The value of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and hemoglobin-to-red blood cell distribution width ratio in the progression of non-small cell lung cancer

Jin-liang Chen; Jin-nan Wu; Xue-dong Lv; Qi-chang Yang; Jian-rong Chen; Dong-mei Zhang

doi:10.1371/journal.pone.0237947

. 2020 Aug 24;15(8):e0237947. doi: 10.1371/journal.pone.0237947

The value of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and hemoglobin-to-red blood cell distribution width ratio in the progression of non-small cell lung cancer

Jin-liang Chen ^1,^#, Jin-nan Wu ^2,^#, Xue-dong Lv ¹, Qi-chang Yang ³, Jian-rong Chen ^1,^*, Dong-mei Zhang ^4,^*

Editor: Jeffrey Chalmers⁵

PMCID: PMC7446848 PMID: 32833961

Abstract

Background

Lung cancer is the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer (NSCLC) accounting for 85% of all lung cancer cases. Inflammation has been proven to be one of the characteristics of malignant tumors. Chronic inflammatory response mediated by cytokines in the tumor microenvironment is an important factor in tumorigenesis. The purpose of this study was to observe and evaluate the value of red blood cell distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), and hemoglobin-to-red blood cell distribution width ratio (HRR) in the progression of NSCLC.

Methods

A total of 245 patients with NSCLC, 97 patients with benign pulmonary nodules, and 94 healthy volunteers were included in this study. Factors, such as age, gender, smoking history, histological type, lymph node metastasis, distant metastasis, TNM stage, and differentiation degree were statistically analyzed. The correlation of RDW, NLR, and HRR of patients with NSCLC with other clinical experimental parameters were also analyzed. Then, the diagnostic value of RDW, NLR, and HRR in the progression of NSCLC was evaluated.

Results

RDW, NLR, and HRR could be used to distinguish patients with NSCLC from healthy controls (p < 0.05). In addition, only the RDW in the NSCLC group with III-IV stage was significantly different from that in the benign pulmonary nodules group (p = 0.033), while NLR and HRR could significantly distinguish patients with NSCLC and benign pulmonary nodules (p < 0.001). RDW and NLR were positively correlated with NSCLC stage, whereas HRR was negatively correlated with NSCLC stage. RDW, NLR, and HRR were also significantly associated with the differentiation degree of NSCLC (p < 0.05). The ROC curve analysis showed that the combination of RDW with NLR, HRR, and CEA could show significantly higher diagnostic value than any one marker alone (AUC = 0.925, 95% CI: 0.897–0.954, and sensitivity and specificity of 79.60% and 93.60%, respectively).

Conclusion

RDW, NLR, and HRR can be utilized as simple and effective biomarkers for the diagnosis and evaluation of NSCLC progression.

Introduction

Lung cancer is the leading cause of cancer-related deaths worldwide and has the highest death rate among all malignancies [1]. Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer cases [2]. The vast majority of patients with NSCLC were diagnosed in the middle and advanced stages, and their 5-year overall survival rate was less than 20% [3]. This is mainly because the late diagnosis and poor treatment of NSCLC. Inflammation has been proven to be the eighth feature of malignant tumors [4]. Chronic inflammatory response mediated by cytokines in the tumor microenvironment is an important factor in tumorigenesis [5]. Considering that human lungs are susceptible to a variety of poisons and pathogens, chronic injuries and inflammation may occur, which may cause lung cancer [6].

Routine blood test for lung cancer is a prerequisite for the hospital admission of patients. Neutrophils are a type of white blood cells that play an important role in the defense and protection of the body. Lymphocytes are another type of white blood cells and an quite important component of the body’s immune response. Neutrophils and lymphocytes have been shown to be widely involved in cancer progression [7–9]_. Studies have demonstrated that neutrophil-to-lymphocyte ratio (NLR) is a simple and easily accessible inflammation biomarker that can be potentially used to predict cancer progression and prognosis [10–12]. Red blood cell distribution width (RDW) mainly reflects the heterogeneity of red blood cell volume, and is usually applied to diagnose and distinguish types of anemia. Recent studies have found that RDW is related to the systemic inflammatory response and nutritional status of the body, and this relationship may have implications for tumor occurrence and development in the body [12–14]. Hemoglobin-to-red blood cell distribution width ratio (HRR) is a new biomarker. HRR was first proposed to have predictive value for cancer by Sun et al. [15]. They found that HRR had important implications in evaluating the prognosis of patients with esophageal squamous cell carcinoma. In addition, HRR has been reported in other types of tumors in recent years, such as head and neck tumors and small cell lung cancer [16,17].

Therefore, the purpose of this study was to conduct a retrospective study based on routine clinical blood test results of patients with NSCLC to explore and evaluate whether RDW, NLR, and HRR can be used as simple, cheap and effective biomarkers for the diagnosis and assessment of clinical progression conditions in NSCLC.

