Front. Oncol. Frontiers in Oncology Front. Oncol. 2234-943X Frontiers Media S.A. 10.3389/fonc.2024.1475348 Oncology Systematic Review The prognostic values of lymph node ratio for gynecological cancer: a systematic review and meta-analysis Chen Mengmeng 1 2 Wang Yisi 1 2 Chen Yali 1 2 Han Ling 1 2 * Zheng Ai 1 2 * 1 Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China 2 Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Sichuan, China

Edited by: Sophia George, University of Miami, United States

Reviewed by: Mikel Gorostidi, University of the Basque Country, Spain

Angelo Finelli, ULSS2 Marca Trevigiana, Italy

*Correspondence: Ling Han, hanlingluobo@sina.com; Ai Zheng, aizheng716@163.com

01 10 2024 2024 14 1475348 03 08 2024 09 09 2024 Copyright © 2024 Chen, Wang, Chen, Han and Zheng 2024 Chen, Wang, Chen, Han and Zheng

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 Background

The aim of this study was to determine the relationship between the lymph node ratio (LNR) and the prognostic values of gynecological cancer.

 Materials and methods

PubMed, Web of Science, Embase, and the Central Cochrane Library were used to search for studies on LNR and gynecological cancer published before 18 April 2024. The effect measure for meta-analysis of primary outcomes was the hazard ratio (HR) for overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). Pooled HRs and 95% confidence intervals (CIs) were calculated using random- or fixed-effects models. Sensitivity analysis was applied to evaluate the robustness of the results. The I 2 statistic was used to measure heterogeneity. Subgroup analysis and meta-regression were chosen to illustrate the potential heterogeneity of the risk factors for outcomes. Publication bias was assessed using Egger’s test and Begg’s funnel plots.

 Results

A total of 34 studies with 23,202 cases were included in this meta-analysis. A meta-analysis found that higher LNR was associated with worse OS (HR = 2.42, 95% CI: 2.07–2.83; I 2 = 77.4%, p < 0.05), PFS (HR = 1.97, 95% CI: 1.66-2.32; I 2 = 0.00%, p > 0.05), and DFS (HR = 3.18, 95% CI: 2.12–4.76; I 2 = 64.3%, p < 0.05). Moreover, meta-analysis revealed significant differences in the association between LNR and OS of cervical cancer (CC) (HR = 2.53, 95% CI: 1.94–3.31; I 2 = 72.6%, p < 0.05), ovarian cancer (OC) (HR = 2.05, 95% CI: 1.66–2.54; I 2 = 76.7%, p < 0.05), endometrial cancer (EC) (HR = 2.16, 95% CI: 1.48–3.16; I 2 = 53.6%, p < 0.05), and vulvar cancer (VC) (HR = 8.13, 95% CI: 3.41–19.43; I 2 = 57.2%, p < 0.05).

 Conclusion

We observed a clear association between higher LNR and poorer prognosis in our study of patients with gynecological cancer. Further prospective studies are warranted to determine the optimal LNR and whether LNR can guide adjuvant therapy use in gynecological cancer. It is essential to conduct further prospective studies to establish the optimal LNR threshold, determine the minimum threshold of lymph node removal, and investigate whether LNR can serve as a reliable marker for guiding adjuvant therapy choices in gynecological cancer.

 Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/#recordDetails, CRD42024541187.

 lymph node ratio gynecological cancer prognosis systematic review meta-analysis section-in-acceptance Gynecological Oncology Introduction

Lymph node metastasis is a common occurrence in gynecological cancers and has a significant impact on patient prognosis. However, the number of positive nodes during pelvic lymphadenectomy can be influenced by surgical technique and the accuracy of pathological examination. To overcome potential confounding effects, the use of lymph node ratio (LNR) has been proposed. LNR calculates the ratio of positive lymph nodes to the total number of resected lymph nodes and provides a more accurate representation of pelvic lymph node metastasis status. It has been identified as an independent predictor of survival in various cancers, including colon cancer (1), oral cancer (2), pancreatic cancer (3), breast cancer (4), esophageal cancer (5), and lung cancer (6).

