BioMed Research International

BioMed Research International / 2017 / Article

Review Article | Open Access

Volume 2017 |Article ID 9503025 | 11 pages | https://doi.org/10.1155/2017/9503025

High Platelet-to-Lymphocyte Ratio Predicts Poor Prognosis and Clinicopathological Characteristics in Patients with Breast Cancer: A Meta-Analysis

Academic Editor: Sabine Rohrmann
Received27 Mar 2017
Revised10 Jun 2017
Accepted11 Jul 2017
Published31 Aug 2017

Abstract

Background. We aimed to evaluate the correlation of platelet-to-lymphocyte ratio (PLR) with prognosis and clinicopathological characteristics of breast cancer. Methods. The PubMed and Embase databases were searched. Hazard ratio (HR) with 95% confidence interval (CI) was used to summarize disease-free survival (DFS) and overall survival (OS). Odds ratio (OR) was used to summarize tumor clinicopathological characteristics. Results. High PLR was associated with poor DFS and OS (DFS: HR = 1.47, 95% CI = 1.16–1.85, and Tau2 = 0.070; OS: HR = 1.88, 95% CI = 1.27–2.80, and Tau2 = 0.192). A Galbraith plot indicated that the studies by Allan et al. and Cihan et al. contributed the heterogeneity of DFS and OS, respectively. There were significant differences in the incidence of high PLR between stage II–IV and stage I groups (OR = 1.86, 95% CI = 1.20–2.90, and Tau2 < 0.001), between lymph node-positive and lymph node-negative groups (OR = 1.52, 95% CI = 1.22–1.91, and Tau2 =0.014), and between metastasis-positive and metastasis-negative groups (OR = 4.24, 95% CI = 2.73–6.59, and Tau2 < 0.001). Conclusions. Our results indicated that PLR was associated with poor prognosis of breast cancer and adequately predicted clinicopathological characteristics.

1. Introduction

Breast cancer is the most frequently diagnosed cancer in women worldwide, accounting for approximately 25% (1.68 million) of all cancer cases [1]. Clinical treatment decisions based on tumor characteristics, patient characteristics, and response to treatment have been widely used for breast cancer and contribute to the decreased mortality [24]. Nevertheless, the heterogeneity of breast cancer is characterized by its variable natural course and patients’ response to treatment and, for this reason, breast cancer is still the leading cause of cancer death in women, accounting for approximately 15% (521,900) of all cancer deaths worldwide [1].

Several translational medicine studies have reported that the interaction between tumor and host microenvironments, including inflammation and immune response, plays an important role in tumor progression and prognosis [59]. Recently, platelet-to-lymphocyte ratio (PLR) has become an attractive, convenient, and cost-effective blood-derived prognostic marker as well as an inflammation-related and immune-related prognostic score to evaluate the prognosis of several solid tumors. Indeed, studies showed that elevated PLR was associated with poor prognosis for colorectal cancer [10, 11]. Moreover, several studies showed that elevated PLR was a good predictor for poor prognosis in gastric cancer and lung cancer [1214]. However, the relationship between PLR and prognosis of breast cancer is controversial and has not been confirmed [1522]. In addition, it is not known whether the PLR can predict the clinicopathological characteristics of breast cancer.

To date, no meta-analyses have evaluated the relationship between PLR and clinicopathological characteristics and prognosis in breast cancer. Therefore, the purpose of our study was to evaluate the clinicopathological and prognostic significance of the PLR in breast cancer.

2. Materials and Methods

2.1. Search Strategy

A systematic literature search of relevant studies was conducted using PubMed and Embase databases, with no restrictions on country of publication or year of publication (up to May 2017). We included English publications because English is the most widely used language worldwide. The main keywords and MeSH terms used were “platelet-to-lymphocyte ratio”, “platelet-lymphocyte ratio”, “platelet to lymphocyte ratio”, “PLR”, “thrombocyte-lymphocyte ratio”, “thrombocyte to lymphocyte ratio”, “breast cancer”, “breast tumor”, “breast neoplasms”, and “breast carcinoma”. We also performed a manual search of the reference lists of the retrieved studies and reviews to identify potential studies.

2.2. Eligibility Criteria

The studies that met the following inclusion criteria were (1) those that evaluated patients with breast cancer only; (2) those that measured PLR in the blood of patients with breast cancer; (3) those that evaluated the prognostic values of PLR in breast cancer; and (4) the outcome measures of interest that could be extracted directly or could be calculated from the published data indirectly. And studies were excluded from the meta-analysis if the outcome measures were not reported and could not be calculated from the published data. If several duplicated studies based on the same patient population met the inclusion criteria, only the most informative study was included.

2.3. Data Extraction and Quality Assessment

Data extraction and quality assessment of the studies included were performed by two reviewers (Miao Zhang and Xuan-zhang Huang), independently. The following data were extracted: first author, country, year of publication, sample size, patient characteristics, sampling time, cut-off point, follow-up period, clinicopathological characteristics of the tumor, and prognostic value (overall survival [OS] and disease-free survival [DFS]).

We used Newcastle-Ottawa Scale (NOS) criteria to assess the quality of the included studies [23]. Moreover, any disagreements in data extraction and quality assessment were resolved via discussion.

