C-reactive protein to albumin ratio combined with the Systemic Inflammatory Response Index predicts the prognosis of patients undergoing radical hepatectomy

C-reactive protein to albumin ratio combined with the Systemic Inflammatory Response Index predicts the prognosis of patients undergoing radical hepatectomy | 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 C-reactive protein to albumin ratio combined with the Systemic Inflammatory Response Index predicts the prognosis of patients undergoing radical hepatectomy Shao-Long Lu, Qing-Yuan Zhang, Yuan-Quan Zhao, Hua-Lin Wu, Jie Lin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5924552/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2025 Read the published version in BMC Cancer → Version 1 posted 11 You are reading this latest preprint version Abstract Purpose: Many prognostic scores based on systemic inflammation have been developed. Most of these prognostic scores have been shown to influence the prognosisof hepatocellular carcinoma (HCC) patients. This research aims to develop a novel prognostic system based on inflammatory markers for patients with HCC. Patients and Methods: This researchencompassed 920 HCC patients who underwent potentially radical surgical resection. We employed receiver-operating characteristic (ROC) curve analysis to determine the optimal cutoff value for the preoperative inflammatory prognostic score. Univariate and multivariate Cox regression analyses were conducted to pinpoint features that significantly influence outcomes for patients with HCC. We employed a calibration curve and decision curve analysis (DCA) to appraise the application of the nomogram. Results: The multivariate Cox regression identified that systemic immunoinflammatory response index (SIRI), C-reactive protein-albumin ratio (CAR), tumor size, hepatitis B virus (HBV)-DNA, prothrombin time, microvascular invasion, macroscopic vascular invasion, and Edmondson-Steiner grade were all independent predictors of overall survival (OS). The predictive accuracy of the nomogram for estimating 1-, 3-, and 5-year OS was measured by the area under the receiver operating characteristic curve (AUC). Inthe training cohort, the AUC scores for the 1-, 3-, and 5-year OS were 0.815, 0.805, and 0.776. For the validation cohort, the respective AUC scores were 0.814, 0.737, and 0.730. Additionally, our nomogram shows a high capacity for distinguishing between different risk groups and is practical for clinical use. Conclusion: The nomogram demonstrates strong predictive performance for the 1-, 3-, and 5-year OS of HCC patients undergoing radical surgery, surpassing BCLC and CNLC staging systems in ability to assess patient prognosis. systemic immunoinflammatory response index C-reactive protein-albumin ratio hepatocellular carcinoma prognosis nomogram Figures Figure 1 Figure 2 Figure 3 Introduction Liver cancer ranks as the sixth most prevalent type of cancer worldwide and ranks third among the leading causes of death from cancer. Hepatocellular carcinoma (HCC) is responsible for about 75 to 85 percent of primary liver cancer diagnoses [1]. Currently, a wide array of therapeutic options for HCC includes surgery, ablation, transarterial chemoembolization, hepatic artery infusion chemotherapy, and other targeted therapies. However, surgery continues to be the main therapeutic approach for those with a diagnosis of HCC [2]. The survival prospects for HCC patients continue to be unfavorable, and the 5-years age-standardized relative survival rate of Chinese HCC patients is only 12.1% [3]. Research indicates that HCC patients almost universally have a background of chronic inflammation, regardless of the cause. In patients with HCC, approximately 80 to 90 percent suffer from cirrhosis due to chronic liver inflammation. Chronic liver inflammation leads to liver cell damage, and while the liver has a robust capacity for regeneration, this leads to a continuous cycle of cell death and regeneration. These can result in liver fibrosis and an increased rate of DNA mutations, thereby elevating the risk of malignant progression. Moreover, chronic inflammation induces changes in the liver's immune system, such as reducing the ratio of M1/M2 tumor-associated macrophages and promoting the production of pro-tumorigenic cytokines, which can help cancer cells sidestep immune surveillance. Therefore, tumor-associated inflammation is essential in tumor initiation, growth, and metastasis [4-6]. And more and more inflammation-related markers are being confirmed as indicators that can predict the outcomes for HCC patients. Most of them are composed of two or three combinations of neutrophils, lymphocytes, monocytes, C-reactive protein (CRP), platelets, and albumin. Such as modified glasgow prognostic score (mGPS) [7], C-reactive protein-albumin ratio (CAR) [8], platelet-lymphocyte ratio (PLR) [9], monocyte-lymphocyte ratio (MLR) [10], neutrophil-lymphocyte ratio (NLR) [11], systemic Inflammatory response Index (SIRI) [12], systemic Immune-inflammation index (SII) [13] and prognostic nutritional index (PNI) [14]. These markers can reflect the inflammatory and immune status of patients. In this research, we developed a predictive tool, the nomogram, integrating the aforementioned inflammatory markers with relevant clinical and tumor characteristics, which aims to improve the accuracy of prognostic estimations for HCC patients. 1 Patients and Methods 1.1 Patients The subjects of this study were patients screened at the Affiliated Tumor Hospital of Guangxi Medical University who underwent radical hepatectomy from April 2014 through June 2021. Radical hepatectomy involved completely removing visible tumors with no tumor cells remaining at the resection edge. Inclusion criteria: (1) the first surgery; (2) pathology confirmed for HCC, (3) no other infections; (4) Child-Pugh A or B; (5) Successful follow-up. Exclusion criteria: (1) history of other cancers; (2) Lack of complete preoperative laboratory data and clinical data. We included a total of 920 HCC patients in our screening process. 1.2 Data Collection Clinical data included preoperative liver function parameters, neutrophils, monocytes, lymphocytes), prothrombin time (PT), alpha-fetoprotein (AFP) level and c-reactive protein (CRP) level, radiologically reported tumor characteristics (size, number, macrovascular invasion), postoperative pathological examination, and relevant medical records. 1.3 Definition of Markers The definition of mGPS is consistent with previous studies [5]. The inflammation-related indicators in peripheral blood were calculated by the formula: CAR: CRP/albumin; PLR: platelet/lymphocyte; MLR: monocytes/lymphocytes; NLR: neutrophils/lymphocytes; SIRI: neutrophil*monocyte/lymphocyte; SII: platelets*neutrophils/lymphocytes; PNI: (lymphocytes*5) + albumin. 1.4 Follow-up For patients with HCC who had undergone radical surgery, we recommend a follow-up schedule that imaging and blood tests at one month post-surgery, with subsequent testing every three months for five years, and then semi-annual assessments from the sixth year. Patients who did not attend our hospital at scheduled times were followed up by telephone to obtain treatment information and living conditions. The endpoint of our research was overall survival (OS), which we specified as the time from the initial diagnosis of HCC to either the final follow-up appointment or the patient's demise. 