Age-Stratified Insights in Colorectal Cancer: A Four-Tier Analysis of Presentation, Treatment, and Outcomes

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Tashkandi, Hosam Ali Alghanmi, A. H. Almatari, M. H. Elsafty, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7088094/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Colorectal cancer (CRC) exhibits significant age-related heterogeneity in tumor biology, clinical presentation, and treatment response. However, real-world, age-stratified data from the Middle East remain limited. Methods We conducted a retrospective cohort study of 761 patients with histologically confirmed colorectal adenocarcinoma treated at a tertiary cancer center in Saudi Arabia between 2015 and 2021. Patients were stratified into four age groups (≤ 40, 41–50, 51–64, ≥ 65 years). Clinicopathologic features, treatment patterns, and survival outcomes were compared using Kaplan–Meier and Cox regression analyses. Results Younger patients (≤ 40) were more likely to present with metastatic disease (61.4%), rectal primaries (57.6%), mucinous/signet ring histology, and peritoneal spread. Older patients (≥ 65) exhibited a higher prevalence of right-sided tumors (37.3%), BRAF mutations (9.7%), and functional impairment. Treatment intensity declined significantly with age, with older adults receiving fewer surgeries, adjuvant therapies, and later-line systemic regimens. Despite more aggressive disease at diagnosis, younger patients achieved superior median overall survival (38.2 vs. 24.8 months) and progression-free survival across all therapy lines. In multivariable analysis, age ≥ 65, ECOG ≥ 2, stage IV disease, right-sided location, absence of surgery, and BRAF mutation independently predicted worse survival. Conclusion This study highlights stark age-related disparities in CRC presentation, molecular profile, treatment delivery, and outcomes. Younger patients benefit from intensive therapy despite biologically aggressive disease, whereas older adults remain under-treated and experience poorer survival. These findings support the need for age-adapted, biology-informed CRC care and underscore the importance of integrating geriatric and molecular assessment into clinical decision-making. Colorectal cancer Age-stratified analysis Early-onset CRC Elderly Survival outcomes Treatment disparities Saudi Arabia Introduction Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and a leading cause of cancer-related mortality worldwide[ 1 ]. While overall incidence and mortality have declined among older adults—largely due to widespread screening and treatment advances—CRC is increasingly being diagnosed in younger individuals, often at more advanced stages and with aggressive tumor biology[ 2 ]. This evolving epidemiology poses new challenges for early detection and age-tailored treatment strategies. Age has emerged as a key determinant influencing CRC presentation, tumor characteristics, treatment decisions, and outcomes[ 3 ]. Patients under 50 years (early-onset CRC) are more likely to present with left-sided or rectal tumors, adverse histology, and metastatic disease at diagnosis[ 4 ]. In contrast, older adults—who account for most CRC cases—tend to have right-sided tumors, higher comorbidity burdens, and more limited access to intensive therapies[ 5 ]. Despite growing recognition of these age-related disparities, real-world, age-specific data guiding optimal management remain scarce. Recent international and regional trends highlight the need for age-stratified analyses to better understand CRC heterogeneity and improve outcomes[ 6 ], [ 7 ]. In Saudi Arabia, CRC is among the most common cancers, with a significant proportion of cases diagnosed at advanced stages despite increasing awareness and national screening efforts. Nearly one-third of patients present with metastatic disease, and survival rates remain modest relative to global benchmarks[ 8 ], [ 9 ]. Barriers such as limited screening uptake, provider inaction, and cultural concerns contribute to delayed diagnosis—particularly in younger and rural populations[ 10 ]. To address this gap, we conducted a comprehensive, age-stratified analysis of CRC patients treated at a tertiary oncology center in Saudi Arabia. By classifying patients into four age groups (≤ 40, 41–50, 51–64, and ≥ 65 years), we examined variations in clinicopathologic features, treatment patterns, and survival outcomes, with the goal of informing more personalized, age-adapted management across the CRC care continuum. METHODS Study Design and Setting This retrospective observational cohort study was conducted at King Abdullah Medical City (KAMC), a tertiary referral oncology center in Makkah, Saudi Arabia. The study design adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for retrospective cohort investigations[ 11 ]. Ethical approval was obtained from the institutional review board, and all data were handled in accordance with institutional confidentiality protocols. Study Population The study included all adult patients aged 18 years and older who were diagnosed with histologically confirmed colorectal adenocarcinoma between January 1, 2015, and December 31, 2021. Patients were excluded if they had a non-adenocarcinoma histology, such as neuroendocrine tumors or lymphomas, if the colorectal involvement was secondary to another primary malignancy, or if diagnostic, treatment, or survival data were incomplete. Eligible patients were identified through institutional cancer registries and electronic health records, with final inclusion confirmed through manual verification. Age Stratification Patients were stratified into four predefined age groups based on age at initial diagnosis. The groups included those aged 40 years or younger, representing very early-onset colorectal cancer (VEOCRC); those aged 41 to 50 years, categorized as early-onset CRC (EOCRC); individuals aged 51 to 64 years, representing mid-aged patients; and those aged 65 years and older, classified as elderly. This stratification approach was selected to capture clinically meaningful differences across life stages in tumor biology, treatment patterns, and outcomes. Data Collection and Variables Clinical data were extracted from electronic medical records using a structured data abstraction form. Data collection was carried out by trained research coordinators under the supervision of the principal investigator. All personally identifying information was removed prior to analysis to ensure compliance with privacy regulations. The collected variables included demographic details such as age at diagnosis, sex, and body mass index (BMI), categorized according to World Health Organization guidelines. Performance status at diagnosis was recorded using the Eastern Cooperative Oncology Group (ECOG) scale and dichotomized as 0–1 versus ≥ 2. Tumor-related characteristics included primary tumor location (right colon, left colon, or rectum), American Joint Committee on Cancer (AJCC) 8th edition stage at presentation, histologic subtype, and molecular profiling, specifically RAS and BRAF mutation status when available. Treatment data encompassed surgical resection, use of adjuvant chemotherapy, and systemic therapy across first, second, third, and fourth-line regimens. Outcome variables included overall survival (OS), defined as the time from diagnosis to death or last follow-up, and progression-free survival (PFS), and defined as the time from initiation of systemic treatment to radiologic progression or death. Statistical Analysis All statistical analyses were performed using IBM Statistics (version 25) and Microsoft Excel. with independent validation performed by a consulting biostatistician. Categorical variables were summarized using frequencies and percentages, and comparisons across age groups were made using Chi-square or Fisher’s exact tests, as appropriate. Continuous variables were expressed as medians with interquartile ranges and compared using the Kruskal–Wallis test due to non-normal distributions. Survival analyses for OS and PFS were performed using the Kaplan–Meier method, and differences among age groups were assessed using the log-rank test. Overall survival was calculated from the date of diagnosis, while PFS was calculated from the start of systemic treatment for each respective line