Seminal plasma PGK2 serves as a predictive biomarker for post-varicocelectomy sperm motility improvement in varicocele subjects

Introduction

Varicocele (VC) refers to a pathological condition characterized by abnormal dilation, tortuosity, and impaired venous return in the pampiniform plexus of the spermatic cord, primarily involving the internal spermatic vein. VC can lead to impaired male fertility by causing a decline in sperm motility, reduced sperm concentration, and abnormal sperm morphology (1). Epidemiological study has indicated that the prevalence of VC is approximately 15.7% in the general population. Among patients with primary and secondary infertility, the prevalence has been reported to be 30–40% and 80–90%, respectively (2), indicating that VC is a major cause of male infertility (3). Surgical intervention, specifically varicocelectomy, is recognized as a definitive and effective treatment for VC. It has been demonstrated that varicocelectomy significantly improves semen quality and increases pregnancy rates in partners (4). However, another study has indicated that only 50–80% of patients derive significant benefit from the procedure (5). Therefore, identifying novel biomarkers is beneficial not only for elucidating the pathophysiology of the disease but also for predicting surgical outcomes, thereby supporting urologists in making informed clinical decisions (6).

Seminal plasma can influence sperm motility and the capacitation process, and through interactions with the female reproductive system, it also impacts normal fertilization (7). Therefore, seminal plasma is considered an ideal biological specimen for research in the field of male reproduction (8). Proteins, as the fundamental building blocks of life, play indispensable physiological roles at every stage of sperm production, maturation, and capacitation. It has been shown that approximately 70% of proteins in seminal plasma originate from spermatozoa (9), indicating that changes in sperm health and quality are reflected in alterations to the seminal proteome. Thus, omics analysis of seminal plasma provides a feasible approach to identify and discover novel candidate protein biomarkers for disease, and to explore the specific roles played by these proteins at various levels. Early proteomic studies compared seminal plasma from healthy men and VC patients (10,11), as well as pre- and post-varicocelectomy seminal plasma from VC patients (12). However, these studies primarily focused on describing mass spectrometry results without further exploration of potential biomarkers, which is urgently demanded.

Phosphoglycerate kinase 2 (PGK2) is a crucial enzyme in the glycolytic pathway, responsible for catalyzing the conversion of 1,3-bisphosphoglycerate to 3-phosphoglycerate and producing adenosine triphosphate (ATP) in the process (13). Studies have shown that PGK2 is specifically expressed in the testes, highlighting its essential role in spermatogenesis (13,14). Recent researches suggest that PGK2 levels in seminal plasma could be a valuable indicator of sperm quality in individuals with non-obstructive azoospermia (15,16). However, the exact functions of PGK2 in VC are still not fully understood.

Aim and objectives

In this study, we aimed to investigate the predictive ability of PGK2 in seminal plasma to the improvement of sperm motility in VC subjects receiving varicocelectomy. Firstly, we collected semen samples from 60 patients with VC-associated asthenospermia who underwent varicocelectomy, as well as 33 healthy donors at the Third Affiliated Hospital of Southern Medical University. Follow-up assessments were carried out to determine the impact of varicocelectomy on the subjects with VC. The concentration of PGK2 in seminal plasma was measured using enzyme-linked immunosorbent assay (ELISA), and a series of statistical analyses were performed to evaluate the predictive value of PGK2 in seminal plasma for the efficacy of varicocelectomy. Additionally, to enhance predictive accuracy, we employed 11 machine learning algorithms to develop predictive models based on PGK2 concentration, hematological parameters, and semen quality indicators. This study provides valuable insights to assist clinicians in making earlier clinical decisions, clarifying the surgical value of varicocelectomy, and prompting patients with VC who have lower benefit rates to consider assisted reproductive technologies at an earlier stage, thereby optimizing clinical treatment outcomes. We present this article in accordance with the STARD reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-24-629/rc).

