Abstract
Background
Microvascular invasion (MVI) plays an important role in tumor progression. The aim of this study is to establish and validate an effective hematological nomogram for MVI prediction in hepatocellular carcinoma (HCC).
Methods
A retrospective study was performed in a primary cohort that includes 1306 patients clinicopathologically diagnosed with HCC, and a validation cohort contained 563 continuous patients. Univariate logistic regression was used to assess the association between variables included both clinicopathologic factors and coagulation parameters (prothrombin time, activated partial thromboplastin time, fibrinogen, and thrombin time [TT]) and MVI. Multiple logistic regression was used to construct a prediction nomogram. We tested the accuracy of the nomogram by discrimination and calibration, and then plotted decision curves to assess the benefits of the nomogram-assisted decisions in a clinical context.
Results
In the two cohorts, patients without MVI had the longest overall survival (OS), compared the OS with MVI. The multivariate analysis indicated that age, sex, tumor node metastasis (TNM) stage, aspartate aminotransferase, alpha fetoprotein, C-reactive protein, and TT were identified as significant independent predictors of MVI of HCC patients. The Hosmer–Lemeshow test showed good point estimate associated P value between predicted risk and observed risk across the deciles. Moreover, the calibration performance of the nomogram risk scores in each decile of the primary cohort was within 5 percentage points of the mean predicted risk score, and in the validation cohort, the observed risk in 90% decile was within 5 percentage points of the mean predicted risk score.
Conclusions
A noninvasive and easy-to-use nomogram was established and may be used to predict preoperative MVI in HCC.
Keywords: hepatocellular carcinoma, microvascular invasion, biomarker, nomogram, prediction
Introduction
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death and poses major challenges to health-care systems worldwide. 1 At early stages of the disease, surgical resection, liver transplantation, and local ablation are considered the main treatments. For remaining patients, available therapies include stereotactic body radiation therapy, radiofrequency or microwave ablation, catheter-based therapy, and systemic therapy.2,3 Nevertheless, the overall response to these therapies is inadequate, and the long-term prognosis of patients with HCC remains poor because of its high recurrence rate.4,5 The 5-year tumor recurrence rate is as high as 70% after resection, while 15% to 30% after liver transplantation, leading to a large proportion of patients succumbing to tumor-related death. 6 Currently, tumor size, tumor number, and vascular invasion are considered as the main risk factors responsible for HCC recurrence and overall survival, especially the microvascular invasion (MVI).7–9
The vascular invasion of HCC comprises of macrovascular invasion and MVI, 10 and their presence signifies an aggressive biological behavior for HCC recurrence. In comparison, macrovascular invasion can be identified through noninvasive imaging examination or postoperative pathological examination. However, MVI only can be detected postoperatively under the microscope, and difficult to be accurately identified or evaluated using conventional preoperative imaging techniques such as CT and MRI.11,12 Over the past years, researchers have made persistent endeavors towards the preoperative prediction of MVI and have suggested that radiologic features and histological components could be used to identified MVI. But, there is still lack a consensus for the best predictive feature of MVI in HCC.
The HCC is usually associated with an environment of chronic inflammation and an exposure to toxins. The MVI is an advanced phase in tumor progression, which might invade blood vessels and change the hemostatic activity. 13 Meanwhile, cancer-induced hemostatic activity has been shown to promote tumor development, progression, and metastasis. Coagulation system plays a pivotal role in maintaining the balance of the physiological coagulation process, which is closely associated with liver functions, including generation of clotting factors.14,15 Patients with HCC, and particularly cirrhosis, have a dysregulated coagulation system. 16 Therefore, we hypothesized that HCC patients with MVI might have abnormal coagulation system, involving coagulation, fibrinolysis, and other complex pathological changes, which closely related to the tumor progression, the prognosis, and survival of cancer patients. Prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (Fbg), and platelet (PLT) are the most common coagulation tests used in the laboratory, but few studies focused on the value of these simple and effective factors in MVI. Therefore, the aim of this study is to develop and validate a nomogram that included both clinicopathologic factors and hemostatic factors for individual preoperative prediction of MVI status in patients with HCC.
