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. 2025 Jan 8;15:1322. doi: 10.1038/s41598-025-85287-6

Novel metabolic prognostic score for predicting survival in patients with cancer

Jinyu Shi 1,2,3,4,#, Chenan Liu 1,2,3,4,#, Xin Zheng 1,2,3,4,#, Yue Chen 1,2,3,4, Heyang Zhang 1,2,3,4, Tong Liu 1,2,3,4, Qi Zhang 5, Li Deng 1,2,3,4, Hanping Shi 1,2,3,4,✉
PMCID: PMC11711642  PMID: 39779840

Abstract

Cancer is a fatal disease with a high global prevalence and is associated with an increased incidence of metabolic disorders. This study aimed to develop a novel metabolic prognostic system to evaluate the overall metabolic disorder burden in cancer patients and its relationship with their prognosis. The patients in this study were enrolled from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) project. The least absolute shrinkage and selection operator (LASSO) analysis was used to screen for indicators of metabolic disorders. Cox regression analysis was used to evaluate the independent association between indicators of metabolic disorders and mortality in patients. The Kaplan–Meier method was used to evaluate the survival of patients with varying burdens of metabolic disorders. Finally, nomogram prognostic models and corresponding calculators were constructed and evaluated using the areas under the receiver operating characteristic curves (AUC), decision curve analysis (DCA), and calibration curves. Five of the 19 hematological indexes, including hemoglobin, neutrophils, direct bilirubin, albumin, and globulin, were selected as the evaluation indicators of metabolic disorder burden and independent risk factors for prognosis in cancer patients. Patients with a higher metabolic disorder burden had poorer survival rates. The AUC of the 1-year, 3-year, and 5-year overall survival of the prognostic nomogram was 0.678, 0.664, and 0.650, respectively. DCA and calibration curves indicated that the clinical benefit rate of metabolic disorder burden prognostic markers was high. Patients with a higher metabolic disorder burden had poorer survival rates. The nomogram and corresponding calculator can accurately evaluate the metabolic disorder burden and predict the prognosis of patients with cancer.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-85287-6.

Keywords: Metabolic disorder burden, Prognosis, Cancer, Nomogram

Subject terms: Cancer, Biomarkers

Introduction

The global incidence of cancer has dramatically increased over the past few decades. The 2020 global cancer statistics have shown that there are an estimated 19.3 million new cancer cases and nearly 10 million cancer-related deaths worldwide. By 2040, the global cancer burden is expected to reach 28.4 million cases1. The occurrence and development of cancer is complex, and the significant increase in morbidity and mortality may be attributable to increased risk factors in modern life, including poor diet (ultra-processed food, high fat and sugar, and excessive nutrition), lack of adequate activity (sedentary lifestyle), and increased obesity2–5. These factors inevitably lead to the occurrence of metabolic disorders.

Metabolic abnormalities are a key feature of tumour cells. Increasingly, epidemiological and basic research and clinical evidence have shown that metabolic disorders are closely related to the occurrence and development of cancer6–8. Tumour cells alter their ability to metabolise sugars, lipids, proteins, and other substances to support rapid cell proliferation9. Rapidly proliferating tumour cells rely on the glycolytic pathway to produce ATP10. Increasing evidence suggests that lipid metabolism in tumour cells is influenced by several factors, such as cell membrane synthesis, lipid synthesis and degradation, and signalling capacity11,12. Patients with cancer have dysfunctional protein metabolism, massive skeletal muscle wasting, and cachexia13. Additionally, bilirubin affects tumour initiation and development via antioxidant, anti-inflammatory, and other functions14. An increased level of metabolites often induces chronic inflammation in patients with cancer15. These metabolic abnormalities are reflected in the related haematological parameters in the patients. There are several clinical indicators to evaluate the metabolic status of patients; however, there is a paucity of a unified scoring system to evaluate the overall metabolic disorder burden and its relationship with prognosis.

This study was a large-scale, multicentre, population-based cohort study that investigated the relationship between metabolic disorder burden and cancer mortality. A new prognostic scoring system for cancer patients has been developed, which has the advantages of being noninvasive, simple, objective, and can be performed repeatedly. The system can assist clinicians in paying attention to patients with metabolic disorders and provide timely clinical intervention, thereby improving patients’ prognosis.

