{"paper_id":"492bcd07-7c08-4083-8452-d7adb505e2af","body_text":"Metastatic Burden and TP53 Mutation Overshadow the Independent Prognostic Value of KRAS in Colorectal Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metastatic Burden and TP53 Mutation Overshadow the Independent Prognostic Value of KRAS in Colorectal Cancer Xiao-dong Wang, Jian-bo Liu, Rui Zhao, Li-bin Huang, Yong Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8327424/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 The independent prognostic value of KRAS mutation in colorectal cancer (CRC) remains controversial, often yielding inconsistent results across studies. We hypothesized that its impact might be overestimated when confounding clinical factors, such as metastatic burden, are rigorously controlled. Methods In this single-center retrospective study, we enrolled 253 CRC patients treated at West China Hospital. We integrated clinical data with next-generation sequencing (NGS) profiles to re-evaluate the prognostic significance of KRAS . Kaplan-Meier survival analysis and multivariate Cox regression models were employed to assess the impact of KRAS status, metastatic burden, and co-occurring mutations on overall survival. Results While Kaplan-Meier analysis showed a trend toward poorer survival in KRAS -mutated patients (P = 0.088), this difference did not reach statistical significance. Crucially, multivariate Cox regression revealed that KRAS mutation was not an independent prognostic factor (HR = 0.723, P = 0.497) after adjusting for confounders. Instead, outcomes were dominated by metastatic burden (M stage: HR = 7.2, P < 0.001) and TP53 mutation (HR = 2.53, P = 0.032). Subgroup analyses confirmed the lack of independent KRAS prognostic value across age, tumor location, and treatment strata. Notably, TP53 mutation emerged as a significant independent risk factor specifically in patients receiving neoadjuvant therapy (HR = 3.12, P = 0.044). Conclusions The prognostic weight of KRAS mutation in CRC is largely overshadowed by dominant clinical determinants (metastatic burden) and concurrent molecular drivers (TP53). Our findings suggest that for high-risk patients, particularly those undergoing neoadjuvant therapy, TP53 status may serve as a superior prognostic biomarker compared to KRAS . We propose moving beyond a \" KRAS -centric\" assessment toward a multi-parameter model integrating TNM staging with comprehensive genomic profiling. Colorectal cancer KRAS mutation Prognosis Retrospective study Personalized treatment Cox regression Figures Figure 1 1. Introduction Colorectal Cancer (CRC) is one of the most common malignancies worldwide, posing a significant threat to human health [ 1 , 2 ]. In recent years, the rising incidence and mortality rates of CRC have presented a substantial challenge for clinical management [ 3 , 4 ]. The long-term survival of CRC patients is influenced by various factors, with molecular heterogeneity playing a pivotal role [ 5 , 6 ]. KRAS mutation is a prevalent genetic alteration in CRC, occurring in approximately 30%-50% of patients, and serves as a critical biomarker for guiding targeted therapy [ 7 , 8 ]. Clinical practice has established that patients harboring KRAS mutations generally derive limited benefit from anti-epidermal growth factor receptor (EGFR) inhibitors and are prone to chemoresistance [ 9 , 10 ]. However, the independent prognostic impact of KRAS mutation on CRC patient survival remains controversial [ 11 , 12 ]. Previous studies have largely been confined to univariate analyses or lacked a systematic evaluation of the complex interactions between KRAS mutation and other clinicopathological features (e.g., TNM staging, metastasis patterns), treatment parameters (e.g., surgical approaches, chemotherapy regimens), and co-existing genetic alterations (e.g., BRAF/NRAS mutations, microsatellite instability [MSI] status) [ 13 , 14 ]. These limitations hinder precise prognostic assessment, making it difficult to formulate highly individualized and effective treatment strategies [ 15 , 16 ]. Despite the routine implementation of KRAS testing, two critical issues remain unresolved. First, the prognostic weight of KRAS mutation necessitates precise re-quantification based on multi-dimensional clinical-genomic data [ 17 , 18 ]. Specifically, it remains unclear whether KRAS mutation acts as an independent survival predictor after rigorously controlling for confounding factors such as age, comorbidities, and tumor burden. Second, the potential synergistic effects between KRAS mutation and other clinical or molecular characteristics require clarification [ 19 , 20 ]. However, few studies have systematically evaluated whether the prognostic weight of KRAS is overshadowed by dominant clinical factors (such as metastatic burden) or concurrent driver gene mutations (such as TP53). This study aims to re-evaluate the independent prognostic value of KRAS in a multi-parameter context, hypothesizing that clinical staging and TP53 status may serve as more robust determinants of survival than KRAS status alone . 2. Materials and Methods 2.1. Study Design and Patient Selection This single-center retrospective study was conducted at West China Hospital, Sichuan University. We enrolled 253 patients diagnosed with CRC who underwent treatment between January 2017 and December 2023. Inclusion criteria were: (1) pathologically confirmed adenocarcinoma or mucinous adenocarcinoma; (2) availability of complete clinical and next-generation sequencing (NGS) genomic data; and (3) complete follow-up information. Patients with incomplete clinical records or missing survival data were excluded. Patient data were retrieved from electronic medical records, covering demographics (age, gender), clinicopathological characteristics (tumor location, differentiation, TNM stage, BMI, comorbidities), and treatment modalities (surgery, chemotherapy, targeted therapy, neoadjuvant therapy). For statistical purposes, well and moderately differentiated tumors were grouped together due to the small sample size of the well-differentiated cohort. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of West China Hospital (Approval Nos. 2019 − 140, 2021 − 155, and 2023 − 669), covering the entire data collection period. 2.2. Genomic Profiling and Outcome Definitions Genomic data were obtained via NGS of tumor tissue, covering KRAS, NRAS, BRAF, TP53, and other relevant driver genes. KRAS mutation status was defined as \"Mutated\" (MU) if any non-synonymous mutation was detected in exons 2, 3, or 4, and \"Wild-type\" (WT) otherwise. During data preprocessing, eight variables (Inflammatory Bowel Disease, NTRK1/2/3 rearrangements, PMS2 germline mutation, ALK fusion, STK11 inactivation, and KEAP1 inactivation) were excluded from the analysis as no positive variants were identified in the cohort. Overall survival (OS) was defined as the interval from the date of diagnosis to the date of death from any cause or the last follow-up. Patients lost to follow-up were censored at the date of last contact. 2.3. Statistical analyses Statistical analyses were performed using R software (version 4.2.2) and SPSS version 26.0. Continuous variables were assessed for normality; normally distributed data were expressed as mean ± standard deviation (SD) and compared using the t-test, while non-normally distributed data were described as median with interquartile range (IQR) and compared using the Mann-Whitney U test. Categorical variables were compared using the Chi-square test or Fisher's exact test. Survival curves were generated using the Kaplan-Meier method and compared using the log-rank test. To identify prognostic factors, univariate and multivariate Cox proportional hazards regression models were employed. Variables with a p-value < 0.1 in the univariate analysis were initially considered for the multivariate model. We utilized the Variance Inflation Factor (VIF) to detect multicollinearity; variables with high VIF or extremely low event frequency (e.g., CHEK1, ERCC3, and operation duration) were excluded to ensure model stability. Subgroup analyses were conducted to evaluate the prognostic value of KRAS mutation across different strata (age, tumor location, neoadjuvant therapy). Furthermore, a sensitivity analysis was performed by relaxing the univariate screening threshold to p-value < 0.2 to assess the robustness of the findings. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant. 3. Results 3.1. Baseline Characteristics The study cohort comprised 253 CRC patients, stratified into the KRAS-mutated (MU) group (n = 62, 24.5%) and the wild-type (WT) group (n = 191, 75.5%). The median follow-up duration was 37 months (interquartile range [IQR]: 27–49 months). At the study endpoint, the overall survival rate was 87.3% (222/253), with survival rates of 89.5% (171/191) in the WT group and 82.3% (51/62) in the MU group. As summarized in Table 1 , patients in the MU group had significantly shorter median survival months compared to the WT group (35.00 vs. 41.00 months, P = 0.004). Notably, the MU group presented with a significantly higher proportion of distant metastasis (M stage: M1) compared to the WT group (29.0% vs. 9.4%, P < 0.001), suggesting a strong correlation between KRAS mutation status and advanced clinical stage. No statistically significant differences were observed between the two groups regarding other baseline clinicopathological characteristics, such as age, gender, tumor location, or TNM stage (T and N categories). Table 1 Baseline characteristics of MU vs WT patients Characteristics WT (n = 191) MU (n = 62) P-value Age (median [IQR]) 64.00 [54.50, 70.00] 62.50 [54.25, 70.00] 0.539 Gender (Male), n (%) 107 (56.0) 33 (53.2) 0.812 BMI, kg/m 2 (mean ± SD) 23.54 (3.06) 23.63 (3.07) 0.851 Tumor location Restum), n (%) 147 (77.0) 43 (69.4) 0.301 Differentiation (Poor), n (%) 41 (22.4) 