SYSTEMATIC REVIEW article

Front. Oncol., 14 February 2025

Sec. Gastrointestinal Cancers: Gastric and Esophageal Cancers

Volume 15 - 2025 | https://doi.org/10.3389/fonc.2025.1536920

The prognostic value of pretreatment neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in patients with esophageal cancer undergoing immunotherapy: a systematic review and meta-analysis

  • Department of Oncology, Bishan Hospital of Chongqing Medical University, Chongqing, China

Abstract

Background:

Esophageal cancer (EC) is associated with a high morbidity and mortality rate. Immunotherapy has demonstrated effective antitumor activity in patients with EC, making it imperative to investigate easily accessible prognostic factors. Consequently, we conducted a meta-analysis to explore the prognostic significance of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in EC patients treated with immunotherapy.

Methods:

The literature search was conducted across three databases: PubMed, Embase, and Web of Science. The primary deadline for literature retrieval was July 2024. Hazard ratio (HR) with a 95% confidence interval (CI) was utilized to assess the association between NLR or PLR and overall survival (OS) as well as progression-free survival (PFS). Statistical analysis was performed using Review Manager version 5.4 and STATA version 15.0.

Results:

The meta-analysis included a total of 16 studies involving 1,481 patients. The results indicated a significant correlation between high pretreatment NLR and poor PFS (HR=1.76, 95%CI:1.38-2.25, p<0.001) as well as poor OS (HR=2.61,95%CI:1.86-3.67, p<0.001). Subgroup analyses based on tumor stage revealed that the association between elevated NLR and poor PFS was only observed in advanced EC patients. Regarding PLR, an increased PLR was found to be indicative of inferior PFS (HR=1.44, 95%CI: 1.20-1.72, p<0.001) and OS (HR=1.72,95%CI:1.08-2.74, p=0.020). However, the sensitivity analyses suggested that the observed increase in PLR lack robustness in terms of its impact on inferior OS.

Conclusion:

Elevated NLR and PLR are associated with inferior PFS and OS in EC patients receiving immunotherapy. These findings suggest that NLR and PLR levels hold promise as prognostic biomarkers in clinical practice, offering valuable guidance for personalized immunotherapy strategies.

Systematic Review Registration:

PROSPERO https://www.crd.york.ac.uk/prospero/, identifier CRD42024596737.

1 Introduction

The incidence of esophageal cancer (EC) ranks seventh, while its mortality rate ranks sixth in the world. Approximately 70% of cases occur in men. Eastern Asia exhibits the highest regional incidence rates, primarily due to the substantial burden in China (1). The predominant histopathological subtypes encompass esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), with ESCC accounting for approximately 90% of annual cases (2).

The current treatment options encompass multimodality therapy, which comprises the mainstays of surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. The latest research findings demonstrated that immunotherapy has yielded substantial survival advantages for the patients diagnosed with EC (35), and ESCC was more sensitive to immunotherapy than EAC (6). Among EC patients who underwent resection after receiving neoadjuvant chemoradiotherapy, CheckMate577 demonstrated that those who received adjuvant therapy with nivolumab had a significantly longer disease-free survival (DFS) compared to those who received placebo (5). For patients with advanced EC, the combination of chemotherapy and immunotherapy offers a more significant survival advantage compared to chemotherapy alone. In the first-line treatment of advanced EC patients, the efficacy of immunotherapy and chemotherapy has been demonstrated in numerous phase III clinical trials. For example, KEYNOTE-590 found that pembrolizumab plus chemotherapy improved OS and PFS in patients with previously untreated, locally advanced, unresectable or metastatic EC (3). The CheckMate648 study found that the addition of nivolumab to chemotherapy as first-line treatment led to a significantly prolonged OS compared to chemotherapy alone (13.2 vs. 10.7 months; HR=0.74, 99.1% CI: 0.58 to 0.96; P = 0.002) in patients with advanced ESCC. Additionally, the combination of nivolumab and ipilimumab as first-line treatment also resulted in a significantly longer OS than chemotherapy alone (median, 12.7 vs. 10.7 months; hazard ratio, 0.78; 98.2% CI, 0.62 to 0.98; P = 0.01) (7). The studies of JUPITER-06, ORIENT-15 and ESCORT-1st have also confirmed that the combination of toripalimab, sintilimab or camrelizumab with chemotherapy leads to significant benefits in OS and PFS (4, 8, 9). In the second-line treatment of advanced EC patients, KEYNOTE-181 revealed pembrolizumab prolonged OS compared to chemotherapy in patients with PD-L1 CPS ≥ 10, while also presenting a reduced incidence of treatment-related adverse events (10). The ESCORT trial and RATIONALE-302 trial demonstrated that second-line camrelizumab and tislelizumab improved OS in patients with advanced or metastatic ESCC compared to chemotherapy (11, 12). Furthermore, in patients with PD-L1 TAP ≥ 10%, tislelizumab demonstrated a statistically significant survival advantage over chemotherapy (12). Therefore, the utilization of immunotherapy is progressively increasing, necessitating the requirement for convenient and cost-effective indicators to assess the prognosis.

The neutrophil-to-lymphocyte ratio (NLR), a systemic inflammatory marker, is determined by the ratio of circulating neutrophil counts to lymphocyte counts. The Platelet-to-Lymphocyte Ratio (PLR) is a quantitative measure of systemic inflammation, obtained by dividing the circulating platelet count by the lymphocyte count. Previous studies have demonstrated the prognostic role of NLR and PLR in many malignant tumors, such as lung cancer, breast cancer and prostate cancer (1316). The results of a meta-analysis have demonstrated that raised NLR and PLR are associated with unfavorable OS and PFS in advanced gastric cancer and gastroesophageal junction cancer patients undergoing immunotherapy (17). However, the prognostic significance of NLR and PLR in EC patients treated with immunotherapy remains controversial.

Therefore, we conducted a systematic review and meta-analysis to assess the prognostic roles of NLR and PLR in EC patients receiving immunotherapy.

2 Materials and methods

2.1 Search strategy

This systematic review and meta-analysis followed the PRISMA guidelines for the reporting of meta-analyses. The literature search was conducted across three databases: PubMed, Embase, and Web of Science. The primary deadline for literature retrieval was July 2024. The search strategy employed the following terms: (“Esophageal neoplasms” OR “Esophageal cancer” OR “Carcinoma, Esophagus”) AND (“NLR” OR “PLR” OR “neutrophil” OR “platelet”) AND (“immunotherapy” OR “PD” OR “checkpoint” OR “pembrolizumab” OR “nivolumab” OR “atezolizumab” OR “ipilimumab” OR “avelumab” OR “durvalumab” OR “camrelizumab” OR “tislelizumab” OR “Sintilimab”). The specific retrieval strategy is detailed in Supplementary Text S1.

