The combination of TLSs and the neutrophil-to-macrophage ratio predicts early recurrence in patients with hepatocellular carcinoma

The combination of TLSs and the neutrophil-to-macrophage ratio predicts early recurrence in patients with hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The combination of TLSs and the neutrophil-to-macrophage ratio predicts early recurrence in patients with hepatocellular carcinoma Zhuangzhuang Chen, Binwei Duan, Xinxin Wang, Gongming Zhang, Feng Wu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3806961/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction: Liver cancer, predominantly hepatocellular carcinoma (HCC), ranks among the deadliest malignancies worldwide, and effective predictive models for early recurrence and poor prognosis are limited. Methods This study retrospectively analyzed 180 HCC patients and explored the prognostic value of tertiary lymphoid structures (TLSs), peripheral blood immune parameters, and clinical factors in HCC. Results The results showed that TLSs could significantly reduce early recurrence rates but that they were not related to late recurrence. The interaction of peripheral blood immune parameters, especially the neutrophil–monocyte ratio (NMR), plays a pivotal role in early recurrence prediction. A novel clinical prediction model was constructed by combining the tumor-node-metastasis (TNM) staging system (8th edition), TLS status, and NMR data, and the results demonstrated substantial predictive accuracy for early HCC recurrence. Conclusions These findings highlight the multifaceted impact of TLSs and peripheral blood immunity on HCC prognosis and provide a valuable tool for personalized patient management, particularly for identifying early recurrence risk. Carcinoma Hepatocellular Tertiary lymphoid structures Neutrophil–monocyte ratio Clinical prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Liver cancer is the sixth most common cancer globally and the third leading cause of tumor-related deaths [ 1 , 2 ]. HCC is the predominant type of primary liver cancer, accounting for approximately 90% of cases [ 1 , 3 , 4 ]. Risk factors for HCC include hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, alcohol consumption, fatty liver disease, aflatoxin exposure, and liver cirrhosis, among others [ 1 , 3 – 6 ]. Despite the various treatment options available for HCC, surgery has the greatest survival benefit, but the recurrence rate is as high as 80% five years after surgery [ 5 ]. Additionally, HCC often lacks specific biomarkers and presents with atypical clinical symptoms in its early stages, leading to delayed diagnosis and missed opportunities for optimal surgical interventions [ 4 , 7 ]. Therefore, the 5-year survival rate for HCC patients is only 15% [ 1 , 4 , 7 – 9 ]. Given the poor prognosis of HCC patients, there is an urgent need for biological markers and predictive models to guide treatment and predict outcomes. While many experts and scholars have proposed various prognostic and predictive models for liver cancer patients, a fully applicable model has yet to be found [ 2 , 9 , 10 ]. TLSs, also known as ectopic lymph node structures, are specific aggregations of lymphocytes in nonlymphoid tissues and are frequently found in conditions such as chronic inflammation, transplant rejection reactions, autoimmune diseases, and cancers [ 7 , 10 , 11 ]. Like secondary lymph nodes, TLSs likely function similarly to secondary lymph nodes, leading most researchers to believe that TLSs play a role in HCC similar to that of lymph nodes [ 12 , 13 ]. Numerous prospective clinical trials and retrospective experiments have confirmed that the presence of TLSs is associated with increased overall survival (OS) and reduced recurrence rates in HCC patients, as well as in other cancers, such as gastric cancer [ 14 , 15 ], colorectal cancer [ 16 ], skin cancer [ 11 ], head and neck squamous cell carcinoma [ 17 ], lung cancer [ 18 ], pancreatic cancer [ 19 ], ovarian cancer [ 20 ], and breast cancer [ 21 ]. Several studies have further indicated that TLSs at different locations within the tumor microenvironment have varying effects on HCC [ 8 , 22 ], with peritumoral TLSs significantly reducing early recurrence rates [ 5 , 22 ]. However, Frink et al. reported that TLSs play opposite roles in HCC [ 23 ], an inflammation-driven cancer that provides a niche for HCC progenitor cells and promotes HCC progression. Moreover, they observed that the presence of TLSs in the nontumoral liver correlated with an increased risk for late recurrence and a trend toward decreased overall survival after surgical resection of HCC [ 23 ]. Therefore, the prognostic value of TLSs, along with other clinical indicators and peripheral blood immune parameters, in HCC was investigated, and a clinical prediction model in which HCC could be constructed to guide personalized treatments for HCC patients was constructed. Materials and methods Patients and samples Patients with HCC diagnosed by surgical pathology at Youan Hospital between April 2014 and February 2021 were enrolled retrospectively. All patients were treated with radical surgery and followed up until December 30, 2022. The following patients were excluded: I. had preoperative anticancer treatments such as radiofrequency ablation, transarterial chemoembolization (TCAE), or radiotherapy or chemotherapy; II. mixed tumor histology, such as combined hepatocellular cholangiocarcinoma; III. Clinical data were missing, and patients were lost to follow-up or experienced intraoperative or postoperative death within one week (Fig. 1 A). A total of 180 HCC patients were included, with the majority having HBV-related HCC and a minimal number having HCV-related (5 patients) or alcohol-related (3 points) HCC. Overall survival (OS) was calculated from the date of liver resection surgery to the date of death, and survival was considered to occur before the last follow-up date. Recurrence-free survival (RFS) was defined as the time from the date of surgical resection to the time of diagnosis of recurrence or the time of last follow-up without reproduction. The data were divided into validation sets at a 7:3 ratio. This study obtained informed consent from participants prior to surgery for the use of surgical specimens and clinical data, and was approved by the Ethical Review Committee of Beijing Youan Hospital, Capital Medical University, and conducted under the guidance of the Declaration of Helsinki. Clinical and laboratory data The clinical characteristics and laboratory parameter data of the patients were obtained from electronic medical records. Clinical features included age, sex, tumor size as revealed by imaging examinations combined with pathological reports, Barcelona Clinic Liver Cancer (BCLC) stage, histology, hepatitis etiology, tumor differentiation, microvascular invasion (MVI), portal vein tumor thrombus (PVTT), portal hypertension (PH), TNM, and survival time, among others. Other laboratory parameters, including liver function indices, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alpha-fetoprotein (AFP), activated partial thromboplastin time (APTT), albumin (ALB), and peripheral blood cell counts, as well as white blood cell (WBC) counts, were collected during the seven days before surgery. Systemic inflammation parameters, including the neutrophil-to-lymphocyte ratio (NLR), NMR, and lymphocyte-to-monocyte ratio (LMR), were also calculated. (NLR = absolute neutrophil count/absolute lymphocyte count, NMR = absolute neutrophil count/total monocyte count, LMR = absolute lymphocyte count/absolute monocyte count) Pathological examination According to the definition of TLSs established by the World Health Organization, TLSs refer to the structural aggregation of lymphoid tissue within nonlymphoid tissue. These structures can be classified into three levels of maturity: I: Agg. This level is characterized by the abundant aggregation of lymphocytes. II: Follicular cells (FOLs) indicate the presence of follicles or follicular structures within the tissue. III: Germinal center (GC) - represents the presence of germinal centers within the structure [ 7 , 10 ]. A germinal center is a central area where a significant aggregation of lymphocytes, mainly CD20 + B cells, forms a dark zone surrounded by CD3 + T cells, CD4 + T cells, dendritic cells (DCs), Tregs, and macrophages. Additionally, peripheral high endothelial venules are observed in this region [ 1 , 10 , 12 , 24 – 28 ]. Two pathologists conducted a double-blind examination of tissue sections and systematically recorded various criteria, including tumor size, satellite nodules, invasion of major blood vessels, microvascular invasion, tumor differentiation as per the World Health Organization, presence of TLSs, TNM, and BCLC clinical staging. A discussion is necessary to come to a unanimous conclusion when there are inconsistent interpretations. The location of TLSs was categorized based on whether TLSs were found within the tumor or within 2 mm of the tumor edge (peritumoral): Intratumor-TLSs (I-TLSs) and Peritumor-TLSs (P-TLSs) (Fig. 1 E, F). If at least one TLS was found within the tumor or peritumor region, the patient was considered I-TLS-positive or P-TLS-positive, respectively. Likewise, if fibrils or GCs were observed within I-TLSs or P-TLSs, the tumor was considered to be an I-TLS or a P-TLS with fibrils or GC positivity. The number of TLS structures was recorded in the area with the highest TLS grade, and the most numerous structures were observed via high-power microscopy (Fig. 1 B, C, D). Statistical analyses All the statistical analyses were performed by using SPSS software (version 26.0, SPSS), GraphPad Prism software (version 9.0), Adobe Illustrator (version 2021), and R software (version 4.0). Categorical variables were compared using the chi-square test or Fisher's exact test. Continuous variables were compared using the t test, Mann–Whitney U test, or Kruskal–Wallis rank test. Survival and RFS curves were drawn using the Kaplan–Meier method and compared using the log-rank test. Multivariate analysis was performed with a Cox proportional hazards regression model. Spearman analysis was used to test the correlation of skewed data. A p value < 0.05 was considered to indicate statistical significance. Cox proportional hazards regression analysis was used to determine the indicators associated with overall survival (OS) and disease-free survival (RFS). Variables with p values less than 0.1 from the univariate Cox regression analysis were included in the multivariate analysis to determine which variables were significantly associated with survival and recurrence rates. We constructed nomograms for predicting 24-month and 60-month OS and RFS in HCC patients based on the identified predictive factors. The concordance index (C-index) was calculated to evaluate the predictive ability of each factor, with 0.5 indicating random chance and values closer to 1.0 indicating a better ability to distinguish outcomes. Calibration curves were used to assess the correlation between the actual outcomes and the predicted probabilities. Results Patient characteristics We retrospectively included 180 patients with primary HCC after surgery, among whom 89 patients were TLS-positive, resulting in a TLS-positive rate of 49.44%, similar to the international rate of 48%. Among the TLS-positive patients, 81 had Agg, four had follicle (FOL), five had GC, 21 had I-TLSs, 29 had P-TLSs, and 39 had both I-TLSs and P-TLSs (Supplementary Table 5). The baseline characteristics of the included population can be found in Table 2 (Supplementary Table 3, 4). All included patients were HCC patients who had not received any antitumor treatment or surgical treatment before surgery. The median OS and median RFS (recurrence-free) were 46 months and 32 months, respectively (Supplementary Table 1, 2). We first divided the samples into training and validation cohorts at a 7:3 ratio and found that among the 125 patients in the training cohort, 65 were TLS-positive and 60 were TLS-negative. The median