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. 2017 Jun 19;10:3059–3070. doi: 10.2147/OTT.S137002

The prognostic value of C-reactive protein/albumin ratio in human malignancies: an updated meta-analysis

Hong-jun Xu 1, Yan Ma 1, Fang Deng 1, Wen-bo Ju 1, Xin-yi Sun 1, Hua Wang 1,✉
PMCID: PMC5488759  PMID: 28790840

Abstract

Purpose

This study aims to investigate the prognostic value of pretreatment C-reactive protein/albumin ratio (CAR) in human malignancies by an updated meta-analysis.

Methods

PubMed, Web of Science, Cochrane Library and Wanfang databases were searched. Pooled hazard ratios (HRs) and odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were used as effective values.

Results

A total of 25 studies with 12,097 patients were included in this meta-analysis. Pooled results showed that high pretreatment CAR was associated with poor overall survival (OS) (HR =1.99, 95% CI: 1.65–2.40, P=0.000) and poor disease-free survival (HR =1.55, 95% CI: 1.34–1.79, P=0.000). In addition, high pretreatment CAR was associated with increased 5-year mortality (OR =2.74, 95% CI: 2.11–3.55, P=0.000). Moreover, subgroup analysis demonstrated that high CAR was associated with poor OS despite variations in publication year, country, sample size, CAR cut-off value and treatment. However, high CAR was associated with poor OS in human malignancies except colorectal cancer (HR =1.64, 95% CI: 0.96–2.80, P=0.069).

Conclusion

High pretreatment CAR indicates poor prognosis in human malignancies except colorectal cancer. Thus, pretreatment CAR serves as a prognostic marker in human malignancies and could be used in the evaluation of prognosis in clinical work.

Keywords: human malignancies, C-reactive protein/albumin ratio, prognosis, meta-analysis

Introduction

Human malignancy remains a public health problem worldwide, and is reported to be the second leading cause of death in the US.1 Based on GLOBOCAN estimates, ~14.1 million new cancer cases and 8.2 million deaths occurred in 2012 worldwide, and the burden is projected to grow worldwide due to the growth and aging of the population.2 With the advance in early detection and treatment modality, the number of cancer survivors has increased steadily.3 However, many challenges remain unsolved, such as identifying economical and practical markers for prognosis.

It is reported that the response of the body to cancer is not a unique mechanism but has many parallels with inflammation and wound healing, during which inflammatory cells and cytokines in tumors are more likely to contribute to tumor growth, progression and immunosuppression.4 In clinical work, the commonly used inflammation markers include C-reactive protein (CRP), neutrophil, lymphocyte, platelet, D-dimer, fibrinogen, procalcitonin, and so on. Coincidentally, many inflammation-based scores, including Glasgow Prognostic Score, modified Glasgow Prognostic Score, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and lymphocyte-to-monocyte ratio, have been reported to be associated with the prognosis of human malignancies.5–7 Recently, numerous publications have tried to explore the correlation of pretreatment C-reactive protein/albumin ratio (CAR) with the prognosis of human malignancies, including one published meta-analysis based on only 10 available studies.8

However, the prognostic role of pretreatment CAR in human malignancies remains inconclusive. Therefore, to clarify this issue, we performed this updated meta-analysis based on more available studies.

Methods

Search strategy

A comprehensive search of PubMed, Web of Science, Cochrane Library and Wanfang databases was performed up to March 13th 2017. The following keywords were used: (“C-reactive protein/Albumin ratio” or “C-reactive protein Albumin ratio” or “CRP/Alb ratio”) and (“Prognosis” or “Prognostic” or “recurrence” or “overall survival”). The detailed search strategy used in PubMed was as follows: “((((prognostic[Title/Abstract]) OR prognosis[Title/Abstract]) OR overall survival[Title/Abstract]) OR recurrence[Title/Abstract]) AND ((C-Reactive Protein[Title/Abstract]) AND albumin[Title/Abstract])”. During this process, we also screened the references list of retrieved articles manually, in order to gain more potential eligible studies.