Methods

Study population

All patients were enrolled from the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) project, which was registered at chictr.org.cn (registration number: ChiCTR1800020329)16. The INSCOC project included clinical data of patients with cancer from more than 40 hospitals in China from June 2012 to June 2021. In this study, 22,783 cancer patients were screened, 10,461 patients with incomplete clinical data or survival data were excluded, and a total of 12,322 patients were included in the final data analysis (Fig. S1). This study was conducted in accordance with the Declaration of Helsinki and approved by the institutional review boards of all participating institutions. Written informed consent was obtained from the patients for the use of clinical data in the study.

Data collection

Following admission, clinical characteristics of all patients was recorded in detail, fasting venous blood was collected for laboratory data, and patient survival was monitored continuously.

The clinical characteristics included age, sex, height, weight, diabetes, hypertension, coronary heart disease (CHD), family history, smoking history, drinking history, tumour stage, treatment, cachexia, Nutritional Risk Screening 2002 (NRS-2002) score, Patient-Generated Subjective Global Assessment (PG-SGA) score, and quality of life. Baseline laboratory indicators included the following: (1) blood routine: hemoglobin (Hb), white blood cells (WBC), neutrophils (Neu), lymphocytes, platelets (PLT); (2) blood lipids and blood glucose: total cholesterol (Tcho), triglycerides, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and fasting blood glucose; and (3) biochemical indicators: total bilirubin (Tbil), direct bilirubin (Dbil), aspartate aminotransferase (AST), glutamate aminotransferase (ALT), total protein, albumin (Alb), globulin (Glo), creatinine, and blood urea nitrogen (BUN).

Laboratory indicators were classified as normal or abnormal based on the clinical criteria. An Hb level of 110–150 g/L for women and 120–160 g/L for men was considered normal. Normal ranges for other indicators were as follows: Neu 1.8–6.3 × 109/L, Dbil 0–3.42 µmol/L, Alb 40–55 g/L, and Glo 20–30 g/L. TNM stage of tumor was performed according to the 8th edition of the American Joint Committee on Cancer guidelines.

The follow-up information included overall survival (OS) and survival status. All patients were followed-up continuously through hospitalisation records, outpatient follow-up, or regular telephone follow-up, and their survival data were recorded. The follow-up was continued until the patient died or was lost to follow-up.

Statistical analysis

All statistical analyses were performed using the R software (version 4.2.1). Normally distributed continuous variables were expressed as mean ± standard deviation (SD), and non-normally distributed data were expressed as median (range) and compared using the Student’s t-test or Mann–Whitney U test. Categorical variables were expressed as frequencies or percentages and compared using the κ2 test or Fisher’s exact test.

In COX regression analyses, model a was not adjusted; model b was adjusted for age, TNM stage, and BMI; and model c was adjusted for age, TNM stage, BMI, smoking, alcohol consumption, diabetes mellitus, hypertension, coronary heart disease, chemotherapy, radiotherapy, surgery, and family history of cancer.

The least absolute shrinkage and selection operator (LASSO) analysis was applied to the entire dataset to screen for the most significant prognostic factors in the three-part criteria of blood routine, blood lipid and blood sugar, and biochemical indicators. The indicators screened in each criterion then were summarised, and the total prognostic risk factors were screened again using LASSO analysis. A nomogram and calculator were constructed to evaluate the metabolic disorder burden and predict the 1-year, 3-year, and 5-year OS of patients with cancer. To obtain an accurate estimate of each indicator of the metabolic disorder burden, a weighted mean was assigned to each selected indicator based on the nomogram score. Kaplan–Meier survival analysis and Cox regression were used to evaluate the survival of the population with different metabolic disorder burden scores. The areas under the receiver operating characteristic curves (AUC), decision curve analysis (DCA), and calibration curves were used to evaluate the predictive accuracy of the scoring system. Random internal validation was performed to test the robustness of the model.

Results

Patient clinical characteristics

This study screened 22,783 patients, and barring incomplete clinical and survival data, 12,322 patients were included in the final data analysis. Among them, there were 6910 males (56.08%) and 5412 females (43.92%), with a mean age of 57.59 (± 12.05) years. There were 3575 patients (29.01%) in stages I–II, 3298 (26.77%) in stage III, and 5449 (44.22%) in stage IV. The mean follow-up time was 27.47 months, during which 4526 patients (36.73%) died. The baseline clinical characteristics of patients are shown in Table S1. There were 8626 and 3696 patients in internal validation cohorts A and B, respectively. The baseline characteristics of randomized internal validation cohorts A and B are shown in Table S2.