Materials and Methods
Study Population
The primary cohort of our study comprised of 1306 patients histologically diagnosed HCC who were retrospectively reviewed from the information system from April 2008 to December 2013. Only the first record of hospitalizations was retained. All the patients met the diagnostic criteria for HCC. The demographic details are described in Table 1.Exclusion criteria were included any as follows: (1) history of cancer treatments or regular procoagulant or anticoagulant therapy; (2) concomitant diseases associated with influenced plasma coagulation levels (ie, venous thromboembolism (VTE), pulmonary embolism or disseminated intravascular coagulation within 1 month of study onset); and (3) other types of malignancy. From January 2014 to January 2016, 563 consecutive patients were enrolled in validation cohort using the same inclusion and exclusion criteria as that in the primary cohort. All the patients of surgical treatments have been performed with liver resections. In this study, the diagnosis of MVI in most HCC patients (88.36%) relies on postoperative histopathological examination, while the diagnosis of MVI before ablation or chemotherapy in a small number of patients (11.64%) relies on liver biopsy. Following surgery or liver biopsy, all lesions were histologically classified according to the 2019 WHO classification of tumors of the digestive system. And, no patients with recurrence were enrolled in this study. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. In addition, the Institute Research Ethics Committee of the Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou 510060, PR China approved the study (NO: SL-B2023-108-01). All the patients provided written informed consent for the use of their data, and we have de-identified all patient details in this study. Reporting of this study is in accordance with the relevant Equator guidelines. 17 The authenticity of this article has been validated by uploading the key raw data onto the Research Data Deposit public platform (www.researchdata.org.cn), with the approval RDD number as RDDA2023747592.
Table 1.
Patient Characteristics in the Primary and Validation Cohorts.
| Primary cohort | Validation cohort | |||
|---|---|---|---|---|
| Mean/number | SD/percentage | Mean/number | SD/percentage | |
| Demographic and clinical characteristics | ||||
| Age, year | 51.28 | 11.71 | 52.87 | 11.36 |
| Sex | ||||
| Male | 1146 | 87.75% | 479 | 85.11% |
| Female | 160 | 12.25% | 84 | 14.89% |
| Treatment | ||||
| Surgery | 1154 | 88.36% | 507 | 89.89% |
| Ablation | 29 | 2.22% | 6 | 1.06% |
| Chemoembolization | 123 | 9.42% | 51 | 9.04% |
| TNM stage | ||||
| I | 578 | 44.26% | 258 | 45.74% |
| II | 357 | 27.34% | 162 | 28.72% |
| III + IV | 371 | 28.40% | 144 | 25.53% |
| Differentiation | ||||
| Low | 526 | 40.28% | 266 | 47.13% |
| Middle | 724 | 55.44% | 275 | 48.76% |
| High | 56 | 4.28% | 23 | 4.08% |
| Maximum tumor diameter, mm | 58.78 | 33.76 | 57.78 | 35.37 |
| Tumor number | ||||
| 1 | 1104 | 84.53% | 449 | 79.75% |
| 2 | 47 | 3.60% | 45 | 7.99% |
| 3 | 7 | 0.54% | 4 | 0.71% |
| >3 | 59 | 4.52% | 25 | 4.44% |
| HBV DNA | ||||
| <103 | 442 | 33.84% | 167 | 29.61% |
| ≥103 | 864 | 64.78% | 397 | 70.39% |
| AST, U/L | ||||
| <40 | 725 | 55.51% | 341 | 60.46% |
| ≥40 | 581 | 44.49% | 223 | 39.54% |
| ALT, U/L | ||||
| <50 | 907 | 69.45% | 391 | 69.33% |
| ≥50 | 399 | 30.55% | 173 | 30.67% |
| AFP, ng/mL | ||||
| <5 | 264 | 18.84% | 118 | 20.92% |
| ≥5 | 1042 | 79.79% | 446 | 79.08% |
| TBIL, μmol/L | ||||
| <20.5 | 1105 | 84.61% | 497 | 88.12% |
| ≥20.5 | 201 | 15.39% | 67 | 11.88% |
| CRP, mg/L | ||||
| <3 | 698 | 53.35% | 328 | 58.16% |
| ≥3 | 608 | 46.55% | 236 | 41.84% |
| HBsAg | ||||
| Negative | 173 | 13.25% | 69 | 12.23% |
| Positive | 1133 | 86.75% | 495 | 87.77% |
| HBsAb | ||||
| Negative | 1157 | 88.59% | 515 | 91.47% |
| Positive | 149 | 11.41% | 45 | 7.99% |
| PLT, 109/L | 185.57 | 71.90 | 183.02 | 70.34 |
| PT, second | 12.02 | 1.13 | 11.76 | 1.44 |
| TT, second | 18.54 | 1.33 | 18.98 | 1.37 |
| APTT, second | 26.43 | 3.44 | 28.30 | 3.82 |
| Fbg, g/L | 3.05 | 1.07 | 2.81 | 0.91 |
Abbreviations: AST, aspartate aminotransferase; ALT, alanine aminotransferase; AFP, alpha fetoprotein; TBIL, total bilirubin; PLT, platelet; PT, prothrombin time; TT, thrombin time; APTT, activated partial thromboplastin time; Fbg, fibrinogen.
