Abstract
Introduction
Pretreatment platelet-to-lymphocyte ratio (PLR) has been considered a prognostic factor in various cancers. However, the application of PLR in the assessment of patients with cholangiocarcinoma remains controversial. This study aimed to evaluate the prognostic value of pretreatment PLR in cholangiocarcinoma.
Methods
A systematic search was performed in MEDLINE, EMBASE, and Cochrane Library to identify studies assessing the prognostic significance of the pretreatment PLR in cholangiocarcinoma. Three databases were searched from inception to August 5, 2018. The primary outcome was overall survival (OS), and the secondary outcomes were recurrence-free survival (RFS) and progression-free survival (PFS). Pooled hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using random-effects models.
Results
A total of 9 studies including 2395 patients were finally enrolled in the meta-analysis based on the inclusion and exclusion criteria. All of the included studies were retrospective observational cohorts. Elevated PLR predicted poor OS (HR: 1.38, 95% CI: 1.19-1.62, P < 0.001) and RFS or PFS (HR = 1.55; 95% CI = 1.27-1.88; P < 0.001). Moreover, elevated PLR was highly associated with male sex (male versus female OR = 0.59, 95% CI: 0.44-0.80, P < 0.001) and R1 resection margin (OR = 2.09, 95% CI: 1.24-3.54, P = 0.006).
Conclusion
The present meta-analysis demonstrated that pretreatment PLR might serve as a useful prognostic biomarker in cholangiocarcinoma.
1. Introduction
Cholangiocarcinoma (CCA) is a primary liver cancer with features of differentiation of cholangiocytes, the epithelial cells lining the intra- and extrahepatic portions of the biliary tree [1]. An increasing incidence of CCA has been reported over the last few decades [2]. It is the second most frequent type of primary liver cancer and comprises malignancies with high inter- and intratumor heterogeneities. It is currently classified into intrahepatic, perihilar, and distal extrahepatic cholangiocarcinoma [3]. Surgical resection remains the best therapeutic approach for CCA, but unfortunately most patients are diagnosed at an unresectable stage of the disease. Although the accuracy of current diagnostic methods has greatly improved, the 5-year overall survival (OS) remains poor [4, 5]. Therefore, a reliable and readily accessible preoperative prognostic biomarker is required to determine the optimal therapeutic strategies.
A growing number of studies have shown that cancer-related inflammation results in poor prognosis. Moreover, inflammation plays a strong role in tumor development, progression, and metastasis [6]. Accordingly, inflammation-based prognostic indicators, such as the Glasgow prognostic score (GPS), C-reactive protein (CRP), and neutrophil-to-lymphocyte ratio (NLR), have been investigated in various cancers [7–9]. The NLR has been associated with worse prognosis in various cancers [10–12]. However, because of the inconsistent results, whether PLR is associated with the prognosis in CCA remains controversial [13–15]. We therefore conducted a meta-analysis to assess the prognostic role of PLR and analyze the relationships between PLR and clinicopathological parameters in patients with CCA.
2. Materials and Methods
2.1. Search Strategies
A systematic search of electronic databases, including MEDLINE, EMBASE, and Cochrane Library, was performed up to August 5, 2018, to obtain relevant articles for the meta-analysis. Studies were selected using the following key words: “cholangiocarcinoma” or “bile duct cancer” and “tumor” or “cancer” or “neoplasm” or “carcinoma” or “malignancy” and “platelet lymphocyte ratio” or “PLR”. Other relevant studies were also obtained by manually screening the references list.
2.2. Selection Criteria
The inclusion criteria were as follows: (1) studies investigate the PLR and survival in CCA; (2) CCA was confirmed by pathological examination; (3) the HR and 95% CI, or Kaplan–Meier survival curves from which an HR could be calculated, were reported; and (4) a cut-off value for PLR was reported. The exclusion criteria were as follows: (1) reviews, letters, or conference abstracts; (2) insufficient data or unavailable data; and (3) studies with duplicate data.