Selection and exclusion criteria

Studies were included if they met all the following criteria: (1) investigating the prognostic role of CAR in human primary malignancies; (2) CAR was obtained before any treatment; (3) survival outcomes including overall survival (OS) or/and disease-free survival (DFS) were included; and (4) relative hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs) were available or could be calculated according to the provided survival data or Kaplan–Meier curve.

Studies were excluded if they met any of the following criteria: (1) studies were reviews, letters, case reports and duplicates; (2) studies did not include any survival outcomes; (3) HRs with corresponding 95% CIs were not available or could not be calculated as mentioned. If multiple publications were based on the same origin of population, only the most informative or most recent one was enrolled.

Data extraction

Two investigators performed data extraction independently, during which discrepancies were solved by a consensus in this research team. The survival outcomes including OS, DFS and 5-year mortality were our main concerns. In addition, we extracted other relative basic information, including first author, publication year, country, cancer stage, cancer type, cut-off value for CAR, major treatment, data source and follow-up time. If results for both multivariate and univariate analyses were available, those for the former ones were extracted and used in the meta-analysis. When HRs with their 95% CIs were unavailable, the total number of events (including observed deaths, cancer-specific death and cancer recurrences) and the sample numbers of each group were extracted to calculate HRs.9 In case neither crude HRs nor outcome data were available, Engauge Digitizer version 4.1 (http://sourceforge.net) was used to read the Kaplan–Meier curves, and then relative HRs with their 95% CIs were calculated using the methods described by Tierney et al.9

Statistical analysis

In this meta-analysis, the pooled HRs and odds ratios (ORs) of included studies were calculated using STATA 10.0. Pooled HRs with corresponding 95% CIs were used to assess the effect of CAR on OS and DFS, and pooled ORs with corresponding 95% CIs were used to assess the effect of CAR on 5-year mortality. A combined HR or OR >1 indicated poor prognosis for patients with a high pretreatment CAR; otherwise, high pretreatment CAR served as a marker of good prognosis. Cochrane Q test and I2 statistic were used to test the potential heterogeneity across studies.10 If heterogeneity was significant (I2.50% or/and P<0.1), we used a random-effect model; otherwise, a fixed-effect model was used. Both Begg’s test and Egger’s test were used to evaluate the potential publication bias.11,12 All P-values were two-sided, and statistical significance was defined as P<0.05.

Results

Literature search information

Initially, 1,021 studies were identified through systematic research in available databases. Next, 992 studies were excluded after reading title and abstract: including reviews, duplicates, letters and articles without survival outcomes. Finally, 25 studies (24 in English and 1 in Chinese)13–37 were included in this meta-analysis after excluding 4 studies (3 studies with survival outcomes unavailable, 1 study based on metastasizing tumor rather than primary cancer) (Figure 1).

Figure 1.

Figure 1

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Flow diagram of searching relevant studies for this meta-analysis.

Characteristics of included studies

The characteristics of included studies are presented in Table 1. A total of 25 studies with 12,097 patients were included in this meta-analysis. The publication time ranged from 2014 to 2017, and all the studies were conducted in Asia (15 in China, 8 in Japan and 2 in Korea). The study sample sizes ranged from 40 to 2,685, with the median size of 386. The cut-off values for high pretreatment CAR ranged from 0.028 to 0.54, with the median cut-off value of 0.095. As shown in the table, this meta-analysis was based on multiple cancer sites (oral cavity in 1, larynx in 1, nasopharynx in 4, esophagus in 2, lung in 3, liver in 2, stomach in 2, pancreas in 4, colorectum in 4 and kidney in 2). The treatment methods were diverse, including surgical resection, chemotherapy, radiotherapy and multidisciplinary treatments. Among the 25 studies, all provided OS results, 8 provided DFS results and 7 provided 5-year mortality results.

Table 1.