OS and median RFS were 46.5 months and 35 months, respectively (Supplementary Table 6, 7). We first explored the prognostic relationship between TLSs and HCC in the HMN cohort (Fig. 2 A-D), which showed that TLSs were associated with early recurrence and death in HCC but not with late prognosis. Considering that previous studies have investigated the prognostic value of peripheral blood immune parameters such as the NLR, LMR, NMR, and PLR in HCC, we studied the relationship between peripheral blood immune parameters (white blood cells, neutrophils, lymphocytes, macrophages) and TLSs and further explored the impact of the NLR, LMR, and NMR on HCC prognosis. We first determined the optimal cutoff values based on the ROC curves and subsequently plotted the corresponding KM curves for the indicators. We found that, overall, NMR performed the best (but only for early recurrence) among the three indicators (Fig. 2 E-J). Therefore, our KM curves suggested that TLSs and the NMR may be associated with early recurrence in HCC patients. Based on these findings, we preliminarily concluded that TLSs and the NMR are factors influencing the early recurrence of HCC. To further explore the factors affecting HCC prognosis, we grouped patients based on TLS status and NMR level and observed whether there were statistically significant differences in the distributions of other factors between the two groups. The intragroup heterogeneity analysis between the TLSs and NMRs revealed differences in the distribution of AST (p = 0.044) and tumor size (p = 0.048), while the distributions of other factors were not significantly different. There was no significant difference in the distribution of the metabolites between the NMR groups (Table 1). Therefore, we explored the factors influencing HCC prognosis, especially early recurrence, through univariate and multivariate Cox analyses. According to the Cox univariate analysis for early recurrence, sex, MVI, tumor size, PVTT, BCLC status, portal hypertension, NMR, TLS, and TNM were found to be factors influencing early recurrence (p < 0.1). However, in the multivariate analysis, TNM grade, TLS, and the NMR were identified as independent factors affecting early HCC recurrence (p < 0.05), with TLS and the NMR acting as protective factors, while TNM was identified as a risk factor. According to the Cox univariate analysis for late recurrence, MVI, PVTT, tumor size, BCLC status, NMR, TNM grade, and ALT level were found to be influencing factors (p < 0.1). However, in the Cox multivariate analysis, only the NMR and BCLC grade were identified as independent factors (p < 0.05) affecting late recurrence (Table 2). TLS and TLS positions and HCC patient survival and recurrence Patients were initially divided into two groups based on the pathological findings, TLS + and TLS-, and the early and late prognoses of these two groups were assessed. TLSs were associated with early recurrence in HCC patients but not with late prognosis recurrence (Fig. 2 A, B). Furthermore, the relationships between the location and maturity of TLSs and early and late recurrence in HCC patients were explored. However, the study revealed that the location and maturity of TLSs were not significantly related to early or late recurrence in HCC patients (Fig. 2 C, D). These results suggest that while TLSs may play a role in early recurrence, TLS location and maturity do not appear to be predictive factors for late recurrence. This study revealed that the location of TLSs is not associated with the prognosis of HCC patients, contrary to the findings of other studies. Their research suggested a close relationship between the location of TLSs and the prognosis of HCC patients, indirectly confirming the heterogeneity of TLSs in HCC patients, which warrants further investigation. This understanding may be crucial for obtaining a better understanding of disease prognosis in HCC patients and for guiding treatment decisions. The prognostic value of peripheral blood immune parameters in HCC patients The prognostic value of peripheral blood immune parameters in HCC patients was assessed, and the optimal cutoff values for each parameter were determined using receiver operating characteristic (ROC) curves. The optimal cutoff values and AUC areas for the NLR, LMR, and NMR for early recurrence-free survival (RFS) were 1.665 (AUC = 0.551), 4.650 (AUC = 0.507), and 11.600 (AUC = 0.547), respectively. For late RFS, the optimal cutoff values and AUC areas for the NLR, LMR, and NMR were 2.845 (AUC = 0.504), 3.285 (AUC = 0.523), and 7.820 (AUC = 0.530), respectively (Fig. 2 E-J). We observed that among the systemic validation parameters, the NMR had a significant impact on both early and late recurrence in HCC patients, while TLS structures exhibited significant differences in early prognosis (Fig. 2 A). Therefore, exploring the combined effects of TLSs and the NMR on HCC prognosis may be valuable, especially in the context of early recurrence. Combining TLSs, the TNM signature and the NMR to predict survival in HCC patients By exploring the relationships between TLSs and the NMR and HCC prognosis, we found that TLSs and the NMR were closely related to early recurrence in HCC patients. Therefore, we grouped the patients based on TLS status and NMR level to investigate whether there were significant differences in these factors between the two groups (Table 1). Table 1 shows that, except for tumor size and AST, there were no differences in the distributions of the other factors between the two groups. Next, we explored the factors influencing early recurrence in HCC patients. Through univariate and multivariate Cox regression analyses, we found that in univariate Cox analysis, MVI, sex, TNM grade, tumor size, tumor differentiation, TLS, NMR, BCLC, and PH (portal hypertension) were closely related to early recurrence in HCC patients (p < 0.1). However, according to the multivariate Cox analysis, only TNM stage, NMR, and TLS were found to be significant factors influencing early recurrence in patients with HCC (p < 0.05) (Table 2). PVTT, TNM, MVI, tumor size, BCLC, and NMR (p < 0.1) are factors that influence late recurrence in HCC patients, while BCLC NMR (p < 0.05) is a factor affecting late recurrence (Table 2). The study indicated that TLSs and the NMR have a significant impact on early recurrence in HCC patients but not on late recurrence. In previous studies, there was no correlation between TLSs and peripheral blood immune cells, and there was also no correlation between TLSs and TNM stage. Furthermore, our correlation analysis did not reveal any association between these two factors. Before conducting a Cox multivariate analysis, collinearity diagnostics were performed on the included factors, and the study showed that there was no collinearity among the included factors (Supplementary Table 11). As a precaution, a correlation analysis was conducted on TNM, NMR, and TLS data, which revealed that there was no correlation between any pair of these factors (Supplementary Table 12–14). Therefore, TLS, TNM grade, and the NMR are considered independent influencing factors for early recurrence in HCC patients. Through multivariate Cox analysis, early recurrence in HCC patients can be predicted through the use of three indicators: TNM grade, TLS, and the NMR. (Fig. 3 A) Construction of the nomogram for RFS and performance evaluation Based on the multivariate Cox analysis, predictive factors for early recurrence in HCC, including TNM grade, TLSs, and the NMR, were used to construct a nomogram for predicting the probability of early recurrence in HCC patients (Fig. 3 B). A nomogram and a calibration curve were constructed to predict early recurrence in HCC patients based on three factors: TNM grade, TLS, and the NMR. The nomogram predicts the 24-month recurrence probability in HCC patients using these three factors. The probability can be obtained as a function of the total points calculated as the sum of the points for each specific variable. Points were assigned for each factor by drawing a line upward from the corresponding values to the ‘point’ line. The total sum of the points added by each factor was plotted on the “total points” line. A line was drawn down to read the corresponding predictions of probability. Internal validation was performed by the bootstrap method with 1000 replicate samples. Each factor in the nomogram was assigned a quantified score, and the scores for all factors were summed to obtain a total score, helping doctors predict the probability of early recurrence in patients. The AUC for early HCC recurrence was 0.792 (p < 0.0001), and the AUC for early RFS in the validation cohort was 0.716 (p = 0.008) (Fig. 4 A, B). The calibration curve demonstrated consistency between the predictions and actual observations, indicating a high degree of agreement between the training and validation cohorts (Fig. 4 C). Furthermore, decision curve analysis (DCA) showed that the nomogram had high predictive efficacy for early recurrence in HCC patients, both for early and late RFS. In summary, a nomogram based on TNM staging, TLSs, and the NMR offers a valuable tool for predicting the risk of early recurrence in HCC patients and has good predictive performance, as evidenced by the AUC and DCA. Discussion In this study, the impacts of TLSs, peripheral blood leukocytes, and other clinical indicators on the prognosis of HCC patients were investigated. We discovered that the combination of TLSs with TNM and NMR data can effectively predict early recurrence in HCC patients. Previous research has suggested that early recurrence indicates in situ tumor recurrence, while late recurrence suggests the development of new tumors [ 24 ]. TNM and TLS, as independent prognostic factors for HCC, were found to be unrelated to each other [ 1 , 7 , 10 ]. Although TLSs function as lymphocyte aggregates within nonlymphoid areas [ 7 , 10 , 25 ], which are theoretically related to peripheral blood neutrophils (N), lymphocytes (L), and monocytes (M), past studies have shown no significant correlation between TLSs and these cells [ 10 , 22 , 25 ], and our research also revealed no correlation between peripheral lymphocytes and TLSs. Finally, NMR and TNM have been shown to be unrelated in previous studies. These findings suggested that TLSs, the TNM grade, and the NMR are independent factors for early recurrence in HCC patients. A nomogram constructed based on these factors provides clinicians with a scoring tool for predicting the early prognosis of HCC patients. According to the nomogram, the ratio of TLSs to the NMR serves as a good discriminant, but the TNM score for T1 was significantly lower than that for the other three stages, which is consistent with clinical observations. According to the TNM staging, the multiplicity of the tumor and microvascular invasion were suspected to be important risk factors for HCC prognosis. However, this phenomenon was not observed in this study, possibly due to the small sample size or lack of collinearity analysis. Thus, future investigations may refine TNM staging to consider the multiplicity, size, and vascular invasion of tumors as separate factors influencing early recurrence in HCC patients. This study provides insights for surgeons to consider operating on HCC patients at the T1N0M0 stage, which could significantly improve patient prognosis. TLSs, as lymphocyte aggregates found in nonlymphoid areas, are known to be involved in chronic inflammation, transplant rejection, autoimmune inflammation, and tumors [ 1 , 7 , 10 , 11 , 25 , 26 ]. They share functional and developmental similarities with lymph nodes and play a role in local immune function as part of the tumor microenvironment (TME) [ 10 ]. However, their role in HCC is controversial [ 1 , 5 , 22 ]. This study revealed that TLSs significantly reduce early recurrence rates in HCC but are not correlated with late recurrence. Furthermore, the location of TLSs was found to be unrelated to HCC prognosis, which differs from the findings of other studies [ 1 , 5 , 20 , 22 , 23 ]. This finding