J Cancer 2019; 10(18):4305-4317. doi:10.7150/jca.31598 This issue Cite


Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies

Peng-Fei Wang1,#, Si-Ying Song2,#, Hang Guo1,3,#, Ting-Jian Wang1, Ning Liu1, Corresponding address, Chang-Xiang Yan1, Corresponding address

1. Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
2. School of Basic Medical Sciences, Capital Medical University, Beijing, China
3. Department of Orthopedics, Shanghai Pudong New Area Gongli Hospital, Naval Military Medical University, Shanghai, China
#Peng-fei Wang, Si-ying Song & Hang Guo contributed equally to the work.

Wang PF, Song SY, Guo H, Wang TJ, Liu N, Yan CX. Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies. J Cancer 2019; 10(18):4305-4317. doi:10.7150/jca.31598. https://www.jcancer.org/v10p4305.htm
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Red blood cell distribution width (RDW) has been recently demonstrated to be a predictor of inflammation. High pretreatment RDW level is associated with poor survival outcomes in various malignancies, although the results are controversial. We aimed to investigate the prognostic role of RDW. A systematic literature search was performed in MEDLINE and EMBASE till April 2018. Pooled hazard ratios (HRs) were estimated for overall survival (OS) and combined disease-free survival, progression-free survival, and recurrence-free survival (DFS/PFS/RFS). 49 studies with 19,790 individuals were included in the final analysis. High RDW level adversely affected both OS and DFS/PFS/RFS. For solid cancers, colorectal cancer (CRC) had the strongest relationship with poor OS, followed by hepatic cancer (HCC). Negative OS outcomes were also observed in hematological malignancies. Furthermore, patients at either early or advanced stage had inverse relationship between high pretreatment RDW and poor OS. Studies with cut-off values between 13% and 14% had worse HRs for OS and DFS/PFS/RFS than others. Furthermore, region under the curve (ROC) analysis was used widely to define cut-off values and had relatively closer relationship with poorer HRs. In conclusion, our results suggested that elevated pretreatment RDW level could be a negative predictor for cancer prognosis.

Keywords: red blood cell distribution width, malignancies, prognosis, meta-analysis


Red blood cell distribution width (RDW) is a conventional biomarker for erythrocyte volume variability and an indicator of erythrocyte homeostasis [1]. Recent evidence shows that anisocytosis is involved in a variety of human diseases such as cardiovascular diseases [2,3], thrombosis [3], diabetes [4], and cancers [5,6]. High RDW level is a negative prognoistic marker for these diseases, and inflammation is the leading mechanism [1].

Inflammation is a key regulator of cancer initiation and progression [7]. Recently, RDW, which plays a critical role in inflammatory response, has attracted attention because of the connection between inflammation and cancer. RDW increases in malignant tumors [8,9]. Furthermore, higher RDW levels are also significantly associated with advanced stages of cancer and metastasis [10,9].

A mounting body of evidence suggests that elevated RDW level also correlated with poor prognosis for various cancers, which included esophageal cancer [11-15], gastrointestinal tumors [16-18], HCC [19-22], lung cancer [23-26], and hematological malignancies [27-30]. However, the prognostic impact of RDW has not been comprehensively investigated because of the inevitable heterogeneity of the samples studied. The aim of the present study was to assess the relationship between RDW and clinical outcomes in patients with cancer.


Search strategy

Our meta-analysis was registered in PROSPERO with the number CRD42018093419. Studies were identified from MEDLINE and EMBASE up to April 2018. Medical subject headings and Emtree headings were searched and combined with the following key-words: “red blood cell distribution width OR RDW” and “prognosis OR prognostic OR survival OR outcome” and “cancer OR tumor OR carcinoma OR neoplasm”. The references of the included articles were also scanned to identify additional studies. Supplementary Table 1 presents the full search strategy.

Study selection

We included prospective or retrospective studies that assessed RDW level prior to any treatment in patients with proven pathological diagnosis of cancer. Furthermore, eligible studies should provide hazard ratio (HR) with a 95% confidence interval (CI) for clinical outcomes, or enough data to calculate these quantities. We excluded studies based on the time when blood samples were collected; studies were eliminated if they involved patients who received any therapy within two weeks prior to blood donation. Conference abstracts, review articles, case reports, letter, animal studies, or in vitro studies were not eligible for our analysis. Studies with duplicate or overlapping data were also excluded. Two reviewers (PF-W and SY-S) independently performed the study selection and resolved any disagreements via discussion.

