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. 2018 Aug 13;19:150. doi: 10.1186/s12931-018-0843-7

Augmented expression of Ki-67 is correlated with clinicopathological characteristics and prognosis for lung cancer patients: an up-dated systematic review and meta-analysis with 108 studies and 14,732 patients

Dan-ming Wei 1, Wen-jie Chen 1, Rong-mei Meng 1, Na Zhao 1, Xiang-yu Zhang 1, Dan-yu Liao 1, Gang Chen 1,✉
PMCID: PMC6088431  PMID: 30103737

Abstract

Background

Lung cancer ranks as the leading cause of cancer-related deaths worldwide and we performed this meta-analysis to investigate eligible studies and determine the prognostic effect of Ki-67.

Methods

In total, 108 studies in 95 articles with 14,732 patients were found to be eligible, of which 96 studies reported on overall survival (OS) and 19 studies reported on disease-free survival (DFS) with relation to Ki-67 expression in lung cancer patients.

Results

The pooled hazard ratio (HR) indicated that a high Ki-67 level could be a valuable prognostic factor for lung cancer (HR = 1.122 for OS, P < 0.001 and HR = 1.894 for DFS, P < 0.001). Subsequently, the results revealed that a high Ki-67 level was significantly associated with clinical parameters of lung cancer including age (odd ratio, OR = 1.246 for older patients, P = 0.018), gender (OR = 1.874 for males, P < 0.001) and smoking status (OR = 3.087 for smokers, P < 0.001). Additionally, significant positive correlations were found between Ki-67 overexpression and poorer differentiation (OR = 1.993, P = 0.003), larger tumor size (OR = 1.436, P = 0.003), and higher pathologic stages (OR = 1.867 for III-IV, P < 0.001). Furthermore, high expression of Ki-67 was found to be a valuable predictive factor for lymph node metastasis positive (OR = 1.653, P < 0.001) and advanced TNM stages (OR = 1.497 for stage III-IV, P = 0.024). Finally, no publication bias was detected in any of the analyses.

Conclusions

This study highlights that the high expression of Ki-67 is clinically relevant in terms of the prognostic and clinicopathological characteristics for lung cancer. Nevertheless, more prospective well-designed studies are warranted to validate these findings.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0843-7) contains supplementary material, which is available to authorized users.

Keywords: Ki-67, Lung cancer, Meta-analysis, Prognosis, Clinicopathological characteristics

Background

Lung cancer is the most frequent diagnosed malignant neoplasms, and it was the first cause of cancer death in 2016 globally [1]. In the United States, lung cancer accounted for 27% of all cancer deaths in 2016. Non-small cell lung cancer (NSCLC), accounting for over 80% of all lung cancers, is the major cause of death worldwide [2]. Although the treatment of NSCLC patients includes surgery, radiotherapy and chemotherapy, the progress in lung cancer treatment is still slow, for which the 5-year relative survival is currently 18%. Diagnosis at an advanced stage is the major reason for this low survival rate [3]. Several prognostic factors were well characterized in lung cancer including sex, age, loss of weight, TNM stage, LDH, neutrophilia, haemoglobin as well as serum calcium [4]. Importantly, The IASLC Lung Cancer Staging Project in 2015 also carried out the 8th edition of the anatomic classification of lung cancer, which redefined the tumor-size cut-points in TNM stage for the lung cancer patients. The prognostic value of reclassification of tumor size were confirmed in 70,967 non–small-cell lung cancer patients from 1999 to 2010 [5]. To improve the survival of lung cancer patients, the choice of targeted treatments is increasingly being based on oncogenic drivers including ALK rearrangements, KRAS and epidermal growth factor receptor (EGFR) mutations [6, 7]. Additionally, BRAF mutations represent promising new therapeutic targets for lung cancer [8]. Likewise, several driver biomarkers also shed new light on the target treatment for lung cancer patients such as Her2 [9], NUT [10], DDR2 [11], FGFR1 [12], and PTEN [13]. Furthermore, several new molecular targets been highlighted in lung cancer, including ROS1 fusions [14], NTRK1 fusions [15] and exon 14 skipping mutations [16]. Recently, the checkpoint inhibitors targeting programmed death protein 1 (PD-1) have shown for durable clinical responses in NSCLC patients with advanced stage [17]. The immunomodulatory monoclonal antibodies against cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) also present the promising results for the treatment of advanced -stages lung cancer patients. However, due to the complex molecular mechanism of lung cancer, the identification of biomarkers in a large proportion of lung cancer patients is still required for a deeper understanding of the underlying epigenetic heterogeneity, and this will benefit the discovery of targeted therapies against lung cancer.

Ki-67, encoded by the MKI67 gene, is expressed throughout the cell cycle in proliferating but absent in in quiescent (G0) cells [18]. Ki-67 appears in the middle or late G1 stage, and then its expression increases through the S and G2 stage until it reaches a peak during the M stage [19]. The high expression of Ki-67 may contribute to aggressive and infiltrative growth of lung squamous carcinoma (SQC), cervical SQC and laryngeal SQC [20–22]. In addition, overexpression of Ki-67 has been positively associated with lymph node metastasis in gastric carcinoma and breast cancer [23, 24]. Ki-67 expression has been reported to be associated with a poor outcome in many malignancies including prostate, bladder and breast cancer [25–29].

Although two meta-analyses have previously reported that Ki-67 could be a possible indicator of short-term survival in lung cancer patients [30, 31], studies on a larger number of lung cancer patients and more reliable evidence are still needed to confirm the prognostic and clinicopathological role of Ki-67 for patients with lung cancer. Thus, our investigation aimed to evaluate the prognostic value and clinicopathological significance of Ki-67 in lung cancer patients via the review of previously published articles.

Results

Study selection and characteristics

As shown in Fig. 1, two investigators read the full text and considered 108 studies in 95 articles [18, 25, 32–124] consisting of 14,732 patients as applicable. The baseline characteristics of the included articles are indicated in Table 1. Ninety-six studies included data regarding OS, and nineteen studies included data regarding DFS. The number of cohorts of each study ranged from 32 to 778. In terms of the study region, 62 studies were from Asia, 30 were from Europe, and 15 were from America. In total, 104 studies consisted of 14,596 NSCLC patients: of these, 28 studies reported on ADC patients, and 10 studies reported on SQC patients. All the studies detected Ki-67 reactivity using IHC.

