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Research Papers:

Prognostic role of the lymph node ratio in node positive colorectal cancer: a meta-analysis

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Oncotarget. 2016; 7:72898-72907. https://doi.org/10.18632/oncotarget.12131

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Ming-Ran Zhang, Tian-Hang Xie, Jun-Lin Chi, Yuan Li, Lie Yang, Yong-Yang Yu, Xiao-Feng Sun and Zong-Guang Zhou _

Abstract

Ming-Ran Zhang1,3,*, Tian-Hang Xie2,*, Jun-Lin Chi1,3, Yuan Li3, Lie Yang1, Yong-Yang Yu1, Xiao-Feng Sun3,4, Zong-Guang Zhou1,3

1Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China

2Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, China

3Institute of Digestive Surgery and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China

4Department of Oncology, Department of Clinical and Experiment Medicine, Linköping University, Linköping, Sweden

*These authors contributed equally to this work

Correspondence to:

Zong-Guang Zhou, email: zhou767@163.com

Keywords: colorectal cancer, lymph node, lymph node ratio, prognostic role, meta-analysis

Received: March 01, 2016     Accepted: September 13, 2016     Published: September 20, 2016

ABSTRACT

The lymph node ratio (LNR) (i.e. the number of metastatic lymph nodes divided by the number of totally resected lymph nodes) has recently emerged as an important prognostic factor in colorectal cancer (CRC). However, the tumor node metastasis (TNM) staging system for colorectal cancer does not consider it as a prognostic parameter. Therefore, we conducted a meta-analysis to evaluate the prognostic role of the LNR in node positive CRC. A systematic search was performed in PubMed, Embase and the Cochrane Library for relevant studies up to November 2015. As a result, a total of 75,838 node positive patients in 33 studies were included in this meta-analysis. Higher LNR was significantly associated with shorter overall survival (OS) (HR = 1.91; 95% CI 1.71–2.14; P = 0.0000) and disease free survival (DFS) (HR = 2.75; 95% CI: 2.14–3.53; P = 0.0000). Subgroup analysis showed similar results. Based on these results, LNR was an independent predictor of survival in colorectal cancer patients and should be considered as a parameter in future oncologic staging systems.


Prognostic role of the lymph node ratio in node positive colorectal cancer: a meta-analysis | Zhang | Oncotarget

INTRODUCTION

Colorectal cancer (CRC) is the third most common cancer and the third leading cause of cancer death in the United States [1]. Lymph node status is accepted as one of the most important prognostic factors in colorectal cancer [2]. The classic staging system for colorectal cancer is the tumor node metastasis (TNM) staging system, which stages lymph node involvement according to the absolute number of positive lymph nodes [2]. However, the TNM system does not take into account examined tumor-free lymph nodes. Therefore, lymph node ratio (LNR) has recently emerged as an important prognostic factor and a suitable staging method for node positive patients [35]. Nevertheless, it was still under controversy due to contradictory LNR consequences in the previous studies [6, 7]. A previous systematic review considered the evidence on LNR as a prognostic factor in the colorectal cancer [3]. However, the main research tool for this study is systemic review (only four series submitted for meta analysis). Since many new studies in the last years have investigated this topic and the last review date was around ten year ago, we aimed to clarify the prognostic role of NLR in patients with lymph node-positive colorectal cancer and conduct the first meta-analysis on this topic.

RESULTS

Eligible and characteristics of studies

We identified 1598 potentially relevant articles from our search of the published literature. After removing duplications, scanning titles and abstracts and reading the full-text, 33 records [5, 738] encompassing a total of 81,331 (75,838 node positive) CRC patients were eligible for the present study based on our inclusion and exclusion criteria (Figure 1).

A flow chart showed the selection of studies.

Figure 1: A flow chart showed the selection of studies.

Demographic details and clinicopathologic characteristics of the included studies were summarized in Table 1 and Table 2. The 75838 node positive colorectal cancer patients were all underwent curative surgery, and their median age ranged from 54 to 75 years. Of all the 33 studies, 16 were focused on colon cancer, 5 on rectal cancer, and 12 considered both the colon and the rectum. We also investigated the situation of lymph nodes harvested and the treatment strategy (Table 2). The follow-up time ranged from 30.2 months to 86 months. The patients included in this study were diagnosed between 1991 and 2012.

