Yu SS, Zheng X, Li XS, Xu QJ, Zhang W, Liao ZL, Lei HK. Development of a nomogram for overall survival in patients with esophageal carcinoma: A prospective cohort study in China. World J Gastrointest Oncol 2025; 17(1): 96686 [DOI: 10.4251/wjgo.v17.i1.96686]
Corresponding Author of This Article
Hai-Ke Lei, Doctor, Associate Professor, Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing 400030, China. tohaike@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Prospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Shi-Shi Yu, Xi Zheng, Zhong-Li Liao, Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing 400030, China
Xiao-Sheng Li, Wei Zhang, Hai-Ke Lei, Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China
Qian-Jie Xu, Department of Health Statistics, School of Public Health, Chongqing Medical University, Chongqing 400016, China
Co-corresponding authors: Zhong-Li Liao and Hai-Ke Lei.
Author contributions: Yu SS conceived and designed the study; Zheng X wrote initial drafts of the paper; Li XS handled the data collection and statistical analysis; Xu QJ, Zhang W, performed analysis and interpretation of statistics; Liao ZL and Lei HK designed the study, revised the article and final approval of the version to be published. All authors collectively designed the methods and experiments, read, and approved the final manuscript. Yu SS and Zheng X contributed equally to this work as co-first authors. Liao ZL works at Department of Gastroenterology, Chongqing University Cancer Hospital and found there was need for a more scientifically robust, systematic, and practical model that incorporates clinically significant indicators to support decision-making. Lei HK works at Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital and is mainly responsible for follow-up of cancer patients. For this reason, Liao ZL and Lei HK designed the study, revised the article and final approval of the version to be published. They are designated as co-corresponding authors.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of Chongqing University Tumor Hospital (Approval No. CZLS2023338-A).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hai-Ke Lei, Doctor, Associate Professor, Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, No. 181 Hanyu Road, Shapingba District, Chongqing 400030, China. tohaike@163.com
Received: May 13, 2024 Revised: September 2, 2024 Accepted: September 9, 2024 Published online: January 15, 2025 Processing time: 213 Days and 2.1 Hours
Abstract
BACKGROUND
Esophageal carcinoma (EC) presents a significant public health issue in China, with its prognosis impacted by myriad factors. The creation of a reliable prognostic model for the overall survival (OS) of EC patients promises to greatly advance the customization of treatment approaches.
AIM
To create a more systematic and practical model that incorporates clinically significant indicators to support decision-making in clinical settings.
METHODS
This study utilized data from a prospective longitudinal cohort of 3127 EC patients treated at Chongqing University Cancer Hospital between January 1, 2018, and December 12, 2020. Utilizing the least absolute shrinkage and selection operator regression alongside multivariate Cox regression analyses helped pinpoint pertinent variables for constructing the model. Its efficacy was assessed by concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).
RESULTS
Nine variables were determined to be significant predictors of OS in EC patients: Body mass index (BMI), Karnofsky performance status, TNM stage, surgery, radiotherapy, chemotherapy, immunotherapy, platelet-to-lymphocyte ratio, and albumin-to-globulin ratio (ALB/GLB). The model demonstrated a C-index of 0.715 (95%CI: 0.701-0.729) in the training cohort and 0.711 (95%CI: 0.689-0.732) in the validation cohort. In the training cohort, AUCs for 1-year, 3-year, and 5-year OS predictions were 0.773, 0.787, and 0.750, respectively; in the validation cohort, they were 0.772, 0.768, and 0.723, respectively, illustrating the model's precision. Calibration curves and DCA verified the model's predictive accuracy and net benefit.
CONCLUSION
A novel prognostic model for determining the OS of EC patients was successfully developed and validated to help clinicians in devising individualized treatment schemes for EC patients.