suggested that TLSs exhibit heterogeneity not only among different tumor types but also among different individuals with HCC and even within different locations in the same donor. Therefore, further exploration of the heterogeneity of TLSs in HCC is warranted. Although the study did not find a correlation between TLS maturity and HCC prognosis, it is worth noting that the low quantity of fully mature TLSs (FOL and GC) in our study may necessitate an increase in sample size for further investigation. We performed receiver operating characteristic (ROC) analysis of the ratio of systemic inflammatory cells, such as the NLR, NMR, and LMR, taking the values at the optimal cutoff points. We found that the NMR effectively distinguished between early and late recurrence in HCC patients, especially in patients with early recurrence. However, our study revealed that patients with a high NMR ratio had a lower early recurrence rate, which contradicts the findings of the current study and requires further validation. Historically, the focus has been on T cells within TLSs, and studies on the relationship between CD4 + and CD8 + T cells and HCC prognosis have yielded different results, some even showing opposing results [ 1 , 5 ]. A recent study in renal cell carcinoma (RCC) revealed various B-cell stages within TLSs [ 27 ] and mature plasma cells secreting IgA and IgG antibodies and revealed that macrophage interactions with tumor cells led to phagocytic death. Additionally, Zhang Zemin's team reported an increase in exhausted T cells in patients with HCC [ 26 ], which is the primary reason for the decreased antitumor effects within the body. The appearance of exhausted T cells is attributed to an increase in neutrophils, which trap functional T cells in the extracellular matrix and hinder their antitumor effects. This finding contrasts with that of a 2015 study by Frink, which identified the role of neutrophils [ 23 ], especially in promoting HCC development, especially in HEVs (High et al.). This study suggested that while an increased number of local neutrophils within HCC tissue may lead to an adverse prognosis, an increased NMR in peripheral blood may reduce the early recurrence rate in HCC patients. These findings indicate that neutrophils in different body compartments may perform different functions in the context of HCC and raise questions about the distinct immunological statuses they represent. The role of TLSs in the early recurrence of HCC appears to extend beyond T and B lymphocyte antitumor immunity and might involve a systemic antitumor response. The tertiary lymph node-like structure seems to act as a hub, recruiting immune cells and initiating specific immune responses. The role of the peripheral blood NMR as a predictor of early recurrence in HCC patients suggested that the presence of N and M cells in the peripheral blood may indirectly reflect the infiltration of N and M cells within TLSs. NMR may reflect the systemic antitumor immune status, and the presence of N and M cells in peripheral blood may be directly or indirectly involved in the antitumor effects within TLSs. Our research offers a clinical prediction model for clinicians to predict early recurrence in HCC patients. These findings suggest that TLSs and the NMR have a positive role in early recurrence in HCC patients. While TLSs are considered to have a local immune function within the tumor microenvironment, they may play a systemic role in antitumor responses. The results also suggest that N and M cells in peripheral blood play a role in anti-HCC functions, but additional research is needed to understand the complexities of their involvement. This study provides clinical insights and future research directions for treating HCC. Conclusions TLSs are associated with a low early recurrence rate after hepatectomy for hepatocellular carcinoma (HCC), indicating a correlation with effective antitumor immunity. Our analysis revealed that the absence of TLSs is significantly associated with a high early recurrence rate in HCC patients. By constructing a clinical prediction model that combines TNM staging and the NMR, we can accurately predict early recurrence in HCC patients. This finding suggested that inducing the formation of TLSs may be a strategy for improving the prognosis of HCC patients. However, further research is needed to validate the results of the present study and elucidate the role of TLSs in the antitumor immune response in hepatocellular carcinoma. Abbreviations CI: Confidence interval; HR: Hazard ratio; HE: Hematoxylin and eosin; HCC: Hepatocellular Carcinoma; RFS: Recurrence-free survival; OS: Overall survival; TLSs: Tertiary lymphoid structures Declarations Data Sharing Statement All relevant data are available within the manuscript and its supplementary material files. Further enquiries can be directed to the corresponding author (Guangming Li, [email protected] ). Acknowledgments Not applicable Authors’ contributions Y.O. and G.L. designed the present study. Material preparation, data collection, and analysis were performed by Z. C., B. D., X. W., G. Z., F. W. and Y. S.. Pathological examinations were conducted by Z. C. and X. W. Statistical analysis was performed by Z. C., Y. O. and G. L. supervised the study design and manuscript preparation. The first draft of the manuscript was written by Z. C., Y. O. and B. D., and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript. Funding This work was supported by the Beijing Natural Science Foundation (L202024, M21006, 7222096, and L222067), the Key Medical Professional Development Plan of the Beijing Hospital Authority (ZYLX202124) and the Beijing Municipal Institute of Public Medical Research Development and Reform Pilot Project (Y-2023-4). Availability of data and materials The data used in this study are available from the corresponding author upon a reasonable request. Conflict of interest Z. C., B. D., X. W., G. Z., F. W., Y. S., Y. O., and G. L. declare that they have no conflicts of interest. Ethical approval This study was approved by the Ethics Committee of Beijing YouAn Hospital (LL-2022-052-K) and was performed in accordance with the ethical standards established in the Helsinki Declaration of 1975, as revised in 2008. Consent for publication Not applicable. Consent to participate All patient information was deidentified, and informed consent was not required because this study was retrospective. Competing Interests The authors declare that they have no conflicts of interest. References Calderaro, J., et al., Intratumoral tertiary lymphoid structures are associated with a low risk of early recurrence of hepatocellular carcinoma. J Hepatol, 2019. 70 (1): p. 58-65. Wen, S., et al., Combination of Tertiary Lymphoid Structure and Neutrophil-to-Lymphocyte Ratio Predicts Survival in Patients With Hepatocellular Carcinoma. Front Immunol, 2021. 12 : p. 788640. El-Serag, H.B., Hepatocellular Carcinoma. New England Journal of Medicine, 2011. 365 (12): p. 1118-1127. 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Noel, G., et al., Functional Th1-oriented T follicular helper cells that infiltrate human breast cancer promote effective adaptive immunity. J Clin Invest, 2021. 131 (19). Li, H., et al., Peritumoral Tertiary Lymphoid Structures Correlate With Protective Immunity and Improved Prognosis in Patients With Hepatocellular Carcinoma. Front Immunol, 2021. 12 : p. 648812. Finkin, S., et al., Ectopic lymphoid structures function as microniches for tumor progenitor cells in hepatocellular carcinoma. Nat Immunol, 2015. 16 (12): p. 1235-44. Imamura, H., et al., Risk factors contributing to early and late phase intrahepatic recurrence of hepatocellular carcinoma after hepatectomy. J Hepatol, 2003. 38 (2): p. 200-7. Cabrita, R., et al., Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature, 2020. 577 (7791): p. 561-565. Xue, R., et al., Liver tumor immune microenvironment subtypes and neutrophil heterogeneity. Nature, 2022. 612 (7938): p. 141-147. Meylan, M., et al., Tertiary lymphoid structures generate and propagate antitumor antibody-producing plasma cells in renal cell cancer. Immunity, 2022. 55 (3): p. 527-541 e5. Tables Tabel 1. Clinical and biological features of the training set according to the presence of TLS and NMR ratio. Available variables Available data (N=125) TLS+ (N=65) TLS- (N=60) p NMR-High (92) NMR-Low(33) p Age ≥60 50 (40.00%) 24 (36.92%) 26 (45.16%) 0.465 35 (37.50%) 15 (48.94%) 0.457 <60 75 (60.00%) 41 (63.08%) 34 (54.84%) 57 (62.50%) 18 (51.06%) Gender Male 101 (80.80%) 52 (80.00%) 49 (81.67%) 0.813 71 (77.17%) 30 (90.91%) 0.098 Female 24 (20.20%) 13 (20.00%) 11 (18.33%) 21 (22.83%) 3 (9.01%) Tumor differentiation I 52 (41.60%) 28 (43.08%) 24 (40.00%) 42 (45.65%) 10 (30.30%) II 62 (49.60%) 33 (50.77%) 29 (48.33%) 0.447 44 (47.83%) 18 (54.55%) 0.064 III 11 (8.80%) 4 (6.15%) 7 (11.67%) 6 (6.52%) 5 (15.15%) Microvascular invasion Y 50 (40.00%) 22 (33.85%) 28 (46.67%) 0.145 41 (44.57%) 9 (27.27%) 0.086 N 75 (60.00%) 43 (66.15%) 32 (53.33%) 51 (55.43%) 24 (72.73%) PVTT Y 26 (20.80%) 14 (21.54%) 12 (20.00%) 0.832 20 (21.74%) 6 (18.18%) 0.666 N 99 (79.20%) 51 (78.46%) 48 (80.00%) 72 (78.26%) 27 (81.82%) ALT (U/L) ≥40.00 41 (32.80%) 18 (27.67%) 23 (38.33%) 0.207 26 (28.26%) 15 (45.45%) 0.074 <40.00 84 (67.20%) 47 (72.33%) 37 (61.67%) 66 (71.74%) 18 (54.55%) AST (U/L) ≥40.00 41 (32.80%) 16 (24.62%) 25 (41.67%) 0.044 28 (30.43%) 13 (39.39%) 0.348 <40.00 84 (67.20%) 49 (75.38%) 35 (58.33%) 64 (69.57%) 20 (60.61%) TB (u mol/L) ≥17.1 58 (46.40%) 28 (43.08%) 30 (50.00%) 0.438 43 (46.74%) 15 (45.45%) 0.814 <17.1 67 (53.60%) 37 (56.92%) 30 (50.00%) 49 (53.26%) 18 (54.55%) ALB (g/L) ≥35 83 (66.40%) 46 (70.77%) 37 (61.67%) 0.283 62 (67.39%) 21 (63.64%) 0.695 <35 42 (33.60%) 19 (29.23%) 23 (38.33%) 30 (32.61%) 12 (36.36%) AFP (ng/ml) ≥400ng/ml 22 (17.60%) 15 (23.08%) 7 (11.67%) 0.100 19 (20.65%) 3 (9.09%) 0.146 <400ng/ml 103 (82.40%) 50 (76.92%) 53 (88.83%) 73 (79.35%) 30 (90.91%) Tumor Size ≥50mm 49 (39.20%) 20 (30.77%) 29 (48.33%) 0.046 39 (42.39%) 10 (30.30%) 0.225 <50mm 76 (60.80%) 45 (69.23%) 31 (51.67%) 53 (57.61%) 23 (69.70%) BCLC A 22 (17.60%) 14 (21.54%) 8 (13.33%) 15 (16.30%) 7 (21.21%) B 56 (44.80%) 29 (44.62%) 27 (45.00%) 0.370 41 (44.57%) 15 (45.45%) 0.515 C 22 (17.60%) 9 (13.85%) 13 (21.67%) 17 (18.48%) 5 (15.15%) D 25 (20.00%) 13 (20.00%) 12 (20.00%) 19 (20.65%) 6 (18.18%) Portal hypertension Y 18 (14.40%) 9 (13.85%) 9 (15.00%) 0.854 10 (10.87%) 8 (24.24%) 0.067 N 107 (85.60%) 56 (86.15%) 51 (85.00%) 82 (89.13%) 25 (75.76%) TNM T1N0M0 64 (51.20%) 37 (56.92%) 27 (45.00%) 0.299 42 (45.65%) 22 (66.67) 0.159 T2N0M0 18 (14.40%) 9 (13.85%) 9 (15.00%) 16 (17.39%) 2 (6.06%) T3N0M0 16 (12.80%) 5 (7.69%) 11 (18.33%) 13 (14.13%) 3 (9.09%) T4N0M0 27 (21.60%) 14 (21.54%) 13 (21.67%) 21 (22.83%) 6 (18.18%) NMR High 92 (72.60%) 48 (73.85%) 44 (73.33%) 0.948 Low 33 (26.40%) 17 (26.15%) 16 (26.67%) TLS Y 65 (52.00%) 48 (52.17%) 17 (51.52%) 0.948 N 60 (48.00%) 44 (47.83%) 16 (48.48%) PVTT: Portal Vein Tumor Thrombosis，BCLC: Barcelona Clinic Liver Cancer. Logistic regression analysis was employed to examine whether there are differences in the intra-group distribution of TLS and NMR. (P<0.05) Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.rtf Table2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 May, 2024 Reviews received at journal 22 May, 2024 Reviewers agreed at journal 12 May, 2024 Reviews received at journal 25 Apr, 2024 Reviewers agreed at journal 20 Apr, 2024 Reviewers invited by journal 21 Jan, 2024 Editor assigned by journal 21 Jan, 2024 Editor invited by journal 09 Jan, 2024 Submission checks completed at journal 08 Jan, 2024 First submitted to journal 26 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3806961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266030294,"identity":"e07c2821-9b3f-4e97-bb67-8623e9fe9f0f","order_by":0,"name":"Zhuangzhuang Chen","email":"","orcid":"","institution":"Beijing YouAn Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhuangzhuang","middleName":"","lastName":"Chen","suffix":""},{"id":266030295,"identity":"7b4314d4-68b6-40f7-9794-284bb772b048","order_by":1,"name":"Binwei