Predictive Significance Of Preoperative Inflammatory Markers In Postoperative Lung Metastasis Of Colorectal Cancer

Predictive Significance Of Preoperative Inflammatory Markers In Postoperative Lung Metastasis Of Colorectal Cancer | 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 Predictive Significance Of Preoperative Inflammatory Markers In Postoperative Lung Metastasis Of Colorectal Cancer Jiukang Sun, Weili Xiong, Zhang Fang, Chuanjun Song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3923170/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 Purpose: This study aims to investigate the predictive significance of inflammatory markers for postoperative lung metastasis in colorectal cancer (CRC) patients. The focus is on exploring the relationship between traditional inflammatory markers, such as NLR, PLR, and newly introduced indices like CALLY, and their association with CRC progression and lung metastasis. Methods: A retrospective review of medical records from 303 CRC patients at the First Affiliated Hospital of Nanjing Medical University (January 2014 to December 2023) was conducted. Various inflammatory markers, baseline characteristics, and survival data were analyzed. The CALLY index, integrating PNI and CRP levels, was introduced. Statistical analyses, including univariate and multivariate Cox regression and logistic regression, were performed to determine associations between inflammatory factors and CRC outcomes. Results: Among the patients, 55 developed lung metastasis during the study period. The results revealed that lower PNI, LMR, and CALLY were significantly associated with reduced postoperative progression-free survival (PFS). Moreover, CEA, PNI, and CALLY emerged as independent risk factors for postoperative lung metastasis. Conclusion: In conclusion, this study underscores the importance of inflammatory markers in predicting postoperative outcomes for CRC patients. Lower PNI, LMR, and CALLY were identified as significant predictors of reduced PFS, while CEA, PNI, and CALLY were independently associated with an increased risk of postoperative lung metastasis. These findings highlight the potential clinical utility of these inflammatory markers in monitoring CRC recurrence and metastasis. colorectal cancer inflammatory markers progression-free survival lung metastasis Figures Figure 1 Figure 2 1. Introduction Colorectal cancer (CRC) is one of the most common malignancies globally and a leading cause of cancer-related deaths[1]. Current treatment modalities for CRC include surgery and chemotherapy, achieving a 90% 5-year survival rate for patients in various stages. However, once distant metastasis occurs, the prognosis remains poor[2]. Postoperative metastasis is a major cause of death in CRC patients and is closely associated with tumor resistance[3]. The liver is the most common site of CRC metastasis, followed by the lungs[4]. While extensive research has focused on postoperative liver metastasis in CRC patients, there is currently a lack of reports on lung metastasis[5]. In contrast to liver metastasis, patients with lung metastasis exhibit a better prognosis[6]. Therefore, there is a need to develop specific approaches for predicting lung metastasis. A substantial body of research confirms that systemic inflammation is an integral component of the tumor immune microenvironment and plays a crucial role in the initiation, progression, and metastasis of tumors[7, 8]. Studies indicate a correlation between chronic inflammation and over 15% of human cancers[9]. Uncontrolled inflammation can promote tumor advancement through mechanisms such as DNA damage and reduced genomic stability[10]. Several traditional inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin-to-globulin ratio (AGR), C-reactive protein (CRP)-to-albumin ratio (CAR), lymphocyte-to-CRP ratio (LCR), systemic immune-inflammation index (SIRI), lymphocyte-to-monocyte ratio (LMR), and prognostic nutritional index (PNI), have been demonstrated to hold predictive value for tumor progression and metastasis[8, 11–13]. The CRP–albumin–lymphocyte (CALLY) index, developed by Müller et al. in 2021, represents a novel inflammatory marker that integrates both the Prognostic Nutritional Index (PNI) and the CRP level[14]. The CALLY index has been demonstrated to be associated with the prognosis of liver cancer and CRC, with higher CALLY index values correlating with longer survival in patients[14, 15]. However, the relationship between these inflammatory markers and postoperative lung metastasis in CRC patients remains unclear. Therefore, our study aims to explore the predictive significance of inflammatory markers for postoperative lung metastasis in CRC patients. 2. Methods We retrospectively reviewed the medical records of patients diagnosed with primary CRC at the First Affiliated Hospital of Nanjing Medical University from January 2014 to December 2023. Inclusion criteria were age > 18 years, histological or cytological confirmation of primary CRC, undergoing curative surgery for CRC, and sufficient clinical and hematological data. Exclusion criteria were acute inflammation at the time of diagnosis, concomitant inflammatory bowel disease, receipt of neoadjuvant treatment, presence of other malignancies, and concurrent chronic inflammatory conditions. For each eligible patient, we collected the following information: 1. Basic patient information, including gender, age, tumor pathology, smoking history, alcohol consumption history, etc.; 2. Peripheral blood indices within the week before surgery, including routine blood tests and biochemistry; 3. Treatment details, such as postoperative chemotherapy; 4. Survival time. The survival data represent progression-free survival (PFS). PFS is defined as the time from the initiation of operation to radiological or clinical progression or death from any cause. Patients who had no progression or death at the last follow-up were considered censored. The diagnosis of metastasis was independently confirmed by two radiologists through CT scans. The formulas for the relevant indices in this study are as follows: NLR = N/L; PLR = P/L; LMR = L/M; PNI = A (g/L) + 5×L(×10^9/L); AGR = A/G; CAR = C/A; LCR = L/C; CALLY = A (g/L) * L(×10^9/L) / (C(mg/L) * 10); SIRI = N * M / L; LA = L(×10^9/L) * A(g/L). Here, N, L, P, and M represent neutrophil, lymphocyte, platelet, and monocyte counts, respectively. A, G, and C represent albumin, globulin, and C-reactive protein. The optimal cutoff values were calculated using the Youden index, resulting in the following cutoff values: NLR, 1.78; PLR, 157.79; PNI, 52.375; AGR, 1.15; CAR, 0.12; LCR, 0.41; LMR, 2.08; CALLY, 2.29; SIRI, 0.87; LA, 75.41; CEA, 15.33; CA199, 11.21. Data analysis was conducted using IBM SPSS version 27.0. Kaplan-Meier curves were employed to analyze progression-free survival (PFS). All variables underwent univariate Cox regression analysis, and only those with a significance level of P < 0.05 were included in the multivariate Cox regression analysis. Logistics regression analysis were used to explore the associations between inflammatory factors and baseline characteristic. A two-tailed P-value < 0.05 was considered statistically significant. This study obtained approval from the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University and adheres to the principles of the Helsinki Declaration. As a retrospective study, the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University waived the requirement for written informed consent. 3. Result 3.1. Baseline characteristic This study included a total of 303 patients with an average age of 62 years. Among them, 175 (57.8%) were male, and 128 (42.2%) were female. The mean follow-up duration was 22.2 months, during which 124 patients experienced progression, with 55 patients developing lung metastasis at the last follow-up. The specific baseline characteristics and inflammatory markers of the patients are presented in Table 1. Table 1 Baseline clinicopathological characteristics of patients. Characteristics Number(%) (n = 303) Age (years) Median 62 70 90(29.7) Gender Male 175(57.8) Female 128(42.2) BMI (kg/m 2 ) 24 43(14.2) Tumor location Right 118(38.7) Left 185(61.3) Smoking status Yes 33(10.9 No 270(89.1) Drinking status Yes 19(6.3) No 284(93.7) Hypertension Yes 94(31) No 209(69) Diabetes Yes 25(8.3) No 278(91.7) Hyperlipemia Yes 175(57.8) No 128(42.2) Adjuvant chemotherapy Yes 290(95.7) No 13(4.3) T stage 2 21(6.9) 3 226(74.6) 4 56(18.5) N stage 0 109(36) 1 137(45.2) 2 57(18.8) TNM stage Ⅱ 107(35.3) Ⅲ 196(64.7) Tumor size (maximum diameter/cm) < 2.5 39(12.9) 2.5-5.0 189(62.4) 5 75(24.8) Differentiation Mod 222(73.3) Poor 81(26.7) Venous invasion Yes 202(66.7) No 101(33.3) Tumor deposits Yes 261(86.1) No 42(13.9) Lymph-node metastasis Yes 109(35.9) No 194(64.1) CEA High 58(19.1) Low 245(80.9) CA199 High 167(55.1) Low 134(44.9) NLR High 199(65.7) Low 104(34.3) PLR High 132(43.6) Low 171(56.4) PNI High 249(82.2) Low 54(17.8) AGR High 268(88.4) Low 35(11.6) CAR High 116(38.3) Low 187(61.7) LCR High 174(57.4) Low 129(42.6) LMR High 249(92.1) Low 24(7.9) CALLY High 138(45.5) Low 165(54.5) SIRI High 159(52.5) Low 144(47.5) LA High 104(34.3) Low 199(65.7) Abbreviations: NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; PNI, prognostic nutritional index; AGR, albumin- globulin ratio; CAR, C-reactive protein-albumin ratio; LCR, lymphocyte-C-reactive protein ratio; LMR, lymphocyte–monocyte ratio; CALLY,CRP–albumin–lymphocyte index; SIRI, systemic immune-inflammation index; LA, lymphocyte–albumin product, CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199. 