2.3. Data Extraction and Quality Assessment
Two investigators (G.H. and Q.L.) performed the data extraction independently. Data were extracted as follows: first author's name, publication year, country, number of patients, follow-up period, treatment, gender, age, CA199, differentiation, lymph node metastasis, vascular invasion, postoperative complication, postoperative mortality, margin status, survival analysis methods, HR estimate, and cut-off values. Margin status included R0 (microscopically negative resection margins) and R1 (microscopically positive resection margins).
The methodological quality of included studies was independently assessed by two independent reviewers (G.H. and Q.L.) according to the Newcastle-Ottawa Scale (NOS) [16], which included three primary domains: selection, comparability, and outcome. Studies with an NOS score of ⩾6 were deemed high-quality studies. Any discrepancy was resolved by joint discussion.
2.4. Statistical Analysis
We used Stata 13.0 statistical software (Stata, College Station) to estimate HRs for OS, PFS, and RFS and odd ratios (ORs) for clinicopathological parameters. If the statistical variables were described in the study, we extracted them directly. Otherwise, they were calculated with Kaplan-Meier survival curves, which were read according to the methods described by Tierney et al. and Parmar [17, 18]. The heterogeneity among the studies was evaluated by the chi-square value and the I2 value. If I2 ⩽ 50% or P > 0.05, a fixed-effects model was used for analysis. If not (I2 > 50% or P ⩽ 0.05), a random-effects model was used. We then performed subgroup analyses to examine the potential source of heterogeneity. To validate the credibility of the result, sensitivity analyses were performed by removing each study. A P value less than 0.05 was considered statistically significant.
3. Results
3.1. Study Characteristics
As shown in the flow diagram (Figure 1), 111 potentially relevant articles were obtained through electronic searches. 99 articles remained after exclusion of duplicated data. After screening the titles and abstracts carefully, 75 articles were excluded. Finally, a total of 9 studies were included in the meta-analysis [13–15, 19–24]. All of the included studies were retrospective observational cohorts. Most of these studies have been published since 2017. Of the 9 studies, four studies were from China, three were from Japan, one was from Korea, and one was from multiple centers. The treatments were surgery and mixed methods. All studies assessed the association between pretreatment PLR and OS, whereas 4 studies reported RFS or PFS. Cut-off values of PLR ranged from 123 to 190. The main characteristics of the 9 enrolled studies are shown in Table 1. NOS scores of all the studies were at least 6 or more (Table 2).
Figure 1.
The flow chart of study selection procedure in the meta-analysis.
Table 1.
Characteristics of the studies included in the meta-analysis.
| Author | Year | Area | Follow-up (months) | Treatment | No. of patients | Stage | Cut-off value | Survival analysis | Analysis |
|---|---|---|---|---|---|---|---|---|---|
| Buettner | 2018 | Multicenter | 29 (4.8-53.3) | Surgery | 991 | NA | 190 | OS | UV |
| Chen | 2015 | China | 57.8±11.2 | Surgery | 322 | Mixed | 123 | OS/RFS | MV |
| Cho | 2018 | Korea | 25 (19.6-30.4)) | Chemotherapy | 257 | Metastatic | 123.8 | OS/PFS | UV |
| Hu | 2018 | China | NA | Surgery | 173 | Mixed | 150 | OS | UV |
| Kitano | 2017 | Japan | NA | Mixed | 120 | Mixed | 185 | OS/RFS | MV |
| Ramen | 2018 | China | NA | Surgery | 90 | NA | 148 | OS/RFS | MV |
| Saito | 2015 | Japan | 70 (42-82) | Surgery | 121 | Mixed | 150 | OS | MV |
| Yoh | 2017 | Japan | 65 (26-84) | Surgery | 134 | Mixed | 120 | OS | MV |
| Zhang | 2016 | China | NA | NA | 187 | Mixed | 138 | OS | MV |
OS: overall survival; PFS: progress-free survival; RFS: recurrence-free survival; UV: univariate; MV: multivariate; NA: not available.
Table 2.