Core Tip: In this study, we identified nine key independent risk factors associated with esophageal carcinoma patients. These factors span clinical characteristics (body mass index, Karnofsky performance status), the TNM stage, treatment approaches (surgery, radiotherapy, chemotherapy, and immunotherapy), and laboratory markers (platelet-to-lymphocyte ratio, albumin-to-globulin ratio). And then, a novel prognostic model was successfully developed and validated. It could be considered as a more systematic and practical model that incorporates clinically significant indicators to support decision-making in clinical settings.
Citation: Yu SS, Zheng X, Li XS, Xu QJ, Zhang W, Liao ZL, Lei HK. Development of a nomogram for overall survival in patients with esophageal carcinoma: A prospective cohort study in China. World J Gastrointest Oncol 2025; 17(1): 96686
Esophageal carcinoma (EC) stands as one of the predominant malignant tumors globally[1]. Data from global cancer statistics in 2020 indicate that there were 604000 new EC cases, with nearly half originating from China[2,3]. The five-year overall survival (OS) rate for those diagnosed with EC is a mere 21%[4]. Early diagnosis and comprehensive treatment strategies are currently the most effective means of improving patient outcomes. Identifying high-risk factors is crucial for crafting multidisciplinary treatment plans. However, the widely-used TNM staging system falls short of providing an accurate survival prognosis for EC patients due to its omission of clinical traits, treatment options, and laboratory data. Recent studies have identified several significant independent risk factors for EC, including body mass index (BMI), Karnofsky performance status (KPS), TNM stage, treatment modalities, the platelet-to-lymphocyte ratio (PLR), and the albumin-to-globulin ratio (ALB/GLB)[5-11]. Despite the advances, genetic research integrating factors like CDK8, ARID1A, and certain autophagy-related lncRNAs into prognostic models has been hindered by the high costs and complexity of gene testing, which adds to patient financial strain[12,13]. Additionally, the absence of valuable laboratory predictors has diminished the precision of some models[14]. This highlights the need for a more scientifically robust, systematic, and practical model that incorporates clinically significant indicators to support decision-making in clinical settings.
Moreover, Chongqing has been identified as a region with a notably high incidence of EC in China, where early detection rates are below 10%[15-17]. The vast majority of EC cases are diagnosed at advanced stages, resulting in poor survival outcomes. Thus, creating a more dependable prognostic model is crucial for guiding clinicians towards more effective anti-cancer treatment strategies, ultimately aiming to enhance the survival rates of patients with EC. In light of this, our study has pinpointed 9 high-risk factors linked to EC and constructed an accessible online predictive model.
MATERIALS AND METHODS
Subjects
Chongqing is recognized as a region with a notably high rate of EC in China. In our study, we gathered data from patients diagnosed with EC at the Chongqing University Cancer Hospital over a period spanning from January 1, 2018, to December 12, 2020. Eligibility criteria for inclusion in this study were set as follows: (1) Being 18 years or older; (2) Receiving a new EC diagnosis confirmed by pathology; (3) Undergoing and completing the prescribed course of treatment, which included options such as surgery, radiotherapy, chemotherapy, immunotherapy, or targeted therapy; and (4) Having a complete set of baseline clinical data and follow-up records. On the other hand, patients were excluded if they lacked follow-up records or had received treatment for cancer prior to this study. The methodology and progression of the study are illustrated in Figure 1.
Figure 1 Flow diagram of study design.
PLR: Platelet-to-lymphocyte ratio; ALB/GLB: Albumin-to-globulin ratio; LASSO: Least absolute shrinkage and selection operator; DCA: Decision curve analysis; ROC: Receiver operating characteristic.
Clinical evaluations
We collected a comprehensive set of data including demographic information (age and gender), clinical characteristics (KPS, BMI, pathological type, and TNM staging), treatment modalities (surgery, radiotherapy, chemotherapy, immunotherapy, and targeted therapy), laboratory markers (β2-microglobulin, neutrophil-lymphocyte ratio, lymphocyte-monocyte ratio, PLR, ALB/GLB, and the CD4/CD8 ratio), as well as follow-up details. The laboratory tests were conducted at the Chongqing University Cancer Hospital's laboratory. We excluded patients who were exclusively undergoing traditional Chinese medicine treatments due to the challenges associated with analyzing their incomplete or missing data.