Materials and methods

1. Patients

A total of 245 patients with NSCLC who were admitted to the Second Affiliated Hospital of Nantong University from June 2016 to June 2019 were diagnosed through lung tissue or lymph node biopsy, pleural effusion cytology and pathological examination. The participants had not received radiochemotherapy, immune-targeted therapy or surgery prior to selection. A total of 97 patients with benign pulmonary nodules who were admitted to the Second Affiliated Hospital of Nantong University at the same time were selected and confirmed by histopathology. These patients had no history of acute and chronic infectious diseases, no history of malignant tumors, and no important systemic organ diseases. In addition, 94 volunteers who underwent physical examination in the same period were selected as the healthy control group. The healthy volunteers had no acute and chronic infectious diseases; no vital organ diseases; and no genetic family tumor history. The details of the number of people in each group are shown in Fig 1. Tumor staging was performed in accordance with the lung cancer TNM staging guidelines published by the Union for International Cancer Control in 2018. This study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Nantong University. All patients signed the informed written consent. The electronic medical record system was used to collect baseline characteristics, including patient age, gender, smoking status, performance status, and medical history, as well as the statistics and analysis of clinical blood routines, tumor screening, and other test data.

2. NLR and HRR calculations

The following equations were used: NLR = neutrophil count (×10⁹/L) / lymphocyte count (×10⁹/L), HRR = hemoglobin (g / L) / red blood cell distribution width (%).

3. Statistical analysis

All data were analyzed using the statistical software IBM SPSS Statistics 22.0 and the graphing software Graphpad Prism 8.0. All data were tested by Kolmogorov-Smirnov test or Shapiro-Wilk test to determine whether they were normally distributed. For normally distributed data, the t-test was used to compare the mean between two groups with equal variances. When the variance was not the same, the t’test (Satterthwaite method) was used. For non-normally distributed data, use the non-parametric test (Mann-Whitney U test), and described as the median value (interquartile range) [M (Q1-Q3)]. Count data were compared using the χ2 test. Correlation analysis between two variables was performed using linear correlation analysis. Pearson correlation analysis was used for data that corresponded to bivariate normal distribution, and Spearman correlation analysis was used for data that fitted non-parametric distribution. The diagnostic value of RDW, NLR and HRR for NSCLC was analyzed using ROC curves. p <0.05, the difference was statistically significant.

Results

1. Baseline characteristics of all participants

A total of 245 patients with NSCLC were included in this study. Among these patients, 107 had squamous cell carcinoma and 138 had adenocarcinoma. In addition, 71.43% of the patients with NSCLC were in TNM III-IV stage, and 68.16% of the patients had poorly differentiated tumors (Table 1). The study also included 97 patients with benign pulmonary nodules and 94 healthy volunteers. No difference in age and sex was observed among the three groups, but between NSCLC patients and benign pulmonary nodule patients, smoking history (p < 0.05) was closely related (Table 2).

Table 1. Demographic and clinical characteristics of NSCLC patients(n = 245).

Variables	NSCLC patients
Age (years)	66(58–73)
Male n(%)	132(53.88%)
Smoking history n(%)	134(54.69%)
Squamous cell carcinoma n(%)	107(43.67%)
Lymph node metastasis n(%)	156(63.67%)
Distant metastasis n(%)	129(52.65%)
TNM Ⅲ-Ⅳ stage n(%)	175(71.43%)
Poorly differentiation n(%)	167(68.16%)

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Data were expressed as number (%) and median (interquartile range, 25th–75th).

NSCLC non-small cell lung cancer.

Table 2. Baseline characteristics of all participants.

Variables	Normal range	NSCLC patients	Benign lung nodules patients	Healthy volunteers	P value
Number		245	97	94
Clinical characteristics
Age (years)	-	66(58–73)	65(56–69)	64(49.75–78.00)	0.052
Gender (male/female)	-	132/113	53/44	40/54	0.062
Smoking history(yes/no)	-	134/111^a	38/59	27/67^c	<0.05
Laboratory parameters
Leukocytes, ×10⁹/L	3.5–9.5	6.40(5.35–8.10)^a	5.20(4.35–6.00)^b	5.70(4.90–6.43)^c	<0.05
Neutrophils, ×10⁹/L	1.8–6.3	4.30(3.31–5.85)^a	2.80(2.40–3.50)	3.15(2.70–3.85)^c	<0.001
Lymphocytes, ×10⁹/L	1.1–3.2	1.40(1.00–1.80)^a	1.80(1.40–2.10)^b	1.95(1.60–2.23)^c	<0.05
Hemoglobin, g/L	115–150	132(117–141)^a	140(132–152)	137.5(131–151.3)^c	<0.001
RDW, %	10.0–15.0	13.4(12.90–14)	13.2(12.80–13.85)^b	12.9(12.70–13.50)^c	<0.05
NLR	-	3.21(2.21–5.34)^a	1.73(1.51–2.08)	1.63(1.33–2.00)^c	<0.001
HRR	-	9.853(8.67–10.85)^a	10.48(9.85–11.45)	10.75(9.78–11.57)^c	<0.001
CEA, ng/mL	-	4.06(2.53–16.63)^a	2.02(1.49–2.55)^b	1(0.6–1.93)^c	<0.001

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Data were expressed as number and median (interquartile range, 25th–75th).

