Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

The prognostic impact of decreased pretreatment haemoglobin level on the survival of patients with lung cancer: a systematic review and meta-analysis

BMC Cancer volume 18, Article number: 1235 (2018) Cite this article

Abstract

Background

Many studies have reported the prognostic value of haemoglobin level for cancers. Whereas the prognostic impact of decreased pretreatment haemoglobin level on the survival of patients with lung cancer remains controversial, herein, a systematic review and meta-analysis were conducted to investigate whether a decreased haemoglobin level before treatment is a significant predictor of survival in patients with lung cancer.

Methods

We performed a systematic review and meta-analysis of observational studies to evaluate the prognostic impact of a decreased haemoglobin level on the survival of patients with lung cancer. Relevant studies were retrieved from databases including PubMed, Embase, Web of Science and the Cochrane Library. Reference lists were hand-searched for potentially eligible studies. The Newcastle-Ottawa scale was used to assess the quality of included studies. Observational studies were included if they provided sufficient information for the extraction of the pooled hazard ratios (HR) and 95% confidence intervals (95% CI) for overall survival, disease-free survival, relapse-free survival, progression-free survival, event-free survival and time to progression. Subgroup analysis, meta-regression and sensitivity analyses were applied to explain the heterogeneity.

Results

Fifty-five articles involving a total of 22,719 patients were obtained to evaluate the correlation between haemoglobin level and survival. The results indicated that decreased haemoglobin level was significantly associated with poor overall survival of patients with lung cancer (HR 1.51, 95% CI 1.42–1.61), both in non-small cell lung cancer (HR 1.57, 95% CI 1.44–1.72) and in small cell lung cancer (HR 1.56, 95% CI 1.21–2.02). We also found that the lower the haemoglobin level, the shorter was the overall survival of patients with lung cancer (HR 1.11, 95% CI 1.06–1.16). However, the relationship between decreased haemoglobin and relapse-free survival was not significant (HR 1.37, 95% CI 0.91–2.05).

Conclusion

A decreased pretreatment haemoglobin level among patients with lung cancer is a prognostic factor of poor survival that can serve as an important indicator in survival prediction, risk stratification and treatment selection. In clinical practice, more attention should be paid to monitoring pretreatment haemoglobin levels among patients with lung cancer.

Peer Review reports

Background

Lung cancer is the most prevalent cancer and the leading cause of cancer-related death in both men and women [1, 2]. Although integrated treatment strategies and multidisciplinary nursing interventions based on surgery, radiotherapy and chemotherapy have provided improvements in the survival of patients with lung cancer, more effective prognostic factors should be identified to guide therapy and assess disease progression in individuals. In previous studies, the tumour-node-metastasis (TNM) staging system and tumour markers have made great contributions to the prediction of clinical outcomes, though most of these markers are clinicopathological parameters determined after surgery and are associated with high costs. Thus, it is important to detect new predictors to satisfy clinical requirements [3, 4].

Decreased haemoglobin (Hb) is the most commonly observed haematological abnormality in patients with cancers; it is induced by the direct or the indirect effects of malignancy or its treatment [5]. The National Comprehensive Cancer Network considered that Hb levels less than 11 g/dl can be diagnostic of cancer-related decreased Hb [6]. The mechanism of Hb degradation in lung cancer is complex. Blood loss, haemolysis, marrow infiltration and nutritional deficiencies may all be responsible for the development of Hb decline. Cancer-stimulated production of inflammatory cytokines (e.g. TNF-α, IL-1, IL-6, INF-γ) can inhibit erythropoiesis resulting in Hb reduction [7, 8]. The Hb level is a convenient and well-known parameter in clinical practice. An increasing body of evidence indicates that decreased Hb is related to poor prognosis in cancers [4, 9, 10]. However, the prognostic value of Hb level in patients with lung cancer has not been well confirmed. Numerous previous studies that have examined this relationship provide conflicting results [11,12,13,14]. Some studies showed that overall survival (OS) was significantly shorter in lung cancer patients with a low Hb level before treatment [11, 12], while some showed that the correlation between low Hb level and shorter OS was not significant [13, 14]. Therefore, in this study, a meta-analysis was conducted to determine the prognostic value of decreased Hb level in patients with lung cancer.

Method

Search strategy

Relevant studies that referred to the prognostic value of the Hb level in patients with lung cancer were identified by searching several databases up to November 2017, including PubMed, Embase, Web of Science and Cochrane Library. We used the following terms as MeSH terms and free-text terms (‘lung neoplasm’, ‘lung cancer’, ‘lung carcinoma’, ‘lung tumor’), (‘hemoglobin’, ‘Hb’ ‘hemoglobinometry’, ‘anemia’) and (‘mortality’, ‘prognosis’, ‘prognostic’, ‘predict’, ‘predictive’, ‘predictor’, ‘survival’, ‘outcome’); only studies published in English were retrieved. The references of candidate studies were also reviewed.

Inclusion and exclusion criteria

The identified studies were independently selected by two reviewers following the inclusion and exclusion criteria below. Disagreements were discussed in a group to reach consensus. Studies were included if they met the following criteria: (1) The study population was patients who were diagnosed with lung cancer; (2) The serum Hb level was measured before treatment; (3) The relationship between the Hb level and survival was provided; and (4) A univariate Log-rank test or multivariate Cox proportional hazards model was used for statistical analysis; only observational studies were selected. Studies were excluded if they met one of the following criteria: (1) Studies were not published in English; (2) The full-text could not be obtained; (3) Data were not sufficient to extract the hazard ratio (HR) and 95% confidence interval (CI); and (4) Survival data were only provided as Kaplan-Meier curves; repeated studies or duplicate data were excluded. If one author reported the same population in different articles, only the most detailed one was included.

Quality assessment

Two reviewers evaluated the quality of each study independently. The Newcastle-Ottawa scale (NOS) was used to assess the quality of included studies. The scale contains 8 items in 3 dimensions (selection, comparability and outcome) [15]. The assessment was carried out by awarding stars for high-quality studies, ranging from zero up to nine stars. A score of more than 6 stars indicates a high quality [16].

Data extraction

Two reviewers extracted data from the eligible studies independently. Any discrepancy in data extraction was resolved through a cross-check and discussion. The primary data extracted were HR for poor prognosis with 95% CI, or the data necessary to calculate the HR and 95% CI. HRs from multivariate analyses were extracted if both univariate and multivariate analyses were provided. The characteristics of the studies and patients were collected, including the first author, year published, country, number of patients, gender, mean or median age of patients, duration of follow-up, subtype of lung cancer, stage of the tumour, treatment modalities, Hb cut-off value, indicator of survival analysis, and statistical methods.