2.4. Statistical Analysis

Our meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement (Supplemental File 1: PRISMA Checklist in Supplementary Material available online at https://doi.org/10.1155/2017/9503025) [24]. We used hazard ratios (HR) with corresponding 95% confidence intervals (CI) as effect measures to evaluate the relationship between PLR and breast cancer prognosis. The included studies were retrospective studies, and the PLR level was measured at the time of diagnosis without a follow-up period; thus PLR was used as a variable but not a predictor, and odds ratio (OR) was used as an effect measure when assessing the association between PLR and clinicopathological characteristics. The OR represented the odds of occurrence of an unfavorable event in a high PLR level compared with a low level. In cases in which the HR, OR, and 95% CI could not be extracted directly, they were manually calculated from published data [25, 26]. Then, the log HR, log OR, and corresponding standard error (SE) were used to compute a pooled effect measures. Moreover, subgroup analyses were performed on the basis of the sampling time, sample size, cut-off point, and the status of receptors (estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 [HER2]).

The heterogeneity between the studies was calculated using tau-squared (Tau2) statistics, statistics, and Cochran test [27]. The Tau2 statistics represented the real measure of between-study heterogeneity, and the statistics represented the approximate proportion of total variability that could be attributed to heterogeneity [28]. The heterogeneity was considered statistically significant when the value was <0.05 in the test and/or was >50%, and then the random-effect model was used to pool the effect measures; otherwise, a fixed effects model was used. The potential sources of heterogeneity were explored by subgroup analyses and Galbraith plot. Publication bias was evaluated using Begg’s and Egger’s tests and funnel plot. In addition, in cases of significant publication bias, we performed trim-and-fill analysis to assess the effect of this bias [2931].

All statistical analyses were performed using Stata software version 12.0 (Stata Corporation, College Station, TX, USA). Two-sided values < 0.05 were considered statistically significant.

3. Results

3.1. Study Selection and Study Characteristics

Three hundred and ten relevant studies were initially identified; of these, 244 studies were excluded after reviewing the titles and abstracts. The full texts of the remaining 66 studies were evaluated. An additional 54 studies were excluded and twelve studies were identified as eligible in the meta-analysis (Figure 1) [1522, 3235].

The twelve eligible studies evaluated 5542 patients with breast cancer (median and range of sample size: 668 and 62–1435; average sample size: 462). The studies were performed in Turkey, Malaysia, China, Austria, USA, Japan, Costa Rica, and the United Kingdom. The analysis of the sampling time indicated that nine studies included patients with preoperative PLR [1622, 32, 33], two studies included patients with preoperative or prechemoradiotherapy PLR [15, 34], and one study did not report the sampling time [35]. Of the eligible studies, eleven studies assessed the relationship between DFS and PLR [1522, 32, 33, 35], and eight studies assessed the relationship between OS and PLR [1621, 33, 35]. The detailed study characteristics and study quality are summarized in Table 1.


ArticleCountryYearNumber of patientsAge: mean ± SD/median (range)Follow-up: mean ± SD/median (range)Cut-off pointOutcomeStudy quality

Takeuchi et al. [22]Japan2017296NRMean: 41162.28DFS: 2.61 (1.07–6.36)7
Asano et al. [20]Japan2016177NRRange: 1.2–72150DFS: 2.84 (1.37–5.91)
OS: 2.62 (1.09–6.34)
7
Liu et al. [21]China2016318Median: 45 (19–71)Median: 58.1 (5.9–136.1)147DFS: 1.18 (0.9–1.55)
OS: 1.30 (0.95–1.79)
6
Allan et al. [19]Costa Rica2016172Mean: 54.2 ± 12.7Median: 79.3 (2–90)250DFS: 4.13 (1.60–10.71)
OS: 3.86 (1.51–9.89)
7
Ulas et al. [18]Turkey2015187Mean: 51.4 ± 10.7Median: 26.0 (6.0–84.0)161.28DFS: 0.89 (0.42–1.88)
OS: 1.88 (0.57–6.21)
7
Koh et al. [15]Malaysia20151435Median: 52NR185DFS: 1.25 (1.04–1.52)6
Gunduz et al. [32]Turkey201562Median: 52 (24–73)Median: 48.4200DFS: 2.35 (1.33–4.16)6
Yao et al. [33]China2014608Mean: 52.4 ± 10.8; range: 26–86Median: 42 (8–62)107.64DFS: 1.33 (0.79–2.25)
OS: 2.54 (1.00–6.45)
6
Krenn-Pilko et al. [16]Austria2014793Mean: 57.9 ± 12.2Median: 98 ± 29.2292DFS: 2.03 (1.03–4.02)
OS: 1.92 (1.01–3.67)
7
Cihan et al. [17]Turkey2014350Mean: 55.2 ± 0.3; range: 26–86Range: 0.3–112160DFS: 0.90 (0.50–1.70)
OS: 0.70 (0.40–1.40)
6
Azab et al. [34]USA2013437Mean: 63.6Mean: 60 ± 0.7185OS: 2.90 (1.84–4.58)7
Cordiner et al. [35]UK2011707NRMedian: 48.1NRDFS: 0.96 (0.58–1.59)NR

Note. DFS: disease-free survival; NR: not reported; OS: overall survival; PLR: platelet to lymphocyte ratio; SD: standard deviation; UK: United Kingdom; USA: United States of America.

