Scoring system related inflammation and nutrition to predict prognoss of patients with hepatocellular carcinoma undergoing liver transplantation

Scoring system related inflammation and nutrition to predict prognoss of patients with hepatocellular carcinoma undergoing liver transplantation | 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 Scoring system related inflammation and nutrition to predict prognoss of patients with hepatocellular carcinoma undergoing liver transplantation Xiaoyong Ye, Xinxue Zhang, Lin Zhou, Qiang He, Jun Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7620800/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hepatocellular carcinoma is a common malignant tumor in the liver. The prognostic predictive roles of inflammatory and nutrition-related markers in liver transplant patients with liver cancer remain unclear. Methods : 239 patients with hepatocellular carcinoma undergoing liver transplantation were randomly divided into a training set and a validation at a 7:3 ratio. The optimal cut-off values for each indicator were determined using Maximally Selected Rank Statistics; a risk score (RS) was constructed using LASSO-Cox regression, and the optimal cut-off value of the RS was determined in the training set. A nomogram was further established and its predictive efficacy was evaluated. Results: The risk score (RS) was ultimately included in three variables: FAR, GAR, and GPAR, with a cutoff value of 0.47. Patients in the high RS group had significantly poorer overall survival than those in the low RS group (P < 0.05), and this result was confirmed in the validation set and the subgroups meeting the Milan/Hangzhou criteria. Multivariate Cox analysis showed that high RS (HR = 2.174) and microvascular invasion (HR = 2.633) were independent risk factors. Based on these two factors, a nomogram was constructed, and the 1/3/5-year AUCs were 0.764/0.762/0.747 (training set) and 0.722/0.716/0.722 (validation set), and both calibration curves and decision curve analysis showed good consistency and clinical net benefit. Conclusion: The preoperative risk score integrating FAR, GAR and GPAR can independently predict the overall survival of HCC patients after liver transplantation, providing a simple and practical new tool for clinical rapid prognosis assessment, individualized follow-up and adjuvant treatment strategy formulation. Liver transplantation Hepatocellular carcinoma Fibrinogen‑to‑albumin ratio Prognosis overall survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Backgroud Hepatocellular carcinoma (HCC) ranks as the sixth most prevalent malignant tumor globally and the fourth leading cause of cancer-related mortality (1), with persistently high incidence and mortality rates. Currently, various therapeutic strategies, including partial hepatectomy, are available for HCC treatment (2), yet liver transplantation remains the most effective intervention (2). However, preoperative candidate selection, postoperative recurrence, and prognostic evaluation for liver transplant recipients with HCC remain critical challenges in clinical practice. Consequently, establishing reliable biomarkers to accurately predict patient outcomes holds paramount clinical significance. Inflammatory responses play a pivotal role in the pathogenesis and progression of hepatocellular carcinoma (HCC), adversely affecting patient prognosis by promoting tumor advancement and metastasis (3–4). Concurrently, the nutritional status of HCC patients significantly influences tumor progression and clinical outcomes. Multiple inflammation-nutrition-related biomarkers have been identified as closely associated with HCC prognosis, including the fibrinogen-to-albumin ratio (FAR) (5), neutrophil-to-lymphocyte ratio (NLR) (6), platelet-to-lymphocyte ratio (PLR)(7), monocyte-to-lymphocyte ratio (MLR) (8), systemic immune-inflammation index (SII) (9), prognostic inflammatory index (PII)(10), aspartate aminotransferase-to-neutrophil ratio index (ANRI)(11), albumin-to-globulin ratio (AGR)(12), gamma-glutamyl transpeptidase-to-albumin ratio (GAR)(13), gamma-glutamyl transpeptidase-to-prealbumin ratio (GPAR) (14), albumin-to-alkaline phosphatase ratio (AAPR)(15), prognostic nutritional index (PNI)(16), ALBI grade(17), and PALBI grade(18). This study aims to systematically evaluate the optimal combination of these biomarkers for effectively predicting post-liver transplantation outcomes in HCC patients, thereby providing clinicians with a comprehensive prognostic assessment tool and valuable evidence-based guidance for clinical decision-making. Methods Patient selection This study constitutes a retrospective analysis encompassing all patients diagnosed with hepatocellular carcinoma (HCC) who underwent liver transplantation at Beijing Chaoyang Hospital, Capital Medical University, between April 2013 and July 2023. All enrolled cases were pathologically confirmed as hepatocellular carcinoma through postoperative histological examination.The information retrieval of patient clinical information was supported by the Ethics Committee of Beijing Chaoyang Hospital (No. 2020-D.-303, date of approval: 28 May 2020). The inclusion criteria for this study were as follows: (1) histopathologically confirmed diagnosis of primary hepatocellular carcinoma; (2) absence of other malignant tumors or metastatic lesions; (3) availability of follow-up data; (4) complete clinical and pathological records; (5) comprehensive blood test results obtained within one week prior to surgery. Exclusion criteria comprised: (1) concurrent malignancies or metastases; (2) perioperative mortality; (3) absence of critical clinical data such as complete blood test records. Ultimately, 239 patients were enrolled in this study. Data collection This study obtained a list of patients who underwent liver transplantation for hepatocellular carcinoma through the hospital's information department, with relevant laboratory data cross-verified and reviewed via the electronic medical record system. All data were systematically entered and managed in an Excel database. The collected variables primarily included: gender, age, alpha-fetoprotein (AFP) levels, pre-operative hematological parameters (such as albumin, globulin, white blood cell count, lymphocyte count, monocyte count, neutrophil count, and platelet count), T stage, N stage, TNM stage, number of intrahepatic tumors, maximum tumor diameter, total intrahepatic tumor diameter, gross tumor morphology, histological type, tumor differentiation grade, macrovascular invasion, microvascular invasion, as well as follow-up information (including survival status and overall survival time). The study identified missing values across all clinical and laboratory variables and employed complete case analysis for data processing, excluding patients with missing data in key variables such as tumor staging and biomarkers. Follow-up All transplant recipients underwent close outpatient follow-up, with a follow-up duration ranging from 4 to 134 months (median: 53 months). During the first 6 months postoperatively, recipients were followed up monthly. From 6 months to 2 years post-transplantation, the follow-up frequency was adjusted to every 3 to 6 months. After 2 years, the follow-up interval was further extended to biennially. Routine follow-up protocols included contrast-enhanced abdominal CT or MRI examinations every 6 months. For suspected local recurrence or distant metastasis, additional targeted imaging examinations such as CT, MRI, bone scanning, or PET-CT were arranged based on clinical indications. Overall survival (OS) was defined as the duration from the date of liver transplantation to death from any cause or the last confirmed survival follow-up. Definition of Inflammatory and Nutritional Related Markers The following inflammatory and nutrition-related markers were calculated based on the laboratory test results obtained from the patients within one week before the operation: FAR = Fibrinogen (mg/dL) / Serum albumin (g/L) NLR = Neutrophils count(10 9 /L)/ Lymphocytes(10 9 /L) PLR = Platelets count(10 9 /L)/ Lymphocytes(10 9 /L) MLR = Monocytes count(10 9 /L) / Lymphocytes(10 9 /L) SII = Neutrophil count (10 9 /L)× Platelet count(10 9 /L) / Lymphocyte count(10 9 /L) PII = Neutrophil (10 9 /L)× Monocyte(10 9 /L) / Lymphocyte count(10 9 /L) ANRI = Aspartate aminotransferase (U/L) / Neutrophil count(10 9 /L) AGR = Serum albumin (g/L) / Serum globulin (g/L) GAR = γ-Aminotransferase (U/L) / Serum albumin (g/L) GPAR = γ-Aminotransferase (U/L) / Serum prealbumin (g/L) AAPR = Serum albumin (g/L) / Alkaline phosphatase (U/L) PNI = Serum albumin (g/L) + 5 × Lymphocyte count (10 9 /L) ALBI = log 10 Total bilirubin (mol/L) × 0.66 - Albumin (g/L) × 0.085 PALBI = [2.02 × log 10 Total bilirubin (mol/L)] + [-0.37 (log 10 Total bilirubin (mol/L))²] + (-0.04 × Serum albumin (g/L) ) + (-3.48 × log 10 Platelets count(10 9 /L)) + [1.01 × (log 10 Platelets count(10 9 /L))²] Development of the risk scoring system The complete dataset was randomly partitioned into training and validation sets at a 7:3 ratio. Within the training cohort, optimal cutoff values for inflammatory and nutritional biomarkers were determined using Maximally Selected Rank Statistics. Prognostically significant inflammatory and nutritional markers were subsequently selected through univariate and multivariate Cox regression analyses, followed by LASSO-Cox regression to identify the most prominent prognostic variables. Biomarkers with non-zero coefficients at the minimum error value were incorporated into a risk score (RS) model. Finally, Maximally Selected Rank Statistics was reapplied to evaluate the predictive performance of RS for overall survival (OS) in hepatocellular carcinoma patients post-liver transplantation and to establish its optimal cutoff threshold. Statistical analysis This study employed SPSS 26.0 and R 4.4.2 for statistical analysis. Normally distributed continuous variables were presented as mean ± standard deviation, with between-group comparisons conducted using independent samples t-tests. Non-normally distributed continuous variables were expressed as median (interquartile range), analyzed via Kruskal-Wallis tests. Categorical variable comparisons between groups utilized χ² tests, with P-values adjusted for multiple comparisons using the Bonferroni method. Survival analysis was performed using the Kaplan-Meier method with corresponding survival curves; prognostic factors were evaluated through univariate and multivariate Cox proportional hazards regression models. Model predictive performance was assessed by calculating the concordance index (C-index) and constructing time-dependent receiver operating characteristic (ROC) curves. Calibration curves were plotted to examine