16 (26.7) 0.617 T stage, n (%) T0 8 (4.2) 2 (3.2) 0.207 T1 14 (7.3) 1 (1.6) T2 41 (21.5) 9 (14.5) T3 107 (56.0) 39 (62.9) T4 21 (11.0) 11 (17.7) N stage, n (%) N0 127 (66.5) 31 (50.0) 0.064 N1 44 (23.0) 22 (35.5) N2 20 (10.5) 9 (14.5) Distant Metastasis (M1), n(%) 18 (9.4) 18 (29.0) < 0.001 Comorbidities, n(%) Hypertension 71 (37.2) 20 (32.3) 0.583 Diabetes 37 (19.4) 8 (12.9) 0.334 Anemia 72 (37.7) 32 (51.6) 0.074 Abnormal liver function 53 (27.7) 18 (29.0) 0.974 Cardiovascular disease 17 (8.9) 3 (4.8) 0.448 Cardiac insuffciency 30 (15.7) 7 (11.3) 0.517 Chronic nephrosis 8 (4.2) 2 (3.2) 1.000 Thrombocytosis 13 (6.8) 6 (9.7) 0.640 COPD 1 (0.5) 0 (0.0) 1.000 Clinical Features, n(%) Obstruction 117 (61.3) 43 (69.4) 0.319 Perforation 24 (12.6) 6 (9.7) 0.700 Invagination 6 (3.1) 0 (0.0) 0.351 Ascites 72 (37.7) 31 (50.0) 0.118 First CEA (median [IQR]) 3.36 [1.90, 8.22] 5.16 [2.33, 11.90] 0.051 NRS2002 (%) 0.130 1 60 (31.4) 17 (27.4) 2 55 (28.8) 18 (29.0) 3 45 (23.6) 21 (33.9) 4 16 (8.4) 6 (9.7) 5 15 (7.9) 0 (0.0) Treatment History, n(%) Neoadjuvant therapy 59 (30.9) 23 (37.1) 0.453 Enterostomy 119 (62.3) 33 (53.2) 0.263 Extended resection 82 (42.9) 33 (53.2) 0.205 Targeted therapy 38 (19.9) 12 (19.4) 1.000 Genomic Markers, n(%) TMB (High/Positive) 8 (4.2) 2 (3.2) 1.000 MSI (High) 8 (4.2) 2 (3.2) 1.000 Note : Data are presented as n (%) unless otherwise indicated. IQR: interquartile range; SD: standard deviation; BMI: body mass index; COPD: chronic obstructive pulmonary disease; CEA: carcinoembryonic antigen; NRS2002: Nutritional Risk Screening 2002; TMB: tumor mutational burden; MSI: microsatellite instability. 3.2. Prognostic Value of KRAS Mutation and Other Factors Kaplan-Meier survival analysis illustrated a clear trend of separation toward poorer prognosis in the MU group compared to the WT group (log-rank P = 0.088, Fig. 1 ). Although this difference did not reach statistical significance likely due to the limited sample size, the separation of the curves indicates a potential negative impact of KRAS mutation on survival. In the subsequent multivariate Cox regression analysis, after adjusting for potential confounders, KRAS mutation was not identified as an independent prognostic factor (HR = 0.723, 95% CI: 0.284–1.84, P = 0.497). Instead, distant metastasis (M stage) emerged as the dominant prognostic determinant, exhibiting the highest hazard ratio (M1 vs. M0: HR = 7.2, 95% CI: 2.67–19.5, P < 0.001). Additionally, TP53 mutation (HR = 2.53, P = 0.032) and regional lymph node metastasis (N1: HR = 3.57, P = 0.015; N2: HR = 4.14, P = 0.013) were confirmed as significant independent risk factors for poor survival (Table 2 ). The detailed outcomes of the univariate Cox regression analysis are provided in Table 1 S. Table 2 Multivariate Cox regression analysis on overall survival of all patients. Variable Category P-value HR (95% CI) KRAS Negative Ref Positive 0.497 0.723 (0.284 ~ 1.84) Age - 0.055 1.04 (0.999 ~ 1.09) N stage* N0 Ref N1 0.015 3.57 (1.27 ~ 9.99) N2 0.013 4.14 (1.35 ~ 12.7) M stage* M0 Ref M1 < 0.001 7.2 (2.67 ~ 19.5) BMI - 0.191 0.903 (0.775 ~ 1.05) NRS2002 1 Ref 2 0.253 0.427 (0.099 ~ 1.84) 3 0.558 1.57 (0.349 ~ 7.04) 4 0.448 1.86 (0.374 ~ 9.24) 5 0.874 0.874 (0.166 ~ 4.61) Obstruction No Ref Yes 0.331 0.583 (0.196 ~ 1.73) Extended/combined resection No Ref Yes 0.571 1.34 (0.483 ~ 3.74) Neoadjuvant therapy No Ref Yes 0.078 2.75 (0.894 ~ 8.48) Targeted therapy No Ref Yes 0.965 1.03 (0.328 ~ 3.21) TP53* Negative Ref Positive 0.032 2.53 (1.08 ~ 5.91) HLA-I germline genotyping (homozygous) Negative Ref Positive 0.217 0.35 (0.0663 ~ 1.85) Note : *: These are significant variables identified by multivariate Cox regression analysis. -: These are quantitative data without category. Ref: Reference category. 3.3. Subgroup Analysis To further explore the potential context-dependent prognostic role of KRAS mutation, we conducted multivariate Cox regression subgroup analyses stratified by age, tumor location, and treatment history. Contrary to the assumption that KRAS might drive prognosis in specific populations, our results showed that KRAS mutation was not an independent prognostic factor in any of the analyzed subgroups, including patients with colon cancer (P = 0.537), those aged ≥ 60 years (P = 0.805), and those who received neoadjuvant chemotherapy (P = 0.427). These findings reinforce the consistency of our primary analysis. However, notably, in the subgroup of patients receiving neoadjuvant therapy, TP53 mutation emerged as a significant independent risk factor for poor sur-vival (HR = 3.12, 95% CI: 1.03–9.42, P = 0.044), suggesting a specific prognostic role for TP53 in this treatment setting. The detailed outcomes of the subgroup analyses are presented in Table 3 and Table 4 . Table 3 Subgroup analysis of overall survival on age and tumor location. Age Tumor location < 60 >=60 Colon Rectal Characteristics P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) KRAS Positive 0.506 1.52 (0.44–5.28) 0.805 1.13 (0.427-3) 0.537 0.456 (0.0376-5.52) 0.352 1.47 (0.653-3.3) N stage N1 0.756 1.28 (0.272–6.01) 0.029 3.77 (1.15–12.4) 0.896 1.17 (0.105–13.1) 0.002 4.76 (1.75-13) N stage N2 0.0935 3.51 (0.809–15.2) 0.013 5.86 (1.45–23.7) 0.915 0.867 (0.0621-12.1) 0.001 6.26 (2.09–18.7) M stage M1 < 0.001 26 (5.3–128) 0.004 4 (1.55–10.3) 0.014 11.4 (1.65–79.5) 0.000 6.8 (3.02–15.3) TP53 Positive 0.985 0.988 (0.286–3.42) 0.117 2.12 (0.828–5.44) 0.918 1.11 (0.149–8.29) 0.057 2.22 (0.977–5.03) Table 4 Subgroup analysis of overall survival on (neo)adjuvant therapy received. Neoadjuvant therapy Adjuvant therapy No Yes No Yes Characteristics P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) Kras Positive 0.62 0.672 (0.14–3.24) 0.427 1.5 (0.554–4.04) 0.933 0.958 (0.354–2.59) 0.951 0.952 (0.197–4.59) N stage N1 0.456 1.72 (0.415-7.1) 0.001 7.73 (2.19–27.3) 0.0496 3.42 (1-11.6) 0.058 4.02 (0.954–16.9) N stage N2 0.0917 3.73 (0.808–17.3) 0.019 5.08 (1.31–19.7) 0.00356 6.8 (1.87–24.7) 0.644 1.63 (0.204–13.1) M stage M1 < 0.001 11 (3.3–36.9) 0.020 3.57 (1.22–10.4) < 0.001 6.39 (2.42–16.8) 0.046 5.62 (1.03–30.6) TP53 Positive 0.536 1.46 (0.443–4.78) 0.044 3.12 (1.03–9.42) 0.25 1.74 (0.676-4.5) 0.066 3.68 (0.916–14.8) 4. Discussion Our study provides a comprehensive re-evaluation of the prognostic value of KRAS mutation in a cohort of 253 CRC patients. Our findings suggest that the independent prognostic significance of KRAS mutation may have been overestimated in previous studies, as its impact appears to be largely overshadowed by dominant clinical factors, particularly metastatic burden. Consequently, this discussion will focus on two pivotal aspects: the re-assessment of KRAS mutation as a standalone prognostic marker and the necessity of a multi-parameter approach that integrates tumor staging and co-existing genetic alterations (such as TP53) for accurate risk stratification. 4.1. The Prognostic Role of KRAS Mutation is Overshadowed by Metastatic Burden Our primary finding, that KRAS mutation was not an independent prognostic factor for overall survival in the overall patient cohort, aligns with findings from several previous studies [ 11 , 21 , 22 ]. Although a trend toward poorer survival was observed in the univariate analysis, the lack of statistical significance in the multivariate model highlights the dominant influence of classical clinical factors. Specifically, distant metastasis (M stage) exhibited a much stronger prognostic impact (HR = 7.2, P < 0.001) compared to KRAS status. This suggests that when a patient's prognosis is already dictated by the advanced stage of their disease, the effect of a single molecular marker like KRAS mutation may be masked [ 15 , 17 ]. Furthermore, our subgroup analyses verified that this \"masking effect\" persists across different clinical contexts. Contrary to some previous reports suggesting that the prognostic value of KRAS might be specific to colon cancer or older patients [ 16 , 18 , 20 ], our data showed that KRAS mutation did not independently predict survival in any subgroup, including patients with colon cancer, those over 60 years old, or those receiving neoadjuvant chemotherapy. This discrepancy with prior positive findings may be attributed to two factors. First, the strong correlation between KRAS mutation and M1 stage observed in our cohort (29.0% in MU vs. 9.4% in WT, P < 0.001) suggests a biological hierarchy in prognosis. From a statistical perspective, this phenomenon can be interpreted as a 'mediation effect', where distant metastasis acts as a strong mediator in the pathway from KRAS mutation to survival outcomes. While KRAS mutation likely drives the initial metastatic seeding, our multivariate results indicate that once this distant metastasis is established, the overwhelming tumor burden becomes the dominant determinant of mortality, effectively absorbing the statistical impact of the initial driver mutation. This explains why KRAS appears prognostic in univariate analysis (due to its correlation with M1) but loses its independent value when metastatic burden is rigorously adjusted. 