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Predictors of inguinal lymph node metastasis in penile cancer patients: a meta-analysis of retrospective studies

Purpose Inguinal lymph node metastasis (LNM) is one of the most significant prognostic factors for patients with penile cancer. This study aimed to identify potential predictors of inguinal LNM. Patients and methods A comprehensive search of the PubMed, Embase, and Cochrane Library databases for studies that reported predictors of inguinal LNM in penile cancer was performed. Finally, we selected 42 eligible studies with 4,802 patients. Accumulative analyses of odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were performed. All analyses were performed by using Review Manager software version 5.3. Results Among the 4,802 patients, 1,706 (36%) were diagnosed with inguinal LNM. Predictors of LNM included two categories: tumor-associated biomarkers and invasive clinicopathologic characteristics. Biomarker-specific predictors: the program death ligand 1 (PD-L1) overexpression (OR=2.55, p=0.002), higher neutrophil-to-lymphocyte ratio (NLR) (OR=4.22, p=0.010), higher C-reactive protein (CRP) (OR=4.78, p<0.001), squamous cell carcinoma antigen (SCC-Ag) overexpression (OR=8.52, p<0.001), P53 protein overexpression (OR=3.57, p<0.001). Clinicopathological predictors: positive clinical lymph node (cN+) (OR=5.86, p<0.001), high-risk histopathological subtype (OR=14.63, p<0.001) and intermediate-risk subtype (OR=3.37, p<0.001), vertical growth pattern (OR=1.97, p=0.020), higher stage (AJCC: OR=3.66, p<0.001; UICC: OR=2.43, p<0.001), higher tumor grade (OR=3.37, p<0.001), tumor size (>3 cm) (OR=2.00, p=0.002), LVI (OR=3.37, p<0.001), invasion depth (>5 mm) (OR=2.58, p=0.002), nerve invasion (OR=2.84, p<0.001), corpora cavernosum invasion (OR=2.22, p<0.001), corpus spongiosum invasion (OR=1.73, p=0.002) and urethra invasion (OR=1.81, p=0.030). Conclusion Current meta-analysis conclusively identified valuable predictors of inguinal LNM for patients with penile cancer. However, high-quality studies are warranted to further validate our conclusions. The intrinsic link between these predictors needs to be further investigated to create an accurate mathematical prediction model for LNM.


Introduction
Penile cancer is a rare malignant tumor, which results in significant physiological and psychological effects on patients. 1 Inguinal lymph nodes are the first site of metastasis in penile carcinoma. 2 The presence of inguinal lymph node metastasis (LNM) is one of the most significant prognostic factors for patients with penile cancer. 3 Therefore, optimal management of inguinal lymph nodes is crucial for long-term survival after local treatment of the primary tumor. 4 The best management of clinically negative nodes (cN0) is controversial because of the approximately 25% likelihood of micrometastatic disease, 5,6 and complete detection of micro-metastases by current imaging techniques is difficult. 7 Close surveillance, dynamic sentinel node biopsy, and modified lymphadenectomy were recommended in the treatment of patients with cN0 over the past decades, aiming to decrease the complications caused by radical inguinal lymph node dissection (ILND). 4,8 But these methods remained a notable risk of missing micro-metastatic disease. 9,10 Comparing these methods, prophylactic lymphadenectomy has the best overall survival benefit from early resection of occult metastasis. 11,12 However, concurrent complications related to lymph drainage and wound healing are relatively high despite surgical modifications. [13][14][15] Therefore, accurate inguinal LNM prediction could pinpoint patients who are the best candidates for inguinal lymphadenectomy, which could not only achieve the best survival rate for patients with occult metastasis but also avoid unnecessary treatment for patients with a low risk of developing LNM.
Given the rarity of penile cancer, prior studies that attempted to identify predictors of LNM had several methodological limitations, such as small series, single-center, and lack of randomized controlled trials. Moreover, the results of these studies were discrepant. Several predictive models had been developed to stratify the risk of developing LNM, such as Solsona risk groups and Hungerhuber risk stratification. 16,17 Unfortunately, further validation denied the accuracy of these tools. 18 Furthermore, the prognostic value of several potential biomarkers, such as PD-L1, P53 protein, NLR, CRP, SCC-Ag, Ki-67, and HPV DNA, had not been conclusively established. Hence, we performed this comprehensive meta-analysis to determine the significant predictors of LNM in penile cancer.

Methods
This meta-analysis was conducted according to the preferred reporting items for systematic review and metaanalyses (PRISMA) statement and was registered with PROSPERO (https://www.crd.york.ac.uk/PROSPERO ID: CRD42018107232). 19 It was approved by the institutional review board before initiation. The need for ethical standard approval or informed consent was waived due to the nature of the research design.