Recently, there has been interest in using LNR as a prognostic tool in gynecologic malignancies such as cervical cancer (CC), ovarian cancer (OC), endometrial cancer (EC), and vulvar cancer (VC). However, the conclusions of studies in this area are not consistent. To address this, we conducted a systematic search of scientific databases to identify relevant publications and to explore the relationship between lymph node ratio and overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) in gynecological cancers.

 Materials and methods Protocol registration

This meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (7). Before data extraction, the review was registered with the International Prospective Register of Systematic Reviews (PROSPERO, Registration Number CRD42024541187).

 Data collection

PubMed, Web of Science, Embase, and the Central Cochrane Library were used to search for studies on LNR and gynecological cancer published before 18 April 2024. The following keywords were used for literature retrieval: (“lymph node ratio” or “Ratio, Lymph Node”) and (“Uterine Cervical Neoplasms” or “Neoplasm, Uterine Cervical “ or “Ovarian Neoplasms” or “Neoplasm, Ovary” or “Neoplasm, Endometrial” or “Endometrial Neoplasms” or “Vulvar Neoplasms” or “Neoplasm, Vulva”). Additionally, the references in the obtained papers were examined to find any other relevant research outside of these two key phrases in the query.

 Eligibility criteria and exclusion criteria

Inclusion criteria were as follows: (1) studies that investigated the relationship between LNR and OS, PFS, or DFS; (2) studies with CC, OC, EC, and VC confirmed by pathology; (3) studies that reported a hazard ratio (HR) with 95% CI for OS, PFS, or DFS; and (4) full articles published in English.

Exclusion criteria were as follows: (1) useful data could not be extracted; (2) the survival data or 95% confidence interval (CI) were not reported; and (3) editorials, reviews, and comments. In addition, when the data of a patient were used in multiple studies, we selected the most recent study.

 Data extraction of data and quality assessment

The data extracted mainly included the following: the first author, publication date, sample size, cancer type, country, average age, duration of follow-up, cutoff value for LNR, and patient outcomes, including OS, PFS, and DFS.

In this meta-analysis, the quality assessment for the non-randomized studies was evaluated by two reviewers independently based on the Newcastle–Ottawa quality assessment scale (NOS) (8). The NOS was based on three categories: selected cases, comparability between groups, and outcome assessment. A score ≥ 6 was considered high-quality literature, to be included in our study.

 Main outcomes

OS refers to the period from the date of the initial therapy to the date of all-cause mortality. PFS refers to the time from the date of the initial therapy to the date of disease progression, which was the time following successful treatment without disease progression or symptoms. DFS refers to the time from surgery to the last follow-up with no evidence of recurrence or distant metastasis.

 Statistical analysis

We used STATA 15.0 software (StataCorp LP, College Station, TX, United States) to pool the extracted data for this meta-analysis. Hazard ratios with 95% CI were collected from individual studies, then combined using a random- or fixed-effects model, and finally presented in forest plots. Statistical heterogeneity was quantified by I 2 statistics. A random-effects model was used if there was prominent heterogeneity (p < 0.1 or I 2 > 50%); otherwise, a fixed-effects model was adopted (p > 0.1 or I 2 < 50%) (9). Sensitivity analysis was used to determine the robustness and stability of the results by calculating the heterogeneity in each situation in which a single study was removed in turn to evaluate the effect of a single study on the overall outcome. The risk of publication was assessed by visual inspection of Begg’s funnel plot and Egger’s linear regression test. In these two-tailed statistical tests, p < 0.05 (95% CI) was regarded as statistically significant. Meta-regression analysis, subgroup analysis, and publication bias were evaluated in analyses that included more than 10 studies.

