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Peripheral hemoglobin to albumin ratio predicts prognosis in patients with nasopharyngeal carcinoma underwent concurrent chemoradiotherapy

BMC Cancer volume 24, Article number: 1012 (2024) Cite this article

Abstract

Background

Recently, the hemoglobin to albumin ratio (HAR) has been shown to be closely associated with the survival of certain malignancies. However, its prognostic value in nasopharyngeal carcinoma (NPC) remained to be elucidated. Herein, we aimed to explore the correlation between HAR and overall survival (OS) in NPC patients treated with concurrent chemoradiotherapy (CCRT).

Methods

This retrospective study included a total of 858 patients with NPC receiving CCRT between January 2010 and December 2014 in Sun Yat-sen University Cancer Center. We randomly divided them into the training cohort (N = 602) and the validation cohort (N = 206). We performed univariate and multivariate Cox regression analyses to identify variables associated with OS, based on which, a predictive nomogram was constructed and assessed.

Results

In both the training and validation cohorts, patients were classified into low- and high-HAR groups according to the cutoff value determined by the maximally selected rank statistics. This HAR cutoff value effectively divided patients into two distinct prognostic groups with significant differences. Multivariable Cox analysis revealed that higher T-stage, N-stage, and HAR values were significantly related to poorer prognosis in NPC patients and served as independent prognostic factors for NPC. Based on these, a predictive model was constructed and graphically presented as a nomogram, whose predictive performance is satisfactory with a C-index of 0.744 [95%CI: 0.679–0.809] and superior to traditional TNM staging system [C-index = 0.609, 95%CI: 0.448–0.770].

Conclusion

The HAR value was an independent predictor for NPC patients treated with CCRT, the predictive model based on HAR with superior predictive performance than traditional TNM staging system might improve individualized survival predictions.

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Introduction

As a head and neck epithelial malignancy, nasopharyngeal cancer (NPC) exhibits distinct geographical distribution patterns, with the highest incidence rates observed in East Asia and Southeast Asia [1, 2]. With the widely use of concurrent chemoradiotherapy (CCRT) and advancements in diagnostic and therapeutic approaches, the mortality rate of NPC has significantly declined [3]. Presently, the severity of NPC in clinical practice is primarily assessed by the tumor-node-metastasis (TNM) staging system [4]. However, it is worth noting that this anatomically based staging system has limited predictive ability for prognosis and treatment outcomes in certain patients [5, 6]. Consequently, numerous studies have been conducted to assess whether incorporating additional clinical factors and molecular biomarkers into the system can enhance the predictive efficiency of survival status, such as EBV DNA [4, 7], DNA methylation markers [8, 9], non-coding RNAs etc. [10, 11].

In recent years, “liquid biopsy” has gained increasing popularity for several diseases including cancers. Compared to conventional detection methods (tissue histopathology and biopsies), liquid biopsy typically utilizes substances present in easily accessible body fluids like blood and urine as detection indicators [12,13,14]. It offers significant advantages in terms of simplicity, cost-effectiveness, and excellent reproducibility [13]. Significant advancements have been made in the early diagnosis, auxiliary subtyping, prognostic prediction, and treatment response prediction of various malignancies [13, 15, 16]. Nevertheless, the application of liquid biopsy in NPC is currently eminently limited. Therefore, delving deeper into exploring novel biomarkers in body fluids that are associated with treatment response or prognosis of NPC holds tremendous prospects.

In recent years, a plethora of blood-derived markers have been utilized in the construction of prognostic models for various malignancies, including NPC [7, 17]. Most previous studies have included these biochemical tests. These encompass indices such as the hemoglobin, albumin, albumin to globulin ratio, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, monocyte-lymphocyte ratio, and systemic immune-inflammation index, among others [18,19,20,21,22]. Recently, researchers have highlighted the correlation between hemoglobin level with the response to radiotherapy and chemotherapy as well as the prognosis of patients with NPC [23, 24]. Moreover, decreased hemoglobin and albumin levels were significantly associated with worse OS in a cohort study of 8093 patients with NPC [25]. Low hemoglobin below 11.4 was an independent adverse prognostic factor for worse survival in head and neck cancer [26]. Additionally, HAR, the ratio of hemoglobin to albumin, in gastric cancer patients has demonstrated predictive significance for short-term prognosis [27]. Nevertheless, the role of HAR in NPC remains unclear.