3.2. Associations between inflammatory markers and PFS We conducted univariate regression Cox analysis on the baseline characteristics and inflammatory markers of the patients. The results indicated that age, hypertension, diabetes, hyperlipemia, adjuvant chemotherapy, T stage, N stage, TNM stage, tumor differentiation, venous invasion, tumor deposits, lymph-node metastasis, CEA, CA199, NLR, PNI, AGR, CAR, LCR, LMR, CALLY, and SIRI were significant influencing factors (all p < 0.05). Subsequently, these factors were included in multivariate Cox regression analysis. We found that the PFS of CRC patients was independently associated with T stage (HR 1.664, 95% CI 1.092–2.534; p = 0.018, Table 2 ), N stage (HR 2.159, 95% CI 1.307–3.568; p = 0.003, Table 2 ), CEA (HR 2.237, 95% CI 1.398–3.579; p < 0.001, Table 2 ), PNI (HR 0.503, 95% CI 0.307–0.825; p = 0.006, Table 2 ), LMR (HR 0.544, 95% CI 0.297–0.993; p = 0.047, Table 2 ), and CALLY (HR 0.375, 95% CI 0.175–0.801; p = 0.011, Table 2 ). The Kaplan–Meier survival curve also indicates that patients with earlier T stage (1 versus 2 versus 3, 48.9 versus 45.8 versus 19.4 months, P < 0.001, Fig. 1), earlier N stage (0 versus 1 versus 2, 62.5 versus 40.3 versus 27.5 months, P < 0.001, Fig. 1), low CEA group (High versus Low, 18 versus 45.8 months, P < 0.001, Fig. 1), high PNI group (High versus Low, 44.6 versus 32.9 months, P = 0.034, Fig. 1), high LMR group (High versus Low, 48.9 versus 19.4 months, P < 0.001, Fig. 1), and high CALLY group (High versus Low, 62.5 versus 21.5 months, P 70/<70 1.891 1.316–2.718 < 0.001 1.891 1.316–2.718 24/18.5–24/<18.5 2.932 0.586–4.238 0.421 Tumor location Right/Left 0.824 0.576–1.181 0.292 Smoking status Yes/No 1.013 0.58–1.769 0.963 Drinking status Yes/No 0.958 0.467–1.966 0.908 Hypertension Yes/No 1.489 1.033–2.146 0.033 1.052 0.656–1.686 0.834 Diabetes Yes/No 1.912 1.144–3.195 0.013 0.901 0.456–1.781 0.765 Hyperlipemia Yes/No 1.715 1.179–2.494 0.005 1.458 0.956–2.225 0.08 Adjuvant chemotherapy Yes/No 0.335 0.188–0.599 < 0.001 0.412 0.166–1.02 0.055 T stage 2/3/4 2.196 1.546–3.118 < 0.001 1.664 1.092–2.534 0.018 N stage 0/1/2 1.594 1.253–2.027 < 0.001 2.159 1.307–3.568 0.003 TNM stage II/III 1.959 1.307–2.934 0.001 1.546 0.458–5.214 0.482 Tumor size(maximum diameter/cm) < 2.5/2.5-5.0/5.0 0.993 0.739–1.333 0.962 Differentiation Mod/Poor 1.506 1.035–2.193 0.033 1.486 0.952–2.319 0.081 Venous invasion Yes/No 1.571 1.051–2.19 0.026 1.122 0.744–1.691 0.583 Tumor deposits Yes/No 1.998 1.264–3.16 0.003 1.179 0.69–2.014 0.547 Lymph-node metastasis Yes/No 1.931 1.294–2.881 0.001 0.483 0.173–1.345 0.164 CEA High/Low 2.954 2.018–4.325 < 0.001 2.237 1.398–3.579 < 0.001 CA199 High/Low 1.53 1.062–2.204 0.023 1.188 0.764–1.848 0.445 NLR High/Low 2.194 1.423–3.384 < 0.001 1.146 0.639–2.055 0.649 PLR High/Low 1.318 0.924–1.879 0.127 PNI High/Low 0.647 0.43–0.972 0.036 0.503 0.307–0.825 0.006 AGR High/Low 0.526 0.325–0.85 0.009 1.528 0.41–1.527 0.488 CAR High/Low 6.397 4.301–9.515 < 0.001 1.785 0.857–3.719 0.122 LCR High/Low 0.151 0.1-0.229 < 0.001 0.617 0.267–1.426 0.259 LMR High/Low 0.329 0.203–0.533 < 0.001 0.544 0.297–0.993 0.047 CALLY High/Low 0.151 0.095–0.241 < 0.001 0.375 0.175–0.801 0.011 SIRI High/Low 1.626 1.131–2.338 0.009 0.97 0.584–1.612 0.906 LA High/Low 1.284 0.893–1.847 0.177 Abbreviations: NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; PNI, prognostic nutritional index; AGR, albumin- globulin ratio; CAR, C-reactive protein-albumin ratio; LCR, lymphocyte-C-reactive protein ratio; LMR, lymphocyte–monocyte ratio; CALLY,CRP–albumin–lymphocyte index; SIRI, systemic immune-inflammation index; LA, lymphocyte–albumin product, CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199. 3.3. Associations between inflammatory factors and postoperative lung metastasis Among the 303 patients, 55 developed lung metastasis at the last follow-up. In the univariate Cox regression analysis incorporating inflammatory markers and baseline characteristics, we found that Age, hypertension, hyperlipemia, T stage, N stage, TNM stage, tumor deposits, lymph-node metastasis, CEA, CA199, NLR, PNI, CAR, LCR, CALLY, and LA had statistically significant impacts on patients' lung metastasis (all < 0.05). Further inclusion of these factors in multivariate Cox regression analysis revealed that CEA (HR 3.703, 95% CI 1.917–7.152; p < 0.001, Table 3 ), PNI (HR 0.401, 95% CI 0.168–0.955; p = 0.039, Table 3 ), and CALLY (HR 0.14, 95% CI 0.047–0.418; p < 0.001, Table 3 ) were independent risk factors for postoperative lung metastasis in CRC patients. This was also corroborated in the Kaplan–Meier survival curve, where the CRC patients in the CEA-High group (High versus Low, 25.8 versus 62.5 months, P < 0.001, Fig. 2), PNI-Low group (High versus Low, 62.5 versus 45.8 months, P < 0.001, Fig. 2), and CALLY-Low group (High versus Low, 62.5 versus 40.3 months, P < 0.001, Fig. 2) were more prone to developing lung metastasis postoperatively. Table 3 Associations between inflammatory factors and postoperative lung metastasis. Univariate analysis Multivariate analysis Characteristics HR 95% CI P-value HR 95% CI P-value Age (years) > 70/ 24/18.5–24/<18.5 0.945 0.823–1.215 0.842 Tumor location Right/Left 0.721 0.421–1.233 0.232 Smoking status Yes/No 1.813 0.911–3.609 0.09 Drinking status Yes/No 1.479 0.586–3.733 0.407 Hypertension Yes/No 1.983 1.154–3.407 0.013 0.937 0.473–1.854 0.851 Diabetes Yes/No 1.941 0.914–4.123 0.084 Hyperlipemia Yes/No 2.041 1.141–3.651 0.016 1.437 0.754–2.737 0.271 Adjuvant chemotherapy Yes/No 0.438 0.172–1.113 0.083 T stage 2/3/4 1.923 1.128–3.275 0.016 1.62 0.847–3.095 0.145 N stage 0/1/2 1.672 1.159–2.41 0.006 1.728 0.796–3.747 0.166 TNM stage II/III 3.372 1.645–6.913 < 0.001 2.231 0.473–9.523 0.311 Tumor size(maximum diameter/cm) < 2.5/2.5-5.0/5.0 0.936 0.602–1.456 0.77 Differentiation Mod/Poor 1.569 0.895–2.751 0.116 Venous invasion Yes/No 1.354 0.778–2.354 0.284 Tumor deposits Yes/No 3.4 1.852–6.243 < 0.001 2.039 0.978–4.248 0.057 Lymph-node metastasis Yes/No 3.47 1.693–7.111 < 0.001 0.707 0.192–7.152 0.602 CEA High/Low 4.853 2.821–8.349 < 0.001 3.703 1.917–7.152 < 0.001 CA199 High/Low 1.962 1.109–3.471 0.021 1.692 0.867–3.303 0.123 NLR High/Low 1.994 1.049–3.789 0.035 1.184 0.561–2.499 0.658 PLR High/Low 1.096 0.641–1.874 0.738 PNI High/Low 0.404 0.231–0.706 < 0.001 0.401 0.168–0.955 0.039 AGR High/Low 0.735 0.313–1.726 0.48 CAR High/Low 6.666 3.624–9.264 < 0.001 1.793 0.525–6.117 0.351 LCR High/Low 0.154 0.082–0.289 < 0.001 1.097 0.313–3.841 0.885 LMR High/Low 0.493 0.21–1.16 0.105 CALLY High/Low 0.107 0.05–0.232 < 0.001 0.14 0.047–0.418 < 0.001 SIRI High/Low 1.189 0.696–2.032 0.527 LA High/Low 1.859 1.083–3.191 0.024 1.146 0.497–2.643 0.749 Abbreviations: NLR, neutrophil–lymphocyte ratio; PLR, platelet–lymphocyte ratio; PNI, prognostic nutritional index; AGR, albumin- globulin ratio; CAR, C-reactive protein-albumin ratio; LCR, lymphocyte-C-reactive protein ratio; LMR, lymphocyte–monocyte ratio; CALLY,CRP–albumin–lymphocyte index; SIRI, systemic immune-inflammation index; LA, lymphocyte–albumin product, CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199. 3.4. Associations between inflammatory factors and baseline characteristic We further explored the associations between PNI, LMR, CALLY, and baseline characteristics in CRC patients. The results indicated that PNI was associated with hyperlipidemia, LMR was related to T stage and tumor size, while CALLY was associated with diabetes, T stage, TNM stage, tumor size, and lymph-node metastasis(all 70/<70 0.159 0.327 24/18.5–24/<18.5 0.466 0.413 0.992 Tumor location Right/Left 0.544 0.776 0.68 Smoking status Yes/No 0.138 0.66 0.265 Drinking status Yes/No 0.704 0.965 0.756 Hypertension Yes/No 0.686 0.508 0.342 Diabetes Yes/No 0.402 0.998 0.03 Hyperlipemia Yes/No 0.019 0.425 0.272 T stage 2/3/4 0.379 < 0.001 0.017 N stage 0/1/2 0.496 0.226 0.136 TNM stage II/III 0.983 0.909 0.047 Tumor size(maximum diameter/cm) < 2.5/2.5-5.0/5.0 0.612 0.002 0.008 Differentiation Mod/Poor 0.596 0.779 0.821 Venous invasion Yes/No 0.576 0.375 0.898 Tumor deposits Yes/No 0.386 0.096 0.69 Lymph-node metastasis Yes/No 0.894 0.779 0.025 Abbreviations: PNI, prognostic nutritional index; LMR, lymphocyte–monocyte ratio; CALLY, CRP–albumin–lymphocyte index. 4. Conclusion CRC is one of the most common malignancies worldwide, and metastasis is a major cause of postoperative mortality in CRC patients[3]. The liver is the most common site of metastasis in CRC patients, followed by lung metastasis[4]. Currently, research on the relationship between liver metastasis and inflammatory markers is well-established, but the association between lung metastasis and inflammatory markers remains unclear[16]. Therefore, this study explores the connection between inflammatory markers and postoperative PFS as well as postoperative lung metastasis in colorectal cancer (CRC) patients. We found that lower PNI, LMR, and CALLY were significantly associated with reduced postoperative PFS in CRC patients, and lower PNI and CALLY were simultaneously significantly associated with postoperative lung metastasis in CRC patients. Systemic inflammation is closely associated with the occurrence, development, and metastasis of tumors[7]. Inflammatory cells can release various cytokines and promote tumor progression and metastasis through oxidative stress and other mechanisms[17, 18]. Ulcerative colitis is a chronic inflammatory bowel disease, and patients with inflammatory bowel disease have an increased risk of developing colorectal cancer. Burr et al. found that non-steroidal anti-inflammatory drugs (NSAIDs) can reduce the risk of malignancy in inflammatory bowel disease, suggesting that inflammation plays an important role in the malignant transformation of inflammatory bowel disease[19]. Neutrophils, lymphocytes, and monocytes are important components of the tumor microenvironment, playing crucial roles in tumor progression and metastasis[10]. Neutrophils can promote tumor occurrence by releasing reactive oxygen species or proteases and participate in tumor angiogenesis, thereby facilitating tumor progression[8]. Neutrophils can also inhibit the cytotoxic effects of lymphocytes on tumors by releasing various enzymes. When neutrophils are reduced or CXC chemokine receptor 2(CXCR2) is blocked to inhibit their chemotaxis, tumor angiogenesis can be reduced[20]. As a traditional inflammatory marker, the predictive value of the neutrophil-to-lymphocyte ratio (NLR) has long been demonstrated in various tumors, including CRC[8]. In our study, NLR also demonstrated predictive significance for the prognosis and metastasis of postoperative CRC patients, but it was not an independent risk factor. We consider that this may be related to other inflammatory markers we included. Lymphocytes are crucial components of the body's defense against tumors, with CD8 + T cells directly attacking tumor cells and CD4 + T cells secreting cytokines and antibodies to activate NK cells and CD8 + T lymphocytes, thereby exerting their anti-tumor effects[21]. Higher infiltration of lymphocytes in the tumor microenvironment often signifies a better prognosis[22]. Conversely, reduced lymphocytes are considered adverse prognostic factors for cancer patients. Additionally, individuals with immune deficiencies are more susceptible to developing tumors, particularly those induced by infections[23]. During the process of carcinogenesis, there is often an increase in circulating neutrophils and a decrease in lymphocytes[16]. Huang et al demonstrated that the percentage of lymphocytes before treatment serves as a reliable prognostic indicator for lung