the agreement between predicted and observed survival probabilities, while decision curve analysis (DCA) was applied to evaluate the clinical utility of the nomogram model. A P-value < 0.05 was considered statistically significant. Results Baseline characteristics of the patients The median follow-up duration for the entire cohort was 53 months. The majority of patients were male (92.05%), with 43.93% aged over 55 years. A history of hepatitis B virus infection was prevalent (90.38%). The proportion of TNM stage III cases was relatively low (21.34%), while microvascular invasion was observed in 54.81% of patients. Portal vein tumor thrombosis occurred infrequently (10.04%). All patients were randomly allocated into training (n = 168) and validation (n = 71) sets at a 7:3 ratio, with no statistically significant differences in baseline characteristics between the two groups (Table 1 ). Table 1 Patient demographics and baseline characteristics Variables Overall (n = 239) Training cohort (n = 168) Valid cohort (n = 71) p-value Gender(Male/Female) 220/19 157/11 63/8 0.332 TNM stages (III/I-II) 51/188 36/132 15/56 0.999 Age, years (> 55/≤55) 105/134 70/98 35/36 0.346 Positive HBsAg (+/−) 216/23 153/15 63/8 0.749 MVI (Yes/No) 131/108 96/72 35/36 0.331 Tumor number (Multiple/Single) 129/110 86/82 43/28 0.235 Maximum tumor size, cm (> 5/≤5) 62/177 43/125 19/52 0.979 Total tumor size, cm (> 8/≤8) 48/191 35/133 13/58 0.788 Differentiation (Poor,undifferentiated/ Well,moderate) 184/55 133/35 51/20 0.288 AFP, ng/mL (> 400/≤400) 80/159 54/114 26/45 0.603 PVTT (+/−) 24/215 18/150 6/65 0.767 Milan criteria (Yes/No) 111/128 73/95 38/33 0.199 Hangzhou criteria (Yes/No) 193/46 133/35 60/11 0.437 Re-treatment resection 1 (0.60%) 4 (5.63%) resection + RFA 1 (0.60%) 0 (0.00%) resection + TACE 3 (1.79%) 0 (0.00%) RFA 6 (3.57%) 9 (12.7%) TACE 38 (22.6%) 14 (19.7%) TACE + RFA 18 (10.7%) 4 (5.63%) TACE + RFA + sorafenib 1 (0.60%) 0 (0.00%) FAR median(IQR) 5.15 (3.93–6.91) 4.82 (3.74–6.70) 5.28 (4.07–6.92) 0.177 NLR median(IQR) 2.86 (1.89–5.02) 3.03 (1.97–4.96) 2.73 (1.81–5.06) 0.416 PLR median(IQR) 102.65 (68.11–151.09) 100.00 (67.60–152.00) 103.52 (68.26–151.02) 0.419 MLR median(IQR) 0.32 (0.23–0.51) 0.31 (0.23–0.51) 0.33 (0.23–0.50) 0.867 SII median(IQR) 266.38 (131.24–563.27) 287.70 (150.93–504.97) 246.52 (125.61–583.50) 0.953 PII median(IQR) 0.80 (0.45–1.61) 0.75 (0.48–1.86) 0.83 (0.45–1.50) 0.997 ANRI median(IQR) 24.05 (12.52–51.71) 35.52 (13.85–62.56) 22.68 (12.14–44.53) 0.055 AGR median(IQR) 1.25 (0.95–1.55) 1.32 (0.96–1.52) 1.24 (0.96–1.56) 0.753 GAR median(IQR) 2.11 (1.20–4.10) 2.01 (1.02–3.55) 2.19 (1.23–4.23) 0.239 GPAR median(IQR) 920.83 (385.71–2541.67) 881.82 (384.52–2666.67) 936.61 (410.49–2408.33) 0.761 AAPR median(IQR) 0.31 (0.22–0.48) 0.33 (0.22–0.50) 0.31 (0.22–0.42) 0.365 PNI median(IQR) 39.75 (34.95–46.53) 37.90 (34.53–45.40) 40.33 (35.20–47.06) 0.409 ALBI median(IQR) -2.06 (-2.56–-1.54) -2.04 (-2.32–-1.59) -2.07 (-2.63–-1.54) 0.549 PALBI median(IQR) -2.17 (-2.47–-1.87) -2.17 (-2.42–-1.85) -2.17 (-2.49–-1.88) 0.622 Determination of the optimal cutoff values for inflammatory and nutritional markers We employed the Maximally Selected Rank Statistics method to determine the optimal cutoff values for inflammatory and nutrition-related biomarkers in the training cohort, subsequently stratifying patients into high- and low-level groups. Univariate Cox regression analyses were then performed on all inflammatory and nutritional biomarkers to identify predictors significantly associated with overall survival (OS) (Table 2 ). Table 2 Univariate Cox analyses for overall survival of inflammation-related and nutrition-related biomarkers in the training set Variable Cutoff value Categories HR 95%CI p-value FAR 6.23 High(≥ 6.23) vs low(< 6.23) 3.85 2.33,6.67 < 0.001 NLR 1.54 High(≥ 1.54) vs low(< 1.54) 3.70 1.33,10 0.011 PLR 123.33 High(≥ 123.33) vs low(< 123.33) 2.13 1.28,3.45 0.003 MLR 0.43 High(≥ 0.43) vs low(< 0.43) 1.85 1.12,3.03 0.017 SII 155.19 High(≥ 155.19) vs low(< 155.19) 3.13 1.54,6.25 0.002 PII 2.13 High(≥ 2.13) vs low(< 2.13) 2.08 1.19,3.7 0.01 ANRI 17.00 High(≥ 17.00) vs low(< 17.00) 1.54 0.9,2.63 0.117 AGR 2.01 High(≥ 2.01) vs low(< 2.01) 0.27 0.07,1.09 0.065 GAR 4.09 High(≥ 4.09) vs low(< 4.09) 4.35 2.56,7.14 < 0.001 GPAR 6600.00 High(≥ 6600.00) vs low(< 6600.00) 3.70 1.96,7.14 < 0.001 AAPR 0.13 High(≥ 0.13) vs low(< 0.13) 0.31 0.16,0.58 < 0.001 PNI 33.40 High(≥ 33.40) vs low(< 33.40) 1.69 0.84,3.45 0.138 ALBI -2.75 High(≥-2.75) vs low(<-2.75) 0.67 0.38,1.18 0.162 PALBI -2.41 High(≥-2.41) vs low(<-2.41) 1.59 0.89,2.86 0.116 RS calculation In the training set, biomarkers with P < 0.05 in the univariate Cox regression analysis were incorporated into LASSO-Cox regression analysis, ultimately identifying three potential predictors from nine candidate biomarkers (Fig. 1 ). Based on the coefficients obtained at the optimal λ value in LASSO-Cox regression, the risk score (RS) calculation formula was established as follows: RS = 0.07214 × FAR + 0.00234 × GAR + 0.00003 × GPAR .. The optimal cutoff value of RS was determined to be 0.47 using the maximal selected rank statistics method, thereby stratifying patients into high-RS and low-RS groups (Fig. 2 A). Kaplan-Meier survival analysis demonstrated that patients in the high-RS group exhibited significantly shorter overall survival compared to the low-RS group(Fig. 2 B-C). This trend was consistently validated in subgroup patient cohorts meeting both the Hangzhou(Fig. 2 D) and Milan criteria(Fig. 2 E), with the high-RS group persistently showing significantly inferior survival outcomes. Univariate and multivariate Cox regression analysis In the training cohort, we incorporated the dichotomized RS based on the optimal cutoff value along with other clinical and pathological characteristics into both univariate and multivariate Cox regression analyses, with detailed results presented in the supplementary table. Univariate analysis demonstrated that TNM stage, microvascular invasion, maximum intrahepatic tumor diameter, number of intrahepatic tumors, tumor differentiation grade, portal vein tumor thrombus, sum of tumor diameters, and RS were all significantly associated with overall survival (P < 0.05). To further account for potential interactions among variables, all factors showing significant associations with overall survival in univariate analysis were included in the multivariate Cox regression model. The results identified microvascular invasion (HR: 2.633; 95% CI: 1.239–5.594; p = 0.012) and high RS (≥ 0.47) (HR: 2.174; 95% CI: 1.126–4.184; p = 0.021) as independent risk factors for overall survival (Table 3 ). Table 3 Univariate and multivariate Cox analyses of baseline characteristics and risk score on overall survival in patients with hepatocellular carcinoma undergoing liver transplantation Variables Univariate analysis Multivariate analysis HR 95%CI p-value HR 95%CI p-value Age, years (≤ 55 vs > 55) 1.344 0.798–2.261 0.266 TNM stage (III vs I + II) 3.395 2.04–5.649 < 0.001 1.254 0.638–2.463 0.512 MVI (Yes vs No) 5.035 2.557–9.918 400) 1.241 0.717–2.148 0.44 Maximum tumor size, cm (≤ 5 vs > 5) 0.316 0.191–0.524 < 0.001 0.674 0.272–1.671 0.395 Tumor number (Single vs Multiple) 0.505 0.3–0.85 0.01 0.703 0.343–1.441 0.336 Gender (Male vs Female) 2.772 0.677–11.35 0.156 Differentiation (Well,moderate vs Poor,undifferentiated) 0.463 0.22–0.973 0.042 0.777 0.352–1.713 0.532 Positive HBsAg (+ vs −) 6.877 0.953–49.621 0.056 PVTT (+ vs −) 2.315 1.205–4.448 0.012 1.255 0.612–2.576 0.536 Total tumor size, cm (≤ 8 vs > 8) 0.274 0.164–0.459 < 0.001 0.719 0.313–1.652 0.437 RS grade (≥ 0.47 vs < 0.47) 4.444 2.544–8.333 < 0.001 2.174 1.126–4.184 0.021 Construction and evaluation of the nomogram We constructed a nomogram model incorporating two independent risk factors (recurrence score and microvascular invasion) identified through multivariate Cox analysis to predict 1-, 3-, and 5-year survival rates following liver transplantation in hepatocellular carcinoma patients(Fig. 3 A). In the training cohort, the model demonstrated area under the receiver operating characteristic curve (AUC) values of 0.764, 0.762, and 0.747 for 1-, 3-, and 5-year survival predictions, respectively. Corresponding AUC values in the validation cohort were 0.722, 0.716, and 0.722 (Fig. 3 B). Calibration curves indicated excellent concordance between nomogram-predicted survival probabilities and actual observations. Furthermore, decision curve analysis (DCA) revealed substantial clinical net benefits across all time points(Fig. 4 ), confirming the model's robust clinical utility for 1-, 3-, and 5-year survival probability predictions(Fig. 5 ). Discussion This study was conducted on 239 hepatocellular carcinoma (HCC) patients who underwent liver transplantation. Preoperative inflammatory and nutritional biomarkers measured within one week prior to surgery were incorporated to develop and validate a prognostic model for predicting overall survival (OS) post-liver transplantation. Patients were randomly allocated into training and validation cohorts at a 7:3 ratio, with no significant differences in baseline clinical or pathological characteristics between groups, ensuring model stability. Univariate Cox regression analysis identified FAR, NLR, PLR, MLR, SII, PII, GAR, and GPAR as risk factors for poor postoperative prognosis, while AAPR showed positive correlation with OS. ANRI, AGR, PNI, ALBI, and PALBI demonstrated no statistically significant associations even after optimal cutoff-based dichotomization. LASSO-Cox regression subsequently selected three key variables (FAR, GAR, GPAR) from these nine biomarkers to construct a risk score (RS). The optimal RS cutoff value was determined using the maximum selected rank statistics method, categorizing patients into high- and low-RS groups. Kaplan-Meier analysis demonstrated significantly shorter OS in high-RS groups across the training cohort, validation cohort, and subgroups meeting either Hangzhou or Milan criteria. This comprehensive risk score, integrating multiple clinically relevant biomarkers, provides enhanced assessment of patients' inflammatory and nutritional status, demonstrating superior discriminative capacity and clinical applicability within current liver transplantation standards. In recent years, accumulating evidence has demonstrated the pivotal role of inflammatory responses in the pathogenesis and progression of hepatocellular carcinoma (HCC)(19). Various chronic liver diseases, including viral hepatitis, alcoholic hepatitis, and non-alcoholic fatty liver disease, can induce persistent hepatic inflammation, subsequently driving the progression from chronic hepatitis to cirrhosis and ultimately leading to hepatocarcinogenesis. During this process, inflammation-associated