Metabolic disorder burden indicators

LASSO analysis was used to screen the related indicators of routine blood tests, and the results showed that Hb and Neu were prognostic factors (Fig. S2). Screening for blood lipids and blood glucose-related indicators showed that triglycerides and HDL were prognostic factors. The screening results for biochemical indicators suggested that Dbil, Alb, and Glo were prognostic factors. Further LASSO analysis was performed on Hb, Neu, triglycerides, HDL, Dbil, Alb, and Glo, and the results showed that Hb, Neu, Dbil, Alb, and Glo were the best evaluation indicators for metabolic disorder burden (Fig. 1).

Fig. 1.

Fig. 1

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Multivariate analysis and cross-validation based on LASSO analysis. Notes: (A) LASSO coefficient profiles of 7 indicators in patients with cancer (1: hemoglobin, 2: neutrophils, 3: triglycerides, 4: high-density lipoprotein cholesterol, 5: direct bilirubin, 6: albumin, 7: globulin). (B) Plots of the crossvalidation error rates.

A COX regression analysis was conducted on the above five indicators. When these five indicators were used as continuous variables, univariate and multivariate Cox regression analyses showed that Hb, Neu, Dbil, Alb, and Glo were significantly correlated with the mortality of cancer patients (all p < 0.05) (Table S3). Similarly, when these five indicators were divided into dichotomous variables based on clinical criteria, the relationship between the indicators and OS remained robust (Table S3, Fig. S3).

Development and validation of metabolic disorder burden nomogram in cancer patients

Based on the indicators of metabolic disorder burden screened out by LASSO regression, a nomogram was constructed to predict the prognosis of patients with cancer. As shown in Fig. 2A, each risk factor is scored on the “point” line. The total score was obtained by adding the scores of each predictor and a vertical line was drawn downward from this point in “total points” to obtain the 1-year, 3-year, and 5-year survival probabilities, respectively. Based on the nomogram, a calculator was developed to enable convenient calculation of the survival probability in cancer patients (Fig. 2B). This calculator is provided in the supplemental material. Users only need to enter Hb, Neu, Dbil, Alb, Glo, and sex to obtain the metabolic disorder burden score and 1-year, 3-year, and 5-year survival probabilities.

Fig. 2.

Fig. 2

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Nomogram and calculator for predicting the metabolic disorder burden score and survival of cancer patients. Notes: (A) Nomogram. (B) Calculator.

The model was evaluated using AUC curves, DCA, and a calibration curve. The AUC of the 1-year, 3-year, and 5-year models were 0.678, 0.664, and 0.650, respectively (Fig. 3). The DCA curve indicated that the model had good clinical practicability and the calibration curve was in line with the standard curve (Fig. 3).

Fig. 3.

Fig. 3

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The receiver operating characteristic curve (A,B), decision curve analysis (C–E) and calibration curve (F–H) of the prognostic nomogram. Notes: (C–E) DCA of 1-year, 3-year, and 5-year nomogram. (F–H) Calibration curve of 1-year, 3-year, and 5-year nomogram.

Association between metabolic disorder burden and OS

According to the nomogram score, a weighted average was assigned to each selected indicator. When Hb, Neu, Dbil, Alb, and Glo were abnormal values, the weighted average values were 47, 36, 33, 100, and 44 points, respectively (Fig. 2A). When these indicators were within normal ranges, the weighted average for each was 0 (clinical criteria were used as reference values). Thus, the metabolic disorder burden was calculated as follows: metabolic prognostic score = 47*Hb + 36*Neu + 33*Dbil + 100*Alb + 44*Glo. When all indicators were normal, the metabolic prognostic score was 0 point. If one indicator was abnormal, the score was at least 33 points. Similarly, if two, three, or four indicators were abnormal, the scores were at least 69, 113, and 160 points, respectively. When all indicators were abnormal, the score reaches 260 points.

Patients were grouped based on their metabolic prognostic scores as follows: 0 point, 33–68 points, 69–112 points, 113–159 points, 160–259 points, 260 points, and Kaplan–Meier survival curves were constructed accordingly. As shown in Fig. S4A, survival benefits were similar for patients with scores of 0 point and 33–69 points, while significant differences in survival were observed for the other score groups. Therefore, patients with scores of 0 point and 33–68 points were combined into group 1, while those with scores 69–112 points, 113–159 points, 160–259 points, 260 points were classified as groups 2, 3, 4, and 5, respectively (group 1: 0–68 points, group 2: 69–112 points, group 3: 113–159 points, group 4: 160–259 points, group 5: 260 points). Kaplan–Meier survival analysis showed significant differences in survival between groups 1–5 (Fig. S4B).

