Systemic Inflammatory Response Markers Improve the Discrimination for Prognostic Model in Hepatocellular Carcinoma

Systemic Inflammatory Response Markers Improve the Discrimination for Prognostic Model in Hepatocellular Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Systemic Inflammatory Response Markers Improve the Discrimination for Prognostic Model in Hepatocellular Carcinoma Alba Rocco, Costantino Sgamato, Filippo Pelizzaro, Vittorio Simeon, and 28 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5441902/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2025 Read the published version in Hepatology International → Version 1 posted 5 You are reading this latest preprint version Abstract Background/purpose of the study: We aimed to evaluate the performance of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and their combination (combined NLR-PLR, CNP) on overall survival (OS) and recurrence-free survival (RFS) in a large cohort of unselected hepatocellular carcinoma (HCC) patients. Methods: Training and validation cohort data were retrieved from the Italian Liver Cancer (ITA.LI.CA) database. The optimal cut-offs of NLR and PLR were calculated according to the multivariable fractional polynomial and the minimum p-value method. The continuous effect and best cut-off categories of NLR and PLR were analyzed using multivariable Cox regression analysis. A shrinkage procedure adjusted over-fitting HR estimates of best cut-off categories. C-statistic and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination properties of the biomarkers when added to clinical survival models. Results: 2,286 patients were split into training (n=1,043) and validation (n=1,243) cohorts.The optimal cut-offs for NLR and PLR were 1.45 and 188, respectively. NLR (HR 1.58, 95%CI 1.11-2.28, p=0.014) and PLR (HR 1.79, 95%CI 1.11-2.90, p=0.018) were independent predictors of OS. When added to the clinical prognostic model, including age, alpha-fetoprotein (AFP), CHILD-Pugh score and Barcelona Clinic Liver Cancer (BCLC) staging system, CNP had a significant incremental value in predicting OS (IDI 1.3%, p=0.04). Data were confirmed in the validation cohort. NLR (p=0.027) and CNP (p=0.023) predicted RFS in the training cohort. Conclusions: NLR, PLR, and CNP independently predicted shorter OS in HCC patients. The addition of CNP into the survival prediction model significantly improved the model's predictive accuracy for OS. Neutrophil-to-lymphocyte ratio platelet-to-lymphocyte ratio survival recurrence-free survival liver cancer prognosis Figures Figure 1 Figure 2 Figure 3 Introduction Hepatocellular carcinoma (HCC) is the seventh most common cancer and the second leading cause of cancer-related deaths worldwide. The overlap between incidence and mortality (830,000 deaths per year) reflects the dismal prognosis of this disease, likely due to the late diagnosis and the high recurrence rate. [ 1 ] Accurate prognostic evaluation of patients with HCC is essential to optimize treatment choices and improve the outcomes. However, stratifying death risk can be challenging because HCC is a unique malignancy that typically develops in the setting of pre-existing liver disease (primarily cirrhosis), thus raising the competing risk of dying from cancer progression and liver decompensation. [ 2 ] Over the last 20 years, several prognostic systems have been introduced, mainly based on variables commonly accepted as survival-associated factors in patients with HCC, such as tumour burden, residual liver function and general clinical conditions (performance status). [ 3 ] Alpha-fetoprotein (AFP) is the only serum biomarker included in some of these models despite its suboptimal accuracy as a prognostic indicator. [ 4 ] Therefore, it is a compelling argument to identify other potential non-invasive biomarkers to refine the patient's prognosis. In recent years, increasing evidence has demonstrated that the systemic inflammatory response (SIR) plays a crucial role in the pathogenesis and progression of liver cirrhosis as well as in the development and advancement of different types of cancer. [ 5 – 7 ] Circulating SIR markers, including the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), or their combination (combined NLR and PLR: CNP) reflect the balance between pro-tumor inflammation associated with platelets and neutrophils and the anti-tumor immune response mediated by lymphocytes. These convenient, easily obtainable and repeatable biomarkers have been proven to have prognostic significance in patients with different cancers. [ 8 – 10 ] However, studies analyzing the prognostic accuracy of serum SIR markers in HCC patients do not allow definitive conclusions on their utility in clinical practice. [ 11 – 16 ] Based on these premises, we aimed to analyze the prognostic accuracy of NLR, PLR, and CNP in a large cohort of Caucasian patients with HCC included in the Italian Liver Cancer (ITA.LI.CA.) database. Material and methods Patients The ITA.LI.CA registry currently includes 7,782 patients consecutively diagnosed with HCC and managed in 24 Italian centres from January 1987 to December 2022. Participating institutions prospectively collect clinical data about patients with HCC and update them every two years. The group coordinator (F.T., Bologna University) regularly checks entries for consistency. Whenever a clarification or additional information is deemed necessary, relevant cases are returned to the recruiting centre before the final inclusion. The ITA.LI.CA database management conforms to the past and current Italian legislation regarding privacy, and the present study conforms to the ethical guidelines of the Declaration of Helsinki. The study was approved by the Institutional Review Board of the ITA.LI.CA coordinating centre, Alma Mater Studiorum University of Bologna, on 15th May 2012 (approval number 99/2012/O/Oss). Written informed consent was obtained from all participants. In the ITA.LI.CA registry, demographic data, aetiology of the liver disease and comorbidities are systematically collected. For this study, from the entire population of patients in the database, we selected those patients diagnosed with HCC between January 2000 and December 2022, with available pertinent laboratory data at the time of the first HCC treatment and a lag time of less than six months between HCC diagnosis and treatment. Exclusion criteria were as follows: (1) ongoing acute infection at the time of enrolment, (2) concomitant severe comorbidities (advanced renal, cardiac, pulmonary, and/or thyroid disorders), and (3) incomplete data. Data from 1,043 HCC patients (from January 2000 to December 2018) and 1,243 HCC patients (from January 2019 to December 2023) were reviewed as training and internal validation cohorts, respectively. Treatment of chronic viral hepatitis was performed according to current guidelines at the time of enrollment. Diagnosis of liver cirrhosis was defined at the time of enrolment based on the combination of laboratory and imaging tests. The Child-Pugh class, Model for End-Stage Liver Disease (MELD) score, ascites and hepatic encephalopathy, and clinically significant portal hypertension (CSPH) were also recorded. Since hepatic venous pressure gradient (HVPG) measurement, the gold standard method for diagnosing CSPH, is not generally performed in clinical practice, the diagnosis was based on unequivocal signs of CSPH (splenomegaly, oesophageal varices, ascites and platelet count < 100 x 10 9 /L). Primary laboratory data, such as white blood cell (WBC) count, neutrophil count, lymphocyte count, platelet (PLT) count, international normalized ratio (INR), C-reactive protein (CRP), albumin, total bilirubin, creatinine, and alpha-fetoprotein (AFP) are regularly registered in the database. The prognostic cut-off value of AFP was arbitrarily set at 400 ng/mL. SIR biomarkers definition The neutrophil-to-lymphocyte ratio (NLR) was calculated as absolute neutrophil count (the number of neutrophils/𝜇L) divided by absolute lymphocyte count (number of lymphocytes/𝜇L). Platelet-to-lymphocyte ratio (PLR) was calculated as absolute platelet count (the number of platelets/𝜇L) divided by absolute lymphocyte count (number of lymphocytes/𝜇L). Diagnosis and staging of HCC The diagnosis of HCC was made according to the guidelines available at the time of inclusion. Among the patients selected for this study, the diagnosis of HCC was confirmed by histology in 211 (19%) cases and based on radiologic criteria at imaging (4-phase multidetector computed tomography [CT]) or dynamic contrast-enhanced magnetic resonance [MRI] in the remaining cases. Dynamic imaging techniques assessed the location, size and number of nodules, macrovascular invasion (MVI), and extrahepatic spread (EHS). HCC was staged according to Barcelona Clinic Liver Cancer (BCLC) [ 17 ] and ITA.LI.CA staging systems [ 18 ]. The diagnosis of HCC recurrence was similar to that of the initial disease diagnosis. Treatment modalities for primary HCC and HCC recurrence followed the BCLC treatment guidelines. Treatment and follow-up As far as HCC treatment is concerned, patients were classified according to the main (most effective) therapy performed in each patient following this hierarchical order: liver transplantation (LT), liver resection (LR), percutaneous radiofrequency ablation or other ablative techniques (ABL), transarterial chemoembolization/embolization or radioembolization (IAT, intra-arterial therapies), systemic therapies (mainly sorafenib, SOR) and best supportive care (BSC). Although all the treatments performed during