cells such as neutrophils, monocytes, and platelets have been substantiated to actively promote HCC progression and metastasis. Tumor-associated neutrophils (TANs) facilitate HCC development and metastasis through multiple mechanisms, including the release of reactive oxygen species (ROS), immunosuppressive cytokines, and neutrophil extracellular traps (NETs)(20). Neutrophil-enriched HCC typically exhibits aggressive characteristics such as poor differentiation and sarcomatoid changes, correlating with unfavorable prognosis and significantly increased risks of recurrence and metastasis(21). Conversely, tumor-associated monocytes (TAMs) interact with immune effector cells to suppress T-cell function and induce an immunosuppressive tumor microenvironment(22–23). Platelets contribute to HCC progression through multifaceted mechanisms: they can be activated by HCC cells to release growth factors like VEGF, promoting tumor angiogenesis(24); they also enhance epithelial-mesenchymal transition (EMT) in tumor cells to accelerate metastatic processes(25). Furthermore, circulating platelets interact with circulating tumor cells (CTCs), facilitating CTC retention and colonization in distant organs to establish metastatic niches. In contrast, lymphocyte subsets—including CD3⁺ T cells, CD8⁺ T cells, CD20⁺ B cells(26), and natural killer (NK) cells(27)—exert antitumor effects through direct cytotoxicity or secretion of cytokines such as IFN-γ. The infiltration density of these cells in tumor stroma shows significant correlation with favorable patient prognosis(28). Fibrinogen is a plasma protein composed of three polypeptide chains (α, β, and γ) that plays a pivotal role in blood coagulation, inflammatory responses, and tissue repair(29). Research has demonstrated that it promotes tumor angiogenesis(30) within the stromal microenvironment and facilitates metastatic(31) progression through interactions with platelets, vascular growth factors, transforming growth factor-β (TGF-β), and fibroblast growth factors. In hepatocellular carcinoma (HCC), the fibrinogen alpha chain (FGA) has been proposed as a potential serological biomarker for disease progression from chronic hepatitis to cirrhosis and ultimately HCC(32). A study conducted by Sun et al. involving 366 HCC patients further revealed that an elevated fibrinogen-to-albumin ratio (FAR) was significantly associated with poor patient prognosis(33). Moreover, preoperative FAR levels have been shown to serve as an important prognostic indicator for postoperative recurrence in HCC patients undergoing hepatectomy(34). Gamma-glutamyltransferase (γ-GT or GGT) is a membrane-bound enzyme that plays a pivotal role in antioxidant defense, xenobiotic metabolism, and the glutathione cycle by catalyzing the transfer of γ-glutamyl groups (35). Through its involvement in glutathione metabolism and oxidative stress responses, GGT may promote tumor progression and metastasis via pro-oxidant and pro-inflammatory effects(36). In hepatocellular carcinoma (HCC), the mRNA expression levels of GGT exhibit a significant correlation with conventional tumor markers such as alpha-fetoprotein (AFP)(37). Current research demonstrates that GGT levels can serve as a predictive biomarker for HCC risk in patients with chronic hepatitis B and C(38), while preoperative GGT levels also constitute an independent risk factor for post-hepatectomy recurrence(39). Furthermore, the GGT-to-albumin ratio (GAR) has been established as an independent prognostic factor for HCC patients, with elevated GAR levels showing significant associations with poor outcomes in individuals undergoing liver transplantation (LT), transarterial chemoembolization (TACE)(40), or hepatic resection (41). Multiple studies have demonstrated a significant correlation between malnutrition in cirrhotic patients and adverse clinical outcomes following liver transplantation, manifesting as increased postoperative complications, prolonged hospitalization, and elevated healthcare costs(42). Severe malnutrition may further compromise post-transplant overall survival, particularly among elderly patients with alcoholic cirrhosis, where malnourished individuals exhibit markedly reduced postoperative survival rates(43). Additionally, severe malnutrition contributes to systemic immunosuppression, exacerbates intestinal barrier dysfunction, thereby predisposing patients to postoperative infections and inducing post-transplant immune dysregulation(44). Albumin and prealbumin, as critical indicators of visceral protein reserves, serve as effective screening tools for malnutrition assessment(45). Consequently, early assessment of inflammatory response and nutritional status in liver transplantation candidates with hepatocellular carcinoma holds significant clinical value for predicting postoperative outcomes, facilitating early interventions, and improving prognosis. For the majority of HCC patients, comprehensive preoperative nutritional evaluation coupled with targeted interventions can effectively reduce postoperative complication rates, shorten hospital stays, and potentially enhance overall survival rates(46). To identify adverse prognostic factors in hepatocellular carcinoma patients following liver transplantation at an early stage, researchers have focused on developing various biomarkers to predict postoperative recurrence and unfavorable clinical outcomes, thereby assisting clinicians in promptly identifying high-risk patients requiring aggressive therapeutic interventions(47–49). Inflammatory responses and nutritional status are closely associated with the prognosis of hepatocellular carcinoma patients. However, the predictive efficacy of inflammation-nutrition-related biomarkers may exhibit population heterogeneity across different studies, leading to inconsistent findings. Consequently, this study comprehensively analyzed 14 common inflammation-nutrition related biomarkers, including the fibrinogen-to-albumin ratio (FAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), prognostic inflammatory index (PII), aspartate aminotransferase-to-neutrophil ratio index (ANRI), albumin-to-globulin ratio (AGR), gamma-glutamyl transferase-to-albumin ratio (GAR), gamma-glutamyl transferase-to-prealbumin ratio (GPAR), albumin-to-alkaline phosphatase ratio (AAPR), prognostic nutritional index (PNI), ALBI score, and PALBI score, with the aim of identifying predictive indicators for post-liver transplantation prognosis in hepatocellular carcinoma patients. The LASSO regression method was further employed to eliminate multicollinearity among variables, resulting in the selection of three biomarkers with the highest predictive value, which were subsequently used to construct a risk score (RS). To enhance model reliability, 10-fold cross-validation was implemented during the analysis to ensure the robustness of LASSO selection. The final risk score was established as an independent risk factor for poor prognosis and demonstrated strong predictive capability for post-transplantation outcomes in both training and validation cohorts. Nevertheless, this study has several limitations. Firstly, as a single-center retrospective study, all cases were sourced from our institution's liver transplantation database spanning 2013 to 2023. The retrospective design inherently constrained data collection to existing electronic medical records and laboratory reports, lacking standardized prospective protocols. Consequently, certain critical variables (such as inflammatory markers including C-reactive protein) were unavailable, and some inflammation-nutrition related biomarkers could not be incorporated into the analysis. Secondly, the single-center data exhibited strong homogeneity in clinical protocols, perioperative management, and follow-up strategies, potentially limiting the generalizability of findings due to regional variations and population heterogeneity. Furthermore, despite rigorous exclusion of known confounding factors, the limited sample size precluded more sophisticated stratified analyses and complete control of all potential confounders. To enhance the reliability and generalizability of conclusions, future large-scale, multicenter prospective studies are warranted to validate current findings, minimize selection bias, and improve the study's representativeness and external validity. Furthermore, this study is limited by its cross-sectional design, which relies solely on preoperative single-timepoint measurements of inflammation-nutrition biomarkers, thus failing to capture their dynamic fluctuations during disease progression or therapeutic interventions. Existing literature indicates that indices such as FAR and GAR may exhibit variability during the perioperative period and postoperative adjuvant therapy due to inflammatory status alterations, nutritional interventions, and tumor recurrence(50–51). Consequently, single-timepoint measurements might inadequately reflect their true prognostic predictive value. To enhance research reliability, future investigations should systematically collect longitudinal data on inflammation-nutrition markers at multiple timepoints (preoperative, intraoperative, and postoperative) in liver transplantation patients, enabling comprehensive analysis of dynamic trajectories for more robust evaluation of their prognostic efficacy in hepatocellular carcinoma. This study employed the Maximally Selected Rank Statistics method to determine the optimal cutoff values for each indicator and the Risk Score (RS) within the training set. Although internal validation demonstrated favorable discriminatory capability, these cutoff values may be influenced by the distribution characteristics of the patient population at our center and have not yet been validated in independent external cohorts. Notably, baseline levels of indicators such as fibrinogen, albumin, and gamma-glutamyltransferase (γ-GT) vary across patients with different geographical origins, etiological backgrounds, and hepatic functional reserves, which may lead to shifts in the optimal cutoff values. Therefore, future multicenter, large-sample, prospective studies are warranted to establish standardized universal cutoff values with external validation, thereby enhancing the model's applicability across diverse populations and institutions. Furthermore, the study cohort predominantly comprised male patients (92.05%) with HBV-related hepatocellular carcinoma (90.38%), while female patients and those with non-HBV etiologies (e.g., alcoholic liver disease, non-alcoholic fatty liver disease) were underrepresented. Consequently, caution is advised when extrapolating these findings to hepatocellular carcinoma patients with other etiological types. Despite these limitations, the results indicate that the Risk Score (RS), constructed based on LASSO regression, serves as an independent risk factor for adverse post-liver transplantation outcomes in hepatocellular carcinoma patients. A higher preoperative RS was significantly associated with poorer prognosis. This scoring tool aids clinicians in comprehensively assessing patients' inflammatory and nutritional statuses preoperatively and provides valuable reference for therapeutic decision-making. Conclusion This study developed a risk scoring system that integrates biomarkers of both inflammatory response and nutritional status, providing a convenient and reproducible tool for rapid preoperative assessment of post-liver transplantation prognosis. This innovative model shows promising potential for guiding individualized follow-up protocols and informing adjuvant therapeutic strategies. However, the clinical applicability of this scoring model requires further validation through multicenter, large-scale prospective studies. Declarations Author Contributions: X.