Literature search
Initially, electronic search yielded 521 potentially relevant studies, of which 276 were left after removing duplicates in the databases. 52 were excluded because of obvious irrelevance based on titles or abstracts. The remaining 224 full-text manuscripts were assessed for eligibility, of which 167 were excluded as they failed to meet the requirements of data extraction, and further 48 were eliminated as they mismatched the eligibility criteria. Consequently, 9 articles were eligible and included. The above 9 studies included a total of 3,862 patients for meta-analysis of predictive value of NLR for HCC prognosis. The stepwise screening of included studies was shown in detail in Fig. 1.
The Characteristics And Quality Of The Included Studies
All of the 9 included studies were written in English. For quality assessment of included study, baseline data of these studies were extracted and exhibited in Table 1. Regions of these 9 studies were all from East Asian with 7 from mainland China [34–40], 1 from Korea [41] and 1 from Taiwan [42]. All these studies were single-center trials published from 2012 to 2018. The 9 observational studies involved 3,862 HCC patients in total, of which 3,491 were from mainland China, 213 from Korea and 158 from Taiwan. The predictive performance of NLR for the prognosis of HCC patients was summarized in Table 2. The AUCs of the included studies ranged from 0.606 to 0.855 and the cut-off values from 1.505 to 2.979. Meanwhile, the sensitivity and specificity of the included studies were calculated or provided, with a range from 0.301 to 0.840 and 0.470 to 0.887 respectively.
Table 1
The main characteristics of the studies enrolled in the meta-analysis.
Author(Year) | Type of publication | Study design | Regions | Sample size (male) | Enrollment period | Median age (years) | NOS |
Chen (2012) | Full-text | Retrospective | Taipei, Taiwan | 158(95) | 2003.07-2010.12 | 65.7 (31.8–82.8) | 8 |
Du (2018) | Full-text | Retrospective | Xi’an, China | 230(174) | 2000.01-2012.12 | 44 (20–66) | 7 |
Gao (2015) | Full-text | Retrospective | Beijing, China | 825(690) | 2008.10-2012.05 | 54.5 (25–75) | 8 |
Hu (2016) | Full-text | Retrospective | Suwon, Korea | 213(166) | 2001.03-2011.12 | 53 (20–79) | 8 |
Hu (2018) | Full-text | Retrospective | Beijing, China | 545(442) | 2013.07-2016.07 | 56.91 | 7 |
Li (2014) | Full-text | Retrospective | Beijing, China | 506(420) | 2005.04-2014.04 | 59.2 (28–85) | 8 |
Liu (2016) | Full-text | Retrospective | Nanjing, China | 223(189) | 2004.07-2011.04 | 54 (21–82) | 7 |
Liu (2017) | Full-text | Retrospective | Chengdu, China | 760(643) | 2007.01-2013.12 | 56.5 (19–89) | 7 |
Tan (2018) | Full-text | Retrospective | Qingdao, China | 402(299) | 2008.09-2017.05 | 51.7(18–92) | 8 |
Note: NR, no result. |
Table 2
Predictive value of NLR to predict HCC in individual studies.
Study | AUC | 95% CI | Cut-off value(ng/mL) | Sensitivity(%) | Specificity(%) | Number of patients |
TP FP FN TN |
Chen (2012) | 0.630 | 0.520–0.720 | 2.400 | 0.730 | 0.470 | 59 | 41 | 22 | 36 |
Du (2018) | 0.625 | 0.527–0.732 | 2.270 | 0.639 | 0.653 | 57 | 49 | 32 | 92 |
Gao (2015) | 0.811 | NR | 2.700 | 0.662 | 0.848 | 220 | 75 | 112 | 418 |
Hu (2016) | 0.643 | NR | 1.505 | 0.775 | 0.486 | 83 | 54 | 24 | 52 |
Hu (2018) | 0.738 | 0.699–0.774 | 2.979 | 0.539 | 0.858 | 199 | 25 | 170 | 151 |
Li (2014) | 0.824 | NR | 2.140 | 0.780 | 0.690 | 143 | 100 | 40 | 223 |
Liu (2016) | 0.606 | NR | 2.750 | 0.301 | 0.887 | 43 | 9 | 100 | 71 |
Liu (2017) | 0.664 | 0.630–0.698 | 2.200 | 0.752 | 0.545 | 415 | 95 | 137 | 113 |
Tan (2018) | 0.855 | NR | 2.200 | 0.840 | 0.860 | 210 | 21 | 40 | 131 |
Note: NR, no result. |
Assessment Of Methodological Quality And Publication Bias
All studies were clearly defined by eligibility criteria and reasons for patient exclusion. The quality of each included study was assessed by QUADAS tool, and all of them had high QUADAS scores (≥ 10). Overall quality of included trials was moderate. The outcome of QUADAS-2 evaluation was provided in the ESM and shown in Fig. 2.