Fig. 1.

Fig. 1

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Flow chart of study selection process

Table 1.

Characteristics of studies included into the meta-analysis

Study(author/year) Region Tumor stage Histological type Patients Sample size Cutoff value Sample type Assay NOS score Extract method Survival
Scagliotti 1993 [91] Italy I-IIIA NSCLC 111 Large 25% Tumor tissue IHC 7 Survival curve OS
Pence 1993 [120] USA I-IV NSCLC 61 Small 4% Tumor tissue IHC 7 Survival curve OS
Fontanini 1996 [46] Italy NA NSCLC 70 Small 30% Tumor tissue IHC 7 Survival curve OS
Bohm 1996 [37] Germany NA SCLC 32 Small 27% Tumor tissue IHC 7 Original data OS
Harpole 1996 [50] USA I NSCLC 275 Large 7% Tumor tissue IHC 9 Multivariate OS
Pujoll 1996 [88] France I-IV LC 97 Small NA Tumor tissue IHC 7 Survival curve OS
Mehdi 1999 [119] USA I-IV NSCLC 203 Large 25% Tumor tissue IHC 9 Multivariate OS and DFS
Demarchi 1999 [41] Brazil I-III ADC 64 Small 22% Tumor tissue IHC 7 Original data OS
Dingemans 1999 [42] Netherland I-III SCLC 93 Small 30% Tumor tissue IHC 7 Survival curve OS
Wang 1999 [99] China NA NSCLC 85 Small 30% Tumor tissue IHC 7 Survival curve OS
Shiba 2000 [92] Japan I-III NSCLC 95 Small 20% Tumor tissue IHC 7 Univariate OS
Hommura 2000 [54] Japan I-II SQC 91 Small 30% Tumor tissue IHC 9 Multivariate OS
Hommura 2000 [54] Japan I-II NSCLC 124 Large 30% Tumor tissue IHC 9 Multivariate OS
Nguyen 2000 [80] Czech Republic I-IV NSCLC 89 Small 30% Tumor tissue IHC 7 Survival curve OS
Puglisi 2001 [87] Italy I-III NSCLC 81 Small 30% Tumor tissue IHC 9 Multivariate OS
Hayashi 2001 [52] Japan I-IV NSCLC 98 Small 13% Tumor tissue IHC 9 Multivariate OS
Pelosi 2001 [84] Italy I SQC 119 Large NA Tumor tissue IHC 9 Multivariate OS and DFS
Ramnath 2001 [121] USA I-IV NSCLC 160 Large 24% Tumor tissue IHC 7 Univariate OS
Ramnath 2001 [121] USA I-IV NSCLC 41 Small 50% Tumor tissue IHC 7 Univariate OS
Wang 2001 [101] China I-IV LC 166 Large 18% Tumor tissue IHC 7 Survival curve OS
Mojtahedzadeh 2002 [78] Japan I-III ADC 141 Large 10% Tumor tissue IHC 8 Multivariate OS
Minami 2002 [76] Japan I ADC 47 Small 20% Tumor tissue IHC 8 Multivariate OS
Takahashi 2002 [94] Japan I-IV NSCLC 62 Small 25% Tumor tissue IHC 9 Multivariate DFS
Wakabayashi 2003 [97] Japan I-IV NSCLC 140 Large 13% Tumor tissue IHC 9 Multivariate OS
Pelosi 2003 [85] UK I ADC 96 Small NA Tumor tissue IHC 9 Multivariate OS
Haga 2003 [49] Japan I ADC 58 Small 10% Tumor tissue IHC 9 Multivariate OS
Hashimoto 2003 [51] Japan I-III ADC 122 Large 20% Tumor tissue IHC 8 Multivariate OS
Poleri 2003 [86] Argentina I NSCLC 50 Small 67% Tumor tissue IHC 7 Survival curve DFS
Matheus 2004 [75] Brazil I-III ADC 33 Small 22% Tumor tissue IHC 7 Original data OS
Niemiec 2004 [81] Poland I-III SQC 78 Small 28% Tumor tissue IHC 7 Survival curve OS
Ahn 2004 [33] Korea II-IIIA NSCLC 65 Small 15% Tumor tissue IHC 9 Multivariate OS
Huang 2005 [55] Japan I NSCLC 97 Small 25% Tumor tissue IHC 8 Multivariate OS
Gasinska 2005 [48] Poland I-III SQC 81 Small 39% Tumor tissue IHC 9 Multivariate OS
Wang 2005 [100] China I-III NSCLC 51 Small 5% Tumor tissue IHC 7 Survival curve OS
Dong 2005 [43] Japan I-IV ADC 131 Large 18% Tumor tissue IHC 9 Multivariate OS
Niemiec 2005 [81] Poland I-III SQC 78 Large 28% Tumor tissue IHC 7 Survival curve OS
Huang 2005 [55] Japan II-III NSCLC 76 Small 25% Tumor tissue IHC 8 Multivariate OS
Tsubochi 2006 [64] Japan I-III NSCLC 219 Large 20% Tumor tissue IHC 9 Multivariate OS
Yang 2006 [110] USA I-III NSCLC 128 Large 25% Tumor tissue IHC 9 Multivariate OS
Nozawa 2006 [82] Japan IV ADC 35 Small 40% Tumor tissue IHC 7 Survival curve OS
Maddau 2006 [118] Italy II-III NSCLC 88 Large 25% Tumor tissue IHC 7 Univariate OS
Maddau 2006 [118] Italy I NSCLC 92 Large 25% Tumor tissue IHC 7 Univariate OS
Inoue 2007 [58] Japan I-III ADC 97 Small 5% Tumor tissue IHC 9 Multivariate DFS
Mohamed 2007 [77] Japan I-IV NSCLC 61 Small 20% Tumor tissue IHC 9 Multivariate OS
Zhou 2007 [116] China I-II NSCLC 70 Small NA Tumor tissue IHC 6 Multivariate OS
Morero 2007 [79] Argentina III NSCLC 32 Small 66% Tumor tissue IHC 7 Survival curve OS
Yoo 2007 [112] Korea I-III NSCLC 219 Large 30% Tumor tissue IHC 9 Multivariate OS
Fujioka 2008 [47] Japan I ADC 73 Small 14% Tumor tissue IHC 9 Multivariate OS
Imai 2008 [57] Japan I NSCLC 248 Large 25% Tumor tissue IHC 9 Multivariate OS
Woo 2008 [103] Japan Ia ADC 131 Large 10% Tumor tissue IHC 8 Univariate DFS
Woo 2008 [103] Japan Ib ADC 59 Small 10% Tumor tissue IHC 8 Univariate DFS
Saad 2008 [89] USA I-III NSCLC 54 Small 30% Tumor tissue IHC 7 Survival curve OS