Methods

Semen sample collection

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional ethics board of The Third Affiliated Hospital of Southern Medical University (No. 2024-ER-089) and informed consent was taken from all the patients. The study retrospectively enrolled patients with VC-associated asthenospermia and healthy donors between June 2022 and December 2023 at the Third Affiliated Hospital of Southern Medical University. The subjects with VC-associated asthenospermia combined with oligozoospermia or teratozoospermia were also included in this study. The inclusion and exclusion criteria can be found in Figure S1. Semen samples were meticulously collected following standard procedures, both before and 6 months after the varicocelectomy. Asthenospermia was defined in accordance with the World Health Organization (WHO) 5th edition criteria (17) as a progressive motility rate (PR%) of less than 32% in three consecutive assessments. Samples were obtained through masturbation after a period of 3–7 days of abstinence and allowed to liquefy at room temperature. All VC patients had confirmed left-sided degree II or III VC without any other comorbidities. The semen analysis included measurements of total sperm count, sperm concentration, total sperm motility, PR%, and total motile sperm count (TMSC). Although our diagnostic criteria for VC-associated asthenospermia were primarily based on reduced progressive motility, the evaluation of varicocelectomy success was determined by improvements in total sperm motility, in accordance with previous research finding (18). The success of varicocelectomy was determined by a more than 40% increase in total sperm motility on postoperative semen analysis. The cases with missing sperm quality parameters were excluded from this study. All the clinical data could be available from the corresponding author upon reasonable request.

Surgical intervention

All patients with VC underwent laparoscopic high ligation of the spermatic vein. The procedures were performed by a single experienced urologist to ensure consistency in surgical technique.

Collection of hematological parameters

The following hematological parameters were recorded for each VC patient: hemoglobin (HB), mean platelet volume (MPV), neutrophils (NEU), neutrophil-to-lymphocyte ratio (NLR), plateletcrit (PCT), platelet distribution width (PDW), platelet-to-lymphocyte ratio (PLR), platelets (PLT), total leukocyte count (TLC), and white blood cell count (WBC). The hematological parameters, including WBC, were analyzed by the Department of Laboratory Medicine at the Third Affiliated Hospital of Southern Medical University, following standardized protocols.

Semen sample processing

After routine semen analysis, the semen samples were centrifuged at 1,000 ×g for 10 minutes at 4 °C to collect the supernatant seminal plasma. The seminal plasma was further centrifuged at 2,400 ×g for 10 minutes to remove residual sperm, followed by centrifugation at 12,000 ×g for 30 minutes to eliminate remaining cellular debris.

ELISA

The human PGK2 ELISA kit (ELK Biotechnology, China) was used to detect the concentration of PGK2 in seminal plasma according to the manufacturer’s instructions. Briefly, diluted standards or test samples (100 µL) were added to each well and incubated at 37 °C for 90 minutes. After discarding the liquid, 100 µL of biotinylated antibody working solution was added and incubated at 37 °C for 50 minutes, followed by three washes. Subsequently, 100 µL of enzyme conjugate working solution was added and incubated at 37 °C for 50 minutes, followed by washing. Then, 90 µL of 3,3’,5,5’-tetramethylbenzidine (TMB) substrate solution was added and incubated in the dark at 37 °C for 20 minutes. After adding 50 µL of stop solution, the optical density (OD) was measured at 450 nm, and a standard curve was used to determine the protein concentration in each sample.

Western blot (WB)

WB analyses were conducted to confirm the accuracy of the ELISA results. Based on the ELISA findings, we chose semen samples with the highest PGK2 concentrations (top 3) and the lowest PGK2 concentrations (top 3) for WB analysis. The protocol of WB was in accordance with our previous study (19). The antibodies used in this study are shown as follows: anti-PGK2 (dilution: 1:500, ABclonal, China), anti-β-tubulin (dilution: 1:5,000, ABclonal, China), and HRP-conjugated Goat anti-Rabbit IgG (H + L) (dilution: 1:10,000, ABclonal, China).