Note: Data are presented as mean (SD) or N (%).
Laboratory Measurements
Patients received routine tests at the first visit in our hospital. Pretreatment blood samples were collected and clotted at room temperature, then centrifuged at 3500 r/min for 10 min, which could be used to estimate the level of serum biomarkers, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), alpha fetoprotein (AFP), C-reactive protein (CRP), PT, APTT, TT, Fbg, and PLT. The baseline clinical data, including age, gender, preoperative histologic grade, Hepatitis B Virus (HBV) infection, and treatment strategies, were extracted from the Electronic Medical Record (EMR) system. All the optimal cutoff points in our study were evaluated by reference ranges in our lab, and continuous variables were transformed to categorical variables.
Statistical Analysis
Statistical analyses were performed using the R software for Windows (version 3.4.2, http://www.r-project.org/). For the development of the nomograms, we sought outcome predictor from a search of the published work and based on our clinical experience. Logistic regression analysis was used for univariate and multivariate analyses. A final model selection was performed by all subsets regression process with the Mallows Cp statistic. A nomogram was formulated based on the results of multivariate analysis by the package of rms.
We tested the accuracy of the nomograms by discrimination and calibration both in primary and external validation cohorts. The calibration accuracy in predicting the probability of MVI was calculated using Hosmer–Lemeshow test, which assessed whether or not the observed event rates matched the expected event rates in subgroups of the patients. Furthermore, we plotted decision curves to assess the benefits of the nomogram-assisted decisions in a clinical context.
All statistical tests were two-sided, and P values of <.05 were considered as statistically significant.
Results
Patient Characteristics
Patients were divided into two groups based on the time of assessment: the primary cohort (n = 1306) and the validation cohort (n = 563). The demographic and clinical characteristics of the training and validation sets were evaluated in Table 1. In the primary cohort, the mean age is 51.28, 1154 (88.36%) patients received surgery, 29 (2.22%) received ablation treatment, and 123 (9.42%) received chemoembolization. There are 864 (64.78%) patients with HBV infection. In the validation cohort, the mean age is 52.87. A total of 507 (89.89%) patients received surgery, 6 (1.06%) received ablation treatment, and 51 (9.04%) received chemoembolization. There are 397 (70.39%) patients with HBV infection. There were no differences between the primary and validation cohorts in terms of age and sex.
Risk Stratification of OS by Microvascular Invasion
In the primary cohort, patients without MVI had the longest OS (33.39 ± 14.73 months), compared the OS with MVI (28.68 ± 16.35 months). Also, patients without MVI had the longest OS (16.33 ± 6.28 months), compared the OS with MVI (13.87 ± 7.71 months) in the validation cohort . Furthermore, we drew the Kaplan–Meier curves, and the differences between the two group were significant (P < .001) (Figure 1).
Figure 1.
Graphs showing the results of Kaplan–Meier curves for the two groups based on microvascular invasion (MVI) in the primary cohort (A) and those in the validation set (B).
Abbreviation: MVI, microvascular invasion.