Kaira 2008 [124] Japan I-III NSCLC 321 Large 25% Tumor tissue IHC 9 Multivariate OS
Ikeda 2008 [56] Japan I-III NSCLC 200 Large 5% Tumor tissue IHC 7 Univariate OS and DFS
Anami 2009 [34] Japan I-IV ADC 139 Large 10% Tumor tissue IHC 8 Multivariate OS
Yuan 2009 [113] China I-III NSCLC 140 Large 25% Tumor tissue IHC 8 Multivariate OS
Kaira 2009 [62] Japan 1 ADC 139 Large 20% Tumor tissue IHC 9 Multivariate OS
Erler 2010 [44] USA NA SCLC 68 Small 50% Tumor tissue IHC 7 Survival curve OS
Filipits 2011 [45] Austria I-III NSCLC 778 Large NA Tumor tissue IHC 9 Multivariate OS and DFS
Werynska 2011 [102] Poland I-IV NSCLC 145 Large 25% Tumor tissue IHC 7 Univariate OS
Yamashita 2011 [109] Japan I NSCLC 44 Small 5% Tumor tissue IHC 8 Multivariate DFS
Wu 2011 [106] China I-IV NSCLC 160 Large 10% Tumor tissue IHC 8 Multivariate OS
Oka 2011 [83] Japan I-III ADC 183 Large 20% Tumor tissue IHC 9 Multivariate DFS
Sterlacci 2011 [122] Austria I-IV NSCLC 386 Large 3% Tumor tissue IHC 9 Multivariate OS
Werynska 2011 [102] Poland NA NSCLC 145 Large 25% Tumor tissue IHC 7 Univariate OS
Liu 2012 [71] China I-IV NSCLC 494 Large 50% Tumor tissue IHC 9 Multivariate OS
Wang 2012 [98] China NA SCLC 42 Small 10% Tumor tissue IHC 7 Survival curve OS
Liu 2012 [71] China I-IV ADC 97 Small 10% Tumor tissue IHC 7 Survival curve OS
Wu 2012 [105] China I-IV ADC 309 Large 50% Tumor tissue IHC 9 Multivariate OS
Salvi 2012 [90] Italy I-III NSCLC 81 Small 15% Tumor tissue IHC 9 Multivariate OS
Yang 2012 [111] China I-III NSCLC 68 Small 38% Tumor tissue IHC 7 Survival curve OS
Wu 2013 [104] China I-IV NSCLC 192 Large 10% Tumor tissue IHC 9 Multivariate OS and DFS
Maki 2013 [74] Japan I ADC 105 Large 15% Tumor tissue IHC 9 Multivariate DFS
Lei 2013 [69] China I-IV NSCLC 279 Large 30% Tumor tissue IHC 9 Multivariate OS
Berghoff 2013 [36] Austria I-IV NSCLC 230 Large 40% Tumor tissue IHC 9 Multivariate OS
Ji 2013 [61] China I-III NSCLC 67 Small 5% Tumor tissue IHC 9 Multivariate OS
Kobyakov 2013 [68] USA I-III SQC 118 Large 30% Tumor tissue IHC 7 Survival curve OS
Zu 2013 [117] China I-III ADC 96 Small 25% Tumor tissue IHC 7 Survival curve OS
Liu 2013 [70] China I-III NSCLC 105 Large 50% Tumor tissue IHC 8 Multivariate OS
Hokka 2013 [53] Japan I-IV ADC 125 Large NA Tumor tissue IHC 9 Multivariate OS
Xue 2013 [73] China I-III NSCLC 83 Small 50% Tumor tissue IHC 8 Multivariate OS
Zhong 2014 [115] China I-IV NSCLC 270 Large 50% Tumor tissue IHC 8 Multivariate OS
Ahn 2014 [32] Korea I-III NSCLC 108 Large 40% Tumor tissue IHC 9 Multivariate DFS
Shimizu 2014 [93] Japan I-III SQC 32 Small 10% Tumor tissue IHC 8 Multivariate DFS
Shimizu 2014 [93] Japan I-III ADC 52 Small 10% Tumor tissue IHC 8 Multivariate DFS
Kim 2014 [136] Korea I-IV ADC 122 Large 10% Tumor tissue IHC 7 Univariate OS
Tsoukalas 2014 [95] Greece I-IV NSCLC 112 Large NA Tumor tissue IHC 9 Multivariate OS
Kawatsu 2014 [63] Japan I-IV NSCLC 183 Large 10% Tumor tissue IHC 8 Multivariate OS
Corzani 2014 [39] Italy III NSCLC 50 Small 50% Tumor tissue IHC 8 Multivariate OS
Warth 2014 [18] Germany I-IV ADC 482 Large 25% Tumor tissue IHC 7 Univariate OS and DFS
Warth 2014 [18] Germany I-IV SQC 233 Large 50% Tumor tissue IHC 8 Multivariate OS
Tabata 2014 [25] Japan I-IV NSCLC 74 Small 10% Tumor tissue IHC 9 Multivariate OS
Xu 2014 [108] China I-IV ADC 80 Small 5% Tumor tissue IHC 7 Survival curve OS
Liu 2014 [70] China I-IV NSCLC 96 Small 30% Tumor tissue IHC 7 Survival curve OS
Ji 2014 [60] China I-III NSCLC 83 Small NA Tumor tissue IHC 8 Multivariate OS
Shimizu 2014 [93] Japan I-IV SQC 32 Large 10%% Tumor tissue IHC 9 Multivariate OS
Shimizu 2014 [93] Japan I-IV ADC 52 Large 10%% Tumor tissue IHC 9 Multivariate OS
Kobierzycki 2014 [67] Poland I-IV NSCLC 218 Large 25% Tumor tissue IHC 6 Univariate OS
Xu 2014 [107] China I-III NSCLC 114 Large 50% Tumor tissue IHC 7 Univariate OS
Zhang 2015 [114] China I-IV ADC 616 Large NA Tumor tissue IHC 8 Multivariate OS
Gobbo 2015 [40] Italy NA NSCLC 383 Large 20% Tissue microarray IHC 7 Univariate OS
Stewart 2015 [123] USA II-IIIA NSCLC 230 Large NA Tumor tissue IHC 7 Univariate DFS
Vigouroux 2015 [96] France I-IV NSCLC 190 Large 40% Tumor tissue IHC 7 Survival curve OS
Jethon 2015 [59] Poland I-IV SQC 89 Small 25% Tumor tissue IHC 7 Univariate OS
Jethon 2015 [59] Poland I-IV ADC 98 Small 25% Tumor tissue IHC 7 Univariate OS
Apostolova 2016 [35] Germany I-IV NSCLC 83 Small 75% Tumor tissue IHC 8 Multivariate OS
Cardona 2016 [38] USA NA NSCLC 144 Large 30% Tumor tissue IHC 7 Original data OS
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Prognostic value of Ki-67 for survival outcome in lung cancer patients