This meta-analysis was registered in the PROSPERO network with the following ID: CRD42024596737.

2.2 Exclusion and inclusion criteria

The included studies met the following criteria: (a) patients with EC who received immunotherapy were included, regardless of treatment line; (b) investigation was conducted to determine the prognostic significance of baseline NLR or PLR in relation to OS or PFS; (c) the 95% confidence interval (CI) and hazard ratio (HR) could be obtained from the original studies; (d) publication in English literature was required.

The exclusion criteria were as follows: (a) systematic reviews, case reports, abstracts, letters, and expert opinions; (b) populations of patients with other primary tumors; (c) studies lacking sufficient data to conclude on the HR and 95% CI; (d) literature with Newcastle-Ottawa Scale (NOS) scores below 6; (e) non-English publications.

2.3 Literature’s data extraction and quality validation

Two authors independently extracted the following information from all eligible studies: The first author’s name, the year of publication, period of study, median follow-up (months), study design, country, sample size, pathological category, cut-off values, survival data (PFS or OS), and hazard ratio (HR) and 95% confidence interval (CI). The HRs from the multivariate analysis were initially extracted when both multivariate and univariate analyses were conducted. In this meta-analysis, we employed the median value of NLR or PLR cut-off from the studies included to determine subgroup analysis cut-off values.

We assessed the quality of the literature involved based on the scoring system of the Newcastle–Ottawa Scale (NOS) (18). The NOS encompasses three key components: patient selection, comparability, and outcome assessment. The studies rated 6 or higher were deemed to possess high quality. Studies with lower scores were considered low quality and thus excluded from the analysis.

2.4 Statistical analysis

HRs and their corresponding 95% CIs were pooled employing the generic inverse variance and random effects model. Heterogeneity was assessed using the Higgins I2 model. Significant heterogeneity was indicated when the values of I2≥50% and p<0.05, in which case a random-effects model was employed. Conversely, studies that did not exhibit significant heterogeneity were evaluated using a fixed-effects model. Subgroup analyses were also conducted to investigate potential factors influencing the prognostic significance of NLR and PLR. Subsequent sensitivity analyses were conducted to identify the sources of heterogeneity and evaluate the stability of the results. Egger’s test and funnel plots were conducted to assess potential publication bias, where a value of p<0.05 was considered a statistically significant difference. The statistical analysis was conducted using Review Manager version 5.4 and STATA version 15.0.

3 Results

3.1 Literature search and study characteristics

A total of 1170 articles were retrieved from the three databases. After removing duplicate entries, a preliminary screening based on titles and abstracts was conducted for 922 articles, out of which 897 were deemed irrelevant to the subject matter under review. Subsequently, a thorough examination of the full texts of the remaining 25 studies was performed, resulting in the exclusion of 9 studies according to our predefined exclusion criteria. Ultimately, this systematic review comprised 16 selected studies (1934). The selection process is summarized in Figure 1.

Figure 1

The primary characteristics of the studies included in the meta-analysis are presented in Table 1. In summary, the sample sizes of the cases ranged from 41 to 322 across these studies, which were all published between 2019 and 2024. 13 studies focused on patients diagnosed with ESCC, whereas the remaining 3 studies encompassed patients with various histological subtypes. The study conducted by Gao et al. analyzed data from 140 patients, with 130 (92.86%) diagnosed with squamous cell carcinoma, 4 (2.86%) with adenocarcinoma, and 6 (4.29%) with an unspecified histological subtype. Inoue et al.’s study included 41 patients, of whom 38 (92.68%) were diagnosed with squamous cell carcinoma. Sugase et al. examined data from 65 patients, comprising 62 cases (95.38%) of squamous cell carcinoma and 3 cases (4.62%) of adenocarcinoma. Among the included studies, 4 focused solely on NLR, while 12 evaluated both NLR and PLR. The meta-analysis comprised sixteen NLR studies with a total of 1481 cases and twelve PLR studies with a total of 1164 cases. All the studies were conducted in Asia, including 11 studies in China, 4 studies in Japan, and 1 study in Korea. The quality assessment using NOS scores revealed that the involved literature demonstrated high quality, with scores ranging from 7 to 9 among the sixteen studies. Detailed information regarding the quality assessment can be found in Supplementary Table S1.

Table 1

StudyYearStudy periodMedian follow-upcountryStudy designSample sizeMedian age(years)HistologystageICIBiomarkerCutoff methodNLR cut-offPLR cut-offOutcomesCenter disign
Chen
et al. (19)
2023Aug 2019- Aug 2021UnclearChinaR5467ESCCadvancedPembrolizumab,camrelizumab, sintilimab, tislelizumabNLR,PLRLiterature5170.5PFSSingle-center
Da
et al. (20)
2023Aug 2019- Feb 202216.9 monthsChinaR16266ESCCadvancedCamrelizumab, sintilimab, toripalimabNLR,PLRROC4.748250.505PFS,OSSingle-center
Gao
et al. (21)
2022Jan 2016-Mar 202020.0 monthsChinaR14060ECI-IVPembrolizumab,oripalizumab, Nivolumab, sintilimab, camrelizumabNLRUncler5NRPFS,OSSingle-center
GUO
et al. (22)
2019Aug 2015- Dec 2017UnclearChinaR4956.7ESCCadvancedundisclosedNLRMedian6.4NRPFS,OSMulti-center
Hamai et al. (23)2023Jun 2016- Dec 2021UnclearJapanR5969.4ESCCadvancedNivolumabNLRROC4.9NRPFS,OSSingle-center
Ikoma et al. (24)2023Jan 2017–Jun 20219.1 monthsJapanR9370ESCCadvancedNivolumabNLR,PLRROC3.18277OSMulti-center
INOUE et al. (25)2022Feb 2020- Apr 2022294 daysJapanR4168ECI-IVNivolumabNLR,PLRMedian3.401242.6PFS,OSSingle-center
Ji
et al. (26)
2023Oct 2016- Jul 2020unclearChinaR32260ESCCAdvancedCamrelizumab, sintinimabNLR,PLRROC4145PFS,OSMulti-center
Kim
et al. (27)
20222015-201916.0 monthsKoreaR6068ESCCAdvancedNivolumab, PembrolizumabNLR,PLRROC2.71216.35PFS,OSSingle-center
Liu
et al. (28)
2022Aug 2019- Oct 2021.11.4 monthsChinaR9067ESCCAdvancedCamrelizumabNLR,PLRROC3.84157.7PFS,OSSingle-center
Qi
et al. (29)
2023Mar 2019-Mar 202220 monthsChinaR5162ESCCII-IVAPembrolizumabNLR,PLRROC2.6150.63PFSSingle-center
Shang et al. (30)2024Jul 2020- Jun 2022.UnclearChinaR6463ESCCAdvancedCamrelizumabNLR,PLRROC4.6194.5PFS,OSSingle-center
Sugase et al. (31)2024Dec 2021- May 202311.6 monthsJapanP6565ECAdvancedPembrolizumabNLR,PLRROC4.6203PFS,OSSingle-center
Wang
et al. (33)
2022May 2016- Jul 2018.48.6 monthsChinaP6961ESCCAdvancedCamrelizumabNLRLiterature4NRPFS,OSSingle-center
Wang
et al. (32)
2023Oct 2019 -Oct 2021UnclearChinaR4361ESCCII-IVPembrolizumabNLR,PLRMedian2.43139.7OSSingle-center
Wu
et al. (34)
2021Dec 2018 -Sep 2020UnclearChinaR11961ESCCI-IVCamrelizumab, nivolumab, pembrolizumab, toripalimab, sintilimabNLR,PLRMedian3.23174.72PFSSingle-center