Herein, we aimed to elucidate the prognostic significance of HAR in NPC and attempt to construct a prognostic nomogram to evaluate the prognosis of NPC patients receiving CCRT.

Methods

Study population

This retrospective study included a consecutive cohort of NPC patients receiving platinum-based CCRT at the Sun Yat-sen University Cancer Center (SYSUCC) between January 2010 and December 2014. All patients underwent intensity-modulated radiotherapy (IMRT). IMRT have been performed since 2010 in our center. There was significant consistency in IMRT during this time, and the patient population was good representative of the standard treatment for CCRT. Primary inclusion criteria employed in this study consist of the following: (I) Previously untreated NPC confirmed through histological and radiological assessments without evidence of metastasis; (II) Definitive IMRT combined with weekly or tri-weekly platinum-based concurrent chemotherapy; (III) Age ≥ 18 years old; (IV) availability of clinical, histological and follow-up information; (V) No previous antitumor therapy. The primary exclusion criteria concluded here were as follows: (I) History of secondary cancer; (II) With chronic inflammatory diseases; (III) Karnofsky performance score ≤ 70. All patients recruited in current study were restaged according to the updated 8th edition TNM system of the American Joint Committee on Cancer (AJCC) [28].

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Research Ethics Committee of SYSUCC (approval number: B2023-573-01). Given the retrospective nature of this study, written informed consent from patients was waived.

Study design and data collection

We randomly allocated NPC patients recruited current study to the training and validation cohorts in a ratio of 7:3. The predictive model was established based on patient characteristics and survival outcomes in the training cohort, and the validation cohort was utilized to estimate this model.

The blood test items, namely the complete blood count and blood chemistry analysis, included hemoglobin and albumin values. Blood samples were collected after conformed diagnosis within one week. Hemoglobin and albumin were analyzed using Roche COBAS702 automated biochemistry. Clinical pathological information was extracted from the patients’ medical records. Based on previous studies, variables related to NPC patients’ prognosis were incorporated in current study, including age, gender, body mass index (BMI), T stage, N stage and routine inspection index (serum lactate dehydrogenase, HGB and ALB) [29]. We calculated BMI as weight (kg)/square of the height in meters (m2) and classified patients into normal (BMI ≤ 24), overweight (24 < BMI < 28) and obese (BMI ≥ 28). EBV-DNA, the most powerful biomarker of NPC, was also included [30]. HAR was calculated as the ratio of HGB to ALB.

Follow-up and the endpoint

The treatment and follow-up plan were carried out following guidelines previously described [31]. The primary endpoint of our study was overall survival (OS), defined as the time from the date of NPC diagnosis to the date of death from any case or the last follow-up.

Statistical analysis

Categorical variables are reported as frequencies along with their respective percentages. The optimal threshold of HAR value was established using the “maxstat” package through maximally selected rank statistics [32], and according to which, NPC patients were allocated into distinct high and low groups. Kaplan-Meier method was used to estimate their survival curves and we utilized the log-rank test to further compare them. Variables met with P < 0.05 in the univariate Cox regression analysis were included in the multivariate Cox regression analysis, which have been tested on basis of the Schoenfeld residuals [33], wherein variables reached to the pre-stated P value (< 0.05) were considered as independent predictors. Then, we incorporated all predictors to construct a predictive model and graphically presented it as a nomogram. Subsequently, the predictive performance and discriminative ability of the model in cohorts were evaluated using the C-index calculated by the “rms” package, time-dependent receiver operating characteristics (tROC) via the “timeROC” package. A two-tailed P-value of < 0.05 was considered statistically significant. All statistical analyses were conducted using R 4.3.1.

Results

Patient characteristics

Between January 2010 and December 2014, 858 NPC patients treated with platinum based CCRT were included in this study, we randomly divided them into the training (n = 602) and the validation cohorts (n = 256) in a 7:3 ratio. The baseline clinicopathological characteristics of two cohorts were presented in Table 1, and they matched well with each other.