Duan","email":"","orcid":"","institution":"Beijing YouAn Hospital","correspondingAuthor":false,"prefix":"","firstName":"Binwei","middleName":"","lastName":"Duan","suffix":""},{"id":266030296,"identity":"ba1f13e7-e82d-4b1e-bc15-63680b3569e0","order_by":2,"name":"Xinxin Wang","email":"","orcid":"","institution":"Beijing YouAn Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Wang","suffix":""},{"id":266030297,"identity":"b1790893-efe7-47fe-a0b1-3c8127dd742d","order_by":3,"name":"Gongming Zhang","email":"","orcid":"","institution":"Beijing YouAn Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gongming","middleName":"","lastName":"Zhang","suffix":""},{"id":266030298,"identity":"61aa1ff2-ba98-4761-9bc8-c382763286fa","order_by":4,"name":"Feng Wu","email":"","orcid":"","institution":"Beijing YouAn Hospital","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Wu","suffix":""},{"id":266030299,"identity":"cd7e0fab-680a-4cff-b2c4-6c5374f36dc7","order_by":5,"name":"Yibo Sun","email":"","orcid":"","institution":"Beijing YouAn Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yibo","middleName":"","lastName":"Sun","suffix":""},{"id":266030300,"identity":"03cb1085-693b-4c2b-8966-40d81f90c0b1","order_by":6,"name":"Yabo Ouyang","email":"","orcid":"","institution":"Beijing Institute of Hepatology","correspondingAuthor":false,"prefix":"","firstName":"Yabo","middleName":"","lastName":"Ouyang","suffix":""},{"id":266030301,"identity":"178c7776-c74e-4f21-9ad4-cd8abd0916c6","order_by":7,"name":"Guangming Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACPmYog42BgfEBlG2AVwsbkhZmmFICWpDZEsRpYWe/JvFzR200n3T7tYofNdsSG9ibt0kw1NzB4zCeMsneM8dz22TOlN3sOXY7sYHnWJkEw7Fn+LSkSfC2Hcttk8hJu83YANQikWMmwdhwGK8Wyb9QLcVgLfJvCGlhPybN21YD1JJ+jBliCw9BW5itZdsOgGxhlgT6xbiNJ63YIuEYbi38/Mcf3nzbVpc7f0b6ww8/am7L9rMf3njjQw1uLQwMPKBYOAxjQGMqAY8GBgb2B0CiDsYYBaNgFIyCUYAJADOTUXKJMrYlAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing YouAn Hospital","correspondingAuthor":true,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-12-26 08:00:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3806961/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3806961/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49436153,"identity":"3d83881c-bef1-4d93-9438-8b7305ae9325","added_by":"auto","created_at":"2024-01-10 20:06:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7795886,"visible":true,"origin":"","legend":"\u003cp\u003eA): A total of 180 hepatocellular carcinoma (HCC) patients were included and divided into training and validation cohorts at a 7:3 ratio. TLS maturity: B) Agg, lymphoid aggregates; C) Fol, follicular-like lymphoid structures; D) GC, mature TLS structures with germinal centers. TLS location: E) Intratumoral (I-TLS) F) peritumoral (P-TLS)\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-3806961/v1/6cc9b2ef46d1ff55dc0dcd56.png"},{"id":49436152,"identity":"835149b0-88f9-4798-a886-7405f99bed53","added_by":"auto","created_at":"2024-01-10 20:06:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":273097,"visible":true,"origin":"","legend":"\u003cp\u003eA) Early recurrence in HCC patients with TLSs+ is low (\u003cem\u003ep\u003c/em\u003e=0.0002), but it is not correlated with late recurrence (\u003cem\u003ep\u003c/em\u003e=0.1032). B) Indicate that the location of TLSs is not associated with early or late recurrence in HCC \u003cem\u003epatients (\u003c/em\u003e=0.425, \u003cem\u003ep \u003c/em\u003e=0.933). E-J) represent the KM curves for the NLR, LMR, and NMR at their optimal cutoff values for early recurrence (2RFS). The respective p values are as follows: \u003cem\u003ep\u003c/em\u003e=0.269, \u003cem\u003ep\u003c/em\u003e=0.068, and \u003cem\u003ep\u003c/em\u003e=0.019. For late recurrence (5RFS), the p values were \u003cem\u003ep\u003c/em\u003e=0.271, \u003cem\u003ep\u003c/em\u003e=0.364,\u003cem\u003e and p\u003c/em\u003e=0.009.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3806961/v1/934fe81722c05e3571906a88.png"},{"id":49436937,"identity":"c5ba178e-f5d9-4907-a0b3-e5a6147cc632","added_by":"auto","created_at":"2024-01-10 20:14:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":558677,"visible":true,"origin":"","legend":"\u003cp\u003eA) The forest plot (log10 scale) of early recurrence-free survival (RFS) in HCC patients is shown below. TLS positivity, high NMR, and low TNM stage are protective factors for HCC patients. B) Calibration curves for the nomogram for predicting the 24-month recurrence rate in the training cohort. C) The calibration curve for the nomogram for predicting the 24-month recurrence rate in the validation cohort.\u003c/p\u003e","description":"","filename":"FIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-3806961/v1/162a3c936a74c9f3e6ab7cc5.png"},{"id":49436150,"identity":"efce7248-daf1-4027-90a4-3febe17e801d","added_by":"auto","created_at":"2024-01-10 20:06:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":905029,"visible":true,"origin":"","legend":"\u003cp\u003eA) The area under the curve (AUC) shows that in the training set, the ROC AUC values for TNM, TLS, and NMR were 0.665, 0.650, and 0.589, respectively. However, the combined ROC AUC for all three parameters in the training set was 0.792 (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001). B) In the validation set, the combined ROC AUC for all three parameters was 0.716 (\u003cem\u003ep\u003c/em\u003e=0.008). C, D) Points were assigned for each factor by drawing a line upward from the corresponding values to the ‘point’ line. The total sum of the points added by each factor was plotted on the “total points” line. A line was drawn down to read the corresponding predictions of probability. Internal validation was performed by the bootstrap method with 1000 replicate samples. E) Calibration curves for the nomogram for predicting the 24-month recurrence rate in the training cohort. F) The calibration curve for the nomogram for predicting the 24-month recurrence rate in the validation cohort. The net benefit curves in both the training and validation sets indicate that the nomogram can effectively assist doctors in selecting personalized treatment approaches and interventions for HCC, thereby reducing the recurrence rate among HCC patients. G) Based on the hazard ratios (HRs) of TNM, TLS, and NMR, patients were assigned scores, and the final values were divided into three groups: high-risk, moderate-risk, and low-risk. As shown in the graph, the high-risk group had a significantly worse prognosis than the low-risk group did, while the moderate-risk group had a score between these two groups, demonstrating good discriminative ability.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3806961/v1/1764094d68345761bc10e280.png"},{"id":49437155,"identity":"b2298b58-33ad-40fb-803e-785d0ebd27e1","added_by":"auto","created_at":"2024-01-10 20:22:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1913345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3806961/v1/8c206683-de2b-47ce-b957-d67b20070798.pdf"},{"id":49436155,"identity":"a9682468-822a-4f1b-af17-ae73f40b8bb2","added_by":"auto","created_at":"2024-01-10 20:06:53","extension":"rtf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10900538,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.rtf","url":"https://assets-eu.researchsquare.com/files/rs-3806961/v1/18abf1b1e6cfe82051d46460.rtf"},{"id":49436149,"identity":"87674c05-dddf-4812-8f12-548a4b024c55","added_by":"auto","created_at":"2024-01-10 20:06:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30782,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3806961/v1/128465a76db923099139e1d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The combination of TLSs and the neutrophil-to-macrophage ratio predicts early recurrence in patients with hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer is the sixth most common cancer globally and the third leading cause of tumor-related deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. HCC is the predominant type of primary liver cancer, accounting for approximately 90% of cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Risk factors for HCC include hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, alcohol consumption, fatty liver disease, aflatoxin exposure, and liver cirrhosis, among others [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite the various treatment options available for HCC, surgery has the greatest survival benefit, but the recurrence rate is as high as 80% five years after surgery [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, HCC often lacks specific biomarkers and presents with atypical clinical symptoms in its early stages, leading to delayed diagnosis and missed opportunities for optimal surgical interventions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, the 5-year survival rate for HCC patients is only 15% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Given the poor prognosis of HCC patients, there is an urgent need for biological markers and predictive models to guide treatment and predict outcomes. While many experts and scholars have proposed various prognostic and predictive models for liver cancer patients, a fully applicable model has yet to be found [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTLSs, also known as ectopic lymph node structures, are specific aggregations of lymphocytes in nonlymphoid tissues and are frequently found in conditions such as chronic inflammation, transplant rejection reactions, autoimmune diseases, and cancers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Like secondary lymph nodes, TLSs likely function similarly to secondary lymph nodes, leading most researchers to believe that TLSs play a role in HCC similar to that of lymph nodes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Numerous prospective clinical trials and retrospective experiments have confirmed that the presence of TLSs is associated with increased overall survival (OS) and reduced recurrence rates in HCC patients, as well as in other cancers, such as gastric cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], colorectal cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], skin cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], head and neck squamous cell carcinoma [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], lung cancer [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], pancreatic cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], ovarian cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and breast cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Several studies have further indicated that TLSs at different locations within the tumor microenvironment have varying effects on HCC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], with peritumoral TLSs significantly reducing early recurrence rates [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, Frink et al. reported that TLSs play opposite roles in HCC [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], an inflammation-driven cancer that provides a niche for HCC progenitor cells and promotes HCC progression. Moreover, they observed that the presence of TLSs in the nontumoral liver correlated with an increased risk for late recurrence and a trend toward decreased overall survival after surgical resection of HCC [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the prognostic value of TLSs, along with other clinical indicators and peripheral blood immune parameters, in HCC was investigated, and a clinical prediction model in which HCC could be constructed to guide personalized treatments for HCC patients was constructed.