The primary objective of this study was to assess the 1-year, 3-year, and 5-year OS rates. OS was measured from the date of EC diagnosis to the date of death or the last known follow-up. Follow-ups were conducted semiannually for the first two years post-diagnosis and annually thereafter until the demise of the subject. The study was concluded on December 31, 2023, with a one-month follow-up period acting as the cut-off point.
Statistical analysis
Continuous variables are presented as the mean ± SD, whereas categorical variables are shown as frequencies and percentages. To compare demographic and clinical variables between the training and validation cohorts, we utilized the Pearson χ2 test for categorical data. All statistical procedures were carried out using R software version 4.2.1 (Institute for Statistics and Mathematics, Vienna, Austria). The dataset was split into training and validation groups in a 7:3 ratio. We employed least absolute shrinkage and selection operator (LASSO) regression to identify risk factors, which were then evaluated through univariate Cox regression analysis.
Construction and validation of the prognostic prediction model
To develop an effective clinical prognostic model, we meticulously selected and identified variables for the prediction model through a two-step process. Initially, LASSO regression was applied to sift through patient characteristics within the training cohort. We determined the optimal parameter (λ) for LASSO regression via cross-validation, selecting crucial variables based on the minimum λ principle. Subsequent to this, we conducted both univariate and multivariate Cox regression analyses on these variables (Figure 2). Recognizing that the clinical relevance of these variables during the selection process is not solely based on their statistical significance, we ultimately pinpointed 9 predictors: BMI, KPS, TNM stage, surgery, radiotherapy, chemotherapy, immunotherapy, the PLR, and the ALB/GLB ratio. These 9 risk factors were incorporated into the prediction model, with each factor being allocated a score. This score can be derived from the illustrated ruler, allowing for the prediction of 1-, 3-, and 5-year OS rates by overlaying it onto the subsequent ruler.
Figure 2 Variable selection by least absolute shrinkage and selection operator Cox regression model.
A coefficient profile plot was produced against the log (lambda) sequence. A: 22 variables with nonzero coefficients were selected by optimal lambda; B: By verifying the optimal parameter (lambda) in the least absolute shrinkage and selection operator model, the partial likelihood deviance (binomial deviance) curve was plotted vs log (lambda) and dotted vertical lines were drawn based on 1 standard error criteria.
The model's capability to forecast survival outcomes was further appraised in the validation cohort by examining both its discrimination and calibration. The concordance index (C-index) was utilised to articulate the model’s proficiency in predicting the survival of patients whose prognoses have been objectively verified. To assess the model’s prediction accuracy and discriminative power, a calibration curve was plotted. Additionally, a receiver operating characteristic (ROC) curve was employed to confirm the model's generalization potential.
RESULTS
Demographic characteristics
A cohort of 3127 patients diagnosed with EC participated in the study and were systematically divided into two groups for analysis: A training group consisting of 2189 patients (approximately 70%) and a validation group comprising 938 patients (around 30%), following a 7:3 distribution ratio. According to statistical analysis, no significant differences were observed between the training and validation groups, as presented in Table 1. For the entire cohort, the median survival time was recorded at 27.50 months, with a range spanning from 0.10 to 60.45 months. Within the training cohort, there were 1267 fatalities over a median duration of 27.40 months (ranging from 0.10 to 61.10 months). In the validation group, 391 patients succumbed, with a median survival time extending to 28.00 months, also within a broad range of 0.10 to 59.30 months.
Table 1 Patient demographics and clinical characteristics.