 Results Study selection

As shown in Figure 1 , through electronic searching on PubMed, Web of Science, Embase, and the Central Cochrane Library, 1,428 potential articles were screened. After excluding duplicate studies there were 1,237 records. Then, the titles and abstracts were screened, and 1,109 publications were removed as irrelevant. Finally, 119 full-text articles were identified for qualification, and 85 ineligible papers were eliminated because they did not provide primary outcome measurements. In the end, a total of 34 studies with 23,202 cases were eligible for the current meta-analysis.

Flow diagram of the included studies.

 Characteristics of the included studies

The main characteristics of the included studies are presented in Table 1 . In this study, all necessary data were extracted from 34 studies from different countries including China (n = 10), Türkiye (n = 6), USA (n = 3), Germany (n = 3), Poland (n = 3), Republic of Korea (n = 2), Austria (n = 2), Thailand (n = 1), Brazil (n = 1), Tunisia (n = 1), Netherlands (n = 1), and France (n = 1). All included studies were retrospective studies. Among these articles, 14 studies involved 6,289 patients diagnosed with CC, 9 studies involved 15,058 patients diagnosed with OC, 8 studies involved 1,017 patients diagnosed with EC, and 3 studies involved 838 patients diagnosed with VC. All studies were retrospective cases, and all were rated seven or more stars according to the NOS criteria ( Table 2 ).

Main characteristics of the included literature.

First author Year Country Recruitment period Size Cancer type Age Mean/median months of follow-up Survival analysis Cutoff LNR NOS score
Ying Chen (10) 2013 China NM 93 Cervical cancer 46 67 OS 0.050.2 8
Qinhao Guo (11) 2020 China 2006–2014 928 Cervical cancer 46.58 35.7 OSPFS 0.16 7
Juan Zhou (12) 2016 China 1988–2010 2,269 Cervical cancer 43 78 OS 0.16 8
Se lk Kim (13) 2021 Republic of Korea 2010–2018 55 Cervical cancer 52.6 NM DFS 0.08831 7
S Polterauer (14) 2012 Austria 1996–2009 139 Cervical cancer 47.9 45.7 OS Continuous 7
Xiang Fan (15) 2023 China 2012–2017 102 Cervical cancer NM 63 OSDFS 0.3 8
Dan Li (16) 2019 China 2008–2013 1,435 Cervical cancer 47 77 OS 0.19 8
Chen Li (17) 2016 China 2007–2009 198 Cervical cancer 44 NM OSDFS 0.2 7
Juan Zhou (18) 2015 China 1980–2012 60 Cervical cancer 37 30.5 OS Continuous 7
Yoon Hee Lee (19) 2021 Republic of Korea 2007–2016 49 Cervical cancer 48.5 58 OSDFS 0.0625 7
Nicole D. Fleming (20) 2015 USA 1990–2011 95 Cervical cancer 39.7 64.8 OSPFS 0.076 8
S Polterauer (21) 2010 Austria 1995–2008 88 Cervical cancer 49.9 37.1 OSDFS 0.1 7
Koray Aslan (22) 2020 Türkiye 2006–2018 185 Cervical cancer 50 45.5 OSDFS 0.05 7
E Olthof (23) 2021 Netherlands 1995–2020 593 Cervical cancer NM NM OS 0.177 7
Koray Aslan (24) 2020 Türkiye 1997–2017 62 Ovarian cancer 47 45 OSPFS 0.09 7
Ali Ayhan (25) 2018 Türkiye 2007–2016 229 Ovarian cancer 56 36 OS 0.10.5 7
Xiaoxia Tong (26) 2019 China 1973–2013 7,819 Ovarian cancer NM NM OS 0.42 7
Dan Nie (27) 2019 China 2008–2014 265 Ovarian cancer 56 40 OSDFS 0.25 7
Beyhan Ataseven (28) 2014 Germany 2000–2013 398 Ovarian cancer NM 45 OS 0.25 7
Katarzyna Lepinay (29) 2020 Poland 2010–2015 144 Ovarian cancer NM NM OS 0.1 7
Juan Zhou (30) 2016 China 1990–2012 5,926 Ovarian cancer 59 33 OS 0.42 7
M.A. Ayadi (31) 2018 Tunisia 2000–2010 84 Ovarian cancer 54 65 OS 0.5 8
P. Widschwendter (32) 2017 Germany 2000–2012 131 Ovarian cancer NM NM OS Continuous 7
Katarzyna Gorzelnik (33) 2022 Poland 2000–2015 75 Endometrial cancer 60 NM OS 0.3 7
Nicole D. Fleming (34) 2015 Brazil 2000–2011 124 Endometrial cancer 60 49.4 OSPFS 0.4 (OS)0.5 (PFS) 7
Ali Ayhan (25) 2017 Türkiye 1998–2016 207 Endometrial cancer 58 40 OSPFS 0.15 7
Siriwan Tangjitgamol (35) 2019 Thailand 1995–2017 82 Endometrial cancer 59.5 NM PFS 0.1 7
Stephan Polterauer (36) 2012 Now York 1993–2008 216 Endometrial cancer 65.5 30.5 OSPFS 0.10.5 7
Nicole D. Fleming (20) 2015 USA 1990–2011 95 Endometrial cancer 39.7 64.8 OSPFS 0.66 (OS)0.076 (PFS) 8
Tayfun Toptas (37) 2015 Türkiye 2005–2013 38 Endometrial cancer 32.5 64 OSPFS 0.065 8
B. Akkus Yildirim (38) 2018 Türkiye 2001–2016 180 Endometrial cancer 60 50.5 OSPFS 0.1 7
Stephan Polterauer (39) 2017 Germany NM 370 Vulvar cancer 64.5 26.4 OSDFS 0.10.2 7
E. Serre (40) 2019 France 2005–2015 176 Vulvar cancer 68.7 NM OS 0.2 7
Stephan Polterauer (41) 2020 Poland 2001–2005 292 Vulvar cancer 69.9 NM OS 0.2 7