Table 1 Patient demographics and clinical characteristics between the training and validation cohorts
Full size table

In the training and validation cohorts, 315 (52.3%) and 123 (48.0%) patients were aged over 45, respectively. More than half of patients (72.6% in the training cohort and 78.1% in the validation cohort) were male. Herein, there were 4 patients with (keratinizing squamous cell carcinoma) WHO I, 8 patients with (differentiated non-keratinizing carcinoma) WHO II type, and 590 patients with (undifferentiated non-keratinizing carcinoma) WHO III type in the training cohorts. In the validation cohort, There were 0 patients with WHO I, 1 patients WHO II type, and 255 patients with WHO III type, respectively. 198 (32.9%) cases in the training cohort and 81 (31.6%) cases in the validation cohort had a value of EBV-DNA ≥ 4000 copy/ml. Patients in the training cohort were subsequently separated into two groups: high-HAR (n = 232, scored ≥ 3.32) and low-HAR (n = 370, scored < 3.32), utilizing the optimal cutoff value of HAR of 3.32 established through the maximally selected rank statistics (Figure S1). By applying the same cutoff value of 3.32, patients in the validation cohort were segregated into high-HAR (n = 111, scored ≥ 3.32,) and low-HAR (n = 145, scored < 3.32) groups.

Prognostic value of HAR for OS in NPC

The median follow-up of the whole cohort was 64.7 months (interquartile range (IQR): 58.5–76.7 months), and the median OS was 62.7 months (IQR: 46.5–74.8 months). There was a total of 83 death events, including 55 events in the training cohort and 28 events in the validation cohort. In the whole cohort, the 1-, 3-, and 5-year OS rates were 98.0%, 94.2%, and 90.1%, respectively. In the training cohort, the 1-, 3-, and 5-year OS rates were 98.2%, 94.3%, and 91.1%, respectively. In the validation cohort, the 1-, 3- and 5-year OS rates were 97.6%, 93.9%, and 87.9%, respectively. There was no significant difference of OS between the training and validation cohorts (P = 0.37) (Figure S2). Kaplan-Meier curves demonstrated a superior survival among patients in the high-HAR group both in training and validation cohorts (Fig. 1A, HR = 0.37; 95% confidence interval (CI): 0.21–0.64, P < 0.001; Fig. 1B, HR = 0.36; 95% CI: 0.16–0.79, P = 0.008).

Fig. 1
figure 1

Kaplan-Meier curves of various survival endpoint between the high-HAR and low-HAR group for overall survival. (A) Training cohort survival curves. (B) Validation cohort survival curves

Full size image

Identification of independent predictors

In the training cohort, In the univariate Cox model, variables met predetermined significance threshold (P < 0.05) in the univariate cox regression analysis, including age, T stage, N stage, EBV-DNA status, and HAR score, were subsequently included in the multivariate Cox regression model. A diagnostic test for multicollinearity was conducted by calculating the variance inflation factors (VIFs) of these variables, all of which were found to be less than 10, indicating no significant presence of multicollinearity among above variables. Based on the proportional hazard diagnostic plots (Figure S3), it could be concluded that the multivariable modeling satisfied the assumption of proportional hazards. Results from multivariate modeling revealed independent associations between T stage, N stage, and HAR score with OS among NPC patients treated with CCRT (Table 2). The HAR correlated with gender and BMI in the training cohort (Table S1), and correlated with gender in the validation cohort (Table S2).

Table 2 Univariate and multivariate Cox regression analyses of overall survival in the training cohort
Full size table

Development of the novel prognostic model based on HAR

A novel prognostic nomogram model was developed for individual survival prediction at 1-, 3-, and 5-year intervals, based on the aforementioned three independent indicators derived from multivariate modeling (Fig. 2). Before the CCRT implementation, the total score for each patient could be calculated by summing the scores from each of the three prognostic factor subclasses. Then, the corresponding probabilities of 1-, 3-, and 5-year OS could be forecasted by locating the total score on the OS rate scale. For instance, if a patient presented with T4 stage, N3 stage, and HAR score ≥ 3.32, their total points were: 10 + 7.74 + 4.69 = 22.43, resulting in respective probabilities of OS at years 1, 3, and 5 follows: 85%, 61%, and 42%.

Fig. 2
figure 2

A nomogram to predict the 1-, 3- and 5-year overall survival of NPC patients receiving concurrent chemoradiotherapy

Full size image

Assessment of predictive performance of the prognostic model

The calibration plots (the Y-axis represents the actual observed survival, while the X-axis represents the nomogram predicted survival) for the 1-, 3-, and 5-year OS present good agreement between predicted OS and observed OS in both the training (Fig. 3A) and validation cohorts (Fig. 3B). The developed prognostic model showed satisfactory efficacy with C-index of 0.744 (95% CI: 0.679–0.809) and 0.736 (95% CI: 0.652–0.820) in the training and validation cohorts. It was obviously superior to the traditional TNM staging system with C-index of 0.640 (95% CI: 0.537–0.742) and 0.609 (95% CI: 0.448–0.770) in the training (Fig. 3C) and validation cohorts (Fig. 3D). Time-dependent ROC curves were then provided to evaluate the predictive performance of the HAR model. The two curves indicated that HAR had better predictive efficiency than HGB, ALB, and traditional TNM staging system. (Figure S4A and Figure S4B).