Binary logistic regression analysis with adjustment age and gender was used to control confounding factors.

RDW red blood cell distribution width; NLR neutrophil-to-lymphocyte ratio; HRR hemoglobin-to-red blood cell distribution width ratio; CEA carcinoembryonic antigen; NSCLC non-small cell lung cancer.

P values were calculated by Kruskai-Wallis tests.

^a Indicates a significant difference (P < 0.05) between NSCLC patients and Benign lung nodules patients.

^b Indicates a significant difference (P < 0.05) between Benign lung nodules patients and Healthy volunteers.

^c Indicates a significant difference (P < 0.05) between NSCLC patients and Healthy volunteers.

2. Expression levels of RDW, NLR, and HRR in the NSCLC patient group, benign pulmonary nodules patient group, and healthy volunteer group

Leukocyte count, neutrophil count, and CEA in patients with NSCLC were significantly higher than those in patients with benign lung nodules and healthy controls (Table 2). The median of RDW in the group of patients with NSCLC was 13.4%, which was significantly higher than that in the healthy control group (12.9%) (p < 0.05) (Table 2). However, compared with the RDW of the patients with benign pulmonary nodules, that of NSCLC patients with I-II stage was no statistically different, whereas that of the patients with III-IV stage was significantly different (p = 0.033) (Fig 2A). We then further calculated the NLR and HRR values of each group. As can be seen in Table 2, the NLR of the group of patients with NSCLC was significantly higher than that of the healthy control group and the benign pulmonary nodule group (p < 0.001). By contrast, the level of HRR in patients with NSCLC was significantly lower than that in patients with benign pulmonary nodules and healthy volunteers (p < 0.001).

In addition, we also found that NSCLC patients with I-II stage had significantly lower RDW values than patients with III-IV stage (p = 0.022, Fig 2A). Similarly, the NLR value of NSCLC patients with III-IV stage was significantly higher than that of patients with I-II stage (p < 0.001, Fig 2B). By contrast, for the levels of HRR, NSCLC patients with III-IV stage were significantly lower than patients with I-II stage (p < 0.001, Fig 2C). In addition, compared with the healthy control group, patients with benign pulmonary nodules did not have significantly different levels of NLR and HRR (Fig 2B and 2C), but had significantly different RDW (p = 0.038, Fig 2A).

The correlation of NSCLC stage with RDW, NLR, and HRR is shown in Fig 3A–3C. Correlation analysis showed that NLR was positively correlated with NSCLC stage (r = 0.212, p < 0.001), whereas HRR was negatively correlated with the cancer stage (r = -0.233, p < 0.001). In addition, there was a very weak correlation between RDW and NSCLC stage (r = 0.128, p = 0.045).

3. Correlation between clinicopathological features and RDW, NLR, and HRR in 245 NSCLC cases

ROC curves were drawn on the basis of the RDW, NLR, and HRR values of patients with NSCLC and healthy volunteers. The Youden ‘s index (sensitivity + specificity -1) was used to determine the cut-off value. Results showed that the optimal cut-off values of RDW, NLR, and HRR were 13.25, 2.14, and 9.48, respectively, and the area under the curve (AUC) of RDW, NLR, and HRR were 0.629, 0.846, and 0.696, respectively (Fig 4). Therefore, we divided the patients with NSCLC into two groups in accordance with the optimal cut-off values of RDW, NLR, and HRR, and then analyzed the relationship of these parameters with the clinicopathological characteristics of the patients. The results were shown in Table 3. The RDW ≥ 13.25 group had more patients with NSCLC with age ≥64 years (p = 0.002), squamous cell carcinoma (p = 0.037), TNM III-IV stage (p = 0.022), and poor tumor differentiation (p = 0.004) than the RDW < 13.25 group. Similarly, compared with those in the NLR < 2.14 group, patients with NSCLC in the NLR ≥ 2.14 group had higher age (p = 0.012), larger tumor tissue (p = 0.002), higher degree of lymph node metastasis (p = 0.002), farther distant metastases (p < 0.001), higher tumor stage (p < 0.001), and poorer tumor differentiation (p < 0.001). In addition, apart from smoking history, HRR and age (p = 0.016), gender (p = 0.009), histology (p = 0.001), tumor size (p <0.001), lymph node metastasis (p <0.001), distant metastasis (p = 0.036), TNM stage (p <0.001), and differentiation degree (p <0.001) were significantly correlated.