In total, 95 studies with 13,678 lung cancer patients investigated the impact of Ki-67 expression on OS (Table 2). The pooled HR of the total population for OS was 1.122 (95%CIs: 1.089–1.156, Z = 7.56, P < 0.001; I2 = 78.20%, P < 0.001, Figs. 2, 3 and 4), showing that a high Ki-67 level indicates worse outcome for lung cancer patients. Furthermore, the correlation of high Ki-67 expression with DFS in 3127 lung cancer patients was then analyzed (Table 3). For the total study population, worse DFS (HR = 1.894, 95%CIs: 1.456–2.463, Z = 4.76, P < 0.001, Fig. 5) was observed among patients with high expression of Ki-67, while the heterogeneity using the random effects model was obvious (I2 = 78.30%, P < 0.001). To investigate the source of heterogeneity, subgroup analyses of publication year, region, histological type, sample size, cut-off value of Ki-67 and estimated method for HR determination were performed. From the subgroup analysis of OS, no heterogeneity was found in the small cell lung cancer (SCLC) group (I2 = 22.30%, P = 0.277). Next, a reduction in heterogeneity was observed after performing subgroup analysis of DFS according to the study region, especially in the studies from America (I2 = 8.40%, P = 0.351) and Asia (I2 = 25.60%, P = 0.179). As indicated in the subgroup of cutoff value, there was a low degree of heterogeneity in both Ki-67 low expression (I2 = 25.30%, P = 0.196) and high expression groups (I2 = 19.40%, P = 0.287). Furthermore, we also performed meta-regression analysis to explore the original of the heterogeneity in the studies. Consistent with the subgroup analysis, the results revealed that the regions and cut-off values might be the potential bias for the heterogeneity (P = 0.017 and P = 0.022, respectively). Altogether, we concluded that the different regions and inconsistent cut-off values might have contributed to the heterogeneity in the results of analyses for DFS. The regression also revealed that the heterogeneity originated from regions and inconsistent cut-off values (Table 4).

Table 2.

Summarized HRs of overall and subgroup analyses for OS

Stratified analysis Study(N) HR z P Heterogeneity
I2 P Estimated method
 OS 95 1.122(1.089–1.156) 7.56 < 0.001 78.20% < 0.001 Random-effect
Subgroup analysis
Publication year
 Early year(~ 2007) 44 1.307(1.212–1.408) 7.01 < 0.001 72.50% < 0.001 Random-effect
 Later year(2007~ 2016) 51 1.101(1.063–1.142) 5.28 < 0.001 81.20% < 0.001 Random-effect
Region
 Europe 30 1.021(1.001–1.042) 2.05 0.041 71.30% < 0.001 Random-effect
 America 13 1.671(1.266–2.205) 3.63 < 0.001 66.60% < 0.001 Random-effect
 Asia 52 1.821(1.623–2.043) 10.21 < 0.001 78.20% < 0.001 Random-effect
Histological type
 SCLC 4 1.023(1.004–1.042) 3.28 0.001 22.30% 0.277 Fixed-effect
 NSCLC 89 1.113(1.081–1.147) 7.11 < 0.001 78.70% < 0.001 Random-effect
 ADC 22 1.219(1.113–1.336) 4.26 < 0.001 76.20% < 0.001 Random-effect
 SQC 9 1.115(0.806–1.542) 0.66 0.512 74.40% < 0.001 Random-effect
Sample size
  < 100 45 1.486(1.340–1.649) 7.71 < 0.001 73.10% < 0.001 Random-effect
  > 100 50 1.083(1.049–1.119) 4.90 < 0.001 81.50% < 0.001 Random-effect
Cutoff value
 L(< 20%) 34 1.962(1.622–2.373) 6.95 < 0.001 74.50% < 0.001 Random-effect
 H(≥20%) 52 1.144(1.094–1.197) 5.91 < 0.001 78.90% < 0.001 Random-effect
Estimated method
 Original data 4 2.043(0.868–4.808) 1.64 0.102 83.90% < 0.001 Random-effect
 Survival curve 23 1.629(1.368–1.940) 5.47 < 0.001 76.30% < 0.001 Random-effect
 HR(univariate) 16 1.511(1.236–1.847) 4.02 < 0.001 56.10% 0.004 Random-effect
 HR(multivariate) 53 1.108(1.063–1.155) 4.87 < 0.001 75.30% < 0.001 Random-effect
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Fig. 2.

Fig. 2

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Hazard ratios and 95% CIs of studies included in meta-analysis of OS

Fig. 3.

Fig. 3

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Hazard ratios and 95% CIs of studies included in meta-analysis of OS

Fig. 4.

Fig. 4

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Hazard ratios and 95% CIs of studies included in meta-analysis of OS

Table 3.