Included study of characteristics.

(R, Retrospective; P, Prospective; PLR, platelet-to-lymphocyte ratio; OS, overall survival;).

3.2 Influence of NLR on PFS

14 studies reported the correlation between NLR and PFS (Figure 2A). The results indicated that elevated NLR was significantly associated with poor PFS outcomes (HR=1.76, 95%CI:1.38-2.25, p<0.001). Due to substantial heterogeneity observed among the included studies (I2 = 71%, p<0.001), a random-effects model was employed for meta-analysis. In the subgroup analyses of tumor stage, a significant association between elevated NLR and poor PFS was observed only in advanced EC. The subgroup analyses, as presented in Table 2, demonstrated no association between elevated PLR and unfavorable PFS within subgroups characterized by prospective study, and geographical origin from Japan.

Figure 2

Table 2

VariablesNEffects modelPFSHeterogeneity
HR (95%CI)p-valueI2p-value
Total14Random1.76 (1.38, 2.25)<0.00171%<0.001
Cut-off value
 <45Random2.17 (1.24, 3.81)0.00667%0.020
 ≥49Random1.56 (1.24, 1.97)<0.00159%0.010
Cut-off method
 ROC8Random1.80 (1.30, 2.48)<0.00177%<0.001
 Not ROC6Random1.72 (1.16, 2.53)0.00652%0.060
Sample size
 <657Random1.76 (1.16, 2.66)0.00863%0.010
 ≥657Random1.78 (1.28, 2.48)<0.00168%0.005
Histology
 ESCC11Random1.71 (1.30, 2.25)<0.00171%<0.001
 EC3Random1.98 (1.08, 3.63)0.03067%0.050
Stage
 Advanced10Random1.75 (1.33, 2.32)<0.00173%<0.001
 I-IV3Random1.87 (0.90, 3.87)0.09075%0.020
 II-IVA11.45 (0.47, 4.47)0.520
Survival analysis
 Multivariate9Random1.80 (1.34, 2.42)<0.00176%<0.001
 Univariate5Random1.68 (1.02, 2.78)0.04058%0.050
Center
 Multicenter2Fixed1.47 (1.14, 1.90)0.00334%0.220
 Single center12Random1.79 (1.32, 2.43)<0.00174%<0.001
Study design
 Retrospective12Random1.83 (1.38, 2.43)<0.00175%<0.001
 Prospective2Fixed1.45 (0.97, 2.18)0.0700%0.430
Country
 China10Random1.81 (1.36, 2.42)<0.00155%0.020
 Japan3Random1.67 (0.89, 3.13)<0.00180%0.007
 Korea12.01 (1.08, 3.74)0.030
Follow-up
 <12months3Random2.60 (1.20, 5.64)0.02078%0.010
 ≥12months5Fixed2.03 (1.57, 2.63)<0.0010%0.480
 Unclear6Fixed1.22 (1.11, 1.34)<0.00111%0.350

NLR subgroup analysis for PFS.

(NLR, neutrophil-to-lymphocyte ratio; PFS, progression-free survival; HR, hazard ratio; CI, confidence interval).

Sensitivity analyses were conducted to assess the impact of individual studies on the relationship between NLR and PFS (Figure 2B; Supplementary Table S2). These analyses revealed that exclusion of any single study did not result in statistically significant changes regarding the influence of NLR on PFS outcomes. However, upon excluding Hamai et al.’s study, a notable decrease in heterogeneity was observed (I2 = 55%, p=0.009), yielding a pooled HR estimate of 1.86(95%CI:1.45-2.40, p<0.001).

3.3 NLR’s impact on OS

A total of 13 studies were included in the analysis to investigate the impact of NLR on OS, revealing high heterogeneity (I2 = 83%, p<0.001). Therefore, a random-effects model was employed, yielding a pooled hazard ratio (HR) of 2.61 (95% CI:1.86–3.67, p<0.001). These findings demonstrated that elevated NLR was significantly associated with worse OS in patients with EC (Figure 3A). Furthermore, apart from tumor stage II-IV, robust associations were confirmed between increased NLR and poor OS across all the subgroups, thus ensuring the reliability of our findings as presented in Table 3.

