The Prognostic Impact of Sarcopenia in Stage I to III Colon Cancer A Retrospective Study

The Prognostic Impact of Sarcopenia in Stage I to III Colon Cancer A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Prognostic Impact of Sarcopenia in Stage I to III Colon Cancer A Retrospective Study Chen Mi, Feng Cui, Yongyue Du, Siyang Wang, Yuanhua She, Yongzhao Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8237628/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Colon cancer (CC) is a common malignancy, and while the TNM staging system is extensively used for prognostic, it has limitations in predicting individual outcomes. In recent years, Sarcopenia has emerged as a significant prognostic factor in cancer patients. This study aimed to investigate the prognostic value of sarcopenia in stage I–III colon cancer patients. Methods This retrospective analysis included 230 CC patients who underwent tumor resection at the Second Hospital of Lanzhou University. Preoperative abdominal computed tomography (CT) scans were used to assess the skeletal muscle index (SMI). Patients were categorized into sarcopenia and non-sarcopenia groups based on the SMI. The primary outcome was overall survival (OS). The prognostic role of sarcopenia was assessed using Kaplan-Meier survival curves, Cox regression modeling and time-dependent Receiver Operator Characteristic (ROC) analysis. In addition, a combined TNM- sarcopenia model was developed. Results Among the 230 patients, 24.34% were diagnosed with sarcopenia. Kaplan-Meier curves showed that patients in the sarcopenia group had significantly lower OS compared to the non-sarcopenia group (P < 0.001). Cox regression identified sarcopenia as an independent adverse prognostic factor (HR, 1.70, 95%CI: 1.05–2.75). Incorporating sarcopenia into the TNM model increased the C-index from 0.570 to 0.604 (P likelihood ratio = 0.027), with improved predictive performance at 1-year, 3-year, and 5-year time points. Conclusion Sarcopenia is not only an independent prognostic factor for OS in patients with stage I–III CC, but also serves as a valuable supplement to existing prognostic assessment tools, which may contribute to personalized treatment strategies. Colon cancer Sarcopenia Prognosis Survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Colon cancer (CC) is one of the most common malignant tumors in the digestive tract globally, with adenocarcinoma accounting for approximately 95% of cases. According to the 2020 Global Cancer Statistics report, more than 1.9 million people have been newly diagnosed with CC, resulting in approximately 935,000 deaths [ 1 ]. This makes CC the third most common cancer in the world and the second leading cause of cancer-related deaths. This presents a significant challenge to public health. Currently, the standard treatment for stage I–III colorectal cancer involves extensive tumor resection followed by adjuvant chemotherapy, such as the FOLFOX or XELOX regimens [ 2 ]. While this treatment approach has significantly improved overall survival (OS) rates in patients, substantial prognostic disparities persist in clinical practice. This is particularly evident in the decision-making process for administering adjuvant chemotherapy to stage II patients, which remains a topic of ongoing debate[ 3 ]. TNM staging, a traditional tool for risk stratification based on tumor anatomical characteristics, holds some prognostic value. However, it fails to account for important factors such as the patient's nutritional status, metabolic condition, and overall physiological state. This limitation hinders its ability to fully reflect the biological behavior of tumors and restricts its application in precision medicine[ 4 , 5 ]. Recent studies have highlighted the significant role of nutritional status and body composition—particularly changes in skeletal muscle mass—in the prognosis of cancer patients[ 6 , 7 ]. Protein-energy malnutrition not only impairs immune function and increases the risk of treatment-related toxicity and postoperative complications, but also significantly reduces patient survival[ 8 – 10 ]. Additionally, micronutrients such as vitamin D, zinc, and selenium can modulate the tumor microenvironment by regulating the gut microbiota-inflammation axis[ 11 , 12 ]. In recent years, sarcopenia has attracted considerable attention as a clinical indicator that reflects a patient's overall functional status and nutritional condition[ 13 , 14 ]. A large number of studies have shown that sarcopenia is closely related to adverse outcomes after surgical intervention in cancer patients, such as higher rates of complications, reduced quality of life and reduced OS[ 15 , 16 ]. An important prospective cohort study conducted in China confirmed that sarcopenia is an independent predictor of poor postoperative prognosis in patients with gastrointestinal cancer[ 17 ]. Notably, a study by Lieffers et al. demonstrated that, in CC patients undergoing surgery, sarcopenia was significantly correlated with prolonged postoperative hospital stay, heightened chemotherapy toxicity, and reduced OS, thereby supporting the importance of sarcopenia in preoperative risk assessment[ 18 ]. Although some studies have suggested a link between preoperative sarcopenia and unfavorable outcomes in CC patients, research specifically targeting stage I-III colon cancer patients remains relatively limited. Therefore, this study aims to thoroughly investigate the prognostic value of sarcopenia in patients with stage I-III colon cancer, clarify its impact on OS, and further construct a combined prognostic prediction model integrating sarcopenia with traditional TNM staging to assess the incremental value of sarcopenia compared to traditional TNM staging in predicting the prognosis of CC patients. Through this study, we aim to provide effective evidence for clinicians to stratify patient risk and make individualized treatment decisions. Patients and Methods Patients and inclusion criteria A retrospective review was performed on 230 patients with CC who underwent primary tumor resection at Lanzhou University Second Hospital between January 2018 and December 2019. None of the patients received neoadjuvant therapy, and all underwent routine abdominal computed tomography scans within one month prior to surgery. As the 8th edition of the American Joint Committee on Cancer (AJCC) staging guidelines was published in 2018, and the patient data for this study began in 2018, the 8th edition of the AJCC staging system was applied[ 19 ]. The inclusion criteria were as follows: (1) Initial diagnosis of CC with subsequent curative surgery; (2) Age > 18 years; (3) Postoperative pathological diagnosis of CC with TNM stages I-III; (4) At least 12 lymph nodes detected in the surgical specimen after radical surgery; (5) Availability of complete follow-up and clinical-pathological records; (6) Availability of complete preoperative computed tomography imaging data. The exclusion criteria were: (1) Received neoadjuvant therapy prior to surgery; (2) Presence of rectal cancer; (3) History of other malignancies; (4) Concurrent malignant tumors. This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Lanzhou University Second Hospital (Project Number: 2025A-638). As a retrospective study, patient privacy and personal identity information were protected, and the Medical Ethics Committee waived the requirement for informed consent. The study has been approved by the Ethics Committee of Lanzhou University Second Hospital. Assessment of sarcopenia A CT scan is routinely performed within one month prior to surgery. The parameters for the CT images include contrast-enhanced or non-enhanced multi-phase acquisition, with a slice thickness of 5 mm. To assess sarcopenia, two adjacent CT images of the third lumbar vertebra (L3) from the same series were selected during the non-contrast phase[ 20 ]. ImageJ2 software (National Institutes of Health, Washington, D.C., USA) was used to measure the total skeletal muscle area (SMA) on the two slices within the range of -29 to + 150 Hounsfield units (HU)[ 21 ]. The SMA was averaged for each patient. The outlined regions for SMA measurement are shown in Fig. 1 . The skeletal muscle index (SMI) was defined as SMA divided by the square of height. Based on current research, sarcopenia was defined as a SMI below the lowest gender-specific quartile. According to this criterion, patients were categorized into sarcopenia and non-sarcopenia groups [ 22 ] (Fig. 1 ). Data collection The preoperative demographic and clinical data collected included age, gender, height, SMI, CEA levels, and cancer location. Tumor location was classified as either right-sided or left-sided CC. Right-sided CC includes cecal cancer, ascending CC, hepatic flexure cancer, and transverse CC, while left-sided CC includes splenic flexure cancer, descending CC, and sigmoid CC. Postoperative data collected included T stage, N stage, differentiation grade, neurovascular invasion, tumor maximum diameter, Ki67%, and cancer staging according to the AJCC 8th edition staging system. The outcome of interest was OS, defined as the time from surgery to death from any cause. All patients were followed up within one month after surgery and then every three months thereafter. The most recent follow-up was conducted in December 2024. Statistical analysis The normality of clinical characteristics was assessed using the Shapiro-Wilk test. For baseline characteristics, continuous variables were expressed as mean ± standard deviation, and categorical variables were presented as