Univariate and multivariate logistic regression results
The univariate logistic regression analysis revealed nine prognostic factors linked to patient outcomes, including KPS (HR 0.98, 95%CI: 0.97-0.99, P < 0.001), BMI ( < 18.5: HR 1.29, 95%CI: 1.11-1.49, P = 0.001), TNM (II: HR 2.16, 95%CI: 1.56-2.98, P < 0.001; III: HR 3.04, 95%CI: 2.22-4.18, P < 0.001; IV: HR 4.12, 95%CI: 2.98-5.70, P < 0.001), surgery (HR 0.56, 95%CI: 0.49-0.64, P < 0.001), radiotherapy (HR 0.72, 95%CI: 0.64-0.81, P < 0.001), chemotherapy (HR 0.74, 95%CI: 0.65-0.84, P < 0.001), immunotherapy (HR 0.73, 95%CI: 0.54-0.98, P = 0.033), and PLR (HR 0.81, 95%CI: 0.67-0.98, P = 0.034), and ALB/GLB (HR 1.01, 95%CI: 1.01-1.01, P < 0.001) (Table 2).
Table 2 Univariate and multivariate analysis for overall survival of the training cohort.
Characteristics
HR (univariable)
HR (multivariable)
Age
1.02 (1.01-1.02, P < 0.001)
KPS
0.95 (0.94-0.95, P < 0.001)
0.98 (0.97-0.99, P < 0.001)
Gender (%)
Male
Female
0.85 (0.74-0.99, P = 0.033)
BMI (%)
18.5-23.9
≥ 24
0.76 (0.66-0.88, P < 0.001)
0.94 (0.82-1.09, P = 0.428)
< 18.5
1.68 (1.45-1.94, P < 0.001)
1.29 (1.11-1.49, P = 0.001)
Base disease (%)
No
Yes
1.04 (0.91-1.20, P = 0.578)
Pathological (%)
SCC
Others
1.41 (1.03-1.92, P = 0.032)
TNM (%)
I
II
2.10 (1.52-2.90, P < 0.001)
2.16 (1.56-2.98, P < 0.001)
III
3.13 (2.29-4.28, P < 0.001)
3.04 (2.22-4.18, P < 0.001)
IV
5.51 (4.03-7.54, P < 0.001)
4.12 (2.98-5.70, P < 0.001)
Radiotherapy (%)
No
Yes
0.74 (0.66-0.83, P < 0.001)
0.72 (0.64-0.81, P < 0.001)
Chemotherapy (%)
No
Yes
0.72 (0.64-0.81, P < 0.001)
0.74 (0.65-0.84, P < 0.001)
Surgery (%)
No
Yes
0.41 (0.37-0.46, P < 0.001)
0.56 (0.49-0.64, P< 0.001)
Immunotherapy (%)
No
Yes
0.54 (0.41-0.72, P < 0.001)
0.73 (0.54-0.98, P = 0.033)
Targeted (%)
No
Yes
0.78 (0.57-1.07, P = 0.120)
LDH
1.01 (1.00-1.01, P = 0.057)
β2. microglobulin
1.14 (1.09-1.19, P < 0.001)
CD4/CD8
0.93 (0.88-0.99, P = 0.018)
ALB/GLB
0.43 (0.35-0.52, P < 0.001)
0.81 (0.67-0.98, P = 0.034)
PLR
1.01 (1.01-1.01, P < 0.001)
1.01 (1.01-1.01, P < 0.001)
LMR
0.99 (0.99-1.00, P = 0.120)
NLR
1.06 (1.05-1.07, P < 0.001)
Prognostic prediction model performance and validation
In the developed prognostic prediction model, each identified risk factor was assigned a corresponding score, using a method where scores are derived from an upper ruler and superimposed onto a lower ruler for the prediction of 1-, 3-, and 5-year OS rates (as shown in Figure 3). Additionally, an online calculator was developed based on this predictive model (available at https://cqchprognosis.shinyapps.io/esophagus/) to estimate the long-term OS for patients with EC. Illustratively, for a patient with stage III EC, a BMI of 25 kg/m2, a KPS score of 85, a PLR of 193, ALB/GLB ratio of 2, without undergoing radiotherapy and immunotherapy, treated with surgery and chemotherapy, the model estimates a 5-year OS probability of 44%.