Figure 3

Table 3

VariablesNEffects modelOSHeterogeneity
HR (95%CI)p-valueI2p-value
Total13Random2.61 (1.86, 3.67)<0.00183%<0.001
Cut-off value
 <45Random3.12 (1.95, 5.00)<0.00150%0.090
 ≥48Random2.33 (1.58, 3.45)<0.00183%<0.001
Cut-off method
 ROC8Random2.14 (1.51, 3.03)<0.00181%<0.001
 Not ROC5Random3.84 (2.23, 6.60)<0.00153%0.070
Sample size
 <656Random2.62 (1.34, 5.13)0.00582%<0.001
 ≥657Fixed2.49 (2.06, 3.01)<0.00130%0.200
Histology
 ESCC10Random2.33 (1.65, 3.29)<0.00181%<0.001
 EC3Random3.96 (1.75, 8.97)0.00169%0.040
Stage
 Advanced10Random2.40 (1.71, 3.38)<0.00182%<0.001
 I-IV2Random5.74 (2.28, 14.43)<0.00156%0.130
 II-IV11.51 (0.49, 4.65)0.47
Survival analysis
 Multivariate9Random2.58 (1.74, 3.83)<0.00186%<0.001
 Univariate4Random2.71 (1.33, 5.50)<0.00161%0.050
Center
 Multicenter3Random2.72 (1.59, 4.65)<0.00164%0.060
 Single center10Random2.56 (1.65, 3.97)<0.00184%<0.001
Study design
 Retrospective11Random2.71 (1.84, 3.98)<0.00186%<0.001
 Prospective2Fixed2.23 (1.37, 3.63)0.0010%0.580
Country
 China9Fixed2.55 (2.10, 3.09)<0.00144%0.080
 Japan4Random2.46 (1.18, 5.13)0.02086%<0.001
 Korea12.25 (1.17, 4.33)0.020
Follow-up
 <12months4Random3.40 (1.97, 5.88)<0.00158%0.070
 ≥12months4Fixed2.87 (2.11, 3.91)<0.0010%0.550
 Unclear5Random1.89 (1.21, 2.95)0.00580%<0.001

NLR subgroup analysis for OS.

(NLR, neutrophil-to-lymphocyte ratio; OS, overall survival; HR, hazard ratio; CI, confidence interval).

Subsequently, sensitivity analyses were conducted to explore potential sources of heterogeneity for OS (Figure 3B; Supplementary Table S3), indicating that exclusion of any single study did not have a statistically significant impact for NLR’s influence on OS in this meta-analysis. After excluding the studies of Hamai et al, heterogeneity decreased to some extent (I2 = 43%, p=0.05), resulting in a merged HR of 2.79(95% CI:2.17-3.59, p<0.001).

3.4 Effect of PLR on PFS

10 studies were included in the analysis of the correlation between PFS and PLR (Figure 4A), employing a fixed-effects model due to low heterogeneity (I2 = 47%, p=0.050). The pooled results demonstrated a significant association between elevated PLR and poorer PFS (HR=1.44, 95% CI:1.20–1.72, p<0.001). As listed in Table 4, the subgroup analyses didn’t reveal the significant association between elevated PLR and poor PFS in subgroups with sample size≥65, tumor stage of I-IV and II-IV, multivariate survival analysis, multicenter studies, study conducted in Korea and follow-up duration≥12 months.

Figure 4

Table 4

VariablesNEffects modelPFSHeterogeneity
HR (95%CI)p-valueI2p-value
Total10Fixed1.44 (1.20, 1.72)<0.00147%0.050
Cut-off value
 <1855Fixed1.38 (1.10, 1.75)0.00649%0.100
 ≥1855Random1.61 (1.05, 2.48)0.03055%0.070
Cut-off method
 ROC7Random1.43 (1.04, 1.97)0.03051%0.060
 Not ROC3Fixed1.83 (1.24, 2.69)0.00229%0.240
Sample size
 <655Fixed1.72 (1.26, 2.35)<0.0018%0.360
 ≥655Random1.43 (0.96, 2.13)0.08063%0.030
Histology
 ESCC8Fixed1.33 (1.10, 1.61)0.00439%0.120
 EC2Fixed2.44 (1.47, 4.04)0.0050%0.440
Stage
 Advanced7Random1.51 (1.13, 2.03)0.00650%0.060
 I-IV2Random1.94 (0.83, 4.55)0.13064%0.100
 II-IV10.81 (0.27, 2.43)0.710
Survival analysis
 Multivariate3Random1.36 (0.75, 2.47)0.30077%0.010
 Univariate7Fixed1.69 (1.30, 2.20)<0.0010%0.510
Center
 Multicenter11.16 (0.85, 1.58)0.350
 Single center9Fixed1.60 (1.29, 2.00)<0.00144%0.080
Study design
 Retrospective9Random1.49 (1.12, 1.98)0.00650%0.040
 Prospective12.04 (1.03, 4.04)0.040
Country
 China7Fixed1.32 (1.08, 1.62)0.00848%0.080
 Japan2Fixed2.44 (1.47, 4.04)<0.0010%0.440
 Korea11.45 (0.80, 2.63)0.220
Follow-up
 <12months3Fixed2.61 (1.75, 3.88)<0.0010%0.690
 ≥12months3Fixed1.04 (0.72, 1.52)0.8300%0.370
 Unclear4Fixed1.32 (1.04, 1.68)0.0200%0.520

PLR subgroup analysis for PFS.

(PLR, platelet-to-lymphocyte ratio; PFS, progression-free survival; HR, hazard ratio; CI, confidence interval).

Sensitivity analyses were performed to evaluate the impact of individual studies on the association between PLR and PFS (Figure 4B; Supplementary Table S4). These analyses demonstrated that exclusion of any single study did not lead to statistically significant changes in terms of the influence of NLR on PFS outcomes.

3.5 PLR’s influence on OS

The data from 9 studies (Figure 5A) provided evidence on the impact of PLR on OS. A high degree of heterogeneity was observed among these studies (I2 = 73.00%, p<0.001). Consequently, a meta-analysis was conducted using the random-effect model, revealing a significant association between raised PLR and worse OS outcomes (HR=1.72,95%CI:1.08-2.74, p=0.020). The subgroup analyses, as presented in Table 5, demonstrated a significant correlation between elevated PLR and poor OS exclusively within subgroups characterized by cut-off method of not using receiver operating characteristic (ROC), sample size<65, histology of EC, tumor stage of I-IV, single-center study design, retrospective study, study conducted in China and duration of follow-up less than 12 months.