4. PLR and Survival

4.1. PLR and DFS

Eleven studies evaluated the relationship between DFS and PLR [1522, 32, 33, 35]. Our results indicate that DFS was significantly shorter in breast cancer patients with high PLR level compared with those with low PLR level (HR = 1.47 and 95% CI = 1.16–1.85; Figure 2), with significant heterogeneity (Tau2 = 0.070 and ). Galbraith plot showed that the study by Allan et al. [19] might contribute to the heterogeneity (Figure 3); as expected, the heterogeneity decreased (Tau2 = 0.046) after this study was excluded and the prognostic value of PLR was confirmed (HR = 1.30 and 95% CI = 1.14–1.48; Figure 2).

The subgroup analysis based on preoperative PLR (HR = 1.66, 95% CI = 1.21–2.27, Tau2 = 0.126, and ) and sample size (sample ≥ 400: HR = 1.26, 95% CI = 1.07–1.48, Tau2 = 0.001, and ; sample < 400: HR = 1.61, 95% CI = 1.14–2.28, Tau2 = 0.142, and ) provided a similar result (Table 2). The subgroup analysis stratified by cut-off point indicated that the prognostic value was significant when the cut-off point was set at ≥180 (HR = 1.99, 95% CI = 1.20–3.31, Tau2 = 0.180, and ) and at <180 (HR = 1.27, 95% CI = 1.04–1.56, Tau2 = 0.073, and ). Moreover, the subgroup analysis based on the status of the receptor showed that the prognostic effect of PLR on DFS was similar among the patients with positive hormone receptors (ER+ or PR+) (HR = 1.59, 95% CI = 1.18–2.13, Tau2 = 0.021, and ), HER2 (HR = 1.30, 95% CI = 1.07–1.57, Tau2 < 0.001, and ), or triple-negative status (HR = 1.39, 95% CI = 1.04–1.86, Tau2 < 0.001, and ). And high PLR tended toward a lower effect of DFS on HER2+ (HR = 1.29, 95% CI = 0.85–1.96, Tau2 = 0.155, and ) and negative hormone receptors (ER and PR) (HR = 1.14, 95% CI = 0.93–1.41, Tau2 < 0.001, and ) breast cancer, although statistical significance was not reached. The detailed results of subgroup analyses are summarized in Table 2.


Disease-free survivalOverall survival

OverallHR = 1.47 [1.16–1.85], Tau2 = 0.070, %HR = 1.88 [1.27–2.80], Tau2 = 0.192, %
Without Allan: HR = 1.30 [1.14–1.48], Tau2 = 0.046, %Without Cihan: HR = 1.89 [1.52–2.34], Tau2 = 0.099, %
Sampling time
 PreoperativeHR = 1.66 [1.21–2.27], Tau2 = 0.126, %HR = 1.71 [1.14–2.57], Tau2 = 0.155, %
Without Allan: HR = 1.53 [1.13–2.06], Tau2 = 0.090, %Without Cihan: HR = 1.67 [1.30–2.13], Tau2 = 0.062, %
Sample size
 ≥400HR = 1.26 [1.07, 1.48], Tau2 = 0.001, %HR = 2.53 [1.79, 3.57], , %
 <400HR = 1.61 [1.14–2.28], Tau2 = 0.142, %HR = 1.60 [0.93–2.75], , %
Without Allan: HR = 1.47 [1.06–2.03], Tau2 = 0.099, %Without Allan: HR = 1.32 [0.80–2.16], Tau2 = 0.130, %
Metastatic status
 M0HR = 1.86 [1.18–2.92], Tau2 = 0.192, %HR = 1.62 [1.25–2.08], Tau2 = 0.079, %
Without Allan: HR = 1.63 [1.06–2.50], Tau2 = 0.129, %/
Cut-off point
 ≥180HR = 1.99 [1.20–3.31], Tau2 = 0.180, %HR = 2.68 [1.89–3.78], Tau2 < 0.001, %
Without Allan: HR = 1.68 [1.07, 2.64], Tau2 = 0.103, %
 <180HR = 1.27 [1.04–1.56], Tau2 = 0.073, %HR = 1.46 [0.92–2.31], Tau2 = 0.135, %
/Without Cihan: HR = 1.51 [1.14–1.99], Tau2 = 0.033, %
Receptor status
 Her2+HR = 1.29 [0.85–1.96], Tau2 = 0.155, %/
 Her2−HR = 1.30 [1.07–1.57], Tau2 < 0.001, %/
 HR+HR = 1.59 [1.18, 2.13], Tau2 = 0.021, %/
 HR−HR = 1.14 [0.93–1.41], Tau2 < 0.001, %/
 TNBCHR = 1.39 [1.04–1.86], Tau2 < 0.001, %/

Note. HER2: human epidermal growth factor receptor; HR: hazard ratio; HR+: positive hormone receptor; HR−: negative hormone receptor; M0: no-metastasis; TNBC: triple-negative breast cancer; “/” symbol: no results due to insufficient studies.
4.2. PLR and OS

Eight studies evaluated the relationship between OS and PLR [1621, 33, 35]. The poor prognosis for OS in patients with breast cancer was indicated by the high level of PLR (HR = 1.88 and 95% CI = 1.27–2.80; Figure 4), with significant heterogeneity (Tau2 = 0.192 and ). A Galbraith plot showed that the study by Cihan et al. [17] contributed to the high heterogeneity (Figure 3), and the heterogeneity decreased (Tau2 = 0.099) after excluding this study and the prognostic value of PLR was confirmed (HR = 1.89; 95% CI = 1.52–2.34; Figure 4).