cancer, where a higher percentage of lymphocytes represents a better prognosis[12]. Multiple clinical studies have also suggested an association between elevated lymphocytes and improved tumor prognosis[13, 24, 25]. This aligns with our findings, indicating that CRC patients with higher LMR, PNI, and CALLY (indicating higher lymphocytes) experience longer PFS and are less prone to lung metastasis after surgery. Monocytes play a crucial role in the process of pathogen clearance, but these cells can also simultaneously promote local inflammation[26]. Research indicates that monocytes can facilitate tumor growth and metastasis. In a mouse model of colorectal cancer, monocytes were recruited to primary tumors and lung metastases through chemokines, thereby promoting tumor migration and progression[27]. Katarzyna et al found that CRC patients with low absolute monocyte counts before surgery had a longer 5-year disease-free survival (DFS)[13]. This suggests that monocytes may promote tumor progression by enhancing immune responses, supporting our conclusion that CRC patients with higher LMR (indicating lower monocytes) experience longer PFS after surgery. CRP is one of the most commonly used indicators to assess systemic inflammation, and its levels are generally positively correlated with the degree of inflammation[28]. Elevated CRP levels can predict the severity of a patient's disease, tumor recurrence rates, and overall survival[29]. Studies have shown that high CRP in solid tumor patients is associated with increased recurrence, metastasis, and mortality rates in 90% of reports[11]. Nozoe et al found that preoperative CRP levels could predict early recurrence in CRC, with patients exhibiting elevated preoperative CRP experiencing significantly higher rates of liver and lymph node metastasis and poorer prognosis, suggesting that CRP may serve as an effective prognostic indicator[30]. Yang et al also demonstrated that lower CALLY index (indicating higher CRP) is associated with an increased risk of death in CRC patients[15]. This aligns with our study, where lower CALLY (indicating higher CRP) was significantly correlated with postoperative lung metastasis and poorer prognosis in CRC patients. Despite some limitations in our study, such as its retrospective nature, which may introduce issues of loss to follow-up, and being a single-center study that may warrant further extensive clinical research to explore the predictive value of inflammatory markers for the prognosis of CRC patients. However, by minimizing biases to the best of our ability, our study demonstrates that PNI, LMR, and CALLY are independent predictors of postoperative PFS in CRC patients. Both PNI and CALLY are independent predictors of postoperative lung metastasis in CRC patients. These inflammatory markers, as simple and cost-effective indicators, provide a useful tool for monitoring recurrence and metastasis in CRC patients postoperatively. Declarations Declarations of interest The authors have no relevant financial or non-financial interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. CRediT authorship contribution statement Jiukang Sun : Writing – original draft, Investigation, Conceptualization, Software, Writing – review & editing. Weili Xiong : Conceptualization, Writing – review & editing. Zhang Fang : Conceptualization, Validation, Software, Writing – review & editing. Chuanjun Song : Supervision, Project administration, Writing – original draft, Resources, Investigation, Formal analysis, Software, Conceptualization, Methodology, Writing – review & editing. Data Availability The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request. Ethics approval This study obtained approval from the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University and adheres to the principles of the Helsinki Declaration. As a retrospective study, the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University waived the requirement for written informed consent. Consent to participate Due to the retrospective nature of the study, informed consent was waived. References Siegel, R.L., N.S. Wagle, A. Cercek, R.A. Smith, and A. Jemal, Colorectal cancer statistics, 2023. CA Cancer J Clin, 202310.3322/caac.21772 Lichtenstern, C.R., R.K. Ngu, S. Shalapour, and M. Karin, Immunotherapy, Inflammation and Colorectal Cancer. Cells, 2020. 9 (3)10.3390/cells9030618 Chandra, R., J.D. Karalis, C. Liu, G.Z. Murimwa, J. Voth Park, C.A. Heid, et al., The Colorectal Cancer Tumor Microenvironment and Its Impact on Liver and Lung Metastasis. Cancers, 2021. 13 (24)10.3390/cancers13246206 Kasi, P.B., V.R. Mallela, F. Ambrozkiewicz, A. Trailin, V. Liška, and K. Hemminki, Theranostics Nanomedicine Applications for Colorectal Cancer and Metastasis: Recent Advances. International Journal of Molecular Sciences, 2023. 24 (9)10.3390/ijms24097922 Wang, R., W. Dai, J. Gong, M. Huang, T. Hu, H. Li, et al., Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. Journal of Hematology & Oncology, 2022. 15 (1)10.1186/s13045-022-01225-3 Prasanna, T., C.S. Karapetis, D. Roder, J. Tie, R. Padbury, T. Price, et al., The survival outcome of patients with metastatic colorectal cancer based on the site of metastases and the impact of molecular markers and site of primary cancer on metastatic pattern. Acta Oncol, 2018. 57 (11):1438-1444.http://dx.doi.org/10.1080/0284186x.2018.1487581 Cools-Lartigue, J., J. Spicer, B. McDonald, S. Gowing, S. Chow, B. Giannias, et al., Neutrophil extracellular traps sequester circulating tumor cells and promote metastasis. J Clin Invest, 2013. 123 (8):3446-58.http://dx.doi.org/10.1172/jci67484 An, S., H. Shim, K. Kim, B. Kim, H.-J. Bang, H. Do, et al., Pretreatment inflammatory markers predicting treatment outcomes in colorectal cancer. Annals of Coloproctology, 2022. 38 (2):97-108.http://dx.doi.org/10.3393/ac.2021.01004.0143 Coussens, L.M. and Z. Werb, Inflammation and cancer. Nature, 2002. 420 (6917):860-7.http://dx.doi.org/10.1038/nature01322 Singh, N., D. Baby, J.P. Rajguru, P.B. Patil, S.S. Thakkannavar, and V.B. Pujari, Inflammation and cancer. Ann Afr Med, 2019. 18 (3):121-126.http://dx.doi.org/10.4103/aam.aam_56_18 Zhang, L., S. Shrotriya, D. Walsh, N. Bennani-Baiti, S. Thomas, and C. Lorton, C-Reactive Protein Is an Important Biomarker for Prognosis Tumor Recurrence and Treatment Response in Adult Solid Tumors: A Systematic Review. Plos One, 2015. 10 (12)10.1371/journal.pone.0143080 Huang, H., L. Li, W. Luo, Y. Yang, Y. Ni, T. Song, et al., Lymphocyte percentage as a valuable predictor of prognosis in lung cancer. Journal of Cellular and Molecular Medicine, 2022. 26 (7):1918-1931.http://dx.doi.org/10.1111/jcmm.17214 Jakubowska, K., M. Koda, M. Grudzińska, L. Kańczuga-Koda, and W. Famulski, Monocyte-to-lymphocyte ratio as a prognostic factor in peripheral whole blood samples of colorectal cancer patients. World Journal of Gastroenterology, 2020. 26 (31):4639-4655.http://dx.doi.org/10.3748/wjg.v26.i31.4639 Müller, L., F. Hahn, A. Mähringer-Kunz, F. Stoehr, S.J. Gairing, M. Michel, et al., Immunonutritive Scoring for Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization: Evaluation of the CALLY Index. Cancers (Basel), 2021. 13 (19)10.3390/cancers13195018 Yang, M., S.-Q. Lin, X.-Y. Liu, M. Tang, C.-L. Hu, Z.-W. Wang, et al., Association between C-reactive protein-albumin-lymphocyte (CALLY) index and overall survival in patients with colorectal cancer: From the investigation on nutrition status and clinical outcome of common cancers study. Frontiers in Immunology, 2023. 14 10.3389/fimmu.2023.1131496 Lu, Y., D. Xin, and F. Wang, Predictive Significance Of Preoperative Systemic Immune-Inflammation Index Determination In Postoperative Liver Metastasis Of Colorectal Cancer. OncoTargets and Therapy, 2019. Volume 12 :7791-7799.http://dx.doi.org/10.2147/ott.S223419 Kaler, P., B.N. Godasi, L. Augenlicht, and L. Klampfer, The NF-κB/AKT-dependent Induction of Wnt Signaling in Colon Cancer Cells by Macrophages and IL-1β. Cancer Microenvironment, 2009. 2 (1):69-80.http://dx.doi.org/10.1007/s12307-009-0030-y Liu, H., X. Liu, C. Zhang, H. Zhu, Q. Xu, Y. Bu, et al., Redox Imbalance in the Development of Colorectal Cancer. Journal of Cancer, 2017. 8 (9):1586-1597.http://dx.doi.org/10.7150/jca.18735 Burr, N.E., M.A. Hull, and V. Subramanian, Does aspirin or non-aspirin non-steroidal anti-inflammatory drug use prevent colorectal cancer in inflammatory bowel disease? World Journal of Gastroenterology, 2016. 22 (13)10.3748/wjg.v22.i13.3679 Antonio, N., M.L. Bønnelykke-Behrndtz, L.C. Ward, J. Collin, I.J. Christensen, T. Steiniche, et al., The wound inflammatory response exacerbates growth of pre-neoplastic cells and progression to cancer. Embo j, 2015. 34 (17):2219-36.http://dx.doi.org/10.15252/embj.201490147 Dai, S., P. Ren, J. Ren, L. Yang, and W. Li, The Relationship between Lymphocyte Subsets and the Prognosis and Genomic Features of Lung Cancer: A Retrospective Study. International Journal of Medical Sciences, 2021. 18 (10):2228-2234.http://dx.doi.org/10.7150/ijms.56928 Ohtani, H., Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer. Cancer Immun, 2007. 7 :4.http://dx.doi.org/ Grulich, A.E., M.T. van Leeuwen, M.O. Falster, and C.M. Vajdic, Incidence of cancers in people with HIV/AIDS compared with immunosuppressed transplant recipients: a meta-analysis. Lancet, 2007. 370 (9581):59-67.http://dx.doi.org/10.1016/s0140-6736(07)61050-2 Chen, B., J. Zhou, Y. Ma, Q. Sun, J. Ren, and D. Wang, Evaluation of multiple biological indicators for the combined diagnosis of metastases from colorectal cancer—a retrospective study based on 1163 patients. World Journal of Surgical Oncology, 2023. 21 (1)10.1186/s12957-023-03108-4 Chen, Q., G.-l. Li, H.-q. Zhu, J.-D. Yu, Z.-P. Chen, J.-Y. Wu, et al., The neutrophil-to-lymphocyte ratio and lactate dehydrogenase combined in predicting liver metastasis and prognosis of colorectal cancer. Frontiers in Medicine, 2023. 10 10.3389/fmed.2023.1205897 Shi, C. and E.G. Pamer, Monocyte recruitment during infection and inflammation. Nature Reviews Immunology, 2011. 11 (11):762-774.http://dx.doi.org/10.1038/nri3070 Chun, E., S. Lavoie, M. Michaud, Carey A. Gallini, J. Kim, G. Soucy, et al., CCL2 Promotes Colorectal Carcinogenesis by Enhancing Polymorphonuclear Myeloid-Derived Suppressor Cell Population and Function. Cell Reports, 2015. 12 (2):244-257.http://dx.doi.org/10.1016/j.celrep.2015.06.024 Sproston, N.R. and J.J. Ashworth, Role of C-Reactive Protein at Sites of Inflammation and Infection. Frontiers in Immunology, 2018. 9 10.3389/fimmu.2018.00754 Mahmoud, F.A. and N.I. Rivera, The role of C-reactive protein as a prognostic indicator in advanced cancer. Curr Oncol Rep, 2002. 