Quality of the included studies.

Study Selection Comparability Outcome Total
Representativeness Selection of non-exposed Ascertainment of exposure Outcome not present at the start Comparability of most important factors Comparability on other risk factors Assessment of outcome Long enough follow-up (median ≥ 5 years) Adequacy (completeness of follow-up)
Ying Chen (10) √ √ √ √ √ × √ √ √ 8
Qinhao Guo (11) √ √ √ √ √ × √ × √ 7
Juan Zhou (12) √ √ √ √ √ × √ √ √ 8
Se lk Kim (13) √ √ √ √ √ × √ × √ 7
S Polterauer (14) √ √ √ √ √ × √ × √ 7
Xiang Fan (15) √ √ √ √ √ × √ √ √ 8
Dan Li (16) √ √ √ √ √ × √ √ √ 8
Chen Li (17) √ √ √ √ √ × √ × √ 7
Juan Zhou (18) √ √ √ √ √ × √ × √ 7
Yoon Hee Lee (19) √ √ √ √ √ × √ × √ 7
Nicole D. Fleming (20) √ √ √ √ √ × √ √ √ 8
S Polterauer (21) √ √ √ √ √ × √ × √ 7
Koray Aslan (22) √ √ √ √ √ × √ × √ 7
E. Olthof (23) √ √ √ √ √ × √ × √ 7
Koray Aslan (24) √ √ √ √ √ × √ × √ 7
Ali Ayhan (25) √ √ √ √ √ × √ × √ 7
Xiaoxia Tong (26) √ √ √ √ √ × √ × √ 7
Dan Nie (27) √ √ √ √ √ × √ × √ 7
Beyhan Ataseven (28) √ √ √ √ √ × √ × √ 7
Katarzyna Lepinay (29) √ √ √ √ √ × √ × √ 7
Juan Zhou (30) √ √ √ √ √ × √ × √ 7
M.A. Ayadi (31) √ √ √ √ √ × √ √ √ 8
P. Widschwendter (32) √ √ √ √ √ × √ × √ 7
Katarzyna Gorzelnik (33) √ √ √ √ × × √ × √ 7
Nicole D. Fleming (34) √ √ √ √ √ × × × × 7
Ali Ayhan (25) √ √ √ √ √ × × × × 7
Siriwan Tangjitgamol (35) √ √ √ √ √ × × × × 7
Stephan Polterauer (36) √ √ √ √ √ × × × × 7
Nicole D Fleming (20) √ √ √ √ √ × × √ √ 8
Tayfun Toptas (37) √ √ √ √ √ × √ √ √ 8
B. Akkus Yildirim (38) √ √ √ √ √ × √ × √ 7
Stephan Polterauer (39) √ √ √ √ √ × √ × √ 7
E. Serre (40) √ √ √ √ √ × √ × √ 7
Stephan Polterauer (41) √ √ √ √ √ × √ × √ 7