Fig 4 — Area under ROC curves were 0.629, 0.846, 0.696 and 0.887, respectively. P values were all <0.001. RDW red blood cell distribution width; NLR neutrophil-to-lymphocyte ratio; HRR hemoglobin-to-red blood cell distribution width ratio; CEA carcinoembryonic antigen; ROC receiver operating characteristic.

Table 3. Correlation between clinicopathological features and RDW, NLR, and HRR in 245 all non-small cell lung cancers.

	RDW		P value	NLR		P value	HRR		P value
	<13.25	≥13.25	P value	<2.142	≥2.142	P value	<9.481	≥9.481	P value
Number	104	141		55	190		95	150
Age (years)
<64	53	43	0.002	30	66	0.012	28	68	0.016
≥64	51	98	0.002	25	124	0.012	67	82	0.016
Gender
Male	56	76	>0.999	27	105	0.445	41	91	0.009
Female	48	65	>0.999	28	85	0.445	54	59	0.009
Smoking history
Yes	52	79	0.367	25	106	0.219	45	86	0.149
No	52	62	0.367	30	84	0.219	50	64	0.149
Histology
Squamous cell carcinoma	37	70	0.037	20	87	0.222	55	52	0.001
Adenocarcinoma	67	71	0.037	35	103	0.222	40	98	0.001
Tumor size
T1	31	30	0.470	24	37	0.002	8	53	<0.001
T2	39	63		20	82		45	57
T3	13	18		5	26		15	16
T4	21	30		6	45		27	24
Lymph node metastasis
N0	45	45	0.169	32	58	0.002	18	72	<0.001
N1	8	7		4	11		4	11
N2	19	36		8	47		23	32
N3	32	53		11	74		50	35
Distant metastasis
M0	53	64	0.438	38	79	<0.001	37	80	0.036
M1	51	77	0.438	17	111	<0.001	58	70	0.036
TNM stage
Ⅰ-Ⅱ	38	32	0.022	31	39	<0.001	12	58	<0.001
Ⅲ-Ⅳ	66	109	0.022	24	151	<0.001	83	92	<0.001
Degree of differentiation
Well/moderated	44	34	0.004	33	45	<0.001	13	65	<0.001
Poorly	60	107	0.004	22	145	<0.001	82	85	<0.001

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Data were expressed as number.

Binary logistic regression analysis with adjustment age and gender was used to control confounding factors.

RDW red blood cell distribution width; NLR neutrophil-to-lymphocyte ratio; HRR hemoglobin-to-red blood cell distribution width ratio.

P values were calculated by χ2 tests.

P<0.05 was considered significant.

4. Diagnostic value of RDW, NLR, HRR, and CEA in NSCLC

We used ROC curves to analyze and evaluate the diagnostic value (equal to the area under the curve, AUC) of individual and combined biomarkers (Table 4). Compared with NLR, HRR, and CEA, the diagnostic value of RDW was lower, with the AUC of 0.629 (95% CI: 0.565–0.692), p < 0.001, sensitivity and specificity of 63.83% and 57.55%, respectively. NLR and HRR were more sensitive to distinguish NSCLC patients from healthy volunteers (82.98% and 91.49%, respectively), while NLR and CEA were more specific (77.55% and 83.27%, respectively). When NLR and HRR were used in combination, the specificity of diagnosis increased significantly (93.60%). In addition, the combination of RDW with NLR, HRR, and CEA could show significantly higher diagnostic value compared with the use of any one marker alone, then the AUC was 0.925 (95% CI: 0.897–0.954), sensitivity and specificity were 79.60% and 93.60%, respectively.

Table 4. Diagnostic value of RDW, NLR, HRR and CEA in non-small cell lung cancers.

	Cut-off	Sensitivity,%	Specificity,%	AUC	95%CI
RDW	13.25	63.83	57.55	0.629	0.565–0.692
NLR	2.14	82.98	77.55	0.846	0.804–0.888
HRR	9.48	91.49	38.78	0.698	0.637–0.755
CEA	2.01	76.60	83.27	0.887	0.849–0.924
RDW+NLR	-	74.70	88.30	0.849	0.808–0.891
RDW+HRR	-	38.40	91.50	0.695	0.636–0.754
NLR+HRR	-	70.20	93.60	0.852	0..811–0.893
RDW+NLR+HRR	-	72.70	88.30	0.851	0.810–0.892
RDW+NLR+HRR+CEA	-	79.60	93.60	0.925	0.897–0.954

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5. Correlation analysis among the levels of RDW, NLR, and HRR

Correlation analysis showed that there was a very weak correlation between RDW and NLR (r = 0.160, p = 0.012; Fig 5A). In addition, we found a strong negative correlation between RDW and HRR (r = -0.667, p < 0.001; Fig 5B). Similarly, a negative correlation was found between HRR and NLR (r = -0.239, p < 0.001; Fig 5C).