Similarly, the study by Cihan et al. [17] contributed to the high heterogeneity in the subgroup analysis of preoperative PLR (HR = 1.71, 95% CI = 1.14–2.57, Tau2 = 0.155, and ). After excluding this study, our results also indicated that preoperative high PLR was associated with worse OS without reduced heterogeneity (HR = 1.67, 95% CI = 1.30–2.13, Tau2 = 0.062, and ). The subgroup analysis stratified by sample size and cut-off points indicated that the prognostic value of PLR was significant when the sample size was ≥400 (HR = 2.53, 95% CI = 1.79–3.57, Tau2 < 0.001, and ) and the cut-off point was set at ≥180 (HR = 2.68, 95% CI = 1.89–3.78, Tau2 < 0.001, and ), and high PLR tended toward a worse OS when the sample size was <400 (HR = 1.60, 95% CI = 0.93–2.75, Tau2 = 0.223, and ) and the cut-off point was <180 (HR = 1.46, 95% CI = 0.92–2.31, Tau2 = 0.135, and ). We could not perform a subgroup analysis on the basis of the receptor status owing to the limited number of studies.

5. PLR and Clinicopathologic Characteristics

Our results indicated a significantly higher incidence of high levels of PLR in the stage II–IV group relative to the stage I group (OR = 1.86, 95% CI = 1.20–2.90, Tau2 < 0.001, and ). There were significant differences in the incidence of high levels of PLR between the lymph node-positive and lymph node-negative groups (OR = 1.52, 95% CI = 1.22–1.91, Tau2 = 0.014, and ) and between the metastasis-positive and metastasis-negative groups (OR = 4.24, 95% CI = 2.73–6.59, Tau2 < 0.001, and ). We could not assess the association between tumor pT category and PLR level owing to the limited number of studies.

On the basis of the degree of tumor differentiation, our results indicated a nonsignificant association between the degree of differentiation and the PLR level (poor differentiation versus moderate/high differentiation, OR = 1.15, 95% CI = 0.86–1.54, and ; poor/moderate differentiation versus high differentiation, OR = 1.56, 95% CI = 0.78–3.14, and ). Moreover, there was a significant difference in the incidence of high levels of PLR between and HER2 status (OR = 1.54, 95% CI = 1.11–2.14, Tau2 < 0.001, and ), but not between other receptor statuses (ER+ versus ER: OR = 1.08, 95% CI = 0.69–1.72, Tau2 = 0.128, and ; PR+ versus PR: OR = 0.88, 95% CI = 0.69–1.13, Tau2 < 0.001, and ).

6. Assessment of Publication Bias

Begg’s and Egger’s tests showed no evidence of publication bias in our study (DFS: Begg’s test = 0.09 and Egger’s test = 0.11; OS: Begg’s test = 0.71 and Egger’s test = 0.37). Furthermore, the absence of publication bias was confirmed with a funnel plot (Figure 5). A Galbraith plot showed that the study by Cihan et al. [17] and Allan et al. [19] generated high heterogeneity to the pooled analysis of OS and DFS, respectively (Figure 3). The exclusion of the study could increase the statistical power and reduced heterogeneity.

7. Discussion

The present study assessed the association between the prognostic and clinicopathological values and PLR levels by analyzing 5542 cases of patients with breast cancer. Our results indicated that high PLR level was associated with poor prognosis, including OS and DFS. Moreover, we found an association between PLR and clinicopathological characteristics, including tumor stage, lymph node metastasis, and distant metastasis, suggesting a solid basis for breast tumor staging.

Tumor proliferation, invasion, angiogenesis, and metastasis are affected by the host inflammation and immune response in the tumor microenvironment [3639]. Moreover, our results indicate that PLR can be used as a prognostic factor for breast cancer. Although the underlying causes and mechanisms have not been fully elucidated, there are several possible explanations. A high PLR reflects a decreased number of platelets and/or an increased number of lymphocytes. The platelet may reflect an inflammatory process and play an important role in tumor progression. Bodies of evidence have shown that platelets could influence the metastatic potential of tumor cells via several biological pathways. First, platelets could secrete cellular growth factors including platelet-derived growth factor, vascular endothelial growth factor, transforming growth factor beta, and platelet factor 4, which could stimulate tumor angiogenesis and growth [4042]. Second, platelets could contribute to the stable adhesion of tumor cells to endothelium and transmigration of tumor cells out of the vasculature. Third, platelets could also enhance tumor stroma formation by promoting the migration of inflammatory cells. Furthermore, platelets could facilitate tumor cell metastasis by impeding the clearance of tumor cells by cell-mediated immunity [4345]. Thus, a high platelet count was correlated with a poor prognosis.

Numerous studies have demonstrated that lymphocytes play a crucial role in tumor immune surveillance, which suppresses tumor progression and metastasis [39, 46]. Their cytotoxic activity and induction of apoptosis in tumor cells by lymphocytes could control tumor growth [47]. Clinical data have shown that high tumor-infiltrating lymphocytes are associated with favorable prognosis in breast cancer [48, 49]. Accordingly, Mao et al. and Seo et al. reported that tumor-infiltrating lymphocytes could predict the response to neoadjuvant and adjuvant therapy [50, 51]. In addition, lymphopenia may present the status of deficiency of the immune system caused by tumor cells. Therefore, high PLR, with a high platelet count and/or low lymphocyte count, resulted in a low antitumor activity and poor prognosis.