1.5 Statistical Analysis Using SPSS 27.0 software (SPSS, Chicago, IL, USA) and R 4.3.2 (https://www.r-project.org/) for statistical analysis. P values < 0.05 for the difference was statistically significant. The R package CBCgrps [15] was used for baseline data statistics. For numerical data, we presented values as either the median with interquartile ranges or the mean with standard deviations. For categorical data, we provided the total number of cases and their corresponding percentages. We determined the most effective cutoff point for outcome prediction by applying the Youden index, which is computed from the receiver-operating characteristic (ROC) curve, and then the numerical variables were converted to categorical variables. Tolerance and variance inflation factor （VIF） value is obtained by using the SPSS software, to estimate the multicollinearity between the variables. In line with previous research, we identified multicollinearity when a variable had a tolerance 5. Such factors were omitted from the subsequent statistical analysis to ensure accuracy [16]. We conducted both univariate and multivariate analyses using Cox regression, and calculated the corresponding 95% confidence intervals (CI) for the results. Among them, the results of univariate analysis of P < 0.1 data into multivariate analysis, the multivariate analysis results of P < 0.05 data into the final build of the nomogram. The OS was analyzed using the Kaplan-Meier (K-M) method and the differences in survival curves were tested with the log-rank test.. We assessed efficacy of the recognition of nomogram by calculating the area under the receiver-operating characteristic curve (AUC) and the consistency index (C-index). Additionally, the calibration chart and decision curve analysis (DCA) were employed to determine the calibration and net benefit of the nomogram. 2 Results 2.1 Patient characteristics Altogether, 920 patients with HCC met the screening criteria. Median follow-up was 33 months (2-74 months). In the primary cohort, the OS rates for patients were as follows: 87.9% at one year, 67.9% at three years, and 59.6% at five years. For the training cohort, the OS rates were 88.8% at one year, 68.6% at three years, and 62.1% at five years. For the validation cohort, the OS rates were 85.8% at one year, 66.0% at three years, and 53.1% at five years (Supplementary figure 1). Except for age, the patient characteristics in different cohorts were found to be statistically similar, with no significant differences identified (Table 1). Table 1 Baseline characteristics of the patients 2.2 ROC analysis of inflammatory indicators The following inflammatory indicators were selected for analysis: CAR, MLR, NLR, PLR, PNI, SII, SIRI, mGPS. We established the optimal cutoff value by calculating the Youden index, which allowed us to reclassify the numerical variables into categorical variables. The ROC curve was constructed and the value of AUC was obtained. Among them, the optimal cutoff value of CAR was 0.105, with an AUC of 62.3% (95% CI: 58.4% - 66.2%); the optimal cutoff value of PLR was 146.5, with an AUC of 59.2% (95% CI: 55.2% - 63.2%); the optimal cutoff value of MLR was 0.245, with an AUC of 59.5% (95% CI: 55.7% - 63.3%); the optimal cutoff value of NLR was 2.425, with an AUC of 60.8% (95% CI: 56.9% - 64.7%); the optimal cutoff value of PNI was 47.5, with an AUC of 55.4% (95% CI: 51.4% - 59.3%); the optimal cutoff value of SII was 504.5, with an AUC of 60.5% (95% CI: 56.6% - 64.4%); the optimal cutoff value of SIRI was 1.075, with an AUC of 62.1% (95% CI: 58.2% - 66.0%); and the AUC of mGPS was 62.4% (95% CI: 58.4% - 66.3%) (Supplementary figure 1 and Supplementary table 1). 2.3 Factors affecting the overall survival Univariate Cox regression analysis showed that, hepatitis B virus (HBV) DNA > 100IU/ml, prothrombin time > 13s, AFP level > 400ng/mL, AST > 40U/L, ascites, tumor size, microvascular invasion (MVI), Edmondson–Steiner grading, macrovascular vascular invasion, mGPS and high level of the CAR, MLR, NLR, PLR, SII, SIRI and low level of PNI are poor prognostic factors for HCC patients after radical surgery (Supplementary table 2). Multicollinearity analysis of the above factors showed that tolerance > 0.1 and VIF 100IU/ml, prothrombin time > 13s, tumor size, microvascular invasion, Edmondson-Steiner grading, macroscopic vascular invasion, high level of the CAR and SIRI were independent prognostic factors for HCC patients (Table 2). Table 2 Univariate and multivariate analysis of variables affecting overall survival 2.4 Construction and validation of Nomogram According to the findings from the multivariate Cox regression analysis, HBV-DNA, prothrombin time, tumor size, microvascular invasion, Edmondson-Steiner grade, macroscopic vascular invasion, CAR, and SIRI were used to construct a nomogram for predicting OS at 1-, 3-, and 5-year in HCC patients treated with radical resection (Figure 1). The nomogram's C-index was 0.763 (95% CI:0.732-0.794). The nomogram's predictive accuracy, with the AUC as the measure, was 0.815, 0.805, and 0.776 for 1-, 3-, and 5-year OS in the training cohort. Corresponding values in the validation cohort were 0.814, 0.737, and 0.730 (Supplementary figure 3). In the training and validation cohorts, the calibration curves for OS at 1-, 3-, and 5-year matched well with their standard lines (Figure 2A, B). The nomogram demonstrated higher predictive accuracy compared to both the CNLC and BCLC staging systems, as evidenced by the time-dependent AUC curve (Figure 2C, D). Patients were categorized into various risk groups using the total risk scores derived from the nomogram, which was employed to assess its ability to distinguish between different survival outcomes. The optimal cut-off scores were determined automatically by the X-tile software. The patients in the high-risk category experienced the poorest OS, while those in the low-risk category had the best OS. The same results were acquired when the cutoff values were applied to the validation cohort (Figure 2E, F). This indicates that our nomogram has good stratification ability. Compared with the CNLC and BCLC staging systems, the nomogram offers superior clinical benefits and demonstrates significant clinical application value, as evidenced by its performance in DCA (Figure 3). 