-Y.Y. and X.-X.Z. is the first author of this research; J.M , Q.H are the corresponding authors of this research. (I) Conception and design: J.M , X.-Y.Y. and L.Z; (II) administrative support: J.M , Q.H and L.Z; (III) provision of study materials and patients: Q.H and L.Z; (IV) collection and assembly of data: X.-Y.Y. , X.-X.Z. and L.Z.; (V) data analysis and interpretation: X.-X.Z. , X.-Y.Y. and L.Z.; (VI) manuscript writing: all authors; (VII) final approval of the manuscript: all authors. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest: The authors declare no conflict of interest. Funding: This research received no external funding. Institutional Review Board Statement: The study was approved by the Institutional Review Board of Beijing chaoyang hospital in accordance with the 1964 Helsinki Declaration and its later amendments (No. 2020-D-303). Informed Consent Statement: The participants’ informed consent was not required because of the retrospective study design, and the study design was approved by the appropriate ethics review board. Data Availability Statement: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. References Chidambaranathan-Reghupaty S, Fisher PB, Sarkar D. Hepatocellular carcinoma (HCC): Epidemiology, etiology and molecular classification. Adv Cancer Res. 2021;149:1-61. doi: 10.1016/bs.acr.2020.10.001. Epub 2020 Nov 28. Heimbach JK, Kulik LM, Finn RS, Sirlin CB, Abecassis MM, Roberts LR, Zhu AX, Murad MH, Marrero JA. AASLD guidelines for the treatment of hepatocellular carcinoma. 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Cancer Med. 2023 Oct;12(20):20321-20331. doi: 10.1002/cam4.6606. Epub 2023 Oct 10. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":540932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature selection based on least absolute shrinkage and selection operator regression. A: LASSO coefficient profile; B: The optimal penalty parameter (λ) in the LASSO model determined by 10-fold cross-validation and the minimum criteria in the training set.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7620800/v1/badeaf2a1d9ba4af8946581d.png"},{"id":94225416,"identity":"69b74ff8-e6ee-48a9-8006-50227fc073ee","added_by":"auto","created_at":"2025-10-23 19:29:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":891895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk score analysis A: Maximally Selected Rank Statistics method determined that the optimal truncation value for RS is 0.47. B: The difference in K-M survival curves between the high RS group and the low RS group in the training set.C:Verify the difference in K-M survival curves between the high RS group and the low RS group in the validation set.D:The difference in K-M survival curves between the high RS group and the low RS group in the Hangzhou standard cohort.E:The difference in K-M survival curves between the high RS group and the low RS group in the Milan standard cohort\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7620800/v1/798cb3f829c08580684c476a.png"},{"id":94225415,"identity":"d033ef54-f365-41c1-a76d-3eb13781fa1d","added_by":"auto","created_at":"2025-10-23 19:29:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":711475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival nomogram and Time-dependent ROC curve. A:Survival nomogram for predicting the 1-year, 3-year, and 5-year survival rates of patients.B:Time-dependent ROC curve of Survival nomogram for predicting the 1-year, 3-year, and 5-year survival rates.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7620800/v1/957e1feedc17b628abe6091a.png"},{"id":94225899,"identity":"28ddd1b0-3229-43d1-a06a-3c7789421f1d","added_by":"auto","created_at":"2025-10-23 19:37:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":573759,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves of the nomogram model for survival prediction in the training and validation cohorts. A-C: Calibration curves for 1-\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eyear, 3-year, and 5-year overall survival (OS) in the training cohort; D-F: Calibration curves for 1-year, 3-year, and 5-year OS in the validation cohort\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7620800/v1/a092e9f36da0e4385a1b582a.png"},{"id":94225896,"identity":"4192f468-5f2a-4d1b-a8f9-07b050df9592","added_by":"auto","created_at":"2025-10-23 19:37:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":578405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis of the survival nomogram for predicting overall survival. A-C: Decision curve analysis for 1-year, 3-year, and 5-\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eyear overall survival (OS) in the training cohort; D-F: Decision curve analysis for 1-year, 3-year, and 5-year OS in the validation cohort.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7620800/v1/290cfe1f40682202e7e50d8b.png"},{"id":103904348,"identity":"5e35d738-65bf-4d9c-a142-ca61a2666fe6","added_by":"auto","created_at":"2026-03-04 10:28:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4916254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7620800/v1/e9d95ab8-1302-45d6-9d2c-c04f612cf372.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scoring system related inflammation and nutrition to predict prognoss of patients with hepatocellular carcinoma undergoing liver transplantation","fulltext":[{"header":"Backgroud","content":"\u003cp\u003eHepatocellular carcinoma (HCC) ranks as the sixth most prevalent malignant tumor globally and the fourth leading cause of cancer-related mortality (1), with persistently high incidence and mortality rates. Currently, various therapeutic strategies, including partial hepatectomy, are available for HCC treatment (2), yet liver transplantation remains the most effective intervention (2). However, preoperative candidate selection, postoperative recurrence, and prognostic evaluation for liver transplant recipients with HCC remain critical challenges in clinical practice. Consequently, establishing reliable biomarkers to accurately predict patient outcomes holds paramount clinical significance.\u003c/p\u003e\u003cp\u003eInflammatory responses play a pivotal role in the pathogenesis and progression of hepatocellular carcinoma (HCC), adversely affecting patient prognosis by promoting tumor advancement and metastasis (3\u0026ndash;4). Concurrently, the nutritional status of HCC patients significantly influences tumor progression and clinical outcomes. Multiple inflammation-nutrition-related biomarkers have been identified as closely associated with HCC prognosis, including the fibrinogen-to-albumin ratio (FAR) (5), neutrophil-to-lymphocyte ratio (NLR) (6), platelet-to-lymphocyte ratio (PLR)(7), monocyte-to-lymphocyte ratio (MLR) (8), systemic immune-inflammation index (SII) (9), prognostic inflammatory index (PII)(10), aspartate aminotransferase-to-neutrophil ratio index (ANRI)(11), albumin-to-globulin ratio (AGR)(12), gamma-glutamyl transpeptidase-to-albumin ratio (GAR)(13), gamma-glutamyl transpeptidase-to-prealbumin ratio (GPAR) (14), albumin-to-alkaline phosphatase ratio (AAPR)(15), prognostic nutritional index (PNI)(16), ALBI grade(17), and PALBI grade(18). This study aims to systematically evaluate the optimal combination of these biomarkers for effectively predicting post-liver transplantation outcomes in HCC patients, thereby providing clinicians with a comprehensive prognostic assessment tool and valuable evidence-based guidance for clinical decision-making.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient selection\u003c/h2\u003e\u003cp\u003eThis study constitutes a retrospective analysis encompassing all patients diagnosed with hepatocellular carcinoma (HCC) who underwent liver transplantation at Beijing Chaoyang Hospital, Capital Medical University, between April 2013 and July 2023. All enrolled cases were pathologically confirmed as hepatocellular carcinoma through postoperative histological examination.The information retrieval of patient clinical information was supported by the Ethics Committee of Beijing Chaoyang Hospital (No. 2020-D.-303, date of approval: 28 May 2020).\u003c/p\u003e\u003cp\u003eThe inclusion criteria for this study were as follows: (1) histopathologically confirmed diagnosis of primary hepatocellular carcinoma; (2) absence of other malignant tumors or metastatic lesions; (3) availability of follow-up data; (4) complete clinical and pathological records; (5) comprehensive blood test results obtained within one week prior to surgery. Exclusion criteria comprised: (1) concurrent malignancies or metastases; (2) perioperative mortality; (3) absence of critical clinical data such as complete blood test records. Ultimately, 239 patients were enrolled in this study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThis study obtained a list of patients who underwent liver transplantation for hepatocellular carcinoma through the hospital's information department, with relevant laboratory data cross-verified and reviewed via the electronic medical record system. All data were systematically entered and managed in an Excel database. The collected variables primarily included: gender, age, alpha-fetoprotein (AFP) levels, pre-operative hematological parameters (such as albumin, globulin, white blood cell count, lymphocyte count, monocyte count, neutrophil count, and platelet count), T stage, N stage, TNM stage, number of intrahepatic tumors, maximum tumor diameter, total intrahepatic tumor diameter, gross tumor morphology, histological type, tumor differentiation grade, macrovascular invasion, microvascular invasion, as well as follow-up information (including survival status and overall survival time). The study identified missing values across all clinical and laboratory variables and employed complete case analysis for data processing, excluding patients with missing data in key variables such as tumor staging and biomarkers.