Table 1: Demographic details of all identified studies

Study

Year

Sample

Patient age

Follow-up time

Country

Endpoint

Xue

2014

180

Median 54 years

Median 49 months

China

DFS

Arda

2014

58

Median 60 years

Mean 4-year

Turkey

OS DFS

Wang

2013

245

Median 61 years

Mean 6-year

China

OS

Yen

2013

612

Median 67 years

Median 52 months

Taiwan

OS,DFS

Tiago

2013

70

NA

Median 33 months

Brazil

DFS

Zhu

2012

161

Mean 59.1 years

NA

China

OS DFS

Liang

2012

174

Mean 62 years

Median 62.5 months

China

OS DFS

Kritsanasakul

2012

227

Mean 62.8 years

Median 86 months

Thailand

OS

Jung

2012

78

Median 64 years

Median 46 months

Korea

OS DFS

Shimomura

2011

266

Median 64 years

Median 42.4 months

Japan

DFS

Hong

2011

130

Mean 64 years

Median 50 months

Korea

DFS

Greenberg

2011

65

Mean 69 years

Mean 34 moths

Israel

OS,DFS

Vaccaro

2009

362

Mean 67.4 years

Median 42 months

Argentina

OS DFS

Galizia

2009

145

Median 66 years

Median 43 months

Italy

DFS

Wang

2012

256

Mean 57.9

Median 37 months

China

OS

Jing

2012

145

Median 66 years

Median 35.4 months

China

DFS

Tong

2011

505

Median 61 years

Median 31.08 months

China

OS

Shao

2011

282

NA

NA

China

OS

Jung

2010

514

Median 63 years

Median 48.5 months

Korea

OS DFS

Wang

2008

24477

Mean 69.2 years

NA

America

OS

Peng

2008

318

Mean 55.3 years

Median 41 months

China

OS, DFS

Derwinger

2008

265

Mean 72 years

Mean 3-year

Sweden

DFS

Lee

2007

201

Median 59 years

Median 41 months

Korea

DFS

Chin

2009

624

Mean 64.1 years

Mean 5-year

Taiwan

DFS

Arslan

2014

440

Median 66 years

Median 30.6 months

Turkey

OS

Kim

2009

232

NA

Median 53 months

Korea

OS

Kobayashi

2011

452

NA

Median 5.3 years

Japan

OS

Lykke

2013

3119

Median 72 years

Mean 5-year

Denmark

OS

Moug

2014

1514

Mean 71.9 years

Median 5.3 years

Scotland

OS

Thoma

2012

1908

Mean 68 years

Median 30.2 months

England

OS

Parnaby

2015

921

Median 75 years

Median 52.8 months

England

OS,DFS

Chen

2011

36712

Mean 69.6 years

NA

America

OS

Zhou

2015

180

Mean 59 years

Median 41.8 months

China

OS

“NA”: not available; “OS”: overall survival;”DFS”: disease free survival.

Table 2: Clinicopathologic characteristics of all studies

Study

Stage

Location

Inclusion period

Treatment

No. of nodes (N+)

Xue

III

colorectum

2007–2012

R0 surgery

median 8,(2)

Arda

III

colon

2006–2014

R0 surgery

NA

Wang

III

colorectum

2000–2006

R0 surgery + AT

NA

Yen

III

colorectum

2004–2008

R0 surgery + AT

median 18,(3)

Tiago

III

colon

2005–2010

R0 surgery

median 18.5

Zhu

III

rectum

2005–2010

R0 surgery

mean 13.4

Liang

III

colorectum

2000–2003

R0 surgery

median 10,(3)

Kritsanasakul

I–III

colorectum

1998–2007

R0 surgery + AT

median 10 (1.7)

Jung

I–III

colon

1999–2007

R0 surgery + AT

median 7

Shimomura

III

colorectum

1991–2008

R0 surgery + AT

median 14,(2)

Hong

III

colon

2000–2006

R0 surgery + AT

median 28,(2)

Greenberg

I–III

colorectum

2003–2009

R0 surgery + AT

median 16

Vaccaro

III

colorectum

1980–2005

R0 surgery + AT

median 20,(2)

Galizia

III

colon

1996–2007

R0 surgery + AT

median 15,(2)

Wang

III

colon

1999–2008

R0 surgery + AT

mean 23.3(4.2)

Jing

III

colon

1998–2008

R0 surgery + AT

mean 13.22(3.77)

Tong

III

colorectum

1994–2007

R0 surgery

median 12,(2)

Shao

II–III

colorectum

2000–2005

R0 surgery

mean 11.44(2.21)