of therapy. Outliers, including patients with survival durations exceeding 10 years, were examined separately in sensitivity analyses. To assess the independent effect of age and other clinical factors on survival, multivariable analysis was performed using a Cox proportional hazards regression model. Covariates included age group, ECOG status, tumor stage, primary site, and receipt of systemic treatment. Results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). The proportional hazards assumption was verified using Schoenfeld residuals. Missing data were handled based on the extent of missingness. Variables with less than 10% missing data were analyzed using complete-case methods. For variables with more than 10% missingness, multiple imputation by chained equations (MICE) was employed to reduce potential bias and improve analytic robustness. All statistical tests were two-sided, and a p-value of less than 0.05 was considered statistically significant. Results A total of 761 patients were stratified into four age groups: ≤40 (n=132), 41–50 (n=135), 51–64 (n=205), and ≥65 years (n=289). The mean age within each subgroup aligned with expected stratification boundaries (36.2, 46.2, 57.1, and 72.8 years, respectively). The male predominance was consistent across all age groups, with no significant difference in sex distribution (p=0.312). Body mass index (BMI) categories varied significantly by age (p=0.039), with younger patients more likely to be underweight, and older patients more likely to be overweight or obese. Functional status, as measured by ECOG performance, showed a statistically significant age-related decline (p<0.001), with 86.4% of patients ≤40 years having ECOG 0–1 versus only 71.3% in the ≥65 group (Table 1). Table 1. Baseline Demographics by Age Group Variable ≤40 (n=132) 41–50 (n=135) 51–64 (n=205) ≥65 (n=289) p-value Age (mean ± SD) 36.2 ± 3.2 46.2 ± 2.3 57.1 ± 3.9 72.8 ± 5.4 — Sex – Male – Female 80 (60.6%) 52 (39.4%) 75 (55.6%) 60 (44.4%) 128 (62.4%) 77 (37.6%) 165 (57.1%) 124 (42.9%) 0.312 BMI Categories – Underweight (<18.5) – Healthy (18.5–24.9) – Overweight (25–29.9) – Obese (≥30) 12 (9.1%) 53 (40.2%) 42 (31.8%) 25 (18.9%) 9 (6.7%) 51 (37.8%) 49 (36.3%) 26 (19.2%) 6 (2.9%) 70 (34.1%) 81 (39.5%) 48 (23.4%) 10 (3.5%) 96 (33.2%) 111 (38.4%) 72 (24.9%) 0.039 ECOG Status – ECOG 0–1 – ECOG ≥2 114 (86.4%) 18 (13.6%) 114 (84.4%) 21 (15.6%) 166 (81.0%) 39 (19.0%) 206 (71.3%) 83 (28.7%) <0.001 BMI categorized per WHO standards. ECOG: Eastern Cooperative Oncology Group performance status. P-values calculated using Chi-square or Fisher’s exact tests, as appropriate. Tumor stage at diagnosis was inversely associated with age (p=0.009). Stage IV disease was more frequent among the youngest cohort (61.4%) and decreased progressively with age, reaching 46.9% in the ≥65 group. Conversely, early-stage diagnoses (Stage I–II) were more common in older patients. Primary tumor location showed a notable age gradient (p<0.001). Rectal cancers were predominant in younger patients (57.6% in ≤40), while right-sided tumors were significantly more common in the elderly (37.3% in ≥65 vs 11.4% in ≤40). The metastatic pattern also varied with age (p=0.017). Liver and lung were the most frequent metastatic sites across all groups, but peritoneal involvement was disproportionately higher in younger patients (18.9% vs 11.4% in ≥65). Histologic subtype also differed by age (p=0.020), with mucinous and signet ring histologies being more prevalent in younger patients. BRAF mutation rates increased significantly with age (2.3% in ≤40 vs 9.7% in ≥65, p=0.038), while RAS mutation rates were relatively stable across groups. Median CEA levels were highest in older patients (13.5 ng/mL in ≥65) and lowest in the ≤40 group (6.8 ng/mL), with a statistically significant difference (p=0.004) (Table2). Table 2. Tumor and Molecular Characteristics by Age Group Variable ≤40 (n=132) 41–50 (n=135) 51–64 (n=205) ≥65 (n=289) p-value Stage Group – Local (I–III) – Metastatic (IV) 38.6% 61.4% 42.2% 57.8% 48.3% 51.7% 53.1% 46.9% 0.006 Stage at Diagnosis – Stage I – Stage II – Stage III – Stage IV 4.5% 10.6% 23.5% 61.4% 7.4% 12.6% 22.2% 57.8% 10.7% 14.1% 23.5% 51.7% 14.9% 18.1% 20.1% 46.9% 0.009 Primary Tumor Site – Right colon – Left colon – Rectum 11.4% 31.1% 57.6% 18.2% 35.6% 46.2% 25.4% 39.0% 35.6% 37.3% 35.0% 27.7% <0.001 Site of Metastases – Liver – Lung – Peritoneum – Other (e.g., brain, bone) 49.2% 34.1% 18.9% 9.8% 46.3% 31.1% 15.6% 8.9% 45.8% 27.3% 14.6% 10.2% 41.6% 24.6% 11.4% 12.4% 0.017 Histologic Type – Adenocarcinoma – Mucinous – Signet Ring – Other (e.g., mixed type) 86.4% 9.1% 4.5% 1.5% 83.5% 11.1% 5.4% 2.2% 82.0% 13.7% 4.3% 2.0% 80.7% 14.6% 4.7% 2.8% 0.020 Molecular Profile – RAS Mutation (%) – BRAF Mutation (%) – CEA (Median, IQR ng/mL) 45.5% 2.3% 6.8 (2.9–21.3) 42.8% 4.4% 7.9 (3.2–24.6) 50.7% 6.3% 11.2 (4.1–33.8) 51.6% 9.7% 13.5 (5.5–39.1) 0.319 0.038 0.004 Primary tumor site classified anatomically. Molecular data include RAS and BRAF mutation status when available. CEA: Carcinoembryonic antigen. P-values derived using Chi-square and Kruskal–Wallis tests. Surgical intervention rates declined with age (85.6% in ≤40 vs 74.7% in ≥65, p=0.018). Similarly, adjuvant chemotherapy usage decreased across age groups (77.3% in ≤40 vs 58.4% in ≥65, p<0.001). The choice of adjuvant regimen differed slightly, with younger patients more likely to receive XELOX and older patients showing increased use of capecitabine monotherapy (p=0.041).Systemic therapy exposure declined with advancing age. While all patients received first-line chemotherapy, the proportions receiving second-, third-, and fourth-line therapies declined significantly in older age groups (p<0.001 for all). Notably, only 7.6% of patients ≥65 received fourth-line therapy compared to 21.2% in the ≤40 group (Table 3). Table 3. Systemic Treatment Exposure by Age Group Treatment Variable ≤40 (n=132) 41–50 (n=135) 51–64 (n=205) ≥65 (n=289) p-value Surgery – Yes – No 113 (85.6%) 19 (14.4%) 111 (82.2%) 24 (17.8%) 163 (79.5%) 42 (20.5%) 216 (74.7%) 73 (25.3%) 0.018 Adjuvant Treatment – Yes – No 102 (77.3%) 30 (22.7%) 98 (72.6%) 37 (27.4%) 137 (66.8%) 68 (33.2%) 169 (58.4%) 120 (41.6%) <0.001 Adjuvant Regimen – XELOX – Capecitabine – FOLFOX 81 (61.4%) 24 (18.2%) 27 (20.5%) 79 (58.5%) 27 (20.0%) 29 (21.5%) 113 (55.1%) 52 (25.4%) 40 (19.5%) 143 (49.5%) 81 (28.0%) 65 (22.5%) 0.041 Systemic Chemotherapy Usage 1st-line therapy received 2nd-line therapy received 3rd-line therapy received 4th-line therapy received 132 (100%) 95 (72.0%) 56 (42.4%) 28 (21.2%) 135 (100%) 88 (65.2%) 52 (38.5%) 25 (18.5%) 205 (100%) 121 (59.0%) 70 (34.1%) 27 (13.2%) 289 (100%) 142 (49.3%) 75 (25.8%) 22 (7.6%) — <0.001 <0.001 <0.001 Systemic therapies reflect real-world usage across multiple treatment lines. Adjuvant regimens include XELOX (capecitabine + oxaliplatin), capecitabine monotherapy, and FOLFOX (5-FU + oxaliplatin). All p-values two-sided. Median overall survival (OS) declined progressively with age (38.2 months in ≤40 vs 24.8 months in ≥65; p<0.001). Similarly, 5-year OS rates dropped from 56.1% in the youngest group to 36.8% in the oldest. Kaplan–Meier estimates revealed a significant stepwise decline in survival at 12, 36, and 60 months across age groups (p-values ranging from 0.018 to <0.001). Progression-free survival (PFS) followed a similar trend. Median first-line PFS decreased from 14.4 months in ≤40 to 10.5 months in ≥65 (p=0.012). This pattern persisted for second-line (9.1 vs 5.9 months, p=0.026) and third-/fourth-line therapies (6.4 vs 3.6 months, p=0.037) (Table 4). Table 4. Survival and Progression-Free Outcomes by Age Group Metric ≤40 (n=132) 41–50 (n=135) 51–64 (n=205) ≥65 (n=289) p-value Median Overall Survival (months) 38.2 35.6 31.5 24.8 <0.001 5-year Overall Survival (%) 56.1% 52.3% 48.7% 36.8% — OS by Age Group (KM Estimate) – 12-month survival rate (%) – 36-month survival rate (%) – 60-month survival rate (%) 90.2% 68.9% 56.1% 87.4% 63.3% 52.3% 83.1% 58.0% 48.7% 76.9% 45.1% 36.8% 0.018 <0.001 <0.001 Median PFS – 1st-line (months) 14.4 13.6 12.2 10.5 0.012 Median PFS – 2nd-line (months) 9.1 8.3 7.6 5.9 0.026 Median PFS – 3rd/4th-line (months) 6.4 5.8 4.2 3.6 0.037 OS: Overall survival. PFS: Progression-free survival. KM: Kaplan–Meier estimate. Survival calculated from