Data extraction

Data from all included studies were extracted by one author (SY-S) and was cross-checked by another author (PF-W). Data were extracted using the name of the first author, year of publication, country, tumor type, clinical/pathological tumor stage, study characteristics (sample size, age, and gender), stage criteria, statistical methods used to calculate the cut-off value for RDW, survival outcomes, and sources of HRs (univariate or multivariate). Furthermore, we calculated the male-to-female gender ratio (M/F gender ratio) to precisely assess the various gender distributions among the included cohorts. The interval of the M/F gender ratio of a balanced composition ranged from one to two; the M/F ratio of a female-dominant composition was less than one, whereas that of male-dominant cohorts was more than two. HRs and 95% CIs were extracted for overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and recurrence-free survival (RFS). We used the Engauge digitizer to estimate HRs and their 95% CIs if eligible studies provided only Kaplan-Meier curves and we received no response from the investigators after two requests for HRs [31]. All disagreements were resolved by consensus.


We defined OS as the time from the study enrollment to the date of death from any cause or last follow-up. As DFS, PFS, and RFS share similar endpoints, they were analyzed together as one outcome, DFS/PFS/RFS [32-34].

Statistical analyses

We used STATA version 14.0 (STATA, College Station, TX) in all analyses. Multivariate-adjusted HRs were used when possible, and univariate HRs were included in the meta-analysis if multivariate-adjusted HRs were missing. Pooled estimates with 95% CIs, separately for studies providing OS and DFS/PFS/RFS, were derived using the Mantel-Haenszel method. Further analyses for exploring heterogeneity were comprehensively conducted through subgroup analysis, sensitivity analyses, and meta-regression. Heterogeneity was assessed using the χ2 test and expressed as the I2 index (25% = low, 50% = medium, 75% = high) [35]. A random effects model was used when heterogeneity was > 50%. Alternatively, a fixed effects model was conducted for the meta-analysis. Publication bias was assessed by visual inspection of funnel plots, combined with Egger's test or Begg's test [36,37]. Additionally, we applied Duval and Tweede's trim and fill method to estimate corrected effect size after adjustment for publication bias [38]. A set of modified predefined criteria was utilized to evaluate the risk of bias in eligible studies [39-41]. P-values < 0.05 were considered statistically significant.


Study characteristics

Our literature search identified 401 potentially relevant records. Eighty-nine articles were further removed due to duplication. Two-hundred and fifteen studies with irrelevant content were excluded after screening titles and abstracts. Ninety-seven articles were reviewed with full texts. In total, forty-nine studies consisting of 19,790 patients were finally included in our analysis according to the inclusion and exclusion criteria (Fig. 1) [42-47,23,48,11,49,27,28,19,50-52,12,53,24,54,25,55,29,13,26,14,15,20,10,21,56,16,57-62,30,63-66,22,67,17,68,18,69].

 Fig 1 

Flow diagram of the study selection process.

J Cancer Image

The characteristics of the included studies are shown in Table 1. OS and DFS/PFS/RFS were reported in 45 and 26 articles, respectively. Sixteen different solid cancer types and five different hematological malignancies were investigated in the eligible studies. For solid tumors, the most frequently evaluated cancer was upper gastrointestinal cancer (UGI) (including patients with pancreatic, esophageal, and gastric cancer) (n = 8), followed by hepatic cancer (HCC) (n = 4), non-small cell lung cancer (NSCLC) (n = 4), colorectal cancer (CRC) (n = 3), breast cancer (n = 3), and glioma (n = 3). Multiple myeloma (MM) (n = 5) and diffuse large B-cell lymphoma (DLBCL) (n = 2) were the most-studied diseases among hematological malignancies. A large number of studies (90%) enrolled patients with mixed-stage, whereas only a few studies specifically investigated patients with early- (10%) and advanced-stage (12%) disease. Five different methods for defining cut-off values were observed in the included studies. Region under the curve (ROC) analysis was used most frequently (n = 23), followed by the upper limit of reference range (n = 12) and empirical values based on previous studies (n = 6). With respect to cut-off values, most studies (94%) selected coefficient of variation (CV) to evaluate RDW, whereas others used standard deviation (SD). The cut-off values ranged from 12.20% to 20.00%. However, thirty-six studies (80%) applied cut-off values in the range of 13-15%. Furthermore, we evaluated the demographic characteristics among the cohorts, such as age, gender, and country of origin. Twenty-two studies (52%) enrolled elderly population, the median or mean age of whom was > 60 years. The number of cohorts with balanced gender composition (n = 22) was nearly equal to that of cohorts with female or male dominant composition (n = 24). Sixty-three percent cohorts were originally from Asian countries, whereas the others were from Western countries. In our assessment of study quality, nine studies had quality scores ≤ 7, and the remaining 40 studies had scores > 7 (Supplementary Table 3).

 Table 1 

Main characteristics of 49 eligible studies included in the meta-analysis.