Statistical analysis

All statistical analyses were performed with Stata statistical software, version 15.0 (Stata Corp LLC, College Station, TX, USA). The association between Hb level and prognosis of patients with lung cancer was estimated by calculating the pooled HR and 95% CI. We used the random-effect model to combine the effective value based on heterogeneity [17]. A p value < 0.05 was considered to be significant in all statistical tests. HR > 1 indicated a negative prognosis in patients with a low Hb level. The heterogeneity of the pooled HRs was assessed using the Cochran’s Q test and I2 test, and a p value less than 0.05 or an I2 more than 50% was considered to be statistically significant [18]. To explain heterogeneity, subgroup analyses were performed by stratifying the included studies by lung cancer subtype and statistical method. To further explore the sources of heterogeneity, meta-regression analyses were conducted. We also performed sensitivity analyses by deleting one study at a time to estimate the contribution of included studies to heterogeneity. Egger’s indicator test and Begg’s funnel plot were applied to scrutinize publication bias [19, 20].

Result

Study retrieval

A total of 5723 citations were retrieved using the search strategy described above. Four hundred twelve duplicate records were removed. After screening and scanning the titles and abstracts of the publications, 5044 studies were excluded for being reviews, animal experiments, case reports, letters, comments, drug clinical trials, or otherwise irrelevant to our studies. After reviewing the full texts of 267 candidate studies, 213 articles were excluded according to the criteria above. Of these, 67 articles were excluded for being irrelevant to our study. For example, one study investigated the effect of abnormal Hb level (< 12 g/l or > 18 g/l) on the prognosis of lung cancer instead of investigating decreased Hb specifically, and some studies focused on the relationship between outcomes and decreased Hb during therapy rather than pretreatment levels. Fifty-five articles were excluded for reporting insufficient data to calculate HR, 44 articles for not having full text available, 42 for being published in other languages, and 5 for being duplicate publications. Two additional non-duplicate studies were identified from study reference lists. Finally, a total of 56 studies including 22,719 patients were included in this meta-analysis. The detailed search process is shown in Fig. 1.

Fig. 1
figure 1

Flow diagram following the searching strategy for studies included in this meta-analysis

Full size image

Study characteristics

The main characteristics of all eligible studies are shown in Table 1. Forty-eight studies were analysed with decreased Hb level as the categorical variable, 38 of which provided data on the relationship between OS and Hb in patients with non-small cell lung cancer (NSCLC); 6 studies were conducted in patients with small cell lung cancer (SCLC); and 4 studies included both patients with NSCLC and SCLC. Eight of the 56 included studies were analysed with pretreatment Hb as a continuous variable. Moreover, 3 studies were also available for disease-free survival (DFS), relapse-free survival (RFS) and progression-free survival (PFS) extraction, respectively. Only one study reported the relationship between the Hb level, event-free survival (EFS) and time to progression (TTP).

Table 1 Characteristics of studies included for meta-analysis
Full size table

OS and decreased Hb

Forty-eight articles with data on overall survival and decreased Hb (categorical variable: decreased Hb vs. normal Hb) were included in the pooled analysis. There was significant heterogeneity among these studies (I2 = 39.1%, p = 0.004), and thus, the random effect model was employed to calculate the pooled HR and its 95% CI. Lower Hb was significantly correlated with poor OS (HR 1.51, 95% CI 1.42–1.61). For further exploration, subgroup analyses were conducted. Forty-eight studies were re-classified by “analysis method”. In univariate analysis studies, there appeared to be no heterogeneity among HRs (I2 = 0.0%, p = 0.517), and we found that decreased Hb was a negative prognostic factor for OS (HR 1.45, 95% CI 1.29–1.63). Similarly, as shown in multivariate analyses, 36 studies also indicated that decreased pretreatment Hb predicted a significantly worse OS in patients with lung cancer (HR 1.53, 95% CI 1.42–1.65) (Fig. 2).

Fig. 2
figure 2

Forest plot and pooled HR and 95% CI for OS in patients with lung cancer: pretreatment decreased Hb vs. normal Hb. The pooled HR for OS showed that the patients with pretreatment decreased Hb level possessed a worse outcome in OS. HR hazard ratios, OS overall survival, CI confidence interval, Hb hemoglobin

Full size image

Cut-off values of 10 g/dl, 11 g/dl, and 12 g/dl, along with gender-specific values of 13 g/dl (males) and 12 g/dl (females), were mostly used in the included studies. We divided these studies into 4 subgroups based the Hb cut-off values used: 10 g/dl, 11 g/dl, 12 g/dl and gender-specific (male 13 g/dl, female 12 g/dl). In total, the HRs of 32 studies were pooled in this meta-analysis. The results showed that decreased Hb before treatment was a significant predictor of OS in patients with lung cancer (HR 1.56, 95% CI 1.43–1.70). Although the heterogeneity was still significant in the 11 g/dl group (I2 = 71%, p = 0.002), there was no significant heterogeneity overall or in the 10 g/dl, 12 g/dl and gender-specific (male 13 g/dl, female 12 g/dl) subgroups with I2 of 35.5, 55.3 and 4.2%, respectively (Fig. 3).

Fig. 3
figure 3

Forest plot and pooled HR and 95% CI for OS in patients with lung cancer: pretreatment decreased Hb vs. normal Hb with different Hb cut-off values. The pooled HR for OS showed the pretreatment decreased Hb was an independent prognostic factor of survival in patients with lung cancer. HR hazard ratios, CI confidence interval, Hb hemoglobin

Full size image

Eight cohorts analysed the Hb level data as a continuous variable and evaluated the correlation between pretreatment Hb level and OS. We found that a decreased Hb level was significantly related to OS (HR 1.11, 95% CI 1.06–1.16) with no significant heterogeneity (I2 = 0.0%, p = 0.770) (Fig. 4).

Fig. 4
figure 4

Forest plot and pooled HR and 95% CI for the association between pretreatment Hb level (continuous variable) and OS in patients with lung cancer. The pooled HR indicated that decreased Hb was related to the poor OS. HR hazard ratios, CI confidence interval, Hb hemoglobin

Full size image

Prognostic impact of decreased Hb on patients with NSCLC

Twenty-eight studies evaluated the prognostic impact of decreased Hb (categorical variable: decreased Hb vs. normal Hb) on NSCLC in multivariate analyses. We found that decreased Hb was a poor prognostic marker for OS (HR 1.57, 95% CI 1.44–1.72) with moderate heterogeneity (I2 = 47.1%, p = 0.003). Subgroup analyses were conducted according to tumour stage. The result indicated that decreased Hb had a prognostic impact on OS for patients in early stage (HR 1.81, 95% CI 1.33–2.46), advanced stage (HR 1.60, 95% CI 1.34–1.92) and both (HR 1.50, 95% CI 1.37–1.64), although the heterogeneity was significant in the advanced stage subgroup (I2 = 70%, p = 0.001) (Fig. 5).