diagnosis (OS) or treatment start (PFS). P-values based on log-rank test. In Cox regression analysis, age ≥65 was independently associated with worse overall survival compared to patients ≤40 (HR 1.48; 95% CI, 1.21–1.81; p<0.001). Other independent negative prognostic factors included ECOG ≥2 (HR 1.91), metastatic disease (HR 2.63), right-sided primary (HR 1.37), absence of surgery (HR 1.72), RAS mutation (HR 1.22), and BRAF mutation (HR 1.58) (all p<0.05) (Table5). Table 5. Multivariable Cox Proportional Hazards Regression for Overall Survival Variable HR (95% CI) p-value Age ≥ 65 vs ≤40 1.48 (1.21–1.81) <0.001 ECOG ≥ 2 1.91 (1.55–2.35) <0.001 Stage IV vs I–III 2.63 (2.15–3.22) <0.001 Right-sided vs left-sided 1.37 (1.11–1.69) 0.003 No surgery 1.72 (1.38–2.14) <0.001 RAS Mutation 1.22 (1.01–1.47) 0.041 BRAF Mutation 1.58 (1.12–2.23) 0.012 HR: Hazard ratio. CI: Confidence interval. Model adjusted for age, ECOG status, tumor stage, site, surgery, and molecular profile. Proportional hazards assumption verified using Schoenfeld residuals. Discussion This age-stratified cohort study offers comprehensive insights into how CRC varies across the lifespan in clinical presentation, tumor biology, treatment patterns, and survival outcomes. By analyzing four predefined age groups, we identified consistent and clinically meaningful gradients that underscore the importance of age-adapted strategies in CRC management. Our cohort revealed distinct age-related patterns in body composition, with underweight status more prevalent among younger patients and overweight or obesity more common in older adults. These trends align with population-level evidence linking both persistent underweight in older individuals and significant weight gain—particularly into obesity—with increased CRC risk[ 12 ]. Together, these findings underscore the dual impact of nutritional extremes on CRC susceptibility across the age spectrum. Functional status, as measured by ECOG performance, declined with increasing age. This trend is consistent with findings from metastatic CRC cohorts, where older adults are more likely to present with impaired baseline function—an important factor influencing treatment intensity, tolerance, and overall outcomes[ 13 ]. A key finding was the inverse relationship between age and disease stage at diagnosis. Very early-onset CRC (VEOCRC) patients had the highest proportion of metastatic disease (61.4%), in line with U.S. registry data showing similarly advanced presentations in this age group [ 4 ]. Notably, our study also revealed distinct anatomical distributions: rectal cancers predominated among younger patients, whereas right-sided tumors were more frequent in the elderly. This contrasts with prior cross-sectional analyses that found no significant age-related differences in tumor location Popovici et al.,[ 14 ], suggesting that deeper age-tiered stratification may uncover clinically relevant anatomic trends. Consistent with earlier reports, the liver and lung were the most common metastatic sites across all age groups, while peritoneal involvement was notably more frequent in younger patients—an observation also documented in early-onset CRC populations[ 15 ], [ 16 ]. These patterns may reflect distinct metastatic biology in younger-onset disease. Histologic subtypes also varied with age. Mucinous, signet ring, and poorly differentiated tumors were more common among younger patients, reinforcing prior findings that early-onset CRC is enriched with aggressive histopathological features Aldriwesh et al.[ 17 ]. On the molecular level, BRAF mutation rates increased significantly with age, consistent with data showing that BRAF mutations are rarely detected in patients under 50 and become more prevalent in sporadic MSI tumors with advancing age[ 18 ]. While RAS mutation rates remained relatively stable across age groups in our cohort, this contrasts with the recognized role of RAS-driven pathways in aging and tumorigenesis. These findings suggest that while downstream signaling may influence age-related tumor biology, RAS mutational frequency itself may not be age-dependent in real-world settings[ 19 ]. Surgical intervention rates declined with age, consistent with prior findings indicating that older adults are more likely to undergo conservative procedures without significantly worse outcomes[ 20 ]. Similarly, the use of adjuvant chemotherapy decreased with age, reflecting a broader trend toward treatment de-intensification in elderly patients. In our cohort, younger patients were more likely to receive oxaliplatin-based adjuvant regimens (e.g., XELOX), whereas older adults more often received capecitabine monotherapy. Although clinical trials have demonstrated that fit elderly patients can derive meaningful benefit from adjuvant therapy, its real-world utilization remains limited likely due to concerns about tolerability, comorbidity, and life expectancy[ 21 ]. While all patients received first-line chemotherapy, subsequent treatment lines were progressively less utilized in older age groups, reflecting prior findings where therapy was often de-escalated beyond the first line, particularly among elderly patients or those with higher comorbidity burdens[ 22 ]. Our study demonstrated a stepwise decline in OS with advancing age, with markedly lower 5-year OS among older patients compared to their younger counterparts. This age-dependent survival gap is well-documented in large population-based datasets, including SEER analyses, where older adults consistently exhibit inferior relative survival—even after adjusting for tumor stage, site, and histology suggesting that factors such as frailty, comorbidity, and under-treatment contribute to poorer outcomes[ 4 ], [ 23 ]. Notably, in metastatic CRC subgroups, younger patients often present with more aggressive disease yet may paradoxically show improved survival, particularly in early-stage diagnosis, possibly due to better performance status and treatment tolerance[ 24 ], [ 25 ]. In contrast, real-world data confirm that elderly patients not only present with worse postoperative outcomes but also experience higher early mortality and reduced access to optimal therapy, further compounding the survival disparity[ 4 ], [ 26 ]. Our findings reinforce the need for age-sensitive treatment planning and underscore the complex interplay between biology, treatment intensity, and survivorship across the CRC continuum. In our cohort, PFS declined progressively with age, from 14.4 months in first-line to 3.6 months in third/fourth-line therapy among older adults. This trend reflects reduced treatment durability and highlights real-world challenges in managing older mCRC patients. Although existing literature focuses primarily on regimen-specific outcomes in refractory settings, meaningful parallels exist. Yang et al., and Wu et al., reported median PFS ranging from 3.0 to 6.3 months with chemo targeted agent combinations closely matching the later-line PFS seen in our older subgroup[ 27 ], [ 28 ]. Ciardiello et al., similarly reported a median PFS of 4.0 months with biologic rechallenge, with notably better outcomes in patients without liver metastases[ 29 ]. While differences in study designs and populations exist, our findings underscore that advancing age is independently associated with diminished PFS across treatment lines, likely reflecting the combined effects of frailty, comorbidities, and treatment de-intensification. These results support the relevance of PFS not only as a measure of therapeutic efficacy, but also as a surrogate marker of physiological resilience in age-stratified mCRC care. In our multivariable analysis, older age (≥ 65) remained an independent predictor of worse OS, consistent with findings from large-scale cohorts such as Liu et al., who reported a 48% increase in CRC-specific mortality among patients ≥ 80 years, emphasizing both age and treatment underutilization as key contributors to poor outcomes[ 30 ]. Poor performance status (ECOG ≥ 2) and absence of surgery were also independently associated with decreased OS, aligning with Kam et al., who identified ECOG status and extent of metastatic spread as pivotal survival determinants[ 31 ]. Right-sided tumor location in our cohort conferred worse prognosis, echoing Kamran et al., where left-sided primaries