4 (3):250-5.http://dx.doi.org/10.1007/s11912-002-0023-1 Nozoe T, M.T., Kitamura M, Sugimachi K., Significance of preoperative elevation of serum C-reactive protein as an indicator for prognosis in colorectal cancer. Am J Surg, 1998176:335-8 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-3923170","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271246663,"identity":"a2c1f2a9-7a0a-469d-a4ab-36a60d48c799","order_by":0,"name":"Jiukang Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiukang","middleName":"","lastName":"Sun","suffix":""},{"id":271246664,"identity":"21aa77f2-0771-42b3-851c-2b8b8cb86798","order_by":1,"name":"Weili Xiong","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weili","middleName":"","lastName":"Xiong","suffix":""},{"id":271246665,"identity":"95904e1c-b82f-404a-94d7-bcfe87414f5b","order_by":2,"name":"Zhang Fang","email":"","orcid":"","institution":"Taikang Xianlin Drum Tower Hospital, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Fang","suffix":""},{"id":271246666,"identity":"a7516329-6af2-4394-80d1-ac7c2e6a2ff8","order_by":3,"name":"Chuanjun Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYHCCxMMMBjZ2/AyMDURrSTjMUJCWLNlAghaGwwwfDjNuOECscvn2Aw8OFxgwMxufP9z24AeDnZwuIcsMziQkHJ5hwMZndiOx3bCHIdnYjJB1BhJAv/AY8DCb3WBsk+BhOJC4jZAW+RlgLRKMm/sPtkn+IUYLww2wFgPGDQyJbdJE2QL2C49BQrLEDaAWGQMi/CLffibxMc+f/3b8/cefSb6psJMjqIWBgScB2VKCykGAnbCpo2AUjIJRMMIBAL6aQhgzLxlfAAAAAElFTkSuQmCC","orcid":"","institution":"Xinghua People's Hospital Affiliated to Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Chuanjun","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2024-02-03 08:14:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3923170/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3923170/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50746837,"identity":"1e5755b1-2390-4e91-8cda-70ca1b2523ef","added_by":"auto","created_at":"2024-02-06 17:09:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91527,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier analysis based on T stage, N stage, CEA, PNI, LMR and CALLY in all patients with CRC. (A),T stage; (B), N stage; (C), CEA; (D),PNI; (E), LMR; (F),CALLY\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3923170/v1/351fd761a04282c45b9f10a0.png"},{"id":50746836,"identity":"7869a2da-a380-4e26-8ba0-8b6c649928c1","added_by":"auto","created_at":"2024-02-06 17:09:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78270,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier analysis based on CEA, PNI and CALLY in all patients with CRC.(A),CEA; (B),PNI; (C),CALLY\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3923170/v1/e515e8b21814251e6f3db24f.png"},{"id":52546685,"identity":"34493d1c-1572-4f0c-9bbc-023f25347082","added_by":"auto","created_at":"2024-03-12 18:53:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":589384,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3923170/v1/81c04b11-fe08-4bf6-8136-5cfe229b07ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Significance Of Preoperative Inflammatory Markers In Postoperative Lung Metastasis Of Colorectal Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is one of the most common malignancies globally and a leading cause of cancer-related deaths[1]. Current treatment modalities for CRC include surgery and chemotherapy, achieving a 90% 5-year survival rate for patients in various stages. However, once distant metastasis occurs, the prognosis remains poor[2]. Postoperative metastasis is a major cause of death in CRC patients and is closely associated with tumor resistance[3]. The liver is the most common site of CRC metastasis, followed by the lungs[4]. While extensive research has focused on postoperative liver metastasis in CRC patients, there is currently a lack of reports on lung metastasis[5]. In contrast to liver metastasis, patients with lung metastasis exhibit a better prognosis[6]. Therefore, there is a need to develop specific approaches for predicting lung metastasis.\u003c/p\u003e \u003cp\u003eA substantial body of research confirms that systemic inflammation is an integral component of the tumor immune microenvironment and plays a crucial role in the initiation, progression, and metastasis of tumors[7, 8]. Studies indicate a correlation between chronic inflammation and over 15% of human cancers[9]. Uncontrolled inflammation can promote tumor advancement through mechanisms such as DNA damage and reduced genomic stability[10]. Several traditional inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin-to-globulin ratio (AGR), C-reactive protein (CRP)-to-albumin ratio (CAR), lymphocyte-to-CRP ratio (LCR), systemic immune-inflammation index (SIRI), lymphocyte-to-monocyte ratio (LMR), and prognostic nutritional index (PNI), have been demonstrated to hold predictive value for tumor progression and metastasis[8, 11\u0026ndash;13].\u003c/p\u003e \u003cp\u003eThe CRP\u0026ndash;albumin\u0026ndash;lymphocyte (CALLY) index, developed by M\u0026uuml;ller et al. in 2021, represents a novel inflammatory marker that integrates both the Prognostic Nutritional Index (PNI) and the CRP level[14]. The CALLY index has been demonstrated to be associated with the prognosis of liver cancer and CRC, with higher CALLY index values correlating with longer survival in patients[14, 15].\u003c/p\u003e \u003cp\u003eHowever, the relationship between these inflammatory markers and postoperative lung metastasis in CRC patients remains unclear. Therefore, our study aims to explore the predictive significance of inflammatory markers for postoperative lung metastasis in CRC patients.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eWe retrospectively reviewed the medical records of patients diagnosed with primary CRC at the First Affiliated Hospital of Nanjing Medical University from January 2014 to December 2023. Inclusion criteria were age\u0026thinsp;\u0026gt;\u0026thinsp;18 years, histological or cytological confirmation of primary CRC, undergoing curative surgery for CRC, and sufficient clinical and hematological data. Exclusion criteria were acute inflammation at the time of diagnosis, concomitant inflammatory bowel disease, receipt of neoadjuvant treatment, presence of other malignancies, and concurrent chronic inflammatory conditions.\u003c/p\u003e \u003cp\u003eFor each eligible patient, we collected the following information: 1. Basic patient information, including gender, age, tumor pathology, smoking history, alcohol consumption history, etc.; 2. Peripheral blood indices within the week before surgery, including routine blood tests and biochemistry; 3. Treatment details, such as postoperative chemotherapy; 4. Survival time. The survival data represent progression-free survival (PFS). PFS is defined as the time from the initiation of operation to radiological or clinical progression or death from any cause. Patients who had no progression or death at the last follow-up were considered censored. The diagnosis of metastasis was independently confirmed by two radiologists through CT scans.\u003c/p\u003e \u003cp\u003eThe formulas for the relevant indices in this study are as follows: NLR\u0026thinsp;=\u0026thinsp;N/L; PLR\u0026thinsp;=\u0026thinsp;P/L; LMR\u0026thinsp;=\u0026thinsp;L/M; PNI\u0026thinsp;=\u0026thinsp;A (g/L)\u0026thinsp;+\u0026thinsp;5\u0026times;L(\u0026times;10^9/L); AGR\u0026thinsp;=\u0026thinsp;A/G; CAR\u0026thinsp;=\u0026thinsp;C/A; LCR\u0026thinsp;=\u0026thinsp;L/C; CALLY\u0026thinsp;=\u0026thinsp;A (g/L) * L(\u0026times;10^9/L) / (C(mg/L) * 10); SIRI\u0026thinsp;=\u0026thinsp;N * M / L; LA\u0026thinsp;=\u0026thinsp;L(\u0026times;10^9/L) * A(g/L). Here, N, L, P, and M represent neutrophil, lymphocyte, platelet, and monocyte counts, respectively. A, G, and C represent albumin, globulin, and C-reactive protein. The optimal cutoff values were calculated using the Youden index, resulting in the following cutoff values: NLR, 1.78; PLR, 157.79; PNI, 52.375; AGR, 1.15; CAR, 0.12; LCR, 0.41; LMR, 2.08; CALLY, 2.29; SIRI, 0.87; LA, 75.41; CEA, 15.33; CA199, 11.21.\u003c/p\u003e \u003cp\u003eData analysis was conducted using IBM SPSS version 27.0. Kaplan-Meier curves were employed to analyze progression-free survival (PFS). All variables underwent univariate Cox regression analysis, and only those with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in the multivariate Cox regression analysis. Logistics regression analysis were used to explore the associations between inflammatory factors and baseline characteristic. A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e This study obtained approval from the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University and adheres to the principles of the Helsinki Declaration. As a retrospective study, the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University waived the requirement for written informed consent.\u003c/p\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline characteristic\u003c/h2\u003e \u003cp\u003eThis study included a total of 303 patients with an average age of 62 years. Among them, 175 (57.8%) were male, and 128 (42.2%) were female. The mean follow-up duration was 22.2 months, during which 124 patients experienced progression, with 55 patients developing lung metastasis at the last follow-up. The specific baseline characteristics and inflammatory markers of the patients are presented in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;1 Baseline clinicopathological characteristics of patients.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNumber(%) (n\u0026thinsp;=\u0026thinsp;303)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e213(70.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e90(29.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e175(57.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e128(42.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e34(11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e226(74.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e43(14.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e118(38.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e185(61.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e33(10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e270(89.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19(6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e284(93.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e94(31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e209(69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25(8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e278(91.