Study, YearCountryTumor typeStudy designStageCriteriaSample sizeAgeaGender (Female/male)Definition of cut-offs Cut-offs valueOutcome measuresHRs sourcevariables
et al 2009
USANRprospectiveNRNRNRNRNR4th quartile14.35%OSUV
et al 2013
JapanLung cancerretrospectiveI-IVUICC-733271.5 (38-94)109/223Upper limit15.00%OSUV; MVRDW; Stage; ECOG PS; Other diseases; Treatment; Albumin; CRP
et al 2014
TurkeyMalignant mesotheliomaretrospectiveNRNR15258.2 ± 11.965/90Arbitraryc20.00%OSMVRDW; Histopathological subtype; NLR
et al 2014
KoreaMMretrospectiveI-IIIISS14661 (32-83)55/91Upper limit14.50%OS; PFSUV; MVRDW; Age at diagnosis; ECOG; Cytogenetic risk; B2MG; Albumin; LDH; Hemoglobin; Calcium; Induction with novel agents; ASCT
et al 2014
AustriaMultiple malignanciesbprospectiveLocalized; Distant metastasis; Not classifiableNR184062 (52-68)843/997Upper limit; 4th quartile16%; 14.6%OSUV; MVRDW; Age; Sex
et al 2014
ChinaRCCretrospectiveI-IVAJCC-731656.83 ± 11.68108/210ROC12.85%OSMVRDW; Smoking; Hemoglobin; MCV; Platelet; WBC; Albumin; ESR
et al 2014
UKNSCLCretrospectiveT1-3; N0-1AJCC-791767.21 (17-90)440/4774th quartile15.30%OSMVRDW; Age; Alcohol intake; Emphysema; Squamous carcinoma; predicted postoperative FEV1; T stage I; T stage III; N stage I
et al 2014
ChinaBreast cancerretrospectiveTis-T3;
NR60852.4 ± 10.8608/0ROC13.45%OSMVRDW; Node stage; Molecular subtype; NLR
et al 2015
ChinaESCCretrospectiveT1-4; N0-3NR277NR37/240Mean14.50%CSSMVRDW; Tumor length; Vessel invasion; Differentiation; T stage; N stage
et al 2015
AJCC-642068 ± 10.3116/79Within central 80 % distribution.14.00%OS; CSSUV; MVRDW; T stage; LN metastasis; Tumor grade; Adjuvant chemotherapy; WBC; NLR
et al 2015
JapanCMLretrospectiveNRNR8451 (22-85)30/54Arbitraryc15.00%OS; EFSUV
et al 2015
CroatiaDLBLretrospectiveI-IVAnn Arbor8164.0 (52.5-72.5)52/29ROC15.00%OS; EFSMVRDW; Age; Sex; IPI; LDH; Clinical stage AA; ECOG PS
et al 2015
ItalyHCCretrospectiveA-DBCLC314Training cohort 70 (62-77); Validation cohort 67 (59-74)Training cohort 52/156; Validation cohort 26/80Upper limit14.60%OSMVRDW; Age at diagnosis; BCLC stage; Child-Pugh-Turcotte score; tumor size; serum AFP
et al 2015
USABreast cancerretrospectiveI-IVAJCC-61816Black 57.26 ± 13.99; White 60.05 ± 13.431816/0NR14.50%OSMVRDW; Age; Year of diagnosis; Ethnicity; Smoking status, Drinking status; Stage; Grade; Estrogen receptor status; progesterone receptor status
et al 2015
USASCLCprospectiveExtensive; LimitedNR93865.4 ± 11.0438/500Upper limit15.00%OSUV; MVRDW; NLR; PLR; Age at diagnosis; Gender; ECOG performance status; Chest radiation; Chemotherapy; Liver metastases; Numbers of metastatic sites
et al 2016
KazakhstanGliomasretrospectiveGrade I-IVWHO 200717841.58 ± 1.0485/93ROC13.95%OSUV
et al 2016
JapanESCCretrospectiveI-IIIAJCC-7144NR15/129Upper limit50fLCSSUV; MVRDW; Stage; Tumor size; Operation time
et al 2016
ChinaBreast cancerretrospectiveI-IIIAJCC-620337 (24-40)203/0ROC13.75%OS; DFSMVRDW; PVI present; PR positive; Stage
et al 2016
JapanNSCLCretrospectiveT1-4; N0-2UICC-7992NRNRMedian13.80%OS; DFSMVRDW; Gender; T factor; N factor; Sub-lobar resection; CEA; NLR; Albumin; Smoking
et al 2016
TurkeyLaryngeal carcinomaretrospectiveT1-4; N0-2; M0AJCC-710365.01 ± 9.01NRROC14.05%OSMVRDW; Tumor stage
et al 2016
TurkeyNSCLCretrospectiveI-IVUICC-714656.5 (26-83)15/131Median; ROC; Upper limit; Arbitraryc14%; 14.2%; 14.5%;
et al 2016
ChinaGlioblastomaretrospectiveNRNR10954 (19-85)42/67ROC14.10%OSMVRDW; Age; Tumor location; Extent of resection; Adjuvant radio/chemotherapy; MCV; MCHC
et al 2016
PolandCLLretrospective0-IVRai6663 (38-85)25/38Upper limit14.50%OSUV