the patient's clinical history are registered in the ITA.LI.CA database, only the main treatment was considered in this study. Overall survival (OS) was defined as the time interval from treatment to the date of death, last follow-up, or data censoring (i.e. 31st December 2018 for the training cohort and 31st December 2022 for the validation cohort). Recurrence-free survival (RFS) was defined as the time between treatment and the first evidence of disease recurrence and was evaluated in the subset of patients who underwent potentially curative therapies, LR, ABL and IAT. Statistical analysis Continuous variables were described using mean and standard deviation (SD) or median values and interquartile range (IQR) according to their distribution. The differences were compared using the Student's t-test and the Mann–Whitney U test. Categorical variables were reported as the number of cases and percentages, and the differences were compared using the chi-square test. A histogram graphically described the distribution of NLR and PLR, whereas a scatterplot and the Spearman rho coefficient highlighted the correlation between biomarkers. The associations between NLR and PLR and clinical factors or staging systems known to be related to HCC prognosis, such as age at diagnosis, AFP, BCLC stage and Child-Pugh score, were analyzed using the Wilcoxon Rank Sum and Signed Rank Tests for dichotomous variables and the Kruskal-Wallis Rank Sum Test for categorical variables. The Cox proportional hazard model was used to test the prognostic role of NLR and PLR on both OS and RFS. In the first univariate analysis, NLR and PLR were tested as continuous variables after the linearity assumption was tested using fractional polynomials. In a second univariate analysis, NLR and PLR were tested as categorical variables dichotomized according to the best cut-off value that minimizes the p-value of hazard ratio (HR). According to the identified cut-offs, OS and RFS curves were estimated using the Kaplan–Meier method and compared with a two-sided log-rank test. For each biomarker (and for both continuous effect and best cut-off categories), a multivariable analysis was performed using covariates: age, AFP, BCLC stage and Child-Pugh score. A shrinkage procedure with 95% CI was calculated using the bootstrap-percentile method to adjust for over-fitting HR estimates of best cut-off categories. [ 19 ] To evaluate the incremental clinical value and the discrimination properties of SIR biomarkers, we compared the basic clinical model with those including biomarkers (continuous or categorical) using C-statistic and integrated discrimination improvement (IDI) measures. Data were analyzed using R software version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria). Results Demographic and tumour characteristics of the enrolled population A total of 2,286 patients who met the eligibility criteria were enrolled. One thousand forty-three were included in the training cohort and 1,243 in the validation cohort. The baseline demographic, clinical and laboratory findings of HCC patients are summarised in Table 1 . Inflammatory markers and association with clinical prognostic factors The median NLR and PLR were 2.22 (IQR 1.63–3.18) and 87 (IQR 58–124). Both biomarkers showed a skewed distribution and were positively correlated (rho = 0.56) ( Supplementary Fig. 1 ). As far as concern the association between NLR and PLR and clinical prognostic factors (age, AFP, Child-Pugh score and BCLC stage), PLR correlated with age [PLR rho: 0.15, p 400 ng/mL than in those with AFP > 400 ng/ml (84 [IQR 56–119] vs 96 [IQR 65–133], p = 0.002). NLR was associated with the Child-Pugh score (p < 0.001), whereas both biomarkers were associated with BCLC staging, progressively increasing with tumour burden ( p < 0.001 and p < 0.001 for NLR and PLR, respectively ( Supplementary Table 1 ). SIR biomarkers are independent prognostic factors of OS According to fractional polynomial analysis, NLR was associated with OS as a logarithmic function [HR 1.61 (95% CI 1.39–1.86, p < 0.001)] while PLR was linearly associated with OS [HR 1.16 (95% CI 1.04–1.29, p < 0.01)] (Fig. 1 , panel A & C ). The best cut-offs minimizing the p-value of HR in the training cohort were 1.45 for NLR and 188 for PLR, respectively (Fig. 1 , panels B & D ). Eight hundred and forty-four patients (81.4%) had NLR > 1.45, and 76 (7.3%) patients had PLR > 188. In the univariate model, both biomarkers were associated with OS irrespective of shrinkage procedure (Shrunken coefficients: NLR HR 1.86, 95%CI 1.40–2.48, p < 0.001, and PLR HR 1.57, 95%CI 1.08–2.30, p = 0.019, respectively). In the multivariate model adjusted for the clinical covariates reported to be prognostic (age, AFP, Child-Pugh score and BCLC stage), both NLR and PLR were significantly associated with OS (NLR: HR 1.65, 95%CI 1.18–2.29, p = 0.003, and PLR: HR 1,87, 95%CI 1.24–2.81, p = 0.003). By applying a shrinkage procedure to adjust for over-fitting HR estimates, both NLR > 1.45 (HR 1.58, 95%CI 1.11–2.28, p = 0.014) and PLR > 188 (HR 1.79, 95%CI 1.11–2.90, p = 0.018) remained associated with OS ( Table 2 ). An inflammation-based score was created by combining NLR with PLR (combined NLR-PLR score, CNP) and analyzed as a continuous and dichotomous variable after identifying the best cut-off. Patients were then classified as follows: those with both NLR and PLR below their respective cut-off entered in the CNP = 0 group (n = 197, 18.8%), those with either NLR or PLR above the cut-off were classified in the CNP = 1 group (n = 772, 74%), and those with both NLR and PLR above their respective cut-off were classified in the CNP = 2 group (n = 74, 7%). Survival analysis When the study was censored (31st December 2023), the median follow-up times were 40 and 27 months in the training and validation cohorts, respectively. Figure 2 shows the survival trees of OS in the training cohort. Median OS time was significantly higher in patients with NLR and PLR below the cut-off values (p < 0.0001 for NLR and p = 0.00015 for PLR, respectively) (Fig. 2 , panels A and B ). Likewise, CNP stratified patients according to their prognosis since median OS was significantly lower in CNP 2 [26 months (95%CI 20–41)] than in CNP 1 [38 months (95% CI 33–42)] or CNP 0 [76.8 months, 95%CI 54.6- not achieved] patients (p < 0.0001) ( Fig. 2 , panel C ). Similar results were observed in the validation cohort ( Supplementary Fig. 2 ) . Patients with NLR and PLR below the cut-off values had significantly lower median OS time (p < 0.001 for NLR and p < 0.001 for PLR, respectively) ( Supplementary Fig. 2 , panels A and B) . CNP was able to stratify patients according to their prognosis since median OS was significantly lower in CNP 2 [18 months (95%CI 12.9–38.5)] than in CNP 1 [45.3 months (95% CI 31.8 - NA)] or CNP 0 [NA months, 95%CI 41.4 - NA] patients (p < 0.0001) ( Supplementary Fig. 2, panel C ). Discrimination properties of SIR biomarkers Three different models were fitted to evaluate the potential role of NLR, PLR, and CNP in predicting survival, together with established prognostic factors. The basic model included standard prognostic factors: age, AFP, Child-Pugh score, and the BCLC staging system. The second model included all the abovementioned variables plus the biomarkers (log-transformed NLR and PLR) considered as continuous variables, with tests for possible interactions. The third model replaced the continuous biomarkers with the CNP score. This progression of models allows for a comparison of predictive accuracy and interpretability by assessing if the addition of serum SIR biomarkers (in continuous or composite form) significantly enhances survival prediction beyond traditional prognostic factors. The results are shown in Table 3 . Log NLR (HR 1.33, 95%CI 1.09–1.62, p = 0.004) and CNP (HR 2.69, 95%CI 1.79–4.06, p < 0.001) were significantly associated with OS. Adding Log-NLR to the basic model did not improve the reclassification index (IDI 0.8 [-0.1-2.2%], p = 0.07). On the other hand, compared to the basic model, the addition of CNP significantly improves the model's reclassification index (IDI 1.3 [0.1–2.7%], p = 0.04). When we included the ITA.LI.CA staging system among baseline prognostic factors, the addition of both continuous biomarkers or CNP did not improve the reclassification index (IDI 0.8 [-0.1-1.7%], p = 0.11 and IDI 0.8 [-0.1-2.3%], p = 0.06, respectively) ( Supplementary Table 2 ). The prognostic performance of SIR markers was confirmed in the validation cohort ( Supplementary Table 3 and Supplementary Table 4 ). Recurrence analysis A subsample of 871 patients who underwent curative treatments (LR, ABL or IAT) was used for the recurrence analysis (Fig. 3 ). Three-hundred and seventy-nine recurrences were recorded during the follow-up, with a median RFS of 31 months (95%CI 26–36). Median RFS time was lower in patients with NLR > 1.45 than in patients with NLR 0.027), whereas it did not significantly differ in patients with PLR above the cut-off (p = 0.17) (Fig. 3 , panels A and B ). CNP was significantly associated with RFS (p = 0.023) ( Fig. 3 , panel C ). However, none of the SIR markers had prognostic value in recurrence analysis in the validation cohort ( data not shown ). Discussion SIR markers are progressively gaining consensus as predictors of cancer survival, although the mechanisms by which they impact tumour biology remain unclear. Inflammatory cells, such as platelets and neutrophils, can contribute to tumour cell invasion into the peripheral blood. [ 7 ] Platelets could protect circulating tumour cells from shear stresses during circulation, induce epithelial-mesenchymal transition, and promote tumour cell extravasation to metastatic sites. [ 20 – 22 ] Neutrophils can enhance the adhesion and seeding of tumour cells in distant organs through the secretion of circulating growth factors. [ 12 , 23 , 24 ] Conversely, lymphocytes are crucial in defence against tumours, dictating the host immune response to malignancy by inducing cytotoxic cell death and inhibiting tumour cell proliferation and migration. [ 25 ] The relationship between NLR and HCC prognosis was first described by Halazun et al. , who demonstrated that an NLR > 5 predicted poor OS and a high recurrence rate in patients undergoing LT for HCC. [ 12 ] The prognostic performance of NLR was then confirmed in HCC patients, mainly of Asian ethnicity, who received curative or palliative therapy. [ 13 , 14 ] Likewise, other reports demonstrated that elevated pre-treatment PLR values predicted an unfavourable OS (HR = 1.73; 95% CI 1.46–2.04; p < 0.00001) and RFS (HR = 1.30; 95% CI 1.06–1.60; p = 0.01) irrespective of therapy.