\u003c/p\u003e\n\u003ch3\u003eFollow-up\u003c/h3\u003e\n\u003cp\u003eAll transplant recipients underwent close outpatient follow-up, with a follow-up duration ranging from 4 to 134 months (median: 53 months). During the first 6 months postoperatively, recipients were followed up monthly. From 6 months to 2 years post-transplantation, the follow-up frequency was adjusted to every 3 to 6 months. After 2 years, the follow-up interval was further extended to biennially. Routine follow-up protocols included contrast-enhanced abdominal CT or MRI examinations every 6 months. For suspected local recurrence or distant metastasis, additional targeted imaging examinations such as CT, MRI, bone scanning, or PET-CT were arranged based on clinical indications. Overall survival (OS) was defined as the duration from the date of liver transplantation to death from any cause or the last confirmed survival follow-up.\u003c/p\u003e\n\u003ch3\u003eDefinition of Inflammatory and Nutritional Related Markers\u003c/h3\u003e\n\u003cp\u003eThe following inflammatory and nutrition-related markers were calculated based on the laboratory test results obtained from the patients within one week before the operation:\u003c/p\u003e\u003cp\u003eFAR\u0026thinsp;=\u0026thinsp;Fibrinogen (mg/dL) / Serum albumin (g/L)\u003c/p\u003e\u003cp\u003eNLR\u0026thinsp;=\u0026thinsp;Neutrophils count(10\u003csup\u003e9\u003c/sup\u003e/L)/ Lymphocytes(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003cp\u003ePLR\u0026thinsp;=\u0026thinsp;Platelets count(10\u003csup\u003e9\u003c/sup\u003e/L)/ Lymphocytes(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003cp\u003eMLR\u0026thinsp;=\u0026thinsp;Monocytes count(10\u003csup\u003e9\u003c/sup\u003e/L) / Lymphocytes(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003cp\u003eSII\u0026thinsp;=\u0026thinsp;Neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)\u0026times; Platelet count(10\u003csup\u003e9\u003c/sup\u003e/L) / Lymphocyte count(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003cp\u003ePII\u0026thinsp;=\u0026thinsp;Neutrophil (10\u003csup\u003e9\u003c/sup\u003e/L)\u0026times; Monocyte(10\u003csup\u003e9\u003c/sup\u003e/L) / Lymphocyte count(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003cp\u003eANRI\u0026thinsp;=\u0026thinsp;Aspartate aminotransferase (U/L) / Neutrophil count(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003cp\u003eAGR\u0026thinsp;=\u0026thinsp;Serum albumin (g/L) / Serum globulin (g/L)\u003c/p\u003e\u003cp\u003eGAR\u0026thinsp;=\u0026thinsp;γ-Aminotransferase (U/L) / Serum albumin (g/L)\u003c/p\u003e\u003cp\u003eGPAR\u0026thinsp;=\u0026thinsp;γ-Aminotransferase (U/L) / Serum prealbumin (g/L)\u003c/p\u003e\u003cp\u003eAAPR\u0026thinsp;=\u0026thinsp;Serum albumin (g/L) / Alkaline phosphatase (U/L)\u003c/p\u003e\u003cp\u003ePNI\u0026thinsp;=\u0026thinsp;Serum albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; Lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003cp\u003eALBI\u0026thinsp;=\u0026thinsp;log\u003csub\u003e10\u003c/sub\u003e Total bilirubin (mol/L) \u0026times; 0.66 - Albumin (g/L) \u0026times; 0.085\u003c/p\u003e\u003cp\u003ePALBI = [2.02 \u0026times; log\u003csub\u003e10\u003c/sub\u003e Total bilirubin (mol/L)] + [-0.37 (log \u003csub\u003e10\u003c/sub\u003eTotal bilirubin (mol/L))\u0026sup2;] + (-0.04 \u0026times; Serum albumin (g/L) ) + (-3.48 \u0026times; log\u003csub\u003e10\u003c/sub\u003e Platelets count(10\u003csup\u003e9\u003c/sup\u003e/L)) + [1.01 \u0026times; (log\u003csub\u003e10\u003c/sub\u003e Platelets count(10\u003csup\u003e9\u003c/sup\u003e/L))\u0026sup2;]\u003c/p\u003e\n\u003ch3\u003eDevelopment of the risk scoring system\u003c/h3\u003e\n\u003cp\u003eThe complete dataset was randomly partitioned into training and validation sets at a 7:3 ratio. Within the training cohort, optimal cutoff values for inflammatory and nutritional biomarkers were determined using Maximally Selected Rank Statistics. Prognostically significant inflammatory and nutritional markers were subsequently selected through univariate and multivariate Cox regression analyses, followed by LASSO-Cox regression to identify the most prominent prognostic variables. Biomarkers with non-zero coefficients at the minimum error value were incorporated into a risk score (RS) model. Finally, Maximally Selected Rank Statistics was reapplied to evaluate the predictive performance of RS for overall survival (OS) in hepatocellular carcinoma patients post-liver transplantation and to establish its optimal cutoff threshold.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThis study employed SPSS 26.0 and R 4.4.2 for statistical analysis. Normally distributed continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with between-group comparisons conducted using independent samples t-tests. Non-normally distributed continuous variables were expressed as median (interquartile range), analyzed via Kruskal-Wallis tests. Categorical variable comparisons between groups utilized χ\u0026sup2; tests, with P-values adjusted for multiple comparisons using the Bonferroni method. Survival analysis was performed using the Kaplan-Meier method with corresponding survival curves; prognostic factors were evaluated through univariate and multivariate Cox proportional hazards regression models. Model predictive performance was assessed by calculating the concordance index (C-index) and constructing time-dependent receiver operating characteristic (ROC) curves. Calibration curves were plotted to examine the agreement between predicted and observed survival probabilities, while decision curve analysis (DCA) was applied to evaluate the clinical utility of the nomogram model. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of the patients\u003c/h2\u003e\u003cp\u003eThe median follow-up duration for the entire cohort was 53 months. The majority of patients were male (92.05%), with 43.93% aged over 55 years. A history of hepatitis B virus infection was prevalent (90.38%). The proportion of TNM stage III cases was relatively low (21.34%), while microvascular invasion was observed in 54.81% of patients. Portal vein tumor thrombosis occurred infrequently (10.04%). All patients were randomly allocated into training (n\u0026thinsp;=\u0026thinsp;168) and validation (n\u0026thinsp;=\u0026thinsp;71) sets at a 7:3 ratio, with no statistically significant differences in baseline characteristics between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics and baseline characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;239)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraining cohort\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValid cohort \u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(Male/Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220/19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157/11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stages (III/I-II)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51/188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36/132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15/56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years (\u0026gt;\u0026thinsp;55/\u0026le;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105/134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70/98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35/36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive HBsAg (+/\u0026minus;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e216/23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153/15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMVI (Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131/108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96/72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35/36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor number (Multiple/Single)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129/110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86/82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43/28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum tumor size, cm (\u0026gt;\u0026thinsp;5/\u0026le;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62/177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43/125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19/52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal tumor size, cm (\u0026gt;\u0026thinsp;8/\u0026le;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48/191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35/133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13/58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifferentiation (Poor,undifferentiated/\u003c/p\u003e\u003cp\u003eWell,moderate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e184/55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133/35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51/20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFP, ng/mL (\u0026gt;\u0026thinsp;400/\u0026le;400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80/159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54/114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26/45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVTT (+/\u0026minus;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24/215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18/150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6/65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMilan criteria (Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111/128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73/95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38/33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHangzhou criteria (Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e193/46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133/35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60/11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRe-treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eresection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (5.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eresection\u0026thinsp;+\u0026thinsp;RFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eresection\u0026thinsp;+\u0026thinsp;TACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (1.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (3.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (12.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTACE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (19.