et al 2016
ChinaESCCretrospectiveI-IIIAJCC-6362Median 58; Mean 57.9694/268ROC13.60%OS; DFSUV
et al 2016
TurkeyNSCLCretrospectiveIA-IIIANR24960.8 ± 9.141/208Upper limit14.60%OS; DFSUV
et al 2016
17963.0 (42-77)29/150Upper limit15.00%OS; DFSMVRDW; Stage (III vs. I&II); Node metastasis status; Tumor length; WBC; Albumin; CRP; NLR
et al 2016
ChinaESCCretrospectiveI-IIIAJCC-746859.5 ± 9.0;
60 (36-81)
92/376ROC12.20%OS; DFSMVRDW; Age; N metastasis; Adjuvant radio/chemotherapy; Smoking; Maximum tumor diameter; MCV; CA19-9; NLR; PLR; COP-MPV
et al 2016
ChinaHCCretrospectiveI-IVNR10652 (22-75)13/93Upper limit14.50%OS; DFSMV; UVRDW; TNM stage; Tumor size; Tumor number; Vascular invasion
et al 2017
ChinaGCretrospectiveI-IVAJCC-7227NR51/176Median13.00%OS; DFSUV
et al 2017
Japan, Italy and UKHCCprospectiveA-DBCLC; CLIP scores44269.92 ± 10.0696/346NRNROSMVTreatment-naïve HCC; NLR; CLIP score; Diarrhea on sorafenib; RDW
et al 2017
2396Male 55.98 ± 9.81; Female 57.93 ± 9.41574/1822NRNROSMVAge, body mass index, smoking, drinking, family history of cancer, systolic blood pressure, fasting blood glucose, TNM stage, tumor embolus and tumor size
et al 2017
CroatiaCRCretrospectiveI-IVAJCC-79066.8 ± 9.737/53ROC14.00%OSMVRDW; Age; Gender; AJCC stage; NLR
Li B
et al 2017
ChinaHilar cholangiocarcinomaretrospectiveI-IVAJCC-729260 (20-78)131/161ROC14.95%OSMVRDW; Histologic grade; T stage; N stage; AJCC stage; Portal vein invasion; Hepatic artery invasion
Li Z
et al 2017
USAEpithelial ovarian cancerretrospectiveI-IVNR65463 (28-93)654/0ROC14.15%OSMVRDW; NLR; PLR; MLR; Combined RDW+NLR; Stage; Origin of cancer; Age; Histology; Grade; Residual disease
et al 2017
ChinaNasal-type, extranodal natural killer/T-cell lymphomaretrospectiveI-IVAnn Arbor19144 (15-86)57/134ROC46.2 fLOS; PFSMVRDW; Local invasiveness; Hemoglobin
et al 2017
ChinaMMretrospectiveI-IIIDSS16661.6 ± 10.878/88Arbitraryc14.00%OS; PFSUV
et al 2017
ChinaProstate cancerretrospectiveNRNR17168.5 ± 8.40/171ROC12.90%OSUV
et al 2017
ThailandOral cancerretrospectiveI-IVAJCC-737460 (21-92)133/241Arbitraryc14.05%OS; DFS; RFSUV; MVRDW; Stage; PLR
et al 2017
ChinaMMretrospectiveI-IIIISS19665 (33-82)86/110ROC18.05%OSMVRDW; Age; gender; Albumin; Lactate dehydrogenase; Creatinine
et al 2017
ChinaGliomaretrospectiveLow grade; High gradeWHO 200716844.1 ± 14.6168/0NR13.20%PFSUV
et al 2017
17361.7 ± 1262/110Mean16.00%OSMVRDW; Gender; Age; Tumor diameter; Vascular invasion; PNI; Metastatic LN; PRBC; Complication; T1; PDW; MCV
et al 2017
ChinaCervical cancerretrospectiveIA1-IIA2FIGO80049.5 ± 10.7800/0ROC12.70%OS; DFSUV
et al 2017
ChinaDLBLretrospectiveI-IVAnn Arbor16159.1±11.470/91ROC14.10%OS; PFSMV
et al 2017
ChinaHCCretrospectiveI-IIINR31652.2 (22.0-80.0)Training cohort 26/159; Validation cohort 20/111ROC13.25%OS; DFSMV; UVRDW; FIB-4; NLR; PLR; Liver cirrhosis; Tumor size; Tumor capsule; Tumor thrombus; TNM stage
et al 2017
PolandRCCretrospectiveI-IVAJCC-743462.0 (54.0-69.0)203/231ROC13.90%CSSMVRDW; Age; Gender; T stage; Distant metastases; Nephrectomy; Tumor necrosis; Grading
et al 2018
ChinaCRCretrospectiveI-IVNR128NR167/73ROC13.45%OS; DFSUV; MVRDW; Differentiation; CA19‐9
et al 2018
ChinaMMretrospectiveI-IIIISS; DSS7860.7 (43-81)31/47ROC15.50%OS; PFSUVRDW; B symptoms; IPI; ECOG PS; LDH; Stage; Bone marrow involvement; Extranodal sites of disease; Hemoglobin
et al 2018
ChinaRectal cancerretrospectiveI-IIIAJCC-7625NR241/384ROCRDW-cv 14.1%; RDW-sd 48.2fLOS; DFSMVRDW; Tumor location; Tumor size; Differentiation; TNM; Vascular invasion; Perineural invasion
et al 2018
ChinaMMretrospectiveI-IIIISS16261 (40-87)75/87Upper limit14.00%OS; PFSUV