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and samples\u003c/h2\u003e \u003cp\u003ePatients with HCC diagnosed by surgical pathology at Youan Hospital between April 2014 and February 2021 were enrolled retrospectively. All patients were treated with radical surgery and followed up until December 30, 2022. The following patients were excluded: I. had preoperative anticancer treatments such as radiofrequency ablation, transarterial chemoembolization (TCAE), or radiotherapy or chemotherapy; II. mixed tumor histology, such as combined hepatocellular cholangiocarcinoma; III. Clinical data were missing, and patients were lost to follow-up or experienced intraoperative or postoperative death within one week (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). A total of 180 HCC patients were included, with the majority having HBV-related HCC and a minimal number having HCV-related (5 patients) or alcohol-related (3 points) HCC. Overall survival (OS) was calculated from the date of liver resection surgery to the date of death, and survival was considered to occur before the last follow-up date. Recurrence-free survival (RFS) was defined as the time from the date of surgical resection to the time of diagnosis of recurrence or the time of last follow-up without reproduction. The data were divided into validation sets at a 7:3 ratio. This study obtained informed consent from participants prior to surgery for the use of surgical specimens and clinical data, and was approved by the Ethical Review Committee of Beijing Youan Hospital, Capital Medical University, and conducted under the guidance of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical and laboratory data\u003c/h2\u003e \u003cp\u003eThe clinical characteristics and laboratory parameter data of the patients were obtained from electronic medical records. Clinical features included age, sex, tumor size as revealed by imaging examinations combined with pathological reports, Barcelona Clinic Liver Cancer (BCLC) stage, histology, hepatitis etiology, tumor differentiation, microvascular invasion (MVI), portal vein tumor thrombus (PVTT), portal hypertension (PH), TNM, and survival time, among others. Other laboratory parameters, including liver function indices, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alpha-fetoprotein (AFP), activated partial thromboplastin time (APTT), albumin (ALB), and peripheral blood cell counts, as well as white blood cell (WBC) counts, were collected during the seven days before surgery. Systemic inflammation parameters, including the neutrophil-to-lymphocyte ratio (NLR), NMR, and lymphocyte-to-monocyte ratio (LMR), were also calculated. (NLR\u0026thinsp;=\u0026thinsp;absolute neutrophil count/absolute lymphocyte count, NMR\u0026thinsp;=\u0026thinsp;absolute neutrophil count/total monocyte count, LMR\u0026thinsp;=\u0026thinsp;absolute lymphocyte count/absolute monocyte count)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePathological examination\u003c/h2\u003e \u003cp\u003eAccording to the definition of TLSs established by the World Health Organization, TLSs refer to the structural aggregation of lymphoid tissue within nonlymphoid tissue. These structures can be classified into three levels of maturity: I: Agg. This level is characterized by the abundant aggregation of lymphocytes. II: Follicular cells (FOLs) indicate the presence of follicles or follicular structures within the tissue. III: Germinal center (GC) - represents the presence of germinal centers within the structure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A germinal center is a central area where a significant aggregation of lymphocytes, mainly CD20\u0026thinsp;+\u0026thinsp;B cells, forms a dark zone surrounded by CD3\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, dendritic cells (DCs), Tregs, and macrophages. Additionally, peripheral high endothelial venules are observed in this region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTwo pathologists conducted a double-blind examination of tissue sections and systematically recorded various criteria, including tumor size, satellite nodules, invasion of major blood vessels, microvascular invasion, tumor differentiation as per the World Health Organization, presence of TLSs, TNM, and BCLC clinical staging. A discussion is necessary to come to a unanimous conclusion when there are inconsistent interpretations.\u003c/p\u003e \u003cp\u003eThe location of TLSs was categorized based on whether TLSs were found within the tumor or within 2 mm of the tumor edge (peritumoral): Intratumor-TLSs (I-TLSs) and Peritumor-TLSs (P-TLSs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F). If at least one TLS was found within the tumor or peritumor region, the patient was considered I-TLS-positive or P-TLS-positive, respectively. Likewise, if fibrils or GCs were observed within I-TLSs or P-TLSs, the tumor was considered to be an I-TLS or a P-TLS with fibrils or GC positivity. The number of TLS structures was recorded in the area with the highest TLS grade, and the most numerous structures were observed via high-power microscopy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, C, D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were performed by using SPSS software (version 26.0, SPSS), GraphPad Prism software (version 9.0), Adobe Illustrator (version 2021), and R software (version 4.0). Categorical variables were compared using the chi-square test or Fisher's exact test. Continuous variables were compared using the t test, Mann\u0026ndash;Whitney U test, or Kruskal\u0026ndash;Wallis rank test. Survival and RFS curves were drawn using the Kaplan\u0026ndash;Meier method and compared using the log-rank test. Multivariate analysis was performed with a Cox proportional hazards regression model. Spearman analysis was used to test the correlation of skewed data. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003cp\u003eCox proportional hazards regression analysis was used to determine the indicators associated with overall survival (OS) and disease-free survival (RFS). Variables with p values less than 0.1 from the univariate Cox regression analysis were included in the multivariate analysis to determine which variables were significantly associated with survival and recurrence rates. We constructed nomograms for predicting 24-month and 60-month OS and RFS in HCC patients based on the identified predictive factors. The concordance index (C-index) was calculated to evaluate the predictive ability of each factor, with 0.5 indicating random chance and values closer to 1.0 indicating a better ability to distinguish outcomes. Calibration curves were used to assess the correlation between the actual outcomes and the predicted probabilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eWe retrospectively included 180 patients with primary HCC after surgery, among whom 89 patients were TLS-positive, resulting in a TLS-positive rate of 49.44%, similar to the international rate of 48%. Among the TLS-positive patients, 81 had Agg, four had follicle (FOL), five had GC, 21 had I-TLSs, 29 had P-TLSs, and 39 had both I-TLSs and P-TLSs (Supplementary Table\u0026nbsp;5). The baseline characteristics of the included population can be found in Table\u0026nbsp;2 (Supplementary Table\u0026nbsp;3, 4). All included patients were HCC patients who had not received any antitumor treatment or surgical treatment before surgery. The median OS and median RFS (recurrence-free) were 46 months and 32 months, respectively (Supplementary Table\u0026nbsp;1, 2). We first divided the samples into training and validation cohorts at a 7:3 ratio and found that among the 125 patients in the training cohort, 65 were TLS-positive and 60 were TLS-negative. The median OS and median RFS were 46.5 months and 35 months, respectively (Supplementary Table\u0026nbsp;6, 7). We first explored the prognostic relationship between TLSs and HCC in the HMN cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D), which showed that TLSs were associated with early recurrence and death in HCC but not with late prognosis. Considering that previous studies have investigated the prognostic value of peripheral blood immune parameters such as the NLR, LMR, NMR, and PLR in HCC, we studied the relationship between peripheral blood immune parameters (white blood cells, neutrophils, lymphocytes, macrophages) and TLSs and further explored the impact of the NLR, LMR, and NMR on HCC prognosis. We first determined the optimal cutoff values based on the ROC curves and subsequently plotted the corresponding KM curves for the indicators. We found that, overall, NMR performed the best (but only for early recurrence) among the three indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-J). Therefore, our KM curves suggested that TLSs and the NMR may be associated with early recurrence in HCC patients. Based on these findings, we preliminarily concluded that TLSs and the NMR are factors influencing the early recurrence of HCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the factors affecting HCC prognosis, we grouped patients based on TLS status and NMR level and observed whether there were statistically significant differences in the distributions of other factors between the two groups. The intragroup heterogeneity analysis between the TLSs and NMRs revealed differences in the distribution of AST (p\u0026thinsp;=\u0026thinsp;0.044) and tumor size (p\u0026thinsp;=\u0026thinsp;0.048), while the distributions of other factors were not significantly different. There was no significant difference in the distribution of the metabolites between the NMR groups (Table\u0026nbsp;1). Therefore, we explored the factors influencing HCC prognosis, especially early recurrence, through univariate and multivariate Cox analyses. According to the Cox univariate analysis for early recurrence, sex, MVI, tumor size, PVTT, BCLC status, portal hypertension, NMR, TLS, and TNM were found to be factors influencing early recurrence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). However, in the multivariate analysis, TNM grade, TLS, and the NMR were identified as independent factors affecting early HCC recurrence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with TLS and the NMR acting as protective factors, while TNM was identified as a risk factor. According to the Cox univariate analysis for late recurrence, MVI, PVTT, tumor size, BCLC status, NMR, TNM grade, and ALT level were found to be influencing factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). However, in the Cox multivariate analysis, only the NMR and BCLC grade were identified as independent factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) affecting late recurrence (Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTLS and TLS positions and HCC patient survival and recurrence\u003c/h2\u003e \u003cp\u003ePatients were initially divided into two groups based on the pathological findings, TLS\u0026thinsp;+\u0026thinsp;and TLS-, and the early and late prognoses of these two groups were assessed. TLSs were associated with early recurrence in HCC patients but not with late prognosis recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Furthermore, the relationships between the location and maturity of TLSs and early and late recurrence in HCC patients were explored. However, the study revealed that the location and maturity of TLSs were not significantly related to early or late recurrence in HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003eThese results suggest that while TLSs may play a role in early recurrence, TLS location and maturity do not appear to be predictive factors for late recurrence. This study revealed that the location of TLSs is not associated with the prognosis of HCC patients, contrary to the findings of other studies. Their research suggested a close relationship between the location of TLSs and the prognosis of HCC patients, indirectly confirming the heterogeneity of TLSs in HCC patients, which warrants further investigation. This understanding may be crucial for obtaining a better understanding of disease prognosis in HCC patients and for guiding treatment decisions.