[ 26 ] Nevertheless, these biomarkers are far from routinely used in clinical practice due to the heterogeneity in study design, sample size and lack of standardized cut-off values, usually set up by the receiver operating characteristic (ROC) method. Indeed, this conventional statistical approach, widely used and easy to apply for determining an "optimal" cut-off in binary outcomes, can lead to a loss of information, reduced statistical power, and an increased risk of false positives. [ 27 ] Our study used the multivariable fractional polynomial and minimum p-value methods that considered the effect of each possible functional form of NLR and PLR, or cut-off points, for survival analysis. These approaches enabled us to identify the cut-off most strongly correlated with the outcomes (OS and RFS). By applying these cut-offs (1.45 for NLR and 188 for PLR), we could confirm that NLR and PLR effectively stratify HCC patients in terms of prognosis. Indeed, patients with biomarker values above their respective cut-off had a median OS time significantly lower than their counterparts (NLR, p < 0.0001, and PLR p = 0.00015). Furthermore, at univariable and multivariable analysis adjusted for clinical covariates associated with HCC prognosis, both NLR and PLR remained independent prognostic factors for OS. Since our cut-offs remain highly data-dependent, carrying with a serious risk of the type I error and an overestimation of the effect of the prognostic value in absolute terms, we also applied a bootstrap resampling approach that, together with a shrunk estimate, allowed us to obtain confidence intervals with the desired coverage. Notably, even after applying the shrinkage procedure, both NLR and PLR remained significant predictors of survival. Interestingly, when we tested the prognostic performance of CNP in our population, it was able to stratify patients into three groups with different median OS, confirming that patients with both NLR and PLR above the cut-off had a worse prognosis. Generally, even when a biomarker has been proven to predict a disease outcome, it remains to be demonstrated whether it can enhance survival prediction compared to commonly used prognostic models (such as cancer stage) and, therefore, if it deserves routine measurement. The discrimination of a risk prediction model, i.e. the ability to differentiate between individuals who will experience the event of interest and those who will not, is typically assessed using the area under the ROC curve (AUC). However, the AUC has been criticized for its limited sensitivity when comparing models, especially if the baseline model already performs well. [ 28 ] In contrast, IDI is not dependent on risk categorization but considers changes in predicted risk, overcoming some of the limitations of AUC. Notably, adding CNP to the survival prediction model, including age, AFP levels, and BCLC staging, improved the IDI value (0.013, p = 0.04), suggesting that CNP significantly enhanced the model's ability to predict OS. Regarding HCC recurrence, literature data on the prognostic significance of NLR and PLR are somewhat controversial. [ 29 ] In our population, although NLR and CNP significantly predicted RFS in the training cohort, their prognostic performance was not confirmed in the validation cohort. It is plausible that the shorter median of the observation period in the validation cohort may have obscured the detection of recurrence events. Consequently, this may have led to an underestimation of the true recurrence rate, as cases that would potentially manifest later remain unobserved. In recent years, the approval of antiprogrammed cell death-1 (PD-1) antibodies, which act as immune checkpoint inhibitors (ICIs), has revolutionized the treatment landscape for HCC. We have not had long-term observations of patients undergoing ICI therapy. However, a recent pooled meta-analysis of 44 studies involving 5,322 patients confirmed the predictive value of baseline SIR biomarkers, such as NLR, for OS also in HCC patients receiving ICI treatment (HR: 1.951, P < 0.001) [ 30 ]. We are aware that some shortcomings may have influenced our study. First, this is a retrospective study, so a potential selection bias and some unintended biases are predictable. Second, we lack external validation although the bootstrap resampling method used in the training set and the internal validation, at least in part, overcomes this limitation, allowing us to establish a stable prognostic "multiparametric" model taking tumour burden, liver function and inflammatory status into account. Lastly, since neutrophil, platelet, and lymphocyte levels are influenced by infections, inflammation in other tissues, and medications taken before HCC treatment, these factors should be considered when interpreting NLR and PLR measurements. On the other hand, we believe that including a large cohort of HCC patients managed with different strategies, extended follow-up, and a rigorous statistical approach strengthens our results. Indeed, to the best of our knowledge, this is the largest Western series of HCC patients in whom the reliability of pre-treatment SIR markers in predicting OS and RFS has been rigorously tested and validated. In conclusion, our study showed that NLR, PLR, and their combination, CNP, are reliable predictors of prognosis in patients with HCC, enhancing the accuracy of traditional factors like cancer stage and liver function. Thus, due to their non-invasiveness, ease of determination, repeatability, and low cost, these biomarkers are strong candidates for improving prognosis prediction in HCC patients. External validation by prospective and well-powered studies would be needed before their routine adoption in clinical practice. Declarations Conflict of interest and ethical standards : The authors have no relevant conflicts of interest. Statement of Human and Animal Rights : The ITA.LI.CA database management conforms to the past and current Italian legislation regarding privacy, and the present study conforms to the ethical guidelines of the Declaration of Helsinki. The study was approved by the Institutional Review Board of the ITA.LI.CA coordinating centre, Alma Mater Studiorum University of Bologna, on 15th May 2012 (approval number 99/2012/O/Oss). Informed consent : Written informed consent was obtained from all participants. Funding: This research received no external funding. Authors contributions: Conceptualization: A Rocco, C Sgamato and F Pelizzaro; Methodology: V Simeon, P Chiodini; Formal analysis and investigation: A Rocco, C Sgamato, F Pelizzaro, V Simeon, P Chiodini; Data Curation: A Rocco, C Sgamato, F Pelizzaro, P Coccoli, D Compare, E Pinto, G Palano, FG Foschi, G Raimondo, G Missale, G Svegliati-Baroni, F Trevisani, E Caturelli, MR Brunetto, G Vidili, A Masotto, D Magalotti, C Campani, A Gasbarrini, F Azzaroli, GL Rapaccini, B Stefanini, R Sacco, A Mega, EG Giannini, G Cabibbo, M Di Marco, M Guarino, P Chiodini, F Farinati, G Nardone, Italian Liver Cancer (ITA.LI.CA) group; Writing - original draft preparation: A Rocco, C Sgamato, F Pelizzaro, V. Simeon; Writing Review and editing: P Coccoli, D Compare, E Pinto, G Palano, FG Foschi, G Raimondo, G Missale, G Svegliati-Baroni, F Trevisani, E Caturelli, MR Brunetto, G Vidili, A Masotto, D Magalotti, C Campani, A Gasbarrini, F Azzaroli, GL Rapaccini, B Stefanini, R Sacco, A Mega, EG Giannini, G Cabibbo, M Di Marco, M Guarino, P Chiodini, F Farinati, G Nardone; Supervision: P Chiodini, F Farinati and G Nardone. All authors have read and agreed to the published version of the manuscript. Acknowledgement Other members of the ITA.LI.CA group: Department of Medical and Surgical Sciences, Semeiotics Unit, University of Bologna, Bologna : Maurizio Biselli, Paolo Caraceni, Annagiulia Gramenzi, Lorenzo Lani, Davide Rampoldi, Nicola Reggidori, Valentina Santi, Benedetta Stefanini. Azienda Ospedaliero-Universitaria S. Orsola-Malpighi, Internal Medicine–Piscaglia Unit, Bologna : Alessandro Granito, Luca Muratori, Fabio Piscaglia, Vito Sansone, Francesco Tovoli. Department of Surgical and Medical Sciences, Gastroenterology Unit, Alma Mater Studiorum–University of Bologna, Bologna : Elton Dajti, Giovanni Marasco, Federico Ravaioli. Department of Specialist, Diagnostic and Experimental Medicine, Radiology Unit, University of Bologna, Bologna : Alberta Cappelli, Rita Golfieri, Cristina Mosconi, Matteo Renzulli. Department of Surgery, Oncology and Gastroenterology, Gastroenterology Unit, University