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTACE\u0026thinsp;+\u0026thinsp;RFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (5.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTACE\u0026thinsp;+\u0026thinsp;RFA\u0026thinsp;+\u0026thinsp;sorafenib\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFAR median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.15 (3.93\u0026ndash;6.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.82 (3.74\u0026ndash;6.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.28 (4.07\u0026ndash;6.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.86 (1.89\u0026ndash;5.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.03 (1.97\u0026ndash;4.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.73 (1.81\u0026ndash;5.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.65 (68.11\u0026ndash;151.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.00 (67.60\u0026ndash;152.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.52 (68.26\u0026ndash;151.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32 (0.23\u0026ndash;0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31 (0.23\u0026ndash;0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33 (0.23\u0026ndash;0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e266.38 (131.24\u0026ndash;563.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e287.70 (150.93\u0026ndash;504.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e246.52 (125.61\u0026ndash;583.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePII median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.80 (0.45\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75 (0.48\u0026ndash;1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83 (0.45\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANRI median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.05 (12.52\u0026ndash;51.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.52 (13.85\u0026ndash;62.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.68 (12.14\u0026ndash;44.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGR median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.25 (0.95\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32 (0.96\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24 (0.96\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGAR median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.11 (1.20\u0026ndash;4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.01 (1.02\u0026ndash;3.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.19 (1.23\u0026ndash;4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPAR median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e920.83 (385.71\u0026ndash;2541.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e881.82 (384.52\u0026ndash;2666.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e936.61 (410.49\u0026ndash;2408.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.761\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAPR median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31 (0.22\u0026ndash;0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33 (0.22\u0026ndash;0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31 (0.22\u0026ndash;0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNI median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.75 (34.95\u0026ndash;46.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.90 (34.53\u0026ndash;45.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.33 (35.20\u0026ndash;47.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALBI median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.06 (-2.56\u0026ndash;-1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.04 (-2.32\u0026ndash;-1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.07 (-2.63\u0026ndash;-1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePALBI median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.17 (-2.47\u0026ndash;-1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.17 (-2.42\u0026ndash;-1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.17 (-2.49\u0026ndash;-1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDetermination of the optimal cutoff values for inflammatory and nutritional markers\u003c/h2\u003e\u003cp\u003eWe employed the Maximally Selected Rank Statistics method to determine the optimal cutoff values for inflammatory and nutrition-related biomarkers in the training cohort, subsequently stratifying patients into high- and low-level groups. Univariate Cox regression analyses were then performed on all inflammatory and nutritional biomarkers to identify predictors significantly associated with overall survival (OS) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate Cox analyses for overall survival of inflammation-related and nutrition-related biomarkers in the training set\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCutoff value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;6.23) vs low(\u0026lt;\u0026thinsp;6.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.33,6.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;1.54) vs low(\u0026lt;\u0026thinsp;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.33,10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e123.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;123.33) vs low(\u0026lt;\u0026thinsp;123.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.28,3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;0.43) vs low(\u0026lt;\u0026thinsp;0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.12,3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e155.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;155.19) vs low(\u0026lt;\u0026thinsp;155.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.54,6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;2.13) vs low(\u0026lt;\u0026thinsp;2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.19,3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;17.00) vs low(\u0026lt;\u0026thinsp;17.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.9,2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;2.01) vs low(\u0026lt;\u0026thinsp;2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07,1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;4.09) vs low(\u0026lt;\u0026thinsp;4.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.56,7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6600.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;6600.00) vs low(\u0026lt;\u0026thinsp;6600.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.96,7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAAPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;0.13) vs low(\u0026lt;\u0026thinsp;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16,0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;\u0026thinsp;33.40) vs low(\u0026lt;\u0026thinsp;33.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84,3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;-2.75) vs low(\u0026lt;-2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.38,1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePALBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh(\u0026ge;-2.41) vs low(\u0026lt;-2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89,2.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRS calculation\u003c/h2\u003e\u003cp\u003eIn the training set, biomarkers with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate Cox regression analysis were incorporated into LASSO-Cox regression analysis, ultimately identifying three potential predictors from nine candidate biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on the coefficients obtained at the optimal λ value in LASSO-Cox regression, the risk score (RS) calculation formula was established as follows: \u003cb\u003eRS\u0026thinsp;=\u0026thinsp;0.07214 \u0026times; FAR\u0026thinsp;+\u0026thinsp;0.00234 \u0026times; GAR\u0026thinsp;+\u0026thinsp;0.00003 \u0026times; GPAR\u003c/b\u003e.. The optimal cutoff value of RS was determined to be 0.47 using the maximal selected rank statistics method, thereby stratifying patients into high-RS and low-RS groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Kaplan-Meier survival analysis demonstrated that patients in the high-RS group exhibited significantly shorter overall survival compared to the low-RS group(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). This trend was consistently validated in subgroup patient cohorts meeting both the Hangzhou(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and Milan criteria(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), with the high-RS group persistently showing significantly inferior survival outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eUnivariate and multivariate Cox regression analysis\u003c/h2\u003e\u003cp\u003eIn the training cohort, we incorporated the dichotomized RS based on the optimal cutoff value along with other clinical and pathological characteristics into both univariate and multivariate Cox regression analyses, with detailed results presented in the supplementary table. Univariate analysis demonstrated that TNM stage, microvascular invasion, maximum intrahepatic tumor diameter, number of intrahepatic tumors, tumor differentiation grade, portal vein tumor thrombus, sum of tumor diameters, and RS were all significantly associated with overall survival (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To further account for potential interactions among variables, all factors showing significant associations with overall survival in univariate analysis were included in the multivariate Cox regression model. The results identified microvascular invasion (HR: 2.633; 95% CI: 1.239\u0026ndash;5.594; p\u0026thinsp;=\u0026thinsp;0.012) and high RS (\u0026ge;\u0026thinsp;0.47) (HR: 2.174; 95% CI: 1.126\u0026ndash;4.184; p\u0026thinsp;=\u0026thinsp;0.021) as independent risk factors for overall survival (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox analyses of baseline characteristics and risk score on overall survival in patients with hepatocellular carcinoma undergoing liver transplantation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years (\u0026le;\u0026thinsp;55 vs\u0026thinsp;\u0026gt;\u0026thinsp;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.798\u0026ndash;2.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage (III vs I\u0026thinsp;+\u0026thinsp;II)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.04\u0026ndash;5.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.638\u0026ndash;2.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMVI (Yes vs No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.557\u0026ndash;9.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.239\u0026ndash;5.