Figure 5

Table 5

VariablesNEffects modelOSHeterogeneity
HR (95%CI)p-valueI2p-value
Total9Random1.72 (1.08, 2.74)0.02073%<0.001
Cut-off value
 <1853Random2.02 (0.67, 6.11)0.21087%<0.001
 ≥1856Random1.60 (0.91, 2.81)0.01065%0.010
Cut-off method
 ROC7Random1.47 (0.92, 2.36)0.11071%0.002
 Not ROC2Fixed3.70 (1.70, 8.05)0.00140%0.200
Sample size
 <654Fixed2.17 (1.40, 3.35)<0.00148%0.120
 ≥655Random1.36 (0.74, 2.51)0.32078%0.001
Histology
 ESCC7Random1.48 (0.92, 2.38)0.11072%0.002
 EC2Random3.29 (1.10, 9.89)0.03055%0.140
Stage
 Advanced7Random1.47 (0.92, 2.36)0.11071%0.002
 I-IV15.69 (2.06, 15.72)<0.001
 II-IV12.01 (0.60, 6.73)0.260
Survival analysis
 Multivariate5Random1.70 (0.87, 3.31)0.12080%<0.001
 Univariate4Random1.80 (0.85, 3.79)0.12066%0.030
Center
 Multicenter2Fixed0.96 (0.71, 1.31)0.8000%0.780
 Single center7Random2.17 (1.21, 3.87)0.00970%0.003
Study design
 Retrospective8Random1.72 (1.04, 2.85)0.04076%<0.001
 Prospective11.85 (0.63, 5.43)0.260
Country
 China5Random1.72 (0.87, 3.39)0.12080%<0.001
 Japan3Random2.02 (0.66, 6.18)0.22076%0.020
 Korea11.37 (0.73, 2.57)0.330
Follow-up
 <12months4Random2.52 (1.02, 6.23)0.04076%0.005
 ≥12months2Fixed1.03 (0.67, 1.59)0.89033%0.220
 Unclear3Random1.54 (0.74, 3.20)0.25059%0.090

PLR subgroup analysis for OS.

(PLR, platelet-to-lymphocyte ratio; OS, overall survival; HR, hazard ratio; CI, confidence interval).

Regarding the sensitivity analyses of OS (Figure 5B; Supplementary Table S5), exclusion of the study conducted by Liu et al. resulted in a partial decrease in heterogeneity (I2 = 59%, p=0.020). However, the combined hazard ratio (HR) didn’t maintain statistical significance upon exclusion of the study conducted by Liu et al. (HR=1.45, 95%CI:0.96-2.18, p=0.020) or Inoue et al. (HR=1.50, 95%CI: 0.97-2.33, p=0.070), suggesting that the observed increase in PLR may lack robustness in terms of its impact on inferior OS.

3.6 Publication bias

The funnel plots exhibited visual asymmetry in terms of the influence of NLR on PFS (Figure 2C) and OS (Figure 3C), suggesting a significant presence of publication bias. This observation was further substantiated by the results obtained from Egger’s test for PFS (p = 0.019) and OS (p < 0.001). Subsequently, trim and fill methods were employed to investigate the impact of publication bias on effect estimates. No statistically significant alterations were observed in the findings (Figures 2D, 3D).

Regarding the influence of PLR on PFS (Figure 4C), the p-values of Egger’s test (p=0.255) did not indicate publication bias, as supported by symmetrical funnel plots upon visual inspection. The absence of publication bias detected by Egger’s test led us to refrain from employing additional trim and fill methods. In terms of the impact of PLR on OS (Figure 5C), the p-values of Egger’s test (p=0.022) revealed significant publication bias, which was evident from asymmetrical funnel plots upon visual examination. Furthermore, employing trim and fill methods once again demonstrated no statistically significant alterations in the results (Figures 5D).

4 Discussion

Esophageal cancer is a prevalent malignancy, and its management has been improved through continuous exploration of the disease and advancements in science and technology. Immune checkpoint inhibitors (ICIs) have demonstrated effective antitumor activity in patients with EC.

The utilization of immunotherapy in esophageal cancer is progressively expanding. However, the efficacy of immunotherapy may not be enhanced in all patients diagnosed with EC. Programmed cell death ligand 1 (PD-L1) and Tumor Mutational Burden (TMB) have emerged as biomarkers for predicting the efficacy of immunotherapy (35, 36). But their assessment requires complex and expensive laboratory techniques. Therefore, we need cost-effective, convenient, and rapid predictive biomarkers. Peripheral blood specimens, which exhibit high patient acceptance rates, are easier to obtain in clinical practice. Previous literatures have demonstrated that cancer-related inflammatory indicators, such as NLR and PLR, exhibit prognostic significance in patients with esophageal cancer undergoing immunotherapy (21, 22, 31).

The NLR, determined by the ratio of circulating neutrophil counts to lymphocyte counts, serves as a prognostic indicator for cancer patients (37, 38). Several studies have confirmed that neutrophils contribute to the processes of angiogenesis and immunosuppression (3941). Coussens et al. revealed that the MMP-9 produced by neutrophils contributes to the carcinogenesis of squamous carcinogenesis (42). Christoffersson et al. demonstrated that the extracellular matrix is degraded by MMP-9 released from neutrophils, leading to the release of vascular endothelial growth factor (VEGF) and promotion of angiogenesis (43). Moreover, the release of Arg-1 from neutrophils leads to the downregulation of CD3ζ chain translation in T cells, thereby contributing to the inhibition of T cell proliferation. This mechanism establishes an immunosuppressive microenvironment that also facilitates cancer growth (44). Neutrophils also contribute to cancer progression by releasing cytokines and growth factors, such as IL-6, TNF, epidermal growth factor, hepatocyte growth factor (HGF), and platelet-derived growth factor (41, 45). The high PLR indicates an elevated platelet count or a reduced lymphocyte count, which may be indicative of tumor recurrence and metastasis. Platelets provides a procoagulant surface that enhances the amplification of cancer-related coagulation, and can be recruited to envelop tumor cells, thereby shielding them from immune responses and promoting cancer growth and dissemination (46). Tao et al. proposed that platelets have been implicated in inducing epigenetic modifications, such as upregulation of oncoproteins within circulating tumor cells, and secretion of potent growth factors may contribute to the promotion of mitogenesis, angiogenesis, and metastatic outgrowth (47). Additionally, numerous clinical and experimental studies have established the crucial role of lymphocytes in the immune response against tumors (48), and lymphopenia is correlated with an unfavorable prognosis in patients with recurrent metastatic EC patients who undergo immunotherapy (49).