Abbreviations: GC = gastric cancer; ESCC = esophageal squamous cell carcinoma; CRC = colorectal carcinoma; HCC = hepatocellular carcinoma; NSCLC = non-small cell lung cancer; SCLC = small cell lung cancer; RCC = renal cell cancer; UTUC = Upper tract urothelial carcinoma; MM = multiple myeloma; chronic lymphocytic leukemia = CLL; CML = Chronic Myeloid Leukemia; DLBL = diffuse large B-cell lymphomas; AJCC = The American Joint Committee on Cancer; BCLC = Barcelona Clinic Liver Cancer guidelines; UICC = International Union Against Cancer; DSS = Durie and Salmon staging system; ISS = International Staging System; OS = overall survival; PFS = progression free survival; RFS = recurrence free survival; DFS = disease free survival; event-free survival = EFS; MV = multivariate; UV = univariate; RDW-CV = red blood cell distribution width coefficient of variation; RDW-SD = red blood cell distribution width standard deviation; NR = not reported

a. Age reported as either mean ± standard deviation or median (range), if not otherwise specified.

b. Multiple malignancies include brain, breast, lung, upper or lower gastrointestinal tract, pancreas, kidney, prostate or gynecological system; sarcoma and hematologic malignancies (lymphoma, multiple myeloma)

c. Studies defined cut-offs value based on previous studies.

 Fig 2 

Meta-analysis of the association between RDW and OS in patients. Results are presented as individual and pooled hazard ratios (HRs) with 95% confidence intervals (CIs).

J Cancer Image

Overall survival

Forty-five studies with 18,767 patients were analyzed for OS. The pooled HRs of higher pretreatment RDW level was 1.508 (95% CI = 1.387-1.639; Fig. 2). Next, we performed comprehensive analysis to explore the high heterogeneity, including subgroup analyses, sensitivity analysis, and meta-regression.

Table 2 shows the subgroup analysis of the included studies, based on eight factors, including tumor type, tumor stage, age, gender distribution, country of origin, cut-off value, method of defining the cut-off value, and HR calculation. In solid tumors, CRC had the strongest relationship with poor OS (HR = 1.932; 95% CI = 1.397-2.673), followed by HCC (HR = 1.430; 95% CI = 1.232-1.660) and NSCLC (HR = 1.440; 95% CI = 1.103-1.880). However, UGI cancer and breast cancer with elevated RDW were not associated with worse OS (UGI cancer: HR = 1.091; 95% CI = 0.925-1.286. Breast cancer: HR = 2.092, 95% CI = 0.833-5-255). For hematological malignancies, negative OS outcomes were observed in MM and DLBCL (MM: HR = 1.692; 95% CI = 1.256-2.281. DLBCL: HR = 3.178, 95% CI = 1.853-5.450). In addition, patients in either early or advanced stage showed adverse relationship between increased pretreatment RDW and poor OS. Furthermore, combined HR remained significant in subgroups stratified by demographic factors, including age, gender, and country of origin. Studies with cut-off values between 13% and 14% had worse HR than others. However, considerable variety was present in the methodologies used for defining cut-off values. ROC analysis was the most widely used method and had relatively closer relationship with poorer HRs. Finally, studies using univariate (HR = 1.525; 95% CI = 1.380-1.686) and multivariate analyses (HR = 1.477; 95% CI = 1.342-1.626) showed that higher RDW levels were associated worse OS.