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFP, ng/mL (\u0026le;\u0026thinsp;400 vs\u0026thinsp;\u0026gt;\u0026thinsp;400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.717\u0026ndash;2.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum tumor size, cm (\u0026le;\u0026thinsp;5 vs\u0026thinsp;\u0026gt;\u0026thinsp;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.191\u0026ndash;0.524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.272\u0026ndash;1.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor number (Single vs Multiple)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3\u0026ndash;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.343\u0026ndash;1.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Male vs Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.677\u0026ndash;11.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifferentiation (Well,moderate vs Poor,undifferentiated)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u0026ndash;0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.352\u0026ndash;1.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive HBsAg (+\u0026thinsp;vs \u0026minus;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.953\u0026ndash;49.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVTT (+\u0026thinsp;vs \u0026minus;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.205\u0026ndash;4.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.612\u0026ndash;2.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal tumor size, cm (\u0026le;\u0026thinsp;8 vs\u0026thinsp;\u0026gt;\u0026thinsp;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.164\u0026ndash;0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.313\u0026ndash;1.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRS grade (\u0026ge;\u0026thinsp;0.47 vs\u0026thinsp;\u0026lt;\u0026thinsp;0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.544\u0026ndash;8.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.126\u0026ndash;4.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eConstruction and evaluation of the nomogram\u003c/h2\u003e\u003cp\u003eWe constructed a nomogram model incorporating two independent risk factors (recurrence score and microvascular invasion) identified through multivariate Cox analysis to predict 1-, 3-, and 5-year survival rates following liver transplantation in hepatocellular carcinoma patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In the training cohort, the model demonstrated area under the receiver operating characteristic curve (AUC) values of 0.764, 0.762, and 0.747 for 1-, 3-, and 5-year survival predictions, respectively. Corresponding AUC values in the validation cohort were 0.722, 0.716, and 0.722 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Calibration curves indicated excellent concordance between nomogram-predicted survival probabilities and actual observations. Furthermore, decision curve analysis (DCA) revealed substantial clinical net benefits across all time points(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), confirming the model's robust clinical utility for 1-, 3-, and 5-year survival probability predictions(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study was conducted on 239 hepatocellular carcinoma (HCC) patients who underwent liver transplantation. Preoperative inflammatory and nutritional biomarkers measured within one week prior to surgery were incorporated to develop and validate a prognostic model for predicting overall survival (OS) post-liver transplantation. Patients were randomly allocated into training and validation cohorts at a 7:3 ratio, with no significant differences in baseline clinical or pathological characteristics between groups, ensuring model stability. Univariate Cox regression analysis identified FAR, NLR, PLR, MLR, SII, PII, GAR, and GPAR as risk factors for poor postoperative prognosis, while AAPR showed positive correlation with OS. ANRI, AGR, PNI, ALBI, and PALBI demonstrated no statistically significant associations even after optimal cutoff-based dichotomization. LASSO-Cox regression subsequently selected three key variables (FAR, GAR, GPAR) from these nine biomarkers to construct a risk score (RS). The optimal RS cutoff value was determined using the maximum selected rank statistics method, categorizing patients into high- and low-RS groups. Kaplan-Meier analysis demonstrated significantly shorter OS in high-RS groups across the training cohort, validation cohort, and subgroups meeting either Hangzhou or Milan criteria. This comprehensive risk score, integrating multiple clinically relevant biomarkers, provides enhanced assessment of patients' inflammatory and nutritional status, demonstrating superior discriminative capacity and clinical applicability within current liver transplantation standards.\u003c/p\u003e\u003cp\u003eIn recent years, accumulating evidence has demonstrated the pivotal role of inflammatory responses in the pathogenesis and progression of hepatocellular carcinoma (HCC)(19). Various chronic liver diseases, including viral hepatitis, alcoholic hepatitis, and non-alcoholic fatty liver disease, can induce persistent hepatic inflammation, subsequently driving the progression from chronic hepatitis to cirrhosis and ultimately leading to hepatocarcinogenesis. During this process, inflammation-associated cells such as neutrophils, monocytes, and platelets have been substantiated to actively promote HCC progression and metastasis.\u003c/p\u003e\u003cp\u003eTumor-associated neutrophils (TANs) facilitate HCC development and metastasis through multiple mechanisms, including the release of reactive oxygen species (ROS), immunosuppressive cytokines, and neutrophil extracellular traps (NETs)(20). Neutrophil-enriched HCC typically exhibits aggressive characteristics such as poor differentiation and sarcomatoid changes, correlating with unfavorable prognosis and significantly increased risks of recurrence and metastasis(21). Conversely, tumor-associated monocytes (TAMs) interact with immune effector cells to suppress T-cell function and induce an immunosuppressive tumor microenvironment(22\u0026ndash;23).\u003c/p\u003e\u003cp\u003ePlatelets contribute to HCC progression through multifaceted mechanisms: they can be activated by HCC cells to release growth factors like VEGF, promoting tumor angiogenesis(24); they also enhance epithelial-mesenchymal transition (EMT) in tumor cells to accelerate metastatic processes(25). Furthermore, circulating platelets interact with circulating tumor cells (CTCs), facilitating CTC retention and colonization in distant organs to establish metastatic niches.\u003c/p\u003e\u003cp\u003eIn contrast, lymphocyte subsets\u0026mdash;including CD3⁺ T cells, CD8⁺ T cells, CD20⁺ B cells(26), and natural killer (NK) cells(27)\u0026mdash;exert antitumor effects through direct cytotoxicity or secretion of cytokines such as IFN-γ. The infiltration density of these cells in tumor stroma shows significant correlation with favorable patient prognosis(28).\u003c/p\u003e\u003cp\u003eFibrinogen is a plasma protein composed of three polypeptide chains (α, β, and γ) that plays a pivotal role in blood coagulation, inflammatory responses, and tissue repair(29). Research has demonstrated that it promotes tumor angiogenesis(30) within the stromal microenvironment and facilitates metastatic(31) progression through interactions with platelets, vascular growth factors, transforming growth factor-β (TGF-β), and fibroblast growth factors. In hepatocellular carcinoma (HCC), the fibrinogen alpha chain (FGA) has been proposed as a potential serological biomarker for disease progression from chronic hepatitis to cirrhosis and ultimately HCC(32). A study conducted by Sun et al. involving 366 HCC patients further revealed that an elevated fibrinogen-to-albumin ratio (FAR) was significantly associated with poor patient prognosis(33). Moreover, preoperative FAR levels have been shown to serve as an important prognostic indicator for postoperative recurrence in HCC patients undergoing hepatectomy(34).\u003c/p\u003e\u003cp\u003eGamma-glutamyltransferase (γ-GT or GGT) is a membrane-bound enzyme that plays a pivotal role in antioxidant defense, xenobiotic metabolism, and the glutathione cycle by catalyzing the transfer of γ-glutamyl groups (35). Through its involvement in glutathione metabolism and oxidative stress responses, GGT may promote tumor progression and metastasis via pro-oxidant and pro-inflammatory effects(36). In hepatocellular carcinoma (HCC), the mRNA expression levels of GGT exhibit a significant correlation with conventional tumor markers such as alpha-fetoprotein (AFP)(37). Current research demonstrates that GGT levels can serve as a predictive biomarker for HCC risk in patients with chronic hepatitis B and C(38), while preoperative GGT levels also constitute an independent risk factor for post-hepatectomy recurrence(39). Furthermore, the GGT-to-albumin ratio (GAR) has been established as an independent prognostic factor for HCC patients, with elevated GAR levels showing significant associations with poor outcomes in individuals undergoing liver transplantation (LT), transarterial chemoembolization (TACE)(40), or hepatic resection (41).\u003c/p\u003e\u003cp\u003eMultiple studies have demonstrated a significant correlation between malnutrition in cirrhotic patients and adverse clinical outcomes following liver transplantation, manifesting as increased postoperative complications, prolonged hospitalization, and elevated healthcare costs(42). Severe malnutrition may further compromise post-transplant overall survival, particularly among elderly patients with alcoholic cirrhosis, where malnourished individuals exhibit markedly reduced postoperative survival rates(43). Additionally, severe malnutrition contributes to systemic immunosuppression, exacerbates intestinal barrier dysfunction, thereby predisposing patients to postoperative infections and inducing post-transplant immune dysregulation(44). Albumin and prealbumin, as critical indicators of visceral protein reserves, serve as effective screening tools for malnutrition assessment(45).\u003c/p\u003e\u003cp\u003eConsequently, early assessment of inflammatory response and nutritional status in liver transplantation candidates with hepatocellular carcinoma holds significant clinical value for predicting postoperative outcomes, facilitating early interventions, and improving prognosis. For the majority of HCC patients, comprehensive preoperative nutritional evaluation coupled with targeted interventions can effectively reduce postoperative complication rates, shorten hospital stays, and potentially enhance overall survival rates(46).