Univariate Analysis and Multivariate Analysis for the Biomarker Selection
A logistic regression analysis was used to identified the value of the pretreatment hemostatic and clinical characteristics (including age, sex, treatment, TNM stage [American Joint Committee on Cancer (AJCC) eighth], differentiation, HBV DNA, AST, ALT, AFP, TBIL, CRP, Hepatitis B surface antigen (HBsAg), Hepatitis B surface antibody (HBsAb), PLT, PT, APTT, TT, and Fbg) in MVI diagnosis in HCC patients. Univariate analysis showed that age (P < .001), sex (P < .001), TNM stage (P < .001, P < .001), differentiation (P = .017, P < .001), HBV DNA (P = .035), AST (P < .001), AFP (P < .001), CRP (P < .001), PLT (P < .001), PT (P = .0043), APTT (P = .0011), TT (P = .0129), and Fbg (P = .0003) were associated with MVI in HCC. All of the potential important biomarkers identified in univariate analysis were further included in the multivariate analysis. The results indicated that age (P = .0004, odds ratio (OR) = 0.9818), sex (P = .0021, OR = 0.5428), TNM stage (P < .001, OR = 2.0084; P = .0013, OR = 1.6047), AST (P < .001, OR = 1.6526), AFP (P < .001, OR = 1.6245), CRP (P < .001, OR = 1.6593), and TT (P = .0129, OR = 0.8612) were identified as significantly independent predictors of MVI of HCC patients (Table 2).
Table 2.
Analyses of the Association Between Clinical Features and Microvascular Invasion.
| Univariate analysis | Multivariate analysis | |||
|---|---|---|---|---|
| OR | P value | OR | P value | |
| Age, year | 0.9784 | <.0001 | 0.9818 | .0004 |
| Sex | ||||
| Male | Ref | Ref | ||
| Female | 0.4867 | .0002 | 0.5428 | .0021 |
| Treatment | ||||
| Surgery | Ref | |||
| Ablation | 0.6668 | .318 | ||
| Chemoembolization | 0.7683 | .186 | ||
| TNM stage | ||||
| I | Ref | Ref | ||
| II | 2.0532 | <.0001 | 2.0084 | <.0001 |
| III + IV | 1.9529 | <.0001 | 1.6047 | .0013 |
| Differentiation | ||||
| Low | Ref | |||
| Middle | 0.7567 | .0168 | ||
| High | 0.3074 | .0007 | ||
| Maximum tumor diameter | 1.019 | <.0001 | ||
| Tumor number | ||||
| 1 | Ref | |||
| 2 | 0.699 | .2703 | ||
| 3 | 1.236 | .7825 | ||
| >3 | 2.091 | .0062 | ||
| HBV DNA | ||||
| 0 | Ref | |||
| 1 | 1.2905 | .0351 | ||
| AST | ||||
| 1 | Ref | Ref | ||
| 2 | 1.8774 | <.0001 | 1.6526 | <.0001 |
| ALT | ||||
| 1 | Ref | |||
| 2 | 1.1003 | .4350 | ||
| AFP | ||||
| 1 | Ref | Ref | ||
| 2 | 1.8040 | <.0001 | 1.6245 | .0024 |
| TBIL | ||||
| 1 | Ref | |||
| 2 | 1.8080 | .3810 | ||
| CRP | ||||
| 1 | Ref | Ref | ||
| 2 | 2.0888 | <.0001 | 1.6593 | <.0001 |
| HBsAg | ||||
| 0 | Ref | |||
| 1 | 1.1971 | .2893 | ||
| HBsAb | ||||
| 0 | Ref | |||
| 1 | 0.7745 | .162 | ||
| PLT | 1.0031 | <.0001 | ||
| PT | 1.1596 | .0043 | ||
| TT | 0.8979 | .0129 | 0.8612 | .0013 |
| APTT | 1.0556 | .0011 | ||
| Fbg | 1.2139 | .0003 | ||
Abbreviations: AST, aspartate aminotransferase; ALT, alanine aminotransferase; AFP, alpha fetoprotein; TBIL, total bilirubin; CRP, C-reactive protein; PLT, platelet; PT, prothrombin time; TT, thrombin time; APTT, activated partial thromboplastin time; Fbg, fibrinogen.
Results of the univariate and multivariate logistic regression models for microvascular invasion (MVI) using variable selection based on Akaike Information Criterion (AIC) of backward stepwise regression.