Characteristics of studies included in this meta-analysis

Author Year Country Study sample size (M/F) Mean/median age (years) Cancer stage Cancer sites Cut-off value for CAR (> CV/, CV) Major treatment Study end points HR with its 95% CI Source Follow-up time (months)
Park et al13 2016 Korea 40 (27/13) Mean 66 (31–83) I–IV (AJCC) Oral 0.085 (13/27) Resection OS OS (M), 6.08 (1.97–18.74) Direct Mean 35.58
Li et al14 2016 China 409 (288/121) Median 45 (18–77) I–IV (AJCC) Nasopharynx 0.037 (228/181) Multiple OS, 5-year survival rate OS (M), 2.09 (1.22–3.59) Direct >60
Zhang et al15 2016 China 1,572 (1,172/400) Median 45 (14–78) I–IV (AJCC) Nasopharynx 0.05 (614/958) Multiple OS, DFS OS (M), 1.39 (1.00–1.94)
DFS (M), 1.24 (0.97–1.57)		 Direct
Direct		 Median 50.0 (1.4–76.4)
Tao et al16 2016 China 719 (495/224) Median 48 (14–81) I–IV (AJCC) Nasopharynx 0.141 (341/378) Radiotherapy OS, 5-year survival rate OS (M), 2.17 (1.13–3.06) Direct >60
He et al17 2016 China 2,685 (2,535/150) NA I–III (NA) Nasopharynx 0.064 (1,022/1,663) Multiple OS, DFS, 5-year survival rate OS (M), 1.36 (1.11–1.65) Direct Median 46.30 (0.07–188.43)
Yu et al18 2017 China 129 Median 61 (29–82) I–IV (AJCC) Laryngeal 0.047 (56/73) Multiple OS, DFS OS (M), 2.13 (1.15–3.93)
DFS (M), 2.36 (1.28–4.36)		 Direct
Direct		 Median 77 (66–94)
Zhang et al19 2017 China 617 (461/156) Median 60 (30–82) I–III (clinical stage) Lung 0.424 (125/492) Resection OS, DFS OS (M), 1.87 (1.41–2.49)
DFS (M), 1.54 (1.10–2.16)		 Direct
Direct		 Median 50 (1–108)
Miyazaki et al20 2016 Japan 108 (69/39) Median 82 I–IV (clinical stage) Lung 0.028 (49/59) Resection OS, 5-year survival rate OS (M), 2.13 (1.07–4.30) Direct >60
Zhou et al21 2015 China 367 (316/51) Median 59 (23–82) Limited/extensive Lung 0.441 (128/239) Multiple OS OS (M), 1.34 (1.04–1.73) Direct Median 29.40 (0.03–116.07)
Wei et al22 2015 China 423 (341/82) Median 58 (24–88) I–IV (AJCC) Esophageal 0.095 (147/276) Resection OS OS (M), 1.39 (1.03–1.88) Direct Median 35.7 (0.6–95.6)
Xu et al23 2015 China 468 (416/52) Median 58 I–IIIC (AJCC) Esophageal 0.50 (87/381) Resection OS, 5-year survival rate OS (M), 2.44 (1.82–3.26) Direct >36
Liu et al24 2015 China 455 (314/141) Median 59 (19–86) I–III (AJCC) Gastric 0.25 (302/153) Resection OS, 5-year survival rate OS (M), 1.63 (1.19–2.20) Direct Median 25 (1–76)