Fig. 5
figure 5

Forest plot and pooled HR and 95% CI for OS in patients with NSCLC: pretreatment decreased Hb vs. normal Hb. The pooled HR for OS indicated that pretreatment decreased Hb level had a negative impact on survival of patients with NSCLC both in early stage and advanced stage. NSCLC non-small cell lung cancer, HR hazard ratios, OS overall survival, CI confidence interval, Hb hemoglobin

Full size image

Prognostic impact of decreased Hb on patients with SCLC

Six cohorts with 3203 cases reported the data of pretreatment Hb (categorical variable: decreased Hb vs. normal Hb) and OS in patients with SCLC. The pooled HR from the 6 cohorts showed that patients with decreased Hb were associated with shorter OS (HR 1.56, 95% CI 1.21–2.02), although there was significant heterogeneity among the studies (I2 = 60.6%, p = 0.026) (Fig. 6).

Fig. 6
figure 6

Forest plot and pooled HR and 95% CI for OS in patients with SCLC: pretreatment decreased Hb vs. normal Hb. The pooled HR for OS showed decreased Hb level was associated with shorter OS. SCLC small cell lung cancer, HR hazard ratios, OS overall survival, CI confidence interval, Hb hemoglobin

Full size image

DFS and decreased Hb

Three studies presented the data from their investigation of pretreatment Hb (categorical variable: decreased Hb vs. normal Hb) and DFS. The combined data suggested that decreased pretreatment Hb was significantly correlated with DFS, with a pooled HR estimate of 1.98 (95% CI 1.21–3.23) and no heterogeneity (I2 = 0.0%, P = 0.419) (Fig. 7).

Fig. 7
figure 7

Forest plot and pooled HR and 95% CI for DFS in patients with lung cancer: pretreatment decreased Hb vs. normal Hb. The pooled HR for DFS showed pretreatment decreased Hb level was associated with shorter DFS. HR hazard ratios, DFS disease-free survival, CI confidence interval, Hb hemoglobin

Full size image

RFS and decreased Hb

Three studies reported the correlation between RFS and decreased Hb (categorical variable: decreased Hb vs. normal Hb). Interestingly, the pooled HR indicated that decreased pretreatment Hb was not significantly associated with shorter RFS (HR 1.37, 95% CI 0.91–2.05), and the heterogeneity was not significant (I2 = 63.9%, p = 0.063) (Fig. 8).

Fig. 8
figure 8

Forest plot and pooled HR and 95% CI for RFS in patients with lung cancer: pretreatment decreased Hb vs. normal Hb. The pooled HR for RFS showed pretreatment decreased Hb level was not significantly associated with shorter RFS. HR hazard ratios, RFS relapse-free survival, CI confidence interval, Hb hemoglobin

Full size image

Meta-regression analyses

To further explore the potential causes of the heterogeneity, treatment method and sample size were used to conduct meta regression after the subgroup analysis. The results showed that these two factors were not the source of heterogeneity.

Sensitivity analysis and publication bias

In our meta-analysis, the Begg’s funnel plot and Egger’s indicator test were used to evaluate potential publication bias for OS. As our results show in Additional file 1: Figure S1 and Additional file 2: Figure S2, both the Begg’s funnel plot and Egger’s publication bias plot indicate the existence of publication bias among the included studies (p < 0.001). Interestingly, sensitivity analysis revealed that none of the HR point estimates lay outside the 95% CI of the pooled analysis, which confirmed that our results were stable and reliable.

Discussion

Lung cancer is a leading cause of cancer death worldwide with about 15% of 5-year survival rate [1]. It is well known that the TNM system has played an important role in the evaluation of clinical outcome and the decision-making process of selecting effective therapies. However, the complexity of its pathogenic mechanism means that the progression and prognosis of cancer can be caused by many factors. Patients with the same pathological stage often present with different outcomes, which suggests that the TNM system alone cannot precisely predict the survival of patients with lung cancer. Moreover, the TNM stage should be confirmed by biopsy; therefore, it is difficult to track stage changes in the process of cancer progression. Peripheral blood samples are easily obtained by nurses with less clinical practice cost. The current viewpoint considers that some haematological biomarkers are related to the prognosis of cancers, including the neutrophil to lymphocyte ratio [73], leucocyte [74], platelet [75], white blood cell [54] and Hb levels [76] before treatment. However, the prognostic value of the Hb level in patients with lung cancer remains controversial.

Many researchers aimed to develop a new evaluation or model to predict the expected lifetime of patients with lung cancer [66, 77]. The creation of such instruments requires to identify the survival prediction value of pretreatment peripheral blood markers and other clinicopathological factors. Hb is an important hematological marker to predict the survival in patient with cancer. However, the prognostic value of decreased pretreatment Hb level on survival remains controversial. This systematic review and meta-analysis are the first evidence-based research to determine the prognostic impact of decreased pretreatment Hb on the OS, DFS and RFS of patients with lung cancer, which can make contributions to the personalized treatment programs.

In this systematic review with meta-analyses of 55 eligible studies, we first evaluated the relationship between decreased Hb and OS in patients with lung cancer. The results showed that patients with a Hb reduction at the time of diagnosis or before treatment were significantly associated with poor OS in both univariate and multivariate analysis. A significant heterogeneity was observed, but the pooled HRs were stable when deleting each study one by one. Thus, a random effect model was selected to analyse the pooled HR, and subgroup analyses and meta-regression were conducted. We also found that there were more studies of the prognostic value of decreased Hb in patients with NSCLC than in patients with SCLC. However, similar results confirmed that a decreased Hb level was a negative prognostic factor for OS in both patients with NSCLC and SCLC. Other survival indicators were also applied to this meta-analysis. Interestingly, different results were found for the prognostic value of preoperative Hb on DFS and RFS. As shown in Fig. 7 and Fig. 8, a decreased pretreatment Hb level was significantly associated with poor DFS, while in three studies addressing RFS, the pooled HR indicated that the prognostic value of Hb was not significant. In the pooled analysis of the continuous variable Hb level and OS, it can be postulated that, even if the Hb level was in the normal range, a lower Hb level was significantly associated with worse survival in patients with lung cancer.