3 Discussion An increasing body of research indicates a close link between inflammation and cancer. Inflammation is the body's response to tissue injury, If the body is in a long-term inflammatory state, it will lead to cell mutation and proliferation, which will lead to the occurrence and promote the development of tumors [6]. Inflammatory cells reflect the prognosis of patients to some degree [17]. Neutrophils have been linked to a poor prognosis in HCC patients due to their strong immunosuppressive activity and damage to the function of T cells and antigen-presenting cells, which can promote the invasion, migration and angiogenesis of cancer cells. In addition, the tumor origin of transforming growth factor β (TGF-β) can be neutrophils from antitumor "N1" phenotype is converted into a "N2" phenotype that promote tumor, which further illustrates the carcinogenic potential of neutrophils [17,18]. In response to tumor-released chemokines, circulating monocytes aggregated in the tumor stroma are transformed into tumor-associated macrophages (TAM). The cytokines including TGF-β, vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF) and tumor necrosis factor (TNF) secreted by TAM affect the tumor microenvironment and stimulate the development of blood vessels in tumors, increase cell proliferation, and facilitate the spread of cancer [19,20]. Lymphocytes play a crucial role in immune surveillance and response to tumors; an increased presence of tumor-infiltrating lymphocytes is associated with a good prognosis [21]. Therefore, SIRI can reflect the changes of the three factors at the same time, and the increase of its level reflects the activity of tumor-related inflammation, suggesting a poor prognosis of HCC patients. CRP is a kind of acute phase protein, which can indirectly form a microenvironment conducive to tumor cell angiogenesis [15]. It has been suggested that CRP is not only produced by normal liver in response to various tumor stimuli, but also by hepatocellular carcinoma tissues [22]. Albumin, produced by the liver, reflects not only liver function but also the patient's nutritional status. As an ideal biomarker, CAR has not only been proved to be related to patient prognosis, but also has the potential to be used as HCC screening and tumor staging prediction [8,23]. Multivariate cox regression analysis showed that except the tumor characteristics, HBV-DNA is an independent prognostic factors for patients with HCC. An increase in HBV-DNA replication indicates the activity of HBV infection, it is generally believed that the carcinogenic mechanisms of HBV include genetic damage caused by immune-mediated inflammation, induction of oxidative stress, etc. A more important carcinogenic mechanism is the integration of HBV-DNA in liver cell genes [24]. Integration of the HBV genome into the host genome can activate cellular genes with oncogenic potential. This activation allows for a selective growth advantage and clonal expansion of hepatocytes, ultimately leading to malignancy. In addition, the genetic instability resulting from HBV integration is considered a key factor in the pathogenesis of HCC [25]. Therefore, in this research, readily available preoperative inflammatory markers were selected to construct a prognostic model for HCC patients treated with radical resection. The results showed that among these inflammatory markers, SIRI and CAR were independent prognostic factors in HCC patients. The nomogram constructed by SIRI, CAR, HBV-DNA, prothrombin time, tumor size, microvascular invasion, Edmondson-Steiner grade and macroscopic vascular invasion can effectively predict the OS of HCC patients. In addition, the nomogram had a higher AUC value, a better calibration curve, and more net benefit compared to the BCLC and CNLC Staging system. In clinical practice, the nomogram provided a more direct prognosis prediction tool for clinicians and patients. The advantage of this research had the following points. 1) While previous studies targeted only a few inflammatory indicators, our research explored a broader range of the reported inflammatory indicators. 2) SIRI was composed of peripheral neutrophils, monocytes and lymphocytes, while CAR was composgureed of C-reactive protein and albumin. A nomogram that included both can provide a more accurate and comprehensive representation of the patient's inflammatory and immune response. 3) Earlier researches were limited to investigating how inflammatory markers predict patient outcomes. The nomograms were not compared with current tumor staging systems to evaluate their predictive efficacy. In our research, by combining inflammatory biomarkers with relevant clinical and tumor characteristics, the nomogram established by us can predict the OS of patients more accurately than the common staging system. It was important to note that this research had certain limitations. Being a retrospective analysis, it was subject to the constraints of a smaller sample size, which may lead to inherent selection bias. Secondly, due to the limitation of region and population, most of the research subjects were HBV-related HCC, and HCC patients caused by other causes were not involved. Future validation of our research's findings will require large-scale, prospective, multicenter trials that also consider various causes of HCC. 4 Conclusion In conclusion, the nomogram constructed based on SIRI, CAR, and related clinical and tumor features in this research has high predictive efficacy for OS in HCC patients treated with radical resection. The nomogram can guide clinical treatment decisions and prognosis assessment for HCC patients. Declarations Author Contributions SLL, QYZ, JC, XBW are responsible for analysis, writing and revises. YQZ participate in the writing. YQZ, HLW, JL, ZJQ, and PZ participate in data collecting and screening. Conflict of Interest The authors declare that they have no competing interestsReferences ETHICS STATEMENT This study was a retrospective study, and the ethical approval of the Ethics Committee of the Cancer Hospital of Guangxi Medical University and the exemption of informed consent were obtained before the study began. Acknowledgments: We thank everyone who provided support for this study. Data Availability Statement: The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors. Funding This study was supported by grants from the National Natural Science Foundation of China (82260345), Guangxi Key Technologies R&D Program (Guike AB22080066), Guangxi Science and Technology Base and Talent Specialization(Guike 2024AC43021), the "139" Plan for Training High-level Backbone Medical Talents in Guangxi (No. G202003008), the Guangxi Medical University Outstanding Young Talents Training Program, and Guangxi Natural Youth Science Foundation (2024JJB140755). References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin . 2021;71(3):209-249. 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Neutrophils: Driving inflammation during the development of hepatocellular carcinoma. Cancer Lett . 2021;522:22-31. Dou L, Shi X, He X, Gao Y. Macrophage Phenotype and Function in Liver Disorder. Front Immunol . 2020;10:3112. Sung PS. Crosstalk between tumor-associated macrophages and neighboring cells in hepatocellular carcinoma. Clin Mol Hepatol . 2022;28(3):333-350. Labani-Motlagh A, Ashja-Mahdavi M, Loskog A. The Tumor Microenvironment: A Milieu Hindering and Obstructing Antitumor Immune Responses. Front Immunol . 2020;11:940. Carr BI, Akkiz H, Guerra V, et al. C-reactive protein and hepatocellular carcinoma: analysis of its relationships to tumor factors. Clin Pract (Lond) . 2018;15(Spec Issue):625-634. Lin N, Li J, Ke Q, Wang L, Cao Y, Liu J. Clinical Significance of C-Reactive Protein to Albumin Ratio in Patients with Hepatocellular Carcinoma: A Meta-Analysis. Dis Markers . 2020:4867974. Yeh SH, Li CL, Lin YY, et al. Hepatitis B Virus DNA Integration Drives Carcinogenesis and Provides a New Biomarker for HBV-related HCC. Cell Mol Gastroenterol Hepatol . 2023;15(4):921-929. Bousali M, Papatheodoridis G, Paraskevis D, Karamitros T. Hepatitis B Virus DNA Integration, Chronic Infections and Hepatocellular Carcinoma. Microorganisms . 2021;9(8):1787. Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables12.docx Supplementarytable1.docx Supplementary Table 1 Definition and cut-offs of the Inflammation-based Score System Supplementarytable2.docx Supplementary Table 2 The results of multicollinearity analysis for factors with P < 0.1 in Univariate COX regression analysis Supplementaryfigure1.pdf Supplementary Information Supplementary Figure 1 Kaplan-Meier survival curves of OS for training cohort and validation cohort. Supplementaryfigure2.pdf Supplementary Figure 2 ROC of different inflammation-based score systems. Supplementaryfigure3.pdf Supplementary Figure 3 ROC of nomogram at 1-, 3- and 5-year in the training cohort (A) and validation (B) cohorts Cite Share Download PDF Status: Published Journal Publication published 26 Apr, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 25 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers agreed at journal 14 Mar, 2025 Reviews received at journal 12 Mar, 2025 Reviewers agreed at journal 12 Mar, 2025 Reviewers invited by journal 04 Feb, 2025 Editor invited by journal 31 Jan, 2025 Editor assigned by journal 31 Jan, 2025 Submission checks completed at journal 31 Jan, 2025 First submitted to journal 29 Jan, 2025 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. 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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-5924552","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":410226083,"identity":"77f6e41e-1f80-4fdd-99af-6b35a227d2ba","order_by":0,"name":"Shao-Long Lu","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shao-Long","middleName":"","lastName":"Lu","suffix":""},{"id":410226084,"identity":"8086685d-a1c4-448d-911e-988868e3f00e","order_by":1,"name":"Qing-Yuan Zhang","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qing-Yuan","middleName":"","lastName":"Zhang","suffix":""},{"id":410226085,"identity":"ab6e71c1-6504-4364-b018-2e8a2c4d3e2a","order_by":2,"name":"Yuan-Quan Zhao","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan-Quan","middleName":"","lastName":"Zhao","suffix":""},{"id":410226086,"identity":"a0729621-02f8-4409-b04e-36fe963d3f2b","order_by":3,"name":"Hua-Lin Wu","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hua-Lin","middleName":"","lastName":"Wu","suffix":""},{"id":410226087,"identity":"afb27d34-839e-43e7-8f32-fb23d6242a60","order_by":4,"name":"Jie Lin","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Lin","suffix":""},{"id":410226088,"identity":"69a37b1e-6fd2-421a-b5b0-48cdae980ecb","order_by":5,"name":"Peng Zhu","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhu","suffix":""},{"id":410226089,"identity":"c7d0c476-a2da-4233-ab89-a235f46c6bb6","order_by":6,"name":"Zheng-Jun Qin","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zheng-Jun","middleName":"","lastName":"Qin","suffix":""},{"id":410226090,"identity":"cc400539-b34f-4074-ae3d-45ea1ac95697","order_by":7,"name":"Xiao-Bo Wang","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Bo","middleName":"","lastName":"Wang","suffix":""},{"id":410226091,"identity":"a914c8a3-8d46-4f79-afb5-020793e447fc","order_by":8,"name":"Jie Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACxmYGBmYgzcMvf/jAgQ8/SNAiIzmDLfHgzB4ibQJpsTG4wWN8mIONGOXtzAc/F1Tc4ZGc3fPhMAMPgzy/2AFCDmNLlp5x5hkPv8zZDYcLLBgMZ85OIKSFx4yZt+0wj2RD7obDM3gYEgxuE9TC/42Z999hHoMDOQ8O87ARpYWHjZm3AajlRg4DsVrYjKV5jgEd1nPMABjIEoT9Yth/+OFnnprD9vzszY8/fPhhI88vTUhLAypfAr9yEJAnrGQUjIJRMApGPAAA1+1DZov7KkMAAAAASUVORK5CYII=","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-01-29 13:23:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5924552/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5924552/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-14163-3","type":"published","date":"2025-04-26T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75411642,"identity":"9d9e2687-8421-43c1-84bf-2ee73eaa6cec","added_by":"auto","created_at":"2025-02-04 09:12:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132035,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for HCC patients treated with radical resection.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/9c8639667184abfa14587463.png"},{"id":75411644,"identity":"1b38a57f-25fa-4e46-839f-16028d1eaea2","added_by":"auto","created_at":"2025-02-04 09:12:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":499509,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots predicting 1, 3, and 5-year OS in the training cohort (A) and validation cohorts (B); the time-dependent ROC values of our nomogram, BCLC, and CNLC in training cohort (C) and validation cohort (D); after grouping by x-tile, the K-M plots of our nomogram in training cohort (E) and validation cohort (F).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/a1b8dd9889d78d45bdf4e1d7.png"},{"id":75409668,"identity":"27e0a863-e9c8-4b0c-96d8-b901a5387511","added_by":"auto","created_at":"2025-02-04 09:04:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":467854,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram. Decision curve analysis of the nomogram for 1-year (A), 3-year (C), and 5-year (E) OS prediction in training cohort. Decision curve analysis of the nomogram for 1-year (B), 3-year (D), and 5-year (F) OS prediction in validation cohort.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/732309e49740dc2742f22493.png"},{"id":81569705,"identity":"3fe54275-6d92-4849-855a-c48d5b8cf333","added_by":"auto","created_at":"2025-04-28 16:10:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1509265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/69f86eb4-0272-413d-b74b-b78d9dfb9736.pdf"},{"id":75409661,"identity":"528f612b-0f93-4901-ba24-3975fcbf464c","added_by":"auto","created_at":"2025-02-04 09:04:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Tables12.docx","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/4efac9b2317d4d812cece395.docx"},{"id":75409663,"identity":"b4a05c80-df96-428a-a39f-8a7162395e71","added_by":"auto","created_at":"2025-02-04 09:04:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20032,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1 Definition and cut-offs of the Inflammation-based Score System\u003c/p\u003e","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/b5416940bd9a02eb4a96c2ca.docx"},{"id":75409665,"identity":"da51f53a-cbbc-4370-b064-18daed3e733b","added_by":"auto","created_at":"2025-02-04 09:04:46","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19357,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 2 The results of multicollinearity analysis for factors with P \u0026lt; 0.1 in Univariate COX regression analysis\u003c/p\u003e","description":"","filename":"Supplementarytable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/81b90be9728e8ee450e0feab.docx"},{"id":75409664,"identity":"389f5460-66b6-4d28-9e12-2f76efc939c8","added_by":"auto","created_at":"2025-02-04 09:04:46","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":95081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Figure 1 Kaplan-Meier survival curves of OS for training cohort and validation cohort.\u003c/p\u003e","description":"","filename":"Supplementaryfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/56d7fbe3390cf286137ebdeb.pdf"},{"id":75411645,"identity":"513d1a80-5ca9-4e77-abce-5f3e087a99cc","added_by":"auto","created_at":"2025-02-04 09:12:46","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":674480,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 2 ROC of different inflammation-based score systems.