In this present study, we conducted a meta-analysis by merging 16 studies on NLR involving 1481 EC cases and 12 studies on PLR involving 1164 EC cases to investigate the prognostic impact of NLR and PLR in patients treated with immunotherapy. The results of our meta-analysis revealed a significant association between elevated NLR and adverse PFS and OS outcomes. Additionally, the study by Wang et al. also corroborated that elevated pretreatment NLR is correlated with poorer outcomes in cancer immunotherapy (50). The prognostic effect of NLR with PFS remained robust in subgroup analyses considering various factors such as cut-off value, cut-off method, sample size, histology, survival analysis, research center and follow-up duration. Despite observing high heterogeneity, the association between increased NLR and poor OS was further supported by all subgroup analyses. These findings further support the reliability of our meta-analysis. Additionally, our combined findings regarding PLR demonstrated that increased PLR was also associated with unfavorable PFS and OS outcomes. This observation aligns with the study by Zhou et al., which demonstrated that lung cancer patients with lower PLR had superior OS and PFS when undergoing immunotherapy (51).The subgroup analyses didn’t reveal the significant association between elevated PLR and poor PFS in subgroups with sample size≥65, tumor stage of I-IV and II-IV, multivariate survival analysis, multicenter studies, study conducted in Korea and follow-up duration ≥12 months. Additionally, the subgroup analyses demonstrated a significant correlation between elevated PLR and poor OS exclusively within subgroups characterized by cut-off method of not using receiver operating characteristic (ROC), sample size<65, histology of EC, tumor stage of I-IV, single-center study design, retrospective study, study conducted in China and duration of follow-up less than 12 months. The combined hazard ratio (HR) didn’t remain significant upon exclusion of the study conducted by Liu et al. or Inoue et al. These findings suggest that the combined outcomes of PLR and OS may lack robustness, potentially due to the limited number of studies included in the meta-analysis. In the subgroup analyses of advanced EC patients undergoing immunotherapy, a significant association was observed between increased NLR and poor PFS as well as OS, while an elevated PLR was found to be linked with inferior PFS. Consistent with these findings, Matsas et al. reported that elevated NLR and PLR were also linked to unfavorable OS and PFS outcomes in patients with advanced gastric cancer (GC) and gastroesophageal junction cancer (GEJC) treated with immunotherapy (17).

As a literature-dependent meta-analysis, several limitations of this study should be acknowledged. Firstly, the majority of included studies were retrospective with small sample sizes, potentially introducing selection bias and influencing the findings. Therefore, it is imperative to conduct more large-scale prospective studies to validate our results. Secondly, only English-language publications were considered in this analysis, excluding non-English studies and unpublished data which may have limited the available evidence for analysis. Thirdly, variations in characteristics such as cut-off value, sample size, tumor stage, survival analysis, and follow-up duration across different studies could contribute to substantial heterogeneity observed in the meta-analysis. Fourthly, we can’t disregard the possibility that non-tumor-related factors might impact patients’ blood markers. Lastly but importantly, there was a lack of standardized cut-off values for NLR or PLR among included studies in this meta-analysis. The range of NLR cut-off values varied from 2.43 to 6.40 while PLR cut-off values ranged from 139.7 to 277 across different studies involved herein, thus limiting clinical applicability and necessitating standardization efforts for NLR and PLR thresholds.

5 Conclusion

Despite its limitations, our meta-analysis found the association between elevated peripheral blood NLR or PLR and inferior PFS and OS in EC patients receiving immunotherapy. These findings suggest that NLR and PLR levels hold promise as prognostic biomarkers in clinical practice, offering valuable guidance for personalized immunotherapy strategies. Future investigations should focus on prospective, multi-center, large-scale studies to validate the results of this meta-analysis and facilitate its integration with other prognostic indicators.

Statements

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 author.

Author contributions

MD: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. YQ: Writing – original draft, Writing – review & editing, Conceptualization. DQ: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft. YS: Writing – original draft, Writing – review & editing, Investigation, Methodology. JZ: Writing – original draft, Writing – review & editing, Methodology, Project administration. LS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, 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.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2025.1536920/full#supplementary-material

References

  • 1

    SungHFerlayJSiegelRLLaversanneMSoerjomataramIJemalAet al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clin. (2021) 71:209–49. doi: 10.3322/caac.21660

  • 2

    AbnetCCArnoldMWeiWQ. Epidemiology of esophageal squamous cell carcinoma. Gastroenterology. (2018) 154:360–73. doi: 10.1053/j.gastro.2017.08.023

  • 3

    SunJMShenLShahMAEnzingerPAdenisADoiTet al. Pembrolizumab plus chemotherapy versus chemotherapy alone for first-line treatment of advanced oesophageal cancer (KEYNOTE-590): a randomised, placebo-controlled, phase 3 study. Lancet. (2021) 398:759–71. doi: 10.1016/S0140-6736(21)01234-4

  • 4

    LuZWangJShuYLiuLKongLYangLet al. Sintilimab versus placebo in combination with chemotherapy as first line treatment for locally advanced or metastatic oesophageal squamous cell carcinoma (ORIENT-15): multicentre, randomised, double blind, phase 3 trial. BMJ. (2022) 377:e068714. doi: 10.1136/bmj-2021-068714

  • 5

    KellyRJAjaniJAKuzdzalJZanderTVan CutsemEPiessenGet al. Adjuvant nivolumab in resected esophageal or gastroesophageal junction cancer. N Engl J Med. (2021) 384:1191–203. doi: 10.1056/NEJMoa2032125

  • 6

    KellyRJ. Emerging multimodality approaches to treat localized esophageal cancer. J Natl Compr Canc Netw. (2019) 17:1009–14. doi: 10.6004/jnccn.2019.7337

  • 7

    DokiYAjaniJAKatoKXuJWyrwiczLMotoyamaSet al. Nivolumab combination therapy in advanced esophageal squamous-cell carcinoma. N Engl J Med. (2022) 386:449–62. doi: 10.1056/NEJMoa2111380

  • 8

    WangZXCuiCYaoJZhangYLiMFengJet al. Toripalimab plus chemotherapy in treatment-naive, advanced esophageal squamous cell carcinoma (JUPITER-06): A multi-center phase 3 trial. Cancer Cell. (2022) 40:277288.e273. doi: 10.1016/j.ccell.2022.02.007

  • 9

    LuoHLuJBaiYMaoTWangJFanQet al. Effect of camrelizumab vs placebo added to chemotherapy on survival and progression-free survival in patients with advanced or metastatic esophageal squamous cell carcinoma: the ESCORT-1st randomized clinical trial. JAMA. (2021) 326:916–25. doi: 10.1001/jama.2021.12836

  • 10

    KojimaTShahMAMuroKFrancoisEAdenisAHsuCHet al. Randomized phase III KEYNOTE-181 study of pembrolizumab versus chemotherapy in advanced esophageal cancer. J Clin Oncol. (2020) 38:4138–48. doi: 10.1200/JCO.20.01888