\u003c/p\u003e \u003cp\u003eThe prognostic value of peripheral blood immune parameters in HCC patients\u003c/p\u003e \u003cp\u003eThe prognostic value of peripheral blood immune parameters in HCC patients was assessed, and the optimal cutoff values for each parameter were determined using receiver operating characteristic (ROC) curves. The optimal cutoff values and AUC areas for the NLR, LMR, and NMR for early recurrence-free survival (RFS) were 1.665 (AUC\u0026thinsp;=\u0026thinsp;0.551), 4.650 (AUC\u0026thinsp;=\u0026thinsp;0.507), and 11.600 (AUC\u0026thinsp;=\u0026thinsp;0.547), respectively. For late RFS, the optimal cutoff values and AUC areas for the NLR, LMR, and NMR were 2.845 (AUC\u0026thinsp;=\u0026thinsp;0.504), 3.285 (AUC\u0026thinsp;=\u0026thinsp;0.523), and 7.820 (AUC\u0026thinsp;=\u0026thinsp;0.530), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-J).\u003c/p\u003e \u003cp\u003eWe observed that among the systemic validation parameters, the NMR had a significant impact on both early and late recurrence in HCC patients, while TLS structures exhibited significant differences in early prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Therefore, exploring the combined effects of TLSs and the NMR on HCC prognosis may be valuable, especially in the context of early recurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCombining TLSs, the TNM signature and the NMR to predict survival in HCC patients\u003c/h2\u003e \u003cp\u003eBy exploring the relationships between TLSs and the NMR and HCC prognosis, we found that TLSs and the NMR were closely related to early recurrence in HCC patients. Therefore, we grouped the patients based on TLS status and NMR level to investigate whether there were significant differences in these factors between the two groups (Table\u0026nbsp;1). Table\u0026nbsp;1 shows that, except for tumor size and AST, there were no differences in the distributions of the other factors between the two groups. Next, we explored the factors influencing early recurrence in HCC patients. Through univariate and multivariate Cox regression analyses, we found that in univariate Cox analysis, MVI, sex, TNM grade, tumor size, tumor differentiation, TLS, NMR, BCLC, and PH (portal hypertension) were closely related to early recurrence in HCC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). However, according to the multivariate Cox analysis, only TNM stage, NMR, and TLS were found to be significant factors influencing early recurrence in patients with HCC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;2). PVTT, TNM, MVI, tumor size, BCLC, and NMR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) are factors that influence late recurrence in HCC patients, while BCLC NMR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) is a factor affecting late recurrence (Table\u0026nbsp;2). The study indicated that TLSs and the NMR have a significant impact on early recurrence in HCC patients but not on late recurrence. In previous studies, there was no correlation between TLSs and peripheral blood immune cells, and there was also no correlation between TLSs and TNM stage. Furthermore, our correlation analysis did not reveal any association between these two factors. Before conducting a Cox multivariate analysis, collinearity diagnostics were performed on the included factors, and the study showed that there was no collinearity among the included factors (Supplementary Table\u0026nbsp;11). As a precaution, a correlation analysis was conducted on TNM, NMR, and TLS data, which revealed that there was no correlation between any pair of these factors (Supplementary Table\u0026nbsp;12\u0026ndash;14). Therefore, TLS, TNM grade, and the NMR are considered independent influencing factors for early recurrence in HCC patients. Through multivariate Cox analysis, early recurrence in HCC patients can be predicted through the use of three indicators: TNM grade, TLS, and the NMR. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the nomogram for RFS and performance evaluation\u003c/h2\u003e \u003cp\u003eBased on the multivariate Cox analysis, predictive factors for early recurrence in HCC, including TNM grade, TLSs, and the NMR, were used to construct a nomogram for predicting the probability of early recurrence in HCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). A nomogram and a calibration curve were constructed to predict early recurrence in HCC patients based on three factors: TNM grade, TLS, and the NMR. The nomogram predicts the 24-month recurrence probability in HCC patients using these three factors. The probability can be obtained as a function of the total points calculated as the sum of the points for each specific variable. Points were assigned for each factor by drawing a line upward from the corresponding values to the \u0026lsquo;point\u0026rsquo; line. The total sum of the points added by each factor was plotted on the \u0026ldquo;total points\u0026rdquo; line. A line was drawn down to read the corresponding predictions of probability. Internal validation was performed by the bootstrap method with 1000 replicate samples. Each factor in the nomogram was assigned a quantified score, and the scores for all factors were summed to obtain a total score, helping doctors predict the probability of early recurrence in patients.\u003c/p\u003e \u003cp\u003eThe AUC for early HCC recurrence was 0.792 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and the AUC for early RFS in the validation cohort was 0.716 (p\u0026thinsp;=\u0026thinsp;0.008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). The calibration curve demonstrated consistency between the predictions and actual observations, indicating a high degree of agreement between the training and validation cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Furthermore, decision curve analysis (DCA) showed that the nomogram had high predictive efficacy for early recurrence in HCC patients, both for early and late RFS. In summary, a nomogram based on TNM staging, TLSs, and the NMR offers a valuable tool for predicting the risk of early recurrence in HCC patients and has good predictive performance, as evidenced by the AUC and DCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the impacts of TLSs, peripheral blood leukocytes, and other clinical indicators on the prognosis of HCC patients were investigated. We discovered that the combination of TLSs with TNM and NMR data can effectively predict early recurrence in HCC patients. Previous research has suggested that early recurrence indicates in situ tumor recurrence, while late recurrence suggests the development of new tumors [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTNM and TLS, as independent prognostic factors for HCC, were found to be unrelated to each other [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although TLSs function as lymphocyte aggregates within nonlymphoid areas [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which are theoretically related to peripheral blood neutrophils (N), lymphocytes (L), and monocytes (M), past studies have shown no significant correlation between TLSs and these cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and our research also revealed no correlation between peripheral lymphocytes and TLSs. Finally, NMR and TNM have been shown to be unrelated in previous studies. These findings suggested that TLSs, the TNM grade, and the NMR are independent factors for early recurrence in HCC patients. A nomogram constructed based on these factors provides clinicians with a scoring tool for predicting the early prognosis of HCC patients. According to the nomogram, the ratio of TLSs to the NMR serves as a good discriminant, but the TNM score for T1 was significantly lower than that for the other three stages, which is consistent with clinical observations. According to the TNM staging, the multiplicity of the tumor and microvascular invasion were suspected to be important risk factors for HCC prognosis. However, this phenomenon was not observed in this study, possibly due to the small sample size or lack of collinearity analysis. Thus, future investigations may refine TNM staging to consider the multiplicity, size, and vascular invasion of tumors as separate factors influencing early recurrence in HCC patients. This study provides insights for surgeons to consider operating on HCC patients at the T1N0M0 stage, which could significantly improve patient prognosis.\u003c/p\u003e \u003cp\u003eTLSs, as lymphocyte aggregates found in nonlymphoid areas, are known to be involved in chronic inflammation, transplant rejection, autoimmune inflammation, and tumors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. They share functional and developmental similarities with lymph nodes and play a role in local immune function as part of the tumor microenvironment (TME) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, their role in HCC is controversial [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study revealed that TLSs significantly reduce early recurrence rates in HCC but are not correlated with late recurrence. Furthermore, the location of TLSs was found to be unrelated to HCC prognosis, which differs from the findings of other studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This finding suggested that TLSs exhibit heterogeneity not only among different tumor types but also among different individuals with HCC and even within different locations in the same donor. Therefore, further exploration of the heterogeneity of TLSs in HCC is warranted. Although the study did not find a correlation between TLS maturity and HCC prognosis, it is worth noting that the low quantity of fully mature TLSs (FOL and GC) in our study may necessitate an increase in sample size for further investigation.\u003c/p\u003e \u003cp\u003eWe performed receiver operating characteristic (ROC) analysis of the ratio of systemic inflammatory cells, such as the NLR, NMR, and LMR, taking the values at the optimal cutoff points. We found that the NMR effectively distinguished between early and late recurrence in HCC patients, especially in patients with early recurrence. However, our study revealed that patients with a high NMR ratio had a lower early recurrence rate, which contradicts the findings of the current study and requires further validation. Historically, the focus has been on T cells within TLSs, and studies on the relationship between CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells and HCC prognosis have yielded different results, some even showing opposing results [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A recent study in renal cell carcinoma (RCC) revealed various B-cell stages within TLSs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and mature plasma cells secreting IgA and IgG antibodies and revealed that macrophage interactions with tumor cells led to phagocytic death. Additionally, Zhang Zemin's team reported an increase in exhausted T cells in patients with HCC [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which is the primary reason for the decreased antitumor effects within the body. The appearance of exhausted T cells is attributed to an increase in neutrophils, which trap functional T cells in the extracellular matrix and hinder their antitumor effects. This finding contrasts with that of a 2015 study by Frink, which identified the role of neutrophils [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], especially in promoting HCC development, especially in HEVs (High et al.). This study suggested that while an increased number of local neutrophils within HCC tissue may lead to an adverse prognosis, an increased NMR in peripheral blood may reduce the early recurrence rate in HCC patients. These findings indicate that neutrophils in different body compartments may perform different functions in the context of HCC and raise questions about the distinct immunological statuses they represent. The role of TLSs in the early recurrence of HCC appears to extend beyond T and B lymphocyte antitumor immunity and might involve a systemic antitumor response. The tertiary lymph node-like structure seems to act as a hub, recruiting immune cells and initiating specific immune responses. The role of the peripheral blood NMR as a predictor of early recurrence in HCC patients suggested that the presence of N and M cells in the peripheral blood may indirectly reflect the infiltration of N and M cells within TLSs. NMR may reflect the systemic antitumor immune status, and the presence of N and M cells in peripheral blood may be directly or indirectly involved in the antitumor effects within TLSs.