4.2. Beyond a Single Biomarker: Independent Value of TP53 and the Necessity of Multi-Dimensional Assessment Our study highlights the limitation of relying solely on KRAS status and reinforces the necessity of a multi-parameter prognostic model. While KRAS failed to demonstrate independent prognostic significance, we identified TP53 mutation as a robust independent risk factor for overall survival (HR = 2.53, P = 0.032). This finding aligns with established literature suggesting that TP53, a critical regulator of the cell cycle and DNA repair, reflects a more aggressive tumor biology when mutated [ 23 ]. A particularly novel insight from our subgroup analysis is the specific impact of TP53 in the context of neoadjuvant therapy. We observed that TP53 mutation was significantly associated with poorer outcomes specifically in patients who received neoadjuvant chemotherapy (HR = 3.12, P = 0.044), whereas KRAS mutation showed no such effect. The emergence of TP53 as a significant risk factor specifically in the neoadjuvant setting suggests a potential mechanism of chemoresistance. Since TP53-mediated apoptosis is a key pathway for chemotherapy efficacy, its mutation may render tumor cells refractory to cytotoxic agents. Clinically, this implies that for CRC patients harboring TP53 mutations, standard neoadjuvant chemotherapy might yield limited survival benefit. Therefore, alternative strategies—such as intensified regimens or immunotherapy—should be considered for this high-risk subgroup. This finding highlights the superiority of TP53 over KRAS as a predictive biomarker in the neoadjuvant context. Furthermore, our study identified other molecular signals, such as the correlation between KRAS and BRCA2 mutations, which hints at potential underlying deficits in DNA damage repair mechanisms. Therefore, for a truly accurate prognostic assessment, clinicians should move beyond a \"KRAS-centric\" view. An integrated approach that combines anatomical staging (TNM), treatment history (e.g., neoadjuvant therapy), and a comprehensive molecular panel (including TP53 and KRAS) is essential to capture the full biological complexity of CRC and to guide personalized management strategies. 4.3. Limitations & future directions Our study has several limitations that warrant consideration. First, as a single-center retrospective study, inherent selection bias is unavoidable. The sample size of 253 patients, particularly the subgroup of 62 KRAS-mutated cases, may have restricted the statistical power. Although the Kaplan-Meier analysis showed a clear trend toward poorer prognosis in the mutated group (P = 0.088), the limited sample size might have prevented this difference from reaching statistical significance, potentially leading to a Type II error. Second, our genomic analysis was treated as a binary classification (Mutated vs. Wild-type). We did not utilize Mutation Annotation Format (MAF) files for deep bioinformatic interrogation. This simplified approach precludes the analysis of variant allele frequency (VAF) or specific mutation subtypes (e.g., G12D vs. G12V), which might exhibit distinct biological behaviors and prognostic impacts. Consequently, complex interactions between KRAS and other signaling pathways may have been oversimplified. Finally, while we controlled for major confounders, detailed information regarding specific chemotherapy regimens and patient adherence was not fully accounted for, which could introduce residual confounding. The identification of TP53 and metastatic burden as dominant prognostic factors in this cohort does not imply that clinical attention to KRAS should cease. On the contrary, our findings suggest that the prognostic role of KRAS is nuanced and likely conditional. Future research should move beyond simple binary assessments and leverage comprehensive bioinformatic analyses (e.g., using MAF files) to explore the \"long-tail\" effect of specific KRAS variants and their co-mutation networks. The critical next step is not to discard KRAS as a marker, but to identify the specific, controlled biological contexts—such as specific immune microenvironments or concurrent pathway alterations—under which KRAS mutation decisively impacts survival. By establishing such a refined stratification logic, we can provide more precise evidence for the selection of KRAS-targeted therapies, ensuring that the right treatment strategies are matched to the specific subgroups of patients who will truly benefit. 5. Conclusions In summary, our study demonstrates that the independent prognostic value of KRAS mutation in colorectal cancer has been overestimated, as its impact is largely overshadowed by the dominant influence of metastatic burden (M stage). Instead of KRAS, TP53 mutation emerged as a robust independent risk factor, particularly for patients undergoing neoadjuvant therapy. Consequently, accurate prognosis and personalized management strategies should not rely on KRAS status in isolation but must integrate clinical staging with a comprehensive molecular profile. Declarations Conflicts of Interest: The authors declare no conflicts of interest. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of West China Hospital, Sichuan University (Approval No. 2023 − 669 on 7 April 2023, No. 2021 − 155 on 10 March 2021, and No. 2019 − 140 on 15 February 2019). Informed Consent Statement : Informed consent was obtained from all subjects involved in the study. Funding This research was funded by 1·3·5 projects for Artificial Intelligence, West China Hospital, Sichuan University grant number ZYAI24067. Author Contribution Conceptualization, X.-D.W.; Methodology, X.-D.W., J.-B. L.; Software, J.-B. L.; Validation, L.-B. H., Y. W., P. C.; Formal Analysis, J.-B. L.; Investigation, X.-D. W., R. Z.; Resources, X.-D. W.; Data Cu-ration, R. Z.; Writing – Original Draft Preparation, X.-D. W.; Writing – Review & Editing, L. Y.; Visualization, J.-B. L.; Supervision, L. Y.; Project Administration, X.-D. W.; Funding Acquisition, X.-D.W.. Data Availability All raw data were stored in the computer medical record system of West China Hospital, Sichuan University. To protect the privacy of the research participants, the original data will not be made public. However, you can contact the corresponding author upon reasonable request to obtain the data for this study. References Dekker E, Tanis PJ, Vleugels MJ, et al. Colorectal cancer Lancet. 2019;394:141–59. Siegel RL, Miller KD, Fedewa AB, et al. Colorectal cancer statistics, 2017. CA Cancer J Clin. 2017;67:177–96. Arnold M, Abnet CC, Bray F, Global Cancer S. 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2019, 69, 419–459. Jemal A, Center MM, DeSantis K, et al. Global cancer statistics. CA Cancer J Clin. 2011;61:69–90. Korphaisarn K, Li T, Lee CW, et al. Genetic heterogeneity of colorectal cancer. J Oncol Pharm Pract. 2019;25:32–44. Sinicrope FA, Colon Cancer. JAMA. 2021;325:2200. Ciardiello F, et al. 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Prognostic and predictive roles of KRAS mutation in colon versus rectal cancer: a systematic review and meta-analysis. Oncotarget. 2015;6:3288–97. Eklund EA, et al. Assessing the prognostic value of KRAS mutation combined with tumor size in stage I-II non-small cell lung cancer: a retrospective analysis. Front Oncol. 2024;14:1396285. Ciardiello F, et al. KRAS status and survival of patients with metastatic colorectal cancer: a comprehensive review. J Clin Oncol. 2014;32:268–74. Soussi T, et al. TP53 mutation as an independent prognostic factor in colorectal cancer: a meta-analysis of over 5000 patients. Oncotarget. 2017;8:64432–41. Additional Declarations No competing interests reported. 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Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"West China Hospital of Sichuan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jian-bo\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":560686343,\"identity\":\"e2350ccf-bc30-49ed-a68f-458527d06364\",\"order_by\":2,\"name\":\"Rui Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"West China Hospital of Sichuan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rui\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":560686344,\"identity\":\"d5eec51d-1531-46c9-8d52-239a6803aca5\",\"order_by\":3,\"name\":\"Li-bin Huang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"West China Hospital of Sichuan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Li-bin\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":560686345,\"identity\":\"36366277-1a23-4d98-880b-88bd01dadd43\",\"order_by\":4,\"name\":\"Yong Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"West China Hospital of Sichuan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yong\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":560686346,\"identity\":\"05866a3d-41b3-4bf6-97e9-70c582055e1e\",\"order_by\":5,\"name\":\"Peng Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"West China Hospital of Sichuan University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Peng\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":560686347,\"identity\":\"604e3672-7f0c-4f7e-9e65-6434aa078587\",\"order_by\":6,\"name\":\"Lie Yang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIie3QrQ7CMBDA8Wua3EzZbBMIvMIRLIJHOQxT85NgOsMDoHgM9C2zewPMHmFyho8aNIdD9Je0OdF/0hYgSf5QbuM21tslZo3oEoyJufSHTe56ViZx2Vno9le/I2WSWQGHtgweGKb6prkYMniHVZifxJz7uyZxBORdFRbC1gRVUozA5Ev0TNrEAQgT/5IgmaPwOsRPblVvKYpusI/na7VqmnaYakUSZdNnEtX5JEmS5Ls37QwxjoN2JKAAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"West China Hospital of Sichuan University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Lie\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-12-10 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17:29:29\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":22321,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOverall survival curve comparison of MU and WT patients.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8327424/v1/f158c26a25e80818f78475a6.png\"},{\"id\":104402104,\"identity\":\"9e11de0a-451b-4f14-9179-eca53f512a2b\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:14:19\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1143556,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8327424/v1/e6039875-5224-4359-96c7-d850dbcb539d.pdf\"},{\"id\":98449574,\"identity\":\"73efda6a-3c0f-4ab5-9684-bca9f8112c6f\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 17:29:44\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":21907,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"KRASTP53Supplementary251210.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8327424/v1/a49113d4c7606d471426f7fb.