Albumin plus CEA: a novel biomarker for predicting prognosis in resectable gastric cancer: a case-control study

Albumin plus CEA: a novel biomarker for predicting prognosis in resectable gastric cancer: a case-control study | 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 Albumin plus CEA: a novel biomarker for predicting prognosis in resectable gastric cancer: a case-control study Jie Li, Haozong Zhao, Qianshi Zhang, Shuangyi Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4380786/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Preoperative high levels of serum carcinoembryonic antigen (CEA) and low levels of albumin (ALB) are closely related to poor prognosis among patients with gastric cancer. This study aims to determine the prognostic value of preoperative serum ALB plus CEA levels as a new biomarker in patients with resectable gastric cancer. Methods A total of 329 patients with gastric cancer were included in this study. The optimal cutoff values of ALB and CEA were 4.77 ng/mL and 41.47 g/L, respectively. Patients were stratified into three groups based on these cutoff values: ALB-CEA = 0 (ALB > 41.47 g/L and CEA ≤ 4.77 ng/mL), ALB-CEA = 1 (ALB ≤ 41.47 g/L or CEA > 4.77 ng/mL), and ALB-CEA = 2 (ALB ≤ 41.47 g/L and CEA > 4.77 ng/mL). Kaplan-Meier curve and Cox proportional model were used to determine the predictive effect of the biomarker on the overall survival (OS) of patients in the training and validation sets. Results ALB-CEA had a larger area under the curve than ALB or CEA alone (0.703, 0.671, 0.635 in the validation set; 0.776, 0.694, 0.616 in the validation set respectively). The Kaplan-Meier curve revealed that higher ALB-CEA scores were indicative of lower survival rates (p < 0.001). Additionally, the multivariate analysis revealed that ALB-CEA was an independent risk factor for poor prognosis in patients with gastric cancer (p < 0.05). Conclusion Preoperative ALB-CEA may be a new biomarker for predicting the prognosis of patients with gastric cancer. For those patients with higher preoperative ALB-CEA scores, more extensive postoperative follow-up should be performed to detect tumor progression early and intervene in time. albumin CEA gastric cancer Postoperative mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Gastric cancer is the second most prevalent cancer and the third most deadly cancer among all cancers in China ( 1 ). Surgery is currently the major treatment strategy for gastric cancer. However, the prognosis of gastric cancer remains unfavorable due to the high recurrence rate and risk of distant metastasis after surgery. Thus, the pursuit of a straightforward and readily accessible biomarker holds the potential to enhance the accuracy of prognosis prediction before surgery for gastric cancer patients. This, in turn, would enable more precise and tailored treatment strategies for individual patients, ultimately aiming to improve the survival rates of patients with gastric cancer. Gastric cancer biomarkers encompass pathological signals within the tumor tissue, genetic or epigenetic changes, and non-invasive biomarkers, such as those derived from blood or gastric fluid. These biomarkers can predict, facilitating personalized treatment strategies for various gastric cancer subtypes post-surgery and enabling the development of effective postoperative treatment plans for more patients with cancer ( 2 ). Previous studies have demonstrated that several common biomarkers of systemic inflammation, including the neutrophil-to-lymphocyte ratio (NLR) ( 3 ), lymphocyte-to-monocyte ratio (LMR) ( 4 ), platelet-to-lymphocyte ratio (PLR) ( 5 , 6 ), albumin, and several other common biomarkers can be used to predict the prognosis of gastric cancer. Malnutrition is frequently observed in patients with cancer pre-surgery, with serum albumin levels commonly reflecting this condition. Hypoalbuminemia, indicative of poor nutritional status, correlates with an increased risk of malnutrition. Additionally, malnutrition can lead to a compromise in the body's immunity, which is often more dangerous for patients with cancer, and potentially leads to postoperative complications ( 7 ). Several studies have linked hypoalbuminemia to poor prognosis in patients with colon cancer ( 8 ), pancreatic cancer ( 9 ), non-small-cell lung cancer ( 10 ), ovarian cancer ( 11 ), and gastric cancer ( 12 ). The discovery of tumor markers constitutes a pivotal advancement in tumor diagnosis and treatment. Over recent years, these markers have not only provided valuable diagnostic insights but have also assumed a significant role in prognostic evaluation. Carcinoembryonic antigen (CEA), among these markers, was discovered by Gold and Freedman in 1965 ( 13 ). CEA has a salivary rockweed glycosylated glycoform that acts as a selectin ligand and promotes the metastasis of colon cancer cells ( 14 , 15 ) and several other organ cancer cells ( 16 ). Additionally, CEA has an excellent diagnostic and prognostic value in gastrointestinal tumors. Elevated preoperative CEA levels in patients with gastric cancer have been identified as an independent risk factor for unfavorable prognosis ( 17 – 21 ). Moreover, combining CEA with other tumor markers has demonstrated efficacy in prognostic prediction for patients with gastric cancer in previous studies ( 22 ). This underscores the complementary role of tumor markers alongside traditional TNM staging, nerve invasion, and cancer embolism in predicting gastric cancer prognosis. Therefore, this study aims to evaluate the predictive efficacy of CEA combined with albumin (ALB-CEA) as a new biomarker for predicting the prognosis of patients with gastric cancer. Materials and methods A total of 329 patients with primary gastric cancer who underwent radical gastrectomy in the Second Affiliated Hospital of Dalian Medical University from January 2010 to February 2017 were included in this study. Data collected were retrospectively evaluated. Patients who underwent surgery from January 2010 to December 2015 comprised the training set, while those who underwent surgery from January 2016 to February 2017 comprised the validation set. Clinical data, including age, gender, lesion diameter, tumor location, differentiation, nerve invasion, cancer embolism, pathological TNM stage (pTNM), lymph node metastasis, depth of invasion, and survival status, was collected from the hospital information system. Routine diagnostic and treatment procedures included the collection of preoperative blood samples from each patient to detect serum albumin and CEA levels, alongside other pertinent indicators such as hemoglobin, liver and kidney function, and electrolyte levels. Peripheral blood samples were obtained from fasting patients within one week before surgery. Outpatient and telephone follow-up after discharge are part of routine treatment. After discharge, all patients underwent telephone and outpatient follow-ups every 1–3 months until either death or the conclusion of the study. The last follow-up was in January 2022. Survival time was defined as the duration from the date of diagnosis to the date of death or the last follow-up. 2. Statistical analysis The optimal cutoff values of ALB and CEA in the training set were determined using receiver operating characteristic (ROC) curve analysis, and ALB-CEA scores were subsequently derived based on these values (Table 1 ). Participants in the training set were further divided into three groups based on their ALB-CEA scores: ALB-CEA = 0 (CEA ≤ 4.77 ng/mL and ALB > 41.47 g/L), ALB-CEA = 1 (CEA > 4.77 ng/mL or ALB ≤ 41.47 g/L), and ALB-CEA = 2 (CEA > 4.77 ng/mL and ALB ≤ 41.47 