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e175(57.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e128(42.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e290(95.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13(4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21(6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e226(74.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e56(18.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e109(36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e137(45.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e57(18.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e107(35.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e196(64.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor size (maximum diameter/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e39(12.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5-5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e189(62.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e75(24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDifferentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e222(73.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e81(26.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVenous invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e202(66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e101(33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTumor deposits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e261(86.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e42(13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLymph-node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e109(35.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e194(64.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e58(19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e245(80.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCA199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e167(55.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e134(44.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e199(65.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e104(34.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e132(43.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e171(56.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e249(82.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e54(17.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e268(88.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e35(11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e116(38.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e187(61.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e174(57.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e129(42.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e249(92.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e24(7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCALLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e138(45.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e165(54.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e159(52.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e144(47.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e104(34.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e199(65.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: NLR, neutrophil\u0026ndash;lymphocyte ratio; PLR, platelet\u0026ndash;lymphocyte ratio; PNI, prognostic nutritional index; AGR, albumin- globulin ratio; CAR, C-reactive protein-albumin ratio; LCR, lymphocyte-C-reactive protein ratio; LMR, lymphocyte\u0026ndash;monocyte ratio; CALLY,CRP\u0026ndash;albumin\u0026ndash;lymphocyte index; SIRI, systemic immune-inflammation index; LA, lymphocyte\u0026ndash;albumin product, CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Associations between inflammatory markers and PFS\u003c/h2\u003e \u003cp\u003eWe conducted univariate regression Cox analysis on the baseline characteristics and inflammatory markers of the patients. The results indicated that age, hypertension, diabetes, hyperlipemia, adjuvant chemotherapy, T stage, N stage, TNM stage, tumor differentiation, venous invasion, tumor deposits, lymph-node metastasis, CEA, CA199, NLR, PNI, AGR, CAR, LCR, LMR, CALLY, and SIRI were significant influencing factors (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, these factors were included in multivariate Cox regression analysis. We found that the PFS of CRC patients was independently associated with T stage (HR 1.664, 95% CI 1.092\u0026ndash;2.534; p\u0026thinsp;=\u0026thinsp;0.018, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), N stage (HR 2.159, 95% CI 1.307\u0026ndash;3.568; p\u0026thinsp;=\u0026thinsp;0.003, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), CEA (HR 2.237, 95% CI 1.398\u0026ndash;3.579; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), PNI (HR 0.503, 95% CI 0.307\u0026ndash;0.825; p\u0026thinsp;=\u0026thinsp;0.006, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), LMR (HR 0.544, 95% CI 0.297\u0026ndash;0.993; p\u0026thinsp;=\u0026thinsp;0.047, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and CALLY (HR 0.375, 95% CI 0.175\u0026ndash;0.801; p\u0026thinsp;=\u0026thinsp;0.011, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Kaplan\u0026ndash;Meier survival curve also indicates that patients with earlier T stage (1 versus 2 versus 3, 48.9 versus 45.8 versus 19.4 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;1), earlier N stage (0 versus 1 versus 2, 62.5 versus 40.3 versus 27.5 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;1), low CEA group (High versus Low, 18 versus 45.8 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;1), high PNI group (High versus Low, 44.6 versus 32.9 months, P\u0026thinsp;=\u0026thinsp;0.034, Fig.\u0026nbsp;1), high LMR group (High versus Low, 48.9 versus 19.4 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;1), and high CALLY group (High versus Low, 62.5 versus 21.5 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;1) have longer PFS.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between inflammatory markers and PFS.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \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 \u003cp\u003e\u0026gt;\u0026thinsp;70/\u0026lt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.316\u0026ndash;2.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.316\u0026ndash;2.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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 \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale/Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641\u0026ndash;1.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.648\u003c/p\u003e \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\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;24/18.5\u0026ndash;24/\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.586\u0026ndash;4.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \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\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight/Left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.576\u0026ndash;1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.292\u003c/p\u003e \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\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u0026ndash;1.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \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\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.467\u0026ndash;1.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \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\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.033\u0026ndash;2.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.656\u0026ndash;1.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.144\u0026ndash;3.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.456\u0026ndash;1.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.179\u0026ndash;2.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.956\u0026ndash;2.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u0026ndash;0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.166\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/3/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.546\u0026ndash;3.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.092\u0026ndash;2.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.253\u0026ndash;2.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.307\u0026ndash;3.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII/III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.307\u0026ndash;2.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.458\u0026ndash;5.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size(maximum diameter/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.5/2.5-5.0/5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.739\u0026ndash;1.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.962\u003c/p\u003e \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\u003eDifferentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMod/Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.035\u0026ndash;2.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.952\u0026ndash;2.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenous invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.051\u0026ndash;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.744\u0026ndash;1.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor deposits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.264\u0026ndash;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69\u0026ndash;2.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph-node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.294\u0026ndash;2.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.173\u0026ndash;1.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.018\u0026ndash;4.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.398\u0026ndash;3.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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 \u003cp\u003eCA199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.062\u0026ndash;2.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.764\u0026ndash;1.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.423\u0026ndash;3.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.639\u0026ndash;2.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.924\u0026ndash;1.