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Metastatic Burden and TP53 Mutation Overshadow the Independent Prognostic Value of KRAS in Colorectal Cancer\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eColorectal Cancer (CRC) is one of the most common malignancies worldwide, posing a significant threat to human health [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. In recent years, the rising incidence and mortality rates of CRC have presented a substantial challenge for clinical management [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The long-term survival of CRC patients is influenced by various factors, with molecular heterogeneity playing a pivotal role [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. KRAS mutation is a prevalent genetic alteration in CRC, occurring in approximately 30%-50% of patients, and serves as a critical biomarker for guiding targeted therapy [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eClinical practice has established that patients harboring KRAS mutations generally derive limited benefit from anti-epidermal growth factor receptor (EGFR) inhibitors and are prone to chemoresistance [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. However, the independent prognostic impact of KRAS mutation on CRC patient survival remains controversial [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Previous studies have largely been confined to univariate analyses or lacked a systematic evaluation of the complex interactions between KRAS mutation and other clinicopathological features (e.g., TNM staging, metastasis patterns), treatment parameters (e.g., surgical approaches, chemotherapy regimens), and co-existing genetic alterations (e.g., BRAF/NRAS mutations, microsatellite instability [MSI] status) [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. These limitations hinder precise prognostic assessment, making it difficult to formulate highly individualized and effective treatment strategies [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDespite the routine implementation of KRAS testing, two critical issues remain unresolved. First, the prognostic weight of KRAS mutation necessitates precise re-quantification based on multi-dimensional clinical-genomic data [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Specifically, it remains unclear whether KRAS mutation acts as an independent survival predictor after rigorously controlling for confounding factors such as age, comorbidities, and tumor burden. Second, the potential synergistic effects between KRAS mutation and other clinical or molecular characteristics require clarification [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. However, few studies have systematically evaluated whether the prognostic weight of KRAS is overshadowed by dominant clinical factors (such as metastatic burden) or concurrent driver gene mutations (such as TP53). This study aims to re-evaluate the independent prognostic value of KRAS in a multi-parameter context, hypothesizing that \\u003cb\\u003eclinical staging and TP53 status may serve as more robust determinants of survival than KRAS status alone\\u003c/b\\u003e.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Study Design and Patient Selection\\u003c/h2\\u003e \\u003cp\\u003eThis single-center retrospective study was conducted at West China Hospital, Sichuan University. We enrolled 253 patients diagnosed with CRC who underwent treatment between January 2017 and December 2023. Inclusion criteria were: (1) pathologically confirmed adenocarcinoma or mucinous adenocarcinoma; (2) availability of complete clinical and next-generation sequencing (NGS) genomic data; and (3) complete follow-up information. Patients with incomplete clinical records or missing survival data were excluded. Patient data were retrieved from electronic medical records, covering demographics (age, gender), clinicopathological characteristics (tumor location, differentiation, TNM stage, BMI, comorbidities), and treatment modalities (surgery, chemotherapy, targeted therapy, neoadjuvant therapy). For statistical purposes, well and moderately differentiated tumors were grouped together due to the small sample size of the well-differentiated cohort. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of West China Hospital (Approval Nos. 2019\\u0026thinsp;\\u0026minus;\\u0026thinsp;140, 2021\\u0026thinsp;\\u0026minus;\\u0026thinsp;155, and 2023\\u0026thinsp;\\u0026minus;\\u0026thinsp;669), covering the entire data collection period.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Genomic Profiling and Outcome Definitions\\u003c/h2\\u003e \\u003cp\\u003eGenomic data were obtained via NGS of tumor tissue, covering KRAS, NRAS, BRAF, TP53, and other relevant driver genes. KRAS mutation status was defined as \\\"Mutated\\\" (MU) if any non-synonymous mutation was detected in exons 2, 3, or 4, and \\\"Wild-type\\\" (WT) otherwise. During data preprocessing, eight variables (Inflammatory Bowel Disease, NTRK1/2/3 rearrangements, PMS2 germline mutation, ALK fusion, STK11 inactivation, and KEAP1 inactivation) were excluded from the analysis as no positive variants were identified in the cohort. Overall survival (OS) was defined as the interval from the date of diagnosis to the date of death from any cause or the last follow-up. Patients lost to follow-up were censored at the date of last contact.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Statistical analyses\\u003c/h2\\u003e \\u003cp\\u003eStatistical analyses were performed using R software (version 4.2.2) and SPSS version 26.0. Continuous variables were assessed for normality; normally distributed data were expressed as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD) and compared using the t-test, while non-normally distributed data were described as median with interquartile range (IQR) and compared using the Mann-Whitney U test. Categorical variables were compared using the Chi-square test or Fisher's exact test. Survival curves were generated using the Kaplan-Meier method and compared using the log-rank test.\\u003c/p\\u003e \\u003cp\\u003eTo identify prognostic factors, univariate and multivariate Cox proportional hazards regression models were employed. Variables with a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1 in the univariate analysis were initially considered for the multivariate model. We utilized the Variance Inflation Factor (VIF) to detect multicollinearity; variables with high VIF or extremely low event frequency (e.g., CHEK1, ERCC3, and operation duration) were excluded to ensure model stability. Subgroup analyses were conducted to evaluate the prognostic value of KRAS mutation across different strata (age, tumor location, neoadjuvant therapy). Furthermore, a sensitivity analysis was performed by relaxing the univariate screening threshold to p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.2 to assess the robustness of the findings. All statistical tests were two-sided, and a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Baseline Characteristics\\u003c/h2\\u003e \\u003cp\\u003eThe study cohort comprised 253 CRC patients, stratified into the KRAS-mutated (MU) group (n\\u0026thinsp;=\\u0026thinsp;62, 24.5%) and the wild-type (WT) group (n\\u0026thinsp;=\\u0026thinsp;191, 75.5%). The median follow-up duration was 37 months (interquartile range [IQR]: 27\\u0026ndash;49 months). At the study endpoint, the overall survival rate was 87.3% (222/253), with survival rates of 89.5% (171/191) in the WT group and 82.3% (51/62) in the MU group. As summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, patients in the MU group had significantly shorter median survival months compared to the WT group (35.00 vs. 41.00 months, P\\u0026thinsp;=\\u0026thinsp;0.004). Notably, the MU group presented with a significantly higher proportion of distant metastasis (M stage: M1) compared to the WT group (29.0% vs. 9.4%, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), suggesting a strong correlation between KRAS mutation status and advanced clinical stage. No statistically significant differences were observed between the two groups regarding other baseline clinicopathological characteristics, such as age, gender, tumor location, or TNM stage (T and N categories).\\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 MU vs WT patients\\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=\\\"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\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eWT (n\\u0026thinsp;=\\u0026thinsp;191)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMU (n\\u0026thinsp;=\\u0026thinsp;62)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP-value\\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 (median [IQR])\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64.00 [54.50, 70.00]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62.50 [54.25, 70.00]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.539\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGender (Male), n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e107 (56.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33 (53.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.812\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.54 (3.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.63 (3.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.851\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTumor location Restum), n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e147 (77.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e43 (69.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.301\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDifferentiation (Poor), n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41 (22.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16 (26.