In our study, the prognostic effect of PLR on DFS was significant in patients with ER+ or PR+ breast cancer. However, PLR could not significantly predict prognosis in patients with ER and PR breast cancer. To date, it is not clear whether the prognostic value of the PLR varies among the subpopulations stratified by receptor status. Ulas et al. reported that the PLR has no effect on DFS or OS in HER2+ breast cancer [18]. However, Gunduz et al. showed that PLR was associated with DFS in HER2+ breast cancer [32]. These contradictory results may be attributed to the ER/PR status. Indeed, Koh et al. thoroughly evaluated the association between PLR and mortality by breast cancer subtype and demonstrated that elevated PLR was associated with an increased risk of mortality in patients with ER+ or PR+ and HER2+ breast cancer but not in patients with ER or PR and HER2+ breast cancer [15]. Therefore, further studies are needed to evaluate the prognostic value of PLR in different subpopulations stratified by receptor status.

Most studies primarily focused on the prognostic value of PLR in breast cancer and only a few studies explored its association with clinicopathological characteristics. Therefore, both the prognostic value and the clinicopathological value of PLR were explored in this study. Our results indicated that high levels of PLR could predict clinicopathological characteristics, such as tumor stage, lymph node metastatic status, and distant metastatic status. Recent strategies for the treatment of breast cancer have accounted for the status of receptors, including ER, PR, and HER2. Therefore, we explored whether PLR was associated with the status of receptors. Our results indicated no significant associations between PLR and receptor status. Moreover, we did not assess whether the few number of studies included would impact these results. Therefore, future studies should thoroughly evaluate the association between PLR and receptor status.

For the clinical use of PLR, the optimal cut-off value may be the main concern for physicians. Unfortunately, for predicting prognosis in patients with breast cancer, this value has not been determined. Krenn-Pilko et al. reported that high PLR was associated with poor prognosis using a cut-off value of 292 [16] and Gunduz et al. reported that high PLR was associated with poor prognosis using a cut-off value of 200 [32]. However, Ulas et al., Yao et al., and Cihan et al. reported that PLR could not be used as a prognostic factor for breast cancer using a cut-off value of 161, 107, and 160, respectively [17, 18, 33]. Our subgroup analysis stratified by cut-off point indicated that the HR for OS was significant when the cut-off point was set at ≥180 but not at <180. Moreover, Azab et al. reported that the HR for the 4th quartile of PLR (cut-off point > 185) compared with the 1st quartile of PLR (cut-off point < 109) was 3.68 (95% CI = 1.74–7.77; ) and the HR was 1.02 (95% CI = 1.015–1.027; ) for every 10-unit increase in PLR, considering PLR as a continuous variable [34]. Therefore, a high cut-off point for PLR may be more valuable and useful. However, a higher cut-off value may result in the omission of a substantial number of patients in clinical practice. Indeed, Azab et al. reported that <10% of patients were grouped into the subset of PLR > 292 and the prognostic value of PLR might be different between the lymphopenia subset and the normal lymphocyte count subset, despite using the same cut-off value [34]. Therefore, further studies should be conducted to define the optimal cut-off value of PLR for future individual treatments.

Although a previous meta-analysis assessed the prognostic value of PLR for breast cancer, the number of included studies was small and the cut-off value of PLR was not assessed [52]. Our study had several obvious advantages. First, our study included more eligible studies and so our results were more robust and powerful. Second, our studies evaluated the impact of cut-off value of PLR via subgroup analyses based on cut-off value. Moreover, our study assessed the associations between receptor status and PLR, and subgroup analyses were performed by the receptor status.

Several limitations should be addressed. First, this retrospective study is based on published data from the studies included. Therefore, some HR values were calculated from published data in case the studies evaluated did not provide them directly. Second, there was a considerable degree of heterogeneity among the studies included and could not be explained and eliminated completely. Our subgroup analysis indicated that sample size might be a source of heterogeneity. After including sample sizes ≥400, the heterogeneity among the studies decreased for both DFS (overall: Tau2 = 0.070 and ; ≥400: Tau2 = 0.001 and ) and OS (overall: Tau2 = 0.192 and ; ≥400: Tau2 < 0.001 and ). A possible reason was that a large sample size could improve the reliability and reduce the variability of the results. For the OS, the different cut-off values of PLR also contributed to substantial heterogeneity (overall: Tau2 = 0.192 and ; ≥180: Tau2 < 0.001 and ). For individual studies, the Galbraith plot indicated that the studies by Cihan et al. [17] and Allan et al. [19] contributed for the heterogeneity of OS and DFS, respectively. Indeed, the heterogeneity was reduced after the removal of these studies (OS without the data from Cihan et al.: Tau2 = 0.099; DFS without the data from Allan et al.: Tau2 = 0.046), and the results remained significant. The remaining unexplained heterogeneity may have been caused by differences in the tumor characteristics (i.e., receptor status and TNM stage), population demographics, methodology, and other confounding factors. Heterogeneity did not affect or dominate our results. Third, PLR may be affected by several important factors, including bacterial diseases, viral infections, and oral drugs. These inherently confounding factors may not be controlled completely and therefore may underestimate the prognostic value of PLR in breast cancer.