In sensitivity analysis under “one study removed” model, the pooled HRs for OS were significantly affected by exclusion of Wang et al. (Supplementary Table 4). In addition, meta-regression did not demonstrate any potential source of heterogeneity (Supplementary Table 5).


Twenty-six studies with 7,350 patients provided HRs and 95% CIs for DFS/PFS/RFS. Overall, elevated pretreatment RDW level were associated with worse DFS/PFS/RFS (HR = 1.576; 95% CI = 1.447-1.716; Fig. 3). Subgroup analyses were performed by stratification based on tumor type, tumor stage, age, gender distribution, country of origin, cut-off value, method of defining the cut-off value, and HR calculation (Supplementary Table 2). Higher levels of RDW were associated with shorter DFS/PFS/RFS in patients with HCC (HR = 2.104, 95% CI = 1.577-2.807), CRC (HR = 1.636; 95% CI = 1.211-2.211), and hematological malignancies (HR = 2.077; 95% CI = 1.644-2.625).

 Table 2 

Subgroup analyses of the associations between RDW and OS in cancer.

Stratified analysesNo. of patientsNo. of studiesModelPooled HR (95%CI)P valuePD valueHeterogeneity
I2PH value
Tumor type<0.001
Hematologic malignancies197910fixed2.046 (1.623-2.580)<0.00121.2%0.248
MM7485fixed1.692 (1.256-2.281)0.00118.8%0.295
DLBCL8812fixed3.178 (1.853-5.450)<0.0010.0%0.793
UGI cancer38056random1.091 (0.925-1.286)0.30373.4%0.001
HCC15105random1.430 (1.232-1.660)<0.00179.9%<0.001
NSCLC23044random1.440 (1.103-1.880)0.00757.2%0.053
Breast cancer26273random2.092 (0.833-5.255)0.11680.3%0.006
Colorectal carcinoma8433fixed1.932 (1.397-2.673)<0.0010.0%0.521
Gliomas2872fixed1.466 (1.129-1.904)<0.00123.9%0.252
UTUC4201*fixed2.172 (1.599-2.949)<0.0013.5%0.309
Mix stage1678633random1.494 (1.372-1.626)<0.00180.5%<0.001
Early stage15455fixed1.690 (1.180-2.422)0.00441.0%0.148
Advanced Stage14166random1.717 (1.235-2.386)0.00157.7%0.038
≤60797919random1.590 (1.321-1.914)<0.00182.6%<0.001
>60799222random1.515 (1.351-1.699)<0.00175.7%<0.001
Gender distribution<0.001
Female dominant50599random1.401 (1.153-1.703)0.00174.9%0.001
Balanced641821random1.696 (1.441-1.997)<0.00174.8%<0.001
Male dominant532514random1.413 (1.232-1.620)<0.00181.6%<0.001
Eastern1060828random1.716 (1.458-2.020)<0.00179.8%<0.001
Western818017random1.316 (1.203-1.439)<0.00180.9%<0.001
Cut-off value<0.001
>15%33566random1.608 (1.107-2.335)0.01389.5%<0.001
>14% and ≤ 15%791121random1.510 (1.351-1.688)<0.00179.2%<0.001
>13% and ≤ 14%340911random1.869 (1.493-2.340)<0.00157.5%0.004
≤13%19825fixed1.534 (1.262-1.865)<0.0010.0%0.655
Definition of cut-off value<0.001
ROC curve analysis627622fixed1.569 (1.434-1.718)<0.00142.6%0.015
Upper limit355811random1.504 (1.296-1.746)<0.00170.8%0.000
Median23573random1.400 (0.961-2.040)0.08062.4%0.046
4th quartile27573random1.647 (1.430-1.897)<0.0010.0%0.645
Arbitrary#9225random1.682 (1.073-2.638)0.02363.2%0.028
HR calculation<0.001
Multivariate1357228random1.477 (1.342-1.626)<0.00183.9%<0.001
Univariate427517fixed1.525 (1.380-1.686)<0.0018.5%0.355

Abbreviations: MM = Multiple Myeloma; DLBCL = Diffuse large B-cell lymphoma; UGI cancer = upper gastrointestinal tract (UGI) cancers (including esophagus cancer, gastric cancer, and small intestine cancer); HCC = hepatocellular carcinoma; NSCLC = non-small cell lung cancer; UTUC = upper tract urothelial carcinoma; OS = overall survival; HR = hazard ratio; CI = confidence interval; PD = P for subgroup difference; PH = P for heterogeneity.