g/L) (Table 1 ). To assess the robustness and applicability of this scoring system, the two optimal cutoff values obtained from the training set were applied to the validation set for ALB-CEA scoring and subsequent analysis. The chi-square or Fisher’s exact test was used to analyze categorical variables. Postoperative survival analysis for the two groups of gastric cancer patients was conducted using the Kaplan-Meier method. Univariate analysis was performed to identify statistically significant variables, which were then included in the multivariate analysis to ascertain independent prognostic factors. SPSS 27.0 and R software were used for statistical analysis. P < 0.05 was considered statistically significant. Furthermore, R software (version 4.3.3) was used to the ability of the combined ALB-CEA scoring system to predict overall survival (OS) was compared with that of CEA or ALB values alone using the consistency index (C-index) values, the area under the curve (AUC), Akaike information criteria (AIC), and decision curve analysis (DCA). Table 1 Prognostic Scores of CEA, ALB and ALB-CEA Scoring System Score albumin (g/L) > 41.47 0 ≤ 41.47 1 CEA (ng/mL) ≤ 4.77 0 > 4.77 1 albumin-CEA ALB > 41.47 and CEA ≤ 4.77 0 ALB > 41.47 and CEA > 4.77 1 ALB ≤ 41.47 and CEA ≤ 4.77 1 ALB ≤ 41.47 and CEA > 4.77 2 Results 3.1 Patient characteristics The training set comprised a total of 164 patients diagnosed with gastric cancer, of which 115 were male (70.1%) and 49 were female (29.9%). Among these patients, 105 (64.0%) were aged 60 years or older, while 59 (36.0%) were younger than 60 years. For the validation set, 165 patients with gastric cancer were included, consisting of 113 males (68.5%) and 52 females (31.5%). Within this group, 108 patients (65.5%) were aged 60 years or older, while 57 patients (34.5%) were younger than 60 years. The pathological and clinical characteristics of the two groups of patients in this study are presented in Supplementary Table S1 . 3.2 Relationship between preoperative ALB and CEA and clinicopathological features Preoperative ALB and CEA levels were correlated with postoperative survival and clinicopathological features in the two groups (Supplementary Table S1 ). In the training set, there was a significant difference in survival rate between patients with CEA > 4.77 ng/mL and those with ALB ≤ 41.47g/L, evidenced by a significantly lower survival rate. This observation was further corroborated in the validation set, where outcomes mirrored those observed in the training set. 3.3 Optimal cutoff values for ALB and CEA In the training set, the optimal cutoff values for CEA and ALB for predicting OS were 4.77 ng/mL and 41.47 g/L, respectively (Fig. 1 A). Furthermore, the AUC of ALB-CEA for predicting OS was slightly higher than that of ALB or CEA alone (Fig. 1 A), suggesting that the combination of CEA and ALB has better predictive efficacy than CEA or ALB alone in patients with gastric cancer. Similar results were confirmed in the validation set (Fig. 1 B). 3.4 Prognostic value of ALB and CEA levels The prognostic value of CEA and ALB levels was evaluated using Kaplan-Meier analysis, and the results revealed that the low CEA group exhibited significantly higher OS rates compared to the high CEA group. Similar findings were obtained for median OS time. Additionally, the high ALB group displayed elevated OS rates and longer median OS times compared to the low ALB group. These findings remained consistent across both the training and validation sets (Supplementary Fig. S1 ) 3.5 Prognostic analysis based on ALB-CEA score The pathological and clinical characteristics of patients stratified by ALB-CEA score are presented in Supplementary Table S2. Notably, the ALB-CEA score was closely related to survival in both training and validation sets, with higher scores indicating a poorer prognosis. 3.6 Univariate and multivariate survival analysis The univariate and multivariate analyses revealed that ALB-CEA was an influencing factor for OS among patients with gastric cancer post-surgery. Kaplan-Meier analysis of patients with ALB-CEA scores of 0, 1, and 2 in the two groups showed that higher ALB-CEA scores were associated with poorer OS (p < 0.001; Fig. 2 ). In the training set, both univariate and multivariate analysis revealed that ALB-CEA was significantly associated with prognosis, and ALB-CEA was an independent prognostic factor for OS (Table 2 ). Specifically, patients with higher ALB-CEA scores were more likely to experience a poor prognosis. Similar findings were observed using the validation set (Table 2 ). Table 2 Univariate and Multivariate Analyses of Clinicopathological Characteristics in patients with gastric cancer Characteristics The training set The validation set Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis HR (%95 CI) P value HR (%95 CI) P value HR (%95 CI) P value HR (%95 CI) P value Sex 0.134 0.470 Female Ref Ref Male 1.466 1.222 (0.892–2.343) (0.710–2.104) Age (years) 0.062 0.001* ≤ 60 Ref Ref > 60 1.548 2.846 (0.979–2.449) (1.520–5.329) Tumor location 0.151 0.363 Middle and lower third Ref Ref Upper third 1.475 1.351 (0.868–2.506) (0.706–2.584) Differentiation 0.094 0.045* Well/Moderate Ref Ref Poor 1.449 1.763 (0.938–2.238) (1.013–3.067) Diameter of lesion (cm) 0.001* 4 2.053 3.784 1.875 (1.350–3.124) (2.268–6.196) (1.103–3.189) Cancer embolism < 0.001* 0.001* < 0.001* None Ref Ref Ref Yes 3.687 2.330 3.191 (2.267–5.996) (1.384–3.922) (1.736–5.867) Never invasion < 0.001* 0.017* < 0.001* None Ref Ref Ref Yes 2.608 1.707 3.388 (1.710–3.978) (1.101–2.645) (2.021–5.680) pTNM < 0.001* 0.003* < 0.001* 0.001* I-II Ref Ref Ref Ref III-IV 4.207 2.260 6.056 3.353 (2.568–6.890) (1.326–3.851) (3.158–11.615) (1.680–6.692) Depth of invasion < 0.001* < 0.001* T1-T2 Ref Ref T3-T4 4.276 6.129 (2.409–7.592) (2.918–12.873) LN metastasis < 0.001* < 0.001* N0 Ref Ref N1/N1/N2 4.323 5.586 (2.508–7.449) (2.841–10.983) CEA levels (ng/ml) < 0.001* 3.77 2.441 3.683 (1.587–3.755) (2.216–6.121) ALB levels (g/L) < 0.001* 41.47 Ref Ref ≤ 41.47 2.550 3.623 (1.602–4.059) (2.081–6.310) ALB-CEA 0 Ref Ref Ref Ref 1 2.395 0.002* 1.806 0.039* 2.605 0.002* 3.517 0.001* (1.308–4.155) (1.030–3.166) (1.423–4.767) (1.621–7.630) 2 4.868 < 0.001* 2.888 0.001* 9.585 < 0.001* 9.584 41.47 and CEA ≤ 4.77 represent 0; CEA > 4.77 or ALB ≤ 41.47 represent 1; CEA > 4.77 and ALB ≤ 41.47 represent 2. Note ALB-CEA: ALB > 41.47 and CEA ≤ 4.77 represent 0; CEA > 4.77 or ALB ≤ 41.47 represent 1; CEA > 4.77 and ALB ≤ 41.47 represent 2. 