Search strategy
In accordance with the PRISMA guidelines, a literature search was performed in January 2019 using PubMed, Embase, and the Cochrane Library. Search terms used included the following: (((penile cancer) or (penile tumor) or (penile neoplasm) or (penile squamous cell carcinoma))) AND ((inguinal lymph node metastasis) or (lymph node metastases) or (nodal metastasis) or (inguinal node metastasis) or (inguinal lymphadenopathy) or (inguinal lymphadenectasis) or (inguinal lymph node involvement)) and ((predictors) or (predictive factor)). All studies on this topic were reviewed, and related references of original studies were identified by manual search.

Inclusion and exclusion criteria
The PICOS (Population, Intervention, Comparator, Outcome, and Study design) principle was employed to define study eligibility. Studies that compared penile cancer patients who were pathologically diagnosed with LNM (P) after local treatment of primary tumor (I) to patients without LNM (C) to determine clinicopathologic predictors or biomarkers of inguinal LNM (O) using logistic regression analyses or providing original statistical data (S) were considered relevant to this systematic review and meta-analysis. Eligible studies were selected based on the following: 1) precise definition of potential predictors; 2) sufficient data: odds ratios (ORs) with 95% confidence interval (CIs) or credible original statistical data that could be used to calculate ORs and 95% CIs; 3) pathologically confirmed LNM; 4) moderate or high methodological quality studies according to the Newcastle-Ottawa scale; 20 and 5) English studies with human subjects. Between two studies with similar research populations, study with larger sample size was chosen. All overlapping studies with different predictors were included. Relevant researches in the form of case reports, reviews, case series, editorials, or letters were excluded.

Data extraction
Data of identified studies were extracted by two independent reviewers (J.H. and J.B.C.). Discrepancies were resolved during a consensus meeting with a senior reviewer (X.Z.). The following information was extracted: author, year, country, sample size, LNM predictors, and follow-up period. We extracted the ORs with their 95% CIs directly if available in the article. Otherwise, we extracted the original statistical data to calculate the ORs and 95% CIs. Quality of data assessment and risk of bias evaluation Two independent reviewers evaluated the quality of the included studies using the Newcastle-Ottawa Quality Assessment Scale, which was designed to assess the quality of observational studies. A star system including nine scoring items was adapted to grade each study. A total score of 8 to 9 was defined as a high-quality study; 6 to 7, intermediate quality. Moreover, we evaluated the publication bias by visual inspection of funnel plots. We also performed a sensitivity analysis using the leave-one-out cross-validation to assess the stability of the present metaanalysis results.

Definition of predictors
Definitions of several predictors were recorded as previously published. 21

Statistical analysis
We performed this meta-analysis to identify potential predictors of LNM by pooling the predictor effect and its standard error, which was calculated from the available ORs and their 95% CIs or from original statistical data. The inverse variance method was adapted to evaluate the cumulative effects of these potential predictors. Statistical heterogeneity among the included studies was assessed using the Cochrane Q test and I 2 statistic (I 2 <25%: no heterogeneity; I 2 =25-50%: moderate heterogeneity; I 2 >50%: large heterogeneity). The value of I 2 indicated the degree of heterogeneity. A random-effects model was used when there was a large heterogeneity; otherwise, the fixed-effects model was used. The level of statistical significance was set at 0.05. The meta-analyses were performed using Review Manager (RevMan) software version 5.3 (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen).

Study population
Finally, we selected 42 eligible studies. 5,21-61 Among the included 4,802 patients, 1,706 (36%) were diagnosed with LNM. Figure 1 shows the study selection process. These studies were performed in the following geographical regions: Europe (n=12), South America (n=8), North America (n=10), Africa (n=1), Australia (n=1), and Asia (n=10). The characteristics of the included studies are provided in Table 1. The pooled results of the predictors are provided in Table 2. Original data are summarized in Tables S1-3. The symmetrical funnel plots showed in Figures S1 and S2 revealed low publication bias for these predictors.

Biomarker-specific predictors for LNM
Immune-related biomarkers: PD-L1, CRP, and NLR Immune-related biomarkers as a predictor of LNM were reported in seven studies. For these predictors, heterogeneity was not observed. Cumulative analysis of these homogeneous data revealed that PD-L1 overexpression in tumor cells ( Figure 2A).

SCC-Ag and P53 protein
SCC-Ag overexpression as a predictor was reported in four studies. There was no heterogeneity between these studies (I 2 =0%; p=0.88). Cumulative analysis of these data revealed that it (OR 8.52, 95% CI 4.09-17.78; p<0.001) is a significant predictor of LNM ( Figure 2B). Similarly, there was no heterogeneity between four studies which reported the correlation between P53 and LNM (I 2 =0%; p=0.68). Cumulative analysis of these homogeneous data revealed that P53 overexpression (OR 3.57, 95% CI 1.93-6.62; p<0.001) is a significant predictor of LNM ( Figure 2C).