The cause of Hb degradation is multifactorial and often relates to other comorbidities. It is reported that the systemic inflammatory responses from tumour cells strongly correlate with cancer progression and malignant transformation [78]. Specifically, interleukin-6 (IL-6) is an important inducer of the production of hepcidin, which is involved in iron metabolism. Elevated hepcidin levels lead to reductions in serum iron levels and result in decreased Hb [79]. It should be noted that higher hepcidin levels have been detected in patients with more aggressive diseases [79]. The mechanism underlying the prognostic value of decreased Hb in patients with lung cancer can be explained from several perspectives. Hb reduction contributes to hypoxia of tumour cells, which then stimulates tumour growth and increases the resistance of tumour cells to radiotherapy and chemotherapy by regulating the gene expression and cell-cycle position, subsequently causing progression of cancer and shorter survival [80].

Two principal options for the management of decreased Hb have been proposed by previous studies, including the use of erythropoiesis-stimulating agents (ESAs) and blood transfusion [81]. ESAs could increase Hb levels and reduce transfusion requirements [82]. However, a meta-analysis of randomized controlled trials showed that the use of ESAs was associated with an increased risk of developing venous thromboembolism in cancer patients [83]. Therefore, the safety of treatment with ESAs in cancer patients still needs to be considered. Blood transfusion is effective for correcting Hb decline and improving symptoms or signs induced by decreased Hb in patients with cancer. However, it has been reported that perioperative blood transfusion was associated with an increased recurrence of lung cancer due to transfusion-related immunomodulation [84]. Overall, further studies are needed to investigate how to effectively manage decreased Hb in patients with lung cancer.

There are several limitations presented in this meta-analysis. First, the recruited data were extracted from observational studies, most of which were retrospective cohort studies; only two studies were based on prospective cohorts. Additionally, the cut-off values defining decreased Hb in our meta-analysis were not consistent, 10 g/dl, 11 g/dl, 11.5 g/dl, 11.6 g/dl, 12 g/dl, 12.5 g/dl, 12.7 g/dl, 12.8 g/dl, 13 g/dl, 13.1 g/dl, 13.2 g/dl, 13.6 g/dl, 14 g/dl and 14.6 g/dl. This confounder may influence the outcomes. To strengthen the power of our results, studies with 10 g/dl, 11 g/dl, 12 g/dl and gender-specific (male, 13 g/dl; female, 12 g/dl) cut-off values were analysed in the meta-analysis and similar results were obtained, specifically that decreased Hb was significantly associated with poor OS in patients with lung cancer. In fact, pooled results of the analysis of the continuous variable Hb and OS suggested that, even when the Hb level was within the normal range, lower Hb levels may predict the poor outcomes of survival and still need attention. Third, mild to moderate potential heterogeneity may exist between the included studies. We evaluated the prognostic value of Hb in NSCLC and SCLC separately. Subgroup analyses and meta-regression were conducted to detect the source of heterogeneity. Although the results suggested that region, subtype of lung cancer, treatment method and cut-off value were not the source of heterogeneity, there were still different features between the trials, and these features may be highly correlated and were not easily detected. Fourth, previous systematic review and meta-analysis showed that blood transfusions adversely affected cancer survival [85]. It was reported that the significant correlation between low Hb level and poor OS may be due to erythropoietin treatment or blood transfusion before surgery [86]. In our meta-analysis, since the data on how many patients received a blood transfusion during their survival time were not available, we cannot determine whether decreased pretreatment Hb or blood transfusion was the major factor of survival. However, this meta-analysis still explained the negative impact of decreased Hb on survival in patients with lung cancer to some extent. Further research on whether the decreased Hb levels before treatment directly affect the survival of patients with lung cancer, rather than blood transfusions, remains to be conducted. Fifth, there was significant publication bias for the correlation between decreased pretreatment Hb and OS in patients with lung cancer given the results of Begg’s funnel plot and the Egger’s test. The number of included articles was sufficient, but some of the baseline characteristics of the recruited studies differed in some confounders (gender, sample size, treatment, period of follow-up, etc.), which may contribute to the bias. We improved the stability of our estimation of the impact of decreased Hb on the prognosis of lung cancer by using sensitivity analysis. However, a publication bias still existed for the estimated pooled HR on OS. Finally, it was reported that not only did a lower Hb level lead to poor prognosis but abnormally elevated Hb did as well [87]. In this meta-analysis, we only focused on the impact of decreased Hb on survival, and further investigation and trials about the prognostic effects of abnormally elevated Hb on the survival of patients with lung cancer are needed.

Conclusion

In conclusion, our findings suggested that a decreased Hb level before treatment was a prognostic indicator of shorter OS and DFS both in patients with NSCLC and SCLC. The Hb level, an economical and readily available marker, might serve as an indicator for survival prediction, risk stratification and treatment selection. However, because of the limitation of our current study, additional large prospective cohorts and experimental trials are needed to confirm Hb level as an independent predictor of prognosis in patients with lung cancer. Additionally, targeting the correction of pretreatment Hb degradation may be an effective strategy to increase the survival rate of patients with lung cancer.

Abbreviations

CI:

Confidence interval

DFS:

Disease-free survival

EFS:

Event-free survival

Hb:

Hemoglobin

HR:

Hazard ratio

MV:

Multivariate

NCCN:

National Comprehensive Cancer Network

NOS:

Newcastle-Ottawa scale

NR:

Not reported

NSCLC:

Non-small cell lung cancer

OS:

Overall survival

PFS:

Progress-free survival

RFS:

Relapse-free survival

SCLC:

Small cell lung cancer

TNM:

Tumor-node metastasis

TTP:

Time to progression

UV:

Univariate

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67:7–30.

    Article  PubMed  Google Scholar 

  2. Oser MG, Niederst MJ, Sequist LV, Engelman JA. Transformation from non-small-cell lung cancer to small-cell lung cancer: molecular drivers and cells of origin. Lancet Oncol. 2015;16:e165–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, Templeton AJ, Fruh M. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer. 2017;111:176–81.

  4. Koh WJ, Greer BE, Abu-Rustum NR, Campos SM, Cho KR, Chon HS, Chu C, Cohn D, Crispens MA, Dizon DS, et al. Vulvar Cancer, version 1, 2017, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Netw. 2017;15(1):92–120.