3.7 Predictive performance of ALB-CEA compared to CEA or ALB alone The scoring criteria developed were applied to the training set to facilitate a more intuitive comparison between ALB-CEA and CEA or ALB alone as predictors of the AUC, alongside testing the adaptability of the training set to the scoring criteria. The C-index, AUC, and AIC were used to evaluate the predictive performance of the ALB-CEA score versus ALB or CEA alone (Fig. 3 ) (Supplementary Table S3). In the training set, the ALB-CEA score exhibited the highest C-index and AUC, as well as the lowest AIC, surpassing the predictive performance of ALB or CEA alone (Fig. 3 ) (Supplementary Table S3). Similar findings were observed using the validation set (Fig. 3 ) (Supplementary Table S3). Additionally, DCA revealed that ALB-CEA, as a new biomarker, has better clinical practicability compared to ALB or CEA alone, which are two conventional biomarkers for predicting prognosis (Fig. 4 ). These findings suggest that ALB-CEA can be a better prognostic predictor than ALB or CEA alone. Discussion Hematological biomarkers identified from peripheral blood samples, such as CEA and albumin, are easy to obtain and inexpensive and can predict the prognosis of patients with cancer. This study reaffirms the association between peripheral blood biomarkers in patients with resectable gastric cancer and their final clinical outcomes. Previous studies have established correlations between lower preoperative albumin levels, higher CEA levels, older age, lower degree of differentiation, later postoperative pathological stage, deeper depth of invasion, greater number of lymph node metastasis, presence of vascular tumor thrombus, nerve invasion, and poor prognosis. In the cohort of 329 patients studied, ALB-CEA scores were associated with prognosis, with higher scores indicating a higher likelihood of poor prognosis and tumor progression. Consequently, ALB-CEA emerges as a novel prognostic biomarker strongly indicative of the prognosis of patients with gastric cancer, owing to the association between patient-tumor characteristics and tumor progression. A study explored the combinations of CEA with HB (hemoglobin) (HB-CEA) to predict gastric cancer prognosis ( 23 ), and the predictive efficacy was examined using the AUC of the ROC. In the training set, the AUC of HB-CEA was 0.677, which was lower than that of ALB-CEA (0.703) in this experiment. A similar prediction efficacy was demonstrated in the validation set, where the AUC of HB-CEA was 0.670, which was lower than the AUC of ALB-CEA (0.776). A comparison with another previous study showed similar results ( 24 ). Because there are some differences between the studied samples, the AUC of the two models was not enough to determine the superiority of our model, so we subsequently validated the model. The utilization of the AUC of the ROC solely assesses the diagnostic accuracy of a prediction model, potentially overlooking the clinical utility and implications of false negatives or false positives. To address this limitation, DCA was employed to further evaluate the predictive efficacy of the ALB-CEA model. Unlike ROC analysis, DCA incorporates the preferences of patients or decision-makers, aligning with the practical needs of clinical decision-making. Its widespread use in clinical analysis underscores its value in providing a more comprehensive assessment of predictive models. Additionally, the C-index and AIC were incorporated into the validation of the model to enhance its accuracy and robustness. This complementary analysis reaffirmed the superiority of the combined ALB-CEA assessment over prognostic prediction using individual biomarkers such as ALB and CEA alone. Gastric cancer is a digestive tract tumor, characterized by a high risk of recurrence and metastasis. Its prognosis is worse compared to colon cancer. Previous studies have demonstrated that preoperative evaluation plays a pivotal role in the treatment of gastric cancer. Therefore, exploring an easy and accessible preoperative prognostic factor is beneficial to identify those patients who may have a poor prognosis and to provide them with an individualized treatment plan. Malnutrition is pervasive among tumor patients due to prolonged illness. Given the gastrointestinal tract's crucial role in digestion and absorption, patients with gastrointestinal tumors are particularly prone to malnutrition. ALB level, commonly used to evaluate nutritional status in clinical practice, has recently been used to predict the prognosis of tumors. Studies have consistently associated hypoalbuminemia with poor prognosis in various malignancies, including lung, breast, gastric, and colon cancers ( 25 – 29 ). Cancer is closely related to inflammation and hypoalbuminemia, and several long-term chronic inflammatory lesions eventually transform into cancer. Additionally, malnutrition due to chronic inflammation and cancer can also lead to hypoalbuminemia ( 30 – 35 ). CEA plays a crucial role in the diagnosis, prognosis prediction, and monitoring of postoperative recurrence of gastric cancer. Elevated preoperative CEA levels serve as an independent risk factor for poor prognosis among patients with gastric cancer. Previous studies have demonstrated that CEA combined with HB and Fibrinogen/Albumin Ratio (FAR) has superior predictive value for gastric cancer prognosis compared to the individual indices ( 23 , 24 ). Therefore, we proposed ALB-CEA as a new biomarker that combines a tumor marker and ALB to reflect nutritional status and predict the prognosis of patients with gastric cancer. The ALB-CEA prediction model proposed in this study has demonstrated notable success in predicting the prognosis of gastric cancer patients, as evidenced by its performance in both the training set of 164 patients and the validation set of 165 patients. In both sets, the AUC for ALB-CEA surpassed that for CEA or ALB, indicating its effectiveness and superiority as a prognostic predictor for patients undergoing radical surgery for gastric cancer. Moreover, the performance of the ALB-CEA model compared favorably with other combined biomarkers proposed in recent years, further substantiating its novelty and efficacy. Various validation methods were employed to evaluate the accuracy and robustness of this novel biomarker. Undeniably, there are several limitations to this study. First, being a single-center, retrospective study, there exists a potential for selection bias, compounded by the relatively modest sample size. A larger sample size and inclusion of patients from different geographic regions could have reduced the potential bias in the retrospective study design, resulting in more accurate results. Second, differences in the type of surgery and postoperative chemotherapy regimen can also influence patients' postoperative OS, potentially impacting the accuracy of the ALB-CEA scoring system. Third, the lack of disease-free survival data limits a comprehensive assessment of prognostic outcomes for patients with gastric cancer. Therefore, future research endeavors should focus on multi-center, prospective studies with larger sample sizes to confirm the predictive efficacy of ALB-CEA for the prognosis of patients with gastric cancer. Conclusion In summary, our retrospective study highlights preoperative ALB-CEA as an independent prognostic factor for overall survival (OS) in patients undergoing radical gastrectomy for gastric cancer. We demonstrated that ALB-CEA outperforms ALB or CEA alone in evaluating prognosis and confirmed the robustness of this prediction model using various methods. This novel biomarker has the advantages of convenience, low cost, and high reproducibility. The findings of our study hold promise for informing individualized and precise treatment strategies for gastric cancer in the future. Abbreviations CEA Carcinoembryonic antigen ALB Albumin ALB-CEA The combination of CEA and the ALB OS Overall survival ROC Receiver operating characteristic pTNM Pathological TNM stage LN metastasis Lymph node metastasis AUC Area under the curve C. Index Concordance index AIC Akaike information criteria CI Confidence interval Declarations Acknowledgments Not applicable. Author Contributions LJ and RSY conceptualized the study. Data curation was performed by ZHZ. The formal analysis was done by ZHZ, ZQS, and LJ. ZHZ are responsible for project administration and methodology. Resources and supervision were given by RSY. Standard software was used. The original draft was written by LJ and ZHZ. All authors validated, reviewed, and edited the manuscript. Funding This work was supported by grants from the Dalian Science and Technology Innovation Fund (No.2021JJ13SN65). From: Dalian Science and Technology Bureau. Availability of data and materials The data set that was created during the study is not publicly available due to the restriction of funder. However, suggestion for data analysis can be made to corresponding author. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Institutional Ethics Committee of the Second Affiliated Hospital of Dalian Medical University. Because the data is anonymous, the requirement for informed consent was waived. Consent for publication All authors approved the final manuscript and the submission to this journal. Conflict of interest The authors declare that they have no conflicts of interests. References Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32. Matsuoka T, Yashiro M. Biomarkers of gastric cancer: Current topics and future perspective. World J Gastroenterol. 2018;24(26):2818–32. 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McMillan DC, Watson WS, O'Gorman P, Preston T, Scott HR, McArdle CS. Albumin concentrations are primarily determined by the body cell mass and the systemic inflammatory response in cancer patients with weight loss. Nutr Cancer. 2001;39(2):210–3. Don BR, Kaysen G. Serum albumin: relationship to inflammation and nutrition. Semin Dial. 2004;17(6):432–7. Jensen GL, Mirtallo J, Compher C, Dhaliwal R, Forbes A, Grijalba RF, et al. Adult starvation and disease-related malnutrition: a proposal for etiology-based diagnosis in the clinical practice setting from the International Consensus Guideline Committee. Clin Nutr. 2010;29(2):151–3. Marcason W. Should Albumin and Prealbumin Be Used as Indicators for Malnutrition? J Acad Nutr Diet. 2017;117(7):1144. Gupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010;9:69. Additional Declarations No competing interests reported. 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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-4380786","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301293096,"identity":"c5e744d5-cd17-4bdb-b82d-a8d9b1f0aaa5","order_by":0,"name":"Jie Li","email":"","orcid":"","institution":"Shenzhen University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Li","suffix":""},{"id":301293097,"identity":"c99c2520-95c5-45d8-b691-6203db8c1583","order_by":1,"name":"Haozong Zhao","email":"","orcid":"","institution":"Hongqi Hospital Affiliated to Mudanjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haozong","middleName":"","lastName":"Zhao","suffix":""},{"id":301293098,"identity":"c8370145-d105-486b-8f61-6cdd835a56cc","order_by":2,"name":"Qianshi Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qianshi","middleName":"","lastName":"Zhang","suffix":""},{"id":301293099,"identity":"b59da818-92a3-43a1-8245-c5a8003194fe","order_by":3,"name":"Shuangyi Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYJACZgjF2CDxoEJCTp40LQlnLIwNG4jXwsAgkdhWkchwgIBy8/bew58L2+zy5COSG28kzpNIYGxgfvjoBh4tMmfOpUnPbEsuNryR2GyRuE0ij52Bzdg4B48WCYkcM2beNubEjTMS2ySAWooZG3jYpPFqkX9j/Jm3rR6qZY5EYsMBQlokeAykedsOJ86XAGlpIEYLT46ZNM+544kbeB42WyQckzA2bCbkF/Yzxp95yqoT57enP7zxoaZOTp69+eFjfFrgwOBCApTFjE8ZMpDvP0Cs0lEwCkbBKBhpAADkPEnssjDrGQAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shuangyi","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-05-07 06:47:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4380786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4380786/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56563768,"identity":"a537da2a-fa55-49f9-aa67-07bba3e02306","added_by":"auto","created_at":"2024-05-15 22:40:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":198169,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics curve analysis of ALB, CEA, and ALB-CEA for OS in patients with gastric cancer (the training set, A; the validation set, B).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4380786/v1/c6d5c2d2f1d8c6b04ca134a5.png"},{"id":56564498,"identity":"489e8dfd-2bcd-4bb4-ba03-2d68b03810cb","added_by":"auto","created_at":"2024-05-15 22:48:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63296,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for OS according to ALB-CEA score in patients with gastric cancer (the training set, A; the validation set, B).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4380786/v1/753b12762648ed421b87dda8.png"},{"id":56563767,"identity":"ba81cb8b-f279-4ba3-9a2c-6d09ff732829","added_by":"auto","created_at":"2024-05-15 22:40:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119878,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of CEA, ALB and ALB-CEA grade for predicting overall survival in the training set and validation set (the training set, A; the validation set, B).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4380786/v1/3a42ad4ceaf8c05f76095b8d.png"},{"id":56563769,"identity":"b21078cc-4748-41f3-a276-8276fe45b81e","added_by":"auto","created_at":"2024-05-15 22:40:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":387107,"visible":true,"origin":"","legend":"\u003cp\u003eDCA for OS of three assessment methods (the training set, A; the validation set, B).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4380786/v1/b4b00247317f2f7c4d79467d.png"},{"id":66058357,"identity":"0fc63df5-9df8-4232-9e4a-0df0861be70a","added_by":"auto","created_at":"2024-10-07 09:39:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1452470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4380786/v1/01daddd5-ef5d-4bf6-b08d-73115fa9f627.pdf"},{"id":56563764,"identity":"d21d4955-170a-4da4-97bc-2a2c1c4a3536","added_by":"auto","created_at":"2024-05-15 22:40:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":163765,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4380786/v1/9e6650d30f86c4696a4f16a7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Albumin plus CEA: a novel biomarker for predicting prognosis in resectable gastric cancer: a case-control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer is the second most prevalent cancer and the third most deadly cancer among all cancers in China (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Surgery is currently the major treatment strategy for gastric cancer. However, the prognosis of gastric cancer remains unfavorable due to the high recurrence rate and risk of distant metastasis after surgery. Thus, the pursuit of a straightforward and readily accessible biomarker holds the potential to enhance the accuracy of prognosis prediction before surgery for gastric cancer patients. This, in turn, would enable more precise and tailored treatment strategies for individual patients, ultimately aiming to improve the survival rates of patients with gastric cancer.