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \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\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u0026ndash;0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.307\u0026ndash;0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u0026ndash;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u0026ndash;1.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.301\u0026ndash;9.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.857\u0026ndash;3.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1-0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.267\u0026ndash;1.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.203\u0026ndash;0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.297\u0026ndash;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCALLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u0026ndash;0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.175\u0026ndash;0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.131\u0026ndash;2.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.584\u0026ndash;1.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.893\u0026ndash;1.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \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\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: NLR, neutrophil\u0026ndash;lymphocyte ratio; PLR, platelet\u0026ndash;lymphocyte ratio; PNI, prognostic nutritional index; AGR, albumin- globulin ratio; CAR, C-reactive protein-albumin ratio; LCR, lymphocyte-C-reactive protein ratio; LMR, lymphocyte\u0026ndash;monocyte ratio; CALLY,CRP\u0026ndash;albumin\u0026ndash;lymphocyte index; SIRI, systemic immune-inflammation index; LA, lymphocyte\u0026ndash;albumin product, CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Associations between inflammatory factors and postoperative lung metastasis\u003c/h2\u003e \u003cp\u003eAmong the 303 patients, 55 developed lung metastasis at the last follow-up. In the univariate Cox regression analysis incorporating inflammatory markers and baseline characteristics, we found that Age, hypertension, hyperlipemia, T stage, N stage, TNM stage, tumor deposits, lymph-node metastasis, CEA, CA199, NLR, PNI, CAR, LCR, CALLY, and LA had statistically significant impacts on patients' lung metastasis (all \u0026lt;\u0026thinsp;0.05). Further inclusion of these factors in multivariate Cox regression analysis revealed that CEA (HR 3.703, 95% CI 1.917\u0026ndash;7.152; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), PNI (HR 0.401, 95% CI 0.168\u0026ndash;0.955; p\u0026thinsp;=\u0026thinsp;0.039, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and CALLY (HR 0.14, 95% CI 0.047\u0026ndash;0.418; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were independent risk factors for postoperative lung metastasis in CRC patients. This was also corroborated in the Kaplan\u0026ndash;Meier survival curve, where the CRC patients in the CEA-High group (High versus Low, 25.8 versus 62.5 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;2), PNI-Low group (High versus Low, 62.5 versus 45.8 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;2), and CALLY-Low group (High versus Low, 62.5 versus 40.3 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;2) were more prone to developing lung metastasis postoperatively.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between inflammatory factors and postoperative lung metastasis.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \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 \u003cp\u003e\u0026gt;\u0026thinsp;70/\u0026lt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.051\u0026ndash;3.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.696\u0026ndash;2.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale/Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.585\u0026ndash;1.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.984\u003c/p\u003e \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\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;24/18.5\u0026ndash;24/\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.823\u0026ndash;1.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \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\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight/Left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.421\u0026ndash;1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.232\u003c/p\u003e \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\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.911\u0026ndash;3.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \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\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.586\u0026ndash;3.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.407\u003c/p\u003e \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\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.154\u0026ndash;3.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.473\u0026ndash;1.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.914\u0026ndash;4.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \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\u003eHyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.141\u0026ndash;3.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.754\u0026ndash;2.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.172\u0026ndash;1.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \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\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/3/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.128\u0026ndash;3.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.847\u0026ndash;3.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.159\u0026ndash;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.796\u0026ndash;3.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII/III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.645\u0026ndash;6.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.473\u0026ndash;9.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size(maximum diameter/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.5/2.5-5.0/5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.602\u0026ndash;1.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \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\u003eDifferentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMod/Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.895\u0026ndash;2.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \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\u003eVenous invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.778\u0026ndash;2.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.284\u003c/p\u003e \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\u003eTumor deposits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.852\u0026ndash;6.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.978\u0026ndash;4.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph-node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.693\u0026ndash;7.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.192\u0026ndash;7.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.821\u0026ndash;8.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.917\u0026ndash;7.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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 \u003cp\u003eCA199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.109\u0026ndash;3.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.867\u0026ndash;3.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.049\u0026ndash;3.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.561\u0026ndash;2.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641\u0026ndash;1.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.738\u003c/p\u003e \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\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.231\u0026ndash;0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.168\u0026ndash;0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.313\u0026ndash;1.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \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\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.624\u0026ndash;9.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.525\u0026ndash;6.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.082\u0026ndash;0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.313\u0026ndash;3.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \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\u003eCALLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u0026ndash;0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.047\u0026ndash;0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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 \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.696\u0026ndash;2.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003c/p\u003e \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\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.083\u0026ndash;3.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.497\u0026ndash;2.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: NLR, neutrophil\u0026ndash;lymphocyte ratio; PLR, platelet\u0026ndash;lymphocyte ratio; PNI, prognostic nutritional index; AGR, albumin- globulin ratio; CAR, C-reactive protein-albumin ratio; LCR, lymphocyte-C-reactive protein ratio; LMR, lymphocyte\u0026ndash;monocyte ratio; CALLY,CRP\u0026ndash;albumin\u0026ndash;lymphocyte index; SIRI, systemic immune-inflammation index; LA, lymphocyte\u0026ndash;albumin product, CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Associations between inflammatory factors and baseline characteristic\u003c/h2\u003e \u003cp\u003eWe further explored the associations between PNI, LMR, CALLY, and baseline characteristics in CRC patients. The results indicated that PNI was associated with hyperlipidemia, LMR was related to T stage and tumor size, while CALLY was associated with diabetes, T stage, TNM stage, tumor size, and lymph-node metastasis(all \u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;4 Associations between inflammatory factors and baseline characteristic.