Toiyama et al25 2016 Japan 384 (264/120) Median 67 (32–88) I–III (JC) Gastric 0.051 (NA) Resection OS, DFS OS (M), 2.21 (1.19–4.11)
DFS (M), 1.82 (1.03–3.23)		 Direct
Direct		 Median 47.6 (40.1–54.1)
Shibutani et al26 2016 Japan 99 (57/42) 63 (27–86) Unresectable Colorectal 0.183 (36/63) Chemotherapy OS OS (M), 1.87 (1.06–3.30) Direct Up to 60
Shibutani et al27 2016 Japan 705 (411/294) Median 68 (26–90) I–III (JC) Colorectal 0.028 (358/347) Resection OS, DFS, 5-year survival rate OS (U), 1.76 (1.17–2.64)
DFS (U), 1.50 (1.05–2.14)		 Curve
Direct		 >60
Ishizuka et al28 2015 Japan 627 (400/227) NA 0–IV (JC) Colorectal 0.038 (366/261) Resection OS OS (M), 2.61 (1.62–4.21) Direct Median 30
Tominaga et al29 2016 Japan 136 (79/57) NA T1–4 N1–3 Colorectal 0.1 (30/106) Resection OS, DFS OS (U), 0.96 (0.87–1.06)
DFS (M), 4.43 (1.94–10.15)		 Data
Direct		 Up to 110
Li and Zhang30 2016 China 178 (127/51) Mean 67.9 NA Hepatic 0.46 (97/86) Resection OS OS (M), 2.52 (1.82–3.47) Direct Mean 35.6 (19–55)
Kinoshita et al31 2015 Japan 186 (133/53) Median 72 0–D (BCLC) Hepatic 0.037 (102/84) Resection OS OS (M), 3.39 (1.99–5.80) Direct Median 18 (1–88)
Liu et al32 2017 China 386 (238/148) Median 61 (34–83) I–IV (AJCC) Pancreatic 0.180 (91/295) Resection OS OS (M), 2.07 (1.59–2.70) Direct Median 8.7
Wu et al33 2016 China 233 (156/77) Median 62 (26–85) Advanced Pancreatic 0.54 (74/159) Multiple OS OS (M), 4.00 (2.64–6.03) Direct Up to 12
Haruki et al34 2016 Japan 113 (70/43) Median 66.8 (27–85) I–IV (clinical) Pancreatic 0.03 (58/53) Resection OS, DFS OS (M), 1.73 (1.04–2.87)
DFS (U), 1.54 (1.00–2.37)		 Direct
Direct		 >60
Lee et al35 2016 Korea 82 (49/33) Mean 63.5±10.7 Advanced/metastatic Pancreatic 0.5 (40/42) Chemotherapy OS OS (M), 1.60 (0.84–3.04) Direct Up to 24
Chen et al36 2015 China 406 (253/153) Median 58 (24–80) I–IV (clinical stage) Kidney 0.15 (93/313) Resection OS OS (U), 3.44 (2.43–4.86) Curve Mean 63 (1–151)
Guo et al37 2017 China 570 (382/188) Mean 51.43±13.52 I–IV (TNM stage) Kidney 0.085 (393/177) Resection OS, DFS OS (M), 1.94 (1.12–3.36)
DFS (M), 2.14 (1.22–3.75)		 Direct Median 63.54
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Notes: Multiple: multidisciplinary treatments; Data: available data to calculate HR with corresponding CI; Curve: Kaplan–Meier curve.