\u003c/p\u003e \u003cp\u003eGastric cancer biomarkers encompass pathological signals within the tumor tissue, genetic or epigenetic changes, and non-invasive biomarkers, such as those derived from blood or gastric fluid. These biomarkers can predict, facilitating personalized treatment strategies for various gastric cancer subtypes post-surgery and enabling the development of effective postoperative treatment plans for more patients with cancer (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Previous studies have demonstrated that several common biomarkers of systemic inflammation, including the neutrophil-to-lymphocyte ratio (NLR) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), lymphocyte-to-monocyte ratio (LMR) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), platelet-to-lymphocyte ratio (PLR) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), albumin, and several other common biomarkers can be used to predict the prognosis of gastric cancer. Malnutrition is frequently observed in patients with cancer pre-surgery, with serum albumin levels commonly reflecting this condition. Hypoalbuminemia, indicative of poor nutritional status, correlates with an increased risk of malnutrition. Additionally, malnutrition can lead to a compromise in the body's immunity, which is often more dangerous for patients with cancer, and potentially leads to postoperative complications (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Several studies have linked hypoalbuminemia to poor prognosis in patients with colon cancer (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), pancreatic cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), non-small-cell lung cancer (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), ovarian cancer (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and gastric cancer (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe discovery of tumor markers constitutes a pivotal advancement in tumor diagnosis and treatment. Over recent years, these markers have not only provided valuable diagnostic insights but have also assumed a significant role in prognostic evaluation. Carcinoembryonic antigen (CEA), among these markers, was discovered by Gold and Freedman in 1965 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). CEA has a salivary rockweed glycosylated glycoform that acts as a selectin ligand and promotes the metastasis of colon cancer cells (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and several other organ cancer cells (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Additionally, CEA has an excellent diagnostic and prognostic value in gastrointestinal tumors. Elevated preoperative CEA levels in patients with gastric cancer have been identified as an independent risk factor for unfavorable prognosis (\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Moreover, combining CEA with other tumor markers has demonstrated efficacy in prognostic prediction for patients with gastric cancer in previous studies (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This underscores the complementary role of tumor markers alongside traditional TNM staging, nerve invasion, and cancer embolism in predicting gastric cancer prognosis.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to evaluate the predictive efficacy of CEA combined with albumin (ALB-CEA) as a new biomarker for predicting the prognosis of patients with gastric cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eA total of 329 patients with primary gastric cancer who underwent radical gastrectomy in the Second Affiliated Hospital of Dalian Medical University from January 2010 to February 2017 were included in this study. Data collected were retrospectively evaluated. Patients who underwent surgery from January 2010 to December 2015 comprised the training set, while those who underwent surgery from January 2016 to February 2017 comprised the validation set. Clinical data, including age, gender, lesion diameter, tumor location, differentiation, nerve invasion, cancer embolism, pathological TNM stage (pTNM), lymph node metastasis, depth of invasion, and survival status, was collected from the hospital information system. Routine diagnostic and treatment procedures included the collection of preoperative blood samples from each patient to detect serum albumin and CEA levels, alongside other pertinent indicators such as hemoglobin, liver and kidney function, and electrolyte levels. Peripheral blood samples were obtained from fasting patients within one week before surgery. Outpatient and telephone follow-up after discharge are part of routine treatment. After discharge, all patients underwent telephone and outpatient follow-ups every 1\u0026ndash;3 months until either death or the conclusion of the study. The last follow-up was in January 2022. Survival time was defined as the duration from the date of diagnosis to the date of death or the last follow-up.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe optimal cutoff values of ALB and CEA in the training set were determined using receiver operating characteristic (ROC) curve analysis, and ALB-CEA scores were subsequently derived based on these values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants in the training set were further divided into three groups based on their ALB-CEA scores: ALB-CEA\u0026thinsp;=\u0026thinsp;0 (CEA\u0026thinsp;\u0026le;\u0026thinsp;4.77 ng/mL and ALB\u0026thinsp;\u0026gt;\u0026thinsp;41.47 g/L), ALB-CEA\u0026thinsp;=\u0026thinsp;1 (CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 ng/mL or ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 g/L), and ALB-CEA\u0026thinsp;=\u0026thinsp;2 (CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 ng/mL and ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 g/L) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To assess the robustness and applicability of this scoring system, the two optimal cutoff values obtained from the training set were applied to the validation set for ALB-CEA scoring and subsequent analysis. The chi-square or Fisher\u0026rsquo;s exact test was used to analyze categorical variables. Postoperative survival analysis for the two groups of gastric cancer patients was conducted using the Kaplan-Meier method. Univariate analysis was performed to identify statistically significant variables, which were then included in the multivariate analysis to ascertain independent prognostic factors. SPSS 27.0 and R software were used for statistical analysis. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Furthermore, R software (version 4.3.3) was used to the ability of the combined ALB-CEA scoring system to predict overall survival (OS) was compared with that of CEA or ALB values alone using the consistency index (C-index) values, the area under the curve (AUC), Akaike information criteria (AIC), and decision curve analysis (DCA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrognostic Scores of CEA, ALB and ALB-CEA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScoring System\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealbumin (g/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;41.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;41.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEA (ng/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ealbumin-CEA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u0026thinsp;\u0026gt;\u0026thinsp;41.47 and CEA\u0026thinsp;\u0026le;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u0026thinsp;\u0026gt;\u0026thinsp;41.47 and CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 and CEA\u0026thinsp;\u0026le;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 and CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 Patient characteristics\u003c/p\u003e \u003cp\u003eThe training set comprised a total of 164 patients diagnosed with gastric cancer, of which 115 were male (70.1%) and 49 were female (29.9%). Among these patients, 105 (64.0%) were aged 60 years or older, while 59 (36.0%) were younger than 60 years. For the validation set, 165 patients with gastric cancer were included, consisting of 113 males (68.5%) and 52 females (31.5%). Within this group, 108 patients (65.5%) were aged 60 years or older, while 57 patients (34.5%) were younger than 60 years. The pathological and clinical characteristics of the two groups of patients in this study are presented in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e3.2 Relationship between preoperative ALB and CEA and clinicopathological features\u003c/p\u003e \u003cp\u003ePreoperative ALB and CEA levels were correlated with postoperative survival and clinicopathological features in the two groups (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the training set, there was a significant difference in survival rate between patients with CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 ng/mL and those with ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47g/L, evidenced by a significantly lower survival rate. This observation was further corroborated in the validation set, where outcomes mirrored those observed in the training set.