Discussion

The analysis of global cancer epidemiology has revealed that lung cancer is the main cause of cancer-related deaths worldwide [1]. Therefore, actively searching for relatively simple and rapid biomarkers related to early lung cancer diagnosis is of great importance. In this study, we investigated the relationship of RDW, NLR, and HRR with the clinical and biological characteristics of NSCLC, and found that RDW, NLR, and HRR can be used as effective biomarkers for the diagnosis of NSCLC.

In this retrospective study, we applied logistic regression to adjust the gender and age in the comparison of the medians of groups because we considered that the age and gender of each group as possible confounding factors. Our study found that RDW, NLR, and HRR can be used to distinguish patients with NSCLC from healthy volunteers. In addition, only the RDW in the NSCLC group with III-IV stage was significantly different from that in the benign pulmonary nodules group, while NLR and HRR could significantly distinguish patients with NSCLC and benign pulmonary nodules. We also found that NSCLC patients with III-IV stage had significantly higher RDW and NLR and significantly lower HRR than patients with I-II stage. In addition, we divided patients with NSCLC into two groups on the basis of the optimal cut-off values of RDW, NLR, and HRR, and compared their relationship with the clinicopathological characteristics of NSCLC. Our results showed that RDW was significantly correlated with age, histology, TNM stage, and differentiation degree. NLR was significantly correlated with age, tumor size, lymph node metastasis, distant metastasis, TNM stage, and differentiation degree. In addition to smoking history, HRR was significantly correlated with age, gender, histology, tumor size, lymph node metastasis, distant metastasis, TNM stage, and differentiation degree.

CEA, a classic tumor marker, can not only indicate the occurrence and development of lung cancer, but also be directly related to tumor infiltration and metastasis. CEA can well reflect the biological activities of tumor cells, such as proliferation, infiltration, invasion, migration and other capabilities [18–20]. However, we found that CEA had a high significance in the diagnosis of patients with benign pulmonary nodules, which has a certain interference effect in the diagnosis of lung cancer. Therefore, we considered combining multiple biomarkers to improve the effective diagnosis rate of NSCLC. Through the analysis of ROC curves, we found that the combination of RDW with NLR, HRR, and CEA could show significantly higher diagnostic value, larger AUC area, and higher sensitivity and specificity than any one marker alone. In addition, correlation analysis showed that there was a certain correlation among RDW, NLR, and HRR. Therefore, RDW, NLR and HRR can be used to diagnose NSCLC and assess progression well.

The "tumor microenvironment" concept can be traced back to the "seed and soil" hypothesis proposed by Stephen Paget in 1889 [21]. This hypothesis states that tumor cells can remodel the tumor microenvironment, and in turn tumor microenvironment remodeling can further affect the biological behavior of tumor cells. A growing body of evidence shows that the host’s systemic response to tumor cells can cause inflammation, and systemic inflammation plays an indispensable role in the occurrence and development of various tumors [22]. On the one hand, systemic inflammatory response stimulates the immune process of the body, thereby inhibiting tumor development. On the other hand, it may promote the growth, malignancy and metastasis of tumor cells by disrupting the balance of the internal environment, suppressing immune response, inhibiting apoptosis and activating angiogenesis [23]. Our research on RDW, NLR, and HRR confirms the concept of "tumor microenvironment".

RDW is an evaluation index that reflects the changes in the sizes of the red blood cells involved in the circulatory response in peripheral blood. Similar to hemoglobin, RDW is often used to diagnose and differentiate types of anemia. Many recent studies have shown that RDW is closely related to the occurrence or prognosis of various cancers [12–14]. However, the exact mechanism of this connection has not been fully elucidated. Most studies have found that RDW is closely related to a variety of inflammatory markers, such as C-reactive protein, interleukin-6, and tumor necrosis factor. This relationship indicates that the cancer-mediated inflammatory microenvironment can lead to increased RDW expression in the body [24]. The inflammatory microenvironment may cause damage to iron metabolism in the body and inhibit erythropoietin production. These effects result in the entry of a large number of immature red blood cells from the bone marrow into the peripheral blood circulation. This phenomenon will eventually lead to variations in the sizes of the red blood cells involved in the circulatory response in the peripheral blood, as manifested by increased RDW levels [25]. Moreover, the increase in RDW may be related to oxidative stress response in the body [26]. Red blood cells, the most important oxygen-carrying medium in the body, will induce hypoxia or a hypoxic microenvironment when they differ in size or their functions are impaired. This microenvironment may accelerate the formation of tumor neovascularization by stimulating the production of vascular endothelial growth factor (VEGF) in the body [27]. Hypoxia or a hypoxic microenvironment can also induce the expression of the hypoxia-inducible factor (HIF) family, which may lead to tumor progression, cancer aggravation or poor prognosis [28]. VEGF and HIFs are widely involved in the tumor-mediated inflammatory microenvironment [29]. In addition, RDW has been found to be associated with malnutrition. The occurrence and development of cancer will cause the body to enter a cachexia state, which is characterized by the deficiency of iron, folic acid, and vitamin B12, and then affect the level of RDW in the body [30]. Therefore, RDW can reflect the systemic inflammatory response of cancer and the nutritional status of the body.