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.617\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT stage, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (4.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (3.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.207\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14 (7.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (1.6)\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41 (21.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 (14.5)\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e107 (56.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e39 (62.9)\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21 (11.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11 (17.7)\\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 stage, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e127 (66.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31 (50.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.064\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44 (23.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22 (35.5)\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20 (10.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 (14.5)\\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\\u003eDistant Metastasis (M1), n(%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18 (9.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18 (29.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eComorbidities, n(%)\\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\\u003eHypertension\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e71 (37.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20 (32.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.583\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37 (19.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8 (12.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.334\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e72 (37.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e32 (51.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.074\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAbnormal liver function\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53 (27.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18 (29.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.974\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCardiovascular disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17 (8.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3 (4.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.448\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCardiac insuffciency\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30 (15.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (11.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.517\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChronic nephrosis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (4.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (3.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eThrombocytosis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13 (6.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (9.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.640\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCOPD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (0.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eClinical Features, n(%)\\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\\u003eObstruction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e117 (61.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e43 (69.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.319\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePerforation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (12.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (9.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.700\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInvagination\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (3.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.351\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAscites\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e72 (37.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31 (50.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.118\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFirst CEA (median [IQR])\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.36 [1.90, 8.22]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.16 [2.33, 11.90]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.051\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNRS2002 (%)\\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\\u003e0.130\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e60 (31.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17 (27.4)\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55 (28.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18 (29.0)\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45 (23.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21 (33.9)\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 (8.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6 (9.7)\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (7.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.0)\\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\\u003eTreatment History, n(%)\\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\\u003eNeoadjuvant therapy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e59 (30.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23 (37.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.453\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEnterostomy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e119 (62.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33 (53.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.263\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExtended resection\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e82 (42.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33 (53.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.205\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTargeted therapy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38 (19.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12 (19.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGenomic Markers, n(%)\\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\\u003eTMB (High/Positive)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (4.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (3.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMSI (High)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8 (4.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2 (3.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003cb\\u003eNote\\u003c/b\\u003e: \\u003cem\\u003eData are presented as n (%) unless otherwise indicated. IQR: interquartile range; SD: standard deviation; BMI: body mass index; COPD: chronic obstructive pulmonary disease; CEA: carcinoembryonic antigen; NRS2002: Nutritional Risk Screening 2002; TMB: tumor mutational burden; MSI: microsatellite instability.\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Prognostic Value of KRAS Mutation and Other Factors\\u003c/h2\\u003e \\u003cp\\u003eKaplan-Meier survival analysis illustrated a clear trend of separation toward poorer prognosis in the MU group compared to the WT group (log-rank P\\u0026thinsp;=\\u0026thinsp;0.088, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Although this difference did not reach statistical significance likely due to the limited sample size, the separation of the curves indicates a potential negative impact of KRAS mutation on survival. In the subsequent multivariate Cox regression analysis, after adjusting for potential confounders, KRAS mutation was not identified as an independent prognostic factor (HR\\u0026thinsp;=\\u0026thinsp;0.723, 95% CI: 0.284\\u0026ndash;1.84, P\\u0026thinsp;=\\u0026thinsp;0.497). Instead, distant metastasis (M stage) emerged as the dominant prognostic determinant, exhibiting the highest hazard ratio (M1 vs. M0: HR\\u0026thinsp;=\\u0026thinsp;7.2, 95% CI: 2.67\\u0026ndash;19.5, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Additionally, TP53 mutation (HR\\u0026thinsp;=\\u0026thinsp;2.53, P\\u0026thinsp;=\\u0026thinsp;0.032) and regional lymph node metastasis (N1: HR\\u0026thinsp;=\\u0026thinsp;3.57, P\\u0026thinsp;=\\u0026thinsp;0.015; N2: HR\\u0026thinsp;=\\u0026thinsp;4.14, P\\u0026thinsp;=\\u0026thinsp;0.013) were confirmed as significant independent risk factors for poor survival (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The detailed outcomes of the univariate Cox regression analysis are provided in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eS.\\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\\u003eMultivariate Cox regression analysis on overall survival of all patients.\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\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\\u003eCategory\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP-value\\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 \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKRAS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.497\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.723 (0.284\\u0026thinsp;~\\u0026thinsp;1.84)\\u003c/p\\u003e \\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\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.055\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.04 (0.999\\u0026thinsp;~\\u0026thinsp;1.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN stage*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.57 (1.27\\u0026thinsp;~\\u0026thinsp;9.