frequencies and percentages. Continuous variables were analyzed using t-tests or Mann-Whitney U tests, depending on their distribution characteristics. Categorical variables were analyzed using chi-square tests. Univariate and multivariate Cox regression analyses were conducted to identify potential independent prognostic factors for OS. For survival analysis, Cox proportional hazards models were constructed, and Kaplan-Meier analyses were conducted. Model performance was evaluated using Harrell's C statistic, and likelihood ratio P-values were used to compare the performance of risk prediction models. Samples were stratified based on predicted hazard ratios (HRs), and the significance of group separation was assessed using multivariate log-rank tests. Subsequently, time-dependent receiver operating characteristic analysis was performed to evaluate the predictive ability of the model at different time points (1 year, 3 years, and 5 years). Subgroup analysis was conducted to explore the impact of sarcopenia within each subgroup. All data analyses were performed using R4.2.1 and Python 3.7.12. A P-value of less than 0.05 was considered statistically significant. Results Baseline characteristics of patients This study ultimately included 230 patients with CC who underwent initial tumor resection at the Second Hospital of Lanzhou University. The lowest gender-specific quartile of SMI at the L3 level was 40.6 cm²/m² for males and 34.9 cm²/m² for females. The baseline characteristics of the included patients are presented in Table 1 . Sarcopenia was identified in 56 patients (24.34%). Patients in the sarcopenia group had significantly poorer prognosis, shorter overall survival, and higher mortality (P < 0.05). No statistically significant differences were observed between the sarcopenia and non-sarcopenia groups in terms of age, gender, differentiation degree, or AJCC stage. Table 1 Baseline characteristics of the participants. Variable Total (n = 230) Non sarcopenia (n = 174) Sarcopenia (n = 56) P Age 0.252 ≤ 60 112 (48.70%) 81 (46.55%) 31 (55.36%) >60 118 (51.30%) 93 (53.45%) 25 (44.64%) Gender 0.959 Female 102 (44.35%) 77 (44.25%) 25 (44.64%) Male 128 (55.65%) 97 (55.75%) 31 (55.36%) CEA (µg/L) 0.144 ≤ 5 114 (49.57%) 91 (52.30%) 23 (41.07%) >5 116 (50.43%) 83 (47.70%) 33 (58.93%) Hight 165.72 ± 7.84 165.61 ± 7.85 166.04 ± 7.85 0.728 Tumor location 0.224 Left hemicolon 123 (53.48%) 97 (55.75%) 26 (46.43%) Right hemicolon 107 (46.52%) 77 (44.25%) 30 (53.57%) Tumor maximum diameter(cm) 0.662 ≤ 6 180 (78.26%) 135 (77.59%) 45 (80.36%) >6 50 (21.74%) 39 (22.41%) 11 (19.64%) Neural or vascular invasion 0.861 No 84 (36.52%) 63 (36.21%) 21 (37.50%) Yes 146 (63.48%) 111 (63.79%) 35 (62.50%) Grade 0.156 Poor/Undifferentiated 39 (16.96%) 26 (14.94%) 13 (23.21%) Moderately differentiated 178 (77.39%) 136 (78.16%) 42 (75.00%) Well differentiated 13 (5.65%) 12 (6.90%) 1 (1.79%) T stage 0.398 T1-2 46 (20.00%) 37 (21.26%) 9 (16.07%) T3-4 184 (80.00%) 137 (78.74%) 47 (83.93%) Lymph node metastasis 0.886 N0 166 (72.17%) 126 (72.41%) 40 (71.43%) N1-2 64 (27.83%) 48 (27.59%) 16 (28.57%) TNM stage 0.798 Ⅰ 44 (19.13%) 35 (20.11%) 9 (16.07%) Ⅱ 122 (53.04%) 91 (52.30%) 31 (55.36%) Ⅲ 64 (27.83%) 48 (27.59%) 16 (28.57%) Overall survival (months) 55.29 ± 23.11 57.33 ± 22.07 48.95 ± 25.25 0.018 Survival status 0.027 Survival 155 (67.39%) 124 (71.26%) 31 (55.36%) Death 75 (32.61%) 50 (28.74%) 25 (44.64%) Sarcopenia on overall survival The Kaplan–Meier survival curve (Fig. 2 ) shows that the OS of patients with sarcopenia is significantly lower than that of patients without sarcopenia (log-rank test P 5, lymph node metastasis, and sarcopenia were significantly associated with poorer OS (all P < 0.05). Multivariate analysis demonstrated that lymph node metastasis (N1-2, HR = 1.63, 95% CI: 1.01–2.62, P = 0.0460) and sarcopenia (HR = 1.70, 95% CI: 1.05–2.75, P = 0.0321) were independent predictors of OS. Table 2 Univariate and multivariate Cox regression analyses. Variable Univariable analysis Multivariable analysis HR (95%CI) P HR (95%CI) P Age ≤ 60 1.0 >60 0.89 (0.56, 1.40) 0.6067 Gender Female 1.0 Male 0.93 (0.59, 1.47) 0.7691 CEA (µg/L) ≤ 5 1.0 1.0 >5 1.63 (1.03, 2.59) 0.0373 1.43 (0.89, 2.30) 0.1363 Tumor location Left hemicolon 1.0 Right hemicolon 1.16 (0.74, 1.83) 0.5152 Tumor maximum diameter(cm) ≤ 6 1.0 >6 1.02 (0.58, 1.76) 0.9562 Neural or vascular invasion No 1.0 Yes 1.62 (0.98, 2.68) 0.0612 Grade Poor/Undifferentiated 1.0 Moderately differentiated 0.87 (0.48, 1.55) 0.6320 Well differentiated 0.58 (0.17, 2.01) 0.3876 T stage T1-2 1.0 T3-4 1.38 (0.74, 2.55) 0.3105 Lymph node metastasis N0 1.0 1.0 N1-2 1.76 (1.10, 2.80) 0.0185 1.63 (1.01, 2.62) 0.0460 TNM stage Ⅰ 1.0 Ⅱ 1.19 (0.61, 2.34) 0.6141 Ⅲ 2.00 (1.00, 4.02) 0.0515 Sarcopenia No 1.0 1.0 Yes 1.76 (1.09, 2.85) 0.0204 1.70 (1.05, 2.75) 0.0321 Prognostic Value of Sarcopenia In this study, a Cox proportional hazards model based on the TNM staging system was constructed, with sarcopenia incorporated as a prognostic factor to assess its predictive value for OS in CC patients. The analysis revealed that the C-index of the traditional TNM model was 0.570 (95% CI: 0.508, 0.631), while the C-index for the TNM-sarcopenia model was 0.604 (95% CI: 0.540, 0.669). The inclusion of sarcopenia significantly improved the predictive performance of the traditional TNM model (P likelihood ratio test = 0.027), demonstrating that sarcopenia provides significant prognostic value for OS in patients with stage I-III colon cancer (Table 3 ). Table 3 Multivariate Analysis of Two Models in Patients with Stage I–III Disease Overall survival Concordance index HR (95%CI) P (95%CI) TNM-Sarcopenia model 0.604 (0.540, 0.669) T 1.13(0.59,2.16) 0.7056 N 1.70(1.05,2.77) 0.0325 Sarcopenia 1.75(1.08,2.83) 0.0222 TNM-model 0.570 (0.508, 0.631) T 1.17(0.61,2.22) 0.6395 N 1.70(1.04,2.76) 0.0332 To further evaluate the prognostic value of sarcopenia, risk stratification was performed based on the hazard ratios from the COX model, categorizing patients into high-risk and low-risk groups. The discriminatory performance was assessed using Kaplan-Meier analysis ( Fig. 3 ). The results demonstrated that the TNM-sarcopenia model (log-rank test P = 0.001) outperformed the TNM model (log-rank test P = 0.017) in identifying high-risk patients. To comprehensively assess the models’ predictive capabilities, time-dependent ROC analysis was conducted to compare the performance of the TNM-sarcopenia model and the TNM model at multiple time points specifically at 1, 3, and 5 years ( Table S1 ). The results showed that the TNM-sarcopenia model outperformed the TNM model at all time points ( Figure S1 ). Subgroup analysis We further performed subgroup analyses and interaction tests to examine potential effect modifications by factors such as age and sex on the independent association between sarcopenia and OS(Table 4 ). Subgroup analyses consistently demonstrated that sarcopenia was associated with increased mortality risk across all predefined subgroups of stage I–III colon cancer patients. Notably, significant associations between sarcopenia and poorer survival were observed specifically in patients with left-sided CC (HR = 2.03, 95% CI: 1.02–4.03, P = 0.0423), T3–4 stage disease (HR = 1.81, 95% CI: 1.08–3.05, P = 0.0244), and TNM stage II (HR = 2.18, 95% CI: 1.12–4.27, P = 0.0225). However, no statistically significant interaction effects were detected across any subgroups (all P for interaction > 0.05). Table 4 The effect of sarcopenia on overall survival in patients with stage I–III colon cancer across different subgroups. Variable N HR (95%CI) P P Interaction Age 0.7331 ≤ 60 112 1.84 (0.95, 3.59) 0.0714 >60 118 1.65 (0.82, 3.32) 0.1636 Gender 0.3738 Female 102 1.37 (0.66, 2.87) 0.3988 Male 128 2.15 (1.14, 4.06) 0.0186 CEA (µg/L) 0.1718 ≤ 5 114 2.55 (1.21, 5.36) 0.0137 >5 116 1.28 (0.68, 2.41) 0.4414 Tumor location 0.5301 Left hemicolon 123 2.03 (1.02, 4.03) 0.0423 Right hemicolon 107 1.50 (0.76, 2.94) 0.2414 Tumor maximum diameter(cm) 0.2733 ≤ 6 180 1.56 (0.90, 2.69) 0.1116 >6 50 2.88 (1.04, 7.93) 0.0411 Neural or vascular invasion 0.7536 No 84 1.59 (0.64, 3.94) 0.3171 Yes 146 1.89 (1.07, 3.32) 0.0282 Histological grade 0.6226 Poor/Undifferentiated 39 1.64 (0.57, 4.74) 0.3585 Moderately differentiated 178 1.81 (1.05, 3.14) 0.0338 Well differentiated 13 0.00 (0.00, Inf) 0.9991 T stage 0.7243 T1-2 46 1.40 (0.38, 5.17) 0.6142 T3-4 184 1.81 (1.08, 3.05) 0.0244 Lymph node metastasis 0.4618 N0 166 2.02 (1.11, 3.66) 0.0206 N1-2 64 1.39 (0.61, 3.16) 0.4291 TNM stage 0.6808 Ⅰ 44 1.49 (0.39, 5.60) 0.5591 Ⅱ 122 2.18 (1.12, 4.27) 0.0225 Ⅲ 64 1.39 (0.61, 3.16) 0.4291 Prognostic Value of Sarcopenia in Stage II Colon Cancer This study further explored the effect of sarcopenia on OS in patients with stage II colon cancer, assessing its prognostic significance through Kaplan-Meier survival analysis and both unadjusted and multivariate Cox proportional hazards regression models. The Kaplan-Meier survival curves (Fig. 4 ) revealed that OS for patients with stage II colon cancer in the sarcopenia group was significantly shorter compared to the non-sarcopenia group (P = 0.019). Cox regression analysis revealed that among patients with stage II tumors (n = 122), sarcopenia was associated with shorter overall survival (HR = 2.18, 95% CI: 1.12–4.27, P = 0.0225). Similar results were observed in the adjusted model controlling for gender, age, CEA levels, tumor location, differentiation grade, maximum tumor diameter, neurovascular invasion, and T stage(Table 5 ). These findings indicate that sarcopenia