Figure 3 Prediction model for predicting 1-, 3- and 5-year overall survival of patients with Esophageal carcinoma.
A: Nomogram model; B: The interface of the web-based nomogram. PLR: Platelet-to-lymphocyte ratio; ALB/GLB: Albumin-to-globulin ratio; BMI: Body mass index; KPS: Karnofsky performance status.
The model's C-index for OS was found to be 0.715 (with a 95%CI of 0.701-0.729) in the training cohort, and 0.711 (95%CI: 0.689-0.732) in the validation cohort. In terms of the ROC values within the training cohort, the prediction capabilities for 1-, 3-, and 5-year OS stood at 0.773, 0.787, and 0.750, respectively (Figure 4A); for the validation cohort, these values were 0.772, 0.768, and 0.723, respectively (Figure 4B). Additionally, the calibration curves illustrated good agreement between the predicted and observed probabilities for 1-, 3-, and 5-year OS in both the training (Figure 4C) and validation cohorts (Figure 4D), according to the predictive model.
Figure 4 Receiver operating characteristic and calibration curves of the prediction model for 1-, 3- and 5-year overall survival prediction.
A: Receiver operating characteristic (ROC) in the training cohort; B: ROC in the validation cohort; C: Calibration plot in the training cohort; D: Calibration plot in the validation cohort. AUC: Area under the receiver operating characteristic curve; OS: Overall survival.
Moreover, decision curve analysis (DCA) was employed to evaluate the model's predictive performance, showing that the model yielded a positive net benefit in both training (Figure 5A) and validation cohorts (Figure 5B). Utilizing this newly established model, we grouped patients from both the training set (Figure 5C) and the validation set (Figure 5D) into high or low risk categories. The findings underscored the model's robust capability to distinguish between patients at high and low risk (P < 0.05).
Figure 5 Decision curve analysis for the prediction model’s ability to predict overall survival in Esophageal carcinoma patients and the prediction model distinguished the risk of Esophageal carcinoma patients.
A: Decision curve analysis (DCA) in the training cohort; B: DCA validation cohort; C: The prediction model in the training cohort; D: The prediction model in the validation cohort.
DISCUSSION
In this study, we utilized both univariate and multivariate Cox regression analyses to identify nine key independent risk factors associated with EC. These factors include clinical characteristics (BMI and KPS), TNM stage, treatment approaches (surgery, radiotherapy, chemotherapy, and immunotherapy), and laboratory markers (PLR, ALB/GLB). Leveraging these factors, we developed a prognostic risk prediction model to predict OS rates in EC patients. This model exhibited high reliability and reproducibility, as evidenced by its calibration curve and C-index. Furthermore, DCA demonstrated that this model more accurately predicted EC outcomes than the TNM stage. The key findings of our research include the integration of a broader scope of independent risk factors into our predictive model than that incorporated by previously utilized models, which improved prognostic risk assessment in EC patients. These factors, which align with findings from prior studies, encompass clinical characteristics, treatment methods, and particular laboratory indicators linked to systemic inflammation[18,19]. Notably, our model underscores the significance of comprehensive treatment and monitoring strategies over reliance solely on the TNM stage, highlighting factors such as low BMI and KPSs, types of treatment received, and specific biomarkers as integral to predicting patient outcomes[20-26].
The traditional KPS is an important tool in oncology for assessing functional performance and aiding in prognosis. It has high reliability and adaptability across various scenarios and serves as a crucial prognostic factor in most prognostic models[27]. A study by Freeman et al[28] revealed that KPSs are closely associated with progression-free survival in patients with breast cancer that has metastasized to the brain and can effectively reflect the quality of life of these patients. Similarly, Bao et al[29] reported that a lower KPS was significantly associated with shorter OS in a study of prognostic indicators for glioma, suggesting that the KPS can be a powerful indicator for investigating the prognosis of patients with glioma. Although the KPS is widely recognized for its application in oncology, some argue that it may not fully capture the complexity of patient function in different rehabilitation environments[30], indicating the need for supplementary assessment tools. Therefore, our nomogram model additionally incorporates other clinical feature variables as predictive factors, which significantly improved the model's predictive accuracy and generalizability.