\u003c/p\u003e \u003cp\u003eOur research offers a clinical prediction model for clinicians to predict early recurrence in HCC patients. These findings suggest that TLSs and the NMR have a positive role in early recurrence in HCC patients. While TLSs are considered to have a local immune function within the tumor microenvironment, they may play a systemic role in antitumor responses. The results also suggest that N and M cells in peripheral blood play a role in anti-HCC functions, but additional research is needed to understand the complexities of their involvement. This study provides clinical insights and future research directions for treating HCC.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTLSs are associated with a low early recurrence rate after hepatectomy for hepatocellular carcinoma (HCC), indicating a correlation with effective antitumor immunity. Our analysis revealed that the absence of TLSs is significantly associated with a high early recurrence rate in HCC patients. By constructing a clinical prediction model that combines TNM staging and the NMR, we can accurately predict early recurrence in HCC patients. This finding suggested that inducing the formation of TLSs may be a strategy for improving the prognosis of HCC patients. However, further research is needed to validate the results of the present study and elucidate the role of TLSs in the antitumor immune response in hepatocellular carcinoma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCI: Confidence interval; HR: Hazard ratio; HE: Hematoxylin and eosin; HCC: Hepatocellular Carcinoma; RFS: Recurrence-free survival; OS: Overall survival; TLSs: Tertiary lymphoid structures\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are available within the manuscript and its supplementary material files. \u0026nbsp;Further enquiries can be directed to the corresponding author (Guangming Li, [email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.O. and G.L. designed the present study. Material preparation, data collection, and analysis were performed by Z. C., B. D., X. W., G. Z., F. W. and Y. S.. Pathological examinations were conducted by Z. C. and X. W. Statistical analysis was performed by Z. C., Y. O. and G. L. supervised the study design and manuscript preparation. The first draft of the manuscript was written by Z. C., Y. O. and B. D., and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Beijing Natural Science Foundation (L202024, M21006, 7222096, and L222067), the Key Medical Professional Development Plan of the Beijing Hospital Authority (ZYLX202124) and the Beijing Municipal Institute of Public Medical Research Development and Reform Pilot Project (Y-2023-4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available from the corresponding author upon a\u003c/p\u003e\n\u003cp\u003ereasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e Z. C., B. D., X. W., G. Z., F. W., Y. S., Y. O., and G. L. declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This study was approved by the Ethics Committee of Beijing YouAn Hospital (LL-2022-052-K) and was performed in accordance with the ethical standards established in the Helsinki Declaration of 1975, as revised in 2008.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e All patient information was deidentified, and informed consent was not required because this study was retrospective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCalderaro, J., et al., \u003cem\u003eIntratumoral tertiary lymphoid structures are associated with a low risk of early recurrence of hepatocellular carcinoma.\u003c/em\u003e J Hepatol, 2019. \u003cstrong\u003e70\u003c/strong\u003e(1): p. 58-65.\u003c/li\u003e\n\u003cli\u003eWen, S., et al., \u003cem\u003eCombination of Tertiary Lymphoid Structure and Neutrophil-to-Lymphocyte Ratio Predicts Survival in Patients With Hepatocellular Carcinoma.\u003c/em\u003e Front Immunol, 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 788640.\u003c/li\u003e\n\u003cli\u003eEl-Serag, H.B., \u003cem\u003eHepatocellular Carcinoma.\u003c/em\u003e New England Journal of Medicine, 2011. \u003cstrong\u003e365\u003c/strong\u003e(12): p. 1118-1127.\u003c/li\u003e\n\u003cli\u003eForner, A., J.M. 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Greenson, \u003cem\u003eThe Crohn\u0026apos;s-Like Lymphoid Reaction to Colorectal Cancer-Tertiary Lymphoid Structures With Immunologic and Potentially Therapeutic Relevance in Colorectal Cancer.\u003c/em\u003e Front Immunol, 2019. \u003cstrong\u003e10\u003c/strong\u003e: p. 1884.\u003c/li\u003e\n\u003cli\u003eRuffin, A.T., et al., \u003cem\u003eB-cell signatures and tertiary lymphoid structures contribute to outcome in head and neck squamous cell carcinoma.\u003c/em\u003e Nat Commun, 2021. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 3349.\u003c/li\u003e\n\u003cli\u003eCarrega, P., et al., \u003cem\u003eNCR(+)ILC3 concentrate in human lung cancer and associate with intratumoral lymphoid structures.\u003c/em\u003e Nat Commun, 2015. \u003cstrong\u003e6\u003c/strong\u003e: p. 8280.\u003c/li\u003e\n\u003cli\u003eA, J.G., et al., \u003cem\u003eGerminal center reactions in tertiary lymphoid structures associate with neoantigen burden, humoral immunity and long-term survivorship in pancreatic cancer.\u003c/em\u003e Oncoimmunology, 2021. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 1900635.\u003c/li\u003e\n\u003cli\u003eKroeger, D.R., K. 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Nelson, \u003cem\u003eTumor-Infiltrating Plasma Cells Are Associated with Tertiary Lymphoid Structures, Cytolytic T-Cell Responses, and Superior Prognosis in Ovarian Cancer.\u003c/em\u003e Clinical Cancer Research, 2016. \u003cstrong\u003e22\u003c/strong\u003e(12): p. 3005-3015.\u003c/li\u003e\n\u003cli\u003eNoel, G., et al., \u003cem\u003eFunctional Th1-oriented T follicular helper cells that infiltrate human breast cancer promote effective adaptive immunity.\u003c/em\u003e J Clin Invest, 2021. \u003cstrong\u003e131\u003c/strong\u003e(19).\u003c/li\u003e\n\u003cli\u003eLi, H., et al., \u003cem\u003ePeritumoral Tertiary Lymphoid Structures Correlate With Protective Immunity and Improved Prognosis in Patients With Hepatocellular Carcinoma.\u003c/em\u003e Front Immunol, 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 648812.\u003c/li\u003e\n\u003cli\u003eFinkin, S., et al., \u003cem\u003eEctopic lymphoid structures function as microniches for tumor progenitor cells in hepatocellular carcinoma.\u003c/em\u003e Nat Immunol, 2015. \u003cstrong\u003e16\u003c/strong\u003e(12): p. 1235-44.\u003c/li\u003e\n\u003cli\u003eImamura, H., et al., \u003cem\u003eRisk factors contributing to early and late phase intrahepatic recurrence of hepatocellular carcinoma after hepatectomy.\u003c/em\u003e J Hepatol, 2003. \u003cstrong\u003e38\u003c/strong\u003e(2): p. 200-7.\u003c/li\u003e\n\u003cli\u003eCabrita, R., et al., \u003cem\u003eTertiary lymphoid structures improve immunotherapy and survival in melanoma.\u003c/em\u003e Nature, 2020. \u003cstrong\u003e577\u003c/strong\u003e(7791): p. 561-565.\u003c/li\u003e\n\u003cli\u003eXue, R., et al., \u003cem\u003eLiver tumor immune microenvironment subtypes and neutrophil heterogeneity.\u003c/em\u003e Nature, 2022. \u003cstrong\u003e612\u003c/strong\u003e(7938): p. 141-147.\u003c/li\u003e\n\u003cli\u003eMeylan, M., et al., \u003cem\u003eTertiary lymphoid structures generate and propagate antitumor antibody-producing plasma cells in renal cell cancer.\u003c/em\u003e Immunity, 2022. \u003cstrong\u003e55\u003c/strong\u003e(3): p. 527-541 e5.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTabel 1.\u0026nbsp;\u003c/strong\u003eClinical and biological features of the\u0026nbsp;training set\u0026nbsp;according to the presence of TLS and NMR ratio.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"753\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailable variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailable data (N=125)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLS+ (N=65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLS- (N=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR-High (92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR-Low(33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e50 (40.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e24 (36.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e26 (45.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e35 (37.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e15 (48.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e75 (60.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e41 (63.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e34 (54.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e57 (62.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e18 (51.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e101 (80.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e52 (80.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e49 (81.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e71 (77.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e30 (90.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e24 (20.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e13 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e11 (18.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e21 (22.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e3 (9.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor differentiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e52 (41.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e28 (43.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e24 (40.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e42 (45.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e10 (30.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e62 (49.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e33 (50.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e29 (48.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e44 (47.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e18 (54.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e11 (8.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e4 (6.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e7 (11.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e6 (6.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e5 (15.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrovascular invasion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e50 (40.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e22 (33.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e28 (46.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e41 (44.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e9 (27.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e75 (60.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e43 (66.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e32 (53.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n 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width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e26 (20.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e14 (21.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e12 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e20 (21.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e6 (18.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e99 (79.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e51 (78.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e48 (80.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e72 (78.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e27 (81.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026ge;40.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e41 (32.