of Padova, Padova : Elisa Pinto, Giorgio Palano, Maria Piera Kitenge, Federica Bertellini. Gastroenterology and Digestive Endoscopy Unit, Foggia University Hospital, Foggia : Ester Marina Cela, Antonio Facciorusso. Department of Internal Medicine, Gastroenterology Unit, University of Genova, IRCCS Policlinico San Martino, Genova : Giulia Pieri, Maria Corina Plaz Torres, Andrea Pasta. Internal Medicine and Gastroenterology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, Roma : Nicoletta de Matthaeis, Francesca Romana Ponziani. Liver Injury and Transplant Unit, Polytechnic University of Marche, Ancona : Gloria Allegrini. Gastroenterology Unit, Belcolle Hospital, Viterbo : Giorgia Ghittoni, Valentina Lauria, Giorgio Pelecca. Vascular and Interventional Radiology Unit, Belcolle Hospital, Viterbo : Fabrizio Chegai, Armando Raso, Alessio Bozzi. Medical Oncology Unit, Belcolle Hospital, Viterbo : Marta Schirripa Department of Medicine and Surgery, Infectious Diseases and Hepatology Unit, University of Parma and Azienda Ospedaliero-Universitaria of Parma, Parma : Elisabetta Biasini, Andrea Olivani. Gastroenterology Unit, IRCCS Sacro Cuore Don Calabria hospital, Negrar : Alessandro Inno, Fabiana Marchetti. Department of Health Promotion, Mother & Child Care, Internal Medicine & Medical Specialties, PROMISE, Gastroenterology & Hepatology Unit, University of Palermo, Palermo : Ciro Celsa, Paolo Giuffrida, Caterina Stornello, Mauro Grova, Carmelo Marco Giacchetto, Gabriele Rancatore, Maria Vittoria Grassini, Roberta Ciccia, Alessandro Grova, Mauro Salvato. Department of Clinical and Experimental Medicine, Clinical and Molecular Hepatology Unit, University of Messina, Messina : Maria Stella Franzè, Carlo Saitta. Department of Medicine Surgery and Pharmacy, Centralized Day Hospital of the medical area, University of Sassari, Azienda Ospedaliero-Universitaria di Sassari, Sassari : Marco Arru, Assunta Sauchella, Maria Grazia Serra. Department of Internal Medicine, Ospedale per gli Infermi di Faenza, Faenza IRCCS Meldola : Vittoria Bevilacqua, Alberto Borghi, Fabio Conti, Lucia Napoli, Luca Frassineti, Maria Teresa Migliano, Nicola Reggidori. Department of Experimental and Clinical Medicine, Internal Medicine and Hepatology Unit, University of Firenze, Firenze : Fabio Marra, Valentina Adotti, Martina Rosi. Department of Clinical Medicine and Surgery, Hepato-Gastroenterology Unit, University of Napoli "Federico II", Napoli : Libera Esposito. Department of Clinical Medicine and Surgery, Gastroenterology Unit, University of Napoli "Federico II", Napoli : Filomena Morisco, Valentina Cossiga, Mario Capasso. Department of Clinical and Experimental Medicine, Hepatology and Liver Physiopathology Laboratory, University Hospital of Pisa, Pisa : Veronica Romagnoli. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Neutrophil extracellular traps sequester circulating tumor cells and promote metastasis. J Clin Invest [Internet]. 2013;123:3446–58. http://www.jci.org/articles/view/67484 Placke T, Salih HR, Kopp H-G. GITR ligand provided by thrombopoietic cells inhibits NK cell antitumor activity. J Immunol [Internet]. 2012;189:154–60. https://www.nature.com/articles/nature07205 Lin W, Zhong M, Zhang Y, Wang H, Zhao H, Cheng B, et al. Prognostic Role of Platelet-to-Lymphocyte Ratio in Hepatocellular Carcinoma with Different BCLC Stages: A Systematic Review and Meta-Analysis. Gastroenterol Res Pract. 2018;2018:1–10. Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of Using Optimal Cutpoints in the Evaluation of Prognostic Factors. JNCI J Natl Cancer Inst. 1994;86:829–35. Liu Y, Wang Z-X, Cao Y, Zhang G, Chen W-B, Jiang C-P. Preoperative inflammation-based markers predict early and late recurrence of hepatocellular carcinoma after curative hepatectomy. Hepatobiliary Pancreat Dis Int [Internet]. 2016;15:266–74. http://www.ncbi.nlm.nih.gov/pubmed/27298102 Mouchli M, Reddy S, Gerrard M, Boardman L, Rubio M. Usefulness of neutrophil-to-lymphocyte ratio (NLR) as a prognostic predictor after treatment of hepatocellular carcinoma. Review article. Ann Hepatol [Internet]. 2021;22:100249. http://www.ncbi.nlm.nih.gov/pubmed/32896610 Zhang L, Feng J, Kuang T, Chai D, Qiu Z, Deng W et al. Blood biomarkers predict outcomes in patients with hepatocellular carcinoma treated with immune checkpoint Inhibitors: A pooled analysis of 44 retrospective sudies. Int Immunopharmacol. 2023;118. Tables Tables 1 to 3 are available in the Supplementary Files section. Supplementary Files GA.png Tables.pdf SupplementaryFigures.pdf SupplementaryTables.pdf Cite Share Download PDF Status: Published Journal Publication published 25 Mar, 2025 Read the published version in Hepatology International → Version 1 posted Editorial decision: Major Revisions Needed 29 Nov, 2024 Reviewers agreed at journal 16 Nov, 2024 Reviewers invited by journal 16 Nov, 2024 Editor assigned by journal 15 Nov, 2024 First submitted to journal 14 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5441902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378791642,"identity":"5cbb7092-a549-4e96-b5f5-72da454cbf9d","order_by":0,"name":"Alba Rocco","email":"","orcid":"","institution":"University of Naples Federico II: Universita degli Studi di Napoli Federico II","correspondingAuthor":false,"prefix":"","firstName":"Alba","middleName":"","lastName":"Rocco","suffix":""},{"id":378791643,"identity":"150bfb5d-ba8e-41c8-8b31-cb5853a689f8","order_by":1,"name":"Costantino 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Smoothed graph of the association of NLR (panel \u003cem\u003eA\u003c/em\u003e) and PLR (panel \u003cem\u003eC\u003c/em\u003e) following evaluation of the linearity assumption using fractional polynomials; note the logarithmic shape of NLR. The best cut-off value minimizes the p-value of the hazard ratio for NLR (panel \u003cem\u003eB\u003c/em\u003e) and PLR (panel \u003cem\u003eD\u003c/em\u003e). Value crossing HR = 1 represents the median value for each biomarker.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5441902/v1/1e6b51aa1989d4a158767e8e.png"},{"id":71807238,"identity":"e468ce12-4b31-4307-afbf-910e454f01d7","added_by":"auto","created_at":"2024-12-18 17:43:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132671,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of survival data of HCC patients (n = 1,043) according to NLR (a) and PLR (b) best cut-offs and CNP (c). Time on the x-axis represents months of observation (follow-up extended up to 206 months). The table with subjects at risk is reported for each biomarker at each specific time point. P value is a log-rank test.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5441902/v1/99b7d4122204ad44ab7cd311.png"},{"id":71806126,"identity":"5f899426-b538-4664-b985-343625b3d77b","added_by":"auto","created_at":"2024-12-18 17:35:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105378,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of recurrence data in HCC patients (n = 871) according to NLR (a) and PLR (b) best cut-off and CNP (c). Time on the x-axis represents months of observation (follow-up extended up to 137 months). The table with subjects at risk is reported for each biomarker at each specific time point. P value is a log-rank test.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5441902/v1/2effdc6342540958967f926a.png"},{"id":79604771,"identity":"9437f142-841c-4b84-ad28-bea44177917d","added_by":"auto","created_at":"2025-03-31 16:04:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1385551,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5441902/v1/4db6252e-7cf1-4afb-9840-948e65811db4.pdf"},{"id":71807237,"identity":"c69ad5d1-ab05-4163-84df-8285011654fe","added_by":"auto","created_at":"2024-12-18 17:43:08","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":122693,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-5441902/v1/ad20a3da255174ffa454cc97.png"},{"id":71806131,"identity":"265032bd-299e-445d-9821-343ca1fe5a07","added_by":"auto","created_at":"2024-12-18 17:35:09","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":434801,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5441902/v1/32707c3dae406e31ac195fb0.pdf"},{"id":71806130,"identity":"d1fd41da-c0f4-440e-a0d2-f6213a30673b","added_by":"auto","created_at":"2024-12-18 17:35:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":217638,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5441902/v1/6caf350816ebc146ba93a86a.pdf"},{"id":71806125,"identity":"312aaa9b-fc84-46d6-a521-9395873b285c","added_by":"auto","created_at":"2024-12-18 17:35:08","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":456573,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5441902/v1/70a3d1531558232e7a6e4472.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSystemic Inflammatory Response Markers Improve the Discrimination for Prognostic Model in Hepatocellular Carcinoma\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the seventh most common cancer and the second leading cause of cancer-related deaths worldwide. The overlap between incidence and mortality (830,000 deaths per year) reflects the dismal prognosis of this disease, likely due to the late diagnosis and the high recurrence rate. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Accurate prognostic evaluation of patients with HCC is essential to optimize treatment choices and improve the outcomes. However, stratifying death risk can be challenging because HCC is a unique malignancy that typically develops in the setting of pre-existing liver disease (primarily cirrhosis), thus raising the competing risk of dying from cancer progression and liver decompensation. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOver the last 20 years, several prognostic systems have been introduced, mainly based on variables commonly accepted as survival-associated factors in patients with HCC, such as tumour burden, residual liver function and general clinical conditions (performance status). [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Alpha-fetoprotein (AFP) is the only serum biomarker included in some of these models despite its suboptimal accuracy as a prognostic indicator. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Therefore, it is a compelling argument to identify other potential non-invasive biomarkers to refine the patient's prognosis.\u003c/p\u003e \u003cp\u003eIn recent years, increasing evidence has demonstrated that the systemic inflammatory response (SIR) plays a crucial role in the pathogenesis and progression of liver cirrhosis as well as in the development and advancement of different types of cancer. [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eCirculating SIR markers, including the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), or their combination (combined NLR and PLR: CNP) reflect the balance between pro-tumor inflammation associated with platelets and neutrophils and the anti-tumor immune response mediated by lymphocytes. These convenient, easily obtainable and repeatable biomarkers have been proven to have prognostic significance in patients with different cancers. [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] However, studies analyzing the prognostic accuracy of serum SIR markers in HCC patients do not allow definitive conclusions on their utility in clinical practice. [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eBased on these premises, we aimed to analyze the prognostic accuracy of NLR, PLR, and CNP in a large cohort of Caucasian patients with HCC included in the Italian Liver Cancer (ITA.LI.CA.) database.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThe ITA.LI.CA registry currently includes 7,782 patients consecutively diagnosed with HCC and managed in 24 Italian centres from January 1987 to December 2022. Participating institutions prospectively collect clinical data about patients with HCC and update them every two years. The group coordinator (F.T., Bologna University) regularly checks entries for consistency. Whenever a clarification or additional information is deemed necessary, relevant cases are returned to the recruiting centre before the final inclusion. The ITA.LI.CA database management conforms to the past and current Italian legislation regarding privacy, and the present study conforms to the ethical guidelines of the Declaration of Helsinki. The study was approved by the Institutional Review Board of the ITA.LI.CA coordinating centre, Alma Mater Studiorum University of Bologna, on 15th May 2012 (approval number 99/2012/O/Oss). Written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eIn the ITA.LI.CA registry, demographic data, aetiology of the liver disease and comorbidities are systematically collected. For this study, from the entire population of patients in the database, we selected those patients diagnosed with HCC between January 2000 and December 2022, with available pertinent laboratory data at the time of the first HCC treatment and a lag time of less than six months between HCC diagnosis and treatment. Exclusion criteria were as follows: (1) ongoing acute infection at the time of enrolment, (2) concomitant severe comorbidities (advanced renal, cardiac, pulmonary, and/or thyroid disorders), and (3) incomplete data.\u003c/p\u003e \u003cp\u003e Data from 1,043 HCC patients (from January 2000 to December 2018) and 1,243 HCC patients (from January 2019 to December 2023) were reviewed as training and internal validation cohorts, respectively. Treatment of chronic viral hepatitis was performed according to current guidelines at the time of enrollment. Diagnosis of liver cirrhosis was defined at the time of enrolment based on the combination of laboratory and imaging tests. The Child-Pugh class, Model for End-Stage Liver Disease (MELD) score, ascites and hepatic encephalopathy, and clinically significant portal hypertension (CSPH) were also recorded. Since hepatic venous pressure gradient (HVPG) measurement, the gold standard method for diagnosing CSPH, is not generally performed in clinical practice, the diagnosis was based on unequivocal signs of CSPH (splenomegaly, oesophageal varices, ascites and platelet count\u0026thinsp;\u0026lt;\u0026thinsp;100 x 10\u003csup\u003e9\u003c/sup\u003e/L).\u003c/p\u003e \u003cp\u003ePrimary laboratory data, such as white blood cell (WBC) count, neutrophil count, lymphocyte count, platelet (PLT) count, international normalized ratio (INR), C-reactive protein (CRP), albumin, total bilirubin, creatinine, and alpha-fetoprotein (AFP) are regularly registered in the database. The prognostic cut-off value of AFP was arbitrarily set at 400 ng/mL.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSIR biomarkers definition\u003c/h3\u003e\n\u003cp\u003eThe neutrophil-to-lymphocyte ratio (NLR) was calculated as absolute neutrophil count (the number of neutrophils/\u0026#120583;L) divided by absolute lymphocyte count (number of lymphocytes/\u0026#120583;L). Platelet-to-lymphocyte ratio (PLR) was calculated as absolute platelet count (the number of platelets/\u0026#120583;L) divided by absolute lymphocyte count (number of lymphocytes/\u0026#120583;L).\u003c/p\u003e\n\u003ch3\u003eDiagnosis and staging of HCC\u003c/h3\u003e\n\u003cp\u003e The diagnosis of HCC was made according to the guidelines available at the time of inclusion. Among the patients selected for this study, the diagnosis of HCC was confirmed by histology in 211 (19%) cases and based on radiologic criteria at imaging (4-phase multidetector computed tomography [CT]) or dynamic contrast-enhanced magnetic resonance [MRI] in the remaining cases. Dynamic imaging techniques assessed the location, size and number of nodules, macrovascular invasion (MVI), and extrahepatic spread (EHS). HCC was staged according to Barcelona Clinic Liver Cancer (BCLC) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and ITA.LI.CA staging systems [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The diagnosis of HCC recurrence was similar to that of the initial disease diagnosis. Treatment modalities for primary HCC and HCC recurrence followed the BCLC treatment guidelines.\u003c/p\u003e\n\u003ch3\u003eTreatment and follow-up\u003c/h3\u003e\n\u003cp\u003eAs far as HCC treatment is concerned, patients were classified according to the main (most effective) therapy performed in each patient following this hierarchical order: liver transplantation (LT), liver resection (LR), percutaneous radiofrequency ablation or other ablative techniques (ABL), transarterial chemoembolization/embolization or radioembolization (IAT, intra-arterial therapies), systemic therapies (mainly sorafenib, SOR) and best supportive care (BSC). Although all the treatments performed during the patient's clinical history are registered in the ITA.LI.CA database, only the main treatment was considered in this study. Overall survival (OS) was defined as the time interval from treatment to the date of death, last follow-up, or data censoring (i.e. 31st December 2018 for the training cohort and 31st December 2022 for the validation cohort). Recurrence-free survival (RFS) was defined as the time between treatment and the first evidence of disease recurrence and was evaluated in the subset of patients who underwent potentially curative therapies, LR, ABL and IAT.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were described using mean and standard deviation (SD) or median values and interquartile range (IQR) according to their distribution. The differences were compared using the Student's t-test and the Mann\u0026ndash;Whitney U test. Categorical variables were reported as the number of cases and percentages, and the differences were compared using the chi-square test. A histogram graphically described the distribution of NLR and PLR, whereas a scatterplot and the Spearman rho coefficient highlighted the correlation between biomarkers.\u003c/p\u003e \u003cp\u003eThe associations between NLR and PLR and clinical factors or staging systems known to be related to HCC prognosis, such as age at diagnosis, AFP, BCLC stage and Child-Pugh score, were analyzed using the Wilcoxon Rank Sum and Signed Rank Tests for dichotomous variables and the Kruskal-Wallis Rank Sum Test for categorical variables.\u003c/p\u003e \u003cp\u003eThe Cox proportional hazard model was used to test the prognostic role of NLR and PLR on both OS and RFS. In the first univariate analysis, NLR and PLR were tested as continuous variables after the linearity assumption was tested using fractional polynomials. In a second univariate analysis, NLR and PLR were tested as categorical variables dichotomized according to the best cut-off value that minimizes the p-value of hazard ratio (HR). According to the identified cut-offs, OS and RFS curves were estimated using the Kaplan\u0026ndash;Meier method and compared with a two-sided log-rank test.\u003c/p\u003e \u003cp\u003eFor each biomarker (and for both continuous effect and best cut-off categories), a multivariable analysis was performed using covariates: age, AFP, BCLC stage and Child-Pugh score. A shrinkage procedure with 95% CI was calculated using the bootstrap-percentile method to adjust for over-fitting HR estimates of best cut-off categories. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo evaluate the incremental clinical value and the discrimination properties of SIR biomarkers, we compared the basic clinical model with those including biomarkers (continuous or categorical) using C-statistic and integrated discrimination improvement (IDI) measures. Data were analyzed using R software version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and tumour characteristics of the enrolled population\u003c/h2\u003e \u003cp\u003eA total of 2,286 patients who met the eligibility criteria were enrolled. One thousand forty-three were included in the training cohort and 1,243 in the validation cohort. The baseline demographic, clinical and laboratory findings of HCC patients are summarised in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInflammatory markers and association with clinical prognostic factors\u003c/h3\u003e\n\u003cp\u003eThe median NLR and PLR were 2.22 (IQR 1.63\u0026ndash;3.18) and 87 (IQR 58\u0026ndash;124). Both biomarkers showed a skewed distribution and were positively correlated (rho\u0026thinsp;=\u0026thinsp;0.56) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAs far as concern the association between NLR and PLR and clinical prognostic factors (age, AFP, Child-Pugh score and BCLC stage), PLR correlated with age [PLR rho: 0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and its median value was significantly lower in patients with AFP\u0026thinsp;\u0026gt;\u0026thinsp;400 ng/mL than in those with AFP\u0026thinsp;\u0026gt;\u0026thinsp;400 ng/ml (84 [IQR 56\u0026ndash;119] vs 96 [IQR 65\u0026ndash;133], p\u0026thinsp;=\u0026thinsp;0.002). NLR was associated with the Child-Pugh score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas both biomarkers were associated with BCLC staging, progressively increasing with tumour burden \u003cem\u003e(\u003c/em\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for NLR and PLR, respectively (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSIR biomarkers are independent prognostic factors of OS\u003c/h2\u003e \u003cp\u003eAccording to fractional polynomial analysis, NLR was associated with OS as a logarithmic function [HR 1.61 (95% CI 1.39\u0026ndash;1.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)] while PLR was linearly associated with OS [HR 1.16 (95% CI 1.04\u0026ndash;1.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003epanel\u003c/b\u003e \u003cb\u003eA\u003c/b\u003e \u003cb\u003e\u0026amp;\u003c/b\u003e \u003cb\u003eC\u003c/b\u003e). The best cut-offs minimizing the p-value of HR in the training cohort were 1.45 for NLR and 188 for PLR, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003epanels\u003c/b\u003e \u003cb\u003eB\u003c/b\u003e \u003cb\u003e\u0026amp;\u003c/b\u003e \u003cb\u003eD\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEight hundred and forty-four patients (81.4%) had NLR\u0026thinsp;\u0026gt;\u0026thinsp;1.45, and 76 (7.3%) patients had PLR\u0026thinsp;\u0026gt;\u0026thinsp;188.\u003c/p\u003e \u003cp\u003eIn the univariate model, both biomarkers were associated with OS irrespective of shrinkage procedure (Shrunken coefficients: NLR HR 1.86, 95%CI 1.40\u0026ndash;2.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and PLR HR 1.57, 95%CI 1.08\u0026ndash;2.30, p\u0026thinsp;=\u0026thinsp;0.019, respectively). In the multivariate model adjusted for the clinical covariates reported to be prognostic (age, AFP, Child-Pugh score and BCLC stage), both NLR and PLR were significantly associated with OS (NLR: HR 1.65, 95%CI 1.18\u0026ndash;2.29, p\u0026thinsp;=\u0026thinsp;0.003, and PLR: HR 1,87, 95%CI 1.24\u0026ndash;2.81, p\u0026thinsp;=\u0026thinsp;0.003). By applying a shrinkage procedure to adjust for over-fitting HR estimates, both NLR\u0026thinsp;\u0026gt;\u0026thinsp;1.45 (HR 1.58, 95%CI 1.11\u0026ndash;2.28, p\u0026thinsp;=\u0026thinsp;0.014) and PLR\u0026thinsp;\u0026gt;\u0026thinsp;188 (HR 1.79, 95%CI 1.11\u0026ndash;2.90, p\u0026thinsp;=\u0026thinsp;0.018) remained associated with OS (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAn inflammation-based score was created by combining NLR with PLR (combined NLR-PLR score, CNP) and analyzed as a continuous and dichotomous variable after identifying the best cut-off. Patients were then classified as follows: those with both NLR and PLR below their respective cut-off entered in the CNP\u0026thinsp;=\u0026thinsp;0 group (n\u0026thinsp;=\u0026thinsp;197, 18.8%), those with either NLR or PLR above the cut-off were classified in the CNP\u0026thinsp;=\u0026thinsp;1 group (n\u0026thinsp;=\u0026thinsp;772, 74%), and those with both NLR and PLR above their respective cut-off were classified in the CNP\u0026thinsp;=\u0026thinsp;2 group (n\u0026thinsp;=\u0026thinsp;74, 7%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eWhen the study was censored (31st December 2023), the median follow-up times were 40 and 27 months in the training and validation cohorts, respectively.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the survival trees of OS in the training cohort. Median OS time was significantly higher in patients with NLR and PLR below the cut-off values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for NLR and p\u0026thinsp;=\u0026thinsp;0.00015 for PLR, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003epanels\u003c/b\u003e \u003cb\u003eA\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eB\u003c/b\u003e). Likewise, CNP stratified patients according to their prognosis since median OS was significantly lower in CNP 2 [26 months (95%CI 20\u0026ndash;41)] than in CNP 1 [38 months (95% CI 33\u0026ndash;42)] or CNP 0 [76.8 months, 95%CI 54.6- not achieved] patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003epanel\u003c/b\u003e \u003cb\u003eC\u003c/b\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilar results were observed in the validation cohort \u003cb\u003e(\u003c/b\u003e\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e. Patients with NLR and PLR below the cut-off values had significantly lower median OS time (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for NLR and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for PLR, respectively) (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e, \u003cb\u003epanels A and B)\u003c/b\u003e. CNP was able to stratify patients according to their prognosis since median OS was significantly lower in CNP 2 [18 months (95%CI 12.9\u0026ndash;38.5)] than in CNP 1 [45.3 months (95% CI 31.8 - NA)] or CNP 0 [NA months, 95%CI 41.4 - NA] patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cb\u003e(\u003c/b\u003e\u003cb\u003eSupplementary Fig.\u0026nbsp;2, panel C\u003c/b\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDiscrimination properties of SIR biomarkers\u003c/h2\u003e \u003cp\u003eThree different models were fitted to evaluate the potential role of NLR, PLR, and CNP in predicting survival, together with established prognostic factors. The basic model included standard prognostic factors: age, AFP, Child-Pugh score, and the BCLC staging system. The second model included all the abovementioned variables plus the biomarkers (log-transformed NLR and PLR) considered as continuous variables, with tests for possible interactions. The third model replaced the continuous biomarkers with the CNP score. This progression of models allows for a comparison of predictive accuracy and interpretability by assessing if the addition of serum SIR biomarkers (in continuous or composite form) significantly enhances survival prediction beyond traditional prognostic factors.\u003c/p\u003e \u003cp\u003eThe results are shown in \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e. Log NLR (HR 1.33, 95%CI 1.09\u0026ndash;1.62, p\u0026thinsp;=\u0026thinsp;0.004) and CNP (HR 2.69, 95%CI 1.79\u0026ndash;4.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with OS.\u003c/p\u003e \u003cp\u003eAdding Log-NLR to the basic model did not improve the reclassification index (IDI 0.8 [-0.1-2.2%], p\u0026thinsp;=\u0026thinsp;0.07). On the other hand, compared to the basic model, the addition of CNP significantly improves the model's reclassification index (IDI 1.3 [0.1\u0026ndash;2.7%], p\u0026thinsp;=\u0026thinsp;0.04). When we included the ITA.LI.CA staging system among baseline prognostic factors, the addition of both continuous biomarkers or CNP did not improve the reclassification index (IDI 0.8 [-0.1-1.7%], p\u0026thinsp;=\u0026thinsp;0.11 and IDI 0.8 [-0.1-2.3%], p\u0026thinsp;=\u0026thinsp;0.06, respectively) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). The prognostic performance of SIR markers was confirmed in the validation cohort (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRecurrence analysis\u003c/h2\u003e \u003cp\u003eA subsample of 871 patients who underwent curative treatments (LR, ABL or IAT) was used for the recurrence analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Three-hundred and seventy-nine recurrences were recorded during the follow-up, with a median RFS of 31 months (95%CI 26\u0026ndash;36). Median RFS time was lower in patients with NLR\u0026thinsp;\u0026gt;\u0026thinsp;1.45 than in patients with NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.45 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.027), whereas it did not significantly differ in patients with PLR above the cut-off (p\u0026thinsp;=\u0026thinsp;0.17) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003epanels\u003c/b\u003e \u003cb\u003eA\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eB\u003c/b\u003e). CNP was significantly associated with RFS (p\u0026thinsp;=\u0026thinsp;0.023) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003epanel\u003c/b\u003e \u003cb\u003eC\u003c/b\u003e\u003cb\u003e).