Subsequently, assessment of publication bias was conducted by funnel plot, which was shown in Fig. 3D, indicating that neither significant threshold effect nor significant asymmetry was observed. In other words, this meta-analysis had no obvious publication bias. Therefore, it seems unlikely that our findings would be greatly changed by unpublished studies.
Nlr For Predicting Prognosis In Patients With Hcc
There were 9 sets of data extracted from 9 eligible studies and presented in Table 2, including AUC, 95% CI, various optimal cut-off values of NLR, sensitivities, specificities, as well as TP/FP/FN/TN values. The predictive value of NLR as a biomarker for prognosis of HCC patients was inspected in 9 studies with a total number of 3,862 subjects. The pooled data of these studies were summarized in Table 2. For summary of performance estimates, the pooled sensitivity of NLR was 0.68 (95% CI, 0.58–0.77) (Fig. 3A), specificity 0.73 (95% CI, 0.61–0.82) (Fig. 3B), PLR 2.5 (95% CI, 1.8–3.6) and NLHR 0.43 (95% CI, 0.33–0.57) respectively. The pooled DOR was 6.347 (95% CI, 5.450–7.391) using a random-effects model. The AUC of SROC for diagnostic accuracy summary was 0.76 (95% CI, 0.72–0.80) (Fig. 3C). To summarize, the predictive value of NLR for prognosis of HCC patients was shown in Fig. 4. It is concluded that high baseline NLR was prominently related to poor prognosis or recurrence
Subgroup Analysis
Subgroup analysis was performed and summarized in Table 3. On the basis of comparisons of DOR and AUC, NLR was variable to some extent regarding prognostic value in HCC patients. For subgroup regional analysis, the DOR and AUC of NLR in mainland China were significantly higher than those in Korea (DOR, 7; AUC, 0.79 vs DOR, 3; AUC, 0.64) and Taiwan (DOR, 7; AUC, 0.79 vs DOR, 2; AUC, 0.63), pointing a better prognostic value of NLR in HCC patients from mainland China than that from Korea and Taiwan. For subgroup geographic analysis, NLR showed a much higher prognostic value in North than that in South (DOR, 8; AUC, 0.80 vs DOR, 3; AUC, 0.69). Interestingly, it is not hard to find that the prognostic value of NLR varies significantly across the north-south geographic boundary.
Table 3
Subgroup analysis on the basis of different standards.
Studies | Number | | Sensitivity | Specificity | PLR | NLHR | DOR | AUC |
Regions | 7 | China | 0.66(0.52–0.77) | 0.78 (0.68–0.86) | 3.0 (2.1–3.9) | 0.43(0.31–0.61) | 7 (4–12) | 0.79(0.75–0.83) |
1 | Korea | 0.775 | 0.486 | NR | NR | 3 (2–6) | 0.64 |
1 | Taiwan | 0.730 | 0.470 | NR | NR | 2 (1–4) | 0.63(0.52–0.72) |
Geography | 6 | North | 0.72(0.63–0.79) | 0.76 (0.64–0.84) | 2.9 (1.9–4.4) | 0.37(0.28–0.50) | 8 (4–14) | 0.80(0.76–0.83) |
3 | South | 0.66 (0.45–0.82) | 0.63 (0.40–0.80) | 1.8 (1.3–2.4 | 0.54 (0.41–0.72) | 3 (3–4) | 0.69(0.65–0.73) |
Note: NR, no result. |
Analysis Of Publication Bias And Heterogeneity
Heterogeneity analysis and SROC were performed. There was no “shoulder arm” pattern seen in the SROC space, suggesting an absence of threshold effect. The publication bias, which was detected by Begg’s funnel plot and Egger’s test, was at a very low level of probability as shown in Fig. 5. Furthermore, meta-regression and subgroup analysis were accomplished for seeking other possible explanations of heterogeneity. The characteristics of geography (North: I2 = 88.8%, P ≤ 0.001 or South: I2 = 0, P = 0.532) may harbor the main source, but the region (mainland China: I2 = 90.1%, P ≤ 0.001, Korea or Taiwan) maybe not a heterogeneity source of predictive value of NLR.