cytotoxic effects of MG are highly conserved. MG promotes the generation of reactive oxygen species (ROS), inducing endothelial cell apoptosis and vascular dysfunction, a mechanism that has been well established in diabetic vascular complications (49). Studies have demonstrated that MG markedly suppresses key antioxidant signaling pathways, including the PI3K/Akt and Nrf2/HO-1 pathways, whereas pharmacological pretreatment with agents such as metformin (MET) can effectively reverse this process by inhibiting ROS overproduction and restoring the expression of downstream antioxidant enzymes (50). These findings suggest that the GLO1-MG axis may play an important role in maintaining vascular homeostasis. Indeed, in diabetic microvascular complications, such as nephropathy and retinopathy, downregulation of GLO1 is closely associated with microvascular injury, whereas restoration of GLO1 function exerts well-defined vasculoprotective effects (48). Furthermore, Ang II aggravates vascular fibrosis and atherosclerosis by suppressing GLO1 expression, further supporting the central role of the Ang II-GLO1-MG axis in vascular injury (51).

However, the role of GLO1 exhibits marked heterogeneity across different pathological contexts. Within the tumor microenvironment, its function may be reversed depending on cell type and signaling network. For example, in triple-negative breast cancer, suppression of GLO1 expression has been associated with delayed angiogenesis (52), suggesting that GLO1 may promote tumor angiogenesis. More importantly, in prostate cancer, upregulation of GLO1 is significantly associated with early biochemical recurrence (53), and its expression level is positively correlated with apoptosis (54). Moreover, inhibition of GLO1 activity has been shown to enhance the sensitivity of tumor cells to chemotherapeutic agents (55). This seemingly paradoxical phenomenon may reflect the dependence of tumor cells on metabolic reprogramming, whereby elevated GLO1 expression provides a survival advantage by enabling cells to cope with the increased glycolytic burden.

CARTPT, TOB1, and GLO1 play complex and critical regulatory roles within the prostatic microenvironment. They participate in the pathological processes of BPH and angiogenesis through anti-inflammatory, antioxidant, and signaling regulatory mechanisms, while also exhibiting distinct expression patterns during tumor initiation and progression that may have potential clinical value.

GO and KEGG analyses revealed enrichment in negative regulation of apoptotic processes, positive regulation of apoptotic processes, and cell migration; MFs enriched included protein binding, RNA binding, and protein homodimerization; CCs enriched included the nucleus, cytoplasm, and cytosol. These findings suggest that the regulation of apoptosis likewise influences the progression of BPH and angiogenesis. Lin et al. reported that YAP1 silencing INHIBITS cell survival by reducing proliferation and enhancing apoptosis, while also attenuating fibrosis through reversal of epithelial-mesenchymal transition (EMT) and extracellular matrix (ECM) deposition in BPH-1 and WPMY-1 prostate cells (56). Erythropoietin is not only a regulator of erythropoiesis but also a well-established promoter of angiogenesis, known to induce endothelial cell proliferation and migration, and promote nitric oxide synthesis. Furthermore, angiogenesis is highly dependent on dynamic interactions between cells and ECM. During endothelial cell migration, integrins mediate the formation of focal adhesions, which provide the mechanical anchorage and traction forces required for forward cell movement. Accordingly, enrichment of the focal adhesion pathway strongly suggests enhanced migratory activity, a hallmark of angiogenesis. In addition, the Rap1 signaling pathway plays a central role in regulating cell adhesion, junctional integrity, and cell polarity. Within the vasculature, Rap1 contributes to the maintenance of endothelial barrier function, modulates VEGF-induced vascular permeability, and promotes the stabilization and maturation of newly formed blood vessels.

The foregoing findings indicate a strong association between BPH and angiogenesis. However, the number of relevant experimental studies remains limited, and many of the published reports are dated, thereby constraining the reliability of the present conclusions. Regarding the datasets used, although five BPH-related GSE datasets were collected, the patient sample sizes within these datasets were relatively small. In addition, several datasets were generated years earlier and thus lacked timeliness. Furthermore, although hub genes were identified using ML approaches, the findings have yet to be validated in vitro. Accordingly, future studies should be designed to incorporate in vitro experiments to substantiate the present results and enhance both the credibility and scientific rigor of the conclusions.

Conclusions

This study tentatively suggests that CARTPT, TOB1, and GLO1 could potentially be involved in BPH and might warrant investigation as candidate molecules for diagnostic models. The findings also hint at a possible, though not yet confirmed, association between these genes and angiogenesis. While these genes represent intriguing prospects for future research into BPH diagnosis and treatment, their precise clinical utility remains to be established and requires extensive experimental validation.

Acknowledgments

We would like to thank all study participants for their contribution and Toedit (https://www.toedit.com/) for English language editing services.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0385/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0385/prf

Funding: This work was supported by the Shanghai Municipal Health Commission Scientific Research Project (No. 202140348).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-0385/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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