    Article  Google Scholar 

  5. Ibrahim UA, Yusuf AA, Ahmed SG. The pathophysiologic basis of Anaemia in patients with malignant diseases. The Gulf journal of oncology. 2016;1:80–9.

    PubMed  Google Scholar 

  6. Cancer and chemotherapy-induced anemia. In: NCCN clinical practice guidelines in Oncology National Comprehensive Cancer Network 2011. http://www.nccn.org/index.ASp Accessed 1 Aug 2011.

  7. Baburao A, Narayanswamy H. Clinico-Pathological Profile and Haematological abnormalities associated with lung Cancer in Bangalore, India. Asian Pac J Cancer Prev. 2015;16:8235–8.

    Article  PubMed  Google Scholar 

  8. Buck I, Morceau F, Grigorakaki C, Dicato M, Diederich M. Linking anemia to inflammation and cancer: the crucial role of TNFalpha. Biochem Pharmacol. 2009;77:1572–9.

    Article  CAS  PubMed  Google Scholar 

  9. Caro JJ, Salas M, Ward A, Goss G. Anemia as an independent prognostic factor for survival in patients with cancer: a systemic quantitative review. Cancer. 2001;91:2214–21.

    Article  CAS  PubMed  Google Scholar 

  10. Li J, Chen S, Peng S, Liu Y, Xing S, He X, Chen H. Prognostic nomogram for patients with nasopharyngeal carcinoma incorporating hematological biomarkers and clinical characteristics. Int J Biol Sci. 2018;14(5):549–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lin Y, Yang H, Cai Q, Wang D, Rao H, Lin S, Long H, Fu J, Zhang L, Lin P, et al. Characteristics and prognostic analysis of 69 patients with pulmonary Sarcomatoid carcinoma. Am J Clin Oncol. 2016;39:215–22.

    Article  CAS  PubMed  Google Scholar 

  12. Strouse CS, Arce-Lara C, Whittle J, Santana-Davilla R. A retrospective, population-based comparison of pemetrexed and paclitaxel for first-line treatment of stage IV non-small cell lung cancer. Int J Radiat Oncol Biol Phys. 2014;90:S49.

    Article  Google Scholar 

  13. Garrido P, Rosell R, Massuti B, Cardenal F, Alberola V, Domine M, Maeztu I, Ramos A, Arellano A. Predictors of long-term survival in patients with lung cancer included in the randomized Spanish lung Cancer group 0008 phase II trial using concomitant chemoradiation with docetaxel and carboplatin plus induction or consolidation chemotherapy. Clin Lung Cancer. 2009;10:180–6.

    Article  CAS  PubMed  Google Scholar 

  14. Kishida Y, Hirose T, Shirai T, Sugiyama T, Kusumoto S, Yamaoka T, Okuda K, Adachi M, Nakamura A. Myelosuppression induced by concurrent hemoradiotherapy as a prognostic factor for patients with locally advanced non-small cell lung cancer. Oncol Lett. 2011;2:949–55.

    PubMed  PubMed Central  Google Scholar 

  15. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA, Thacker SB. Meta-analysis of observational studies in epidemiology: a proposal for reporting, Meta-analysis of observational studies in epidemiology (MOOSE) group. JAMA. 2000;283:2008–12.

    Article  CAS  PubMed  Google Scholar 

  16. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25:603–5.

    Article  PubMed  Google Scholar 

  17. Cornell JE, Mulrow CD, Localio R, Stack CB, Meibohm AR, Guallar E, Goodman SN. Random-effects meta-analysis of inconsistent effects: a time for change. Ann Intern Med. 2014;160:267–70.

    Article  PubMed  Google Scholar 

  18. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–58.

    Article  PubMed  Google Scholar 

  19. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Liu JL. The role of the funnel plot in detecting publication and related biases in meta-analysis. Evid Based Dent. 2011;12:121–2.

    Article  PubMed  Google Scholar 

  21. Osterlind K, Andersen PK. Prognostic factors in small cell lung cancer: multivariate model based on 778 patients treated with chemotherapy with or without irradiation. Cancer Res. 1986;46:4189–94.

    CAS  PubMed  Google Scholar 

  22. Albain KS, Crowley JJ, LeBlanc M, Livingston RB. Survival determinants in extensive-stage non-small-cell lung cancer: the southwest oncology group experience. J Clin Oncol. 1991;9:1618–26.

    Article  CAS  PubMed  Google Scholar 

  23. Takigawa N, Segawa Y, Okahara M, Maeda Y, Takata I, Kataoka M, Fujii M. Prognostic factors for patients with advanced non-small cell lung cancer: univariate and multivariate analyses including recursive partitioning and amalgamation. Lung Cancer. 1996;15:67–77.

    Article  CAS  PubMed  Google Scholar 

  24. Wigren T, Oksanen H, Kellokumpu-Lehtinen P. A practical prognostic index for inoperable non-small-cell lung cancer. J Cancer Res Clin Oncol. 1997;123:259–66.

    Article  CAS  PubMed  Google Scholar 

  25. Ohlhauser C, Bülzebruck H, Ebert W, Drings P, Wannenmacher M. Prognostic factors for survival in inoperable non-small-cell lung cancer: a multivariate regression analysis of 456 patients with radiation therapy. Onkologie. 1997;20:126–31.

    Google Scholar 

  26. Jazieh AR, Hussain M, Howington JA, Spencer HJ, Husain M, Grismer JT, Read RC. Prognostic factors in patients with surgically resected stages I and II non-small cell lung cancer. Ann Thorac Surg. 2000;70:1168–71.

    Article  CAS  PubMed  Google Scholar 

  27. Rzyman W, Dziadziuszko R, Skokowski J, Wilimski R, Raiter A, Szymanowska A, Jassem J. The influence of blood transfusion on survival in operated non-small cell lung cancer patients. J Thorac Cardiovasc Surg. 2003;126:755–60.

    Article  PubMed  Google Scholar 

  28. Bremnes RM, Sundstrom S, Aasebo U, Kaasa S, Hatlevoll R, Aamdal S. The value of prognostic factors in small cell lung cancer: results from a randomised multicenter study with minimum 5 year follow-up. Lung Cancer. 2003;39:303–13.

    Article  PubMed  Google Scholar 

  29. Langendijk H, de Jong J, Wanders R, Lambin P, Slotman B. The importance of pre-treatment haemoglobin level in inoperable non-small cell lung carcinoma treated with radical radiotherapy. Radiother Oncol. 2003;67:321–5.

    Article  PubMed  Google Scholar 

  30. Tammemagi CM, Neslund-Dudas C, Simoff M, Kvale P. Impact of comorbidity on lung cancer survival. Int J Cancer. 2003;103:792–802.