Development of a machine learning-based predictive model

To enhance the prediction of treatment response in VC patients, a machine learning-based predictive model was developed using the Mime package in R (20). 60 VC subjects were randomly divided into training and testing groups in a 1:1 ratio. 11 algorithms—generalized linear model boosting (glmBoost), ridge regression (Ridge), partial least squares regression for generalized linear models (plsRglm), eXtreme gradient boosting (XGBoost), naive bayes classifier (NaiveBayes), stepwise generalized linear model forward selection [Stepglm (forward)], stepwise generalized linear model backward elimination [Stepglm (backward)], stepwise generalized linear model bidirectional selection [Stepglm (both)], random forest (RF), support vector machine (SVM), and linear discriminant analysis (LDA)—were applied, and the model with the highest average area under the curve (AUC) was selected.

Statistical analysis

All data were presented as mean ± standard deviation (SD) for continuous variables and as frequency (percentage) for categorical variables. Statistical analyses were performed using R software (version 3.6.3). For the continuous variables, Welch-corrected t-tests or Mann-Whitney U tests were adopted to evaluate the difference. For the Categorical variables, Fisher exact tests or Pearson χ² were adopted. The spearman correlation coefficients were evaluated using the “cor.test” function in R. The univariate or multivariate logistic regression was conducted using the rms package, and the odds ratio (OR) and its corresponding 95% confidence interval (CI) were calculated. Receiver operating characteristic (ROC) analysis was performed using the pROC package, and Delong tests were conducted using the “roc.test” function. All experiments were repeated at least three times, and statistical significance was set at P<0.05.

Results

PGK2 concentration in seminal plasma is down-regulated and positively associated with sperm quality in VC subjects

A sum of 60 VC and 33 healthy control subjects were enrolled in this study (Figures S1,S2), with their baseline clinical information showing in Table 1. Using ELISA, we measured the concentration of PGK2 in seminal plasma from 60 VC and 33 healthy control subjects. Subsequently, WB was conducted to validate the accuracy of the ELISA results (Figure 1A). In comparison to the control group, individuals with VC exhibited a lower concentration of PGK2 in seminal plasma (P<0.001, Figure 1B). However, no significant correlation was observed between the concentration of PGK2 and the degree of VC (P=0.72, Figure 1C), as well as body mass index (BMI) classification among VC subjects (underweight vs. normal weight, P=0.55; underweight vs. overweight & obesity, P=0.38; normal weight vs. overweight & obesity, P=0.61; Figure 1D). Although an analysis of the association between PGK2 concentration, age, and hematological parameters was conducted, no significant correlations were identified (Tables S1,S2). Notably, PGK2 concentration demonstrated a higher diagnostic value in distinguishing VC from control subjects when compared to age, BMI, and hematological parameters (Figure 1E, Table 2), underscoring the superior diagnostic potential of PGK2 in VC cases.

Figure 1 PGK2 concentration in seminal plasma was down-regulated in VC and associated with sperm quality features. (A) WB analyses were performed to confirm the accuracy of ELISA. (B) VC individuals showed lower levels of PGK2 in seminal plasma. (C,D) The association of PGK2 concentration in seminal plasma with VC degree (C) and body mass index (BMI) (D). (E) The diagnosis value of PGK2 concentration, age, BMI, and hematological parameters to VC. (F-G) The association of PGK2 concentration with PR%, sperm concentration, total sperm motility, and total sperm count in all (F) and VC (G) subjects. ns, not significant. AUC, area under curve; WBC, white blood cell count; HB, hemoglobin; PLT, platelets; TLC, total leukocyte count; NEU, neutrophils; PDW, platelet distribution width; MPV, mean platelet volume; PCT, plateletcrit; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PGK2, phosphoglycerate kinase 2; VC, varicocele; WB, western blot; ELISA, enzyme-linked immunosorbent assay; BMI, body mass index; PR%, progressive motility rate.

Furthermore, we investigated the relationship between PGK2 concentration and various sperm quality indicators, including PR%, sperm concentration, total sperm motility, and total sperm count in all subjects (Figure 1F) and specifically in VC subjects (Figure 1G). Our findings revealed a positive association between PGK2 concentration and PR% (R=0.49, P<0.001), sperm concentration (R=0.24, P=0.02), and total sperm motility (R=0.49, P<0.001) in all subjects. Similarly, in VC subjects, PGK2 concentration was positively correlated with PR% (R=0.30, P=0.03) and total sperm motility (R=0.29, P=0.03).