Assessment of study quality.
| Author | Selection | Comparability | Outcome | Total score |
|---|---|---|---|---|
| Buettner | ★★★★ | ★★ | ★★★ | 9 |
| Chen | ★★★★ | ★★ | ★★ | 8 |
| Cho | ★★★★ | ★★ | ★★ | 8 |
| Hu | ★★★ | ★ | ★★ | 6 |
| Kitano | ★★★★ | ★ | ★ | 6 |
| Ramen | ★★★ | ★★ | ★★★ | 8 |
| Saito | ★★★★ | ★ | ★ | 6 |
| Yoh | ★★★★ | ★ | ★★ | 7 |
| Zhang | ★★★★ | ★ | ★ | 6 |
3.2. Meta-Analysis
3.2.1. Impact of PLR on OS
Nine studies, comprising 2395 patients, reported the relationship between PLR and OS. The HR, expressed as the high-PLR group versus the low-PLR group, was 1.00 (95% CI = 1.00-1.00, P = 0.085). Buettner et al.'s study was not included in this meta-analysis of OS. The pooled result showed that patients with high PLR had a worse OS (HR: 1.38, 95% CI: 1.19-1.62, P < 0.001), with no heterogeneity (I2= 16.5%, P = 0.30; Figure 2). The association between PLR and OS was further evaluated by subgroup analysis based on the main features, including tumor stage, cut-off for PLR, treatment, and analysis method (Table 3). The results indicated that elevated PLR significantly predicted shorter OS in patients who received surgery (HR = 1.43; 95% CI = 1.12-1.83; P = 0.005) or mixed treatments (HR = 1.89; 95% CI = 1.11-3.14; P = 0.020). When stratified by disease stage, PLR was a prognostic factor in patients with mixed stages (HR = 1.40; 95% CI = 1.18-1.67; P < 0.001). Pooled HRs for OS were stratified by HR analysis methods. The negative effect of elevated PLR on OS was observed by multivariate analysis (HR = 1.52; 95% CI = 1.27-1.81; P < 0.001). Moreover, PLR showed prognostic value regardless of the cut-off value for NLR (⩾ 150 and < 150).
Figure 2.
Forest plots for the association between PLR and OS.
Table 3.
Pooled hazard ratios (HRs) for OS according to subgroup analyses.
| Subgroup | No. of studies | No. of patients | HR (95% CI) | P value | Heterogeneity | |
|---|---|---|---|---|---|---|
| I2(%) | Ph | |||||
| Overall | 8 | 1404 | 1.38 (1.19-1.62) | <0.001 | 16.5 | 0.30 |
| Treatment | ||||||
| Surgery | 5 | 840 | 1.43 (1.12-1.83) | 0.005 | 30.8 | 0.22 |
| Chemoradiotherapy | 1 | 257 | 1.19 (0.91-1.55) | 0.200 | — | — |
| Mixed | 1 | 120 | 1.89 (1.11-3.14) | 0.020 | — | — |
| Stage | ||||||
| Mixed | 6 | 866 | 1.40 (1.18-1.67) | <0.001 | 8.2 | 0.36 |
| Metastatic | 1 | 257 | 1.19 (0.91-1.55) | 0.200 | — | — |
| Cut-off | ||||||
| ≥150 | 3 | 485 | 1.59 (1.03-2.46) | 0.036 | 56.2 | 0.102 |
| <150 | 5 | 919 | 1.33 (1.14-1.56) | <0.001 | 0 | 0.505 |
| Analysis method | ||||||
| Univariate | 2 | 430 | 1.16 (0.93-1.45) | 0.174 | 0 | 0.778 |
| Multivariate | 6 | 974 | 1.52 (1.27-1.81) | <0.001 | 0 | 0.426 |
3.2.2. Impact of PLR on PFS/RFS
Four studies were included in the analysis of PLR and PFS/RFS. The pooled HR was 1.55, which indicated that elevated PLR was significantly associated with poor PFS/RFS (Figure 3). There was no significant heterogeneity between the included studies (I2 = 19.0%; P = 0.295).
Figure 3.
Forest plots for the association between PLR and PFS/RFS.