HRR, as a new type of biomarker, was first proposed to have predictive value for cancer by Sun et al. [15]. Sun et al. found that there was no significant difference in the overall survival period of patients with esophageal squamous cell carcinoma when they used hemoglobin and RDW as prognostic factors. However, they found that there was a significant association between HRR and the survival outcomes of patients with esophageal squamous cell carcinoma. Thus, HRR can be used as an important prognostic factor that is unaffected by other risk factors. Our study found that RDW had significant difference only between the NSCLC group with III-IV stage and the benign pulmonary nodule group, whereas HRR could be used to significantly distinguish patients with NSCLC and patients with benign pulmonary nodules. Moreover, HRR had higher diagnostic value and higher sensitivity than RDW. Therefore, we believe that HRR has great importance in the diagnosis of NSCLC. In addition, both hemoglobin and RDW may be affected by various diseases other than cancer [15–17], while HRR can minimize the potential effects, and theoretically reflect the systemic inflammatory response and nutritional status of the body. Therefore, HRR can be used as a relatively more reliable biomarker in the assessment of cancer progression.

NLR reflects the quantitative relationship between neutrophils and lymphocytes in the body. Neutrophils are derived from bone marrow stem cells, and account for about 50% - 70% of the total number of leukocytes in the peripheral blood. They are considered as the main immune cells that protect the body from microbial infection and eliminate pathogens [31]. A large number of studies have shown that under the interaction of tumor cells and the tumor microenvironment, neutrophils can reshape their own phenotype and function, and then participate in the occurrence and development of tumor through a variety of mechanisms, including promoting the proliferation, migration and invasion capabilities of tumor cells, and the formation of new blood vessels in tumor cells [8,32]. In contrast to neutrophils, lymphocytes play a key role in the body’s protective immunity by inhibiting the proliferation and migration of tumor cells. Lymphocytes can induce the death of cytotoxic cells, and produce cytokines, such as interleukin-4 and interleukin-12, that inhibit the proliferation and metastasis of tumor cells [33,34]. Therefore, it is believed that the reduction in the number of peripheral blood lymphocytes may lead to the attenuation or disappearance of the immune response to tumor, and thus promote the occurrence and development of tumor. All in all, NLR can be used as a reliable indicator to reflect the systemic inflammatory response in tumor progression in vivo.

This study has several limitations. First, it involved retrospective analysis, which included the manual extraction and input of clinical data. Although there was no missing or erroneous data for laboratory values or basic patient information in our analysis, patient selection bias is a possible risk. Second, this study did not evaluate various potential confounding factors, such as severe infections and local ischemia, that may affect peripheral blood cell counts. Thirdly, due to some constraints, the effect of age and smoking on the indicators was not fully evaluated in this study. Therefore, a large-scale prospective study is warranted to confirm the results of the current study.

Conclusions

In conclusion, RDW, NLR, and HRR can be used as simple, cheap, and effective biomarkers for diagnosing and evaluating the progress of patients with NSCLC in clinical practice, which will be helpful for clinicians to select the appropriate clinical treatment options and predict disease prognosis.

Supporting information

S1 Data

(XLSX)

Click here for additional data file.^{(42.1KB, xlsx)}

Abbreviations

NSCLC: non-small cell lung cancer
RDW: red blood cell distribution width
NLR: neutrophil-to-lymphocyte ratio
HRR: hemoglobin-to-red blood cell distribution width ratio
CEA: carcinoembryonic antigen
ROC: receiver operating characteristic
AUC: area under curve
CI: confidence interval
VEGF: vascular endothelial growth factor
HIF: hypoxia-inducible factor

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

Jin-liang Chen: the Natural Science Foundation of Jiangsu Province (BK20191207); Jin-nan Wu: Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX19_0873); Dong-mei Zhang: the Scientific Research Project of Health Commission of Jiangsu Province (H2018035); the Scientific Research Project of “333 Project” in Jiangsu Province (BRA2018224); Jian-rong Chen: the Science and Technology Program of Nantong City (Grant No. HS2018002). Jin-liang Chen: Conceptualization, Methodology, Software, Investigation; Jin-nan Wu: Formal analysis, Data curation, Software, Investigation, Writing-Original draft preparation; Xue-dong Lv: Visualization, Investigation; Qi-chang Yang: Supervision; Dong-mei Zhang: Software, Validation; Jian-rong Chen: Project administration, Writing-Reviewing and Editing. The authors thank all the staff members in our institution.