demonstrated superior OS across RAS subtypes, even after biologic therapy[ 32 ]. RAS and BRAF mutations were associated with poorer survival in our analysis. This is consistent with evidence highlighting RAS mutations as markers of resistance and aggressive tumor biology, and the well-established poor prognosis of BRAF-mutant CRC, despite recent advances in targeted therapy[ 33 ], [ 34 ]. Collectively, these findings reinforce the multifactorial nature of CRC prognosis, where age, tumor biology, and treatment accessibility interplay to shape survival outcomes. This study represents one of the most comprehensive single-institution, real-world analyses of colorectal cancer across distinct age groups, integrating detailed clinicopathologic, molecular, treatment, and survival data. Stratification by four age tiers allowed nuanced exploration of age-related heterogeneity. Standardized data collection, adherence to STROBE guidelines, and multivariable modeling enhance the methodological rigor and internal validity. However, the retrospective design introduces inherent limitations, including the potential for selection bias and residual confounding. Important geriatric domains such as frailty, cognitive function, and social support were not captured, which could have refined treatment interpretation in older adults. Additionally, the absence of comorbidity indices and patient-reported outcomes limits insight into patient-centered decision-making. Finally, as a single-center study conducted in a tertiary cancer facility, generalizability to broader or community-based populations may be limited. Our findings highlight the need for age-adapted, biology-informed CRC care. The disproportionate rate of metastatic and aggressive disease in younger patients suggests the need for earlier detection, intensified therapy, and molecular profiling. Conversely, the under-treatment and poorer survival observed in older adults call for greater integration of geriatric assessment tools and individualized therapeutic goals, rather than chronological age alone, to guide care. Molecular variations across age, particularly in BRAF and RAS mutations, support more personalized treatment strategies in different age strata. Prospective studies should incorporate molecular profiling, frailty indices, and quality-of-life measures to better define optimal treatment algorithms across age groups. The role of comorbidity-adjusted endpoints, real-time treatment tolerability metrics, and longitudinal geriatric assessments should be explored in elderly CRC cohorts. In younger patients, research should focus on screening strategies, hereditary syndromes, and response to intensified systemic therapies. Multicenter and population-based collaborations will be critical to validate our findings and shape age-stratified clinical guidelines. In conclusion, this age-stratified analysis underscores the clinical and biological heterogeneity of colorectal cancer across the lifespan. Younger patients often present with advanced, aggressive disease but demonstrate superior survival, likely due to better functional status and treatment intensity. In contrast, older patients, even when diagnosed earlier, experience poorer outcomes, driven by under-treatment, frailty, and adverse tumor biology. These findings advocate for a paradigm shift toward precision, age-sensitive oncology, where treatment decisions are tailored not only to tumor characteristics but also to patient age, function, and preferences. Such a shift is essential for equitable, effective CRC care across all age groups. Declarations Supplementary information: Not applicable. Ethics Statement The study protocol was approved by the Institutional Review Board of KAMC, Makkah, Saudi Arabia (IRB no. 21-796). The need for informed consent was waived because de-identified data was used. All procedures were performed in accordance with the principles outlined in the Declaration of Helsinki. Consent for publication : Not applicable. Data Access and Responsibility Emad Tashkandi had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Disclosure The author declares no conflicts of interest. Funding Statement : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors contribution: The research concept and protocol were developed by Hosam Alghanmi. Data collection was carried out by the co-authors M. H. Elsafty, A. Refaat , A. H. Almatari, and Ruqayya Azher. Hosam Alghanmi and E. Tashkandi were responsible for data cleaning and editing. Statistical analysis and the drafting of the results section were completed by D.K Mohrojy. The manuscript writing was divided among contributors, with Hosam Alghanmi composing the introduction, Khalid writing the methods section, and both Hosam Alghanmi and Emad contributing to the discussion. Emad also handled the language review and final manuscript editing. All authors have reviewed and approved the final version of the manuscript. 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Oncol. , vol. 26, no. 1, pp. 69–84, Jan. 2024, doi: 10.1007/S12094-023-03243-0/TABLES/3. D. Niedersüß-Beke et al. , “Clinical Effectiveness of Oncological Treatment in Metastatic Colorectal Cancer Is Independent of Comorbidities and Age,” Cancers (Basel). , vol. 13, no. 9, p. 2091, May 2021, doi: 10.3390/CANCERS13092091. S. Pilleron, D. R. Withrow, B. D. Nicholson, and E. J. A. Morris, “Age-related differences in colon and rectal cancer survival by stage, histology, and tumour site: An analysis of United States SEER-18 data,” Cancer Epidemiol. , vol. 84, p. 102363, Jun. 2023, doi: 10.1016/J.CANEP.2023.102363. E. Cheng et al. , “Analysis of Survival Among Adults With Early-Onset Colorectal Cancer in the National Cancer Database,” JAMA Netw. Open , vol. 4, no. 6, pp. e2112539–e2112539, Jun. 2021, doi: 10.1001/JAMANETWORKOPEN.2021.12539. B. Carbajal-López et al. , “Age as a Predictor of Overall Survival in Colorectal Cancer,” Diagnostics , vol. 14, no. 22, p. 2550, Nov. 2024, doi: 10.3390/DIAGNOSTICS14222550. C. Chen et al. , “Trends in 5-year cancer survival disparities by race and ethnicity in the US between 2002–2006 and 2015–2019,” Sci. Rep. , vol. 14, no. 1, Dec. 2024, doi: 10.1038/S41598-024-73617-Z,. F. Yang, Y. Mao, H. Huang, W. Luo, L. Liu, and W. Chen, “The efficacy and safety of regorafenib/fruquintinib combined with PD-1/PD-L1 for metastatic colorectal cancer: a meta-analysis based on single-arm studies,” Front. Immunol. , vol. 16, p. 1579293, May 2025, doi: 10.3389/FIMMU.2025.1579293/BIBTEX. C. Wu, S. Li, and X. Hou, “A real-world study: third-line treatment options for metastatic colorectal cancer,” Front. Oncol. , vol. 14, p. 1480704, Dec. 2024, doi: 10.3389/FONC.2024.1480704/BIBTEX. D. 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Shinozaki, “BRAF Mutation in Colorectal Cancers: From Prognostic Marker to Targetable Mutation,” Cancers (Basel). , vol. 12, no. 11, p. 3236, Nov. 2020, doi: 10.3390/CANCERS12113236. S. Yermekova, M. Orazgaliyeva, T. Goncharova, F. Rakhimbekova, D. Kaidarova, and O. Shatkovskaya, “Characteristic Mutational Damages in Gastric and Colorectal Adenocarcinomas,” Asian Pac. J. Cancer Prev. , vol. 24, no. 11, p. 3939, 2023, doi: 10.31557/APJCP.2023.24.11.3939. Additional Declarations No competing interests reported. 