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e18 (27.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e23 (38.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e26 (28.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e15 (45.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026lt;40.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e84 (67.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e47 (72.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e37 (61.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e66 (71.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e18 (54.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026ge;40.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e41 (32.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e16 (24.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e25 (41.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e28 (30.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e13 (39.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026lt;40.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e84 (67.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e49 (75.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e35 (58.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e64 (69.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e20 (60.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB (u mol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026ge;17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e58 (46.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e28 (43.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e30 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e43 (46.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e15 (45.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026lt;17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e67 (53.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e37 (56.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e30 (50.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e49 (53.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e18 (54.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALB (g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e83 (66.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e46 (70.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e37 (61.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e62 (67.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e21 (63.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e42 (33.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e19 (29.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e23 (38.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e30 (32.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e12 (36.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP (ng/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026ge;400ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e22 (17.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e15 (23.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e7 (11.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e19 (20.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e3 (9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026lt;400ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e103 (82.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e50 (76.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e53 (88.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e73 (79.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e30 (90.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026ge;50mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e49 (39.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e20 (30.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e29 (48.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e39 (42.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e10 (30.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u0026lt;50mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e76 (60.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e45 (69.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e31 (51.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e53 (57.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e23 (69.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBCLC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e22 (17.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e14 (21.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e8 (13.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e15 (16.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e7 (21.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e56 (44.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e29 (44.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e27 (45.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e41 (44.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e15 (45.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e22 (17.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e9 (13.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e13 (21.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e17 (18.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e5 (15.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e25 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e13 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e12 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e19 (20.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e\u0026nbsp;6 (18.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePortal hypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e18 (14.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e9 (13.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e9 (15.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e10 (10.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e8 (24.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e107 (85.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e56 (86.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e51 (85.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e82 (89.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e25 (75.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eT1N0M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e64 (51.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e37 (56.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e27 (45.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e42 (45.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e22 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eT2N0M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e18 (14.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e9 (13.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e9 (15.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e16 (17.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e2 (6.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eT3N0M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e16 (12.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e5 (7.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e11 (18.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e13 (14.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e3 (9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eT4N0M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e27 (21.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e14 (21.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e13 (21.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e21 (22.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e6 (18.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e92 (72.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e48 (73.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e44 (73.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e33 (26.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e17 (26.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\n \u003cp\u003e16 (26.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e65 (52.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e48 (52.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e17 (51.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.077025232403718%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.01460823373174%\"\u003e\n \u003cp\u003e60 (48.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.889774236387781%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.843293492695883%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.749003984063744%\"\u003e\n \u003cp\u003e44 (47.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.420982735723772%\"\u003e\n \u003cp\u003e16 (48.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.694555112881806%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePVTT: Portal Vein Tumor Thrombosis，BCLC: Barcelona Clinic Liver Cancer. Logistic regression analysis was employed to examine whether there are differences in the intra-group distribution of TLS and NMR. (P\u0026lt;0.05)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Carcinoma, Hepatocellular, Tertiary lymphoid structures, Neutrophil–monocyte ratio, Clinical prediction model","lastPublishedDoi":"10.21203/rs.3.rs-3806961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3806961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eLiver cancer, predominantly hepatocellular carcinoma (HCC), ranks among the deadliest malignancies worldwide, and effective predictive models for early recurrence and poor prognosis are limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study retrospectively analyzed 180 HCC patients and explored the prognostic value of tertiary lymphoid structures (TLSs), peripheral blood immune parameters, and clinical factors in HCC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results showed that TLSs could significantly reduce early recurrence rates but that they were not related to late recurrence. The interaction of peripheral blood immune parameters, especially the neutrophil\u0026ndash;monocyte ratio (NMR), plays a pivotal role in early recurrence prediction. A novel clinical prediction model was constructed by combining the tumor-node-metastasis (TNM) staging system (8th edition), TLS status, and NMR data, and the results demonstrated substantial predictive accuracy for early HCC recurrence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings highlight the multifaceted impact of TLSs and peripheral blood immunity on HCC prognosis and provide a valuable tool for personalized patient management, particularly for identifying early recurrence risk.\u003c/p\u003e","manuscriptTitle":"The combination of TLSs and the neutrophil-to-macrophage ratio predicts early recurrence in patients with hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-10 20:06:48","doi":"10.21203/rs.3.rs-3806961/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-29T09:13:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-23T00:11:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17953401724722726373079029171569132021","date":"2024-05-13T00:07:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-25T09:16:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4dcee0ce-2474-42b5-b707-db6b01ef52ae","date":"2024-04-20T15:21:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-21T23:59:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-21T23:58:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-09T05:08:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-09T04:52:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2023-12-26T07:59:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51ac93c9-5265-4a2c-b797-2f7f180b59ed","owner":[],"postedDate":"January 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T03:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-10 20:06:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3806961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3806961","identity":"rs-3806961","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}