\u003c/p\u003e","description":"","filename":"Supplementaryfigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/b9ee8374a4ca300125f0a546.pdf"},{"id":75412233,"identity":"204dfcb3-8683-44d0-9493-17ca3dc9f8ab","added_by":"auto","created_at":"2025-02-04 09:20:46","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":121804,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 3 ROC of nomogram at 1-, 3- and 5-year in the training cohort (A) and validation (B) cohorts\u003c/p\u003e","description":"","filename":"Supplementaryfigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5924552/v1/98f0a93e1fb294d3f7ed169f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"C-reactive protein to albumin ratio combined with the Systemic Inflammatory Response Index predicts the prognosis of patients undergoing radical hepatectomy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer ranks as the sixth most prevalent type of cancer worldwide and ranks third among the leading causes of death from cancer. Hepatocellular carcinoma (HCC) is responsible for about 75 to 85 percent of primary liver cancer diagnoses\u0026nbsp;[1].\u0026nbsp;Currently, a wide array of therapeutic options for HCC includes surgery, ablation, transarterial chemoembolization, hepatic artery infusion chemotherapy, and other targeted therapies. However, surgery continues to be the main therapeutic approach for those with a diagnosis of HCC [2]. The survival prospects for HCC patients continue to be unfavorable, and the 5-years age-standardized relative survival rate of Chinese HCC patients is only 12.1% [3].\u003c/p\u003e\n\u003cp\u003eResearch indicates that HCC patients almost universally have a background of chronic inflammation, regardless of the cause. In patients with HCC, approximately 80 to 90 percent suffer from cirrhosis due to chronic liver inflammation. Chronic liver inflammation leads to liver cell damage, and while the liver has a robust capacity for regeneration, this leads to a continuous cycle of cell death and regeneration. These can result in liver fibrosis and an increased rate of DNA mutations, thereby elevating the risk of malignant progression. Moreover, chronic inflammation induces changes in the liver\u0026apos;s immune system, such as reducing the ratio of M1/M2 tumor-associated macrophages and promoting the production of pro-tumorigenic cytokines, which can help cancer cells sidestep immune surveillance. Therefore, tumor-associated inflammation is essential in tumor initiation, growth, and metastasis [4-6]. And more and more inflammation-related markers are being confirmed as indicators that can predict the outcomes for HCC patients. Most of them are composed of two or three combinations of neutrophils, lymphocytes, monocytes, C-reactive protein (CRP), platelets, and albumin. Such as modified glasgow prognostic score (mGPS) [7], C-reactive protein-albumin ratio (CAR) [8], platelet-lymphocyte ratio (PLR) [9], monocyte-lymphocyte ratio (MLR) [10], neutrophil-lymphocyte ratio (NLR) [11], systemic Inflammatory response Index (SIRI) [12], systemic Immune-inflammation index (SII) [13] and prognostic nutritional index (PNI) [14]. These markers can reflect the inflammatory and immune status of patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this research, we developed a predictive tool, the nomogram, integrating the aforementioned inflammatory markers with relevant clinical and tumor characteristics, which aims to improve the accuracy of prognostic estimations for HCC patients.\u003c/p\u003e"},{"header":"1 Patients and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1\u003c/strong\u003e \u003cstrong\u003ePatients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe subjects of this study were patients screened at the Affiliated Tumor Hospital of Guangxi Medical University who underwent radical hepatectomy from April 2014 through June 2021. Radical hepatectomy involved completely removing visible tumors with no tumor cells remaining at the resection edge. Inclusion criteria: (1) the first surgery; (2) pathology confirmed for HCC, (3) no other infections; (4) Child-Pugh A or B; (5) Successful follow-up. Exclusion criteria: (1) history of other cancers; (2) Lack of complete preoperative laboratory data and clinical data. We included a total of 920\u0026nbsp;HCC patients in our screening process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data included preoperative liver function parameters, neutrophils, monocytes, lymphocytes), prothrombin time (PT), alpha-fetoprotein (AFP) level and c-reactive protein (CRP) level, radiologically reported tumor characteristics (size, number, macrovascular invasion), postoperative pathological examination, and relevant medical records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Definition of Markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe definition of mGPS is consistent with previous studies\u0026nbsp;[5]. The inflammation-related indicators in peripheral blood were calculated by the formula: CAR: CRP/albumin; PLR: platelet/lymphocyte; MLR: monocytes/lymphocytes; NLR: neutrophils/lymphocytes; SIRI: neutrophil*monocyte/lymphocyte; SII: platelets*neutrophils/lymphocytes; PNI: (lymphocytes*5) + albumin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4\u003c/strong\u003e \u003cstrong\u003eFollow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor patients with HCC who had undergone radical surgery, we recommend a follow-up schedule that\u0026nbsp;imaging and blood tests at one month post-surgery, with subsequent testing every three months for five years, and then semi-annual assessments from the sixth year. Patients who did not attend our hospital at scheduled times were followed up by telephone to obtain treatment information and living conditions. The endpoint of our research was overall survival (OS), which we specified as the time from the initial diagnosis of HCC to either the final follow-up appointment or the patient\u0026apos;s demise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing SPSS 27.0 software (SPSS, Chicago, IL, USA) and R 4.3.2 (https://www.r-project.org/) for statistical analysis. P values \u0026lt; 0.05 for the difference was statistically significant. The R package CBCgrps [15] was used for baseline data statistics. For numerical data, we presented values as either the median with interquartile ranges or the mean with standard deviations. For categorical data, we provided the total number of cases and their corresponding percentages. We determined the most effective cutoff point for outcome prediction by applying the Youden index, which is computed from the receiver-operating characteristic (ROC) curve, and then the numerical variables were converted to categorical variables. Tolerance and variance inflation factor （VIF） value is obtained by using the SPSS software, to estimate the multicollinearity between the variables. In line with previous research, we identified multicollinearity when a variable had a tolerance \u0026lt; 0.1 and a VIF \u0026gt; 5. Such factors were omitted from the subsequent statistical analysis to ensure accuracy [16]. We conducted both univariate and multivariate analyses using Cox regression, and calculated the corresponding 95% confidence intervals (CI) for the results. Among them, the results of univariate analysis of P \u0026lt; 0.1 data into multivariate analysis, the multivariate analysis results of P \u0026lt; 0.05 data into the final build of the nomogram. The OS was analyzed using the Kaplan-Meier (K-M) method and the differences in survival curves were tested with the log-rank test.. We assessed efficacy of the recognition of nomogram by calculating the area under the receiver-operating characteristic curve (AUC) and the consistency index (C-index). Additionally, the calibration chart and decision curve analysis (DCA) were employed to determine the calibration and net benefit of the nomogram.