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Tashkandi","email":"","orcid":"","institution":"Umm Al-Qura University","correspondingAuthor":false,"prefix":"","firstName":"E.","middleName":"","lastName":"Tashkandi","suffix":""},{"id":502071755,"identity":"e8638c4f-822d-4c13-9f5f-307877a7044e","order_by":1,"name":"Hosam Ali Alghanmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACgwMQWoaNmfnggw8VEO6BB0Ro4WFjZ0s2nHHmAAMPSEsCMVoY+HnMpHnbIFoY8Go53v7w0w0GOx4+Zh5jY955d+TsxQ4/BNpiJ6fbgF2L/ZkzxtI5DMk8bMxshQ/nbntmzCOdZgDUkmxsdgCHLTdyGIBamIFamDcbvN12OLFHOgGk5UDiNpxa0h//zmGoB2phMJPgnQPSkv6BgJYEM6Ath4FaWMwkeRtAWnII2HLmjJl1jsFxkF+AgXzssDHP7ZyCAwkGePxyvP3x7ZyKajn5/sPAqKw5LMc+O33zhw8VdnK4tEA1EiEyCkbBKBgFo4AEAABGNF9x47loKAAAAABJRU5ErkJggg==","orcid":"","institution":"King Abdullah Medical City","correspondingAuthor":true,"prefix":"","firstName":"Hosam","middleName":"Ali","lastName":"Alghanmi","suffix":""},{"id":502071756,"identity":"e7ead7eb-62f3-4961-94c0-72eb8b8f0872","order_by":2,"name":"A. H. Almatari","email":"","orcid":"","institution":"King Abdullah Medical City","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"H.","lastName":"Almatari","suffix":""},{"id":502071757,"identity":"7be170a0-c711-4417-93cd-18499bef80a1","order_by":3,"name":"M. H. Elsafty","email":"","orcid":"","institution":"King Abdullah Medical City","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"H.","lastName":"Elsafty","suffix":""},{"id":502071758,"identity":"ae462d13-2efe-42b6-a0b1-0f1fd2308e4b","order_by":4,"name":"A. Refaat","email":"","orcid":"","institution":"King Abdullah Medical City","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"","lastName":"Refaat","suffix":""},{"id":502071759,"identity":"9857dea6-fa02-4ad3-8302-739f7e2e753a","order_by":5,"name":"Ruqayya Azher","email":"","orcid":"","institution":"Medical College, Umm Al-Qura University","correspondingAuthor":false,"prefix":"","firstName":"Ruqayya","middleName":"","lastName":"Azher","suffix":""},{"id":502071760,"identity":"f5064829-198b-453a-aa59-f6b4c423f8d6","order_by":6,"name":"D. K. Mohorjy","email":"","orcid":"","institution":"King Abdullah Medical City","correspondingAuthor":false,"prefix":"","firstName":"D.","middleName":"K.","lastName":"Mohorjy","suffix":""},{"id":502071761,"identity":"386ce7f1-908a-401f-ac03-2954885218ba","order_by":7,"name":"K. A. Naghi","email":"","orcid":"","institution":"Mansoura University Hospital School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"K.","middleName":"A.","lastName":"Naghi","suffix":""}],"badges":[],"createdAt":"2025-07-10 02:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7088094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7088094/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89392000,"identity":"3d035f68-44d2-494b-9f0b-c92b15223105","added_by":"auto","created_at":"2025-08-19 13:09:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1008367,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7088094/v1/1ea47a65-bb51-4364-b9f9-08f5e2bace59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-Stratified Insights in Colorectal Cancer: A Four-Tier Analysis of Presentation, Treatment, and Outcomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the third most commonly diagnosed malignancy and a leading cause of cancer-related mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While overall incidence and mortality have declined among older adults—largely due to widespread screening and treatment advances—CRC is increasingly being diagnosed in younger individuals, often at more advanced stages and with aggressive tumor biology[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This evolving epidemiology poses new challenges for early detection and age-tailored treatment strategies.\u003c/p\u003e\u003cp\u003eAge has emerged as a key determinant influencing CRC presentation, tumor characteristics, treatment decisions, and outcomes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Patients under 50 years (early-onset CRC) are more likely to present with left-sided or rectal tumors, adverse histology, and metastatic disease at diagnosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In contrast, older adults—who account for most CRC cases—tend to have right-sided tumors, higher comorbidity burdens, and more limited access to intensive therapies[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite growing recognition of these age-related disparities, real-world, age-specific data guiding optimal management remain scarce.\u003c/p\u003e\u003cp\u003eRecent international and regional trends highlight the need for age-stratified analyses to better understand CRC heterogeneity and improve outcomes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In Saudi Arabia, CRC is among the most common cancers, with a significant proportion of cases diagnosed at advanced stages despite increasing awareness and national screening efforts. Nearly one-third of patients present with metastatic disease, and survival rates remain modest relative to global benchmarks[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Barriers such as limited screening uptake, provider inaction, and cultural concerns contribute to delayed diagnosis—particularly in younger and rural populations[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo address this gap, we conducted a comprehensive, age-stratified analysis of CRC patients treated at a tertiary oncology center in Saudi Arabia. By classifying patients into four age groups (≤ 40, 41–50, 51–64, and ≥ 65 years), we examined variations in clinicopathologic features, treatment patterns, and survival outcomes, with the goal of informing more personalized, age-adapted management across the CRC care continuum.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eStudy Design and Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective observational cohort study was conducted at King Abdullah Medical City (KAMC), a tertiary referral oncology center in Makkah, Saudi Arabia. The study design adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for retrospective cohort investigations[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Ethical approval was obtained from the institutional review board, and all data were handled in accordance with institutional confidentiality protocols.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study included all adult patients aged 18 years and older who were diagnosed with histologically confirmed colorectal adenocarcinoma between January 1, 2015, and December 31, 2021. Patients were excluded if they had a non-adenocarcinoma histology, such as neuroendocrine tumors or lymphomas, if the colorectal involvement was secondary to another primary malignancy, or if diagnostic, treatment, or survival data were incomplete. Eligible patients were identified through institutional cancer registries and electronic health records, with final inclusion confirmed through manual verification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAge Stratification\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients were stratified into four predefined age groups based on age at initial diagnosis. The groups included those aged 40 years or younger, representing very early-onset colorectal cancer (VEOCRC); those aged 41 to 50 years, categorized as early-onset CRC (EOCRC); individuals aged 51 to 64 years, representing mid-aged patients; and those aged 65 years and older, classified as elderly. This stratification approach was selected to capture clinically meaningful differences across life stages in tumor biology, treatment patterns, and outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection and Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eClinical data were extracted from electronic medical records using a structured data abstraction form. Data collection was carried out by trained research coordinators under the supervision of the principal investigator. All personally identifying information was removed prior to analysis to ensure compliance with privacy regulations.\u003c/p\u003e\u003cp\u003e The collected variables included demographic details such as age at diagnosis, sex, and body mass index (BMI), categorized according to World Health Organization guidelines. Performance status at diagnosis was recorded using the Eastern Cooperative Oncology Group (ECOG) scale and dichotomized as 0–1 versus ≥ 2. Tumor-related characteristics included primary tumor location (right colon, left colon, or rectum), American Joint Committee on Cancer (AJCC) 8th edition stage at presentation, histologic subtype, and molecular profiling, specifically RAS and BRAF mutation status when available. Treatment data encompassed surgical resection, use of adjuvant chemotherapy, and systemic therapy across first, second, third, and fourth-line regimens. Outcome variables included overall survival (OS), defined as the time from diagnosis to death or last follow-up, and progression-free survival (PFS), and defined as the time from initiation of systemic treatment to radiologic progression or death.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using IBM Statistics (version 25) and Microsoft Excel. with independent validation performed by a consulting biostatistician. Categorical variables were summarized using frequencies and percentages, and comparisons across age groups were made using Chi-square or Fisher’s exact tests, as appropriate. Continuous variables were expressed as medians with interquartile ranges and compared using the Kruskal–Wallis test due to non-normal distributions.