8. Conclusions

Our results indicated that high PLR was significantly associated with poor prognosis of breast cancer and adequately predicted clinicopathological characteristics. Future high-quality and well-designed studies are needed to identify the optimal cut-off value of PLR and improve the clinical utility of PLR.

Ethical Approval

The study did not violate the local regulations and was approved by the Research Ethics Committee of China Medical University, China.

Disclosure

The corresponding author had full access to all the data and analyses.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Miao Zhang and Xuan-zhang Huang contributed equally to this work

Acknowledgments

The authors acknowledge the Department of Oncology of Air Force General Hospital of Chinese PLA, Department of Chemotherapy and Radiotherapy of Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, and Department of Surgical Oncology of First Hospital of China Medical University for technical assistance.

Supplementary Materials

Supplemental File 1: PRISMA Checklist.

  1. Supplementary Material

References

  1. L. A. Torre, F. Bray, R. L. Siegel, J. Ferlay, and J. Lortet-Tieulent, “Global cancer statistics, 2012,” CA: A Cancer Journal for Clinicians, vol. 65, no. 2, pp. 87–108, 2015. View at: Publisher Site | Google Scholar
  2. S. H. Giordano, A. U. Buzdar, T. L. Smith, S.-W. Kau, Y. Yang, and G. N. Hortobagyi, “Is Breast Cancer Survival Improving? Trends in Survival for Patients with Recurrent Breast Cancer Diagnosed from 1974 through 2000,” Cancer, vol. 100, no. 1, pp. 44–52, 2004. View at: Publisher Site | Google Scholar
  3. J. Kraeima, S. Siesling, I. M. H. Vliegen, J. M. Klaase, and M. J. Ijzerman, “Individual risk profiling for breast cancer recurrence: Towards tailored follow-up schemes,” British Journal of Cancer, vol. 109, no. 4, pp. 866–871, 2013. View at: Publisher Site | Google Scholar
  4. H. Sezer, M. Yilmaz, H. Gurler, and A. Koyuncu, “Breast cancer risk factors in Turkey: A hospital-based case-control study,” Asian Pacific Journal of Cancer Prevention, vol. 12, no. 9, pp. 2317–2322, 2011. View at: Google Scholar
  5. L. M. Coussens, W. W. Raymond, G. Bergers et al., “Inflammatory mast cells up-regulate angiogenesis during squamous epithelial carcinogenesis,” Genes and Development, vol. 13, no. 11, pp. 1382–1397, 1999. View at: Publisher Site | Google Scholar
  6. I. Esposito, M. Menicagli, N. Funel et al., “Inflammatory cells contribute to the generation of an angiogenic phenotype in pancreatic ductal adenocarcinoma,” Journal of Clinical Pathology, vol. 57, no. 6, pp. 630–636, 2004. View at: Publisher Site | Google Scholar
  7. K. Tsung, J. B. Meko, G. R. Peplinski, Y. L. Tsung, and J. A. Norton, “IL-12 Induces T Helper 1-Directed Antitumor Response,” Journal of Immunology, vol. 158, no. 7, pp. 3359–3365, 1997. View at: Google Scholar
  8. Z. Cui, M. C. Willingham, A. M. Hicks et al., “Spontaneous regression of advanced cancer: Identification of a unique genetically determined, age-dependent trait in mice,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 11, pp. 6682–6687, 2003. View at: Publisher Site | Google Scholar
  9. S. E. A. Street, J. A. Trapani, D. MacGregor, and M. J. Smyth, “Suppression of lymphoma and epithelial malignancies effected by interferon γ,” Journal of Experimental Medicine, vol. 196, no. 1, pp. 129–134, 2002. View at: Publisher Site | Google Scholar
  10. N. Chen, W. Li, K. Huang et al., “Increased platelet-lymphocyte ratio closely relates to inferior clinical features and worse long-term survival in both resected and metastatic colorectal cancer: an updated systematic review and meta-analysis of 24 studies,” Oncotarget, vol. 8, no. 19, pp. 32356–32369, 2017. View at: Publisher Site | Google Scholar
  11. J. You, G.-Q. Zhu, L. Xie et al., “Preoperative platelet to lymphocyte ratio is a valuable prognostic biomarker in patients with colorectal cancer,” Oncotarget, vol. 7, no. 18, pp. 25516–25527, 2016. View at: Publisher Site | Google Scholar
  12. X. Gu, X.-S. Gao, M. Cui et al., “Clinicopathological and prognostic significance of platelet to lymphocyte ratio in patients with gastric cancer,” Oncotarget, vol. 7, no. 31, pp. 49878–49887, 2016. View at: Publisher Site | Google Scholar
  13. L. Lian, Y.-Y. Xia, C. Zhou et al., “Application of platelet/lymphocyte and neutrophil/lymphocyte ratios in early diagnosis and prognostic prediction in patients with resectable gastric cancer,” Cancer Biomarkers, vol. 15, no. 6, pp. 899–907, 2015. View at: Publisher Site | Google Scholar
  14. X. Gu, S. Sun, X.-S. Gao et al., “Prognostic value of platelet to lymphocyte ratio in non-small cell lung cancer: Evidence from 3,430 patients,” Scientific Reports, vol. 6, Article ID 23893, 2016. View at: Publisher Site | Google Scholar