\u003c/p\u003e \u003cp\u003e3.3 Optimal cutoff values for ALB and CEA\u003c/p\u003e \u003cp\u003eIn the training set, the optimal cutoff values for CEA and ALB for predicting OS were 4.77 ng/mL and 41.47 g/L, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Furthermore, the AUC of ALB-CEA for predicting OS was slightly higher than that of ALB or CEA alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), suggesting that the combination of CEA and ALB has better predictive efficacy than CEA or ALB alone in patients with gastric cancer. Similar results were confirmed in the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e3.4 Prognostic value of ALB and CEA levels\u003c/p\u003e \u003cp\u003eThe prognostic value of CEA and ALB levels was evaluated using Kaplan-Meier analysis, and the results revealed that the low CEA group exhibited significantly higher OS rates compared to the high CEA group. Similar findings were obtained for median OS time. Additionally, the high ALB group displayed elevated OS rates and longer median OS times compared to the low ALB group. These findings remained consistent across both the training and validation sets (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.5 Prognostic analysis based on ALB-CEA score\u003c/p\u003e \u003cp\u003eThe pathological and clinical characteristics of patients stratified by ALB-CEA score are presented in Supplementary Table S2. Notably, the ALB-CEA score was closely related to survival in both training and validation sets, with higher scores indicating a poorer prognosis.\u003c/p\u003e \u003cp\u003e3.6 Univariate and multivariate survival analysis\u003c/p\u003e \u003cp\u003eThe univariate and multivariate analyses revealed that ALB-CEA was an influencing factor for OS among patients with gastric cancer post-surgery. Kaplan-Meier analysis of patients with ALB-CEA scores of 0, 1, and 2 in the two groups showed that higher ALB-CEA scores were associated with poorer OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the training set, both univariate and multivariate analysis revealed that ALB-CEA was significantly associated with prognosis, and ALB-CEA was an independent prognostic factor for OS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, patients with higher ALB-CEA scores were more likely to experience a poor prognosis. Similar findings were observed using the validation set (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Analyses of Clinicopathological Characteristics in patients with gastric cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eThe training set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eThe validation set\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (%95 CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (%95 CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (%95 CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (%95 CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.892\u0026ndash;2.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.710\u0026ndash;2.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.979\u0026ndash;2.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.520\u0026ndash;5.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle and lower third\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper third\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.868\u0026ndash;2.506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.706\u0026ndash;2.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.045*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell/Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.938\u0026ndash;2.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.013\u0026ndash;3.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter of lesion (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.350\u0026ndash;3.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.268\u0026ndash;6.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(1.103\u0026ndash;3.189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer embolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.267\u0026ndash;5.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.384\u0026ndash;3.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.736\u0026ndash;5.867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.710\u0026ndash;3.978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.101\u0026ndash;2.645)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.021\u0026ndash;5.680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epTNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.568\u0026ndash;6.890)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.326\u0026ndash;3.851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.158\u0026ndash;11.615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(1.680\u0026ndash;6.692)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth of invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.409\u0026ndash;7.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.918\u0026ndash;12.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1/N1/N2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.508\u0026ndash;7.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.841\u0026ndash;10.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA levels (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.587\u0026ndash;3.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.216\u0026ndash;6.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB levels (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;41.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;41.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.602\u0026ndash;4.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.081\u0026ndash;6.310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB-CEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.308\u0026ndash;4.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.030\u0026ndash;3.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.423\u0026ndash;4.767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(1.621\u0026ndash;7.630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.661\u0026ndash;8.826)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.546\u0026ndash;5.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4.844\u0026ndash;18.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(4.128\u0026ndash;22.251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: ALB-CEA: ALB\u0026thinsp;\u0026gt;\u0026thinsp;41.47 and CEA\u0026thinsp;\u0026le;\u0026thinsp;4.77 represent 0; CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 or ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 represent 1; CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 and ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 represent 2.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eALB-CEA: ALB\u0026thinsp;\u0026gt;\u0026thinsp;41.47 and CEA\u0026thinsp;\u0026le;\u0026thinsp;4.77 represent 0; CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 or ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 represent 1; CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 and ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 represent 2.