Fig. 3
figure 3

The calibration and comparison of the predictive capabilities of this HAR-based prognostic model. (A) Calibration plot in the training cohort. (B) Calibration plot in the validation cohort. (C) Time-dependent ROC curves comparison between the current prognostic model and traditional TNM staging system in the training cohort. (D) Time-dependent ROC curves comparison between the current prognostic model and traditional TNM staging system in the validation cohort

Full size image

Discussion

Herein, we initially identified the HAR as an independent prognostic factor for NPC patients receiving CCRT at our center. Subsequently, a novel prognostic prediction model based on the HAR value was developed and demonstrated to have favorable predictive performance in both training and validation cohorts. Comparison with invasive and experience-dependent histological examinations, as well as costly and less accessible genetic testing methods, the use of sub-parameters, such as HGB and ALB levels, extracted from routine laboratory tests such as complete blood count and serum biochemistry, offers great advantages in clinic. And according to the data from our study, our predictive nomogram constructed based on HAR demonstrated superior prognostic ability to the traditional TNM staging system, which indicated that this HAR-based prognostic model might serve as a convenience, non-invasive, affordable, and reliable tool to improve the prognostic predictions and help clinicians treatment decision-making for NPC patients underwent CCRT.

NPC, as a relatively uncommon epithelial malignancy with prominent regional variations, has not received sufficient attention and research for a period [2]. For NPC patients in China, non-keratinizing carcinoma especially undifferentiated non-keratic carcinoma accounts for the majority of NPC [3]. In recent years, there has been a varying degree of decline in the incidence and mortality rates of NPC owing to the changing lifestyles and advancements in diagnostic and therapeutic approaches [3]. Nevertheless, in specific regions and populations, the incidence of NPC remains to be worthy of attention. Furthermore, NPC exhibits significant biological heterogeneity [34]. According to the latest eighth edition TNM staging system, patients with same stage tend to exhibit diverse survival outcomes following comparable treatments [6], which reflects that current anatomically based staging systems are insufficient to accurately predict treatment efficacy and prognosis.

With the development of molecular biotechnology in recent years, increasing researchers focus on other clinicopathological factors and molecular biomarkers, such as DNA and non-coding RNA found in tumor tissues or circulating in the blood [7, 11]. One of the most representative markers is the EBV-DNA, which demonstrates high sensitivity and specificity for the early diagnosis and screening of NPC [8]. However, its predictive efficacy for NPC staging and prognosis is inadequate. Additionally, due to the low abundance of circulating DNA fragments, its enrichment and detection require precise equipment that may not be available in lower-tier hospitals in counties and townships. Interestingly, some gene expression-based predictive signatures have been proposed as a potential prognostic tool for NPC [35, 36]. While genetic testing remains expensive, with complex procedures and insufficient reliability in terms of reproducibility. Therefore, widespread adoption of genetic testing for NPC still has a long way to go. In the present times, the ideal prognostic biomarkers are often considered as features that can independently identify individual prognoses, apart from traditional classification systems such as the TNM staging system. These biomarkers aim to improve prognosis and possess several advantages, such as reasonably price, easily accessible, minimally invasive, or non-invasive, exhibit clear indicators, and scalable for large-scale implementation in different healthcare settings. Among these, indicators found in routine blood tests demonstrate the most significant advantages.

The decline in HGB levels often occurs early during malignant tumors and persists throughout the disease progression [18, 37]. Some studies have reported that the content of HGB could impact the oxygen transport process in the body, thereby being associated with various treatment-related complications and the efficacy of interventions such as radiotherapy and chemotherapy [24, 38]. As for the serum ALB, it is one of the most utilized indicators to evaluate the nutritional status of cancer patients [18]. Malnutrition is directly associated with a variety of adverse outcomes in tumors, including reduced quality of life, decreased response to drug therapy, increased complications, and increased drug toxicity. As tumor progresses, inadequate intake and inflammatory stress could cause metabolic abnormalities, albumin production decreases and consumption increases, leading to a significant reduction in blood albumin levels, which has been proven as a good predictor of prognosis in many cancers.