Construction a Predict Nomogram for Microvascular Invasion
On the basis of the multivariate analysis, a nomogram was constructed to predict MVI, which included all the biomarkers mentioned above (sex, age, TNM stage, AST,AFP, CRP, and TT). The larger points in the nomogram, the larger possibility the MVI happened (Figure 2).
Figure 2.
Developed hematological nomogram. The nomogram was incorporated with sex, age, TNM stage, aspartate aminotransferase (AST), alpha fetoprotein (AFP), C-reactive protein (CRP), and thrombin time (TT) for the prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. The nomogram is valued by adding up the points identified on the points scale for each variable.
Abbreviations: AST, aspartate aminotransferase; AFP, alpha fetoprotein; CRP, C-reactive protein; TT, thrombin time.
The Hosmer–Lemeshow calibration test, for which the point estimates (mean square difference between predicted risk and observed risk across the deciles), and associated P value were shown (χ2 = 2.5053, P = .9615). Moreover, calibration performance across the full range of the nomogram risk scores is shown in Figure 3A, the observed risk in each decile of the primary cohort was within 5 percentage points of the mean predicted risk score. Decile 1 indicates the lowest risk, and decile 10 indicates the highest risk.
Figure 3.
Agreement between observed incidence and predicted incidence of microvascular invasion (MVI) with the hematological nomogram. (A) The observed versus predicted MVI incidence by deciles of predicted risk in the primary cohort. (B) The observed versus predicted MVI incidence by deciles of predicted risk in the validation cohort. Predicted 4-year incidence for each decile is the mean predicted risk score in each decile. Error bars indicate standard deviation.
Validation of Predictive Accuracy of the Nomogram for Microvascular Invasion
The Hosmer–Lemeshow calibration test, for which the point estimates (mean square difference between predicted risk and observed risk across the deciles), and associated P value were shown (χ = 9.5414, P = .2987). Moreover, calibration performance across the full range of the nomogram risk scores is shown in Figure 3B.
Decision Curve Analysis
The decision curve analysis for the nomogram in the primary cohort and validation cohort is showed in Figure 4. The decision curve presented that if the threshold probability of a patient is >20%, the developed nomogram in predicting MVI is more beneficial than all patients with or without MVI. The straight line represented the assumption of all patients with MVI, and the horizontal line represented the assumption of patients without MVI. Decision curve analysis showed that the nomogram had a higher net clinical benefit than that of AFP in this range.
Figure 4.
Decision curve analysis for predicting microvascular invasion (MVI). Black blue line: All patients have MVI. Black line: No patients have MVI. Red dashed line: Model of nomogram. Green dashed line: Model of alpha fetoprotein (AFP). (A) In the primary cohort. (B) In the validation cohort.
Abbreviation: AFP, alpha fetoprotein.
Discussion
In this study, we developed and validated a diagnostic nomogram for the preoperative individualized prediction of MVI in patients with HCC, which were based on the serum biomarkers. The nomogram incorporated two aspects of the clinical characteristics and hematological risk factors, and showed easy-to-use nomogram facilitates of the preoperative individualized prediction of MVI.
The MVI is an expression of aggressive biological behavior by the tumor progression and is currently one of the most critical factors predictive of HCC recurrence, 18 which is associated with tumor original abnormal coagulation and inflammation. Various studies are needed to provide a prospective method to measure MVI in HCC. The most common ways in MVI diagnosis were imaging techniques and tumor biopsies, both of which were invasive and expensive.19,20 As far as we have known, this is the first study to employ serum biomarkers for evaluation of MVI in HCC. Thus, the noninvasive serum signatures, which we already have for free, could serve as a more convenient biomarker for the prediction of MVI.
For construction of the nomogram, the associations among clinicopathologic factors, TNM staging system, biomarkers in coagulation, and inflammation have been examined by logistic regression analysis. Multivariate analysis showed that sex, age, TNM stage, AST, AFP, CRP, and TT were the predictors for the MVI. All the biomarkers were combined in the nomogram. The nomogram performed well in discrimination ability: the Hosmer–Lemeshow test showed an optimal agreement between the prediction by nomogram and actual observation both in the two cohorts. We also used decision curve analysis (DCA) to evaluate the nomogram, which showed that the nomogram had a higher net clinical benefit across a wider range of threshold probabilities both in the primary and validation cohorts.