  15. C.-H. Koh, N. Bhoo-Pathy, K.-L. Ng et al., “Utility of pre-treatment neutrophil-lymphocyte ratio and platelet-lymphocyte ratio as prognostic factors in breast cancer,” British Journal of Cancer, vol. 113, no. 1, pp. 150–158, 2015. View at: Publisher Site | Google Scholar
  16. S. Krenn-Pilko, U. Langsenlehner, E.-M. Thurner et al., “The elevated preoperative derived neutrophil-to-lymphocyte ratio predicts poor clinical outcome in breast cancer patients,” British Journal of Cancer, vol. 110, no. 10, pp. 2524–2530, 2014. View at: Publisher Site | Google Scholar
  17. Y. B. Cihan, A. Arslan, M. F. Cetindag, and H. Mutlu, “Lack of prognostic value of blood parameters in patients receiving adjuvant radiotherapy for breast cancer,” Asian Pacific Journal of Cancer Prevention, vol. 15, no. 10, pp. 4225–4231, 2014. View at: Publisher Site | Google Scholar
  18. A. Ulas, N. Avci, T. Kos et al., “Are neutrophil/lymphocyte ratio and Platelet/lymphocyte ratio associated with prognosis in patients with HER2-positive early breast cancer receiving adjuvant trastuzumab?” Journal of B.U.ON., vol. 20, no. 3, pp. 714–722, 2015. View at: Google Scholar
  19. R. Allan, R. Luis, and P. Juan, “Neutrophil-lymphocyte ratio and platelet-lymphocyte ratio as prognostic factors in non-metastatic breast cancer patients from a Hispanic population,” Breast Disease, pp. 1–6, 2016. View at: Publisher Site | Google Scholar
  20. Y. Asano, S. Kashiwagi, N. Onoda et al., “Platelet-Lymphocyte Ratio as a Useful Predictor of the Therapeutic Effect of Neoadjuvant Chemotherapy in Breast Cancer,” PLoS ONE, vol. 11, no. 7, p. e0153459, 2016. View at: Publisher Site | Google Scholar
  21. C. Liu, Z. Huang, Q. S. Wang et al., “Usefulness of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in hormone-receptor-negative breast cancer,” OncoTargets and Therapy, vol. 9, pp. 4653–4660, 2016. View at: Publisher Site | Google Scholar
  22. H. Takeuchi, H. Kawanaka, S. Fukuyama et al., “Comparison of the prognostic values of preoperative inflammation-based parameters in patients with breast cancer,” PLOS ONE, vol. 12, no. 5, p. e0177137, 2017. View at: Publisher Site | Google Scholar
  23. A. Stang, “Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses,” European Journal of Epidemiology, vol. 25, no. 9, pp. 603–605, 2010. View at: Publisher Site | Google Scholar
  24. N. Panic, E. Leoncini, G. De Belvis, W. Ricciardi, and S. Boccia, “Evaluation of the endorsement of the preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement on the quality of published systematic review and meta-analyses,” PLoS ONE, vol. 8, no. 12, Article ID e83138, 2013. View at: Publisher Site | Google Scholar
  25. J. F. Tierney, L. A. Stewart, D. Ghersi, S. Burdett, and M. R. Sydes, “Practical methods for incorporating summary time-to-event data into meta-analysis,” Trials, vol. 8, article 16, 2007. View at: Publisher Site | Google Scholar
  26. M. K. B. Parmar, V. Torri, and L. Stewart, “Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints,” Statistics in Medicine, vol. 17, no. 24, pp. 2815–2834, 1998. View at: Publisher Site | Google Scholar
  27. J. P. T. Higgins, S. G. Thompson, J. J. Deeks, and D. G. Altman, “Measuring inconsistency in meta-analyses,” British Medical Journal, vol. 327, no. 7414, pp. 557–560, 2003. View at: Publisher Site | Google Scholar
  28. J. P. T. Higgins, “Commentary: Heterogeneity in meta-analysis should be expected and appropriately quantified,” International Journal of Epidemiology, vol. 37, no. 5, pp. 1158–1160, 2008. View at: Publisher Site | Google Scholar
  29. M. Egger, G. D. Smith, M. Schneider, and C. Minder, “Bias in meta-analysis detected by a simple, graphical test,” British Medical Journal, vol. 315, pp. 629–634, 1997. View at: Publisher Site | Google Scholar
  30. C. B. Begg and M. Mazumdar, “Operating characteristics of a rank correlation test for publication bias,” Biometrics, vol. 50, no. 4, pp. 1088–1101, 1994. View at: Publisher Site | Google Scholar
  31. S. Duval and R. Tweedie, “Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis,” Biometrics, vol. 56, no. 2, pp. 455–463, 2000. View at: Publisher Site | Google Scholar
  32. S. Gunduz, S. S. Goksu, D. Arslan et al., “Factors affecting disease-free survival in patients with human epidermal growth factor receptor 2-positive breast cancer who receive adjuvant trastuzumab,” Molecular and Clinical Oncology, 2015. View at: Publisher Site | Google Scholar
  33. M. Yao, Y. Liu, H. Jin et al., “Prognostic value of preoperative inflammatory markers in Chinese patients with breast cancer,” OncoTargets and Therapy, vol. 7, pp. 1743–1752, 2014. View at: Publisher Site | Google Scholar