serves as an independent prognostic factor for overall survival in patients with stage II colon cancer. Table 5 Multivariate analysis of sarcopenia in patients with stage II colon cancer Overall survival HR (95%CI) P Concordance index (95%CI) Unadjusted Cox regression 0.584 (0.507, 0.661) Sarcopenia 2.18(1.12,4.27) 0.0225 multivariable Cox regression 0.649 (0.567, 0.730) Sarcopenia 2.19(1.08,4.42) 0.0289 Gender 1.14(0.58,2.23) 0.7012 Age 0.87(0.42,1.80) 0.7037 CEA (µg/L) 1.36(0.67,2.75) 0.3887 Tumor location 1.18(0.59,2.35) 0.6387 Histological grade 1.16(0.37,3.64) 0.7951 Tumor maximum diameter(cm) 1.02(0.46,2.25) 0.9593 Neural or vascular invasion 1.61(0.74,3.50) 0.2308 T Stage 1.57(0.67,3.64) 0.2976 Discussion This study examined the impact of sarcopenia on OS in patients with stage I–III colon cancer and confirmed its role as an independent prognostic factor. The study results indicate that sarcopenia is significantly linked to poorer survival outcomes in CC patients, with its impact remaining evident in multivariate Cox regression analysis (HR = 1.70, 95% CI: 1.05–2.75, P = 0.0321). Subgroup analyses further indicate that sarcopenia has a more significant effect on survival in patients with left-sided CC, T3-4 stage, and N1-2 stage disease. To enhance prognostic predictive capability, this study developed a TNM-sarcopenia model based on the TNM staging system. The model showed superior predictive performance with higher C-index and area under the curve (AUC) values compared to the TNM model, suggesting the potential value of incorporating sarcopenia into the prognostic assessment of CC. Sarcopenia is a common metabolic abnormality in patients with malignant tumors, and previous studies have shown that it is associated with poor outcomes in various cancers, including lung, gastric, pancreatic, and CC[ 23 – 26 ]. In the field of colorectal cancer, although several studies have explored the impact of preoperative sarcopenia on short- and long-term outcomes, a meta-analysis by Trejo-Avila M further clarified that sarcopenia is an important predictor of increased postoperative complication rates and reduced survival rates[ 27 ]. Another study demonstrated that sarcopenia is an objective and reliable predictive factor, outperforming current nutritional, functional, biochemical, and clinical indicators in predicting postoperative complications and cancer recurrence after radical resection for CC[ 28 ]. However, to date, insufficient research has been conducted on its prognostic value in patients with stage I–III colon cancer. Therefore, the findings of this study may provide further evidence supporting the need for clinical intervention to address sarcopenia in patients with stage I–III colon cancer. The results of this study showed that patients with sarcopenia had significantly lower OS compared to those without sarcopenia (P = 0.018), and sarcopenia was identified as an independent risk factor for OS (HR = 1.70, P = 0.0321), further confirming that sarcopenia can serve as a reliable prognostic indicator for the long-term prognosis of patients with stage I-III colon cancer. However, the mechanisms by which sarcopenia influences the long-term prognosis of CC patients remain incompletely understood. This phenomenon may be linked to the chronic inflammatory state, impaired immune function, and reduced antitumor capacity associated with sarcopenia[ 29 ]. Sarcopenia in cancer patients is believed to result from skeletal muscle loss and muscle atrophy due to metabolic abnormalities linked to tumor cachexia[ 30 ]. Dietary supplements and appetite stimulants alone are insufficient to reverse this underlying metabolic abnormality, which may partially explain why sarcopenia acts as a prognostic factor for poor outcomes in cancer patients[ 31 ]. Interestingly, patients with sarcopenia often exhibit elevated levels of systemic inflammatory factors, such as C-reactive protein and interleukin-6. These inflammatory markers can accelerate tumor progression and affect treatment outcomes by stimulating tumor cell growth, suppressing antitumor immune responses, and enhancing tumor resistance[ 32 ]. Moreover, studies have demonstrated a significant association between sarcopenia and elevated IL-23 levels, with the combination of sarcopenia and IL-23 serving as an effective prognostic predictor. This suggests that IL-23 and systemic inflammation may play key roles in poor survival outcomes[ 33 ]. Furthermore, high levels of inflammation are closely associated with poor prognosis in CC patients, and sarcopenia may exacerbate this inflammatory state, leading to a cascade of reactions that further reduce patient survival rates. Sarcopenia is also closely linked to malnutrition in cancer patients, which further impairs immune function and treatment tolerance. Malnutrition is particularly prevalent in CC patients, making appropriate nutritional supplementation crucial for achieving good short-term outcomes and long-term survival[ 34 , 35 ]. Previous studies have shown that patients with sarcopenia experience a higher incidence of complications after surgery or chemoradiotherapy, slower postoperative recovery, and may face challenges in treatment implementation[ 16 , 36 ]. Although this study did not directly analyze the impact of treatment modalities on patient outcomes, the results indicate that patients with sarcopenia have poorer survival outcomes, suggesting that clinical practice should prioritize screening for and intervening in sarcopenia to improve patient outcomes. Currently, the TNM staging system is the standard tool for treatment decision-making and prognosis assessment in CC, but it still has certain limitations in terms of individualized prediction[ 37 ]. To overcome this limitation, efforts have been made to classify CC based on molecular subtypes derived from gene expression, with the aim of more effectively guiding treatment decisions. However, these factors have limited practical value in routine clinical practice[ 38 ]. Given the complex mechanisms underlying CC development, a single genetic marker is unlikely to predict individual prognosis. Therefore, an international validation study initiated by the International Immuno-Scoring Consortium [ 39 ] assessed the prognostic value of total tumor-infiltrating T cell counts and cytotoxic tumor-infiltrating T cell counts in stage I-III colon cancer patients using a consensus immuno-scoring assay. The results showed that patients with high immuno-scores had the lowest risk of recurrence at 5 years and could also effectively identify high-risk patients for tumor recurrence in stage II cancer patients. However, in this study, the AUC value for predicting OS in the model combining all clinical variables with the immune score remained below 0.65. This highlights the need for further exploration of additional prognostic predictors. To this end, this study constructed both the traditional TNM model and the TNM-sarcopenia model to assess and validate the predictive ability of sarcopenia in patients with stage I-III colon cancer. The results showed that the C-index of the TNM-sarcopenia model (0.604) was higher than that of the TNM model (0.570), and the AUC values at different time points (1 year, 3 years, 5 years) were superior to those of the TNM model, with the most significant improvement in predictive performance at 3 years (TNM-sarcopenia model AUC = 0.629 vs TNM model AUC = 0.588). These findings indicate that incorporating sarcopenia factors enhances the predictive capability of the model, further validating the role of sarcopenia in CC prognosis assessment. Kaplan-Meier survival analysis further demonstrated that the TNM-sarcopenia model exhibits superior stratified predictive capability compared to the standalone TNM model. These results suggest that, in clinical practice, the TNM-sarcopenia model, by incorporating sarcopenia factors, can provide more precise survival predictions and effectively identify high-risk patients, thereby aiding in individualized treatment decisions. Subgroup analysis in this study further demonstrated that sarcopenia had a more significant impact on survival in patients with left-sided CC, T3-4 stage, and N1-2 stage. This may be related to differences in gut microbiota, tumor microenvironment, and metabolic characteristics among patients with left-sided CC. Previous studies have reported significant differences between left-sided and right-sided CC in terms of long-term prognosis and cancer biology[ 40 ]. A retrospective study indicated that sarcopenia[ 13 ], as assessed by CT, independently predicts shorter OS and recurrence-free survival in patients with left-sided colon or rectal cancer after curative surgery, which is consistent with the subgroup analysis results. Additionally, patients with T3-4 stage and N1-2 stage disease have a greater disease burden, and sarcopenia may further exacerbate their survival disadvantage. Therefore, in clinical practice, sarcopenia should be prioritized for screening and management in high-risk subgroups to optimize survival outcomes. Although this study revealed the adverse impact of sarcopenia on the OS of stage I-III colon cancer patients, particularly those with stage II disease, certain limitations remain. First, this study is a retrospective analysis, which may introduce selection bias, necessitating validation of the results in larger prospective cohort studies. Second, due to the lack of standardized assessment methods for sarcopenia, differences in results across studies may limit the comparability and generalizability of the