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003e\u003cstrong\u003e2.1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Patient\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAltogether, 920 patients with HCC met the screening criteria. Median follow-up was 33 months (2-74 months). In the primary cohort,\u0026nbsp;the OS rates for patients were as follows: 87.9% at one year, 67.9% at three years, and 59.6% at five years. For\u0026nbsp;the training cohort, the OS rates were 88.8% at one year, 68.6% at three years, and 62.1% at five years.\u0026nbsp;For\u0026nbsp;the validation cohort,\u0026nbsp;the OS rates were 85.8% at one year, 66.0% at three years, and 53.1% at five years\u0026nbsp;(Supplementary figure 1).\u0026nbsp;Except for age,\u0026nbsp;the patient characteristics in\u0026nbsp;different\u0026nbsp;cohorts were found to be statistically similar, with no significant differences identified\u0026nbsp;(Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1 Baseline characteristics of the patients\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 ROC analysis of inflammatory indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following inflammatory indicators were selected for analysis: CAR, MLR, NLR, PLR, PNI, SII, SIRI, mGPS. We established the optimal\u0026nbsp;cutoff\u0026nbsp;value\u0026nbsp;by calculating the Youden index,\u0026nbsp;which allowed us to reclassify the\u0026nbsp;numerical variables into categorical variables. The ROC curve was constructed and the value of AUC was obtained. Among them, the optimal cutoff value of CAR was 0.105, with an AUC of 62.3% (95% CI: 58.4% - 66.2%); the optimal cutoff value of PLR was 146.5, with an AUC of 59.2% (95% CI: 55.2% - 63.2%); the optimal cutoff value of MLR was 0.245, with an AUC of 59.5% (95% CI: 55.7% - 63.3%); the optimal cutoff value of NLR was 2.425, with an AUC of 60.8% (95% CI: 56.9% - 64.7%); the optimal cutoff value of PNI was 47.5, with an AUC of 55.4% (95% CI: 51.4% - 59.3%); the optimal cutoff value of SII was 504.5, with an AUC of 60.5% (95% CI: 56.6% - 64.4%); the optimal cutoff value of SIRI was 1.075, with an AUC of 62.1% (95% CI: 58.2% - 66.0%); and the AUC of mGPS was 62.4% (95% CI: 58.4% - 66.3%) (Supplementary figure 1 and Supplementary table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Factors affecting the overall survival\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression analysis showed that,\u0026nbsp;hepatitis B virus (HBV)\u0026nbsp;DNA \u0026gt; 100IU/ml, prothrombin time \u0026gt; 13s, AFP level \u0026gt; 400ng/mL, AST \u0026gt; 40U/L, ascites, tumor size, microvascular invasion (MVI), Edmondson\u0026ndash;Steiner grading, macrovascular vascular invasion, mGPS and high level of the CAR, MLR, NLR, PLR, SII, SIRI and low level of PNI are poor prognostic factors for HCC patients after radical surgery\u0026nbsp;(Supplementary table 2). Multicollinearity analysis of the above factors showed that tolerance \u0026gt; 0.1 and VIF \u0026lt;\u0026nbsp;5. Multivariate Cox regression analysis showed that HBV-DNA \u0026gt; 100IU/ml, prothrombin time \u0026gt; 13s, tumor size, microvascular invasion, Edmondson-Steiner grading, macroscopic vascular invasion, high level of the CAR and SIRI were independent prognostic factors for HCC patients (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2 Univariate and multivariate analysis of variables affecting overall survival\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Construction and validation of Nomogram\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the findings from the multivariate Cox regression analysis, HBV-DNA, prothrombin time, tumor size, microvascular invasion, Edmondson-Steiner grade, macroscopic vascular invasion, CAR, and SIRI were used to construct a nomogram for predicting OS at 1-, 3-, and 5-year in HCC patients treated with radical resection (Figure 1). The nomogram\u0026apos;s C-index was 0.763 (95% CI:0.732-0.794). The nomogram\u0026apos;s predictive accuracy, with the AUC as the measure, was 0.815, 0.805, and 0.776 for 1-, 3-, and 5-year OS in the training cohort. Corresponding values in the validation cohort were 0.814, 0.737, and 0.730 (Supplementary figure 3). In the training and validation cohorts, the calibration curves for OS at 1-, 3-, and 5-year matched well with their standard lines (Figure 2A, B). The nomogram demonstrated higher predictive accuracy compared to both the CNLC and BCLC staging systems, as evidenced by the time-dependent AUC curve (Figure 2C, D). Patients were categorized into various risk groups using the total risk scores derived from the nomogram, which was employed to assess its ability to distinguish between different survival outcomes. The optimal cut-off scores were determined automatically by the X-tile software. The patients in the high-risk category experienced the poorest OS, while those in the low-risk category had the best OS. The same results were acquired when the cutoff values were applied to the validation cohort (Figure 2E, F). This indicates that our nomogram has good stratification ability. Compared with the CNLC and BCLC staging systems, the nomogram offers superior clinical benefits and demonstrates significant clinical application value, as evidenced by its performance in DCA (Figure 3).\u003c/p\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eAn increasing body of research indicates a close link between inflammation\u0026nbsp;and cancer. Inflammation is the body's response to tissue injury, If the body is in a long-term inflammatory state, it will lead to cell mutation and proliferation, which will lead to the occurrence and promote the development of tumors [6].\u0026nbsp;Inflammatory cells reflect the prognosis of patients to some degree [17]. Neutrophils have been linked to a poor prognosis in\u0026nbsp;HCC patients due to their strong immunosuppressive activity and damage to the function of T cells and antigen-presenting cells, which can promote the invasion, migration and angiogenesis of cancer cells. In addition, the tumor origin of transforming growth factor β (TGF-β) can be neutrophils from antitumor \"N1\" phenotype is converted into a \"N2\" phenotype that promote tumor, which further illustrates the carcinogenic potential of neutrophils [17,18]. In response to tumor-released chemokines, circulating monocytes aggregated in the tumor stroma are transformed into tumor-associated macrophages (TAM). The cytokines including TGF-β, vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF) and tumor necrosis factor (TNF) secreted by TAM affect the tumor microenvironment and stimulate the development of blood vessels in tumors, increase cell proliferation, and facilitate the spread of cancer [19,20]. Lymphocytes play a crucial role in immune surveillance and response to tumors; an increased presence of tumor-infiltrating lymphocytes is associated with a good prognosis [21]. Therefore, SIRI can reflect the changes of the three factors at the same time, and the increase of its level reflects the activity of tumor-related inflammation, suggesting a poor prognosis of HCC patients. CRP is a kind of acute phase protein, which can indirectly form a microenvironment conducive to tumor cell angiogenesis [15]. It has been suggested that CRP is not only produced by normal liver in response to various tumor stimuli, but also by hepatocellular carcinoma tissues [22]. Albumin, produced by the liver, reflects not only liver function but also the patient's nutritional status. As an ideal biomarker, CAR has not only been proved to be related to patient prognosis, but also has the potential to be used as HCC screening and tumor staging prediction [8,23].