Ki-67 and HPV infection status
Ki-67 overexpression as a predictor of LNM was reported in three studies. Heterogeneity was observed between the studies (I 2 =55%; p=0.11). Cumulative analysis of these data revealed that Ki-67 overexpression (OR 2.70, 95% CI 0.81-9.05; p=0.110) is not a significant predictor of LNM ( Figure 2D). Six studies reported the association between HPV infection and LNM, in which 208 (42%) and 285 (58%) patients had positive and negative HPV infection, respectively. No heterogeneity was noted between the studies (I 2 =0%; p=0.49); Cumulative analysis of these homogeneous data demonstrated that HPV infection (OR 0.85, 95% CI 0.58-1.25; p=0.410) is not a significant predictor of LNM ( Figure 2E).    Figure 5A). Heterogeneity between the studies in univariable subgroup was significant (I 2 =64%; p<0.001); tumor size as a predictor of LNM was reported in four studies. They used 3 cm as the cutoff value. No heterogeneity between the studies (I 2 =0%; p=0.55) was found, and cumulative analysis of homogeneous data revealed that tumor size (>3 cm) (OR 2.00, 95% CI 1.29-3.10; p=0.002) is a significant predictor of LNM ( Figure 5B).  Figure 6B). Nerve invasion as a predictor of LNM was reported in seven studies, in which 196 (23%) and 670 (77%) patients had positive and negative nerve invasion, respectively. No heterogeneity was noted between the studies (I 2 =7%; p=0.37). Cumulative analysis of these homogeneous data revealed that nerve invasion (OR 2.84, 95% CI 1.99-4.04; p<0.001) is a significant predictor of LNM ( Figure 6C).

Cavernosum invasion, urethra invasion, age, and race
Six studies reported the association between corpora cavernosum invasion and LNM. The data were homogeneous (I 2 =0%; p=0.900) and cumulative results demonstrated that corpora cavernosum invasion (OR 2.22, 95% CI 1.63-3.04; p<0.001) is a significant predictor of LNM ( Figure 7A). Similarly, cumulative analysis revealed that Notes: a High-low risk group and b Intermediate-low risk group. c AJCC TNM stage system and d UICC TNM stage system. Abbreviations: LNM, lymph node metastasis; OR: odds ratio; CI, confidence intervals; I 2 , the heterogeneity between studies; pre, predictors; NR, not reported; PD-L1, program death ligand 1; SCC-Ag, squamous cell carcinoma antigen; NLR, neutrophil-to-lymphocyte ratio; CRP, C-reactive protein.  p=0.002) is also a significant predictor of LNM ( Figure 7B). Seven studies reported the association between urethra invasion and LNM. Pooled results demonstrated that urethra invasion (OR 1.81, 95% CI 1.07-3.05; p=0.030) is a significant predictor of LNM ( Figure 7C). Heterogeneity was observed between included studies (I 2 =59%; p=0.02); Nine studies reported the association between age and LNM. Heterogeneity was moderate (I 2 =29%; p=0.19); Cumulative analysis of available ORs demonstrated that age (OR 0.99, 95% CI 0.95-1.03; p=0.65) was not a significant predictor of LNM ( Figure 7D). Three studies reported the race-related LNM risk. Accumulative analysis of these homogeneous data revealed that race (OR 0.92, 95% CI 0.52-1.63; p=0.77) was not a significant predictor of LNM ( Figure 7E).