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eN2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.013\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.14 (1.35\\u0026thinsp;~\\u0026thinsp;12.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eM stage*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eM1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.2 (2.67\\u0026thinsp;~\\u0026thinsp;19.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.191\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.903 (0.775\\u0026thinsp;~\\u0026thinsp;1.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNRS2002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.253\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.427 (0.099\\u0026thinsp;~\\u0026thinsp;1.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.558\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.57 (0.349\\u0026thinsp;~\\u0026thinsp;7.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.448\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.86 (0.374\\u0026thinsp;~\\u0026thinsp;9.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.874\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.874 (0.166\\u0026thinsp;~\\u0026thinsp;4.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eObstruction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.331\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.583 (0.196\\u0026thinsp;~\\u0026thinsp;1.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExtended/combined resection\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.571\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.34 (0.483\\u0026thinsp;~\\u0026thinsp;3.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeoadjuvant therapy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.078\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.75 (0.894\\u0026thinsp;~\\u0026thinsp;8.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTargeted therapy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.965\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.03 (0.328\\u0026thinsp;~\\u0026thinsp;3.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTP53*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.032\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.53 (1.08\\u0026thinsp;~\\u0026thinsp;5.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHLA-I germline genotyping (homozygous)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRef\\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\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.217\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.35 (0.0663\\u0026thinsp;~\\u0026thinsp;1.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u003cb\\u003eNote\\u003c/b\\u003e: \\u003cem\\u003e*: These are significant variables identified by multivariate Cox regression analysis.\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u003cem\\u003e-: These are quantitative data without category. Ref: Reference category.\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Subgroup Analysis\\u003c/h2\\u003e \\u003cp\\u003eTo further explore the potential context-dependent prognostic role of KRAS mutation, we conducted multivariate Cox regression subgroup analyses stratified by age, tumor location, and treatment history. Contrary to the assumption that KRAS might drive prognosis in specific populations, our results showed that KRAS mutation was not an independent prognostic factor in any of the analyzed subgroups, including patients with colon cancer (P\\u0026thinsp;=\\u0026thinsp;0.537), those aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;60 years (P\\u0026thinsp;=\\u0026thinsp;0.805), and those who received neoadjuvant chemotherapy (P\\u0026thinsp;=\\u0026thinsp;0.427). These findings reinforce the consistency of our primary analysis. However, notably, in the subgroup of patients receiving neoadjuvant therapy, TP53 mutation emerged as a significant independent risk factor for poor sur-vival (HR\\u0026thinsp;=\\u0026thinsp;3.12, 95% CI: 1.03\\u0026ndash;9.42, P\\u0026thinsp;=\\u0026thinsp;0.044), suggesting a specific prognostic role for TP53 in this treatment setting. The detailed outcomes of the subgroup analyses are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\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\\u003eSubgroup analysis of overall survival on age and tumor location.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"13\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c9\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c13\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003eTumor location\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;60\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c8\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026gt;=60\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c11\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eColon\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003eRectal\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP value\\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\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP value\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP value\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP value\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003eHR (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\\u003eKRAS Positive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e0.506\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.52 (0.44\\u0026ndash;5.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e0.805\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.13 (0.427-3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e0.537\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.456 (0.0376-5.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e0.352\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e1.47 (0.653-3.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN stage N1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e0.756\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.28 (0.272\\u0026ndash;6.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e0.029\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.77 (1.15\\u0026ndash;12.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e0.896\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1.17 (0.105\\u0026ndash;13.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e4.76 (1.75-13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN stage N2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0935\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.51 (0.809\\u0026ndash;15.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e0.013\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5.86 (1.45\\u0026ndash;23.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e0.915\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.867 (0.0621-12.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e6.26 (2.09\\u0026ndash;18.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eM stage M1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26 (5.3\\u0026ndash;128)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e4 (1.55\\u0026ndash;10.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e0.014\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e11.4 (1.65\\u0026ndash;79.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e6.8 (3.02\\u0026ndash;15.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTP53 Positive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e0.985\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.988 (0.286\\u0026ndash;3.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e0.117\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.12 (0.828\\u0026ndash;5.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e0.918\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1.11 (0.149\\u0026ndash;8.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e0.057\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e2.22 (0.977\\u0026ndash;5.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\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\\u003eSubgroup analysis of overall survival on (neo)adjuvant therapy received.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eNeoadjuvant therapy\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c9\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eAdjuvant therapy\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP value\\u003c/em\\u003e\\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 value\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP value\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP value\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eHR (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\\u003eKras Positive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.672 (0.14\\u0026ndash;3.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.427\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.5 (0.554\\u0026ndash;4.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.933\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.958 (0.354\\u0026ndash;2.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.951\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.952 (0.197\\u0026ndash;4.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN stage N1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.456\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.72 (0.415-7.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.73 (2.19\\u0026ndash;27.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.0496\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.42 (1-11.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.058\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e4.02 (0.954\\u0026ndash;16.