"√" indicates that the criteria are met, while "×" indicates that the criteria are not met.

 Meta-analysis Primary outcomes LNR and OS

Out of the 34 (10–41) eligible studies, 32 (10–12, 14–34, 36–41) studies, namely, 13 (10–12, 14–23) studies with CC, 9 (24–32) studies with OC, 7 (20, 25, 33, 34, 36–38) studies with EC, and 3 (39–41) studies with VC, analyzed the association between LNR and OS. Using a random-effects model, the pooled results of HR and OS statistics from these 32 studies showed that higher levels of LNR were associated with worse OS (HR = 2.42, 95% CI: 2.07–2.83; I 2 = 77.4%, p < 0.05), as shown in ( Figure 2A ). However, the results also indicated a high heterogeneity between studies (I 2 = 77.4%, p < 0.05).

Forest plots and pooled estimates of the effect for meta-analysis of the association between LNR and OS (A), PFS (B), and DFS (C) in patients with gynecological cancer.

 LNR and PFS

Seven studies (20, 25, 34–38) with EC, two studies (11, 15) with CC, and one study (24) with OC explored the association between LNR and PFS. Using a fixed-effects model, pooled results of HR and PFS statistics from 10 studies indicated that higher LNR levels were associated with worse PFS (HR = 1.97, 95% CI: 1.66–2.32; I 2 < 50%, p > 0.05) ( Figure 2B ). The results showed that there was low heterogeneity between studies (I 2 < 50%, p > 0.05).

 LNR and DFS

Eight studies, consisting of six studies (13, 15, 17, 19, 21, 22) with CC, one study (24) with OC, and one study (39) with VC, were included in the analysis of the association between LNR and PFS. Using a random-effects model, the pooled results indicated that higher levels of LNR were associated with worse DFS (HR = 3.18, 95% CI: 2.12–4.76; I 2 = 64.3%, p < 0.05) ( Figure 2C ). However, there was high heterogeneity between studies (I 2 > 50%, p < 0.05).

 Subgroup and meta-regression analysis

As the outcome of OS shows high heterogeneity, both meta-regression and subgroup analyses were conducted to explore the factors contributing to the high heterogeneity. Subgroup analysis was carried out based on the type of cancer. The analysis revealed significant differences in the association between LNR and OS of CC (HR = 2.53, 95% CI: 1.94–3.31; I 2 = 72.6%, p < 0.05), OC (HR = 2.05, 95% CI: 1.66–2.54; I 2 = 76.7%, p < 0.05), EC (HR = 2.16, 95% CI: 1.48–3.16; I 2 = 53.6%, p < 0.05), and VC (HR = 8.13, 95% CI: 3.41–19.43; I 2 = 57.2%, p < 0.05) ( Figure 3 ). Furthermore, meta-regression analysis was carried out to investigate possible sources of heterogeneity. Single covariate regression was performed using variables such as country, sample size, type of gynecological cancer, and publication year. The results indicated that sample size was the main factor contributing to the heterogeneity, with a p-value of 0.014 ( Table 3 ).