PGK2 concentration in seminal plasma is a significant biomarker to distinguish the VC subjects receiving benefit from varicocelectomy

The concentration of PGK2 in seminal plasma was significantly increased in the postoperative samples compared to the preoperative samples (P<0.001, Figure 2A). The follow-up data indicated that around 65% VC cases showed improved total sperm motility. Individuals with VC who demonstrated improved sperm motility also exhibited elevated levels of PGK2 in seminal plasma (P=0.006, Figure 2B). Moreover, we conducted a comparison of the predictive capabilities of various factors including PGK2 concentration, age, BMI, VC degree, semen quality parameters, and hematological parameters (Figure 2C, Table 3). The AUC for PGK2 concentration was determined to be 0.735 (95% CI: 0.601–0.860), which was significantly higher than the predictive abilities of VC degree (P=0.001), WBC (P=0.04), NEU (P=0.02), MPV (P=0.02), PCT (P=0.01), and PLR (P=0.03).

Figure 2 PGK2 concentration in seminal plasma served as a promising biomarker for the improvement of sperm motility following varicocelectomy. (A) The PGK2 concentration in seminal plasma from the preoperative and postoperative cases. (B) The cases showing significant improvement in sperm motility after varicocelectomy exhibited higher levels of PGK2 in seminal plasma. (C) The predictive ability of PGK2 concentration, age, BMI, VC degree, sperm quality features, and hematological parameters to the efficacy of varicocelectomy. **, P<0.01; ***, P<0.001. AUC, area under the curve; WBC, white blood cell count; HB, hemoglobin; PLT, platelets; TLC, total leukocyte count; NEU, neutrophils; PDW, platelet distribution width; MPV, mean platelet volume; PCT, plateletcrit; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PR%, progressive motility rate; TMSC, total motile sperm count; BMI, body mass index; VC, varicocele; PGK2, phosphoglycerate kinase 2.

PGK2 concentration in seminal plasma is an independent predictor for the efficacy of varicocelectomy

After incorporating age, BMI, VC degree, sperm quality parameters, hematological parameters, and PGK2 concentration, we conducted univariate and multivariate logistic regression analyses to explore the potential of PGK2 concentration as an independent predictor. The findings revealed that PGK2 concentration emerged as an independent predictor for the outcomes of varicocelectomy in individuals with VC in both the univariate (OR =1.533, 95% CI: 1.179–2.095, P=0.003) and multivariate (OR =7.294, 95% CI: 1.846–122.699, P=0.049) logistic regression models (Figure 3).

Figure 3 PGK2 concentration in seminal plasma was an independent predictor for the outcomes of varicocelectomy both in the univariate and multivariate logistic regression analyses. OR, odds ratio; CI, confidence interval; WBC, white blood cell count; HB, hemoglobin; PLT, platelets; TLC, total leukocyte count; NEU, neutrophils; PDW, platelet distribution width; MPV, mean platelet volume; PCT, plateletcrit; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PR, progressive motility rate; TMSC, total motile sperm count; BMI, body mass index; PGK2, phosphoglycerate kinase 2.