\u003c/p\u003e\n\u003cp\u003eMultivariate cox regression analysis showed that except the tumor characteristics, HBV-DNA is an independent prognostic factors for patients with HCC. An increase in HBV-DNA replication indicates the activity of HBV infection, it is generally believed that the carcinogenic mechanisms of HBV include genetic damage caused by immune-mediated inflammation, induction of oxidative stress, etc. A more important carcinogenic mechanism is the integration of HBV-DNA in liver cell genes [24]. Integration of the HBV genome into the host genome can activate cellular genes with oncogenic potential. This activation allows for a selective growth advantage and clonal expansion of hepatocytes, ultimately leading to malignancy. In addition, the genetic instability resulting from HBV integration is considered a key factor in the pathogenesis of HCC [25].\u003c/p\u003e\n\u003cp\u003eTherefore, in this research, readily available preoperative inflammatory markers were selected to construct a prognostic model for HCC patients treated with radical resection. The results showed that among these inflammatory markers, SIRI and CAR were independent prognostic factors in HCC patients. The nomogram constructed by SIRI, CAR, HBV-DNA, prothrombin time, tumor size, microvascular invasion, Edmondson-Steiner grade and macroscopic vascular invasion can effectively predict the OS of HCC patients. In addition, the nomogram had a higher AUC value, a better calibration curve, and more net benefit compared to the BCLC and CNLC Staging system. In clinical practice, the nomogram provided a more direct prognosis prediction tool for clinicians and patients.\u003c/p\u003e\n\u003cp\u003eThe advantage of this research had the following points. 1) While previous studies targeted only a few inflammatory indicators, our research explored a broader range of the reported inflammatory indicators. 2) SIRI was composed of peripheral neutrophils, monocytes and lymphocytes, while CAR was composgureed of C-reactive protein and albumin. A nomogram that included both can\u0026nbsp;provide a more accurate and comprehensive representation of the patient's inflammatory and immune response. 3) Earlier researches were limited to investigating how inflammatory markers predict patient outcomes. The nomograms were not compared with current tumor staging systems to evaluate their predictive efficacy. In our research, by combining inflammatory biomarkers with relevant clinical and tumor characteristics, the nomogram established by us can predict the OS of patients more accurately than the common staging system.\u003c/p\u003e\n\u003cp\u003eIt was important to note that this research had certain limitations. Being a retrospective analysis, it was subject to the constraints of a smaller sample size, which may lead to inherent selection bias. Secondly, due to the limitation of region and population, most of the research subjects were HBV-related HCC, and HCC patients caused by other causes were not involved. Future validation of our research's findings will require large-scale, prospective, multicenter trials that also consider various causes of HCC.\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eIn conclusion, the nomogram constructed based on SIRI, CAR, and related clinical and tumor features in this research has high predictive efficacy for OS in HCC patients treated with radical resection. The nomogram can guide clinical treatment decisions and prognosis assessment for HCC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSLL,\u0026nbsp;QYZ, JC, XBW are responsible for analysis, writing and revises. YQZ participate in the writing. YQZ, HLW, JL, ZJQ,\u0026nbsp;and PZ participate in data collecting and\u0026nbsp;screening.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interestsReferences\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a retrospective study, and the ethical approval of the Ethics Committee of the Cancer Hospital of Guangxi Medical University and the exemption of informed consent were obtained before the study began.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank everyone who provided support for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of China (82260345), Guangxi Key Technologies R\u0026amp;D Program (Guike AB22080066), Guangxi Science and Technology Base and Talent Specialization(Guike 2024AC43021), the \u0026quot;139\u0026quot; Plan for Training High-level Backbone Medical Talents in Guangxi (No. G202003008), the Guangxi Medical University Outstanding Young Talents Training Program, and Guangxi Natural Youth Science Foundation (2024JJB140755).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 2021;71(3):209-249.\u003c/li\u003e\n\u003cli\u003eVogel A, Meyer T, Sapisochin G, Salem R, Saborowski A. Hepatocellular carcinoma. \u003cem\u003eLancet\u003c/em\u003e. 2022;400(10360):1345-1362.\u003c/li\u003e\n\u003cli\u003eZeng H, Chen W, Zheng R, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. \u003cem\u003eLancet Glob Health\u003c/em\u003e. 2018;6(5):e555-e567.\u003c/li\u003e\n\u003cli\u003eRingelhan M, Pfister D, O\u0026apos;Connor T, Pikarsky E, Heikenwalder M. The immunology of hepatocellular carcinoma. \u003cem\u003eNat Immunol\u003c/em\u003e. 2018;19(3):222-232.\u003c/li\u003e\n\u003cli\u003eYang YM, Kim SY, Seki E. Inflammation and Liver Cancer: Molecular Mechanisms and Therapeutic Targets. \u003cem\u003eSemin Liver Dis\u003c/em\u003e. 2019;39(1):26-42.\u003c/li\u003e\n\u003cli\u003eSingh N, Baby D, Rajguru JP, Patil PB, Thakkannavar SS, Pujari VB. Inflammation and cancer. \u003cem\u003eAnn Afr Med\u003c/em\u003e. 2019;18(3):121-126.\u003c/li\u003e\n\u003cli\u003eNi XC, Yi Y, Fu YP, et al. Prognostic Value of the Modified Glasgow Prognostic Score in Patients Undergoing Radical Surgery for Hepatocellular Carcinoma. \u003cem\u003eMedicine (Baltimore)\u003c/em\u003e. 2015;94(36):e1486.\u003c/li\u003e\n\u003cli\u003eKinoshita A, Onoda H, Imai N, et al. The C-reactive protein/albumin ratio, a novel inflammation-based prognostic score, predicts outcomes in patients with hepatocellular carcinoma. \u003cem\u003eAnn Surg Oncol\u003c/em\u003e. 2015;22(3):803-810.\u003c/li\u003e\n\u003cli\u003eHu DH, Yu SM. Association between platelet to lymphocyte ratio (PLR) and overall survival (OS) of hepatocellular carcinoma (HCC): A meta-analysis. \u003cem\u003eCell Mol Biol (Noisy-le-grand)\u003c/em\u003e. 2017;63(8):30-32.\u003c/li\u003e\n\u003cli\u003eNouri-Vaskeh M, Mirza-Aghazadeh-Attari M, Pashazadeh F, et al. Prognostic Impact of Monocyte to Lymphocyte Ratio in Clinical Outcome of Patients with Hepatocellular Carcinoma: A Systematic Review and Meta-analysis. \u003cem\u003eGalen Med J\u003c/em\u003e. 2020;9:e1948.\u003c/li\u003e\n\u003cli\u003eLin S, Hu S, Ran Y, Wu F. Neutrophil-to-lymphocyte ratio predicts prognosis of patients with hepatocellular carcinoma: a systematic review and meta-analysis. \u003cem\u003eTransl Cancer Res\u003c/em\u003e. 2021;10(4):1667-1678.