findings. Future studies should focus on standardizing and validating assessment methods, as well as exploring the consistency and complementarity of different measurement techniques in predicting clinical outcomes, to enhance the scientific rigor and clinical applicability of such research. Additionally, since this study did not further analyze the survival outcomes of sarcopenia patients receiving different treatment regimens (such as adjuvant chemotherapy or intensive nutritional intervention), future studies could explore the impact of these factors on prognosis. Conclusions In summary, sarcopenia is an independent prognostic factor associated with OS in patients with stage I-III colon cancer. Incorporating sarcopenia into a combined model based on TNM staging significantly enhances prognostic predictive ability, particularly in stage II patients, where it is more effective in identifying high-risk populations. Therefore, sarcopenia may serve as a crucial prognostic biomarker, offering an effective supplement for risk stratification and individualized treatment. Abbreviations The following abbreviations are used in this manuscript: HRs Hazard Ratios ROC Receiver Operator Characteristic AUC Area Under the Curve AJCC American Joint Committee on Cancer CT Computed Tomography SMA Skeletal Muscle Area SMI Skeletal Muscle Index OS Overall Survival Declarations Data Availability Statement: The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Author Contributions: CM and FC designed the study and conducted the data analysis. YYD and SYW participated in manuscript modification. YHS, YZL, DDW and WW took part in data collection and analysis. HTY and XJD provided critical revisions to the manuscript. All authors read and approved the final manuscript. Funding: This study was supported by the Nature Science Foundation of Gansu Province (Grant No.24JRRA3221), the “Cuiying Science and Technology Innovation” program (Grant No. CY2024-MS-B04), and the Scientific Research Project of Traditional Chinese Medicine of Gansu Province (Grant No. GZKZ-2024-26). Ethics Statement: This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of the Second Hospital of Lanzhou University (Project Number:2025 A-638). As this study is a retrospective and the privacy and personal identity information of the patients were protected, the need for informed consent was waived by the Medical Ethics Committee of the Second Hospital of Lanzhou University. Acknowledgments: We are grateful for the financial support provided by the Gansu Provincial Natural Science, Cuiying Science and Technology Innovation and Gansu Province Traditional Chinese Medicine. 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09:24:57","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20213,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/a26a72119dd83bb03dd36b28.png"},{"id":98778425,"identity":"70b55bd7-d806-44d8-82df-0df9a3f00c25","added_by":"auto","created_at":"2025-12-22 12:29:14","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13114,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/6af828f1c81360eabf76153f.png"},{"id":98778687,"identity":"b2c0f904-6350-49a7-bf4f-cea484da36ec","added_by":"auto","created_at":"2025-12-22 12:29:32","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208733,"visible":true,"origin":"","legend":"","description":"","filename":"60eb6d56969c47bf8f73f88717bec6851structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/55d4192a4539290332f48472.xml"},{"id":98754699,"identity":"580c811b-f214-4a50-a3d1-6a5b829ccc81","added_by":"auto","created_at":"2025-12-22 09:24:56","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":224062,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/0c48ca122f84247c91eb40a2.html"},{"id":98754669,"identity":"c948e930-d122-4844-a122-7a9b95793eee","added_by":"auto","created_at":"2025-12-22 09:24:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":816773,"visible":true,"origin":"","legend":"\u003cp\u003eExample diagram of the total skeletal muscle area outlined at the L3 level for different muscle conditions. Figures (A) and (B) show patients without sarcopenia and patients with sarcopenia, respectively.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/e25583b72c72070802a58ad7.png"},{"id":98779644,"identity":"b0be4d21-bbf9-41c2-893c-28ae16adbeb6","added_by":"auto","created_at":"2025-12-22 12:30:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":265576,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves estimating overall survival in patients stratified by sarcopenia status.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/1daf182a77de577d00dde955.png"},{"id":98778551,"identity":"a8f784c4-9887-4903-a918-f9c572a8d69d","added_by":"auto","created_at":"2025-12-22 12:29:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":307842,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis comparing the high-risk and low-risk groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/8778651c65c497ba615d1080.png"},{"id":98754675,"identity":"f67443aa-1eb1-45a6-9252-ddbe67b0d2c7","added_by":"auto","created_at":"2025-12-22 09:24:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":190555,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves assessing overall survival based on sarcopenia status in patients with stage II colon cancer.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/b3e6803b31cc8825003c89df.png"},{"id":100379858,"identity":"2b61253e-f257-41fb-8edb-bdd2c5b276ab","added_by":"auto","created_at":"2026-01-16 09:47:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2861068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/6784347f-de49-4dec-9566-028e92d0af51.pdf"},{"id":98778566,"identity":"9b6fde6d-e4b6-45ba-9778-40c3caf84b0c","added_by":"auto","created_at":"2025-12-22 12:29:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":106089,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8237628/v1/04dac021b4c19551c6ab2c33.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Prognostic Impact of Sarcopenia in Stage I to III Colon Cancer A Retrospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColon cancer (CC) is one of the most common malignant tumors in the digestive tract globally, with adenocarcinoma accounting for approximately 95% of cases. According to the 2020 Global Cancer Statistics report, more than 1.9\u0026nbsp;million people have been newly diagnosed with CC, resulting in approximately 935,000 deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This makes CC the third most common cancer in the world and the second leading cause of cancer-related deaths. This presents a significant challenge to public health. Currently, the standard treatment for stage I\u0026ndash;III colorectal cancer involves extensive tumor resection followed by adjuvant chemotherapy, such as the FOLFOX or XELOX regimens [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While this treatment approach has significantly improved overall survival (OS) rates in patients, substantial prognostic disparities persist in clinical practice. This is particularly evident in the decision-making process for administering adjuvant chemotherapy to stage II patients, which remains a topic of ongoing debate[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTNM staging, a traditional tool for risk stratification based on tumor anatomical characteristics, holds some prognostic value. However, it fails to account for important factors such as the patient's nutritional status, metabolic condition, and overall physiological state. This limitation hinders its ability to fully reflect the biological behavior of tumors and restricts its application in precision medicine[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent studies have highlighted the significant role of nutritional status and body composition\u0026mdash;particularly changes in skeletal muscle mass\u0026mdash;in the prognosis of cancer patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Protein-energy malnutrition not only impairs immune function and increases the risk of treatment-related toxicity and postoperative complications, but also significantly reduces patient survival[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, micronutrients such as vitamin D, zinc, and selenium can modulate the tumor microenvironment by regulating the gut microbiota-inflammation axis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, sarcopenia has attracted considerable attention as a clinical indicator that reflects a patient's overall functional status and nutritional condition[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A large number of studies have shown that sarcopenia is closely related to adverse outcomes after surgical intervention in cancer patients, such as higher rates of complications, reduced quality of life and reduced OS[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. An important prospective cohort study conducted in China confirmed that sarcopenia is an independent predictor of poor postoperative prognosis in patients with gastrointestinal cancer[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, a study by Lieffers et al. demonstrated that, in CC patients undergoing surgery, sarcopenia was significantly correlated with prolonged postoperative hospital stay, heightened chemotherapy toxicity, and reduced OS, thereby supporting the importance of sarcopenia in preoperative risk assessment[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although some studies have suggested a link between preoperative sarcopenia and unfavorable outcomes in CC patients, research specifically targeting stage I-III colon cancer patients remains relatively limited.