Summarized HRs of overall and subgroup analyses for DFS

Stratified analysis Study(N) HR Z P Heterogeneity
I2 P Estimated method
DFS 21 1.894(1.456–2.463) 4.76 < 0.001 78.30% < 0.001 Random-effect
Subgroup analysis for DFS
Publication year
 Early year(~ 2007) 6 1.428(0.992–2.055) 1.92 0.055 62.10% 0.022 Random-effect
 Later year(2007~ 2016) 15 2.237(1.54–3.249) 4.23 < 0.001 72.00% < 0.001 Random-effect
Region
 Europe 3 1.023(1.005–1.041) 2.51 0.012 53.40% 0.117 Fixed-effect
 America 4 1.559(1.155–2.105) 2.9 0.004 8.40% 0.351 Fixed-effect
 Asia 14 2.673(2.096–3.409) 7.92 < 0.001 25.60% 0.179 Fixed-effect
Histological type
 SCLC
 NSCLC 21 1.894(1.456–2.463) 4.76 < 0.001 78.30% < 0.001 Random-effect
 ADC 9 3.186(1.797–5.650) 3.96 < 0.001 62.10% 0.007 Random-effect
 SQC 2 1.022(1.004–1.04) 2.42 0.015 0.00% 0.774 Random-effect
Sample size
  < 100 7 2.455(1.392–4.330) 3.10 < 0.001 20.30% 0.28 Fixed-effect
  > 100 14 1.770(1.340–2.338) 4.02 < 0.001 82.80% < 0.001 Random-effect
Cutoff value
 L(< 20%) 12 2.783(2.141–3.619) 7.64 < 0.001 25.30% 0.196 Fixed-effect
 H(≥20%) 6 1.514(1.243–1.844) 4.12 < 0.001 19.40% 0.287 Fixed-effect
Estimated method
 Survival curve 2 1.595(1.053–2.416) 2.21 0.027 52.60% 0.146 Fixed-effect
 HR(univariate) 6 2.126(1.156–3.909) 2.43 0.015 67.40% 0.009 Random-effect
 HR(multivariate) 13 1.892(1.328–2.698 3.53 < 0.001 79.90% < 0.001 Random-effect
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Fig. 5.

Fig. 5

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Hazard ratios and 95% CIs of studies included in meta-analysis of DFS

Table 4.

Meta-regression for the OS and DFS analysis

Variables HR Standard Error t P > |t| Lower limit Upper limit
OS
 Year 0.999 0.104 −0.010 0.991 0.811 1.230
 Region 0.835 0.061 −2.450 0.017 0.722 0.967
 Cancer type 0.954 0.045 −0.990 0.326 0.868 1.049
 Sample size 1.108 0.122 0.930 0.353 0.890 1.380
 Cutoff value 0.777 0.084 −2.330 0.022 0.626 0.964
 Statistical method 1.045 0.044 1.050 0.295 0.961 1.137
DFS
 Year 2.011 1.525 0.920 0.377 0.379 10.678
 Region 0.591 0.289 −1.070 0.306 0.201 1.736
 Cancer type 1.125 0.401 0.330 0.747 0.513 2.467
 Sample size 0.793 0.348 −0.530 0.607 0.302 2.081
 Cutoff value 0.767 0.363 −0.560 0.587 0.271 2.172
 Statistical method 0.994 0.221 −0.030 0.979 0.609 1.622
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The correlation of Ki-67 expression and clinicopathological features in lung cancer patients

An association of Ki-67 expression with age in 2506 lung cancer patients was identified using the fixed effects model in 19 studies, and higher Ki-67 expression was found to be more common in older patients (OR = 1.246, 95%CIs: 1.039–1.494; Z = 2.37, P = 0.018, I2 = 0.00%, P = 0.967, Table 5 and Additional file 1: Figure S1). Subsequently, the results revealed significant differences in Ki-67 level between male and female (OR = 1.874, 95%CIs: 1.385–2.535; Z = 4.07, P < 0.001, I2 = 69.70%, P < 0.0001, Additional file 1: Figure S1B). Meta-analysis of 15 studies including 2152 lung cancer patients revealed a positive association between high Ki-67 level and smoking history (OR = 3.087, 95%CIs: 2.504–3.806, Z = 10.56, P < 0.001; I2 = 39.40%, P = 0.064, Additional file 1: Figure S1C). According to the histological type, a pooled OR of 0.397 (95%CIs: 0.236–0.667) indicated that Ki-67 expression was significantly higher in ADC compared with that in SQC (Z = 3.49, P < 0.001; I2 = 81.20%, P < 0.001, Additional file 2: Figure S2A). Next, tumor differentiation was considered. The results from 11 studies enrolling 1731 lung cancer patients showed that an elevated Ki-67 level was associated with poor differentiation, with a pooled OR of 1.993 (95%CIs:1.262–3.146, Z = 2.96, P = 0.003; I2 = 66.30%, P = 0.001, Additional file 2: Figure S2B). A total of 13 studies with 1851 individuals were analyzed in this meta-analysis, and the results showed that a higher Ki-67 level was positively associated with the pathologic stage III/IV with a low degree of heterogeneity (OR = 1.867, 95%CIs: 1.498–2.327, Z = 5.56, P < 0.001; I2 = 23.1%, P = 0.210, Additional file 2: Figure S2C). A trend toward positive correlation was found between a high Ki-67 level and larger tumor size in 12 studies based on 1707 lung cancer patients, with a pooled OR of 1.436 (95%CIs:1.127~ 1.290,, Z = 2.93, P = 0.003; I2 = 0.00%, P = 0.876, Additional file 3: Figure S3A). Twenty-three studies comprising 2994 cases were used for meta-analysis of Ki-67 expression and lymph node metastasis, and the pooled OR indicated that a high Ki-67 level was significantly correlated with lymph node metastasis positive (OR = 1.653, 95%CIs: 1.285–2.127, Z = 3.91, P < 0.0001; I2 = 46.70%, P = 0.008, Additional file 3: Figure S3B). The association of Ki-67 expression and TNM stage was then incorporated into the meta-analysis. Eight studies with 736 patients showed a trend for correlation between Ki-67 overexpression and advanced TNM stages, with a pooled OR of 1.50 (95%CIs:1.053~ 2.126, Z = 2.25, P = 0.024; I2 = 36.90%, P = 0.134, Additional file 3: Figure S3C). In the meta-analysis, no association between Ki-67 and tumor stage was observed in lung cancer patients (OR = 1.287, 95%CIs:0.882–1.877, Z = 1.31, P = 0.191; I2 = 55.30%, P = 0.013). Additionally, analysis of four selected studies using the random effects model did not reveal any significance for the association between Ki-67 expression and metastasis (OR = 2.609, 95%CIs: 0.667–10.204, Z = 1.38, P = 0.168) or invasion (OR = 0.993, 95%CIs: 0.511–1.930, Z = 0.02, P = 0.984; I2 = 14.20%, P = 0.312).