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PLoS One. doi: 10.1371/journal.pone.0237947.r001

Decision Letter 0

Jeffrey Chalmers

13 Jul 2020

PONE-D-20-13045

The value of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and hemoglobin-to-red blood cell distribution width ratio in the progression of non-small cell lung cancer

PLOS ONE

Dear Dr. Wu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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PLOS ONE

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Reviewer #1: Yes

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Reviewer #1: This manuscript reports the value of several hematologic parameters in the progression of non-small cell lung cancer (NSCLC). Red blood cell distribution width (RDW), neutrophil-to-lymphocyte ratio (NLR), and hemoglobin-to-red blood cell distribution (HRR) values were evaluated in 245 patients with NSCLC, and compared to 97 patients with benign nodules and to 94 healthy volunteers. Results showed that RDW, NLR, and HRR could be used to distinguish patients with NSCLC from healthy controls. Furthermore, NLR and HHR could be used to distinguish between patients with NSCLC and benign nodules, while RDW can be used for distinguishing the NSCLC group at III-IV stage. As these 3 parameters are available in any hematology analyzer, and are fast and cheap to obtain, the information reported in this study can be very useful for those studying new diagnostic tools to predict cancer progression and prognosis. Therefore, I think this manuscript merits publication in Plos One. In the following, authors can find minor comments that can help to improve the quality of the manuscript:

- Page 10: “The healthy volunteers had no acute and chronic infectious diseases; no vital organ diseases. such as heart, liver, kidney and digestive system diseases”. Please, remove point before “such”.

- Tables 1, 2 and 3. Please correct the typographical error in “smoking history”.

- Page 12: “Leukocyte count, neutrophil count, lymphocyte count, hemoglobin, and CEA in patients with NSCLC were significantly higher than those in patients with benign lung nodules and healthy controls”. Please, correct the sentence. Lymphocyte count and hemoglobin are lower among patients with NSCLC, which is the primary reason why NLR is high and HRR is low for these patients.

- I recommend the authors to include either the percentage of patients with abnormal hematological parameters in Table 2, or to include the normal values of these parameters in the table (as another column titled normal range).

- Hemoglobin normal range varies with gender and smoking status. Proof of this is that the only parameter correlated to smoking history and gender is HRR (none of the other 2 parameters are correlated to these clinical characteristics). I wonder if the authors can safely apply this parameter as a diagnostic tool independently of gender and smoking history. Please, discuss this issue.

**********

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Reviewer #1: No

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PLoS One. 2020 Aug 24;15(8):e0237947. doi: 10.1371/journal.pone.0237947.r002

Author response to Decision Letter 0

3 Aug 2020

Dear editors:

Thank you very much for your letter and the comments from the referees about our paper submitted to PLOS ONE (PLOS ONE manuscript number: PONE-D-20-13045).We have checked the manuscript and revised it according to the comments. We submit here the revised manuscript as well as a list of changes.

Part I Responses to Editor

1.Please ensure that your manuscript meets PLOS ONE’s style requirements, including those for file naming.

Reply: We have made modifications in strict accordance with the PLOS ONE’s style requirements. If there are any errors, please don’t hesitate to let us know.

2.In the ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records used in your retrospective study, including: a) whether all data were fully anonymized before you accessed them; b) the date range (month and year) during which patients' medical records were accessed. If patients provided informed written consent to have data from their medical records used in research, please include this information.

Reply: We can provide additional information about the patient records used in our retrospective study, including: a) all data were fully anonymized before we accessed them; b) the date range (month and year) during which patients' medical records were accessed was June 2016 to June 2019. In addition, we can provide the patients’ informed written consent to have data from their medical records used in research. We have uploaded the informed written consent and related materials of patients in the form of attachment.

3.PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly.

Reply: All relevant data are within the manuscript and its Supporting Information files.

4.Please upload a copies of Figures 1 to 5, to which you refer in your text.

Reply: We have uploaded a copies of Figures 1 to 5.

Part II Responses to Referee 1

1.Page 10: “The healthy volunteers had no acute and chronic infectious diseases; no vital organ diseases. such as heart, liver, kidney and digestive system diseases”. Please, remove point before “such”.

Reply: We have revised the description of this sentence. Now the sentence is “The healthy volunteers had no acute and chronic infectious diseases; no vital organ diseases; and no genetic family tumor history”.

2.Tables 1, 2 and 3. Please correct the typographical error in “smoking history”.

Reply: We have corrected the typographical error in “smoking history”.