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to thoroughly investigate the prognostic value of sarcopenia in patients with stage I-III colon cancer, clarify its impact on OS, and further construct a combined prognostic prediction model integrating sarcopenia with traditional TNM staging to assess the incremental value of sarcopenia compared to traditional TNM staging in predicting the prognosis of CC patients. Through this study, we aim to provide effective evidence for clinicians to stratify patient risk and make individualized treatment decisions.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and inclusion criteria\u003c/h2\u003e \u003cp\u003e A retrospective review was performed on 230 patients with CC who underwent primary tumor resection at Lanzhou University Second Hospital between January 2018 and December 2019. None of the patients received neoadjuvant therapy, and all underwent routine abdominal computed tomography scans within one month prior to surgery. As the 8th edition of the American Joint Committee on Cancer (AJCC) staging guidelines was published in 2018, and the patient data for this study began in 2018, the 8th edition of the AJCC staging system was applied[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The inclusion criteria were as follows: (1) Initial diagnosis of CC with subsequent curative surgery; (2) Age\u0026thinsp;\u0026gt;\u0026thinsp;18 years; (3) Postoperative pathological diagnosis of CC with TNM stages I-III; (4) At least 12 lymph nodes detected in the surgical specimen after radical surgery; (5) Availability of complete follow-up and clinical-pathological records; (6) Availability of complete preoperative computed tomography imaging data. The exclusion criteria were: (1) Received neoadjuvant therapy prior to surgery; (2) Presence of rectal cancer; (3) History of other malignancies; (4) Concurrent malignant tumors.\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Lanzhou University Second Hospital (Project Number: 2025A-638). As a retrospective study, patient privacy and personal identity information were protected, and the Medical Ethics Committee waived the requirement for informed consent. The study has been approved by the Ethics Committee of Lanzhou University Second Hospital.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of sarcopenia\u003c/h3\u003e\n\u003cp\u003eA CT scan is routinely performed within one month prior to surgery. The parameters for the CT images include contrast-enhanced or non-enhanced multi-phase acquisition, with a slice thickness of 5 mm. To assess sarcopenia, two adjacent CT images of the third lumbar vertebra (L3) from the same series were selected during the non-contrast phase[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. ImageJ2 software (National Institutes of Health, Washington, D.C., USA) was used to measure the total skeletal muscle area (SMA) on the two slices within the range of -29 to +\u0026thinsp;150 Hounsfield units (HU)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The SMA was averaged for each patient. The outlined regions for SMA measurement are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The skeletal muscle index (SMI) was defined as SMA divided by the square of height. Based on current research, sarcopenia was defined as a SMI below the lowest gender-specific quartile. According to this criterion, patients were categorized into sarcopenia and non-sarcopenia groups [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe preoperative demographic and clinical data collected included age, gender, height, SMI, CEA levels, and cancer location. Tumor location was classified as either right-sided or left-sided CC. Right-sided CC includes cecal cancer, ascending CC, hepatic flexure cancer, and transverse CC, while left-sided CC includes splenic flexure cancer, descending CC, and sigmoid CC. Postoperative data collected included T stage, N stage, differentiation grade, neurovascular invasion, tumor maximum diameter, Ki67%, and cancer staging according to the AJCC 8th edition staging system.\u003c/p\u003e \u003cp\u003eThe outcome of interest was OS, defined as the time from surgery to death from any cause. All patients were followed up within one month after surgery and then every three months thereafter. The most recent follow-up was conducted in December 2024.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe normality of clinical characteristics was assessed using the Shapiro-Wilk test. For baseline characteristics, continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables were presented as frequencies and percentages. Continuous variables were analyzed using t-tests or Mann-Whitney U tests, depending on their distribution characteristics. Categorical variables were analyzed using chi-square tests. Univariate and multivariate Cox regression analyses were conducted to identify potential independent prognostic factors for OS. For survival analysis, Cox proportional hazards models were constructed, and Kaplan-Meier analyses were conducted. Model performance was evaluated using Harrell's C statistic, and likelihood ratio P-values were used to compare the performance of risk prediction models. Samples were stratified based on predicted hazard ratios (HRs), and the significance of group separation was assessed using multivariate log-rank tests. Subsequently, time-dependent receiver operating characteristic analysis was performed to evaluate the predictive ability of the model at different time points (1 year, 3 years, and 5 years). Subgroup analysis was conducted to explore the impact of sarcopenia within each subgroup. All data analyses were performed using R4.2.1 and Python 3.7.12. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of patients\u003c/h2\u003e \u003cp\u003eThis study ultimately included 230 patients with CC who underwent initial tumor resection at the Second Hospital of Lanzhou University. The lowest gender-specific quartile of SMI at the L3 level was 40.6 cm\u0026sup2;/m\u0026sup2; for males and 34.9 cm\u0026sup2;/m\u0026sup2; for females.\u003c/p\u003e \u003cp\u003eThe baseline characteristics of the included patients are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Sarcopenia was identified in 56 patients (24.34%). Patients in the sarcopenia group had significantly poorer prognosis, shorter overall survival, and higher mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No statistically significant differences were observed between the sarcopenia and non-sarcopenia groups in terms of age, gender, differentiation degree, or AJCC stage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;230)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon sarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;174)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112 (48.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81 (46.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (55.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118 (51.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93 (53.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (44.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102 (44.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77 (44.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (44.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128 (55.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97 (55.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (55.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114 (49.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91 (52.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (41.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116 (50.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83 (47.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (58.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165.61\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166.04\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.728\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft hemicolon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123 (53.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97 (55.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (46.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight hemicolon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107 (46.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77 (44.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (53.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor maximum diameter(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180 (78.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135 (77.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (80.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (21.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (22.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (19.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeural or vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84 (36.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (36.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (37.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146 (63.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111 (63.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35 (62.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor/Undifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (16.