\u003c/b\u003e However, none of the SIR markers had prognostic value in recurrence analysis in the validation cohort (\u003cb\u003edata not shown\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSIR markers are progressively gaining consensus as predictors of cancer survival, although the mechanisms by which they impact tumour biology remain unclear. Inflammatory cells, such as platelets and neutrophils, can contribute to tumour cell invasion into the peripheral blood. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Platelets could protect circulating tumour cells from shear stresses during circulation, induce epithelial-mesenchymal transition, and promote tumour cell extravasation to metastatic sites. [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Neutrophils can enhance the adhesion and seeding of tumour cells in distant organs through the secretion of circulating growth factors. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Conversely, lymphocytes are crucial in defence against tumours, dictating the host immune response to malignancy by inducing cytotoxic cell death and inhibiting tumour cell proliferation and migration. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe relationship between NLR and HCC prognosis was first described by Halazun \u003cem\u003eet al.\u003c/em\u003e, who demonstrated that an NLR\u0026thinsp;\u0026gt;\u0026thinsp;5 predicted poor OS and a high recurrence rate in patients undergoing LT for HCC. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] The prognostic performance of NLR was then confirmed in HCC patients, mainly of Asian ethnicity, who received curative or palliative therapy. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Likewise, other reports demonstrated that elevated pre-treatment PLR values predicted an unfavourable OS (HR\u0026thinsp;=\u0026thinsp;1.73; 95% CI 1.46\u0026ndash;2.04; p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001) and RFS (HR\u0026thinsp;=\u0026thinsp;1.30; 95% CI 1.06\u0026ndash;1.60; p\u0026thinsp;=\u0026thinsp;0.01) irrespective of therapy.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Nevertheless, these biomarkers are far from routinely used in clinical practice due to the heterogeneity in study design, sample size and lack of standardized cut-off values, usually set up by the receiver operating characteristic (ROC) method. Indeed, this conventional statistical approach, widely used and easy to apply for determining an \"optimal\" cut-off in binary outcomes, can lead to a loss of information, reduced statistical power, and an increased risk of false positives. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOur study used the multivariable fractional polynomial and minimum p-value methods that considered the effect of each possible functional form of NLR and PLR, or cut-off points, for survival analysis. These approaches enabled us to identify the cut-off most strongly correlated with the outcomes (OS and RFS). By applying these cut-offs (1.45 for NLR and 188 for PLR), we could confirm that NLR and PLR effectively stratify HCC patients in terms of prognosis. Indeed, patients with biomarker values above their respective cut-off had a median OS time significantly lower than their counterparts (NLR, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, and PLR p\u0026thinsp;=\u0026thinsp;0.00015). Furthermore, at univariable and multivariable analysis adjusted for clinical covariates associated with HCC prognosis, both NLR and PLR remained independent prognostic factors for OS.\u003c/p\u003e \u003cp\u003eSince our cut-offs remain highly data-dependent, carrying with a serious risk of the type I error and an overestimation of the effect of the prognostic value in absolute terms, we also applied a bootstrap resampling approach that, together with a shrunk estimate, allowed us to obtain confidence intervals with the desired coverage. Notably, even after applying the shrinkage procedure, both NLR and PLR remained significant predictors of survival. Interestingly, when we tested the prognostic performance of CNP in our population, it was able to stratify patients into three groups with different median OS, confirming that patients with both NLR and PLR above the cut-off had a worse prognosis.\u003c/p\u003e \u003cp\u003eGenerally, even when a biomarker has been proven to predict a disease outcome, it remains to be demonstrated whether it can enhance survival prediction compared to commonly used prognostic models (such as cancer stage) and, therefore, if it deserves routine measurement.\u003c/p\u003e \u003cp\u003eThe discrimination of a risk prediction model, i.e. the ability to differentiate between individuals who will experience the event of interest and those who will not, is typically assessed using the area under the ROC curve (AUC). However, the AUC has been criticized for its limited sensitivity when comparing models, especially if the baseline model already performs well. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] In contrast, IDI is not dependent on risk categorization but considers changes in predicted risk, overcoming some of the limitations of AUC. Notably, adding CNP to the survival prediction model, including age, AFP levels, and BCLC staging, improved the IDI value (0.013, p\u0026thinsp;=\u0026thinsp;0.04), suggesting that CNP significantly enhanced the model's ability to predict OS.\u003c/p\u003e \u003cp\u003eRegarding HCC recurrence, literature data on the prognostic significance of NLR and PLR are somewhat controversial. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] In our population, although NLR and CNP significantly predicted RFS in the training cohort, their prognostic performance was not confirmed in the validation cohort.\u003c/p\u003e \u003cp\u003eIt is plausible that the shorter median of the observation period in the validation cohort may have obscured the detection of recurrence events. Consequently, this may have led to an underestimation of the true recurrence rate, as cases that would potentially manifest later remain unobserved.\u003c/p\u003e \u003cp\u003e In recent years, the approval of antiprogrammed cell death-1 (PD-1) antibodies, which act as immune checkpoint inhibitors (ICIs), has revolutionized the treatment landscape for HCC. We have not had long-term observations of patients undergoing ICI therapy. However, a recent pooled meta-analysis of 44 studies involving 5,322 patients confirmed the predictive value of baseline SIR biomarkers, such as NLR, for OS also in HCC patients receiving ICI treatment (HR: 1.951, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe are aware that some shortcomings may have influenced our study. First, this is a retrospective study, so a potential selection bias and some unintended biases are predictable. Second, we lack external validation although the bootstrap resampling method used in the training set and the internal validation, at least in part, overcomes this limitation, allowing us to establish a stable prognostic \"multiparametric\" model taking tumour burden, liver function and inflammatory status into account. Lastly, since neutrophil, platelet, and lymphocyte levels are influenced by infections, inflammation in other tissues, and medications taken before HCC treatment, these factors should be considered when interpreting NLR and PLR measurements.\u003c/p\u003e \u003cp\u003eOn the other hand, we believe that including a large cohort of HCC patients managed with different strategies, extended follow-up, and a rigorous statistical approach strengthens our results. Indeed, to the best of our knowledge, this is the largest Western series of HCC patients in whom the reliability of pre-treatment SIR markers in predicting OS and RFS has been rigorously tested and validated.\u003c/p\u003e \u003cp\u003eIn conclusion, our study showed that NLR, PLR, and their combination, CNP, are reliable predictors of prognosis in patients with HCC, enhancing the accuracy of traditional factors like cancer stage and liver function. Thus, due to their non-invasiveness, ease of determination, repeatability, and low cost, these biomarkers are strong candidates for improving prognosis prediction in HCC patients. External validation by prospective and well-powered studies would be needed before their routine adoption in clinical practice.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand ethical standards\u003c/strong\u003e: The authors have no relevant conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Human and Animal Rights\u003c/strong\u003e: The ITA.LI.CA database management conforms to the past and current Italian legislation regarding privacy, and the present study conforms to the ethical guidelines of the Declaration of Helsinki. The study was approved by the Institutional Review Board of the ITA.LI.CA coordinating centre, Alma Mater Studiorum University of Bologna, on 15th May 2012 (approval number 99/2012/O/Oss).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e: Written informed consent was obtained from all participants.