\u003c/p\u003e\u003cp\u003eSurvival analyses for OS and PFS were performed using the Kaplan–Meier method, and differences among age groups were assessed using the log-rank test. Overall survival was calculated from the date of diagnosis, while PFS was calculated from the start of systemic treatment for each respective line of therapy. Outliers, including patients with survival durations exceeding 10 years, were examined separately in sensitivity analyses.\u003c/p\u003e\u003cp\u003eTo assess the independent effect of age and other clinical factors on survival, multivariable analysis was performed using a Cox proportional hazards regression model. Covariates included age group, ECOG status, tumor stage, primary site, and receipt of systemic treatment. Results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). The proportional hazards assumption was verified using Schoenfeld residuals.\u003c/p\u003e\u003cp\u003eMissing data were handled based on the extent of missingness. Variables with less than 10% missing data were analyzed using complete-case methods. For variables with more than 10% missingness, multiple imputation by chained equations (MICE) was employed to reduce potential bias and improve analytic robustness. All statistical tests were two-sided, and a p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 761 patients were stratified into four age groups: \u0026le;40 (n=132), 41\u0026ndash;50 (n=135), 51\u0026ndash;64 (n=205), and \u0026ge;65 years (n=289). The mean age within each subgroup aligned with expected stratification boundaries (36.2, 46.2, 57.1, and 72.8 years, respectively). The male predominance was consistent across all age groups, with no significant difference in sex distribution (p=0.312).\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Body mass index (BMI) categories varied significantly by age (p=0.039), with younger patients more likely to be underweight, and older patients more likely to be overweight or obese. Functional status, as measured by ECOG performance, showed a statistically significant age-related decline (p\u0026lt;0.001), with 86.4% of patients \u0026le;40 years having ECOG 0\u0026ndash;1 versus only 71.3% in the \u0026ge;65 group (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline Demographics by Age Group\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;40\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=132)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u0026ndash;50 (n=135)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51\u0026ndash;64\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=205)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;65\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=289)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e36.2 \u0026plusmn; 3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e46.2 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e57.1 \u0026plusmn; 3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e72.8 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Male\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e80 (60.6%)\u003c/p\u003e\n \u003cp\u003e52 (39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75 (55.6%)\u003c/p\u003e\n \u003cp\u003e60 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e128 (62.4%)\u003c/p\u003e\n \u003cp\u003e77 (37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e165 (57.1%)\u003c/p\u003e\n \u003cp\u003e124 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI Categories\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Underweight (\u0026lt;18.5)\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Healthy (18.5\u0026ndash;24.9)\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Overweight (25\u0026ndash;29.9)\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Obese (\u0026ge;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12 (9.1%)\u003c/p\u003e\n \u003cp\u003e53 (40.2%)\u003c/p\u003e\n \u003cp\u003e42 (31.8%)\u003c/p\u003e\n \u003cp\u003e25 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (6.7%)\u003c/p\u003e\n \u003cp\u003e51 (37.8%)\u003c/p\u003e\n \u003cp\u003e49 (36.3%)\u003c/p\u003e\n \u003cp\u003e26 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6 (2.9%)\u003c/p\u003e\n \u003cp\u003e70 (34.1%)\u003c/p\u003e\n \u003cp\u003e81 (39.5%)\u003c/p\u003e\n \u003cp\u003e48 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (3.5%)\u003c/p\u003e\n \u003cp\u003e96 (33.2%)\u003c/p\u003e\n \u003cp\u003e111 (38.4%)\u003c/p\u003e\n \u003cp\u003e72 (24.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG Status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; ECOG 0\u0026ndash;1\u003c/p\u003e\n \u003cp\u003e\u0026ndash; ECOG \u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e114 (86.4%)\u003c/p\u003e\n \u003cp\u003e18 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e114 (84.4%)\u003c/p\u003e\n \u003cp\u003e21 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e166 (81.0%)\u003c/p\u003e\n \u003cp\u003e39 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e206 (71.3%)\u003c/p\u003e\n \u003cp\u003e83 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBMI categorized per WHO standards. ECOG: Eastern Cooperative Oncology Group performance status. P-values calculated using Chi-square or Fisher\u0026rsquo;s exact tests, as appropriate.\u003c/p\u003e\n\u003cp\u003eTumor stage at diagnosis was inversely associated with age (p=0.009). Stage IV disease was more frequent among the youngest cohort (61.4%) and decreased progressively with age, reaching 46.9% in the \u0026ge;65 group. Conversely, early-stage diagnoses (Stage I\u0026ndash;II) were more common in older patients. Primary tumor location showed a notable age gradient (p\u0026lt;0.001). Rectal cancers were predominant in younger patients (57.6% in \u0026le;40), while right-sided tumors were significantly more common in the elderly (37.3% in \u0026ge;65 vs 11.4% in \u0026le;40).\u003cbr\u003e\u0026nbsp;The metastatic pattern also varied with age (p=0.017). Liver and lung were the most frequent metastatic sites across all groups, but peritoneal involvement was disproportionately higher in younger patients (18.9% vs 11.4% in \u0026ge;65). Histologic subtype also differed by age (p=0.020), with mucinous and signet ring histologies being more prevalent in younger patients.\u003cbr\u003e\u0026nbsp;BRAF mutation rates increased significantly with age (2.3% in \u0026le;40 vs 9.7% in \u0026ge;65, p=0.038), while RAS mutation rates were relatively stable across groups. Median CEA levels were highest in older patients (13.5 ng/mL in \u0026ge;65) and lowest in the \u0026le;40 group (6.8 ng/mL), with a statistically significant difference (p=0.004) (Table2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eTumor and Molecular Characteristics by Age Group\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;40\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=132)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u0026ndash;50\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=135)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51\u0026ndash;64\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=205)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;65\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=289)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage Group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Local (I\u0026ndash;III)\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Metastatic (IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38.6%\u003c/p\u003e\n \u003cp\u003e61.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42.2%\u003c/p\u003e\n \u003cp\u003e57.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48.3%\u003c/p\u003e\n \u003cp\u003e51.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53.1%\u003c/p\u003e\n \u003cp\u003e46.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage at Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Stage I\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Stage II\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Stage III\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Stage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003cp\u003e10.6%\u003c/p\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003cp\u003e61.