  • 11

    HuangJXuJChenYZhuangWZhangYChenZet al. Camrelizumab versus investigator’s choice of chemotherapy as second-line therapy for advanced or metastatic oesophageal squamous cell carcinoma (ESCORT): a multicentre, randomised, open-label, phase 3 study. Lancet Oncol. (2020) 21:832–42. doi: 10.1016/S1470-2045(20)30110-8

  • 12

    ShenLKatoKKimSBAjaniJAZhaoKHeZet al. Tislelizumab versus chemotherapy as second-line treatment for advanced or metastatic esophageal squamous cell carcinoma (RATIONALE-302): A randomized phase III study. J Clin Oncol. (2022) 40:3065–76. doi: 10.1200/JCO.21.01926

  • 13

    DengMMaXLiangXZhuCWangM. Are pretreatment neutrophil-lymphocyte ratio and platelet-lymphocyte ratio useful in predicting the outcomes of patients with small-cell lung cancer? Oncotarget. (2017) 8:37200–7. doi: 10.18632/oncotarget.16553

  • 14

    CaoWYuHZhuSLeiXLiTRenFet al. Clinical significance of preoperative neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in the prognosis of resected early-stage patients with non-small cell lung cancer: A meta-analysis. Cancer Med. (2022) 12:7065–76. doi: 10.1002/cam4.5505

  • 15

    KimJYJungEJKimJMLeeHSKwagSJParkJHet al. Dynamic changes of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio predicts breast cancer prognosis. BMC Cancer. (2020) 20:1206. doi: 10.1186/s12885-020-07700-9

  • 16

    HusznoJKoloszaZMrochem-KwarciakJTelkaEJochymekBMiszczykL. Role of neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, lymphocyte-monocyte ratio and platelets in prognosis of patients with prostate cancer. Oncol Lett. (2022) 24:305. doi: 10.3892/ol.2022.13425

  • 17

    MatsasSAguiarPNJr.Del GiglioA. Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as biomarkers to prognosticate survival in advanced gastric cancer patients in the era of immunotherapy: a systematic review and meta-analysis. J Gastrointestinal Oncol. (2024) 15:3351. doi: 10.21037/jgo-23-808

  • 18

    WellsGSheaBO’ConnellDPetersonJWelchVLososMet al. The newcastle-ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Available online at: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (Accessed 25 April 2022).

  • 19

    ChenWLiDBianXWuYXuMWuMet al. Peripheral blood markers predictive of progression-free survival in advanced esophageal squamous cell carcinoma patients treated with PD-1 inhibitors plus chemotherapy as first-line therapy. Nutr Cancer. (2023) 75:207–18. doi: 10.1080/01635581.2022.2123533

  • 20

    DaLQuZZhangCShenYHuangWZhangYet al. Prognostic value of inflammatory markers and clinical features for survival in advanced or metastatic esophageal squamous cell carcinoma patients receiving anti-programmed death 1 treatment. Front Oncol. (2023) 13:1144875. doi: 10.3389/fonc.2023.1144875

  • 21

    GaoYZhangZLiYChenSLuJWuLet al. Pretreatment neutrophil-to-lymphocyte ratio as a prognostic biomarker in unresectable or metastatic esophageal cancer patients with anti-PD-1 therapy. Front Oncol. (2022) 12:834564. doi: 10.3389/fonc.2022.834564

  • 22

    GuoJCLinCCLinCYHsiehMSKuoHYLienMYet al. Neutrophil-to-lymphocyte ratio and use of antibiotics associated with prognosis in esophageal squamous cell carcinoma patients receiving immune checkpoint inhibitors. Anticancer Res. (2019) 39:5675–82. doi: 10.21873/anticanres.13765

  • 23

    HamaiYEmiMIbukiYKurokawaTYoshikawaTOhsawaMet al. Ability of blood cell parameters to predict clinical outcomes of nivolumab monotherapy in advanced esophageal squamous cell carcinoma. OncoTargets Ther. (2023) 16:263–73. doi: 10.2147/OTT.S404926

  • 24

    IkomaTShimokawaMMatsumotoTBokuSYasudaTShibataNet al. Inflammatory prognostic factors in advanced or recurrent esophageal squamous cell carcinoma treated with nivolumab. Cancer Immunology Immunotherapy. (2023) 72:427–35. doi: 10.1007/s00262-022-03265-7

  • 25

    InoueHShiozakiAFujiwaraHKonishiHKiuchiJOhashiTet al. Absolute lymphocyte count and C−reactive protein−albumin ratio can predict prognosis and adverse events in patients with recurrent esophageal cancer treated with nivolumab therapy. Oncol Lett. (2022) 24:257–66. doi: 10.3892/ol.2022.13377

  • 26

    JiSZhaoCLiuRWangYYangQYangHet al. A combined immune prognostic index in esophageal squamous cell carcinoma patients treated with anti-PD-1 therapy. Ther Adv Med Oncol. (2023) 15:1–12. doi: 10.1177/17588359231174869

  • 27

    KimJHAhnBHongS-MJungH-YKimDHChoiKDet al. Real-world efficacy data and predictive clinical parameters for treatment outcomes in advanced esophageal squamous cell carcinoma treated with immune checkpoint inhibitors. Cancer Res Treat. (2022) 54:505–16. doi: 10.4143/crt.2020.1198

  • 28

    LiuJGaoDLiJHuGLiuJLiuD. The predictive value of systemic inflammatory factors in advanced, metastatic esophageal squamous cell carcinoma patients treated with camrelizumab. OncoTargets Ther. (2022) 15:1161–70. doi: 10.2147/OTT.S382967

  • 29

    QiWXWangXLiCLiSLiHXuFet al. Pretreatment absolute lymphocyte count is an independent predictor for survival outcomes for esophageal squamous cell carcinoma patients treated with neoadjuvant chemoradiotherapy and pembrolizumab: An analysis from a prospective cohort. Thorac Cancer. (2023) 14:1556–66. doi: 10.1111/1759-7714.14898

  • 30

    ShangHChenYWangQYangYZhangJ. A correlation evaluation between the peripheral blood index and the prognosis of advanced esophageal squamous cell carcinoma patients treated with camrelizumab. J Inflammation Res. (2024) 17:2009–21. doi: 10.2147/JIR.S450669