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN stage N2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0917\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.73 (0.808\\u0026ndash;17.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.019\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.08 (1.31\\u0026ndash;19.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.00356\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e6.8 (1.87\\u0026ndash;24.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.644\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.63 (0.204\\u0026ndash;13.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eM stage M1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 (3.3\\u0026ndash;36.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.020\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.57 (1.22\\u0026ndash;10.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e6.39 (2.42\\u0026ndash;16.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e5.62 (1.03\\u0026ndash;30.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTP53 Positive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.536\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.46 (0.443\\u0026ndash;4.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.044\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.12 (1.03\\u0026ndash;9.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.74 (0.676-4.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.066\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3.68 (0.916\\u0026ndash;14.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eOur study provides a comprehensive re-evaluation of the prognostic value of KRAS mutation in a cohort of 253 CRC patients. Our findings suggest that the independent prognostic significance of KRAS mutation may have been overestimated in previous studies, as its impact appears to be largely overshadowed by dominant clinical factors, particularly metastatic burden. Consequently, this discussion will focus on two pivotal aspects: the re-assessment of KRAS mutation as a standalone prognostic marker and the necessity of a multi-parameter approach that integrates tumor staging and co-existing genetic alterations (such as TP53) for accurate risk stratification.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1. The Prognostic Role of KRAS Mutation is Overshadowed by Metastatic Burden\\u003c/h2\\u003e \\u003cp\\u003eOur primary finding, that KRAS mutation was not an independent prognostic factor for overall survival in the overall patient cohort, aligns with findings from several previous studies [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Although a trend toward poorer survival was observed in the univariate analysis, the lack of statistical significance in the multivariate model highlights the dominant influence of classical clinical factors. Specifically, distant metastasis (M stage) exhibited a much stronger prognostic impact (HR\\u0026thinsp;=\\u0026thinsp;7.2, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) compared to KRAS status. This suggests that when a patient's prognosis is already dictated by the advanced stage of their disease, the effect of a single molecular marker like KRAS mutation may be masked [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFurthermore, our subgroup analyses verified that this \\\"masking effect\\\" persists across different clinical contexts. Contrary to some previous reports suggesting that the prognostic value of KRAS might be specific to colon cancer or older patients [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], our data showed that KRAS mutation did not independently predict survival in any subgroup, including patients with colon cancer, those over 60 years old, or those receiving neoadjuvant chemotherapy.\\u003c/p\\u003e \\u003cp\\u003eThis discrepancy with prior positive findings may be attributed to two factors. First, the strong correlation between KRAS mutation and M1 stage observed in our cohort (29.0% in MU vs. 9.4% in WT, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) suggests a biological hierarchy in prognosis. From a statistical perspective, this phenomenon can be interpreted as a 'mediation effect', where distant metastasis acts as a strong mediator in the pathway from KRAS mutation to survival outcomes. While KRAS mutation likely drives the initial metastatic seeding, our multivariate results indicate that once this distant metastasis is established, the overwhelming tumor burden becomes the dominant determinant of mortality, effectively absorbing the statistical impact of the initial driver mutation. This explains why KRAS appears prognostic in univariate analysis (due to its correlation with M1) but loses its independent value when metastatic burden is rigorously adjusted.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2. Beyond a Single Biomarker: Independent Value of TP53 and the Necessity of Multi-Dimensional Assessment\\u003c/h2\\u003e \\u003cp\\u003eOur study highlights the limitation of relying solely on KRAS status and reinforces the necessity of a multi-parameter prognostic model. While KRAS failed to demonstrate independent prognostic significance, we identified TP53 mutation as a robust independent risk factor for overall survival (HR\\u0026thinsp;=\\u0026thinsp;2.53, P\\u0026thinsp;=\\u0026thinsp;0.032). This finding aligns with established literature suggesting that TP53, a critical regulator of the cell cycle and DNA repair, reflects a more aggressive tumor biology when mutated [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eA particularly novel insight from our subgroup analysis is the specific impact of TP53 in the context of neoadjuvant therapy. We observed that TP53 mutation was significantly associated with poorer outcomes specifically in patients who received neoadjuvant chemotherapy (HR\\u0026thinsp;=\\u0026thinsp;3.12, P\\u0026thinsp;=\\u0026thinsp;0.044), whereas KRAS mutation showed no such effect. The emergence of TP53 as a significant risk factor specifically in the neoadjuvant setting suggests a potential mechanism of chemoresistance. Since TP53-mediated apoptosis is a key pathway for chemotherapy efficacy, its mutation may render tumor cells refractory to cytotoxic agents. Clinically, this implies that for CRC patients harboring TP53 mutations, standard neoadjuvant chemotherapy might yield limited survival benefit. Therefore, alternative strategies\\u0026mdash;such as intensified regimens or immunotherapy\\u0026mdash;should be considered for this high-risk subgroup. This finding highlights the superiority of TP53 over KRAS as a predictive biomarker in the neoadjuvant context.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, our study identified other molecular signals, such as the correlation between KRAS and BRCA2 mutations, which hints at potential underlying deficits in DNA damage repair mechanisms. Therefore, for a truly accurate prognostic assessment, clinicians should move beyond a \\\"KRAS-centric\\\" view. An integrated approach that combines anatomical staging (TNM), treatment history (e.g., neoadjuvant therapy), and a comprehensive molecular panel (including TP53 and KRAS) is essential to capture the full biological complexity of CRC and to guide personalized management strategies.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3. Limitations \\u0026amp; future directions\\u003c/h2\\u003e \\u003cp\\u003eOur study has several limitations that warrant consideration. First, as a single-center retrospective study, inherent selection bias is unavoidable. The sample size of 253 patients, particularly the subgroup of 62 KRAS-mutated cases, may have restricted the statistical power. Although the Kaplan-Meier analysis showed a clear trend toward poorer prognosis in the mutated group (P\\u0026thinsp;=\\u0026thinsp;0.088), the limited sample size might have prevented this difference from reaching statistical significance, potentially leading to a Type II error. Second, our genomic analysis was treated as a binary classification (Mutated vs. Wild-type). We did not utilize Mutation Annotation Format (MAF) files for deep bioinformatic interrogation. This simplified approach precludes the analysis of variant allele frequency (VAF) or specific mutation subtypes (e.g., G12D vs. G12V), which might exhibit distinct biological behaviors and prognostic impacts. Consequently, complex interactions between KRAS and other signaling pathways may have been oversimplified. Finally, while we controlled for major confounders, detailed information regarding specific chemotherapy regimens and patient adherence was not fully accounted for, which could introduce residual confounding.\\u003c/p\\u003e \\u003cp\\u003eThe identification of TP53 and metastatic burden as dominant prognostic factors in this cohort does not imply that clinical attention to KRAS should cease. On the contrary, our findings suggest that the prognostic role of KRAS is nuanced and likely conditional. Future research should move beyond simple binary assessments and leverage comprehensive bioinformatic analyses (e.g., using MAF files) to explore the \\\"long-tail\\\" effect of specific KRAS variants and their co-mutation networks. The critical next step is not to discard KRAS as a marker, but to identify the specific, controlled biological contexts\\u0026mdash;such as specific immune microenvironments or concurrent pathway alterations\\u0026mdash;under which KRAS mutation decisively impacts survival. By establishing such a refined stratification logic, we can provide more precise evidence for the selection of KRAS-targeted therapies, ensuring that the right treatment strategies are matched to the specific subgroups of patients who will truly benefit.