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (14.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (23.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e178 (77.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136 (78.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (75.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (5.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (6.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (1.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (21.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (16.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184 (80.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137 (78.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47 (83.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166 (72.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126 (72.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40 (71.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (27.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (27.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (28.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (19.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (20.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (16.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122 (53.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91 (52.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (55.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (27.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (27.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (28.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall survival (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.29\u0026thinsp;\u0026plusmn;\u0026thinsp;23.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.33\u0026thinsp;\u0026plusmn;\u0026thinsp;22.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.95\u0026thinsp;\u0026plusmn;\u0026thinsp;25.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155 (67.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124 (71.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (55.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75 (32.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (28.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (44.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\n\u003ch3\u003eSarcopenia on overall survival\u003c/h3\u003e\n\u003cp\u003eThe Kaplan\u0026ndash;Meier survival curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows that the OS of patients with sarcopenia is significantly lower than that of patients without sarcopenia (log-rank test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe risk factors for OS in patients with stage I-III colon cancer are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Univariate analysis revealed that CEA\u0026thinsp;\u0026gt;\u0026thinsp;5, lymph node metastasis, and sarcopenia were significantly associated with poorer OS (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analysis demonstrated that lymph node metastasis (N1-2, HR\u0026thinsp;=\u0026thinsp;1.63, 95% CI: 1.01\u0026ndash;2.62, P\u0026thinsp;=\u0026thinsp;0.0460) and sarcopenia (HR\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.05\u0026ndash;2.75, P\u0026thinsp;=\u0026thinsp;0.0321) were independent predictors of OS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.56, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.59, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63 (1.03, 2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43 (0.89, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1363\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft hemicolon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight hemicolon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.74, 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor maximum diameter(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.58, 1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeural or vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.62 (0.98, 2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor/Undifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.48, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58 (0.17, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38 (0.74, 2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.76 (1.10, 2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.63 (1.01, 2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0460\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.61, 2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.00 (1.00, 4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.76 (1.09, 2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.70 (1.05, 2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePrognostic Value of Sarcopenia\u003c/h3\u003e\n\u003cp\u003eIn this study, a Cox proportional hazards model based on the TNM staging system was constructed, with sarcopenia incorporated as a prognostic factor to assess its predictive value for OS in CC patients. The analysis revealed that the C-index of the traditional TNM model was 0.570 (95% CI: 0.508, 0.631), while the C-index for the TNM-sarcopenia model was 0.604 (95% CI: 0.540, 0.669). The inclusion of sarcopenia significantly improved the predictive performance of the traditional TNM model (P \u003csub\u003e\u003cem\u003elikelihood ratio test\u003c/em\u003e\u003c/sub\u003e = 0.027), demonstrating that sarcopenia provides significant prognostic value for OS in patients with stage I-III colon cancer (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Analysis of Two Models in Patients with Stage I\u0026ndash;III Disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eOverall survival Concordance index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTNM-Sarcopenia model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.604 (0.540, 0.669)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.13(0.59,2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.70(1.05,2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.75(1.08,2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTNM-model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.570 (0.508, 0.631)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.17(0.61,2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.70(1.04,2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further evaluate the prognostic value of sarcopenia, risk stratification was performed based on the hazard ratios from the COX model, categorizing patients into high-risk and low-risk groups. The discriminatory performance was assessed using Kaplan-Meier analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results demonstrated that the TNM-sarcopenia model (log-rank test P\u0026thinsp;=\u0026thinsp;0.001) outperformed the TNM model (log-rank test P\u0026thinsp;=\u0026thinsp;0.017) in identifying high-risk patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo comprehensively assess the models\u0026rsquo; predictive capabilities, time-dependent ROC analysis was conducted to compare the performance of the TNM-sarcopenia model and the TNM model at multiple time points specifically at 1, 3, and 5 years (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The results showed that the TNM-sarcopenia model outperformed the TNM model at all time points (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eWe further performed subgroup analyses and interaction tests to examine potential effect modifications by factors such as age and sex on the independent association between sarcopenia and OS(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Subgroup analyses consistently demonstrated that sarcopenia was associated with increased mortality risk across all predefined subgroups of stage I\u0026ndash;III colon cancer patients. Notably, significant associations between sarcopenia and poorer survival were observed specifically in patients with left-sided CC (HR\u0026thinsp;=\u0026thinsp;2.03, 95% CI: 1.02\u0026ndash;4.03, P\u0026thinsp;=\u0026thinsp;0.0423), T3\u0026ndash;4 stage disease (HR\u0026thinsp;=\u0026thinsp;1.81, 95% CI: 1.08\u0026ndash;3.05, P\u0026thinsp;=\u0026thinsp;0.0244), and TNM stage II (HR\u0026thinsp;=\u0026thinsp;2.18, 95% CI: 1.12\u0026ndash;4.27, P\u0026thinsp;=\u0026thinsp;0.0225). However, no statistically significant interaction effects were detected across any subgroups (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effect of sarcopenia on overall survival in patients with stage I\u0026ndash;III colon cancer across different subgroups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003eInteraction\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84 (0.95, 3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65 (0.82, 3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (0.66, 2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.15 (1.14, 4.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55 (1.21, 5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.68, 2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft hemicolon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03 (1.02, 4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight hemicolon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 (0.76, 2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTumor maximum diameter(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56 (0.90, 2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88 (1.04, 7.