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthors contributions:\u003c/h2\u003e \u003cp\u003eConceptualization: A Rocco, C Sgamato and F Pelizzaro; Methodology: V Simeon, P Chiodini; Formal analysis and investigation: A Rocco, C Sgamato, F Pelizzaro, V Simeon, P Chiodini; Data Curation: A Rocco, C Sgamato, F Pelizzaro, P Coccoli, D Compare, E Pinto, G Palano, FG Foschi, G Raimondo, G Missale, G Svegliati-Baroni, F Trevisani, E Caturelli, MR Brunetto, G Vidili, A Masotto, D Magalotti, C Campani, A Gasbarrini, F Azzaroli, GL Rapaccini, B Stefanini, R Sacco, A Mega, EG Giannini, G Cabibbo, M Di Marco, M Guarino, P Chiodini, F Farinati, G Nardone, Italian Liver Cancer (ITA.LI.CA) group; Writing - original draft preparation: A Rocco, C Sgamato, F Pelizzaro, V. Simeon; Writing Review and editing: P Coccoli, D Compare, E Pinto, G Palano, FG Foschi, G Raimondo, G Missale, G Svegliati-Baroni, F Trevisani, E Caturelli, MR Brunetto, G Vidili, A Masotto, D Magalotti, C Campani, A Gasbarrini, F Azzaroli, GL Rapaccini, B Stefanini, R Sacco, A Mega, EG Giannini, G Cabibbo, M Di Marco, M Guarino, P Chiodini, F Farinati, G Nardone; Supervision: P Chiodini, F Farinati and G Nardone. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eOther members of the ITA.LI.CA group: \u003cem\u003eDepartment of Medical and Surgical Sciences, Semeiotics Unit, University of Bologna, Bologna\u003c/em\u003e: Maurizio Biselli, Paolo Caraceni, Annagiulia Gramenzi, Lorenzo Lani, Davide Rampoldi, Nicola Reggidori, Valentina Santi, Benedetta Stefanini. \u003cem\u003eAzienda Ospedaliero-Universitaria S. Orsola-Malpighi, Internal Medicine\u0026ndash;Piscaglia Unit, Bologna\u003c/em\u003e: Alessandro Granito, Luca Muratori, Fabio Piscaglia, Vito Sansone, Francesco Tovoli. \u003cem\u003eDepartment of Surgical and Medical Sciences, Gastroenterology Unit, Alma Mater Studiorum\u0026ndash;University of Bologna, Bologna\u003c/em\u003e: Elton Dajti, Giovanni Marasco, Federico Ravaioli. \u003cem\u003eDepartment of Specialist, Diagnostic and Experimental Medicine, Radiology Unit, University of Bologna, Bologna\u003c/em\u003e: Alberta Cappelli, Rita Golfieri, Cristina Mosconi, Matteo Renzulli. \u003cem\u003eDepartment of Surgery, Oncology and Gastroenterology, Gastroenterology Unit, University of Padova, Padova\u003c/em\u003e: Elisa Pinto, Giorgio Palano, Maria Piera Kitenge, Federica Bertellini. \u003cem\u003eGastroenterology and Digestive Endoscopy Unit, Foggia University Hospital, Foggia\u003c/em\u003e: Ester Marina Cela, Antonio Facciorusso. \u003cem\u003eDepartment of Internal Medicine, Gastroenterology Unit, University of Genova, IRCCS Policlinico San Martino, Genova\u003c/em\u003e: Giulia Pieri, Maria Corina Plaz Torres, Andrea Pasta. \u003cem\u003eInternal Medicine and Gastroenterology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Universit\u0026agrave; Cattolica del Sacro Cuore, Roma\u003c/em\u003e: Nicoletta de Matthaeis, Francesca Romana Ponziani. \u003cem\u003eLiver Injury and Transplant Unit, Polytechnic University of Marche, Ancona\u003c/em\u003e: Gloria Allegrini. \u003cem\u003eGastroenterology Unit, Belcolle Hospital, Viterbo\u003c/em\u003e: Giorgia Ghittoni, Valentina Lauria, Giorgio Pelecca. \u003cem\u003eVascular and Interventional Radiology Unit, Belcolle Hospital, Viterbo\u003c/em\u003e: Fabrizio Chegai, Armando Raso, Alessio Bozzi. \u003cem\u003eMedical Oncology Unit, Belcolle Hospital, Viterbo\u003c/em\u003e: Marta Schirripa \u003cem\u003eDepartment of Medicine and Surgery, Infectious Diseases and Hepatology Unit, University of Parma and Azienda Ospedaliero-Universitaria of Parma, Parma\u003c/em\u003e: Elisabetta Biasini, Andrea Olivani. \u003cem\u003eGastroenterology Unit, IRCCS Sacro Cuore Don Calabria hospital, Negrar\u003c/em\u003e: Alessandro Inno, Fabiana Marchetti. \u003cem\u003eDepartment of Health Promotion, Mother \u0026amp; Child Care, Internal Medicine \u0026amp; Medical Specialties, PROMISE, Gastroenterology \u0026amp; Hepatology Unit, University of Palermo, Palermo\u003c/em\u003e: Ciro Celsa, Paolo Giuffrida, Caterina Stornello, Mauro Grova, Carmelo Marco Giacchetto, Gabriele Rancatore, Maria Vittoria Grassini, Roberta Ciccia, Alessandro Grova, Mauro Salvato. \u003cem\u003eDepartment of Clinical and Experimental Medicine, Clinical and Molecular Hepatology Unit, University of Messina, Messina\u003c/em\u003e: Maria Stella Franz\u0026egrave;, Carlo Saitta. \u003cem\u003eDepartment of Medicine Surgery and Pharmacy, Centralized Day Hospital of the medical area, University of Sassari, Azienda Ospedaliero-Universitaria di Sassari, Sassari\u003c/em\u003e: Marco Arru, Assunta Sauchella, Maria Grazia Serra. \u003cem\u003eDepartment of Internal Medicine, Ospedale per gli Infermi di Faenza, Faenza IRCCS Meldola\u003c/em\u003e: Vittoria Bevilacqua, Alberto Borghi, Fabio Conti, Lucia Napoli, Luca Frassineti, Maria Teresa Migliano, Nicola Reggidori. \u003cem\u003eDepartment of Experimental and Clinical Medicine, Internal Medicine and Hepatology Unit, University of Firenze, Firenze\u003c/em\u003e: Fabio Marra, Valentina Adotti, Martina Rosi. \u003cem\u003eDepartment of Clinical Medicine and Surgery, Hepato-Gastroenterology Unit, University of Napoli \"Federico II\", Napoli\u003c/em\u003e: Libera Esposito. \u003cem\u003eDepartment of Clinical Medicine and Surgery, Gastroenterology Unit, University of Napoli \"Federico II\", Napoli\u003c/em\u003e: Filomena Morisco, Valentina Cossiga, Mario Capasso. \u003cem\u003eDepartment of Clinical and Experimental Medicine, Hepatology and Liver Physiopathology Laboratory, University Hospital of Pisa, Pisa\u003c/em\u003e: Veronica Romagnoli.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Int Immunopharmacol. 2023;118.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\n\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"hepatology-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hepi","sideBox":"Learn more about [Hepatology International](https://www.springer.com/journal/12072)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/hepi/default.aspx","title":"Hepatology International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, survival, recurrence-free survival, liver cancer, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-5441902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5441902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/purpose of the study: \u003c/strong\u003eWe aimed to evaluate the performance of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and their combination (combined NLR-PLR, CNP) on overall survival (OS) and recurrence-free survival (RFS) in a large cohort of unselected hepatocellular carcinoma (HCC) patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTraining and validation cohort data were retrieved from the Italian Liver Cancer (ITA.LI.CA) database. The optimal cut-offs of NLR and PLR were calculated according to the multivariable fractional polynomial and the minimum p-value method. The continuous effect and best cut-off categories of NLR and PLR were analyzed using multivariable Cox regression analysis. A shrinkage procedure adjusted over-fitting HR estimates of best cut-off categories. C-statistic and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination properties of the biomarkers when added to clinical survival models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e2,286 patients were split into training (n=1,043) and validation (n=1,243) cohorts.The optimal cut-offs for NLR and PLR were 1.45 and 188, respectively. NLR (HR 1.58, 95%CI 1.11-2.28, p=0.014) and PLR (HR 1.79, 95%CI 1.11-2.90, p=0.018) were independent predictors of OS. When added to the clinical prognostic model, including age, alpha-fetoprotein (AFP), CHILD-Pugh score and Barcelona Clinic Liver Cancer (BCLC) staging system, CNP had a significant incremental value in predicting OS (IDI 1.3%, p=0.04). Data were confirmed in the validation cohort. NLR (p=0.027) and CNP (p=0.023) predicted RFS in the training cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e NLR, PLR, and CNP independently predicted shorter OS in HCC patients. The addition of CNP into the survival prediction model significantly improved the model's predictive accuracy for OS.\u003c/p\u003e","manuscriptTitle":"Systemic Inflammatory Response Markers Improve the Discrimination for Prognostic Model in Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 17:35:03","doi":"10.21203/rs.3.rs-5441902/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2024-11-29T19:54:30+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-11-16T12:22:22+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-16T05:11:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-15T11:50:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Hepatology International","date":"2024-11-14T08:52:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"hepatology-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hepi","sideBox":"Learn more about [Hepatology International](https://www.springer.com/journal/12072)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/hepi/default.aspx","title":"Hepatology International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4a9378a6-f07e-4429-921a-0a4e2d71285f","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T15:58:48+00:00","versionOfRecord":{"articleIdentity":"rs-5441902","link":"https://doi.org/10.1007/s12072-025-10806-6","journal":{"identity":"hepatology-international","isVorOnly":false,"title":"Hepatology International"},"publishedOn":"2025-03-25 15:57:02","publishedOnDateReadable":"March 25th, 2025"},"versionCreatedAt":"2024-12-18 17:35:03","video":"","vorDoi":"10.1007/s12072-025-10806-6","vorDoiUrl":"https://doi.org/10.1007/s12072-025-10806-6","workflowStages":[]},"version":"v1","identity":"rs-5441902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5441902","identity":"rs-5441902","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}