\u003c/li\u003e\n\u003cli\u003eXu L, Yu S, Zhuang L, et al. Systemic inflammation response index (SIRI) predicts prognosis in hepatocellular carcinoma patients. \u003cem\u003eOncotarget\u003c/em\u003e. 2017;8(21):34954-34960.\u003c/li\u003e\n\u003cli\u003eHu B, Yang XR, Xu Y, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. \u003cem\u003eClin Cancer Res\u003c/em\u003e. 2014;20(23):6212-6222.\u003c/li\u003e\n\u003cli\u003eWang D, Hu X, Xiao L, et al. Prognostic Nutritional Index and Systemic Immune-Inflammation Index Predict the Prognosis of Patients with HCC. \u003cem\u003eJ Gastrointest Surg\u003c/em\u003e. 2021;25(2):421-427.\u003c/li\u003e\n\u003cli\u003eZhang Z, Gayle AA, Wang J, Zhang H, Cardinal-Fern\u0026aacute;ndez P. Comparing baseline characteristics between groups: an introduction to the CBCgrps package. \u003cem\u003eAnn Transl Med\u003c/em\u003e. 2017;5(24):484.\u003c/li\u003e\n\u003cli\u003eZheng Z, Guan R, Zou Y, et al. Nomogram Based on Inflammatory Biomarkers to Predict the Recurrence of Hepatocellular Carcinoma-A Multicentre Experience. \u003cem\u003eJ Inflamm Res\u003c/em\u003e. 2022;15:5089-5102.\u003c/li\u003e\n\u003cli\u003eSanghera C, Teh JJ, Pinato DJ. The systemic inflammatory response as a source of biomarkers and therapeutic targets in hepatocellular carcinoma. \u003cem\u003eLiver Int\u003c/em\u003e. 2019;39(11):2008-2023.\u003c/li\u003e\n\u003cli\u003eChen H, Zhou XH, Li JR, et al. Neutrophils: Driving inflammation during the development of hepatocellular carcinoma. \u003cem\u003eCancer Lett\u003c/em\u003e. 2021;522:22-31.\u003c/li\u003e\n\u003cli\u003eDou L, Shi X, He X, Gao Y. Macrophage Phenotype and Function in Liver Disorder. \u003cem\u003eFront Immunol\u003c/em\u003e. 2020;10:3112.\u003c/li\u003e\n\u003cli\u003eSung PS. Crosstalk between tumor-associated macrophages and neighboring cells in hepatocellular carcinoma. \u003cem\u003eClin Mol Hepatol\u003c/em\u003e. 2022;28(3):333-350.\u003c/li\u003e\n\u003cli\u003eLabani-Motlagh A, Ashja-Mahdavi M, Loskog A. The Tumor Microenvironment: A Milieu Hindering and Obstructing Antitumor Immune Responses. \u003cem\u003eFront Immunol\u003c/em\u003e. 2020;11:940.\u003c/li\u003e\n\u003cli\u003eCarr BI, Akkiz H, Guerra V, et al. C-reactive protein and hepatocellular carcinoma: analysis of its relationships to tumor factors. \u003cem\u003eClin Pract (Lond)\u003c/em\u003e. 2018;15(Spec Issue):625-634.\u003c/li\u003e\n\u003cli\u003eLin N, Li J, Ke Q, Wang L, Cao Y, Liu J. Clinical Significance of C-Reactive Protein to Albumin Ratio in Patients with Hepatocellular Carcinoma: A Meta-Analysis. \u003cem\u003eDis Markers\u003c/em\u003e. 2020:4867974.\u003c/li\u003e\n\u003cli\u003eYeh SH, Li CL, Lin YY, et al. Hepatitis B Virus DNA Integration Drives Carcinogenesis and Provides a New Biomarker for HBV-related HCC. \u003cem\u003eCell Mol Gastroenterol Hepatol\u003c/em\u003e. 2023;15(4):921-929.\u003c/li\u003e\n\u003cli\u003eBousali M, Papatheodoridis G, Paraskevis D, Karamitros T. Hepatitis B Virus DNA Integration, Chronic Infections and Hepatocellular Carcinoma. \u003cem\u003eMicroorganisms\u003c/em\u003e. 2021;9(8):1787. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"systemic immunoinflammatory response index, C-reactive protein-albumin ratio, hepatocellular carcinoma, prognosis, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-5924552/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5924552/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Many prognostic scores based on systemic inflammation have been developed. Most of these prognostic scores have been shown to influence the prognosisof hepatocellular carcinoma (HCC) patients. This research aims to develop a novel prognostic system based on inflammatory markers for patients with HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients and Methods:\u003c/strong\u003e This researchencompassed 920 HCC patients who underwent potentially radical surgical resection. We employed receiver-operating characteristic (ROC) curve analysis to determine the optimal cutoff value for the preoperative inflammatory prognostic score. Univariate and multivariate Cox regression analyses were conducted to pinpoint features that significantly influence outcomes for patients with HCC. We employed a calibration curve and decision curve analysis (DCA) to appraise the application of the nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The multivariate Cox regression identified that systemic immunoinflammatory response index (SIRI), C-reactive protein-albumin ratio (CAR), tumor size, hepatitis B virus (HBV)-DNA, prothrombin time, microvascular invasion, macroscopic vascular invasion, and Edmondson-Steiner grade were all independent predictors of overall survival (OS). The predictive accuracy of the nomogram for estimating 1-, 3-, and 5-year OS was measured by the area under the receiver operating characteristic curve (AUC). Inthe training cohort, the AUC scores for the 1-, 3-, and 5-year OS were 0.815, 0.805, and 0.776. For the validation cohort, the respective AUC scores were 0.814, 0.737, and 0.730. Additionally, our nomogram shows a high capacity for distinguishing between different risk groups and is practical for clinical use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The nomogram demonstrates strong predictive performance for the 1-, 3-, and 5-year OS of HCC patients undergoing radical surgery, surpassing BCLC and CNLC staging systems in ability to assess patient prognosis.\u003c/p\u003e","manuscriptTitle":"C-reactive protein to albumin ratio combined with the Systemic Inflammatory Response Index predicts the prognosis of patients undergoing radical hepatectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-04 09:04:42","doi":"10.21203/rs.3.rs-5924552/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-25T13:04:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T10:19:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156080612925855893813359678353302883732","date":"2025-03-25T08:04:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60575739637541333401917363624870293989","date":"2025-03-14T21:16:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-12T09:51:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190756943815331967248347815401449859387","date":"2025-03-12T04:12:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-05T01:30:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-31T19:20:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-31T13:22:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-31T13:21:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-01-29T13:09:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b60834c-abe6-4a99-9965-cc013da1ad42","owner":[],"postedDate":"February 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T16:02:26+00:00","versionOfRecord":{"articleIdentity":"rs-5924552","link":"https://doi.org/10.1186/s12885-025-14163-3","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-04-26 15:57:03","publishedOnDateReadable":"April 26th, 2025"},"versionCreatedAt":"2025-02-04 09:04:42","video":"","vorDoi":"10.1186/s12885-025-14163-3","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14163-3","workflowStages":[]},"version":"v1","identity":"rs-5924552","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5924552","identity":"rs-5924552","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}