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eIn summary, our study demonstrates that the independent prognostic value of KRAS mutation in colorectal cancer has been overestimated, as its impact is largely overshadowed by the dominant influence of metastatic burden (M stage). Instead of KRAS, TP53 mutation emerged as a robust independent risk factor, particularly for patients undergoing neoadjuvant therapy. Consequently, accurate prognosis and personalized management strategies should not rely on KRAS status in isolation but must integrate clinical staging with a comprehensive molecular profile.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eConflicts of Interest:\\u003c/h2\\u003e\\n\\u003cp\\u003eThe authors declare no conflicts of interest.\\u003c/p\\u003e\\n\\u003ch2\\u003eInstitutional Review Board Statement\\u003c/h2\\u003e\\n\\u003cp\\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of West China Hospital, Sichuan University (Approval No. 2023\\u0026thinsp;\\u0026minus;\\u0026thinsp;669 on 7 April 2023, No. 2021\\u0026thinsp;\\u0026minus;\\u0026thinsp;155 on 10 March 2021, and No. 2019\\u0026thinsp;\\u0026minus;\\u0026thinsp;140 on 15 February 2019).\\u003c/p\\u003e\\n\\u003ch2\\u003eInformed Consent \\u003cstrong\\u003eStatement\\u003c/strong\\u003e:\\u0026nbsp;\\u003c/h2\\u003e\\n\\u003cp\\u003eInformed consent was obtained from all subjects involved in the study.\\u003c/p\\u003e\\n\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\n\\u003cp\\u003eThis research was funded by 1\\u0026middot;3\\u0026middot;5 projects for Artificial Intelligence, West China Hospital, Sichuan University grant number ZYAI24067.\\u003c/p\\u003e\\n\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\n\\u003cp\\u003eConceptualization, X.-D.W.; Methodology, X.-D.W., J.-B. L.; Software, J.-B. L.; Validation, L.-B. H., Y. W., P. C.; Formal Analysis, J.-B. L.; Investigation, X.-D. W., R. Z.; Resources, X.-D. W.; Data Cu-ration, R. Z.; Writing \\u0026ndash; Original Draft Preparation, X.-D. W.; Writing \\u0026ndash; Review \\u0026amp; Editing, L. Y.; Visualization, J.-B. L.; Supervision, L. Y.; Project Administration, X.-D. W.; Funding Acquisition, X.-D.W..\\u003c/p\\u003e\\n\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\n\\u003cp\\u003eAll raw data were stored in the computer medical record system of West China Hospital, Sichuan University. To protect the privacy of the research participants, the original data will not be made public. However, you can contact the corresponding author upon reasonable request to obtain the data for this study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eDekker E, Tanis PJ, Vleugels MJ, et al. Colorectal cancer Lancet. 2019;394:141\\u0026ndash;59.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSiegel RL, Miller KD, Fedewa AB, et al. Colorectal cancer statistics, 2017. CA Cancer J Clin. 2017;67:177\\u0026ndash;96.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eArnold M, Abnet CC, Bray F, Global Cancer S. 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2019, 69, 419\\u0026ndash;459.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJemal A, Center MM, DeSantis K, et al. Global cancer statistics. CA Cancer J Clin. 2011;61:69\\u0026ndash;90.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKorphaisarn K, Li T, Lee CW, et al. Genetic heterogeneity of colorectal cancer. J Oncol Pharm Pract. 2019;25:32\\u0026ndash;44.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSinicrope FA, Colon Cancer. 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OncoTargets Ther. 2016;9:4769\\u0026ndash;76.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePassalacqua R, et al. KRAS mutation status and prognosis in metastatic colorectal cancer: an update. Eur J Cancer. 2019;115:1\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSorich MJ, et al. Prognostic and predictive markers in metastatic colorectal cancer. Expert Rev Anticancer Ther. 2015;15:111\\u0026ndash;24.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBokemeyer C, et al. KRAS status and efficacy of cetuximab in advanced colorectal cancer: a review. OncoTargets Ther. 2019;12:10329\\u0026ndash;38.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAllegra CJ, et al. KRAS and BRAF mutations as prognostic and predictive markers in metastatic colorectal cancer: A systematic review and meta-analysis. Clin Colorectal Cancer. 2018;17:17\\u0026ndash;25.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi H, Fu J, Lu B, et al. Prognostic and Predictive Role of KRAS Mutations in Colorectal Cancer: A Large-Scale Retrospective Cohort Study. OncoTargets Ther. 2019;12:9763\\u0026ndash;72.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eModest DP, et al. A review of KRAS G12C mutation in colorectal cancer: treatment, survival rates and future perspec-tives. Ther Adv Med Oncol. 2021;13:1758\\u0026ndash;68.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSanson B, et al. KRAS status and survival of patients with metastatic colorectal cancer: a cohort study. J Natl Cancer Inst. 2014;106:djt343.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eArtale S, et al. Prognostic and predictive value of KRAS G12C mutation in metastatic colorectal cancer. Mol Clin Oncol. 2021;14:14.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVasan N, et al. Subtype-specific KRAS mutations in colorectal cancer: a review of current knowledge. Cell Rep Med. 2021;2:100267.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShiu KK, et al. Prognostic and predictive roles of KRAS mutation in colon versus rectal cancer: a systematic review and meta-analysis. Oncotarget. 2015;6:3288\\u0026ndash;97.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEklund EA, et al. Assessing the prognostic value of KRAS mutation combined with tumor size in stage I-II non-small cell lung cancer: a retrospective analysis. Front Oncol. 2024;14:1396285.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCiardiello F, et al. KRAS status and survival of patients with metastatic colorectal cancer: a comprehensive review. J Clin Oncol. 2014;32:268\\u0026ndash;74.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSoussi T, et al. TP53 mutation as an independent prognostic factor in colorectal cancer: a meta-analysis of over 5000 patients. Oncotarget. 2017;8:64432\\u0026ndash;41.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Colorectal cancer, KRAS mutation, Prognosis, Retrospective study, Personalized treatment, Cox regression\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8327424/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8327424/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eThe independent prognostic value of \\u003cem\\u003eKRAS\\u003c/em\\u003e mutation in colorectal cancer (CRC) remains controversial, often yielding inconsistent results across studies. We hypothesized that its impact might be overestimated when confounding clinical factors, such as metastatic burden, are rigorously controlled.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eIn this single-center retrospective study, we enrolled 253 CRC patients treated at West China Hospital. We integrated clinical data with next-generation sequencing (NGS) profiles to re-evaluate the prognostic significance of \\u003cem\\u003eKRAS\\u003c/em\\u003e. Kaplan-Meier survival analysis and multivariate Cox regression models were employed to assess the impact of \\u003cem\\u003eKRAS\\u003c/em\\u003e status, metastatic burden, and co-occurring mutations on overall survival.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eWhile Kaplan-Meier analysis showed a trend toward poorer survival in \\u003cem\\u003eKRAS\\u003c/em\\u003e-mutated patients (P\\u0026thinsp;=\\u0026thinsp;0.088), this difference did not reach statistical significance. Crucially, multivariate Cox regression revealed that \\u003cem\\u003eKRAS\\u003c/em\\u003e mutation was not an independent prognostic factor (HR\\u0026thinsp;=\\u0026thinsp;0.723, P\\u0026thinsp;=\\u0026thinsp;0.497) after adjusting for confounders. Instead, outcomes were dominated by metastatic burden (M stage: HR\\u0026thinsp;=\\u0026thinsp;7.2, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and TP53 mutation (HR\\u0026thinsp;=\\u0026thinsp;2.53, P\\u0026thinsp;=\\u0026thinsp;0.032). Subgroup analyses confirmed the lack of independent \\u003cem\\u003eKRAS\\u003c/em\\u003e prognostic value across age, tumor location, and treatment strata. Notably, TP53 mutation emerged as a significant independent risk factor specifically in patients receiving neoadjuvant therapy (HR\\u0026thinsp;=\\u0026thinsp;3.12, P\\u0026thinsp;=\\u0026thinsp;0.044).\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eThe prognostic weight of \\u003cem\\u003eKRAS\\u003c/em\\u003e mutation in CRC is largely overshadowed by dominant clinical determinants (metastatic burden) and concurrent molecular drivers (TP53). Our findings suggest that for high-risk patients, particularly those undergoing neoadjuvant therapy, TP53 status may serve as a superior prognostic biomarker compared to \\u003cem\\u003eKRAS\\u003c/em\\u003e. We propose moving beyond a \\\"\\u003cem\\u003eKRAS\\u003c/em\\u003e-centric\\\" assessment toward a multi-parameter model integrating TNM staging with comprehensive genomic profiling.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Metastatic Burden and TP53 Mutation Overshadow the Independent Prognostic Value of KRAS in Colorectal Cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-17 16:55:23\",\"doi\":\"10.21203/rs.3.rs-8327424/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"f90ab543-803f-4b5d-ab40-7218dd40f2ec\",\"owner\":[],\"postedDate\":\"December 17th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-05T16:54:59+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-17 16:55:23\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8327424\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8327424\",\"identity\":\"rs-8327424\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}