3.2.3. Associations between PLR and Clinicopathological Parameters
To further exploit the impact of PLR on clinicopathological features, we identified 9 clinicopathological parameters (Table 4). As shown in Table 3, the results demonstrated that elevated PLR was highly correlated with gender (male versus female; OR = 0.59, 95% CI: 0.44-0.80, P < 0.001) and margin status (R1 versus R0; OR = 2.09, 95% CI: 1.24-3.54, P = 0.006). However, elevated PLR was not related to age (⩾ 45 versus < 45; OR = 0.82, 95% CI: 0.38-1.77, P = 0.61), CA199 (>37 ng/mL versus <37 ng/mL; OR = 1.25, 95% CI: 0.92-1.70, P = 0.16), differentiation (low versus moderate/high; OR = 1.05, 95% CI: 0.64-1.73, P = 0.85), lymph node metastasis (pos versus neg; OR = 1.16, 95% CI: 0.82-1.65, P = 0.39), vascular invasion (pos versus neg; OR = 1.27, 95% CI: 0.86-1.89, P = 0.23), postoperative complications (present versus absent; OR = 1.44, 95% CI: 0.97-2.14, P = 0.07), and postoperative mortality (present versus absent; OR = 1.54, 95% CI: 0.56-4.26, P = 0.41).
Table 4.
Meta-analysis of the association between PLR and clinicopathological features of CCA.
| Characteristics | No. of studies | No. of patients | OR (95% CI) | p | Heterogeneity | |
|---|---|---|---|---|---|---|
| I2 (%) | Ph | |||||
| Age (≥ median vs. < median) | 3 | 669 | 0.82 (0.38-1.77) | 0.61 | 70 | 0.03 |
| Gender (male vs. female) | 4 | 789 | 0.59 (0.44-0.80) | < 0.001 | 0 | 0.94 |
| CA199 (>37 ng/mL vs. <37 ng/mL) | 3 | 669 | 1.25 (0.92-1.70) | 0.16 | 0 | 0.56 |
| Differentiation (low vs. moderate/high) | 2 | 442 | 1.05 (0.64-1.73) | 0.85 | 0 | 0.90 |
| Lymph node metastasis (pos vs. neg) | 4 | 1194 | 1.16 (0.82-1.65) | 0.39 | 0 | 0.63 |
| Vascular invasion (pos vs. neg) | 2 | 978 | 1.27 (0.86-1.89) | 0.23 | 0 | 0.56 |
| Postoperative complication (present vs. absent) | 2 | 776 | 1.44 (0.97-2.14) | 0.07 | 0 | 0.39 |
| Postoperative mortality (present vs. absent) | 2 | 776 | 1.54 (0.56-4.26) | 0.41 | 0 | 0.67 |
| Margin status (R1 vs. R0) | 2 | 776 | 2.09 (1.24-3.54) | 0.006 | 0 | 0.69 |
R0: microscopically negative resection margins; R1: microscopically positive resection margins.
3.3. Sensitivity Analysis
Sensitivity analysis was performed to assess the stability of the results. The result was not significantly impacted by removing any eligible study (Figure 4).
Figure 4.
Sensitivity analysis of PLR on OS in CCA patients.
4. Discussion
In this study, a meta-analysis was conducted to investigate the correlations between pretreatment PLR and clinicopathological characteristics and to evaluate the prognostic value of PLR in patients with CCA. The combined results demonstrated that elevated PLR is significantly associated with worse OS and RFS/PFS. Therefore, PLR could serve as biomarker for the prognosis of CCA patients. Additionally, the correlations between PLR and clinicopathological parameters were evaluated. Elevated PLR was correlated with female sex and margin status (R1).
The exact mechanisms by which PLR predicts poor outcome of CCA patients are still undefined. Emerging evidence has indicated strong linkage between systemic inflammatory response and tumor development [6, 25, 26]. Platelets, as a participant in the inflammatory response, protect tumor cells from natural killer-mediated lysis, thus supporting the tumor metastasis [27]. A variety of platelet-associated chemokines can modulate inflammation within the tumor environment and tumor angiogenesis, such as platelet factor 4 (PF-4/CXCL4) and connective tissue-activating peptide III (CTAP-III) [28]. Lymphocytes play a major role in suppressing cancer cell proliferation and migration [29]. Tumor-infiltrating lymphocytes (TILs) are vital components of the antitumor immune microenvironment and are involved in several stages of tumor progression [30, 31]. Tumor-infiltrating CD8+ and CD4+ T lymphocytes induce cytotoxic cell death and inhibit tumor cell proliferation and migration in antitumor immune reactions [32, 33]. Conversely, low lymphocyte counts may lead to inadequate immune responses, resulting in poor survival of many cancers [34, 35]. Thus, PLR may represent a balance between the tumor promotion reaction and antitumor immune function.