Subgroup analyses of PGK2 concentration in seminal plasma

To better clarify the predictive value of PGK2 to the efficacy of varicocelectomy, we performed the subgroup analyses. The optimal cut-off values of the continuous variables were detected by the ROC analysis (Figures S3-S5). The subgroup analysis indicated that PGK2 concentration is a meaningful predictor in the VC subjects with age <31.5 years (OR =1.475, 95% CI: 1.100–2.087, P=0.02), BMI <23.325 kg/m² (OR =1.476, 95% CI: 1.072–2.186, P=0.03), BMI ≥23.325 kg/m² (OR =1.826, 95% CI: 1.100–3.860, P=0.048), degree II (OR =1.751, 95% CI: 1.240–2.759, P=0.005), sperm concentration ≥28.55×10⁶/mL (OR =1.380, 95% CI: 1.062–1.878, P=0.02), PR ≥23.3% (OR =3.150, 95% CI: 1.612–9.369, P=0.007), total sperm motility <31.35% (OR =1.493, 95% CI: 1.109–2.172, P=0.02), total sperm motility ≥31.35% (OR =2.132, 95% CI: 1.115–5.281, P=0.045), TMSC ≥36.684×10⁶ (OR =1.418, 95% CI: 1.058–2.001, P=0.03), WBC ≥5.99×10⁹/L (OR =1.998, 95% CI: 1.253–3.910, P=0.01), HB ≥151.5 g/L (OR =1.511, 95% CI: 1.076–2.320, P=0.03), PLT <249.5×10⁹/L (OR =1.391, 95% CI: 1.024–2.006, P=0.049), PLT ≥249.5×10⁹/L (OR =1.862, 95% CI: 1.136–3.749, P=0.03), TLC ≥1.815×10⁹/L (OR =1.498, 95% CI: 1.113–2.156, P=0.01), NEU <4.565×10⁹/L (OR =1.515, 95% CI: 1.120–2.181, P=0.01), PDW <16.05 fL (OR =1.715, 95% CI: 1.183–2.817, P=0.01), MPV ≥9.65 fL (OR =1.704, 95% CI: 1.180–2.753, P=0.01), PCT <0.289 ng/mL (OR =1.580, 95% CI: 1.201–2.202, P=0.003), PLR <140.24 (OR =1.591, 95% CI: 1.187–2.286, P=0.005), and NLR <2.295 (OR =1.533, 95% CI: 1.148–2.177, P=0.008) (Figure 4).

Figure 4 The subgroup analysis of the predictive ability of PGK2 concentration to the outcomes of varicocelectomy, where the univariate logistic regression analysis was conducted. OR, odds ratio; CI, confidence interval; WBC, white blood cell count; HB, hemoglobin; PLT, platelets; TLC, total leukocyte count; NEU, neutrophils; PDW, platelet distribution width; MPV, mean platelet volume; PCT, plateletcrit; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; PR, progressive motility rate; TMSC, total motile sperm count; BMI, body mass index; PGK2, phosphoglycerate kinase 2.

Machine learning-based model shows increased predictive ability

In order to enhance the predictive accuracy of varicocelectomy outcomes in individuals with VC, we integrated PGK2 concentration, age, BMI, VC degree, sperm quality parameters, and hematological parameters to develop a predictive model using 11 machine learning algorithms. Initially, we randomly divided 60 VC subjects into training and testing groups in a 1:1 ratio, with their baseline clinical characteristics detailed in Table 4. Our findings revealed that the LDA model exhibited the highest average AUC values (0.877), leading to the selection of the LDA algorithm (Figure 5A). The LDA model demonstrated remarkable predictive performance in the training cohort (AUC =1.000, 95% CI: 1.000–1.000, Figure 5B), testing cohort (AUC =0.804, 95% CI: 0.619–0.963, Figure 5C), and combined cohorts (AUC =0.828, 95% CI: 0.706–0.929, Figure 5D). The LDA model demonstrated perfect discrimination in the training cohort (AUC =1.000), which may indicate potential overfitting. However, the model maintained satisfactory performance in both the testing cohort (AUC =0.804) and combined cohort (AUC =0.828), suggesting its generalizability and practical applicability. The confusion matrices of the LDA model for the training, testing, and combined cohorts are presented in Figure 5E-5G, respectively.

Figure 5 A LDA model was constructed to achieve a better predictive capability. (A) According to the average AUC values, the LDA model was selected. (B-D) The ROC analyses indicated the predictive performance of the LDA model in the training (B), testing (C), and all (D) cohorts. (E-G) The confusion matrixes indicated the predictive performance of the LDA model in the training (E), testing (F), and all (G) cohorts. AUC, area under the curve; CI, confidence interval; LDA, linear discriminant analysis; RF, random forest; ROC, receiver operating characteristic; SVM, support vector machine; XGBoost, eXtreme gradient boosting.