\u003c/p\u003e\u003cp\u003eTo identify adverse prognostic factors in hepatocellular carcinoma patients following liver transplantation at an early stage, researchers have focused on developing various biomarkers to predict postoperative recurrence and unfavorable clinical outcomes, thereby assisting clinicians in promptly identifying high-risk patients requiring aggressive therapeutic interventions(47\u0026ndash;49). Inflammatory responses and nutritional status are closely associated with the prognosis of hepatocellular carcinoma patients. However, the predictive efficacy of inflammation-nutrition-related biomarkers may exhibit population heterogeneity across different studies, leading to inconsistent findings.\u003c/p\u003e\u003cp\u003eConsequently, this study comprehensively analyzed 14 common inflammation-nutrition related biomarkers, including the fibrinogen-to-albumin ratio (FAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), prognostic inflammatory index (PII), aspartate aminotransferase-to-neutrophil ratio index (ANRI), albumin-to-globulin ratio (AGR), gamma-glutamyl transferase-to-albumin ratio (GAR), gamma-glutamyl transferase-to-prealbumin ratio (GPAR), albumin-to-alkaline phosphatase ratio (AAPR), prognostic nutritional index (PNI), ALBI score, and PALBI score, with the aim of identifying predictive indicators for post-liver transplantation prognosis in hepatocellular carcinoma patients. The LASSO regression method was further employed to eliminate multicollinearity among variables, resulting in the selection of three biomarkers with the highest predictive value, which were subsequently used to construct a risk score (RS). To enhance model reliability, 10-fold cross-validation was implemented during the analysis to ensure the robustness of LASSO selection. The final risk score was established as an independent risk factor for poor prognosis and demonstrated strong predictive capability for post-transplantation outcomes in both training and validation cohorts.\u003c/p\u003e\u003cp\u003eNevertheless, this study has several limitations. Firstly, as a single-center retrospective study, all cases were sourced from our institution's liver transplantation database spanning 2013 to 2023. The retrospective design inherently constrained data collection to existing electronic medical records and laboratory reports, lacking standardized prospective protocols. Consequently, certain critical variables (such as inflammatory markers including C-reactive protein) were unavailable, and some inflammation-nutrition related biomarkers could not be incorporated into the analysis. Secondly, the single-center data exhibited strong homogeneity in clinical protocols, perioperative management, and follow-up strategies, potentially limiting the generalizability of findings due to regional variations and population heterogeneity. Furthermore, despite rigorous exclusion of known confounding factors, the limited sample size precluded more sophisticated stratified analyses and complete control of all potential confounders. To enhance the reliability and generalizability of conclusions, future large-scale, multicenter prospective studies are warranted to validate current findings, minimize selection bias, and improve the study's representativeness and external validity.\u003c/p\u003e\u003cp\u003eFurthermore, this study is limited by its cross-sectional design, which relies solely on preoperative single-timepoint measurements of inflammation-nutrition biomarkers, thus failing to capture their dynamic fluctuations during disease progression or therapeutic interventions. Existing literature indicates that indices such as FAR and GAR may exhibit variability during the perioperative period and postoperative adjuvant therapy due to inflammatory status alterations, nutritional interventions, and tumor recurrence(50\u0026ndash;51). Consequently, single-timepoint measurements might inadequately reflect their true prognostic predictive value. To enhance research reliability, future investigations should systematically collect longitudinal data on inflammation-nutrition markers at multiple timepoints (preoperative, intraoperative, and postoperative) in liver transplantation patients, enabling comprehensive analysis of dynamic trajectories for more robust evaluation of their prognostic efficacy in hepatocellular carcinoma.\u003c/p\u003e\u003cp\u003eThis study employed the Maximally Selected Rank Statistics method to determine the optimal cutoff values for each indicator and the Risk Score (RS) within the training set. Although internal validation demonstrated favorable discriminatory capability, these cutoff values may be influenced by the distribution characteristics of the patient population at our center and have not yet been validated in independent external cohorts. Notably, baseline levels of indicators such as fibrinogen, albumin, and gamma-glutamyltransferase (γ-GT) vary across patients with different geographical origins, etiological backgrounds, and hepatic functional reserves, which may lead to shifts in the optimal cutoff values. Therefore, future multicenter, large-sample, prospective studies are warranted to establish standardized universal cutoff values with external validation, thereby enhancing the model's applicability across diverse populations and institutions.\u003c/p\u003e\u003cp\u003eFurthermore, the study cohort predominantly comprised male patients (92.05%) with HBV-related hepatocellular carcinoma (90.38%), while female patients and those with non-HBV etiologies (e.g., alcoholic liver disease, non-alcoholic fatty liver disease) were underrepresented. Consequently, caution is advised when extrapolating these findings to hepatocellular carcinoma patients with other etiological types.\u003c/p\u003e\u003cp\u003eDespite these limitations, the results indicate that the Risk Score (RS), constructed based on LASSO regression, serves as an independent risk factor for adverse post-liver transplantation outcomes in hepatocellular carcinoma patients. A higher preoperative RS was significantly associated with poorer prognosis. This scoring tool aids clinicians in comprehensively assessing patients' inflammatory and nutritional statuses preoperatively and provides valuable reference for therapeutic decision-making.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed a risk scoring system that integrates biomarkers of both inflammatory response and nutritional status, providing a convenient and reproducible tool for rapid preoperative assessment of post-liver transplantation prognosis. This innovative model shows promising potential for guiding individualized follow-up protocols and informing adjuvant therapeutic strategies. However, the clinical applicability of this scoring model requires further validation through multicenter, large-scale prospective studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e X.-Y.Y. and X.-X.Z. is the first author of this research; J.M , Q.H are the corresponding authors of this research. (I) Conception and design: J.M , \u0026nbsp;X.-Y.Y. and L.Z; (II) administrative support: J.M , Q.H and L.Z; (III) provision of study materials and patients: Q.H and L.Z; (IV) collection and assembly of data: X.-Y.Y. , X.-X.Z. and L.Z.; (V) data analysis and interpretation: X.-X.Z. , X.-Y.Y. and L.Z.; (VI) manuscript writing: all authors; (VII) final approval of the manuscript: all authors. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e The study was approved by the Institutional Review Board of Beijing chaoyang hospital in accordance with the 1964 Helsinki Declaration and its later amendments (No. 2020-D-303).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e The participants’ informed consent was not required because of the retrospective study design, and the study design was approved by the appropriate ethics review board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChidambaranathan-Reghupaty S, Fisher PB, Sarkar D. 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Epub 2023 Oct 10. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Liver transplantation, Hepatocellular carcinoma, Fibrinogen‑to‑albumin ratio, Prognosis, overall survival","lastPublishedDoi":"10.21203/rs.3.rs-7620800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7620800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Hepatocellular carcinoma is a common malignant tumor in the liver. The prognostic predictive roles of inflammatory and nutrition-related markers in liver transplant patients with liver cancer remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: 239 patients with hepatocellular carcinoma undergoing liver transplantation were randomly divided into a training set and a validation at a 7:3 ratio. The optimal cut-off values for each indicator were determined using Maximally Selected Rank Statistics; a risk score (RS) was constructed using LASSO-Cox regression, and the optimal cut-off value of the RS was determined in the training set. A nomogram was further established and its predictive efficacy was evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The risk score (RS) was ultimately included in three variables: FAR, GAR, and GPAR, with a cutoff value of 0.47. Patients in the high RS group had significantly poorer overall survival than those in the low RS group (P \u0026lt; 0.05), and this result was confirmed in the validation set and the subgroups meeting the Milan/Hangzhou criteria. Multivariate Cox analysis showed that high RS (HR = 2.174) and microvascular invasion (HR = 2.633) were independent risk factors. Based on these two factors, a nomogram was constructed, and the 1/3/5-year AUCs were 0.764/0.762/0.747 (training set) and 0.722/0.716/0.722 (validation set), and both calibration curves and decision curve analysis showed good consistency and clinical net benefit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe preoperative risk score integrating FAR, GAR and GPAR can independently predict the overall survival of HCC patients after liver transplantation, providing a simple and practical new tool for clinical rapid prognosis assessment, individualized follow-up and adjuvant treatment strategy formulation.\u003c/p\u003e","manuscriptTitle":"Scoring system related inflammation and nutrition to predict prognoss of patients with hepatocellular carcinoma undergoing liver transplantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 19:29:39","doi":"10.21203/rs.3.rs-7620800/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2596591-54de-4e48-badb-05dcef32964c","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T10:27:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 19:29:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7620800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7620800","identity":"rs-7620800","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}