Univariate and multivariate Cox regression analyses indicated that the metabolic prognostic score was significantly associated with mortality of cancer patient (all p < 0.001) (Table 1). Higher scores corresponded to increased mortality risk. After excluding patients with short-term mortality (within 90 days), the metabolic prognostic score remained an independent risk factor for mortality in cancer patients, as confirmed by both univariate and multivariate Cox regression analyses (all p < 0.001) (Table S4).

Table 1.

Cox regression analyses for the associations between metabolic disorder burden score and mortality in cancer patients. Related to Fig. S7.

metabolic prognostic score Model a P Model b P Model c P
0–68 points Ref. Ref. Ref.
69–112 points 1.389 (1.268–1.521) < 0.001 1.340 (1.223–1.468) < 0.001 1.314 (1.1990–1.440) < 0.001
113–159 points 1.782 (1.631–1.946) < 0.001 1.507 (1.379–1.647) < 0.001 1.496 (1.369–1.635) < 0.001
160–259 points 2.650 (2.445–2.871) < 0.001 2.047 (1.886–2.222) < 0.001 1.975 (1.819–2.145) < 0.001
260 points 4.104 (3.472–4.851) < 0.001 2.802 (2.367–3.317) < 0.001 2.663 (2.248–3.154) < 0.001
P for trend < 0.001 < 0.001 < 0.001
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Model a: No adjusted.

Model b: Adjusted for age, TNM stage, BMI.

Model c: adjusted for age, TNM stage, BMI, smoking, alcohol drinking, diabetes mellitus, hypertension, coronary heart disease, chemotherapy, radiotherapy, surgery, family history of cancer.

Subgroup analysis

Figure 4 showed that the metabolic prognostic score grouping was independently associated with survival in overall cancer patients. With the increase of the group, the risk of poor prognosis gradually increased. Across different cancer types, higher metabolic disorder burden was significantly associated with an increased risk of poor prognosis (Fig. 4). Subsequently, we explored the association between metabolic disorder burden and mortality within patients categorized by different cancer stages and different treatments, and similar results were obtained (Figs. S5, S6).

Fig. 4.

Fig. 4

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The association between metabolic disorder burden and overall survival in different cancer types. Notes: Group 1: 0–68 points, Group 2: 69–112 points, Group 3: 113–159 points, Group 4: 160–259 points, Group 5: 260 points.

Additionally, Kaplan–Meier survival curves indicate that, across various cancer types, patients with higher metabolic disorder burden have worse prognoses (Fig. 5). Kaplan–Meier survival curves were also constructed for subgroups based on cancer stage, treatment, sex, age, and the presence or absence of metabolic diseases (diabetes, hypertension, coronary heart disease) (Figs. S7–S11). The results showed that regardless of cancer stage, treatment, sex, age, or metabolic disease, patients with higher metabolic disorder burden consistently have poorer prognoses (all p < 0.05).

Fig. 5.

Fig. 5

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Kaplan–Meier curves of patients with different metabolic disorder burden in different cancer type subgroups. Notes: Group 1: 0–68 points, Group 2: 69–112 points, Group 3: 113–159 points, Group 4: 160–259 points, Group 5: 260 points.

Randomized internal validation

Using a random number generator, all populations were randomly assigned to validation cohort A (8626 cases) and validation cohort B (3696 cases) at a ratio of 7:3 for random internal validation. Table S2 shows that the clinical and laboratory characteristics of the validation cohorts A and B were not significantly different, suggesting that the two cohorts were independent. Figure S12 showed that patients with a higher metabolic disorder burden had shorter overall survival than those with a lower burden in validation cohort A or cohort B. Univariate and multivariate COX regression analyses showed that the metabolic disorder burden score was an independent risk factor for OS of cancer patients in both validation cohort A and cohort B (all p < 0.05) (Tables S5, S6).

In addition, the model in validation cohorts A and B using AUC, DCA, and calibration curves was evaluated. The AUC of 1-year, 3-year and 5-year A for cohort A was 0.674, 0.660, and 0.648, respectively, and those of cohort B were 0.691, 0.676, and 0.657, respectively (Fig. S13). The DCA curve suggested that the model had good clinical applicability and the calibration curve was in line with the standard curve (Figs. S14, S15).