Abbreviations: M/F, male/female; CAR, C-reactive protein/albumin ratio; CV, cut-off value; HR, hazard ratio; CI, confidence interval; AJCC, American Joint Committee on Cancer; OS, overall survival; M, multivariate analysis; DFS, disease-free survival; NA, not available; JC, Japanese Classification; U, univariate analysis; BCLC, Barcelona Clinic Liver Cancer.

Meta-analysis results for OS

In total, 25 studies involving 12,097 patients investigated the prognostic role of pretreatment CAR in OS. Since heterogeneity was significant across studies (I2=86.9%, P=0.000), a random-effect model was used. Pooled results demonstrated that high pretreatment CAR was associated with poor OS (HR =1.99, 95% CI: 1.65–2.40, P=0.000), suggesting that patients with a high pretreatment CAR suffered from decreased OS rate (Figure 2).

Figure 2.

Figure 2

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Forest plots for the prognostic impact of CAR on overall survival in human malignancies. Studies were grouped by cut-off value of CAR, and the median value was 0.095.

Note: Weights are from random-effects analysis.

Abbreviations: CAR, C-reactive protein/albumin ratio; HR, hazard ratio; CI, confidence interval.

Meta-analysis results for DFS

A total of 8 studies involving 4,226 patients reported the prognostic role of pretreatment CAR in DFS. The heterogeneity was not significant (I2=45.7%, P=0.075); therefore, a fixed-effect model was used. Combined results showed that high pretreatment CAR was associated with poor DFS (HR =1.55, 95% CI: 1.34–1.79, P=0.000), suggesting that patients with a high pretreatment CAR suffered from high tumor recurrence rate (Figure 3).

Figure 3.

Figure 3

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Forest plots for the prognostic impact of C-reactive protein/albumin ratio on disease-free survival in human malignancies.

Abbreviations: HR, hazard ratio; CI, confidence interval.

Meta-analysis results for 5-year mortality rate

Totally, 5 studies with 5,551 patients assessed the prognostic effect of high pretreatment CAR on 5-year mortality. The pooled results showed that high pretreatment CAR was associated with high 5-year mortality (OR =2.74, 95% CI: 2.11–3.55, P=0.000, random-effect model), which also meant that patients with a high pretreatment CAR suffered from decreased survival rate (Figure 4).

Figure 4.

Figure 4

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Forest plots for the prognostic impact of C-reactive protein/albumin ratio on 5-year mortality in human malignancies.

Note: Weights are from random-effects analysis.

Abbreviations: OR, odds ratio; CI, confidence interval.

Subgroup analysis

Subgroup analysis showed that high pretreatment CAR was associated with poor OS despite variations in publication year, country, sample size, cut-off value for CAR and treatment (Table 2). However, when subgroup analysis was conducted according to cancer sites, the results showed that high pretreatment CAR was associated with poor OS in nasopharyngeal cancer (HR =1.56, 95% CI: 1.25–1.94, P=0.000), esophageal cancer (HR =1.84, 95% CI: 1.06–3.20, P=0.030), lung cancer (HR =1.63, 95% CI: 1.24–2.15, P=0.046), gastric cancer (HR =1.73, 95% CI: 1.31–2.28, P=0.000), hepatic carcinoma (HR =2.73, 95% CI: 2.07–3.60, P=0.000), pancreatic cancer (HR =2.25, 95% CI: 1.52–3.34, P=0.000), kidney cancer (HR =2.69, 95% CI: 1.54–4.69, P=0.000) and other cancers (including oral cancer and laryngeal cancer) (HR =3.22, 95% CI: 1.18–8.80, P=0.022), but not colorectal cancer (HR =1.64, 95% CI: 0.96–2.80, P=0.069) (Figure 5). In addition, the HR for OS increased when the cut-off value of CAR increased, which could also be seen in Figure 2. Therefore, high pretreatment CAR is significantly associated with poor OS despite potential confounding factors.

Table 2.

Subgroup analysis for the prognostic impact of C-reactive protein/albumin ratio on overall survival in human malignancies