    Article  CAS  PubMed  Google Scholar 

  31. Yovino S, Kwok Y, Krasna M, Bangalore M, Suntharalingam M. An association between preoperative anemia and decreased survival in early-stage non-small-cell lung cancer patients treated with surgery alone. Int J Radiat Oncol Biol Phys. 2005;62:1438–43.

    Article  PubMed  Google Scholar 

  32. Berardi R, Brunelli A, Tamburrano T, Verdecchia L, Onofri A, Zuccatosta L, Gasparini S, Santinelli A, Scartozzi M, Valeri G, et al. Perioperative anemia and blood transfusions as prognostic factors in patients undergoing resection for non-small cell lung cancers. Lung Cancer. 2005;49:371–6.

    Article  CAS  PubMed  Google Scholar 

  33. Pradier O, Lederer K, Hille A, Weiss E, Christiansen H, Schmidberger H, Hess CF. Concurrent low-dose cisplatin and thoracic radiotherapy in patients with inoperable stage III non-small cell lung cancer: a phase II trial with special reference to the Hb level as prognostic parameter. J Cancer Res Clin Oncol. 2005;131:261–9.

    Article  CAS  PubMed  Google Scholar 

  34. Aoe K, Hiraki A, Maeda T, Katayama H, Fujiwara K, Tabata M, Kiura K, Ueoka H, Tanimoto M. Serum Hb level determined at the first presentation is a poor prognostic indicator in patients with lung cancer. Intern Med. 2005;44:800–4.

    Article  PubMed  Google Scholar 

  35. Mohan A, Goyal A, Singh P, Singh S, Pathak AK, Bhutani M, Pandey RM, Guleria R. Survival in small cell lung cancer in India: prognostic utility of clinical features, laboratory parameters and response to treatment. Indian J Cancer. 2006;43:67–74.

    Article  CAS  PubMed  Google Scholar 

  36. Mandrekar SJ, Northfelt DW, Schild SE, Foster NR, Bot B, Marks RS, Mailliard JA, Krook JE, Maksymiuk AW, Adjei AA, et al. Impact of pretreatment factors on adverse events: a pooled analysis of north central Cancer treatment group advanced stage non-small cell lung cancer trials. J Thorac Oncol. 2006;1:556–63.

    Article  PubMed  Google Scholar 

  37. Laurie SA, Jeyabalan N, Nicholas G, MacRae R, Dahrouge S. Association between anemia arising during therapy and outcomes of chemoradiation for limited small-cell lung cancer. J Thorac Oncol. 2006;1:146–51.

    Article  PubMed  Google Scholar 

  38. Paul I, Lappin TR, Maxwell P, Graham AN. Pre-operative plasma erythropoietin concentration and survival following surgery for non-small cell lung cancer. Lung Cancer. 2006;51:329–34.

    Article  CAS  PubMed  Google Scholar 

  39. Gauthier I, Ding K, Winton T, Shepherd FA, Livingston R, Johnson DH, Rigas JR, Whitehead M, Graham B, Seymour L. Impact of Hb levels on outcomes of adjuvant chemotherapy in resected non-small cell lung cancer: the JBR 10 trial experience. Lung Cancer. 2007;55:357–63.

    Article  CAS  PubMed  Google Scholar 

  40. Ademuyiwa FO, Johnson CS, White AS, Breen TE, Harvey J, Neubauer M, Hanna NH. Prognostic factors in stage III non-small-cell lung cancer. Clin Lung Cancer. 2007;8:478–82.

    Article  PubMed  Google Scholar 

  41. Panagopoulos ND, Karakantza M, Koletsis E, Apostolakis E, Sakellaropoulos GC, Filos KS, Eleni T, Dougenis D. Influence of blood transfusions and preoperative anemia on long-term survival in patients operated for non-small cell lung cancer. Lung Cancer. 2008;62:273–80.

    Article  PubMed  Google Scholar 

  42. Park MJ, Kim HS, Jun HJ, Kim KH, Chang MH, Yi SY, Lee J, Park K, Ahn JS, Ahn MJ. Prognostic model to predict survival in patients with non-small cell lung cancer treated with gefitinib. Ann Oncol. 2008;19:viii111–2.

    Article  Google Scholar 

  43. Jacot W, Colinet B, Bertrand D, Lacombe S, Bozonnat MC, Daures JP, Pujol JL. Quality of life and comorbidity score as prognostic determinants in non-small-cell lung cancer patients. Ann Oncol. 2008;19:1458–64.

    Article  CAS  PubMed  Google Scholar 

  44. Florescu M, Hasan B, Seymour L, Ding K, Shepherd FA. A clinical prognostic index forpatients treated with erlotinib in National Cancer Institute of Canada clinical trials group study BR.21. J Thorac Oncol. 2008;3:590–8.

    Article  PubMed  Google Scholar 

  45. Stinchcombe TE, Hodgson L, Herndon JE, Kelley MJ, Cicchetti MG, Ramnath N, Niell HB, Atkins JN, Akerley W, Green MR, et al. Treatment outcomes of different prognostic groups of patients on cancer and leukemia group B trial 39801: induction chemotherapy followed by chemoradiotherapy compared with chemoradiotherapy alone for unresectable stage III non-small cell lung cancer. J Thorac Oncol. 2009;4:1117–25.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Belbaraka R, Tredan O, Ray-Coquard I, Chvetzoff G, Bajard A, Perol D, Ismaili N, Ismaili M, Errihani H, Bachelot T, et al. Factors of interrupting chemotherapy in patients with advanced non-small-cell lung Cancer. BMC Res Notes. 2010;3:164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Qiu MZ, Xu RH, Ruan DY, Li ZH, Luo HY, Teng KY, Wang ZQ, Li YH, Jiang WQ. Incidence of anemia, leukocytosis, and thrombocytosis in patients with solid tumors in China. Tumour Biol. 2010;31:633–41.

    Article  CAS  PubMed  Google Scholar 

  48. Ovcaricek T, Triller N, Sadikov A, Cufer T. Prognostic value of C-reactive protein and other classical factors in patients with advanced non-small cell lung carcinoma treated in routine clinical practice. Zdravniski Vestnik-Slovenian Medical Journal. 2010;79:669–76.

    Google Scholar 

  49. Yi SY, Ahn M, Park JY, Lee HR, Lee J, Park YH, Ahn JS, Park K. Prognostic factors in elderly patients with advanced non-small cell lung cancer treated with platinum-based doublet chemotherapy. J Clin Oncol. 2011;29:15.