Table 5.

Main results for meta-analysis between Ki-67 and clinicopathological features in lung cancer

Clinicopathological features Study(n) Pooled OR(95%CIs) z P Heterogeneity Publication bias
I2 P Estimated method P
Age 19 1.246(1.039–1.494) 2.37 0.018 0.00% 0.967 Fixed-effect 0.234
Gender 26 1.874(1.385–2.535) 4.07 < 0.001 69.70% 0.000 Random-effect 1.000
Histological type 16 0.397(0.236–0.667) 3.49 < 0.001 81.20% 0.000 Random-effect 0.324
Differentiation 11 1.993(1.262–3.146) 2.96 0.003 66.30% 0.001 Random-effect 0.893
Pathologic stage 13 1.867(1.498–2.327) 5.56 < 0.001 23.10% 0.210 Fixed-effect 1.000
Tumor size 12 1.436(1.127–1.29) 2.93 0.003 0.00% 0.876 Fixed-effect 0.276
Tumor stage 11 1.287(0.882–1.877) 1.31 0.191 55.30% 0.013 Random-effect 0.086
Metastasis 4 2.609(0.667–10.204) 1.38 0.168 64.50% 0.038 Random-effect 0.428
Lymph node 23 1.653(1.285–2.127) 3.91 < 0.001 46.70% 0.008 Random-effect 0.876
TNM stage 8 1.497(1.053–2.126) 2.25 0.024 36.90% 0.134 Fixed-effect 0.187
Invasion 3 0.993(0.511–1.930) 0.02 0.984 14.20% 0.312 Fixed-effect 0.308
Smoking 15 3.087(2.504–3.8060) 10.56 < 0.001 39.40% 0.064 Fixed-effect 0.711
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Publication bias

To identify potential publication bias, Begg’s test and funnel plots were used. No publication bias was found in the analysis for OS (p = 0.444, Fig. 6a) and DFS (P = 0.246, Fig. 6b). Moreover, there was no publication bias among any of the analyses used to correlate Ki-67 expression and clinicopathological characteristics (all P > 0.05, Table 5, Additional file 4: Figure S4, Additional file 5: Figure S5, Additional file 6: Figure S6 and Additional file 7: Figure S7).

Fig. 6.

Fig. 6

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Funnel plots for publication bias of OS and DFS meta-analysis

Discussion

As previously mentioned, there are two meta-analyses showing that high expression of Ki-67 predicts worse prognosis in lung cancer patients [30] and early-stage NSCLCs [31]. Nevertheless, there is no consensus regarding the clinicopathological significance of Ki-67 in lung cancer patients. Martin et al. performed a meta-analysis on 37 studies to evaluate the prognostic value of Ki-67 in 3983 lung cancer patients in 2004 [30]. Lacking sufficient information for other subtypes of lung cancer and Asian patients, the results from the abovementioned meta-analysis were not convincing. Our meta-analysis includes 108 studies with 14,831 lung cancer patients comprising NSCLC and SCLC cases and thus provides more reliable evidence. Additionally, we also restricted the number of patients in each study to greater than 30 to exclude low-quality studies. To strengthen the evidence, we estimated not only OS data but also DFS to determine the prognostic role of Ki-67 in lung cancer patients. Moreover, multivariate analyses of OS and DFS were performed, and the HR of OS was 1.108(95%CIs: 1.063–1.155), and that for DFS was 1.892(95%CIs: 1.328–2.698), indicating that Ki-67 is an independent prognostic marker for lung cancer. Compared to the previous meta-analysis, our meta-analysis included results on all subtypes of lung cancer and represented broader ethnicity; in addition, subgroups were classified according to region, cut-off value, number of patients and histological type. With the inclusion of high-quality studies and a larger number of patients, the results derived from our study are more convincing.

Ki-67 is present in the active phases of the cell cycle (G1, S, and G2), as well as during mitosis, but it is not expressed in the G0 phase. Thus, it has become an excellent operational marker for the estimation of the proportion of proliferative cells in a given cell population [125]. Our study demonstrated that Ki-67 expression was lower in ADC compared with that in SQC, suggesting it is a useful biomarker for distinguishing ADC from SQC. Consisted with our result, Ki-67 was also revealed to be higher in ADC than in SQC in the same stage, duo to the different tumor biology of histological subtypes in NSCLC [49, 64]. The high-grade Ki-67 was proved to be significantly correlated with a more aggressive tumor infiltration patterns in lung SQC, Indicating the strong association between tumor invasiveness and cell proliferation [20]. It has been reported in the literature that inverted papillomas with high levels of Ki-67 also include squamous cell carcinomas. Regarding the SCLC, Vasudha Murlidhar et al. recently carried out a result that Ki-67 could contribute to the early detection of metastasis in circulating lung cancer cells. Ki-67 was also demonstrated as a potential diagnostic factor for histopathological definition of SCLC [126]. Contrary to our study, previous study revealed that maternal cigarette smoking could dramatically decrease the expression of Ki-67 in cytotrophoblasts [127]. Interestingly, further study also found that Ki-67 was lower expressed in smokers and smokers with COPD compared to the non-smokers. The authors hypothesized that the permanent cellular damage might play a crucial role in the destruction of bronchiolar tissue [128]. Nevertheless, the mechanisms that govern how Ki-67 expression contribute to the tumorigenesis and progression of lung cancer remain to be unveiled.