Variables No of studies No of patients HR (95% CI) P-value Heterogeneity
I2 (%) P-value
Total 25 12,097 1.99 (1.65–2.40) 0.000 86.9 0.000
Publication year
 2016–2017 18 9,165 1.93 (1.54–2.42) 0.000 86.6 0.000
 2014–2015 7 2,932 2.11 (1.56–2.84) 0.000 81.6 0.000
Country
 China 15 9,617 1.97 (1.66–2.34) 0.000 75.3 0.000
 Others 10 2,480 2.03 (1.38–2.97) 0.000 86.4 0.000
Sample size
 ≥386 14 10,426 1.91 (1.64–2.24) 0.000 63.5 0.001
 <386 11 1,671 2.10 (1.45–3.04) 0.000 90.6 0.000
Cut-off value for CAR
 >0.095 12 4,146 2.00 (1.47–2.72) 0.000 92.7 0.000
 ≤0.095 13 7,951 1.88 (1.57–2.25) 0.000 51.5 0.016
Cancer sites
 Nasopharynx 4 5,385 1.56 (1.25–1.94) 0.000 35.8 0.197
 Esophagus 2 891 1.84 (1.06–3.20) 0.030 85.6 0.008
 Lung 3 1,092 1.63 (1.24–2.15) 0.046 45.4 0.160
 Stomach 2 839 1.73 (1.31–2.28) 0.000 0.0 0.388
 Liver 2 364 2.73 (2.07–3.60) 0.000 0.0 0.352
 Pancreas 4 814 2.25 (1.52–3.34) 0.000 69.8 0.019
 Colorectum 4 1,567 1.64 (0.96–2.80) 0.069 89.0 0.000
 Kidney 2 976 2.69 (1.54–4.69) 0.000 66.5 0.084
 Others 2 169 3.22 (1.18–8.80) 0.022 61.0 0.109
Treatments
 Resection 16 5,802 2.08 (1.60–2.71) 0.000 90.1 0.000
 Others 9 6,295 1.82 (1.43–2.33) 0.000 70.9 0.001
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Abbreviations: HR, hazard ratio; CI, confidence interval; CAR, C-reactive protein/albumin ratio.

Figure 5.

Figure 5

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Forest plots for subgroup analysis based on the meta-analysis with overall survival according to different cancer types.

Note: Weights are from random-effects analysis.

Abbreviations: HR, hazard ratio; CI, confidence interval.

Publication bias

In this meta-analysis, both Begg’s test and Egger’s test were used to check the potential publication bias. No publication bias was found in the meta-analysis with OS (P=0.315) or 5-year mortality (P=0.764) when tested by Begg’s test. However, publication bias was found in the meta-analysis with OS (P=0.000) and 5-year mortality (P=0.024) when tested by Egger’s test, and DFS when tested by Begg’s test (P=0.035) as well as Egger’s test (P=0.006).

Sensitivity analysis

Since heterogeneity was found in the meta-analysis with OS, sensitivity analysis was performed for studies included in this meta-analysis. However, Figure 6 shows that the results of most of the included studies are close to the central line without obvious deviation.

Figure 6.

Figure 6

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Sensitivity analysis for the studies included in the meta-analysis with overall survival in human malignancies.

Abbreviation: CI, confidence interval.

Discussion

A meta-analysis is a quantitative method of analyzing and integrating available studies on the same topic, which has evolved as an alternative to the conventional narrative review.38 A meta-analysis may help clinicians and researchers better understand the findings of clinical studies, draw conclusions about therapeutic effectiveness or plan new studies.39 When compared with any single study, meta-analysis results are more convincing, but sometimes, they can also be limited by sampling bias, inadequate data and interpretation of bias outcomes.40

Inflammation is a central aspect of the innate immune system response to tissue damage or infection. The relationship between inflammation and cancer has been recognized since 17th century.4 Chronic inflammation can induce certain cancers and solid tumors (eg, hepatitis infection-related hepatocellular carcinoma, Helicobacter pylori infection-related gastric cancer and pancreatitis-related pancreatic carcinoma). Besides, inflammation is a critical component of tumor progression since tumor microenvironment could contribute to tumor invasion, migration and metastasis.41 Therefore, the relationship between cancer and inflammation has become the forefront of clinical oncology.42

CRP is a major component of any inflammatory reaction, which is generated from liver and belongs to pentraxin family.43 A systematic review with 271 articles demonstrated that high CRP was associated with higher mortality in 90% of reports on people with primary solid tumors, especially gastrointestinal malignancies and kidney malignancies. In addition, high CRP was correlated with poor treatment response and increased tumor recurrence.44