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003cp\u003e12.6%\u003c/p\u003e\n \u003cp\u003e22.2%\u003c/p\u003e\n \u003cp\u003e57.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003cp\u003e14.1%\u003c/p\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003cp\u003e51.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14.9%\u003c/p\u003e\n \u003cp\u003e18.1%\u003c/p\u003e\n \u003cp\u003e20.1%\u003c/p\u003e\n \u003cp\u003e46.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Tumor Site\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Right colon\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Left colon\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Rectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.4%\u003c/p\u003e\n \u003cp\u003e31.1%\u003c/p\u003e\n \u003cp\u003e57.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.2%\u003c/p\u003e\n \u003cp\u003e35.6%\u003c/p\u003e\n \u003cp\u003e46.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25.4%\u003c/p\u003e\n \u003cp\u003e39.0%\u003c/p\u003e\n \u003cp\u003e35.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37.3%\u003c/p\u003e\n \u003cp\u003e35.0%\u003c/p\u003e\n \u003cp\u003e27.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite of Metastases\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Liver\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Lung\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Peritoneum\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Other (e.g., brain, bone)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49.2%\u003c/p\u003e\n \u003cp\u003e34.1%\u003c/p\u003e\n \u003cp\u003e18.9%\u003c/p\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46.3%\u003c/p\u003e\n \u003cp\u003e31.1%\u003c/p\u003e\n \u003cp\u003e15.6%\u003c/p\u003e\n \u003cp\u003e8.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.8%\u003c/p\u003e\n \u003cp\u003e27.3%\u003c/p\u003e\n \u003cp\u003e14.6%\u003c/p\u003e\n \u003cp\u003e10.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41.6%\u003c/p\u003e\n \u003cp\u003e24.6%\u003c/p\u003e\n \u003cp\u003e11.4%\u003c/p\u003e\n \u003cp\u003e12.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistologic Type\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Adenocarcinoma\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Mucinous\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Signet Ring\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Other (e.g., mixed type)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86.4%\u003c/p\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e83.5%\u003c/p\u003e\n \u003cp\u003e11.1%\u003c/p\u003e\n \u003cp\u003e5.4%\u003c/p\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e82.0%\u003c/p\u003e\n \u003cp\u003e13.7%\u003c/p\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e80.7%\u003c/p\u003e\n \u003cp\u003e14.6%\u003c/p\u003e\n \u003cp\u003e4.7%\u003c/p\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular Profile\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; RAS Mutation (%)\u003c/p\u003e\n \u003cp\u003e\u0026ndash; BRAF Mutation (%)\u003c/p\u003e\n \u003cp\u003e\u0026ndash; CEA (Median, IQR ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.5%\u003c/p\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003cp\u003e6.8 (2.9\u0026ndash;21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42.8%\u003c/p\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003cp\u003e7.9 (3.2\u0026ndash;24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50.7%\u003c/p\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003cp\u003e11.2 (4.1\u0026ndash;33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51.6%\u003c/p\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003cp\u003e13.5 (5.5\u0026ndash;39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ePrimary tumor site classified anatomically. Molecular data include RAS and BRAF mutation status when available. CEA: Carcinoembryonic antigen. P-values derived using Chi-square and Kruskal\u0026ndash;Wallis tests.\u003c/p\u003e\n\u003cp\u003eSurgical intervention rates declined with age (85.6% in \u0026le;40 vs 74.7% in \u0026ge;65, p=0.018). Similarly, adjuvant chemotherapy usage decreased across age groups (77.3% in \u0026le;40 vs 58.4% in \u0026ge;65, p\u0026lt;0.001). The choice of adjuvant regimen differed slightly, with younger patients more likely to receive XELOX and older patients showing increased use of capecitabine monotherapy (p=0.041).Systemic therapy exposure declined with advancing age. While all patients received first-line chemotherapy, the proportions receiving second-, third-, and fourth-line therapies declined significantly in older age groups (p\u0026lt;0.001 for all). Notably, only 7.6% of patients \u0026ge;65 received fourth-line therapy compared to 21.2% in the \u0026le;40 group (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eSystemic Treatment Exposure by Age Group\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;40\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=132)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u0026ndash;50\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=135)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51\u0026ndash;64\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=205)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;65\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=289)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Yes\u003c/p\u003e\n \u003cp\u003e\u0026ndash; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e113 (85.6%)\u003c/p\u003e\n \u003cp\u003e19 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e111 (82.2%)\u003c/p\u003e\n \u003cp\u003e24 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e163 (79.5%)\u003c/p\u003e\n \u003cp\u003e42 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e216 (74.7%)\u003c/p\u003e\n \u003cp\u003e73 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjuvant Treatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Yes\u003c/p\u003e\n \u003cp\u003e\u0026ndash; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e102 (77.3%)\u003c/p\u003e\n \u003cp\u003e30 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98 (72.6%)\u003c/p\u003e\n \u003cp\u003e37 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e137 (66.8%)\u003c/p\u003e\n \u003cp\u003e68 (33.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e169 (58.4%)\u003c/p\u003e\n \u003cp\u003e120 (41.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjuvant Regimen\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; XELOX\u003c/p\u003e\n \u003cp\u003e\u0026ndash; Capecitabine\u003c/p\u003e\n \u003cp\u003e\u0026ndash; FOLFOX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81 (61.4%)\u003c/p\u003e\n \u003cp\u003e24 (18.2%)\u003c/p\u003e\n \u003cp\u003e27 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e79 (58.5%)\u003c/p\u003e\n \u003cp\u003e27 (20.0%)\u003c/p\u003e\n \u003cp\u003e29 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e113 (55.1%)\u003c/p\u003e\n \u003cp\u003e52 (25.4%)\u003c/p\u003e\n \u003cp\u003e40 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e143 (49.5%)\u003c/p\u003e\n \u003cp\u003e81 (28.0%)\u003c/p\u003e\n \u003cp\u003e65 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystemic Chemotherapy Usage\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1st-line therapy received\u003c/p\u003e\n \u003cp\u003e2nd-line therapy received\u003c/p\u003e\n \u003cp\u003e3rd-line therapy received\u003c/p\u003e\n \u003cp\u003e4th-line therapy received\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e132 (100%)\u003c/p\u003e\n \u003cp\u003e95 (72.0%)\u003c/p\u003e\n \u003cp\u003e56 (42.4%)\u003c/p\u003e\n \u003cp\u003e28 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e135 (100%)\u003c/p\u003e\n \u003cp\u003e88 (65.2%)\u003c/p\u003e\n \u003cp\u003e52 (38.5%)\u003c/p\u003e\n \u003cp\u003e25 (18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e205 (100%)\u003c/p\u003e\n \u003cp\u003e121 (59.0%)\u003c/p\u003e\n \u003cp\u003e70 (34.1%)\u003c/p\u003e\n \u003cp\u003e27 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e289 (100%)\u003c/p\u003e\n \u003cp\u003e142 (49.3%)\u003c/p\u003e\n \u003cp\u003e75 (25.8%)\u003c/p\u003e\n \u003cp\u003e22 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSystemic therapies reflect real-world usage across multiple treatment lines. Adjuvant regimens include XELOX (capecitabine + oxaliplatin), capecitabine monotherapy, and FOLFOX (5-FU + oxaliplatin). All p-values two-sided.