    Google Scholar 

  50. Castro JG, TKTCRVFJBOFTYT, Hoff PM. Long-term follow-up of advanced nsclc patients treated with cisplatin-inorelbine. Eur Lung Cancer Conf. 2012;7:S33–4.

    Google Scholar 

  51. Janku F, Tsimberidou AM, Wang X, Hong DS, Naing A, Gong J, Garrido-Laguna I, Parsons HA, Zinner RG, Kurzrock R. Outcomes of patients with advanced non-small cell lung cancer treated in a phase I clinic. Oncologist. 2011;16:327–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gioulbasanis I, Georgoulias P, Vlachostergios PJ, Baracos V, Ghosh S, Giannousi Z, Papandreou CN, Mavroudis D, Georgoulias V. Mini nutritional assessment (MNA) and biochemical markers of cachexia in metastatic lung cancer patients: interrelations and associations with prognosis. Lung Cancer. 2011;74:516–20.

    Article  PubMed  Google Scholar 

  53. Hsu C-L, Chen K-Y, Shih J-Y, Ho C-C, Yang C-H, Yu C-J, Yang P-C. Advanced non-small cell lung cancer in patients aged 45 years or younger: outcomes and prognostic factors. BMC Cancer. 2012;12:241.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Holgersson G, Sandelin M, Hoye E, Bergstrom S, Henriksson R, Ekman S, Nyman J, Helsing M, Friesland S, Holgersson M, et al. Swedish lung cancer radiation study group: the prognostic value of anaemia, thrombocytosis and leukocytosis at time of diagnosis in patients with non-small cell lung cancer. Med Oncol. 2012;29:3176–82.

    Article  PubMed  Google Scholar 

  55. Ng T, Ryder BA, Chern H, Sellke FW, Machan JT, Harrington DT, Cioffi WG. Leukocyte-depleted blood transfusion is associated with decreased survival in resected early-stage lung cancer. J Thorac Cardiovasc Surg. 2012;143:815–9.

    Article  PubMed  Google Scholar 

  56. Wu C, Li F, Jiao SC. Prognostic factors for survival of patients with extensive stage small cell lung cancer-a retrospective single institution analysis. Asian Pac J Cancer Prev. 2012;13:4959–62.

    Article  PubMed  Google Scholar 

  57. Kiely BE, Veillard AS, Davidson JA, Trinkaus ME, Briscoe KP, Hughes B, Begbie S, Pavlakis N, Millward M, Boyer M, et al. Prognostic significance, accuracy and usefulness of oncologists' estimates of survival time for patients starting first-line chemotherapy for advanced non-small-cell lung cancer (ANSCLC). J Thorac Oncol. 2013;8:S880.

    Google Scholar 

  58. Tas F, Ciftci R, Kilic L, Karabulut S. Age is a prognostic factor affecting survival in lung cancer patients. Oncol Lett. 2013;6:1507–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Qu X, Zhang T, Ma H, Sui P, J D. Lower mean corpuscular hemoglobin concentration is associated with unfavorable prognosis of resected lung cancer. Future Oncol. 2014;10:2149–59.

    Article  CAS  PubMed  Google Scholar 

  60. Smith MO, Sharkey AJ, George RS, Hubbard R, Rao J, Edwards JG. Prognostic implications of blood tests performed routinely prior to surgical resection of non-small cell lung cancer. Lung Cancer. 2014;83:S73–4.

    Article  Google Scholar 

  61. Kacan T, Babacan NA, Seker M, Yucel B, Bahceci A, Eren AA, Eren MF, Kilickap S. Could the neutrophil to lymphocyte ratio be a poor prognostic factor for non small cell lung cancers? Asian Pac J Cancer Prev. 2014;15:2089–94.

    Article  PubMed  Google Scholar 

  62. Oguz A, Colak D, Ersoy U, Imamoglu IG, Altinbas M, Unal D, Zararsiz G. The effect of Haematological parameters on overall survival in advanced stage non small cell lung Cancer. UHOD-ULUSLARARASI HEMATOLOJI-ONKOLOJI DERGISI. 2014;24(2):82–8.

    Google Scholar 

  63. Crvenkova S. Prognostic factors and survival in non-small cell lung cancer patients treated with chemoradiotherapy. Maced J Med Sci. 2015;3:75–9.

    Google Scholar 

  64. Wu XY, Huang XE. Screening for patients with non-small cell lung cancer who could survive long term chemotherapy. Asian Pac J Cancer Prev. 2015;16:647–52.

    Article  PubMed  Google Scholar 

  65. Imai H, Kaira K, Mori K, Ono A, Akamatsu H, Matsumoto S, Taira T, Kenmotsu H, Harada H, Naito T, et al. Prognostic significance of diabetes mellitus in locally advanced non-small cell lung cancer. BMC Cancer. 2015;15:989.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Xie D, Marks R, Zhang M, Jiang G, Jatoi A, Garces YI, Mansfield A, Molina J, Yang P. Nomograms predict overall survival for patients with small-cell lung Cancer incorporating pretreatment peripheral blood markers. J Thorac Oncol. 2015;10:1213–20.

    Article  PubMed  Google Scholar 

  67. Abazari M, Gholamnejad M, Roshanaei G, Abazari R, Roosta Y, Mahjub H. Estimation of survival rates in patients with lung cancer in West Azerbaijan, the northwest of Iran. Asian Pac J Cancer Prev. 2015;16:3923–6.

    Article  PubMed  Google Scholar 

  68. Cata JP, Gutierrez C, Mehran RJ, Rice D, Nates J, Feng L, Rodriguez-Restrepo A, Martinez F, Mena G, Gottumukkala V. Preoperative anemia, blood transfusion, and neutrophil-to-lymphocyte ratio in patients with stage i non-small cell lung cancer. Cancer Cell Microenviron. 2016;3:e1116.

    PubMed  PubMed Central  Google Scholar 

  69. Shaverdian N, Veruttipong D, Wang J, Kupelian P, Steinberg M, Lee P. Pretreatment Anemia portends poor survival and nonlocal disease progression in patients with stage I non-small cell lung Cancer treated with stereotactic body radiation therapy. J Thorac Oncol. 2016;11:1319–25.