Human albumin (HA) is the most abundant plasma protein accounting for ~50% of the total protein content. HA is a small globular protein with a molecular weight of 66.5 kDa, consisting of a single chain of 585 amino acids.45 As a major plasma protein, HA has been used for prognostic assessment in patients with different conditions, including hemodialysis patients, multiple myeloma patients and patients undergoing replacement albumin therapy.46 In addition, pretreatment serum protein is used as a prognostic marker in many human malignancies, including urinary carcinoma,47,48 head and neck cancer,49 lung cancer,50 gynecological cancer51,52 and gastrointestinal cancers.53,54

As a combined product, CAR, especially its prognostic role in human malignancies, was investigated by many studies recently. On January 27th 2017, Li et al published a meta-analysis, including only 10 studies with 4,592 cancer patients, investigating the prognostic role of CAR in human cancer.8 In their meta-analysis, the impact of pretreatment albumin–globulin ratio (AGR) on OS in human cancers was analyzed, but without DFS or 5-year mortality. As we all know, DFS and 5-year mortality rate are also considered as important indicators in the research on cancer prognosis. Moreover, the number of included studies and cancer patients was limited in this previously published paper. Therefore, we conducted this updated meta-analysis to investigate the prognostic value of pretreatment CAR in cancer patients. In this meta-analysis, we included 25 studies with 12,097 cases diagnosed with malignancy. The pooled results showed that high pretreatment CAR was associated with poor OS, poor DFS and high 5-year mortality, suggesting that pretreatment CAR might serve as a marker of poor survival rate and high tumor recurrence rate in human malignancies. Moreover, our subgroup meta-analysis showed that high pretreatment CAR was significantly associated with poor OS in patients with nasopharyngeal cancer, esophageal cancer, gastric cancer, liver cancer, pancreatic cancer, lung cancer, oral cancer and renal cancer, but not colorectal cancer. When compared with the previously published meta-analysis, our meta-analysis results are more reliable and convincing in addition to being based on more available studies.

However, this meta-analysis also has some limitations. Firstly, we did not perform methodological quality analysis for included studies, as no widely agreed quality criteria have been identified yet for assessing studies investigating prognosis.55 Secondly, heterogeneity was found in the meta-analysis with OS and 5-year mortality. We performed subgroup meta-analysis based on potential confounding factors including publication time, country, sample size, cut-off value for CAR and treatment, but heterogeneity could not be solved. However, when conducting subgroup meta-analysis based on cancer sites, no heterogeneity was found in the meta-analysis with nasopharyngeal cancer, lung cancer, gastric cancer or liver cancer. The other reason is that the cut-off values for CAR were highly diverse, which may also account for the heterogeneity to some extent. Last but not least, publication bias was found in the meta-analysis, which might have been caused by the following reasons. Only articles in Chinese or English were included in this meta-analysis, though we did not set any language limitations during the searching process. In addition, some databases (eg, Embase database) were not available for our research group, and survival results were not provided or could not be calculated in some retracted articles.

At the same time, we also found other combined markers derived from albumin, such as albumin–bilirubin grade used for predicting prognosis in patients with hepatocellular carcinoma (HCC)56,57 and AGR used as a prognostic marker in patients with HCC,58 urinary carcinoma,59,60 lung cancer61 and endometrial cancer.51 Besides, we also found one study investigating the impact of CAR on long-term outcomes following hepatic resection for colorectal liver metastases.62 Thus, pretreatment CAR is a useful prognostic marker in cancer patients

In summary, high pretreatment CAR was associated with poor OS, poor DFS and high 5-year mortality in human malignancies. Pretreatment CAR might serve as a marker of poor survival rate and high tumor recurrence rate in human malignancies except colorectal cancers. Therefore, CAR could be used in the evaluation of prognosis of human malignancies in clinical work. More prospective studies with large sample size are needed to explore the prognostic role of pretreatment CAR in patients with colorectal cancer.

Acknowledgments

The authors are grateful to Lin An (Department of Dermatology, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China) for her help in this research.

Footnotes

Disclosure

The authors report no conflicts of interest in this work.

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