Discussion
The effectiveness of current guidelines in managing inguinal lymph nodes of patients with penile cancer had been challenged by a prospective study. 62 This prospective study revealed that over 80% of the patients, who were categorized as intermediate or high risk for developing LNM according to the current guidelines, accepted unnecessary prophylactic ILND. This inaccuracy may be caused by the small number of predictors included in the current guidelines. In addition, ILND exhibits serious surgical complications. To pinpoint patients who are the best candidates for receiving radical ILND, we conducted this comprehensive meta-analysis. Consequently, we identified numerous valuable predictors which could be classified into two categories, including tumor-associated biomarkers and invasive clinicopathologic characteristics. To  our knowledge, this meta-analysis is the first to combine comprehensive and detailed evidence on extensive potential predictors of LNM for patients with penile cancer. It is well established that inflammation contributes a lot to the initiation and progression of cancers. 63 Several immune-related predictors, such as NLR, CRP, and PD-L1, were conclusively identified in this meta-analysis. NLR combining of neutrophilia and lymphopenia represents systemic inflammatory response and immune response. It is an independent predictor of poor prognosis for several solid cancers including  castration-resistant prostate cancer, cervical adenocarcinoma, lung cancer, and esophageal carcinoma. [64][65][66][67] Similarly, a study enrolling 84 consecutive penile cancer patients investigated the association between NLR with pathologic LNM and prognosis. 24 This study demonstrated that NLR was an independent predictor of overall survival. In addition, patients with an elevated NLR were related to higher risk of pathologic LNM, although this relationship was not significant in adjusted analysis. CRP is an acute-phase protein produced almost exclusively by the liver. Its prognostic value for patients with penile cancer has been demonstrated. 22,42 According to the I 2 statistics, there was no heterogeneity for these biomarkers between included studies. Furthermore, both NLR and CRP are measured easily and economically in clinical practice. These features may facilitate the clinical application of these two biomarkers.
Another important and therapeutic potential biomarker is PD-L1, a transmembrane protein with the ability to suppress host immune system. It is a critical component of tumor-specific immune resistance mechanisms. 68 Cancer immunotherapy by targeting PD-L1 can improve overall survival for patients with advanced cancer. 69 However, evidence on cancer immunotherapy for patients with penile cancer is limited. One target of this metaanalysis was to investigate the association between PDL-1 with inguinal LNM and prognosis in populations with penile cancer. We revealed that higher PD-L1 expression in tumor cells was related to higher risk of LNM and poorer prognosis. This conclusion, to some extent, provided a theoretical basis for the application of targeted anti-PD-L1 immunotherapy in penile cancer patients.
SCC-Ag, a tumor-associated glycoprotein, is bound up with early recurrence of cervical cancer. 70 However, its relationship with early recurrence and risk of LNM in patients with penile cancer has not been summarized. Touloupidis et al revealed elevated SCC-Ag level predicted LNM and distant metastasis. 54 Laniado et al demonstrated elevated SCC-Ag level had a high specificity (100%) and an intermediate sensitivity (57%) for prediction of LNM. 41 However, Hungerhuber et al found that it was just related to tumor burden rather than LNM. 39 Based on an accumulate metaanalysis of these discrepant data, we conclusively demonstrated that elevated SCC-Ag was a predictor of LNM. However, due to the small sample size of prior studies, the clinical application of SCC-Ag to predict LNM should be further validated by more large-scale researches.
For these biochemical predictors, given the limited number of included studies, we are unable to perform subgroup analysis based on different cut-off points, different antibodies, or different measurement methods. This accounted for the cross-study heterogeneity. Meanwhile, there are other immune biomarkers, such as CD8, CD163, and so on. However, we cannot include them in this metaanalysis because of insufficient data. This may be a direction of our future research.
Two TNM stage systems consisting of AJCC and UICC were adapted for penile cancer in this meta-analysis and they are constantly updated. Therefore, we performed a subgroup analysis based on different TNM versions. No matter which version we analyzed, we found that patients with higher stage disease were at higher LNM risk. In addition, we further analyzed all components of these TNM systems as well as other common pathological characters. Conclusively, the LNM risk will increase significantly as long as there is any invasive pathological character, such as LVI, corpora cavernosum invasion, corpus spongiosum invasion, urethra invasion, nerve invasion, larger tumor size, and deeper tumor invasion.
A large-scale study performed by Wang et al showed that LNM rates ranged from 5% to 100% between different risk stratified histological subtypes. 5 This huge risk difference highlighted the importance of a conclusive histological risk stratification. Therefore, we divided patients into three risk groups (high-risk, intermediate-risk, and low-risk groups) and compared the LNM risk between each group. Results suggested that this risk stratification was viable. Patients with high-risk histological subtype (basaloid, sarcomatoid, adenosquamous, and poorly differentiated types) were at obviously higher LNM risk.
For patients with cN+, the LNM risk ranged from 54% to 85% (mean 62%) in these included studies. Results of all individual studies were statistically significant. For tumor grade, we performed subgroups analysis based on multivariable and univariable analysis. Both groups demonstrated that higher grade was related to higher LNM risk. Similarly, such a positive correlation was also revealed between tumor vertical growth pattern and LNM risk. However, we did not find any correlation between LNM risk and age, race or HPV infection.
This study has several limitations. First, we failed to analyze some important factors, such as the tumor site, tumor multifocality, tumor cell keratinization, and koilocytosis. Second, some studies only provided original statistical data rather than direct ORs. Third, several methodological drawbacks of the included studies were noted, such as the small series, single-center and retrospective nature of these studies. Fourth, the number of studies focusing on biomarkers was small, and some other important biomarkers were not included due to insufficient data. Given these drawbacks, high-quality studies are warranted to further validate our conclusion. In addition, future studies should explore the intrinsic links between these predictors and then create an accurate and comprehensive mathematical predictive model for LNM by integrating multiple predictors.