*: Cheng et al 2015 separately evaluated the survival outcome in two cohorts, which were derivation cohort and validation cohort.

#: Definition of cut-offs value of RDW was based on previous study.

‡: HRs were extracted from multivariate cox proportional hazards models, univariate cox proportional hazards models or survival curve analysis.

 Fig 3 

Meta-analysis of the association between RDW and DFS/PFS/RFS in patients. Results are presented as individual and pooled hazard ratios (HRs) with 95% confidence intervals (CIs).

J Cancer Image

Overall, HRs remained significant in subgroups stratified by demographic factors, including age, gender, and country of origin. Furthermore, associations between higher RDW levels and worse DFS/PFS/RFS were also observed with cut-off values > 13% and < 14% (HR = 1.818; 95% CI = 1.474-2.243). Studies which utilized ROC analysis to define cut-off values showed comparatively worse HRs (HR = 1.770; 95% CI = 1.536-2.040). Finally, both univariate and multivariate analyses for HR calculation indicated poor DFS/PFS/RFS outcomes.

Publication bias

We observed evidence of publication bias in studies provided on OS (n = 45) and DFS/PFS/RFS (n = 26) by visual inspection of the funnel plot (Supplementary Fig. 1), which was further confirmed by Egger's tests (P < 0.001) (Supplementary Fig. 2). The trim and fill method was applied to address these problems. Intriguingly, pooled adjusted HRs of OS and DFS/PFS/RFS subsets were consistent with our primary analysis (Supplementary Table 6 and Supplementary Fig. 3).


RDW is an easily acquired, non-invasive, and inexpensive maker, which can be used routinely for clinical purpose. This is the first meta-analysis to comprehensively evaluate the prognostic role of RDW in cancers. High RDW level was correlated with unfavorable clinical outcomes in most tumor types and stages. The prognostic value of RDW was also independent of patient age, gender, or region.

Gradual increase in RDW with age has been reported in healthy people [1]. However, association between gender and RDW is still unclear. Certain studies indicated that RDW was slightly higher in females [70,71], whereas others observed no significant gender-based difference in RDW values [72,73]. Hence, an age- and gender-stratified subgroup analysis was performed. Poor survival outcome was associated with higher RDW in elder or younger patients with cancer. Similarly, both females and males with high RDW levels exhibited poor survival. These results showed that RDW can predict survival independent of age and gender. The cut-off value of 14.6% is conventionally used for anemia [74]. However, the lack of unified RDW cutoff values for cancer survival prediction was a matter of concern [73].

Majority of the studies used ROC analysis to define cut-off values, which ranged from 12.20% to 20.00%. However, 36 studies (80%) applied cut-off values between 13% and 15%. We observed that cut-off values defined by ROC curves were more likely to predict poor clinical outcomes. Furthermore, subgroups with cut-off values between 13% and 14% were mostly negatively associated with poor OS and DFS/PFS/RFS. We conclude that more studies are required to determine uniform cut-off values in specific cancer types.

The mechanisms underlying the prognostic impact of RDW on cancers were due to inflammation [75], poor nutritional status [76], and oxidative stress [77]. First, it is well-known that malignant tumors are accompanied by systemic inflammatory response [76]. RDW was identified as an inflammatory marker in patients with cancer due to its positive association with widely used plasma inflammatory biomarkers such as C-reactive protein (CRP) [43,28,14], erythrocyte sedimentation rate (ESR) [60,47], and interleukin (IL)-6 [78] levels. Elevated RDW level reflected the presence of immature juvenile red blood cells in the periphery. Various cytokines affect erythropoiesis via erythropoietin (EPO) production, inhibition of erythroid progenitors, and reduction in iron release. Previous in vitro and in vivo studies have demonstrated that EPO production was inhibited by inflammatory cytokines [79-81] such as IL-6, interferon-gamma (IFN-γ), IL-1β, and tumor necrosis factor-alpha (TNF-α). In addition, IL-1α and IL-1β play important roles in suppression of erythroid progenitors [82]. Hepcidin, a regulator of iron metabolism, is increasingly expressed when plasma IL-6 level is elevated [83,84], which results in iron deficiency and anemia [80]. In sum, it is plausible to hypothesize that RDW can reflect inflammatory status in cancer. Second, malnutrition is another hallmark of cancer because of reduction in appetite and weight. This results in deficiency of various minerals and vitamins such as iron, folate and vitamin B12, which consequently contribute to the increase in RDW [85,42]. Numerous studies have also shown that low albumin level is associated with increased RDW level in cancer patients [24,60,30,69], which also indicated the relationship between high RDW level and poor nutritional status in patients with cancer. Third, oxidative stress was recognized as a negative factor leading to significant variation in erythrocyte size. Free reactive oxygen species (ROS) can damage protein, lipids, and DNA, which may reduce RBC survival [86]. Taken together, high RDW level is well-suited to reflect both chronic ongoing inflammation and poor nutritional status in patients with cancer.