In comparison to PGK2 concentration, the LDA model exhibited a significant enhancement in predicting varicocelectomy outcomes in the training cohort (P=0.003). However, no statistically significant differences were observed in the testing (P=0.89) and combined cohorts (P=0.31) (Table 5), which may be attributed in part to the limited sample size.

The predictive performance of the LDA model without PGK2 was also analyzed. The LDA model performed impressively in the training cohort (AUC =0.898, 95% CI: 0.769–0.996, Figure S6A), testing cohort (AUC =0.704, 95% CI: 0.497–0.873, Figure S6B), and combined cohort (AUC =0.787, 95% CI: 0.665–0.897, Figure S6C). The confusion matrices of the LDA model without PGK2 can be found in Figure S6D-S6F.

To identify VC patients who are unlikely to benefit from varicocelectomy, we stratified the VC cohort into “Improved” and “Extremely Unimproved” subgroups based on postoperative changes in total sperm motility. Patients demonstrating increased total sperm motility (D-value >0) were classified as “Improved”, while those showing no improvement or decreased motility (D-value ≤0) were categorized as “Extremely Unimproved”. Following this classification, 10 out of 60 VC patients were assigned to the “Extremely Unimproved” subgroup. Remarkably, the LDA model demonstrated robust predictive performance for identifying patients in the “Extremely Unimproved” category across all cohorts. In the training cohort, the model achieved an AUC of 0.841 (95% CI: 0.693–0.960, Figure S7A). Similarly, in the testing cohort, the AUC was 0.702 (95% CI: 0.462–0.885, Figure S7B), and in the combined cohort, the AUC reached 0.720 (95% CI: 0.562–0.858, Figure S7C). Corresponding confusion matrices are provided in Figure S7D-S7F, further validating the model’s predictive accuracy.

Discussion

This study aimed to investigate the predictive ability of PGK2 concentration in seminal plasma for the efficacy of varicocelectomy in individuals with VC-associated asthenospermia. Seminal plasma samples were collected from 60 patients undergoing varicocelectomy and 33 healthy donors at the Third Affiliated Hospital of Southern Medical University. A 6-month follow-up was conducted to assess sperm motility improvement post-surgery in VC subjects. The results revealed that PGK2 concentration in seminal plasma was lower in VC cases compared to healthy donors but increased in postoperative samples. PGK2 concentration emerged as a significant predictor of varicocelectomy outcomes, outperforming routine clinical, sperm quality, and hematological parameters. To enhance predictive efficacy, 11 machine learning algorithms were utilized to develop models predicting varicocelectomy outcomes based on PGK2 concentration, age, BMI, VC degree, sperm quality, and hematological features. Ultimately, an LDA model was constructed, demonstrating superiority to PGK2 concentration alone.

Previous studies have identified various predictive factors for the efficacy of varicocelectomy. For instance, a recent cross-sectional study suggested that individuals with VC and lower BMI experienced greater improvement in sperm motility post-varicocelectomy (21). Additionally, a retrospective study highlighted that the initial total progressively motile sperm count and vein diameter were significant predictors of semen parameter improvement after varicocelectomy (22). Kandevani and colleagues also found that BMI, NLR, and TMSC were strong predictors of varicocelectomy efficacy (23). Furthermore, some studies have focused on the predictive efficacy of biomarkers. For example, 17-hydroxy progesterone (17-OHP), a precursor for testosterone synthesis, was identified as a biomarker for detecting improvements in semen parameters following sub-inguinal micro-varicocelectomy (24). These findings have contributed valuable insights into potential predictors in clinical practice. In this study, we have introduced a novel perspective by demonstrating that PGK2 concentration in seminal plasma could serve as a promising biomarker for assessing semen quality improvement post-varicocelectomy, offering a better understanding of the pathogenesis of VC.