Jung

III

colorectum

1998–2007

R0 surgery + AT

median 14,(2)

Wang

III

colon

1988–2003

curative surgery

NA

Peng

III

rectum

1990–2004

R0 surgery + AT

mean 12(3.8)

Derwinger

III

colon

1999–2003

R0 surgery + AT

median 11

Lee

III

colon

1995–2001

R0 surgery + AT

median 17,(3)

Chin

III

colon

1995–2003

R0 surgery + AT

NA

Arslan

I–III

colon

2005–2011

R0 surgery

median 19

Kim

III

rectum

1996–2006

R0 surgery + AT

median 17,(3)

Kobayashi

III

rectum

1991–1998

R0 surgery + AT

median 37(2)

Lykke

I–III

colon

2003–2008

R0 surgery

median 13(2)

Moug

I–III

colon

2000–2004

R0 surgery + AT

median 11

Thoma

III

colorectum

1997–2007

R0 surgery + AT

median 11(4)

Parnaby

I–III

colon

2006–2012

R0 surgery + AT

median 16

Chen

III

colon

1992–2004

R0 surgery

NA

Zhou

II–III

rectum

2005–2010

R0 surgery + AT

median 11(4)

“AT”: adjuvant treatment; “No. of nodes (N+)”: total number of lymph nodes harvested (number of positive lymph nodes); “NA”: not available.

All the HRs and their 95% CIs in the collected articles were listed in Table 3. We also summarized the methodological quality details. Firstly, the cut-off value of the LNRs was quite different from each other and stratified methods were not consistent (Table 3). Secondly, almost all of researchers used the multivariate statistical analysis models. Thirdly, most studies were retrospective study in design, while 5 articles were designed as the prospectively studies. Regarding the relationship between LNR and the clinicopathological characteristics of node positive colorectal cancer patients, no significant differences emerged for mean age and gender. Furthermore, the LNR was not associated with tumor location or T stage [15, 23, 39]. Higher LNR patients have, however, significant major proportion of a higher lymphovascular invasion and poor differentiation [15, 23, 39].

Table 3: Summary table of HRs (95% CI) and HR calculation

Study

HR (95%CI)

LNR cutoff value

LNR stratification

Statistical analysis

Study design

OS

Arda

1.712 (0.982–2.984)

0.25

NA

MA

R

Wang

1.641 (1.099–2.450)

0.3

Log rank analysis

MA

R

Yen

1.54 (1.05–2.22)

0.17

Log rank analysis

MA

R

Zhu

3.655 (1.939–6.888)

0.43

Mean

MA

R

Liang

1.42 (1.13–1.76)

0.125, 0.26, 0.5

Quartiles

MA

R

Kritsanasakul

2.62 (1.79–3.85)

0.35, 0.69

ROC curve analysis

MA

R

Jung

1.402 (1.265–4.564)

0, 0.01, 0.28

Median value

MA

R

Greenberg

12.2 (2.178–68.622)

0.13

ROC curve analysis

MA

R

Vaccaro

2.3 (1.3–4.1)

0.25

Quartiles

MA

R

Wang

1.754 (1.344–2.289)

0.11, 0.39

Log rank analysis

MA

P

Tong

1.958 (1.652–2.321)

0.35, 0.69

Log rank analysis

MA

R

Shao

1.263 (1.027–1.552)

0, 0.17, 0.41, 0.69

Literature data

MA

R

Jung

1.589 (1.106–2.284)

0.18

Quartiles

MA

R

Wang

2.30 (2.083–2.545)

1/14, 0.25, 0.5

ROC curve analysis

MA

SEER

Peng

3.41 (1.63–7.13)

0.14, 0.49

Literature data

MA

R

Arslan

2.197 (1.357–3.556)

0.05, 0.20

NA

UA

P

Kim

2.261(1.234–4.143)

0.1, 0.2, 0.4

Quartiles

MA

R

Kobayashi

2.114 (1.241–3.600)

0.04, 0.079, 0.15

Quartiles

MA

R

Lykke

1.560 (1.232–1.975)

0, 1/12, 1/4 , 1/2

Literature data

MA

P

Moug

2.117 1.350–3.318)

0.05, 0.19, 0.39

Literature data

MA

P

Thoma

1.799 (1.132–2.859 )

0, 0.11 ,0.21, 0.36, 0.60

NA

MA

P

Parnaby

2.464 (1.487–4.083)