Among solid tumors, CRC and HCC showed relatively strong association between RDW level and negative prognosis. This significant association in CRC may be attributed to chronic inflammatory status and cancer-associated anemia. CRC can develop from inflammatory bowel diseases and inflamed polyps [87-89]. Thus, inflammation plays a crucial role in colorectal carcinogenesis [90]. In addition, chronic blood loss is a common symptom of CRC, which can lead to iron deficiency, anemia, and subsequent rise in RDW values. HCC is one of the most important inflammation-associated cancers [91]; it is closely associated with chronic inflammation and fibrosis, which is known as hepatic inflammation-fibrosis-cancer (IFC) axis. IL-6 and TNF-α expression was elevated and erythrocyte maturation was suppressed in patients with HCC [92]. Furthermore, within the diseased liver, free radicals such as ROS and nitrogen species (NO) were generated by the cells of the hepatic immune system, including recruited neutrophils, monocytes, and Kupffer Cells [92]. In sum, elevated RDW was negatively associated with the prognosis of certain cancer types, which encompassed multiple pathways affecting erythropoiesis.

In our meta-analysis, pretreatment RDW was identified as a robust predictor of cancer prognosis. However, there are several limitations. First, there was considerable heterogeneity when HRs for OS outcomes were pooled. However, subgroup analysis showed that various methodologies for defining cut-off values may be a major cause of heterogeneity. The robustness of our results was further confirmed by sensitivity analysis and meta-regression, which did not significantly alter the pooled effect size for OS. Second, we observed that some studies evaluated the relationship between delta RDW level [17,27,16] or delta MCV level [93-96] and cancer prognosis after the patients had undergone certain therapies. However, we focused on the prognostic role of absolute value of pretreatment RDW level in this analysis as delta RDW level may be dependent on many cofactors such as therapies and types of cancer. Finally, although pretreatment RDW level can reflect both inflammatory and nutritional status, it would be more convincible if combined with other potential predictors, such as neutrophil to lymphocyte ratio (NLR) and prognostic nutritional index (PNI). More studies are required for building a new prognostic and comprehensive model for predicting survival outcomes in patients with cancer.


Pretreatment RDW level is a potential predictor of cancer prognosis, independent of most tumor type and stage and patient age and gender. Optimal RDW cut-off values can be defined by ROC analysis. Cut-off values between 13% and 14% were negatively associated with poor survival outcomes. Uniform cut-off values for specific cancer types are required for further evaluation in future.

Supplementary Material

Supplementary figures and tables.



This work was supported by grants from the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2014BAI04B01).

Author contributions

Conception and design: Chang-xiang Yan, Ning Liu; Collection and assembly of data: Peng-fei Wang, Si-ying Song; Data analysis and interpretation: All authors; Manuscript writing: All authors; Final approval of manuscript: All authors; Accountable for all aspects of the work: All authors.

Competing Interests

The authors have declared that no competing interest exists.


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Author contact

Corresponding address Corresponding authors: Dr. Chang-Xiang Yan, Department of Neurosurgery, Capital Medical University, Building 1, Ward 6, Xiang Shan Yi Ke Song Road 50, Haidian, Beijing, China. Phone: 0086-010-62856706. Email: yancxedu.cn. Dr. Ning Liu, Department of Neurosurgery, Capital Medical University, Building 1, Ward 6, Xiang Shan Yi Ke Song Road 50, Haidian, Beijing, China. Email: liuning301com

Received 2018-11-18
Accepted 2019-5-8
Published 2019-7-10

Citation styles

Wang, P.F., Song, S.Y., Guo, H., Wang, T.J., Liu, N., Yan, C.X. (2019). Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies. Journal of Cancer, 10(18), 4305-4317. https://doi.org/10.7150/jca.31598.

Wang, P.F.; Song, S.Y.; Guo, H.; Wang, T.J.; Liu, N.; Yan, C.X. Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies. J. Cancer 2019, 10 (18), 4305-4317. DOI: 10.7150/jca.31598.

Wang PF, Song SY, Guo H, Wang TJ, Liu N, Yan CX. Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies. J Cancer 2019; 10(18):4305-4317. doi:10.7150/jca.31598. https://www.jcancer.org/v10p4305.htm

Wang PF, Song SY, Guo H, Wang TJ, Liu N, Yan CX. 2019. Prognostic role of pretreatment red blood cell distribution width in patients with cancer: A meta-analysis of 49 studies. J Cancer. 10(18):4305-4317.

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