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e3.7 Predictive performance of ALB-CEA compared to CEA or ALB alone\u003c/p\u003e \u003cp\u003eThe scoring criteria developed were applied to the training set to facilitate a more intuitive comparison between ALB-CEA and CEA or ALB alone as predictors of the AUC, alongside testing the adaptability of the training set to the scoring criteria. The C-index, AUC, and AIC were used to evaluate the predictive performance of the ALB-CEA score versus ALB or CEA alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Supplementary Table S3). In the training set, the ALB-CEA score exhibited the highest C-index and AUC, as well as the lowest AIC, surpassing the predictive performance of ALB or CEA alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Supplementary Table S3). Similar findings were observed using the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Supplementary Table S3). Additionally, DCA revealed that ALB-CEA, as a new biomarker, has better clinical practicability compared to ALB or CEA alone, which are two conventional biomarkers for predicting prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings suggest that ALB-CEA can be a better prognostic predictor than ALB or CEA alone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHematological biomarkers identified from peripheral blood samples, such as CEA and albumin, are easy to obtain and inexpensive and can predict the prognosis of patients with cancer. This study reaffirms the association between peripheral blood biomarkers in patients with resectable gastric cancer and their final clinical outcomes. Previous studies have established correlations between lower preoperative albumin levels, higher CEA levels, older age, lower degree of differentiation, later postoperative pathological stage, deeper depth of invasion, greater number of lymph node metastasis, presence of vascular tumor thrombus, nerve invasion, and poor prognosis. In the cohort of 329 patients studied, ALB-CEA scores were associated with prognosis, with higher scores indicating a higher likelihood of poor prognosis and tumor progression. Consequently, ALB-CEA emerges as a novel prognostic biomarker strongly indicative of the prognosis of patients with gastric cancer, owing to the association between patient-tumor characteristics and tumor progression. A study explored the combinations of CEA with HB (hemoglobin) (HB-CEA) to predict gastric cancer prognosis (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and the predictive efficacy was examined using the AUC of the ROC. In the training set, the AUC of HB-CEA was 0.677, which was lower than that of ALB-CEA (0.703) in this experiment. A similar prediction efficacy was demonstrated in the validation set, where the AUC of HB-CEA was 0.670, which was lower than the AUC of ALB-CEA (0.776). A comparison with another previous study showed similar results (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Because there are some differences between the studied samples, the AUC of the two models was not enough to determine the superiority of our model, so we subsequently validated the model. The utilization of the AUC of the ROC solely assesses the diagnostic accuracy of a prediction model, potentially overlooking the clinical utility and implications of false negatives or false positives. To address this limitation, DCA was employed to further evaluate the predictive efficacy of the ALB-CEA model. Unlike ROC analysis, DCA incorporates the preferences of patients or decision-makers, aligning with the practical needs of clinical decision-making. Its widespread use in clinical analysis underscores its value in providing a more comprehensive assessment of predictive models. Additionally, the C-index and AIC were incorporated into the validation of the model to enhance its accuracy and robustness. This complementary analysis reaffirmed the superiority of the combined ALB-CEA assessment over prognostic prediction using individual biomarkers such as ALB and CEA alone.\u003c/p\u003e \u003cp\u003eGastric cancer is a digestive tract tumor, characterized by a high risk of recurrence and metastasis. Its prognosis is worse compared to colon cancer. Previous studies have demonstrated that preoperative evaluation plays a pivotal role in the treatment of gastric cancer. Therefore, exploring an easy and accessible preoperative prognostic factor is beneficial to identify those patients who may have a poor prognosis and to provide them with an individualized treatment plan. Malnutrition is pervasive among tumor patients due to prolonged illness. Given the gastrointestinal tract's crucial role in digestion and absorption, patients with gastrointestinal tumors are particularly prone to malnutrition. ALB level, commonly used to evaluate nutritional status in clinical practice, has recently been used to predict the prognosis of tumors. Studies have consistently associated hypoalbuminemia with poor prognosis in various malignancies, including lung, breast, gastric, and colon cancers (\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Cancer is closely related to inflammation and hypoalbuminemia, and several long-term chronic inflammatory lesions eventually transform into cancer. Additionally, malnutrition due to chronic inflammation and cancer can also lead to hypoalbuminemia (\u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). CEA plays a crucial role in the diagnosis, prognosis prediction, and monitoring of postoperative recurrence of gastric cancer. Elevated preoperative CEA levels serve as an independent risk factor for poor prognosis among patients with gastric cancer. Previous studies have demonstrated that CEA combined with HB and Fibrinogen/Albumin Ratio (FAR) has superior predictive value for gastric cancer prognosis compared to the individual indices (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Therefore, we proposed ALB-CEA as a new biomarker that combines a tumor marker and ALB to reflect nutritional status and predict the prognosis of patients with gastric cancer.\u003c/p\u003e \u003cp\u003eThe ALB-CEA prediction model proposed in this study has demonstrated notable success in predicting the prognosis of gastric cancer patients, as evidenced by its performance in both the training set of 164 patients and the validation set of 165 patients. In both sets, the AUC for ALB-CEA surpassed that for CEA or ALB, indicating its effectiveness and superiority as a prognostic predictor for patients undergoing radical surgery for gastric cancer. Moreover, the performance of the ALB-CEA model compared favorably with other combined biomarkers proposed in recent years, further substantiating its novelty and efficacy. Various validation methods were employed to evaluate the accuracy and robustness of this novel biomarker. Undeniably, there are several limitations to this study. First, being a single-center, retrospective study, there exists a potential for selection bias, compounded by the relatively modest sample size. A larger sample size and inclusion of patients from different geographic regions could have reduced the potential bias in the retrospective study design, resulting in more accurate results. Second, differences in the type of surgery and postoperative chemotherapy regimen can also influence patients' postoperative OS, potentially impacting the accuracy of the ALB-CEA scoring system. Third, the lack of disease-free survival data limits a comprehensive assessment of prognostic outcomes for patients with gastric cancer. Therefore, future