0, 0.17, 0.41, 0.69

Literature data

MA

L

Chen

1.975 (1.519–2.568)

0.1, 0.24, 0.49, 0.99, 1

Log rank analysis

MA

SEER

Zhou

1.71 (1.1–2.65)

0, 0.19

ROC curve analysis

MA

R

DFS

Xue

2.098 (1.050–4.192)

0.17

ROC curve analysis

MA

R

Arda

1.736 (0.997–3.024)

0.25

NA

MA

R

Yen

1.53 (1.05–2.23)

0.17

Log rank analysis

MA

R

Tiago

74.88 (1.55–3617.01)

0.15

Literature data

MA

R

Zhu

2.775 (1.544–4.988)

0.43

Mean

MA

R

Liang

1.39 (1.15–1.69)

0.125, 0.26, 0.5

Quartiles

MA

R

Jung

3.073 (1.496–6.313 )

0, 0.01, 0.28

Median value

MA

R

Shimomura

2.425 (1.497–3.922)

0.2

ROC curve analysis

MA

R

Hong

5.868 (1.585–21.729)

0.1638

Quartiles

MA

R

Greenberg

3.297 (0.875–12.427)

0.13

ROC curve analysis

MA

R

Vaccaro

2.6 (1.5–4.8)

0.25

Quartiles

MA

R

Galizia

5.56 (3.45–12.5)

0.1818

ROC curve analysis

MA

R

Jing

11.75(3.20–43.12)

0.11, 0.20. 429

Quartiles

MA

R

Jung

1.596 (1.122–2.268)

0.18

Quartiles

MA

R

Peng

3.82 (1.96–7.47)

0.14, 0.49

Literature data

MA

R

Derwinger

10.6 (3.2–31.8)

0.12, 0.27, 0.4

Quartiles

MA

R

Lee

2.880 (1.950–4.253)

0.11, 0.24,

Quartiles

MA

R

Chin

3.915 (1.249–12.269)

0.4, 0.7

Log rank analysis

MA

R

Parnaby

2.877 (1.837–4.507)

0, 0.17, 0.41, 0.69

Literature data

MA

R

Study design is described as prospective (P) or retrospective (R). SEER surveillance, epidemiology, and end results cancer registry; L location cancer registry.

NA, not available; OS, overall survival; DFS, disease -free survival;

ROC curve: receiver operating characteristic curve. LNR: lymph node ratio,

MA, multivariate statistical analysis models; UA, univariate statistical analysis models.

Meta-analysis results

As shown in Figure 2, a pooled HR and its 95%CI were calculated with a random model because of the heterogeneity test showed that statistically significant heterogeneity exists between the studies (for OS: I2 = 60.5%, P = 0.000; for DFS: I2 = 71.7%, P = 0.000). The result showed that elevated LNR may predict poor OS (n = 24) (the pooled HR was 1.91; 95% CI: 1.71–2.14) and DFS (the pooled HR was 2.75; 95% CI: 2.14–3.53). We next conducted subgroup analysis base on some important clinicopathological characteristics. The patients with higher LNR were all associated with decreased OS and DFS (Table 4).

Forest plots show the association between LNR and overall survival (A), disease free survival (B).

Figure 2: Forest plots show the association between LNR and overall survival (A), disease free survival (B).

Table 4: Results of the meta-analysis

Stratifications

No. of studies

Pooled Estimates

Model

Heterogeneity

HR (95% CI)

P value

I2(%)

P value

OS

24

1.91 (1.71–2.14)

0.000

R

60.5

0.000

No. of nodes

No. of nodes≥12

13

1.97 (1.71–2.26)

0.000

F

35.2

0.101

No. of nodes<12

8

1.74 (1.40–2.17)

0.000

R

62

0.015

Location

Colon

9

2.11 (1.95–2.28)

0.000

F

35.1

0.137

rectum

5

2.30 (1.79–2.96)

0.000

F

19.9

0.288

Treatment

R0 surgery +AT

15

1.96 (1.73–2.22)

0.000

F

8.8

0.355

R0 surgery

9

1.83 (1.52–2.20)

0.000

R

81.3

0.000

Stage

Stage III

15

1.91 (1.71–2.14)

0.000

R

50.7

0.013

DFS

19

2.75 (2.14–3.53)

0.000

R

71.7

0.000

No. of nodes

No. of nodes≥12

13

2.87 (2.18–3.77)