The authors suspected that Ki-67 might affect cyclin-dependent kinase1 (CDK1), leading to the entry of inverted papilloma cells into the active phase of cell cycle(G1)and resulting in malignant transformation [129]. Interestingly, our study found that Ki-67 expression in male patients was significantly higher comparing with that in female patients. Previous reports have suggested that testosterone can promote the growth of cancer cells that express androgen receptors, which negatively regulates the Ki-67 level in lung cancer patients [130, 131]. Many previous studies have also revealed that Ki-67 is significantly associated with histopathologic parameters in other tumor because of the correlation between proliferation and those parameters [132–134]. It was found that p53 regulated the p53- and Sp1-dependent pathways, leading to the inhibition of Ki-67 promoter [135, 136]. A recent study confirmed that there was a correlation between the specific Ki-67 splice variants and the progression through the cell cycle in cancer cells. Ki-67 might be involved in a putative extranuclear elimination pathway transported to the Golgi apparatus [137]. Based on these results, we concluded that Ki-67 serves as a valuable indicator for the aggressiveness and prognosis of lung cancer.

Heterogeneity was significant in this meta-analysis. To eliminate the heterogeneity, subgroup analyses according to region, cut-off value, number of patients and histological type were carried out using random effects models. As a result, we revealed that the source of heterogeneity originated from the publication region and the cutoff value by the meta-regression analysis. Our meta-analysis was limited to publications in English or Chinese; nevertheless, the researchers typically tend to publish studies with negative results in local journals and in the native language of the study region. In addition, although detailed exclusion criteria were established to avoid duplication, our meta-analysis was not able to avoid the same patient cohorts in different publications. Other methodological factors might also affect heterogeneity, such as the antibody and cut-off value used in the study. Although anti-MIB-1 antibody is the most frequently used antibody in studies, most of the included studies stratified high and low levels of Ki-67 using a median value varying from 3 to 75%, which might have influenced the results. Several studies used the Ki-67 cut-off less than 10% to assess the prognostic impact of Ki-67 after surgical resection with curative intent in early-stages lung cancer patients. Most of the included studies used the median value of Ki-67 index as cutoff value, which could divide the patient into equally the group but did not reflect the clinical relevant use. A cut-off value of Ki-67 maximizing the hazard ratio across the groups could be used for the clinical management for the diagnosis and prognosis of lung cancers. More importantly, Multiple clinical laboratories have reported the Ki-67 cutoff values between 10 and 14% could be recommended as the gold standard identify the high risk of the survival outcome in cancers [138–141]. Additionally, microarrays were used in several studies, for which the sensitivity of assessment of Ki-67 expression is generally poor. Another possibility of bias may be related to the method of extrapolating the HR; the HR extracted from survival curves was less reliable than direct analysis of variance. Additionally, the subgroup analysis for different stages because most of the included studies recruited the lung cancer patients within three or more tumor stages. Thus, we could not classify the patients into the early stages and advanced stages for the subgroup analysis.

Conclusion

In conclusion, our meta-analysis demonstrated that high expression of Ki-67 is associated with worse prognosis and disease progression in lung cancer patients. Ki-67 can be an independent biological marker for predicting the prognosis of lung cancer patients. Subsequent studies are required to investigate the prognoses and clinical characteristics of lung cancer patients to confirm our findings.

Materials and methods

Literature search and selection

The databases that we searched included PubMed, Web of Science, EMBASE, and Chinese datasets (WanFang, China National Knowledge Infrastructure and Chinese VIP) until June 1, 2017. The key words identifying the articles were as follows: (Ki-67 OR Ki67 OR MIB-1 OR “proliferative index” OR “proliferative activity” OR “mitotic index” OR “labeling index” OR “mitotic count” OR “proliferative marker” OR “mitotic figure” OR “mitotic activity”) AND (Cancer OR carcinoma OR adenocarcinoma OR tumour OR tumor OR malignanc* OR neoplas*) AND (Lung OR pulmonary OR respiratory OR respiration OR aspiration OR bronchi OR bronchioles OR alveoli OR pneumocytes OR “air way”).

Selection criteria

Publications were included if they met the following inclusion criteria: (1) the patients enrolled had been diagnosed with lung cancer; (2) the results for the study included the correlation between Ki-67 and overall survival (OS) or disease-free survival (DFS); (3) the samples used in the studies were human lung tissue, serum or sputum but not animals or cell lines; (4) the techniques used to measure the expression level of Ki-67 in cancer tissue or tumors of the patients were immunohistochemistry (IHC), PCR/RT-PCR, ELISA or western blotting; (5) the study provided hazard ratios (HRs) and their 95% confidence intervals (CIs) or sufficient information for estimating these parameters; (6) the article was fully written in English or Chinese; and (7) the sample size was larger than 30. Studies were excluded if they met the following exclusion criteria:(1) if they included animal experiments or cell lines or were pre-clinical studies, meta-analyses, reviews, comments, conference abstracts, letters or case reports; (2) articles in languages other than English or Chinese; and (3) studies did not include the key information for survival analyses such as HRs and 95%CIs. To avoid data duplication, when the same patient cohort was reported in different publications or the same article was found in different journals, only the most recent and complete publication was included.

Data extraction and quality assessment

All the articles were independently reviewed and selected by two investigators. Discrepancies were resolved by discussion and arbitrated by a third investigator. The following information was extracted from each publication: first author’s name, year of publication year, pathology type, tumor stage, number of patients, sample type, cut-off value of Ki-67, determination assay, method to extract HR and survival type. Additionally, we also obtained the clinicopathological characteristics of the lung cancer patients in the included studies including age (old/young), gender (male/female), histological type (adenocarcinoma/squamous carcinoma, ADC/SQC), smoking status (smoker/non-smoker), differentiation (poor/well or moderate), pathologic stage (III-IV/I-II), tumor size (large/small), tumor stage (T3–4/T1-T2), metastasis (yes/no), lymph node (N1-Nx/N0), TNM stage (T3–4/T1-T2) and invasion (yes/no). Especially, the tumor stage was used to describe the size and extent of tumor. And the TNM stage were used to define the progression of cancer based on the size and extension tumor, lymphatic involvement and metastasis status. Based on the Newcastle Ottawa Scale (NOS) criteria [142], studies with NOS scores higher than 6 are considered high-quality studies, whereas those with NOS scores less than 5 are defined as low-quality studies. This study was strictly performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [143] and the PRISMA checklist were also provided in the Additional file 8.