3.Page 12: “Leukocyte count, neutrophil count, lymphocyte count, hemoglobin, and CEA in patients with NSCLC were significantly higher than those in patients with benign lung nodules and healthy controls”. Please, correct the sentence. Lymphocyte count and hemoglobin are lower among patients with NSCLC, which is the primary reason why NLR is high and HRR is low for these patients.

Reply: We have revised the description of this sentence. Now the sentence is “Leukocyte count, neutrophil count, and CEA in patients with NSCLC were significantly higher than those in patients with benign lung nodules and healthy controls”.

4.Recommend the authors to include either the percentage of patients with abnormal hematological parameters in Table 2, or to include the normal values of these parameters in the table (as another column titled normal range).

Reply: We have include the normal values of these parameters in the table (as another column titled normal range).

5.Hemoglobin normal range varies with gender and smoking status. Proof of this is that the only parameter correlated to smoking history and gender is HRR (none of the other 2 parameters are correlated to these clinical characteristics). I wonder if the authors can safely apply this parameter as a diagnostic tool independently of gender and smoking history. Please, discuss this issue.

Reply: In this article, we divided the patients with NSCLC into two groups in accordance with the optimal cut-off value of HRR, and then analyzed the relationship of HRR with the clinicopathological characteristics of the patients. Therefore, apart from smoking history, HRR and age (p = 0.016), gender (p = 0.009), histology (p = 0.001), tumor size (p <0.001), lymph node metastasis (p <0.001), distant metastasis (p = 0.036), TNM stage (p <0.001), and differentiation degree (p <0.001) were significantly correlated.

Then we considered that the age and gender of each group as possible confounding factors, so we applied logistic regression to adjust the gender and age in the comparison of the medians of groups. And then We divided 245 NSCLC patients into smoking group and non-smoking group according to smoking history, and compared the difference of HRR between the two groups: 9.921(8.593-10.92) vs 9.816(8.694-10.70), p = 0.4462, indicated no significant difference. Then we divided 245 NSCLC patients into male group and female group according to gender, and compared the difference of HRR between the two groups: p > 0.05, indicated no significant difference. We have had to admit that due to some constraints, the effect of age, gender, and smoking on the indicators was not fully evaluated in this study. Therefore, a large-scale prospective study is warranted to confirm the results of the current study.

All the lines and pages indicated above are in the revised manuscript.

Thank you and all the reviewers for the kind advice. If you have any question about this paper, please don’t hesitate to let me know.

Thank you and best regards.

Yours sincerely,

Jinnan Wu.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(16.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0237947.r003

Decision Letter 1

Jeffrey Chalmers

6 Aug 2020

The value of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and hemoglobin-to-red blood cell distribution width ratio in the progression of non-small cell lung cancer

PONE-D-20-13045R1

Dear Dr. Wu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Jeffrey Chalmers, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0237947.r004

Acceptance letter

Jeffrey Chalmers

12 Aug 2020

PONE-D-20-13045R1

The value of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and hemoglobin-to-red blood cell distribution width ratio in the progression of non-small cell lung cancer

Dear Dr. Wu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Jeffrey Chalmers

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Data

(XLSX)

Click here for additional data file.^{(42.1KB, xlsx)}

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

The value of red blood cell distribution width, neutrophil-to-lymphocyte ratio, and hemoglobin-to-red blood cell distribution width ratio in the progression of non-small cell lung cancer

Jin-liang Chen

Jin-nan Wu

Xue-dong Lv

Qi-chang Yang

Jian-rong Chen

Dong-mei Zhang

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

1. Patients

Fig 1. The flow chart of the study.

2. NLR and HRR calculations

3. Statistical analysis

Results

1. Baseline characteristics of all participants

Table 1. Demographic and clinical characteristics of NSCLC patients(n = 245).

Table 2. Baseline characteristics of all participants.

2. Expression levels of RDW, NLR, and HRR in the NSCLC patient group, benign pulmonary nodules patient group, and healthy volunteer group

Fig 2.

Fig 3.

3. Correlation between clinicopathological features and RDW, NLR, and HRR in 245 NSCLC cases

Fig 4. The ROC curves for RDW, NLR, HRR and CEA.

Table 3. Correlation between clinicopathological features and RDW, NLR, and HRR in 245 all non-small cell lung cancers.

4. Diagnostic value of RDW, NLR, HRR, and CEA in NSCLC

Table 4. Diagnostic value of RDW, NLR, HRR and CEA in non-small cell lung cancers.

5. Correlation analysis among the levels of RDW, NLR, and HRR

Fig 5.

Discussion

Conclusions

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Jeffrey Chalmers

Roles

Author response to Decision Letter 0

Decision Letter 1

Jeffrey Chalmers

Roles

Acceptance letter

Jeffrey Chalmers

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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