Previous studies focused on clinical laboratory examinations and radiological imaging results to predict MVI presence at preoperation in HCC patients.21–23 Mao et al 24 found that incorporating tumor diameter, AFP, and TBIL achieved a better concordance in predicting MVI presence. Zhang et al 25 found that Fbg, cirrhosis, and poor tumor border were independent risk factors of MVI. Yang et al 26 revealed that discomfort of right upper abdomen, vascular invasion, lymph node metastases, unclear tumor boundary, tumor necrosis, tumor size, and higher alkaline phosphatase were predictive MVI factors in HCC. However, the model of above that discussed previous studies establishes based on a small sample size from a single center which may have resulted in certain level of selection bias. Our study provided a new perspective, which had several advantages compared to other reports. We included these common systemic coagulation and inflammation parameters, such as PT, APTT, TT, Fbg, and CRP. The MVI has been shown to be involved in the angiogenesis and blood coagulation disorders, and inflammation has been also reported to be involved in the HCC development, which promoted the growth of tumor invasion and metastasis. That is to say that the imbalance of tumor, coagulation, and inflammation is related to the MVI. In our nomogram, TNM stage, AFP, AST, TT, and CRP contributed to the MVI development of HCC. In line with previous studies, TNM stage and AFP were associated with MVI. 27 It has been widely accepted that AFPs were overexpressed in patients with HCC and MVI. Cucchetti et al 12 reported the relationship between the tumor stage and MVI, and also Kim et al 28 described that tumor size, tumor number, and histologic grade have effect on the MVI. This is first to report that AST, TT, and CRP play an important role in MVI, and each of these biomarkers were associated with the OS in HCC patients. To metastasize, HCC cells need to survive in the bloodstream and evade the immune system. 29 One of the most plausible mechanisms to achieve this is that AST and CRP could be used as biomarkers of chronic inflammation in HCC, and changes in vascular phenotype accompanying subclinical inflammation may be associated with the change in AST and CRP; the other mechanisms are that the pathogenesis of the MVI state in cancer is more complex and multifactorial and is involved in tumor-procoagulant activity. Tumor cells directly produce various procoagulant activities and proinflammatory cytokines, including tissue factor, cancer procoagulant (CP), tumor necrosis factor (TNF-a), interleukin-1 (IL-1b), and vascular endothelial growth factor (VEGF).30–33 Therefore, the imbalance of tumor, coagulation, and inflammation in blood coagulation disorders promotes tumor growth, invasion, and metastasis, thus MVI.
There are still several limitations in our study that need to be addressed. First, the nomogram was established based on data obtained from one institution in China, and further multicenter study is necessary to validate the nomogram to provide more convincing evidence in favor of the clinical application of the findings of this study. Second, the follow-up time was shorter in the validation cohort, and close monitoring and 5-year follow-up data are still required for patients. Third, accurate TNM stage is based on the postoperative histopathology characteristics which is hampering the clinical importance of the nomogram. Fourth, because routine test is not conducted in our institution, many other molecular markers, such as VEGF 34 and adhesion molecules, 35 reported in other studies associated with the MVI were not included in our study. Fifth, the majority of patients in this study is HBV-positive, whether the nomogram is applicable to non-HBV patients that remains to be evaluated.
Conclusion
We developed and validated nomograms to predict the MVI in HCC. The proposed nomogram in this study provided statistically significant discrimination, and it may be offered as an inexpensive, easy-to-use, and noninvasive tool for MVI assessment. To generalize the use of this nomogram in other groups, additional validation with data from other institutions is required.
Acknowledgments
We thank the staff of the biochemical laboratory of Sun Yat-sen University Cancer Center who provided various biochemical markers, and all of the staff who supported our study.
Footnotes
Authors’ Note: Yongneng Liang and Hui Sheng contributed to the conception and design of the study and drafted the manuscript; Minjie Mao and Wen Liu contributed to data analysis and interpretation; and Kewei Huang and Hailin Zheng participated in data collection and literature research. All authors read and approved the final manuscript.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethical Approval: This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. In addition, the Institute Research Ethics Committee of the Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou 510060, PR China approved the study (NO: SL-B2023-108-01).
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iD: Yongneng Liang https://orcid.org/0000-0002-9059-9752
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