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeural or vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 (0.64, 3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89 (1.07, 3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor/Undifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64 (0.57, 4.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81 (1.05, 3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (0.38, 5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81 (1.08, 3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02 (1.11, 3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (0.61, 3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49 (0.39, 5.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18 (1.12, 4.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (0.61, 3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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 \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Value of Sarcopenia in Stage II Colon Cancer\u003c/h2\u003e \u003cp\u003eThis study further explored the effect of sarcopenia on OS in patients with stage II colon cancer, assessing its prognostic significance through Kaplan-Meier survival analysis and both unadjusted and multivariate Cox proportional hazards regression models. The Kaplan-Meier survival curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed that OS for patients with stage II colon cancer in the sarcopenia group was significantly shorter compared to the non-sarcopenia group (P\u0026thinsp;=\u0026thinsp;0.019).\u003c/p\u003e \u003cp\u003eCox regression analysis revealed that among patients with stage II tumors (n\u0026thinsp;=\u0026thinsp;122), sarcopenia was associated with shorter overall survival (HR\u0026thinsp;=\u0026thinsp;2.18, 95% CI: 1.12\u0026ndash;4.27, P\u0026thinsp;=\u0026thinsp;0.0225). Similar results were observed in the adjusted model controlling for gender, age, CEA levels, tumor location, differentiation grade, maximum tumor diameter, neurovascular invasion, and T stage(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These findings indicate that sarcopenia serves as an independent prognostic factor for overall survival in patients with stage II colon cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ensp;Multivariate analysis of sarcopenia in patients with stage II colon cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\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\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall survival HR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConcordance index (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnadjusted Cox regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.584 (0.507, 0.661)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.18(1.12,4.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emultivariable Cox regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.649 (0.567, 0.730)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19(1.08,4.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e1.14(0.58,2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.42,1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36(0.67,2.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003e1.18(0.59,2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16(0.37,3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor maximum diameter(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02(0.46,2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeural or vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61(0.74,3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003e1.57(0.67,3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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":"Discussion","content":"\u003cp\u003eThis study examined the impact of sarcopenia on OS in patients with stage I\u0026ndash;III colon cancer and confirmed its role as an independent prognostic factor. The study results indicate that sarcopenia is significantly linked to poorer survival outcomes in CC patients, with its impact remaining evident in multivariate Cox regression analysis (HR\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.05\u0026ndash;2.75, P\u0026thinsp;=\u0026thinsp;0.0321). Subgroup analyses further indicate that sarcopenia has a more significant effect on survival in patients with left-sided CC, T3-4 stage, and N1-2 stage disease. To enhance prognostic predictive capability, this study developed a TNM-sarcopenia model based on the TNM staging system. The model showed superior predictive performance with higher C-index and area under the curve (AUC) values compared to the TNM model, suggesting the potential value of incorporating sarcopenia into the prognostic assessment of CC.\u003c/p\u003e \u003cp\u003eSarcopenia is a common metabolic abnormality in patients with malignant tumors, and previous studies have shown that it is associated with poor outcomes in various cancers, including lung, gastric, pancreatic, and CC[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the field of colorectal cancer, although several studies have explored the impact of preoperative sarcopenia on short- and long-term outcomes, a meta-analysis by Trejo-Avila M further clarified that sarcopenia is an important predictor of increased postoperative complication rates and reduced survival rates[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Another study demonstrated that sarcopenia is an objective and reliable predictive factor, outperforming current nutritional, functional, biochemical, and clinical indicators in predicting postoperative complications and cancer recurrence after radical resection for CC[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, to date, insufficient research has been conducted on its prognostic value in patients with stage I\u0026ndash;III colon cancer. Therefore, the findings of this study may provide further evidence supporting the need for clinical intervention to address sarcopenia in patients with stage I\u0026ndash;III colon cancer.\u003c/p\u003e \u003cp\u003eThe results of this study showed that patients with sarcopenia had significantly lower OS compared to those without sarcopenia (P\u0026thinsp;=\u0026thinsp;0.018), and sarcopenia was identified as an independent risk factor for OS (HR\u0026thinsp;=\u0026thinsp;1.70, P\u0026thinsp;=\u0026thinsp;0.0321), further confirming that sarcopenia can serve as a reliable prognostic indicator for the long-term prognosis of patients with stage I-III colon cancer. However, the mechanisms by which sarcopenia influences the long-term prognosis of CC patients remain incompletely understood. This phenomenon may be linked to the chronic inflammatory state, impaired immune function, and reduced antitumor capacity associated with sarcopenia[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sarcopenia in cancer patients is believed to result from skeletal muscle loss and muscle atrophy due to metabolic abnormalities linked to tumor cachexia[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Dietary supplements and appetite stimulants alone are insufficient to reverse this underlying metabolic abnormality, which may partially explain why sarcopenia acts as a prognostic factor for poor outcomes in cancer patients[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Interestingly, patients with sarcopenia often exhibit elevated levels of systemic inflammatory factors, such as C-reactive protein and interleukin-6. These inflammatory markers can accelerate tumor progression and affect treatment outcomes by stimulating tumor cell growth, suppressing antitumor immune responses, and enhancing tumor resistance[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, studies have demonstrated a significant association between sarcopenia and elevated IL-23 levels, with the combination of sarcopenia and IL-23 serving as an effective prognostic predictor. This suggests that IL-23 and systemic inflammation may play key roles in poor survival outcomes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, high levels of inflammation are closely associated with poor prognosis in CC patients, and sarcopenia may exacerbate this inflammatory state, leading to a cascade of reactions that further reduce patient survival rates. Sarcopenia is also closely linked to malnutrition in cancer patients, which further impairs immune function and treatment tolerance. Malnutrition is particularly prevalent in CC patients, making appropriate nutritional supplementation crucial for achieving good short-term outcomes and long-term survival[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous studies have shown that patients with sarcopenia experience a higher incidence of complications after surgery or chemoradiotherapy, slower postoperative recovery, and may face challenges in treatment implementation[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Although this study did not directly analyze the impact of treatment modalities on patient outcomes, the results indicate that patients with sarcopenia have poorer survival outcomes, suggesting that clinical practice should prioritize screening for and intervening in sarcopenia to improve patient outcomes.