Statistical methods

HR and 95%CIs were used to measure the relationship between Ki-67 expression and prognosis of lung cancer patients. The most accurate determination was made when the study directly provided the HRs and 95%CIs. The multivariate HRs were calculated by using the Cox proportional hazards model, which could independently predict the survival outcome for the lung cancer patients. We preferentially chose the multivariate values when the study provided both univariate and multivariate HRs. If the above data were not available, we used the Engauge Digitizer version 4.1 to extract the survival rates from KM curves and estimated the HR according the method as Tierneyet al. described [144, 145]. Moreover, we calculated the HR from the original survival data that the study provided using SPSS. An observed HR > 1 indicated worse prognosis for the lung cancer patients with high expression of Ki-67, if the 95%CIs for the overall HR was not 1, we considered the prognostic effect of Ki-67 on to be statistically significant. Odds ratio (OR) with 95%CIs were used to analyze the degree of association between Ki-67 level and clinicopathological characteristics. Heterogeneity was determined using the χ2 and inconsistency (I2) tests [146]. I2 > 50% or P < 0.1 indicated substantial heterogeneity among the studies, in which case a random effects model was applied; otherwise, we utilized the fixed effects model. Then, subgroup analysis and meta-regression analysis were used to investigate any source of heterogeneity. Moreover, publication bias was assessed using Begg’s test and funnel plots, and P-values < 0.05 indicated statistically significant publication bias [147].

Additional files

Additional file 1: (4.3MB, tif)

Figure S1. Forest plots for the relationships between Ki-67 expression and clinicopathological features of patients with lung cancer. A. Age B. Gender C. Histological type. (TIF 4444 kb)

Additional file 2: (3.7MB, tif)

Figure S2. Forest plots for the relationships between Ki-67 expression and clinicopathological features of patients with lung cancer. A. Differentiation B. Pathologic stage C. Tumor size. (TIF 3759 kb)

Additional file 3: (3.1MB, tif)

Figure S3. Forest plots for the relationships between Ki-67 expression and clinicopathological features of patients with lung cancer. A. Lymph node B. TNM stage C. Smoking. (TIF 3186 kb)

Additional file 4: (19.6MB, tif)

Figure S4. Funnel plots for publication bias of clinicopathological features meta-analysis (A~C). A. Age B. Gender C. Histological type. (TIF 20061 kb)

Additional file 5: (20.9MB, tif)

Figure S5. Funnel plots for publication bias of clinicopathological features meta-analysis (D~F). A. Differentiation B. Pathologic stage C. Tumor size. (TIF 21452 kb)

Additional file 6: (20.8MB, tif)

Figure S6. Funnel plots for publication bias of clinicopathological features meta-analysis (G~J). A. Tumor stage B. Lymph node C. TNM stage. (TIF 21299 kb)

Additional file 7: (7.3MB, tif)

Figure S7. Funnel plot for publication bias of clinicopathological features meta-analysis (Smoking status). (TIF 7465 kb)

Additional file 8: (63.5KB, doc)

PRISMA 2009 Checklist. (DOC 63 kb)

Acknowledgments

Funding

The current study was supported by the Funds of National Natural Science Foundation of China (NSFC 81360327, NSFC 81560469), Natural Science Foundation of Guangxi, China (2015GXNSFCA139009) and Guangxi Medical University Training Program for Distinguished Young Scholars (2017).

Availability of data and materials

The databases which we collection Literature include PubMed, Web of Science, EMBASE, and Chinese datasets (WanFang, China National Knowledge Infrastructure and Chinese VIP) until June 1, 2017.

Abbreviations

CDK1

Cyclin-dependent kinase1

CIs

Confidence intervals

DFS

Disease-free survival

EGFR

Epidermal growth factor receptor

HR

Hazard ratio

IHC

Immunohistochemistry

NSCLC

Non-small cell lung cancer

OR

Odds ratio

OS

Overall survival

SCLC

Small cell lung cancer

SQC

Squamous carcinoma

Authors’ contributions

DMW and GC conceived of the project and designed the study. DMW and WJC wroted the manuscript. RMM and NZ participated in selecting study and extracting data. WJC and DYL take part in Manuscript Revise. GC was in charge of quality control. All authors read and approved the final manuscript.

Not applicable.

Not applicable.

The authors declare that they have no competing interests.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Dan-ming Wei, Email: danmingwei08@163.com.

Wen-jie Chen, Email: ajchenwenjie@gmail.com.

Rong-mei Meng, Email: 3088601866@qq.com.

Na Zhao, Email: 1803378864@qq.com.

Xiang-yu Zhang, Email: 949195924@qq.com.

Dan-yu Liao, Email: 842060442@qq.com.

Gang Chen, Email: chengang@gxmu.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1: (4.3MB, tif)

Figure S1. Forest plots for the relationships between Ki-67 expression and clinicopathological features of patients with lung cancer. A. Age B. Gender C. Histological type. (TIF 4444 kb)

Additional file 2: (3.7MB, tif)

Figure S2. Forest plots for the relationships between Ki-67 expression and clinicopathological features of patients with lung cancer. A. Differentiation B. Pathologic stage C. Tumor size. (TIF 3759 kb)

Additional file 3: (3.1MB, tif)

Figure S3. Forest plots for the relationships between Ki-67 expression and clinicopathological features of patients with lung cancer. A. Lymph node B. TNM stage C. Smoking. (TIF 3186 kb)

Additional file 4: (19.6MB, tif)

Figure S4. Funnel plots for publication bias of clinicopathological features meta-analysis (A~C). A. Age B. Gender C. Histological type. (TIF 20061 kb)

Additional file 5: (20.9MB, tif)

Figure S5. Funnel plots for publication bias of clinicopathological features meta-analysis (D~F). A. Differentiation B. Pathologic stage C. Tumor size. (TIF 21452 kb)

Additional file 6: (20.8MB, tif)

Figure S6. Funnel plots for publication bias of clinicopathological features meta-analysis (G~J). A. Tumor stage B. Lymph node C. TNM stage. (TIF 21299 kb)

Additional file 7: (7.3MB, tif)

Figure S7. Funnel plot for publication bias of clinicopathological features meta-analysis (Smoking status). (TIF 7465 kb)

Additional file 8: (63.5KB, doc)

PRISMA 2009 Checklist. (DOC 63 kb)

Data Availability Statement

The databases which we collection Literature include PubMed, Web of Science, EMBASE, and Chinese datasets (WanFang, China National Knowledge Infrastructure and Chinese VIP) until June 1, 2017.

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