The clinical relationship between the BMI and the risk of EC has long been ambiguous. Some studies suggest that a high BMI is associated with an increased risk of EC[31], whereas others find that a low BMI is related to a greater risk of developing EC[32]. Recently, a meta-analysis based on evidence from 25 observational studies indicated that being underweight, compared with a normal weight, is significantly associated with an increased risk of EC (RR = 1.78, 95%CI: 1.48-2.14; P < 0.001). Additionally, being overweight or obese was found to increase the risk of esophageal adenocarcinoma (RR = 1.56, 95%CI: 1.42-1.71, P < 0.001; RR = 2.34, 95%CI: 2.02-2.70, P < 0.001) while simultaneously decreasing the risk of esophageal squamous cell carcinoma (RR = 0.71, 95%CI: 0.60-0.84, P < 0.001; RR = 0.63, 95%CI: 0.60-0.84, P = 0.002)[33]. In our study, a BMI of less than 18 was associated with the highest risk of EC compared with other BMI levels. Interestingly, overweight individuals demonstrated a lower risk of EC, which we speculate is because our selected EC patients were predominantly those with esophageal squamous cell carcinoma.
The TNM staging system, an important indicator for assessing tumor progression, also reflects patient survival chances to a certain extent. Recent studies have shown that incorporating the TNM stage as a predictive factor in conventional tumor prognostic models can significantly increase the predictive accuracy of these models[34]. In our research, TNM stage emerged as a crucial predictor of OS in EC patients, showing substantial predictive value in the nomogram plot. Recent studies have shown that the presence of systemic inflammation is associated with poorer survival rates in patients with EC. In this study, patients with a higher PLR often had worse survival rates, indicating that the PLR is a risk factor. Zheng et al[35] investigated the prognostic value of the PLR in EC patients and reported that a high PLR is significantly associated with poorer disease-free survival, which suggested that the PLR can serve as a predictive factor for quality of life in EC patients. Similarly, a lower ALB/GLB ratio has been associated with poorer prognosis[36], which is consistent with our study. Surgery, radiotherapy, chemotherapy and immunotherapy are the primary clinical treatments for EC. A recent review noted that surgical treatment remains the primary therapeutic approach for patients with EC[37]. Consequently, in our study, patients who did not undergo surgical treatment had poorer prognoses. Notably, other treatment modalities, such as immunotherapy, chemotherapy, and radiotherapy, can also effectively prolong survival time and improve the quality of life of patients. Moreover, targeted therapy had a limited influence on the OS of EC patients in our study and warrants further investigation.
Because all of the above factors are easily measured in the clinical setting, our model is clinically practical. Another practical contribution of our model is the creation of an accessible online calculator designed to aid clinicians in tailoring treatment plans through dynamic survival predictions at various time points. This tool aims to minimize the economic burden on patients while maximizing the model’s utility in clinical settings.
However, our work is not without limitations. The exclusive use of data from a single medical institution and the absence of a broader prospective cohort study underline the need for further validation through larger-scale, multicenter research. Furthermore, the lack of imaging data in our study points to an area for future enhancement, potentially increasing the robustness of our findings.
CONCLUSION
In conclusion, our comprehensive predictive model for EC patients, supported by an easy-to-use online calculator, presents a novel approach to improving patient outcomes through personalized interventions.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Oncology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade C, Grade C
Novelty: Grade B, Grade B
Creativity or Innovation: Grade B, Grade B
Scientific Significance: Grade B, Grade B
P-Reviewer: Sun X S-Editor: Qu XL L-Editor: A P-Editor: Yu HG
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