Forest plot for subgroup analysis for OS in gynecological cancer.

Results of the meta-regression.

Co-factor Coefficient 95% confidence interval p-value
Year 0.055 (−0.003,0.114) 0.062
Type 0.193 (−0.063,0.449) 0.133
Country −0.045 (−0.130,0.038) 0.274
Size −0.00 (−0.00,−0.000) 0.014

We did not conduct meta-analyses, and subgroup analyses for PFS and DFS, as they c.

 Sensitivity analysis

In order to assess the stability of the models, a sensitivity analysis was conducted by excluding each individual study and calculating new HRs. The results showed that the HRs were relatively stable, as illustrated in Figure 4 .

Sensitivity analysis of the association between LNR and OS (A), PFS (B), and DFS (C) in gynecological cancer.

 Publication bias

Publication bias was assessed by Begg’s funnel plots and Egger’s test. For OS, the p-values for Egger’s test ( Figure 5A ) and Begg’s test ( Figure 5B ) were p = 0.255 and p < 0.00, respectively. Visual inspection of Begger’s funnel plot ( Figure 5C ) was not symmetrical, suggesting evidence of publication bias. We did not conduct a publication bias analysis for PFS and DFS, as they did not include more than 10 studies.

Egger’s (A), Begg’s (B), and funnel plots (C) show bias for OS.

 Discussion

This meta-analysis revealed that higher LNR was significantly associated with poorer prognosis across multiple metrics. Specifically, it found that higher LNR was correlated with worse OS, PFS, and DFS, indicating that patients with higher LNR often have worse outcomes. Furthermore, the pooled HRs for OS across different types of cancers are 3.42 for CC, 2.05 for OC, 2.16 for EC, and 8.13 for VC, suggesting that higher LNR often correlates with worse survival outcomes in CC, OC, EC, and VC. These findings underscore the significant prognostic implications of LNR in gynecological cancer, highlighting its role as a critical factor in predicting patient outcomes.

For CC, based on the results of previous studies, the cutoff value of LNR in patients with CC ranges from 5% to 30% (9–22), with higher LNR consistently associated with poorer prognosis. A meta-analysis (42) conducted in 2017, comprising eight articles with 3,325 patients diagnosed with CC, confirmed that higher LNR was an unfavorable prognostic factor for OS and DFS, which is consistent with our findings. In 2018, the FIGO Committee added IIICI (pelvic lymph node metastasis only) and IIIC2 (para-aortic lymph node metastasis) to the FIGO staging system for CC (43). Nevertheless, this was solely based on anatomic location and did not consider other lymph node characteristics. Researchers have looked for a reliable lymph node measure in CC, yielding conflicting results. Yoon et al. (19) showed that LNR was the most robust biomarker for predicting tumor recurrence, while Qinhao et al. (11) found that positive lymph nodes had the best prognostic performance for OS and PFS. Therefore, further studies are warranted to explore this aspect and establish the most reliable measure.

In OC, previous studies have identified varying cutoff values for LNR ranging from 9% to 50% (23–31). Consistently, higher LNR was linked to poorer prognosis, which is in line with our study findings. It is worth noting that OC is a heterogeneous group of diseases with varying histology, molecular genetic analysis, and prognosis (44); some researchers have begun to conduct more in-depth research on the classification of epithelial OC. The prognostic value of lymph node ratio has been confirmed in high-grade serous ovarian carcinoma (45), low-grade serous ovarian carcinoma (22), clear cell ovarian carcinoma (27), and borderline ovarian tumors (46). Especially in borderline ovarian tumors (46), David controlled for age, histology, stage, tumor size, and adequate lymphadenectomy status, and LNR remained an independent factor for survival; qualitative assessment of lymph node involvement is not a prognostic factor for survival. Another study (30) based on the Surveillance, Epidemiology, and End Results database showed that there is a significant and independent correlation between higher LNR and poorer OS, and its prognostic value is superior to removed lymph nodes and positive lymph node counts. Despite this, a study conducted by Xiao et al. (26) found that LNR did not reach statistical significance for discriminating OS in stage IV patients, although it showed better performance than the number of positive lymph nodes. Therefore, future research should emphasize the prognostic utility of LNR in various stages of OC.