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value( PNI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value( LMR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eP-value( CALLY)\u003c/p\u003e \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 \u003cp\u003e\u0026gt;\u0026thinsp;70/\u0026lt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale/Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;24/18.5\u0026ndash;24/\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \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 \u003cp\u003eRight/Left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/3/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII/III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size(maximum diameter/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.5/2.5-5.0/5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \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 \u003cp\u003eMod/Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenous invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor deposits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph-node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: PNI, prognostic nutritional index; LMR, lymphocyte\u0026ndash;monocyte ratio; CALLY, CRP\u0026ndash;albumin\u0026ndash;lymphocyte index.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\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":"4. Conclusion","content":"\u003cp\u003eCRC is one of the most common malignancies worldwide, and metastasis is a major cause of postoperative mortality in CRC patients[3]. The liver is the most common site of metastasis in CRC patients, followed by lung metastasis[4]. Currently, research on the relationship between liver metastasis and inflammatory markers is well-established, but the association between lung metastasis and inflammatory markers remains unclear[16]. Therefore, this study explores the connection between inflammatory markers and postoperative PFS as well as postoperative lung metastasis in colorectal cancer (CRC) patients. We found that lower PNI, LMR, and CALLY were significantly associated with reduced postoperative PFS in CRC patients, and lower PNI and CALLY were simultaneously significantly associated with postoperative lung metastasis in CRC patients.\u003c/p\u003e \u003cp\u003eSystemic inflammation is closely associated with the occurrence, development, and metastasis of tumors[7]. Inflammatory cells can release various cytokines and promote tumor progression and metastasis through oxidative stress and other mechanisms[17, 18]. Ulcerative colitis is a chronic inflammatory bowel disease, and patients with inflammatory bowel disease have an increased risk of developing colorectal cancer. Burr et al. found that non-steroidal anti-inflammatory drugs (NSAIDs) can reduce the risk of malignancy in inflammatory bowel disease, suggesting that inflammation plays an important role in the malignant transformation of inflammatory bowel disease[19].\u003c/p\u003e \u003cp\u003eNeutrophils, lymphocytes, and monocytes are important components of the tumor microenvironment, playing crucial roles in tumor progression and metastasis[10]. Neutrophils can promote tumor occurrence by releasing reactive oxygen species or proteases and participate in tumor angiogenesis, thereby facilitating tumor progression[8]. Neutrophils can also inhibit the cytotoxic effects of lymphocytes on tumors by releasing various enzymes. When neutrophils are reduced or CXC chemokine receptor 2(CXCR2) is blocked to inhibit their chemotaxis, tumor angiogenesis can be reduced[20]. As a traditional inflammatory marker, the predictive value of the neutrophil-to-lymphocyte ratio (NLR) has long been demonstrated in various tumors, including CRC[8]. In our study, NLR also demonstrated predictive significance for the prognosis and metastasis of postoperative CRC patients, but it was not an independent risk factor. We consider that this may be related to other inflammatory markers we included.\u003c/p\u003e \u003cp\u003eLymphocytes are crucial components of the body's defense against tumors, with CD8\u0026thinsp;+\u0026thinsp;T cells directly attacking tumor cells and CD4\u0026thinsp;+\u0026thinsp;T cells secreting cytokines and antibodies to activate NK cells and CD8\u0026thinsp;+\u0026thinsp;T lymphocytes, thereby exerting their anti-tumor effects[21]. Higher infiltration of lymphocytes in the tumor microenvironment often signifies a better prognosis[22]. Conversely, reduced lymphocytes are considered adverse prognostic factors for cancer patients. Additionally, individuals with immune deficiencies are more susceptible to developing tumors, particularly those induced by infections[23]. During the process of carcinogenesis, there is often an increase in circulating neutrophils and a decrease in lymphocytes[16]. Huang et al demonstrated that the percentage of lymphocytes before treatment serves as a reliable prognostic indicator for lung cancer, where a higher percentage of lymphocytes represents a better prognosis[12]. Multiple clinical studies have also suggested an association between elevated lymphocytes and improved tumor prognosis[13, 24, 25]. This aligns with our findings, indicating that CRC patients with higher LMR, PNI, and CALLY (indicating higher lymphocytes) experience longer PFS and are less prone to lung metastasis after surgery.\u003c/p\u003e \u003cp\u003eMonocytes play a crucial role in the process of pathogen clearance, but these cells can also simultaneously promote local inflammation[26]. Research indicates that monocytes can facilitate tumor growth and metastasis. In a mouse model of colorectal cancer, monocytes were recruited to primary tumors and lung metastases through chemokines, thereby promoting tumor migration and progression[27]. Katarzyna et al found that CRC patients with low absolute monocyte counts before surgery had a longer 5-year disease-free survival (DFS)[13]. This suggests that monocytes may promote tumor progression by enhancing immune responses, supporting our conclusion that CRC patients with higher LMR (indicating lower monocytes) experience longer PFS after surgery.\u003c/p\u003e \u003cp\u003eCRP is one of the most commonly used indicators to assess systemic inflammation, and its levels are generally positively correlated with the degree of inflammation[28]. Elevated CRP levels can predict the severity of a patient's disease, tumor recurrence rates, and overall survival[29]. Studies have shown that high CRP in solid tumor patients is associated with increased recurrence, metastasis, and mortality rates in 90% of reports[11]. Nozoe et al found that preoperative CRP levels could predict early recurrence in CRC, with patients exhibiting elevated preoperative CRP experiencing significantly higher rates of liver and lymph node metastasis and poorer prognosis, suggesting that CRP may serve as an effective prognostic indicator[30]. Yang et al also demonstrated that lower CALLY index (indicating higher CRP) is associated with an increased risk of death in CRC patients[15]. This aligns with our study, where lower CALLY (indicating higher CRP) was significantly correlated with postoperative lung metastasis and poorer prognosis in CRC patients.\u003c/p\u003e \u003cp\u003eDespite some limitations in our study, such as its retrospective nature, which may introduce issues of loss to follow-up, and being a single-center study that may warrant further extensive clinical research to explore the predictive value of inflammatory markers for the prognosis of CRC patients. However, by minimizing biases to the best of our ability, our study demonstrates that PNI, LMR, and CALLY are independent predictors of postoperative PFS in CRC patients. Both PNI and CALLY are independent predictors of postoperative lung metastasis in CRC patients. These inflammatory markers, as simple and cost-effective indicators, provide a useful tool for monitoring recurrence and metastasis in CRC patients postoperatively.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJiukang Sun\u003c/strong\u003e: Writing \u0026ndash; original draft, Investigation, Conceptualization, Software, Writing \u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eWeili Xiong\u003c/strong\u003e: Conceptualization, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eZhang Fang\u003c/strong\u003e: Conceptualization, Validation, Software, Writing \u0026ndash; review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;Chuanjun Song\u003c/strong\u003e: Supervision, Project administration, Writing \u0026ndash; original draft, Resources, Investigation, Formal analysis, Software, Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study obtained approval from the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University and adheres to the principles of the Helsinki Declaration. As a retrospective study, the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University waived the requirement for written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the retrospective nature of the study, informed consent was waived.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R.L., N.S. Wagle, A. Cercek, R.A. Smith, and A. Jemal, \u003cem\u003eColorectal cancer statistics, 2023.\u003c/em\u003e CA Cancer J Clin, 202310.3322/caac.21772\u003c/li\u003e\n\u003cli\u003eLichtenstern, C.R., R.K. Ngu, S. Shalapour, and M. Karin, \u003cem\u003eImmunotherapy, Inflammation and Colorectal Cancer.\u003c/em\u003e Cells, 2020. \u003cstrong\u003e9\u003c/strong\u003e(3)10.3390/cells9030618\u003c/li\u003e\n\u003cli\u003eChandra, R., J.D. Karalis, C. Liu, G.Z. Murimwa, J. Voth Park, C.A. Heid, et al., \u003cem\u003eThe Colorectal Cancer Tumor Microenvironment and Its Impact on Liver and Lung Metastasis.\u003c/em\u003e Cancers, 2021. \u003cstrong\u003e13\u003c/strong\u003e(24)10.3390/cancers13246206\u003c/li\u003e\n\u003cli\u003eKasi, P.B., V.R. Mallela, F. Ambrozkiewicz, A. Trailin, V. Li\u0026scaron;ka, and K. Hemminki, \u003cem\u003eTheranostics Nanomedicine Applications for Colorectal Cancer and Metastasis: Recent Advances.\u003c/em\u003e International Journal of Molecular Sciences, 2023. \u003cstrong\u003e24\u003c/strong\u003e(9)10.3390/ijms24097922\u003c/li\u003e\n\u003cli\u003eWang, R., W. Dai, J. Gong, M. Huang, T. Hu, H. Li, et al., \u003cem\u003eDevelopment of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients.\u003c/em\u003e Journal of Hematology \u0026amp; Oncology, 2022. \u003cstrong\u003e15\u003c/strong\u003e(1)10.1186/s13045-022-01225-3\u003c/li\u003e\n\u003cli\u003ePrasanna, T., C.S. Karapetis, D. Roder, J. Tie, R. Padbury, T. Price, et al., \u003cem\u003eThe survival outcome of patients with metastatic colorectal cancer based on the site of metastases and the impact of molecular markers and site of primary cancer on metastatic pattern.