research endeavors should focus on multi-center, prospective studies with larger sample sizes to confirm the predictive efficacy of ALB-CEA for the prognosis of patients with gastric cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our retrospective study highlights preoperative ALB-CEA as an independent prognostic factor for overall survival (OS) in patients undergoing radical gastrectomy for gastric cancer. We demonstrated that ALB-CEA outperforms ALB or CEA alone in evaluating prognosis and confirmed the robustness of this prediction model using various methods. This novel biomarker has the advantages of convenience, low cost, and high reproducibility. The findings of our study hold promise for informing individualized and precise treatment strategies for gastric cancer in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003cp\u003eCEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Carcinoembryonic antigen\u003c/p\u003e\n \u003cp\u003eALB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Albumin\u003c/p\u003e\n \u003cp\u003eALB-CEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The combination of CEA and the ALB\u003c/p\u003e\n \u003cp\u003eOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Overall survival\u003c/p\u003e\n \u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Receiver operating characteristic\u003c/p\u003e\n \u003cp\u003epTNM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pathological TNM stage\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLN metastasis \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Lymph node metastasis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the curve\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC. Index \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Concordance index\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Akaike information criteria\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLJ\u0026nbsp;and RSY\u0026nbsp;conceptualized the study. Data curation was performed by ZHZ. The formal analysis was done by ZHZ, ZQS,\u0026nbsp;and LJ. ZHZ\u0026nbsp;are responsible for project administration and methodology. Resources and supervision were given by RSY. Standard software was used. The original draft was written by LJ and ZHZ.\u0026nbsp;All authors validated, reviewed, and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Dalian Science and Technology Innovation Fund (No.2021JJ13SN65). From: Dalian Science and Technology Bureau.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data set that was created during the study is not publicly available due to the restriction of funder. However, suggestion for data analysis can be made to corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Institutional Ethics Committee of the Second Affiliated Hospital of Dalian Medical University.\u0026nbsp;Because the data is anonymous, the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and the submission to this journal.\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 conflicts of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuoka T, Yashiro M. Biomarkers of gastric cancer: Current topics and future perspective. World J Gastroenterol. 2018;24(26):2818\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Liu X, Liu J, Chen S, Xu D, Li W, et al. Preoperative neutrophil-to-lymphocyte ratio plus platelet-to-lymphocyte ratio in predicting survival for patients with stage I-II gastric cancer. Chin J Cancer. 2016;35(1):57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan JC, Chan DL, Diakos CI, Engel A, Pavlakis N, Gill A, et al. The Lymphocyte-to-Monocyte Ratio is a Superior Predictor of Overall Survival in Comparison to Established Biomarkers of Resectable Colorectal Cancer. Ann Surg. 2017;265(3):539\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee BM, Chung SY, Chang JS, Lee KJ, Seong J. The Neutrophil-Lymphocyte Ratio and Platelet-Lymphocyte Ratio Are Prognostic Factors in Patients with Locally Advanced Pancreatic Cancer Treated with Chemoradiotherapy. Gut Liver. 2018;12(3):342\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng J, Li H, Ou Q, Lin J, Wu X, Lu Z, et al. Preoperative lymphocyte-to-monocyte ratio represents a superior predictor compared with neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios for colorectal liver-only metastases survival. Onco Targets Ther. 2017;10:3789\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto M, Saito H, Uejima C, Tanio A, Tada Y, Matsunaga T, et al. Combination of Serum Albumin and Cholinesterase Levels as Prognostic Indicator in Patients ith Colorectal Cancer. 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Nutr J. 2010;9:69.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"albumin, CEA, gastric cancer, Postoperative mortality","lastPublishedDoi":"10.21203/rs.3.rs-4380786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4380786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePreoperative high levels of serum carcinoembryonic antigen (CEA) and low levels of albumin (ALB) are closely related to poor prognosis among patients with gastric cancer. This study aims to determine the prognostic value of preoperative serum ALB plus CEA levels as a new biomarker in patients with resectable gastric cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 329 patients with gastric cancer were included in this study. The optimal cutoff values of ALB and CEA were 4.77 ng/mL and 41.47 g/L, respectively. Patients were stratified into three groups based on these cutoff values: ALB-CEA\u0026thinsp;=\u0026thinsp;0 (ALB\u0026thinsp;\u0026gt;\u0026thinsp;41.47 g/L and CEA\u0026thinsp;\u0026le;\u0026thinsp;4.77 ng/mL), ALB-CEA\u0026thinsp;=\u0026thinsp;1 (ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 g/L or CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 ng/mL), and ALB-CEA\u0026thinsp;=\u0026thinsp;2 (ALB\u0026thinsp;\u0026le;\u0026thinsp;41.47 g/L and CEA\u0026thinsp;\u0026gt;\u0026thinsp;4.77 ng/mL). Kaplan-Meier curve and Cox proportional model were used to determine the predictive effect of the biomarker on the overall survival (OS) of patients in the training and validation sets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eALB-CEA had a larger area under the curve than ALB or CEA alone (0.703, 0.671, 0.635 in the validation set; 0.776, 0.694, 0.616 in the validation set respectively). The Kaplan-Meier curve revealed that higher ALB-CEA scores were indicative of lower survival rates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the multivariate analysis revealed that ALB-CEA was an independent risk factor for poor prognosis in patients with gastric cancer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePreoperative ALB-CEA may be a new biomarker for predicting the prognosis of patients with gastric cancer. For those patients with higher preoperative ALB-CEA scores, more extensive postoperative follow-up should be performed to detect tumor progression early and intervene in time.\u003c/p\u003e","manuscriptTitle":"Albumin plus CEA: a novel biomarker for predicting prognosis in resectable gastric cancer: a case-control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 22:40:44","doi":"10.21203/rs.3.rs-4380786/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d18a3bcf-1aea-4a19-b94a-17769fae63cf","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T09:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 22:40:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4380786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4380786","identity":"rs-4380786","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}