Several limitations should be taken into consideration when interpreting our findings. First, the cut-off value of PLR applied in the enrolled studies was not uniform. Second, all of the included studies were retrospective and published in English. Third, this meta-analysis is not registered online. Fourth, all included studies were from Asia, which means that our data do not represent the CCA picture globally. It remains unclear whether these findings might be applied to other populations. Therefore, more large-scale studies are warranted to assess the prognostic value of pretreatment PLR for cervical cancer patients.
5. Conclusions
Our meta-analysis confirmed that elevated pretreatment PLR is associated with poor prognosis in patients with CCA. Therefore, PLR may serve as a promising biomarker for predicting prognosis in patients with CCA.
Acknowledgments
We gratefully acknowledge the statistical assistance of Professor Sheng Wu from the Department of Medical Statistics, Hubei Polytechnic University.
Conflicts of Interest
The authors report no conflicts of interest in this work.
Authors' Contributions
Gang Hu, Qin Liu, and Cheng-yuan Liu conceived and designed the experiments. Gang Hu, Jian-ying Ma, and Qin Liu performed the experiments. Gang Hu, Qin Liu, and Cheng-yuan Liu analyzed the data. All authors contributed reagents/materials/analysis tools and wrote the paper. Gang Hu and Qin Liu contributed equally to this work.
Supplementary Materials
S1: search strategies in the databases. S2: data related to this study.
References
- 1.Pellino A., Loupakis F., Cadamuro M., et al. Precision medicine in cholangiocarcinoma. Translational Gastroenterology and Hepatology. 2018;3:40–40. doi: 10.21037/tgh.2018.07.02. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Beetz O., Klein M., Schrem H., et al. Relevant prognostic factors influencing outcome of patients after surgical resection of distal cholangiocarcinoma. BMC Surgery. 2018;18(1) doi: 10.1186/s12893-018-0384-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Banales J. M., Cardinale V., Carpino G. Expert consensus document: Cholangiocarcinoma: current knowledge and future perspectives consensus statement from the European Network for the Study of Cholangiocarcinoma (ENS-CCA) Nature Reviews Gastroenterology & Hepatology. 2016;13(5):261–280. doi: 10.1038/nrgastro.2016.51. [DOI] [PubMed] [Google Scholar]
- 4.Maithel S. K., Clark Gamblin T., Kamel I., Corona-Villalobos C. P., Thomas M., Pawlik T. M. Multidisciplinary approaches to intrahepatic cholangiocarcinoma. Cancer. 2013;119(22):3929–3942. doi: 10.1002/cncr.28312. [DOI] [PubMed] [Google Scholar]
- 5.Guro H., Kim J. W., Choi Y., Cho J. Y., Yoon Y.-S., Han H.-S. Multidisciplinary management of intrahepatic cholangiocarcinoma: Current approaches. Surgical Oncology. 2017;26(2):146–152. doi: 10.1016/j.suronc.2017.03.001. [DOI] [PubMed] [Google Scholar]
- 6.Grivennikov S. I., Greten F. R., Karin M. Immunity, Inflammation, and Cancer. Cell. 2010;140(6):883–899. doi: 10.1016/j.cell.2010.01.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kudou K., Saeki H., Nakashima Y., et al. C-reactive protein/albumin ratio is a poor prognostic factor of esophagogastric junction and upper gastric cancer. Journal of Gastroenterology and Hepatology. 2018 doi: 10.1111/jgh.14442. [DOI] [PubMed] [Google Scholar]
- 8.Topkan E., Selek U., Ozdemir Y., et al. Prognostic value of the Glasgow Prognostic Score for glioblastoma multiforme patients treated with radiotherapy and temozolomide. Journal of Neuro-Oncology. 2018;139(2):411–419. doi: 10.1007/s11060-018-2879-4. [DOI] [PubMed] [Google Scholar]