  • 31

    SugaseTKanemuraTTakeokaTMatsuuraNMasuikeYShinnoNet al. Survival impact of inflammation-based prognostic scores in metastatic or unresectable esophageal cancer treated with pembrolizumab plus chemotherapy. J Immunotherapy. (2024) 47:249–57. doi: 10.1097/CJI.0000000000000529

  • 32

    WangH-CHuangXChenJLiYCongYQuB-Let al. Long-term efficacy and predictors of pembrolizumab-based regimens in patients with advanced esophageal cancer in the real world. World J Gastroenterol. (2023) 29:5641–56. doi: 10.3748/wjg.v29.i41.5641

  • 33

    WangLZhuYZhangBWangXMoHJiaoYet al. Prognostic and predictive impact of neutrophil-to-lymphocyte ratio and HLA-I genotyping in advanced esophageal squamous cell carcinoma patients receiving immune checkpoint inhibitor monotherapy. Thorac Cancer. (2022) 13:1631–41. doi: 10.1111/1759-7714.14431

  • 34

    WuXHanRZhongYWengNZhangA. Post treatment NLR is a predictor of response to immune checkpoint inhibitor therapy in patients with esophageal squamous cell carcinoma (vol 21, 356, 2021). Cancer Cell Int. (2021) 21:356–66. doi: 10.1186/s12935-021-02401-0

  • 35

    DoroshowDBBhallaSBeasleyMBShollLMKerrKMGnjaticSet al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol. (2021) 18:345–62. doi: 10.1038/s41571-021-00473-5

  • 36

    BanchereauRLengNZillOSokolELiuGPavlickDet al. Molecular determinants of response to PD-L1 blockade across tumor types. Nat Commun. (2021) 12:3969. doi: 10.1038/s41467-021-24112-w

  • 37

    TempletonAJMcNamaraMGSerugaBVera-BadilloFEAnejaPOcanaAet al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. (2014) 106:dju124. doi: 10.1093/jnci/dju124

  • 38

    LiSQianYXieWLiXWeiJWangLet al. Identification and validation of neutrophils-related subtypes and prognosis model in triple negative breast cancer. J Cancer Res Clin Oncol. (2024) 150:149. doi: 10.1007/s00432-024-05651-3

  • 39

    JaillonSPonzettaADi MitriDSantoniABonecchiRMantovaniA. Neutrophil diversity and plasticity in tumour progression and therapy. Nat Rev Cancer. (2020) 20:485503. doi: 10.1038/s41568-020-0281-y

  • 40

    MackeyJBGCoffeltSBCarlinLM. Neutrophil maturity in cancer. Front Immunol. (2019) 10:1912. doi: 10.3389/fimmu.2019.01912

  • 41

    XiongSDongLChengL. Neutrophils in cancer carcinogenesis and metastasis. J Hematol Oncol. (2021) 14:173. doi: 10.1186/s13045-021-01187-y

  • 42

    CoussensLMTinkleCLHanahanDWerbZ. MMP-9 supplied by bone marrow-derived cells contributes to skin carcinogenesis. Cell. (2000) 103:481–90. doi: 10.1016/s0092-8674(00)00139-2

  • 43

    ChristofferssonGVagesjoEVandoorenJLidenMMassenaSReinertRBet al. VEGF-A recruits a proangiogenic MMP-9-delivering neutrophil subset that induces angiogenesis in transplanted hypoxic tissue. Blood. (2012) 120:4653–62. doi: 10.1182/blood-2012-04-421040

  • 44

    RomanoAParrinelloNLVetroCTibulloDGiallongoCLa CavaPet al. The prognostic value of the myeloid-mediated immunosuppression marker Arginase-1 in classic Hodgkin lymphoma. Oncotarget. (2016) 7:67333–46. doi: 10.18632/oncotarget.12024

  • 45

    JablonskaEKilukMMarkiewiczWPiotrowskiLGrabowskaZJablonskiJ. TNF-alpha, IL-6 and their soluble receptor serum levels and secretion by neutrophils in cancer patients. Arch Immunol Ther Exp (Warsz). (2001) 49:63–9. https://www.ncbi.nlm.nih.gov/pubmed/11266093.

  • 46

    BambaceNMHolmesCE. The platelet contribution to cancer progression. J Thromb Haemost. (2011) 9:237–49. doi: 10.1111/j.1538-7836.2010.04131.x

  • 47

    TaoDLTassi YungaSWilliamsCDMcCartyOJT. Aspirin and antiplatelet treatments in cancer. Blood. (2021) 137:3201–11. doi: 10.1182/blood.2019003977

  • 48

    NagaiSAbouljoudMSKazimiMBrownKAMoonkaDYoshidaA. Peritransplant lymphopenia is a novel prognostic factor in recurrence of hepatocellular carcinoma after liver transplantation. Transplantation. (2014) 97:694701. doi: 10.1097/01.TP.0000437426.15890.1d

  • 49

    ZhaoQBiYXueJLiuYZhuJQinS. Prognostic value of absolute lymphocyte count in patients with advanced esophageal cancer treated with immunotherapy: a retrospective analysis. Ann Transl Med. (2022) 10:744. doi: 10.21037/atm-22-2669

  • 50

    WangHYangRLiuDLiW. Association of pretreatment neutrophil-to-lymphocyte ratio with clinical outcomes in cancer immunotherapy: An evidence synthesis from 30 meta-analyses. Int Immunopharmacol. (2024) 132:111936. doi: 10.1016/j.intimp.2024.111936

  • 51

    ZhouKCaoJLinHLiangLShenZWangLet al. Prognostic role of the platelet to lymphocyte ratio (PLR) in the clinical outcomes of patients with advanced lung cancer receiving immunotherapy: A systematic review and meta-analysis. Front Oncol. (2022) 12:962173. doi: 10.3389/fonc.2022.962173

Summary

Keywords

esophageal cancer (EC), immunotherapy, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), prognosis, meta-analysis

Citation

Deng M, Qing Y, Qiu D, Sheng Y, Zhou J and Sun L (2025) The prognostic value of pretreatment neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in patients with esophageal cancer undergoing immunotherapy: a systematic review and meta-analysis. Front. Oncol. 15:1536920. doi: 10.3389/fonc.2025.1536920

Received

29 November 2024

Accepted

22 January 2025

Published

14 February 2025

Volume

15 - 2025

Edited by

Alessandro Mangogna, University of Udine, Italy

Reviewed by

Yaniv Zager, AdventHealth, United States

Xueyin Zhou, Wenzhou Medical University, China

Updates

Copyright

*Correspondence: Lan Sun,

Disclaimer

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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