Discussion

Metabolic disorders have a high incidence in patients with cancer, which influences tumour initiation and development, and promotes poor prognosis. Therefore, accurate prediction and stratification of the metabolic disorder burden in patients is crucial to guide clinical decisions and improve the OS of cancer patients. The results of this large-scale, nationwide, population-based cohort study of 12,322 cancer patients showed that five simple and non-invasive laboratory indicators, namely Hb, Neu, Dbil, Alb, and Glo, can be used to evaluate the metabolic disorder burden and prognosis in cancer patients. Univariate and multivariate Cox regression analyses showed that patients with a higher burden of metabolic disorders had a higher risk of death. Moreover, this study established a prognostic nomogram model that can further improve the accuracy of prognosis prediction. To facilitate its use in clinical practice, a corresponding free calculator was developed to help us accurately calculate the burden of metabolic disorders and predict the OS of cancer patients.

With the rise in urbanisation and industrialisation, and the increase in unhealthy eating and habits such as consumption of ultra-processed food, high-fat, and high-sugar diets and sedentary and inactive lifestyles, metabolic disorders have gradually become a major public health problem in society2,5,17,18. Previous studies have reported that metabolic disorders increase rates of cancer morbidity and mortality19–26. Cicione et al. have suggested that metabolic disorders are associated with an increased risk of prostate cancer27. Park et al. showed that people with metabolic disorders have a significantly higher risk of thyroid cancer28. Tran et al. suggested that metabolic disorders are risk factors for colorectal morbidities29. Hu et al. suggested that preoperative metabolic disorders are a predictor of mortality in patients who undergo radical gastrectomy30.

Several clinical indicators are used to evaluate metabolic disorders in patients; however, there is a paucity of a unified scoring system that can objectively and conveniently evaluate the overall burden of metabolic disorders and their relationship with the prognosis of patients. This study found that Hb, Neu, Dbil, Alb, and Glo levels were closely related to the prognosis of patients with cancer. Cancer is a chronic, wasting disease with nutrition and inflammation playing key roles in tumour initiation and development31,32. Hb and Alb levels are effective indicators of the nutritional status of patients. In addition, some studies have shown that Alb can participate in the body’s inflammatory response by regulating inflammatory factors, such as interleukin-6 and tumour necrosis factor, thereby changing the tumour microenvironment and influencing prognosis in cancer patients. The cytokines, chemokines, and growth factors secreted by Neu are involved in the formation of an inflammatory tumour microenvironment and play a major role in tumour progression and metastasis by promoting immune escape, tumour angiogenesis, cell apoptosis, and other mechanisms. Glo contains a plethora of proteins, such as immunoglobulin, complement, and cytokines, which play an extremely important role in inflammation and immune response. Previous studies have shown that bilirubin has antioxidant, anti-inflammatory, and anti-cancer effects, and has a significant relationship with tumour initiation and development. Therefore, these five laboratory indicators represent the overall nutritional and immune-inflammatory status of patients and can comprehensively reflect the overall metabolic disorder burden in patients. Tumour initiation and development potentially lead to these metabolic changes, and consequently, these changes may influence further tumour progression.

Previous studies have shown that metabolic disorders lead to a chronic, systemic inflammatory state leading to a surge in reactive oxygen species. Proinflammatory cytokines secreted during this pathological process have been implicated in the initiation and progression of certain tumours as well as mortality. Metabolic disorders may be surrogate markers for other cancer-associated risk factors such as decreased physical activity, consumption of calorie-dense foods, high dietary fat intake, low fibre intake, and oxidative stress. In addition, it has been reported that an unhealthy lifestyle can cause changes in the structure and function of the intestinal flora, thereby promoting metabolic disorders, inflammation, and also cancer. The results of our study showed that the risk of mortality in patients significantly increased with an increase in metabolic disorder burden.

This study developed a new prognostic score for cancer patients based on the patient’s metabolic disorder burden, which has the advantages of being non-invasive, simple, convenient, objective, and repeatable, to assist clinicians in monitoring patients’ metabolic disorders. The clinician can calculate the metabolic prognostic score by nomogram, or input corresponding indicators directly into the calculator to automatically obtain the metabolic prognostic score and 1-year, 3-year, and 5-year survival rates. For patients with high metabolic prognostic score, timely clinical intervention can be carried out to improve the survival rate of patients33.

This study had several limitations. Firstly, the patients included in this study were all from East Asia, which may limit the generalizability of the results. In the future, we plan to collect data of tumour patients from different regions for external validation. Secondly, as this study is a retrospective analysis, potential biases may be present. Additionally, our study only evaluated the baseline metabolic disorder burden of cancer patients, but could not obtain the dynamic metabolic disorder burden of patients. Therefore, it is not possible to definitively determine the impact of dynamic changes in metabolic disorder burden on patient prognosis.