- 9.Rosner S., Kwong E., Shoushtari A. N., et al. Peripheral blood clinical laboratory variables associated with outcomes following combination nivolumab and ipilimumab immunotherapy in melanoma. Cancer Medicine. 2018;7(3):690–697. doi: 10.1002/cam4.1356. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Weng W., Chen X., Gong S., Guo L., Zhang X. Preoperative neutrophil–lymphocyte ratio correlated with glioma grading and glioblastoma survival. Neurological Research. 2018;40(11):917–922. doi: 10.1080/01616412.2018.1497271. [DOI] [PubMed] [Google Scholar]
- 11.Yılmaz B., Şengül E., Gül A., et al. Neutrophil–lymphocyte ratio as a prognostic factor in laryngeal carcinoma. Indian Journal of Otolaryngology and Head & Neck Surgery. 2018;70(2):175–179. doi: 10.1007/s12070-014-0769-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kobayashi H., Okuma T., Oka H., et al. Neutrophil-to-lymphocyte ratio after pazopanib treatment predicts response in patients with advanced soft-tissue sarcoma. International Journal of Clinical Oncology. 2018;23(2):368–374. doi: 10.1007/s10147-017-1199-6. [DOI] [PubMed] [Google Scholar]
- 13.Hu H., Jin Y., Zhou R., et al. Clinical value of inflammation-based prognostic scores to predict the resectability of hyperbilirubinemia patients with potentially resectable hilar cholangiocarcinoma. Journal of Gastrointestinal Surgery. 2018:93892–93899. doi: 10.1007/s11605-018-3892-9. [DOI] [PubMed] [Google Scholar]
- 14.Buettner S., Spolverato G., Kimbrough C. W., et al. The impact of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio among patients with intrahepatic cholangiocarcinoma. Surgery. 2018;164(3):411–418. doi: 10.1016/j.surg.2018.05.002. [DOI] [PubMed] [Google Scholar]
- 15.Yoh T., Seo S., Hatano E., et al. A novel biomarker-based preoperative prognostic grading system for predicting survival after surgery for intrahepatic cholangiocarcinoma. Annals of Surgical Oncology. 2017;24(5):1351–1357. doi: 10.1245/s10434-016-5708-z. [DOI] [PubMed] [Google Scholar]
- 16.Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. European Journal of Epidemiology. 2010;25(9):603–605. doi: 10.1007/s10654-010-9491-z. [DOI] [PubMed] [Google Scholar]
- 17.Parmar M. K. B., Torri V., Stewart L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Statistics in Medicine. 1998;17(24):2815–2834. doi: 10.1002/(SICI)1097-0258(19981230)17:24<2815::AID-SIM110>3.0.CO;2-8. [DOI] [PubMed] [Google Scholar]
- 18.Tierney J. F., Stewart L. A., Ghersi D., Burdett S., Sydes M. R. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8, article 16 doi: 10.1186/1745-6215-8-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Kitano Y., Yamashita Y.-I., Yamamura K., et al. Effects of preoperative neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios on survival in patients with extrahepatic cholangiocarcinoma. Anticancer Reseach. 2017;37(6):3229–3237. doi: 10.21873/anticanres.11685. [DOI] [PubMed] [Google Scholar]
- 20.Chen Q., Dai Z., Yin D., et al. Negative impact of preoperative platelet-lymphocyte ratio on outcome after hepatic resection for intrahepatic cholangiocarcinoma. Medicine (United States) 2015;94(13, article no. e574) doi: 10.1097/MD.0000000000000574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Saito H., Noji T., Okamura K., Tsuchikawa T., Shichinohe T., Hirano S. A new prognostic scoring system using factors available preoperatively to predict survival after operative resection of perihilar cholangiocarcinoma. Surgery. 2016;159(3):842–851. doi: 10.1016/j.surg.2015.10.027. [DOI] [PubMed] [Google Scholar]
- 22.Cho H., Yoo C., Kim K., et al. Prognostic implication of inflammation-based prognostic scores in patients with intrahepatic cholangiocarcinoma treated with first-line gemcitabine plus cisplatin. Investigational New Drugs. 2018;36(3):496–502. doi: 10.1007/s10637-017-0548-7. [DOI] [PubMed] [Google Scholar]