PGK2, a pivotal enzyme in the glycolytic pathway, plays a crucial role in the later stages of spermatogenesis by catalyzing the conversion of 1,3-bisphosphoglycerate to 3-phosphoglycerate, simultaneously generating ATP to provide energy for cellular processes (25). This enzyme is predominantly expressed in the testis, with particularly high levels observed in spermatocytes and sperm cells (26). In mice, the PGK2 gene remains untranslated until the pre-meiotic phase in spermatogenic cells, suggesting its protein expression serves as a potential predictor for both sperm quality and quantity (27). However, the mechanisms underlying the temporal and spatial specificity of PGK2 expression remain unexplored up to date. Furthermore, beyond its role in glycolysis, whether PGK2 influences spermatogenic function through alternative pathways has not been thoroughly investigated.

In this study, we compared the predictive ability of PGK2 concentration and hematological parameters. MPV reflects PLT size and activation and serves as an indicator of PLT function and activity (28). Studies have shown that patients with VC have higher MPV compared to healthy controls, with MPV values positively correlated with the grade of the disease (29-31). NLR is another marker that has been widely used in diagnosing and prognosticating various malignancies, with higher levels generally indicating poorer prognosis. Ates et al. attempted to investigate the predictive value of NLR for surgical outcomes in VC patients, showing that patients who benefited from varicocelectomy had lower preoperative NLR (32). Similarly, Erdogan et al. (33) reported comparable findings, whereas Duran et al. (18) found that the predictive power of NLR was modest, with an AUC of only 0.636 (95% CI: 0.519–0.754). Monocyte-to-lymphocyte ratio (MLR) reflects the role of the innate immune system in modulating inflammatory responses (34). MLR has also been identified as an important parameter for predicting the success of varicocelectomy, with an AUC of 0.652 (95% CI: 0.531–0.773) (18). However, the non-specific, transient nature of inflammatory markers and their susceptibility to interference may limit the utility of relying solely on these markers to predict surgical outcomes, contributing to discrepancies among previous studies. In our study, the limited sample size resulted in no significant differences in serum inflammatory markers between groups. However, combining PGK2 with these markers partially improved predictive performance for surgical response using the LDA algorithm.

There are certain limitations in this study. Firstly, the retrospective design of the study may limit the clinical applicability of PGK2 as a predictive biomarker. Therefore, there is a need for a prospective, large-scale, multi-center, and double-blind clinical trial to validate the findings in the future. Additionally, it should be noted that the surgical approach employed in this study was limited to laparoscopic high ligation of the spermatic vein, rather than the more precise microsurgical varicocelectomy technique, due to its retrospective nature. The methodological difference in surgical approach may affect the generalizability of our findings. Secondly, further research is required to investigate the biological function and underlying mechanisms of PGK2 in the pathogenesis of VC, which might help us better understand the roles that PGK2 plays in VC. Thirdly, the fertility condition follow-up of the subjects with VC was unavailable in this study due to the limited follow-up period, and thus the predictive ability of the PGK2 in seminal plasma to the infertility needs to be clarified in future studies. Fourthly, while the diagnosis of VC in this study was established through scrotal color Doppler ultrasound, a method consistently employed in previous research (35,36), future investigations should incorporate additional diagnostic modalities, such as physical examination and retrograde venography of the internal spermatic vein, to enhance diagnostic precision and reliability. Additionally, paternity tests of the healthy donors are needed in future studies to confirm the normal fertility of these subjects. Lastly, while the current study focused on evaluating the predictive value of seminal PGK2 in assessing varicocelectomy outcomes specifically for VC-associated asthenospermia patients (a subgroup of VC cases), subsequent research should expand its scope to include all VC patients undergoing varicocelectomy. This broader inclusion criterion would yield more comprehensive and clinically relevant conclusions regarding the utility of seminal PGK2 as a predictive biomarker.

Conclusions

In conclusion, PGK2 concentration in seminal plasma can serve as a valuable predictor of improvement in semen quality following varicocelectomy, helping in clinical decision-making regarding therapeutic approaches.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-24-629/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-629/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-629/prf

Funding: This study was supported by National Natural Science Foundation of China (No. 82101667) and President Foundation of The Third Affiliated Hospital of Southern Medical University (No. YL202201).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-24-629/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. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional ethics board of The Third Affiliated Hospital of Southern Medical University (No. 2024-ER-089) and informed consent was taken from all the patients.

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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