0.000

F

48.8

0.062

No. of nodes < 12

4

2.69 (1.32–5.50)

0.000

R

81.5

0.001

Location

Colon

9

3.49 (2.47–4.93)

0.000

R

48.9

0.048

Treatment

R0 surgery + AT

14

3.06 (2.32–4.04)

0.000

R

63.2

0.001

R0 surgery

5

1.91 (1.27–2.86)

0.002

R

59

0.045

Stage

Stage III

16

2.73 (2.06–3.61)

0.000

R

74.6

0.000

“OS”: overall survival; “DFS”: disease free survival; “AT”: adjuvant treatment; “R”: random effects model; “F”: fixed effect model; “No. of nodes”: total number of lymph nodes harvested.

Sensitivity analysis

Obvious heterogeneity was found in some analysis groups (Table 4). The most possible sources of heterogeneity were analyzed by subgroup. But subgroup analysis could not completely explain the heterogeneity. Therefore, we performed sensitivity analysis (Figure 3). In the OS analysis for all, heterogeneity was significant (I2 = 60.5%, P = 0.000). When Shaos’ study and Wangs’ study were removed from analysis, the heterogeneity became insignificant (P = 0.109 and I2 = 28.1%). As to DFS analysis for all (I2 = 71.7%, P = 0.000), we found that Liangs’, Yen’s and Jungs’ study were responsible for the heterogeneity of DFS analysis group (P = 0.091 and I2 = 33.9%). After we excluded the publications with statistically significant heterogeneity and repeated the analysis, the summary estimates for higher LNR did not change statistically significantly (OS for all: the pooled HR was 1.85; 95% CI: 1.72–2.00; DFS for all: the pooled HR was 3.01; 95% CI: 2.55–3.55).

Sensitivity analysis of the association between LNR and overall survival (A), disease free survival (B).

Figure 3: Sensitivity analysis of the association between LNR and overall survival (A), disease free survival (B).

Publication bias

Funnel plots and Egger’s test were conducted to evaluate the publication bias of included studies. No obvious visual asymmetry was observed in funnel plots (Figure 4) for OS, and the P values of the Egger’s test were 0.800. However, statistically significant publication bias was found in the studies of DFS (Egger’s test P value = 0.000). The funnel plot for the studies of DFS showed an asymmetrical distribution of the studies (Figure 4). Therefore we used the trim-and-fill method (Figure 5). As a consequence, there were 6 potential missing studies, and after these 6 potentially unpublished studies were filled, the recalculated pooled HR was 2.24 (95% CI: 1.75–2.88, p < 0.00001) in the random effects model. That indicated a positive outcome even though publication bias still exists.

Funnel plot of the association between LNR and overall survival (A), disease free survival (B).

Figure 4: Funnel plot of the association between LNR and overall survival (A), disease free survival (B).

Trim and fill funnel plot for the source of publication bias.

Figure 5: Trim and fill funnel plot for the source of publication bias.

DISCUSSION

The prognosis of patients with colorectal cancer was largely related to the lymph node status, which helps in tumor staging and clinical decision. According to the current TNM staging system proposed by the AJCC/ UICC [2], N categories were determined by the absolute number of involved lymph nodes (N1, one to three; N2, four or more). Although this categorization has been proven to predict long term outcomes and well accepted [40], it is noteworthy that the TNM system does not take into account some important features of lymph node metastasis. In fact, many features of lymph node such as the number of non metastasis lymph nodes and the extra-nodal extension of nodal metastasis retrieved from the resection specimen which has been shown to have a prognostic significance in CRC [41, 42]. Furthermore, LNR can be considered as a hallmark of aggressiveness, since it was associated with a higher percentage of lymphovascular invasion and poor tumor differentiation [15, 23, 39].

In last decades, many researchers suggested that LNR could be a prognostic factor in different types of malignancies especially most of the gastrointestinal cancers [4346]. This meta-analysis confirmed that higher LNR is statistically significantly associated with a poor survival of colorectal cancer. The results were similar when we subgroup the patients according to some important clinicopathological characteristics. Furthermore, we carried out a sensitivity analysis, which suggested the stability of our meta-analysis. We encountered evidence of publication bias in our main analysis, but our results remained unchanged after we adjusted for this. In current meta-analysis, we excepted the studies which included patients underwent neo-adjuvant treatment because it has reported that the total number of retrieved lymph nodes and positive lymph nodes may decrease after preoperative chemoradiation [47, 48].