\u003c/p\u003e \u003cp\u003eCurrently, the TNM staging system is the standard tool for treatment decision-making and prognosis assessment in CC, but it still has certain limitations in terms of individualized prediction[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. To overcome this limitation, efforts have been made to classify CC based on molecular subtypes derived from gene expression, with the aim of more effectively guiding treatment decisions. However, these factors have limited practical value in routine clinical practice[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Given the complex mechanisms underlying CC development, a single genetic marker is unlikely to predict individual prognosis. Therefore, an international validation study initiated by the International Immuno-Scoring Consortium [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] assessed the prognostic value of total tumor-infiltrating T cell counts and cytotoxic tumor-infiltrating T cell counts in stage I-III colon cancer patients using a consensus immuno-scoring assay. The results showed that patients with high immuno-scores had the lowest risk of recurrence at 5 years and could also effectively identify high-risk patients for tumor recurrence in stage II cancer patients. However, in this study, the AUC value for predicting OS in the model combining all clinical variables with the immune score remained below 0.65. This highlights the need for further exploration of additional prognostic predictors.\u003c/p\u003e \u003cp\u003eTo this end, this study constructed both the traditional TNM model and the TNM-sarcopenia model to assess and validate the predictive ability of sarcopenia in patients with stage I-III colon cancer. The results showed that the C-index of the TNM-sarcopenia model (0.604) was higher than that of the TNM model (0.570), and the AUC values at different time points (1 year, 3 years, 5 years) were superior to those of the TNM model, with the most significant improvement in predictive performance at 3 years (TNM-sarcopenia model AUC\u0026thinsp;=\u0026thinsp;0.629 vs TNM model AUC\u0026thinsp;=\u0026thinsp;0.588). These findings indicate that incorporating sarcopenia factors enhances the predictive capability of the model, further validating the role of sarcopenia in CC prognosis assessment. Kaplan-Meier survival analysis further demonstrated that the TNM-sarcopenia model exhibits superior stratified predictive capability compared to the standalone TNM model. These results suggest that, in clinical practice, the TNM-sarcopenia model, by incorporating sarcopenia factors, can provide more precise survival predictions and effectively identify high-risk patients, thereby aiding in individualized treatment decisions.\u003c/p\u003e \u003cp\u003eSubgroup analysis in this study further demonstrated that sarcopenia had a more significant impact on survival in patients with left-sided CC, T3-4 stage, and N1-2 stage. This may be related to differences in gut microbiota, tumor microenvironment, and metabolic characteristics among patients with left-sided CC. Previous studies have reported significant differences between left-sided and right-sided CC in terms of long-term prognosis and cancer biology[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A retrospective study indicated that sarcopenia[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], as assessed by CT, independently predicts shorter OS and recurrence-free survival in patients with left-sided colon or rectal cancer after curative surgery, which is consistent with the subgroup analysis results. Additionally, patients with T3-4 stage and N1-2 stage disease have a greater disease burden, and sarcopenia may further exacerbate their survival disadvantage. Therefore, in clinical practice, sarcopenia should be prioritized for screening and management in high-risk subgroups to optimize survival outcomes.\u003c/p\u003e \u003cp\u003eAlthough this study revealed the adverse impact of sarcopenia on the OS of stage I-III colon cancer patients, particularly those with stage II disease, certain limitations remain. First, this study is a retrospective analysis, which may introduce selection bias, necessitating validation of the results in larger prospective cohort studies. Second, due to the lack of standardized assessment methods for sarcopenia, differences in results across studies may limit the comparability and generalizability of the findings. Future studies should focus on standardizing and validating assessment methods, as well as exploring the consistency and complementarity of different measurement techniques in predicting clinical outcomes, to enhance the scientific rigor and clinical applicability of such research. Additionally, since this study did not further analyze the survival outcomes of sarcopenia patients receiving different treatment regimens (such as adjuvant chemotherapy or intensive nutritional intervention), future studies could explore the impact of these factors on prognosis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, sarcopenia is an independent prognostic factor associated with OS in patients with stage I-III colon cancer. Incorporating sarcopenia into a combined model based on TNM staging significantly enhances prognostic predictive ability, particularly in stage II patients, where it is more effective in identifying high-risk populations. Therefore, sarcopenia may serve as a crucial prognostic biomarker, offering an effective supplement for risk stratification and individualized treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eHazard Ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eReceiver Operator Characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eAJCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eAmerican Joint Committee on Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eComputed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eSkeletal Muscle Area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eSMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eSkeletal Muscle Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e CM and FC designed the study and conducted the data analysis. YYD and SYW participated in manuscript modification. YHS, YZL, DDW and WW took part in data collection and analysis. HTY and XJD provided critical revisions to the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by the Nature Science Foundation of Gansu Province (Grant No.24JRRA3221), the \u0026ldquo;Cuiying Science and Technology Innovation\u0026rdquo; program (Grant No. CY2024-MS-B04), and the Scientific Research Project of Traditional Chinese Medicine of Gansu Province (Grant No. GZKZ-2024-26).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u003c/strong\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of the Second Hospital of Lanzhou University (Project Number:2025 A-638). As this study is a retrospective and the privacy and personal identity information of the patients were protected, the need for informed consent was waived by the Medical Ethics Committee of the Second Hospital of Lanzhou University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We are grateful for the financial support provided by the Gansu Provincial Natural Science, Cuiying Science and Technology Innovation and Gansu Province Traditional Chinese Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number :\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJanssen, I.; Heymsfield, S.B.; Ross, R. 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In recent years, Sarcopenia has emerged as a significant prognostic factor in cancer patients. This study aimed to investigate the prognostic value of sarcopenia in stage I\u0026ndash;III colon cancer patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective analysis included 230 CC patients who underwent tumor resection at the Second Hospital of Lanzhou University. Preoperative abdominal computed tomography (CT) scans were used to assess the skeletal muscle index (SMI). Patients were categorized into sarcopenia and non-sarcopenia groups based on the SMI. The primary outcome was overall survival (OS). The prognostic role of sarcopenia was assessed using Kaplan-Meier survival curves, Cox regression modeling and time-dependent Receiver Operator Characteristic (ROC) analysis. In addition, a combined TNM- sarcopenia model was developed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 230 patients, 24.34% were diagnosed with sarcopenia. Kaplan-Meier curves showed that patients in the sarcopenia group had significantly lower OS compared to the non-sarcopenia group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Cox regression identified sarcopenia as an independent adverse prognostic factor (HR, 1.70, 95%CI: 1.05\u0026ndash;2.75). Incorporating sarcopenia into the TNM model increased the C-index from 0.570 to 0.604 (P \u003csub\u003e\u003cem\u003elikelihood ratio\u003c/em\u003e\u003c/sub\u003e = 0.027), with improved predictive performance at 1-year, 3-year, and 5-year time points.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSarcopenia is not only an independent prognostic factor for OS in patients with stage I\u0026ndash;III CC, but also serves as a valuable supplement to existing prognostic assessment tools, which may contribute to personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"The Prognostic Impact of Sarcopenia in Stage I to III Colon Cancer A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:24:47","doi":"10.21203/rs.3.rs-8237628/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":"f0294b3f-8707-4162-8731-14fa7d22d2da","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-01T11:53:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 09:24:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8237628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8237628","identity":"rs-8237628","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}