    Article  PubMed  Google Scholar 

  70. Park S, Park S, Lee SH, Suh B, Keam B, Kim TM, Kim DW, Kim YW, Heo DS. Nutritional status in the era of target therapy: poor nutrition is a prognostic factor in non-small cell lung cancer with activating epidermal growth factor receptor mutations. Korean J Intern Med. 2016;31:1140–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Minami S, Ogata Y, Ihara S, Yamamoto S, Komuta K. Outcomes and prognostic factors of chemotherapy for patients with locally advanced or metastatic pulmonary squamous cell carcinoma. Lung Cancer (Auckl). 2016;7:99–110.

    Google Scholar 

  72. Lee S, Eo W, Jeon H, Park S, Chae J. Prognostic significance of host-related biomarkers for survival in patients with advanced non-small cell lung Cancer. J Cancer. 2017;8:2974–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Yin Y, Wang J, Wang X, Gu L, Pei H, Kuai S, Zhang Y, Shang Z. Prognostic value of the neutrophil to lymphocyte ratio in lung cancer: a meta-analysis. Clinics (Sao Paulo). 2015;70:524–30.

    Article  Google Scholar 

  74. Holgersson G, Bergqvist M, Nilsson J, Thureson M, Harmenberg J, Bergstrom S. The prognostic value of pre-treatment leukocytosis in patients with previously treated, stage IIIB/IV non-small cell lung Cancer treated with the IGF-1R pathway modulator AXL1717 or docetaxel; a retrospective analysis of a phase II trial. Asian Pac J Cancer Prev. 2017;18:1555–60.

    PubMed  PubMed Central  Google Scholar 

  75. Men H, Liang C, Yu M. Thrombocytosis as a prognostic factor in patients with renal cell carcinoma: a meta-analysis of literature. J Cancer Res Ther. 2015;11:67–72.

    Article  PubMed  Google Scholar 

  76. Barlas RS, Honney K, Loke YK, McCall SJ, Bettencourt-Silva JH, Clark AB, Bowles KM, Metcalf AK, Mamas MA, Potter JF, et al. Impact of hemoglobin levels and Anemia on mortality in acute stroke: analysis of UK regional registry data, systematic review, and Meta-analysis. J Am Heart Assoc: Cardiovasc Cerebrovasc Dis. 2016;5:e003019.

    Article  Google Scholar 

  77. Rades D, Kaesmann L, Janssen S, Schild SE. A new score for estimating survival after definitive Radiochemotherapy of limited disease small cell lung cancers. LUNG. 2016;194(4):625–9.

    Article  CAS  PubMed  Google Scholar 

  78. Gardenghi S, Renaud TM, Meloni A, Casu C, Crielaard BJ, Bystrom LM, Greenberg-Kushnir N, Sasu BJ, Cooke KS, Rivella S. Distinct roles for hepcidin and interleukin-6 in the recovery from anemia in mice injected with heat-killed Brucella abortus. Blood. 2014;123:1137–45.

  79. Lefebvre T, Lasocki S, Feneant-Thibault M, Lamy PJ, Cunat S, Ropert-Bouchet M, Aguilar-Martinez P, Lehmann S, Delaby C. Added value of hepcidin quantification for the diagnosis and follow-up of anemia-related diseases. Ann Biol Clin (Paris). 2017;75:9–18.

    Google Scholar 

  80. Gaspar BL, Sharma P, Das R. Anemia in malignancies: pathogenetic and diagnostic considerations. Hematology. 2015;20:18–25.

    Article  PubMed  Google Scholar 

  81. Bormanis J, Quirt I, Chang J, Kouroukis CT, MacDonald D, Melosky B, Verma S, Couture F. Erythropoiesis-stimulating agents (ESAs): do they still have a role in chemotherapy-induced anemia (CIA)? Crit Rev Oncol Hematol. 2013;87:132–9.

    Article  PubMed  Google Scholar 

  82. Clark O, Adams JR, Bennett CL, Djulbegovic B. Erythropoietin, uncertainty principle and cancer related anaemia. BMC Cancer. 2002;2:23.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Zhan P, Wang Q, Qian Q, Yu LK. Risk of venous thromboembolism with the erythropoiesis-stimulating agents (ESAs) for the treatment of cancer-associated anemia: a meta-analysis of randomized control trials. Chin Clin Oncol. 2012;1:19.

    PubMed  Google Scholar 

  84. Velásquez JF, Cata JP. Transfusions of blood products and cancer outcomes. Rev Esp Anestesiol Reanim. 2015;62:461–7.

    Article  PubMed  Google Scholar 

  85. Luan H, Ye F, Wu L, Zhou Y, Jiang J. Perioperative blood transfusion adversely affects prognosis after resection of lung cancer: a systematic review and a meta-analysis. BMC Surg. 2014;14:34.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Zhao F, Wang Y, Liu L, Bian M. Erythropoietin for cancer-associated malignant anemia: a meta-analysis. Mol Clin Oncol. 2017;6(6):925–30.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Berghmans T, Lafitte JJ, Thiriaux J, VanHoutte P, Lecomte J, Efremidis A, Koumakis G, Giner V, Richez M, Corhay JL, et al. Survival is better predicted with a new classification of stage III unresectable non-small cell lung carcinoma treated by chemotherapy and radiotherapy. Lung Cancer. 2004;45:339–4.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Author information

Author notes

  1. Yaqi Huang, Siqi Wei and Nan Jiang contributed equally to this work.

Authors and Affiliations

  1. School of Nursing, Tianjin Medical University, Tianjin, 300070, China

    Yaqi Huang, Siqi Wei, Nan Jiang, Siyuan Wang, Xiaona Cao & Yue Zhao

  2. School of Nursing, Liaoning University of Traditional Chinese Medicine, Liaoning, China

    Lijuan Zhang

  3. Department of Radiotherapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China

    Peiguo Wang

Authors
  1. Yaqi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siqi Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lijuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Siyuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaona Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yue Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Peiguo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YZ and PGW designed the systematic review; YQH, NJ, XNC performed the literature search and extracted the data; SQW and SYW evaluate the quality of included studies; YQH and PGW conducted the statistical analysis; YQH, NJ, LJZ were involved in the interpretation of the results. YZ, PGW and NJ were responsible for the writing and critical revision of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yue Zhao or Peiguo Wang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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

Additional files

Additional file 1:

Figure S1. Begg’s funnel plot for included studies. (JPG 75 kb)

Additional file 2:

Figure S2. Egger’s indicator test for included studies (JPG 69 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Y., Wei, S., Jiang, N. et al. The prognostic impact of decreased pretreatment haemoglobin level on the survival of patients with lung cancer: a systematic review and meta-analysis. BMC Cancer 18, 1235 (2018). https://doi.org/10.1186/s12885-018-5136-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-018-5136-5

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us