\u003c/em\u003e Acta Oncol, 2018. \u003cstrong\u003e57\u003c/strong\u003e(11):1438-1444.http://dx.doi.org/10.1080/0284186x.2018.1487581\u003c/li\u003e\n\u003cli\u003eCools-Lartigue, J., J. Spicer, B. McDonald, S. Gowing, S. Chow, B. Giannias, et al., \u003cem\u003eNeutrophil extracellular traps sequester circulating tumor cells and promote metastasis.\u003c/em\u003e J Clin Invest, 2013. \u003cstrong\u003e123\u003c/strong\u003e(8):3446-58.http://dx.doi.org/10.1172/jci67484\u003c/li\u003e\n\u003cli\u003eAn, S., H. Shim, K. Kim, B. Kim, H.-J. Bang, H. Do, et al., \u003cem\u003ePretreatment inflammatory markers predicting treatment outcomes in colorectal cancer.\u003c/em\u003e Annals of Coloproctology, 2022. \u003cstrong\u003e38\u003c/strong\u003e(2):97-108.http://dx.doi.org/10.3393/ac.2021.01004.0143\u003c/li\u003e\n\u003cli\u003eCoussens, L.M. and Z. Werb, \u003cem\u003eInflammation and cancer.\u003c/em\u003e Nature, 2002. \u003cstrong\u003e420\u003c/strong\u003e(6917):860-7.http://dx.doi.org/10.1038/nature01322\u003c/li\u003e\n\u003cli\u003eSingh, N., D. Baby, J.P. Rajguru, P.B. Patil, S.S. Thakkannavar, and V.B. Pujari, \u003cem\u003eInflammation and cancer.\u003c/em\u003e Ann Afr Med, 2019. \u003cstrong\u003e18\u003c/strong\u003e(3):121-126.http://dx.doi.org/10.4103/aam.aam_56_18\u003c/li\u003e\n\u003cli\u003eZhang, L., S. Shrotriya, D. Walsh, N. Bennani-Baiti, S. Thomas, and C. Lorton, \u003cem\u003eC-Reactive Protein Is an Important Biomarker for Prognosis Tumor Recurrence and Treatment Response in Adult Solid Tumors: A Systematic Review.\u003c/em\u003e Plos One, 2015. \u003cstrong\u003e10\u003c/strong\u003e(12)10.1371/journal.pone.0143080\u003c/li\u003e\n\u003cli\u003eHuang, H., L. Li, W. Luo, Y. Yang, Y. Ni, T. Song, et al., \u003cem\u003eLymphocyte percentage as a valuable predictor of prognosis in lung cancer.\u003c/em\u003e Journal of Cellular and Molecular Medicine, 2022. \u003cstrong\u003e26\u003c/strong\u003e(7):1918-1931.http://dx.doi.org/10.1111/jcmm.17214\u003c/li\u003e\n\u003cli\u003eJakubowska, K., M. Koda, M. Grudzińska, L. Kańczuga-Koda, and W. Famulski, \u003cem\u003eMonocyte-to-lymphocyte ratio as a prognostic factor in peripheral whole blood samples of colorectal cancer patients.\u003c/em\u003e World Journal of Gastroenterology, 2020. \u003cstrong\u003e26\u003c/strong\u003e(31):4639-4655.http://dx.doi.org/10.3748/wjg.v26.i31.4639\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller, L., F. Hahn, A. M\u0026auml;hringer-Kunz, F. Stoehr, S.J. Gairing, M. Michel, et al., \u003cem\u003eImmunonutritive Scoring for Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization: Evaluation of the CALLY Index.\u003c/em\u003e Cancers (Basel), 2021. \u003cstrong\u003e13\u003c/strong\u003e(19)10.3390/cancers13195018\u003c/li\u003e\n\u003cli\u003eYang, M., S.-Q. Lin, X.-Y. Liu, M. Tang, C.-L. Hu, Z.-W. Wang, et al., \u003cem\u003eAssociation between C-reactive protein-albumin-lymphocyte (CALLY) index and overall survival in patients with colorectal cancer: From the investigation on nutrition status and clinical outcome of common cancers study.\u003c/em\u003e Frontiers in Immunology, 2023. \u003cstrong\u003e14\u003c/strong\u003e10.3389/fimmu.2023.1131496\u003c/li\u003e\n\u003cli\u003eLu, Y., D. Xin, and F. Wang, \u003cem\u003ePredictive Significance Of Preoperative Systemic Immune-Inflammation Index Determination In Postoperative Liver Metastasis Of Colorectal Cancer\u0026lt;/p\u0026gt;.\u003c/em\u003e OncoTargets and Therapy, 2019. \u003cstrong\u003eVolume 12\u003c/strong\u003e:7791-7799.http://dx.doi.org/10.2147/ott.S223419\u003c/li\u003e\n\u003cli\u003eKaler, P., B.N. Godasi, L. Augenlicht, and L. Klampfer, \u003cem\u003eThe NF-\u0026kappa;B/AKT-dependent Induction of Wnt Signaling in Colon Cancer Cells by Macrophages and IL-1\u0026beta;.\u003c/em\u003e Cancer Microenvironment, 2009. \u003cstrong\u003e2\u003c/strong\u003e(1):69-80.http://dx.doi.org/10.1007/s12307-009-0030-y\u003c/li\u003e\n\u003cli\u003eLiu, H., X. Liu, C. Zhang, H. Zhu, Q. Xu, Y. Bu, et al., \u003cem\u003eRedox Imbalance in the Development of Colorectal Cancer.\u003c/em\u003e Journal of Cancer, 2017. \u003cstrong\u003e8\u003c/strong\u003e(9):1586-1597.http://dx.doi.org/10.7150/jca.18735\u003c/li\u003e\n\u003cli\u003eBurr, N.E., M.A. Hull, and V. Subramanian, \u003cem\u003eDoes aspirin or non-aspirin non-steroidal anti-inflammatory drug use prevent colorectal cancer in inflammatory bowel disease?\u003c/em\u003e World Journal of Gastroenterology, 2016. \u003cstrong\u003e22\u003c/strong\u003e(13)10.3748/wjg.v22.i13.3679\u003c/li\u003e\n\u003cli\u003eAntonio, N., M.L. B\u0026oslash;nnelykke-Behrndtz, L.C. Ward, J. Collin, I.J. Christensen, T. Steiniche, et al., \u003cem\u003eThe wound inflammatory response exacerbates growth of pre-neoplastic cells and progression to cancer.\u003c/em\u003e Embo j, 2015. \u003cstrong\u003e34\u003c/strong\u003e(17):2219-36.http://dx.doi.org/10.15252/embj.201490147\u003c/li\u003e\n\u003cli\u003eDai, S., P. Ren, J. Ren, L. Yang, and W. Li, \u003cem\u003eThe Relationship between Lymphocyte Subsets and the Prognosis and Genomic Features of Lung Cancer: A Retrospective Study.\u003c/em\u003e International Journal of Medical Sciences, 2021. \u003cstrong\u003e18\u003c/strong\u003e(10):2228-2234.http://dx.doi.org/10.7150/ijms.56928\u003c/li\u003e\n\u003cli\u003eOhtani, H., \u003cem\u003eFocus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer.\u003c/em\u003e Cancer Immun, 2007. \u003cstrong\u003e7\u003c/strong\u003e:4.http://dx.doi.org/\u003c/li\u003e\n\u003cli\u003eGrulich, A.E., M.T. van Leeuwen, M.O. Falster, and C.M. Vajdic, \u003cem\u003eIncidence of cancers in people with HIV/AIDS compared with immunosuppressed transplant recipients: a meta-analysis.\u003c/em\u003e Lancet, 2007. \u003cstrong\u003e370\u003c/strong\u003e(9581):59-67.http://dx.doi.org/10.1016/s0140-6736(07)61050-2\u003c/li\u003e\n\u003cli\u003eChen, B., J. Zhou, Y. Ma, Q. Sun, J. Ren, and D. Wang, \u003cem\u003eEvaluation of multiple biological indicators for the combined diagnosis of metastases from colorectal cancer\u0026mdash;a retrospective study based on 1163 patients.\u003c/em\u003e World Journal of Surgical Oncology, 2023. \u003cstrong\u003e21\u003c/strong\u003e(1)10.1186/s12957-023-03108-4\u003c/li\u003e\n\u003cli\u003eChen, Q., G.-l. Li, H.-q. Zhu, J.-D. Yu, Z.-P. Chen, J.-Y. Wu, et al., \u003cem\u003eThe neutrophil-to-lymphocyte ratio and lactate dehydrogenase combined in predicting liver metastasis and prognosis of colorectal cancer.\u003c/em\u003e Frontiers in Medicine, 2023. \u003cstrong\u003e10\u003c/strong\u003e10.3389/fmed.2023.1205897\u003c/li\u003e\n\u003cli\u003eShi, C. and E.G. Pamer, \u003cem\u003eMonocyte recruitment during infection and inflammation.\u003c/em\u003e Nature Reviews Immunology, 2011. \u003cstrong\u003e11\u003c/strong\u003e(11):762-774.http://dx.doi.org/10.1038/nri3070\u003c/li\u003e\n\u003cli\u003eChun, E., S. Lavoie, M. Michaud, Carey A. Gallini, J. Kim, G. Soucy, et al., \u003cem\u003eCCL2 Promotes Colorectal Carcinogenesis by Enhancing Polymorphonuclear Myeloid-Derived Suppressor Cell Population and Function.\u003c/em\u003e Cell Reports, 2015. \u003cstrong\u003e12\u003c/strong\u003e(2):244-257.http://dx.doi.org/10.1016/j.celrep.2015.06.024\u003c/li\u003e\n\u003cli\u003eSproston, N.R. and J.J. Ashworth, \u003cem\u003eRole of C-Reactive Protein at Sites of Inflammation and Infection.\u003c/em\u003e Frontiers in Immunology, 2018. \u003cstrong\u003e9\u003c/strong\u003e10.3389/fimmu.2018.00754\u003c/li\u003e\n\u003cli\u003eMahmoud, F.A. and N.I. Rivera, \u003cem\u003eThe role of C-reactive protein as a prognostic indicator in advanced cancer.\u003c/em\u003e Curr Oncol Rep, 2002. \u003cstrong\u003e4\u003c/strong\u003e(3):250-5.http://dx.doi.org/10.1007/s11912-002-0023-1\u003c/li\u003e\n\u003cli\u003eNozoe T, M.T., Kitamura M, Sugimachi K., \u003cem\u003eSignificance of preoperative elevation of serum C-reactive protein as an indicator for prognosis in colorectal cancer.\u003c/em\u003e Am J Surg, 1998176:335-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"colorectal cancer, inflammatory markers, progression-free survival, lung metastasis","lastPublishedDoi":"10.21203/rs.3.rs-3923170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3923170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e \u003cp\u003eThis study aims to investigate the predictive significance of inflammatory markers for postoperative lung metastasis in colorectal cancer (CRC) patients. The focus is on exploring the relationship between traditional inflammatory markers, such as NLR, PLR, and newly introduced indices like CALLY, and their association with CRC progression and lung metastasis.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003e A retrospective review of medical records from 303 CRC patients at the First Affiliated Hospital of Nanjing Medical University (January 2014 to December 2023) was conducted. Various inflammatory markers, baseline characteristics, and survival data were analyzed. The CALLY index, integrating PNI and CRP levels, was introduced. Statistical analyses, including univariate and multivariate Cox regression and logistic regression, were performed to determine associations between inflammatory factors and CRC outcomes.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAmong the patients, 55 developed lung metastasis during the study period. The results revealed that lower PNI, LMR, and CALLY were significantly associated with reduced postoperative progression-free survival (PFS). Moreover, CEA, PNI, and CALLY emerged as independent risk factors for postoperative lung metastasis.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eIn conclusion, this study underscores the importance of inflammatory markers in predicting postoperative outcomes for CRC patients. Lower PNI, LMR, and CALLY were identified as significant predictors of reduced PFS, while CEA, PNI, and CALLY were independently associated with an increased risk of postoperative lung metastasis. These findings highlight the potential clinical utility of these inflammatory markers in monitoring CRC recurrence and metastasis.\u003c/p\u003e","manuscriptTitle":"Predictive Significance Of Preoperative Inflammatory Markers In Postoperative Lung Metastasis Of Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 17:09:36","doi":"10.21203/rs.3.rs-3923170/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":"90e68ba8-6394-42d7-9459-d2e955c6af21","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-12T18:44:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 17:09:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3923170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3923170","identity":"rs-3923170","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}