In EC, according to prior research (20, 25, 32–37), the cutoff values for LNR in EC patients span from 6.5% to 50%. These studies consistently indicated that higher LNR is associated with a worse prognosis, a trend observed in our study. Previous multi-center retrospective studies (20, 25, 32–37) have found a correlation between LNR and worse OS and PFS. Xi-Lin et al. (47) found that the lymph node ratio had a better predictive performance for these patients than the number of removed lymph nodes, the number of positive lymph nodes, and the number of negative lymph nodes. However, a study conducted by Fleming et al. (34) did not find a statistically significant association between LNR and OS, probably due to the small patient cohort in this single-institution study and the low median count of retrieved lymph nodes. This suggests that the prognostic value of LNR may be limited to patients who have undergone a minimum threshold of lymph node removal. Nevertheless, this threshold has not been universally adopted as a clinical standard. Future research should focus on identifying optimal lymph node removal strategies to mitigate the morbidity associated with systemic lymphadenectomy.

In VC, Kunos et al. (48) initially described LNR for prognostic assessment in patients with VC, and they found that patients with LNR > 20% had an increased likelihood of contralateral positive lymph nodes, recurrence, and cancer-specific death compared with patients with LNR < 20%. Moreover, some studies (38–40) stratified patients into three groups (LNR = 0%, 0% < LNR < 20%, and LNR > 20%), and the LNR > 20% group had the highest risk for OS and recurrence, which is consistent with our study. According to a study conducted by Polterauer et al., LNR appears to be a consistent, independent prognostic parameter for both OS and PFS, and its predictive value is superior to positive lymph node number. These studies support the predictive value of the LNR for VC.

Our research has certain strengths. First, this is the first complete meta-analysis to quantify the role of LNR in the prognosis of gynecological cancer. Second, this meta-analysis included a large number of primary studies (34 papers) and patients (23,202 patients with positive lymph nodes), which allowed for a more robust statistical analysis. Finally, our findings have demonstrated the importance of LNR in the prognosis of gynecological cancer. Therefore, we recommend LNR as a prognostic parameter that should be included in a future gynecological cancer staging system.

While our study has shown that LNR is of significant prognostic value in gynecologic cancers, it also has limitations. First, literature-based meta-analyses rely on published data and may be biased toward positive results, and we found that it does have bias in our study. In addition, the absence of data on tumor size, pathological stages, number of examined lymph nodes, number of metastasized lymph nodes, and surgical methods limited further subgroup analysis. Furthermore, the LNR cutoff in different studies was inconsistent. Finally, all included studies were retrospective, and this study type has intrinsic limitations. Thus, more prospective data are required to further ascertain the prognostic value of LNR in specific populations.

 Conclusion

Higher LNR is linked to lower OS, PFS, and DFS in patients diagnosed with gynecological cancer. The prognostic value of LNR for OS is consistent across different types of gynecological cancer, including CC, OC, EC, and VC. Further prospective studies are essential to establish the optimal LNR threshold, determine the minimum threshold of lymph node removal, and evaluate whether LNR can effectively guide the use of adjuvant therapies in gynecological cancer.

 Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

 Author contributions

MC: Writing – original draft. YW: Writing – original draft. YC: Writing – original draft. LH: Writing – review & editing. AZ: Writing – review & editing.

 Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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