  34. B. Azab, N. Shah, J. Radbel et al., “Pretreatment neutrophil/lymphocyte ratio is superior to platelet/lymphocyte ratio as a predictor of long-term mortality in breast cancer patients,” Medical Oncology, vol. 30, no. 1, article no. 432, 2013. View at: Publisher Site | Google Scholar
  35. R. L. Cordiner, J. Mansell, C. Obondo et al., “Pre-operative haematological markers and prognosis in early breast cancer,” Cancer Research, vol. 71, no. 24 Supplement, pp. P2-12-30–P2-12-30, 2014. View at: Publisher Site | Google Scholar
  36. L. M. Coussens and Z. Werb, “Inflammation and cancer,” Nature, vol. 420, no. 6917, pp. 860–867, 2002. View at: Publisher Site | Google Scholar
  37. D. Hanahan and R. A. Weinberg, “Hallmarks of cancer: the next generation,” Cell, vol. 144, no. 5, pp. 646–674, 2011. View at: Publisher Site | Google Scholar
  38. D. G. DeNardo and L. M. Coussens, “Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression,” Breast Cancer Research, vol. 9, article 212, 2007. View at: Publisher Site | Google Scholar
  39. G. P. Dunn, L. J. Old, and R. D. Schreiber, “The immunobiology of cancer immunosurveillance and immunoediting,” Immunity and Immunoediting, vol. 21, no. 2, pp. 137–148, 2004. View at: Publisher Site | Google Scholar
  40. K. Egan, D. Crowley, P. Smyth et al., “Platelet adhesion and degranulation induce pro-survival and pro-angiogenic signalling in ovarian cancer cells,” PLoS ONE, vol. 6, no. 10, Article ID e26125, 2011. View at: Publisher Site | Google Scholar
  41. S. A. Kono, L. E. Heasley, R. C. Doebele, and D. R. Camidge, “Adding to the mix: Fibroblast growth factor and platelet-derived growth factor receptor pathways as targets in non-small cell lung cancer,” Current Cancer Drug Targets, vol. 12, no. 2, pp. 107–123, 2012. View at: Publisher Site | Google Scholar
  42. M. H. F. Klinger and W. Jelkmann, “Role of blood platelets in infection and inflammation,” Journal of Interferon & Cytokine Research, vol. 22, no. 9, pp. 913–922, 2002. View at: Publisher Site | Google Scholar
  43. K. Suzuki, K. Aiura, M. Ueda, and M. Kitajima, “The influence of platelets on the promotion of invasion by tumor cells and inhibition by antiplatelet agents,” Pancreas, vol. 29, no. 2, pp. 132–140, 2004. View at: Publisher Site | Google Scholar
  44. B. Nieswandt, M. Hafner, B. Echtenacher, and D. N. Männel, “Lysis of tumor cells by natural killer cells in mice is impeded by platelets,” Cancer Research, vol. 59, no. 6, pp. 1295–1300, 1999. View at: Google Scholar
  45. J. S. Palumbo, K. E. Talmage, J. V. Massari et al., “Platelets and fibrin(ogen) increase metastatic potential by impeding natural killer cell-mediated elimination of tumor cells,” Blood, vol. 105, no. 1, pp. 178–185, 2005. View at: Publisher Site | Google Scholar
  46. H. E. Ownby, L. D. Roi, R. R. Isenberg, and M. J. Brennan, “Peripheral lymphocyte and eosinophil counts as indicators of prognosis in primary breast cancer,” Cancer, vol. 52, no. 1, pp. 126–130, 1983. View at: Publisher Site | Google Scholar
  47. K. Hiraoka, M. Miyamoto, Y. Cho et al., “Concurrent infiltration by CD8+ T cells and CD4+ T cells is a favourable prognostic factor in non-small-cell lung carcinoma,” British Journal of Cancer, vol. 94, no. 2, pp. 275–280, 2006. View at: Publisher Site | Google Scholar
  48. V. Kotoula, K. Chatzopoulos, S. Lakis et al., “Tumors with high-density tumor infiltrating lymphocytes constitute a favorable entity in breast cancer: A pooled analysis of four prospective adjuvant trials,” Oncotarget, vol. 7, no. 4, pp. 5074–5087, 2016. View at: Publisher Site | Google Scholar
  49. E. M. Ibrahim, M. E. Al-Foheidi, M. M. Al-Mansour, and G. A. Kazkaz, “The prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancer: a meta-analysis,” Breast Cancer Research and Treatment, vol. 148, no. 3, pp. 467–476, 2014. View at: Publisher Site | Google Scholar
  50. A. N. Seo, H. J. Lee, E. J. Kim et al., “Tumour-infiltrating CD8+ lymphocytes as an independent predictive factor for pathological complete response to primary systemic therapy in breast cancer,” British Journal of Cancer, vol. 109, no. 10, pp. 2705–2713, 2013. View at: Publisher Site | Google Scholar
  51. Y. Mao, Q. Qu, Y. Zhang, J. Liu, X. Chen, and K. Shen, “The value of tumor infiltrating lymphocytes (TILs) for predicting response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis,” PLoS ONE, vol. 9, no. 12, Article ID e0119243, e115103 pages, 2014. View at: Publisher Site | Google Scholar
  52. Y. Zhu, W. Si, Q. Sun, B. Qin, W. Zhao, and J. Yang, “Platelet-lymphocyte ratio acts as an indicator of poor prognosis in patients with breast cancer,” Oncotarget, vol. 8, no. 1, pp. 1023–1030, 2017. View at: Publisher Site | Google Scholar

Copyright © 2017 Miao Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

1568 Views | 406 Downloads | 14 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.