- 23.Ramen K., Zeng Q., Mo Z., Shan Y., Zheng J. Prognostic value of platelet to lymphocyte ratio in patients with intrahepatic cholangiocarcinoma. International Journal of Clinical and Experimental Medicine. 2018;11(6):5945–5952. [Google Scholar]
- 24.Zhang C., Wang H., Ning Z., et al. Prognostic value of systemic inflammatory response markers in patients with intrahepatic cholangiocarcinoma. International Journal of Clinical and Experimental Medicine. 2016;9(6):11502–11509. [Google Scholar]
- 25.Palumbo J. S., Degen J. L. Mechanisms coupling the hemostatic system to colitis-associated cancer. Thrombosis Research. 2010;125:S39–43. doi: 10.1016/S0049-3848(10)70011-6. [DOI] [PubMed] [Google Scholar]
- 26.Mantovani A., Allavena P., Sica A., Balkwill F. Cancer-related inflammation. Nature. 2008;454(7203):436–444. doi: 10.1038/nature07205. [DOI] [PubMed] [Google Scholar]
- 27.Nieswandt B., Hafner M., Echtenacher B., Männel D. N. Lysis of tumor cells by natural killer cells in mice is impeded by platelets. Cancer Research. 1999;59(6):1295–1300. [PubMed] [Google Scholar]
- 28.Pilatova K., Greplova K., Demlova R., Bencsikova B., Klement G. L., Zdrazilova-Dubska L. Role of platelet chemokines, PF-4 and CTAP-III, in cancer biology. Journal of Hematology & Oncology. 2013;6(1, article no. 42) doi: 10.1186/1756-8722-6-42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Bastid J., Bonnefoy N., Eliaou J.-F., Bensussan A. Lymphocyte-derived interleukin-17A adds another brick in the wall of inflammation-induced breast carcinogenesis. OncoImmunology. 2014;3(3) doi: 10.4161/onci.28273. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Chen K.-J., Zhou L., Xie H.-Y., Ahmed T.-E., Feng X.-W., Zheng S.-S. Intratumoral regulatory T cells alone or in combination with cytotoxic T cells predict prognosis of hepatocellular carcinoma after resection. Medical Oncology. 2012;29(3):1817–1826. doi: 10.1007/s12032-011-0006-x. [DOI] [PubMed] [Google Scholar]
- 31.Zhou J., Ding T., Pan W., Zhu L.-Y., Li A., Zheng L. Increased intratumoral regulatory T cells are related to intratumoral macrophages and poor prognosis in hepatocellular carcinoma patients. International Journal of Cancer. 2009;125(7):1640–1648. doi: 10.1002/ijc.24556. [DOI] [PubMed] [Google Scholar]
- 32.Zikos T. A., Donnenberg A. D., Landreneau R. J., Luketich J. D., Donnenberg V. S. Lung T-cell subset composition at the time of surgical resection is a prognostic indicator in non-small cell lung cancer. Cancer Immunology, Immunotherapy. 2011;60(6):819–827. doi: 10.1007/s00262-011-0996-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Minami T., Minami T., Shimizu N., et al. Identification of programmed death ligand 1-derived peptides capable of inducing cancer-reactive cytotoxic T lymphocytes from HLA-A24+ patients with renal cell carcinoma. Journal of Immunotherapy. 2015;38(7):285–291. doi: 10.1097/CJI.0000000000000090. [DOI] [PubMed] [Google Scholar]
- 34.Hoffmann T. K., Dworacki G., Tsukihiro T., et al. Spontaneous apoptosis of circulating T lymphocytes in patients with head and neck cancer and its clinical importance. Clinical Cancer Research. 2002;8(8):2553–2562. [PubMed] [Google Scholar]
- 35.Väyrynen J. P., Tuomisto A., Klintrup K., Mäkelä J., Karttunen T. J., Mäkinen M. J. Detailed analysis of inflammatory cell infiltration in colorectal cancer. British Journal of Cancer. 2013;109(7):1839–1847. doi: 10.1038/bjc.2013.508. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
S1: search strategies in the databases. S2: data related to this study.