In this study, the organic combination of the two indexes indicating the ratio of HGB to ALB was an independent prognostic factor for patients with NPC receiving standard treatment. Further exploration confirmed that the prognostic model containing HAR had significant advantages in prognostic prediction compared with the most utilized TNM staging system. This model predicts 1-, 3-, and 5-prognosis AUC of more than 0.7 in NPC patients. This suggests that our model might provide greater benefits for patients with NPC compared with previous models. At the same time, it also indicated us that some commonly used indicators in body fluids might be combined to construct new indicators to provide more accurate prediction models for diseases in an economical and convenient way.

In our study, the 1-, 3-, and 5-year OS rates were 98.0%, 94.2%, and 90.1%, respectively. The survival rates were higher compared to previous study [39], because according to our exclusion criteria, patients with Karnofsky performance score ≤ 70 were excluded, and the patients were generally in good condition and could receive treatment with sufficient intensity, which might improve the OS rate.

There were several limitations of our study. Firstly, the retrospective studies have some biases, such as selection bias, information bias, or confounding factors that cannot be controlled. Prospective design can improve the repeatability and applicability of study results. Therefore, based on the current results, a prospective study is warrant in future to to validate the HAR model. Secondly, patients included here might not represent a typical sample of all NPC patients diagnosed and treated at our center, as we focused on a population of NPC patients equipped with certain indicators. Thirdly, the hematological markers of NPC patients might fluctuate during treatment, leading to variations in the HAR value at different time points. We intend to collect more data for further dynamic analysis to verify this model. Lastly, due to the unavailability of high-quality data from other hospitals, we were unable to externally validate the prognostic model constructed in this study, which somewhat limits the generalizability of our findings. Therefore, multicenter external validation is warrant in future to strengthen our conclusions.

Conclusion

High HAR was significantly associated with poor prognosis and was an independent predictor of the prognosis of NPC patients treated with CCRT. The HAR-based predictive nomogram model presented superior predictive performance than traditional staging system, thus, this HAR score might help physicians to estimate individualized survival prognosis more accurate for NPC patients underwent CCRT.

Data availability

The data analyzed in this study are available from the corresponding author (Chaxiang Teng, E-mail: tengchaxiang@163.com) on reasonable requests.

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Acknowledgements

We would like to thank all patients involved in this study.

Funding

Not applicable.

Author information

Author notes

  1. Chao Zhang, Si-Fen Wang and Yu-Ling Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Oncology, Shenzhen Qianhai Taikang Hospital, Shenzhen, 518054, People’s Republic of China

    Cha-Xiang Teng

  2. State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, People’s Republic of China

    Chao Zhang & Si-Fen Wang

  3. Department of Breast Oncology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China

    Chao Zhang

  4. Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China

    Si-Fen Wang

  5. Department of Endocrinology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, China

    Yu-Ling Zhang

Authors
  1. Chao Zhang
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  2. Si-Fen Wang
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  3. Yu-Ling Zhang
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  4. Cha-Xiang Teng
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Contributions

Conceptualization, C-XT; methodology, CZ, S-FW, Y-LZ, and C-XT; software, CZ, S-FW, Y-LZ, and C-XT; validation, CZ, S-FW, Y-LZ; formal analysis, CZ; investigation, CZ, S-FW, Y-LZ; resources, CZ, S-FW, C-XT; data curation, CZ, C-XT.; writing-original draft preparation, CZ and S-FW; writing-review and editing, C-XT; visualization, C-XT.; supervision, CZ, C-XT; project administration, CZ, C-XT. All authors have read and agreed to the submitted version of the manuscript.

Corresponding author

Correspondence to Cha-Xiang Teng.

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Ethics approval and consent to participate

The study was approved by the Research Ethics Committee of Sun Yat-sen University Cancer Center (approval number: B2023-573-01). The requirement for informed consent was waived by the Ethics Committee of Sun Yat-sen University Cancer Center because of the retrospective nature of the study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Zhang, C., Wang, SF., Zhang, YL. et al. Peripheral hemoglobin to albumin ratio predicts prognosis in patients with nasopharyngeal carcinoma underwent concurrent chemoradiotherapy. BMC Cancer 24, 1012 (2024). https://doi.org/10.1186/s12885-024-12763-z

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  • DOI: https://doi.org/10.1186/s12885-024-12763-z

Keywords

BMC Cancer

ISSN: 1471-2407

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