Our results have demonstrated the significant weight of LNR in the prognosis of CRC. It is recommended to include LNR as a prognostic parameter in future colorectal staging system. It is important to note that the extent of dissection would influence the LNR. Generally, a more extensive surgical dissection of the specimen results in a higher number of positive nodes. And a ratio based on a small number of lymph nodes ha s a larger standard error, which could affect the reliability of the LNR in those patients who had less extensive dissection [49, 50]. So, adequate lymph nodes retrieved from the operative specimen was still important.

Our study had some advantages. First, this is the first complete meta-analysis identify the prognostic role of LNR in CRC. Second, this meta-analysis included plenty of primary studies (33 papers) and patients (75,838 node positive patients). The statistical power is well enough for our results. However, this study also had several limitations which are largely reflected by those within the primary studies. First, data about other co-morbidities (like cardiovascular diseases) were not reported, but it is known that they play an important prognostic role also in patients with cancer. Second, The cut-off value for defining LNR in each included study is quite different, which may have contributed to heterogeneity. Regarding which cutoff value will be the most reliable for predicting the prognostic values of colorectal cancer patients, the available evidence could not achieve an agreement. This needs a large cohort study or an individual patient data meta-analysis which could stratify and evaluate different LNRs on the CRC prognosis and find out the minute differences in prognostic outcomes. Finally, we also encountered some heterogeneity but were able to investigate sources of this within subgroup analysis and sensitive analysis.

In conclusion, this meta-analysis indicated that higher LNR can be used as a predictor of poor survival and assists in the choice of adjuvant treatment in the clinical setting in patients with CRC. We proposed that the LNR could be a prognostic parameter in future colorectal staging system.

MATERIALS AND METHODS

Search strategy and selection criteria

We systematically searched PubMed, Embase and the Cochrane library (http://www.cochrane.org) using the “lymph node ratio”, “LNR”;”lymph positive node ratio”, “lymph metastatic node ratio” Medical Subject Heading (MeSH) terms “Colorectal Neoplasms” and the individual corresponding free terms such as “colorectal cancer”, ”colon cancer”, “rectal cancer” “colorectal adenocarcinoma”, “colon adenocarcinoma”, “rectal adenocarcinoma”, “colorectal carcinoma”, “colon carcinoma”, “rectal carcinoma”, “colorectal tumor”, “colon tumor”, “rectal tumor”. No language or other restrictions were applied. The last search was updated on 28 November, 2015. In addition, we reviewed references in the retrieved articles to search for additional relevant studies.

Studies eligible in the meta-analysis fulfilled the following inclusion criteria: (1) the patients were pathologically diagnosed as CRC with node-positive who underwent curative surgery (R0 resection);(2) the outcome of interest was overall survival (OS) and disease free survival (DFS);(3) hazard ratio (HR) and 95% confidence intervals (CI) were sufficiently reported. Exclusion criteria were defined as follows: (1) the patients have distant metastasis (TNM stage IV) or received neoadjuvant chemotherapy; (2) Letters, reviews, expert opinions, and case reports.

Data extraction

The following information were extracted from each selected papers if available: first author, year of publication, country of the study population, number of patients, number of nodes examined, type of study, cut-off value for the LNR and definition of the strata, follow-up years, the location and the TNM stage of the tumor, and HRs with 95% CI. Two investigators reviewed and extracted information independently and checked by the other authors. Discrepancies were settled by consensus.

Statistical analysis

The statistical analyses were carried out using STATA 12.0 (STATA Corporation, College Station, TX, USA). The HRs with 95% CI from each study were extracted to generate a pooled HR. Heterogeneity among studies was checked using the chi-squared test and I2 statistics. If the P value < 0.05 and/or I2 > 50% indicating statistical significance, a random effects model was used to obtain summary HRs. Otherwise, a fixed effect model was utilized. In addition, we conducted a sensitivity analysis to investigate the potential sources of heterogeneity and assess the strength of our findings by sequentially excluding one study. Furthermore, factors contributed to heterogeneities were also analyzed by stratifying the subjects according to the tumor location. Publication bias among the studies was investigated by using Begg’s funnel plot and the Egger’s test.

ACKNOWLEDGMENTS

We thank Hao Cheng and Dun Wang for assisting with data collection and management.

CONFLICTS OF INTEREST

The authors declare no competing financial interests.

FUNDING

This study was supported by grants from the National Natural Science Fund of China (NSFC key project 81472304).

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