In summary, the results of this study showed that metabolic disorder burden (including Hb, Neu, Dbil, Alb, and Glo) was independent risk factors for OS in cancer patients. The metabolic prognostic score accurately reflects the metabolic disorder burden in cancer patients, and the burden directly correlates with worsened survival. The nomogram and calculator can be used as tools for predicting the prognosis of cancer patients, to assist clinicians in predicting the survival probability of cancer patients accurately and in a straightforward manner, and to provide timely monitoring and clinical intervention for improved patient prognosis.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (3MB, docx)

Acknowledgements

We would like to thank Editage (www.editage.cn) for English language editing. We are grateful to all participants of the project and to the members of the study teams at different study centers who helped make this research possible.

Author contributions

All authors have read and approved the manuscript. JYS: Methodology, Software, Writing-Original draft preparation; CAL: Writing- Reviewing & editing; XZ: Writing- Reviewing & editing; YC: Methodology, Software, Visualization; HYZ: Methodology, Software, Visualization; TL: Methodology, Software, Visualization; QZ: Supervision, Validation; LD: Supervision, Validation; HPS: Conceptualization, Funding acquisition, Resources, Supervision.

Funding

This work was supported by the National Key Research and Development Program (2022YFC2009600, 2022YFC2009601), Laboratory for Clinical Medicine, Capital Medical University (2023-SYJCLC01), National Multidisciplinary Cooperative Diagnosis and Treatment Capacity Project for Major Diseases: Comprehensive Treatment and Management of Critically Ill Elderly Inpatients (No.2019.YLFW) to Dr. Hanping Shi.

Data availability

All data needed to evaluate the conclusions of the study are presented in this paper and/or the Supplementary Materials. Additional data related to this study is available upon request to authors/corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study followed the Helsinki declaration. All participants signed an informed consent form, and this study was approved by the Institutional Review Board of each hospital (Anhui Provincial Cancer Hospital, Affiliated Cancer Hospital of Harbin Medical University, Affiliated Cancer Hospital of Zunyi Medical College, Guizhou Province, Beijing Shijitan Hospital, Beijing Cancer Hospital, Cancer Hospital of Chinese Academy of Medical Sciences, Chongqing Daping Hospital, Chongqing Third People’s Hospital, Chongqing Cancer Hospital, First Hospital of Jilin University, Foshan First People’s Hospital, Fujian Provincial Cancer Hospital, First Affiliated Hospital of Guangxi Medical University, First Hospital of Shanxi Medical University, Guangxi Zhuang Autonomous Region People’s Hospital, Guangdong Provincial People’s Hospital, Guigang People’s Hospital of Guangxi Province, Hebei province people’s Hospital, Huizhou Central People’s Hospital, Liaoning Provincial Cancer Hospital, Sichuan Provincial Cancer Hospital, Shanghai Ruijin Hospital, Shanghai Tenth People’s Hospital, The Affiliated Hospital of Chengde Medical College, Tangdu Hospital of the Fourth Military Medical University, The Fourth Affiliated Hospital of Harbin Medical University, The First Hospital of Hebei Medical University, The Third Affiliated Hospital of Kunming Medical University, Tianjin Medical University Cancer Hospital, The First Affiliated Hospital of Zhejiang University, The Second Affiliated Hospital of Zhejiang University, The First Affiliated Hospital of Sun Yat-sen University, The Second Hospital of Hebei Medical University, The Fourth Hospital of Hebei Medical University, The First Affiliated Hospital of Kunming Medical University, West China Hospital of Sichuan University, Wuhan Tongji Hospital, Xijing Hospital, Xinjiang Kashgar First People’s Hospital, Xingtai People’s Hospital, Yunnan Cancer Hospital, Yuncheng Central Hospital, Zhejiang First Hospital, Zhejiang People’s Hospital, and Zhejiang Cancer Hospital) (Registration number: ChiCTR1800020329).

Consent for publication

All authors approved the publication.

Footnotes

Publisher’s note

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

Jinyu Shi, Chenan Liu and Xin Zheng contributed equally.

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Associated Data

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

Supplementary Materials

Supplementary Material 1 (3MB, docx)

Data Availability Statement

All data needed to evaluate the conclusions of the study are presented in this paper and/or the Supplementary Materials. Additional data related to this study is available upon request to authors/corresponding author.

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