\u003c/p\u003e\n\u003cp\u003eMedian overall survival (OS) declined progressively with age (38.2 months in \u0026le;40 vs 24.8 months in \u0026ge;65; p\u0026lt;0.001). Similarly, 5-year OS rates dropped from 56.1% in the youngest group to 36.8% in the oldest. Kaplan\u0026ndash;Meier estimates revealed a significant stepwise decline in survival at 12, 36, and 60 months across age groups (p-values ranging from 0.018 to \u0026lt;0.001). Progression-free survival (PFS) followed a similar trend. Median first-line PFS decreased from 14.4 months in \u0026le;40 to 10.5 months in \u0026ge;65 (p=0.012). This pattern persisted for second-line (9.1 vs 5.9 months, p=0.026) and third-/fourth-line therapies (6.4 vs 3.6 months, p=0.037) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Survival and Progression-Free Outcomes by Age Group\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;40 (n=132)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41\u0026ndash;50 (n=135)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51\u0026ndash;64 (n=205)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;65 (n=289)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian Overall Survival (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e35.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5-year Overall Survival (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e56.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e52.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e48.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e36.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS by Age Group (KM Estimate)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ndash; 12-month survival rate (%)\u003c/p\u003e\n \u003cp\u003e\u0026ndash; 36-month survival rate (%)\u003c/p\u003e\n \u003cp\u003e\u0026ndash; 60-month survival rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90.2%\u003c/p\u003e\n \u003cp\u003e68.9%\u003c/p\u003e\n \u003cp\u003e56.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e87.4%\u003c/p\u003e\n \u003cp\u003e63.3%\u003c/p\u003e\n \u003cp\u003e52.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e83.1%\u003c/p\u003e\n \u003cp\u003e58.0%\u003c/p\u003e\n \u003cp\u003e48.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e76.9%\u003c/p\u003e\n \u003cp\u003e45.1%\u003c/p\u003e\n \u003cp\u003e36.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian PFS \u0026ndash; 1st-line (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian PFS \u0026ndash; 2nd-line (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian PFS \u0026ndash; 3rd/4th-line (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOS: Overall survival. PFS: Progression-free survival. KM: Kaplan\u0026ndash;Meier estimate. Survival calculated from diagnosis (OS) or treatment start (PFS). P-values based on log-rank test.\u003c/p\u003e\n\u003cp\u003eIn Cox regression analysis, age \u0026ge;65 was independently associated with worse overall survival compared to patients \u0026le;40 (HR 1.48; 95% CI, 1.21\u0026ndash;1.81; p\u0026lt;0.001). Other independent negative prognostic factors included ECOG \u0026ge;2 (HR 1.91), metastatic disease (HR 2.63), right-sided primary (HR 1.37), absence of surgery (HR 1.72), RAS mutation (HR 1.22), and BRAF mutation (HR 1.58) (all p\u0026lt;0.05) (Table5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariable Cox Proportional Hazards Regression for Overall Survival\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"510\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eAge \u0026ge; 65 vs \u0026le;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.48 (1.21\u0026ndash;1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eECOG \u0026ge; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.91 (1.55\u0026ndash;2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eStage IV vs I\u0026ndash;III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e2.63 (2.15\u0026ndash;3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eRight-sided vs left-sided\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.37 (1.11\u0026ndash;1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eNo surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.72 (1.38\u0026ndash;2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eRAS Mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.22 (1.01\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 194px;\"\u003e\n \u003cp\u003eBRAF Mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e1.58 (1.12\u0026ndash;2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHR: Hazard ratio. CI: Confidence interval. Model adjusted for age, ECOG status, tumor stage, site, surgery, and molecular profile. Proportional hazards assumption verified using Schoenfeld residuals.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis age-stratified cohort study offers comprehensive insights into how CRC varies across the lifespan in clinical presentation, tumor biology, treatment patterns, and survival outcomes. By analyzing four predefined age groups, we identified consistent and clinically meaningful gradients that underscore the importance of age-adapted strategies in CRC management.\u003c/p\u003e\u003cp\u003eOur cohort revealed distinct age-related patterns in body composition, with underweight status more prevalent among younger patients and overweight or obesity more common in older adults. These trends align with population-level evidence linking both persistent underweight in older individuals and significant weight gain\u0026mdash;particularly into obesity\u0026mdash;with increased CRC risk[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Together, these findings underscore the dual impact of nutritional extremes on CRC susceptibility across the age spectrum.\u003c/p\u003e\u003cp\u003eFunctional status, as measured by ECOG performance, declined with increasing age. This trend is consistent with findings from metastatic CRC cohorts, where older adults are more likely to present with impaired baseline function\u0026mdash;an important factor influencing treatment intensity, tolerance, and overall outcomes[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA key finding was the inverse relationship between age and disease stage at diagnosis. Very early-onset CRC (VEOCRC) patients had the highest proportion of metastatic disease (61.4%), in line with U.S. registry data showing similarly advanced presentations in this age group [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, our study also revealed distinct anatomical distributions: rectal cancers predominated among younger patients, whereas right-sided tumors were more frequent in the elderly. This contrasts with prior cross-sectional analyses that found no significant age-related differences in tumor location Popovici et al.,[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], suggesting that deeper age-tiered stratification may uncover clinically relevant anatomic trends.\u003c/p\u003e\u003cp\u003eConsistent with earlier reports, the liver and lung were the most common metastatic sites across all age groups, while peritoneal involvement was notably more frequent in younger patients\u0026mdash;an observation also documented in early-onset CRC populations[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These patterns may reflect distinct metastatic biology in younger-onset disease.\u003c/p\u003e\u003cp\u003eHistologic subtypes also varied with age. Mucinous, signet ring, and poorly differentiated tumors were more common among younger patients, reinforcing prior findings that early-onset CRC is enriched with aggressive histopathological features Aldriwesh et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. On the molecular level, BRAF mutation rates increased significantly with age, consistent with data showing that BRAF mutations are rarely detected in patients under 50 and become more prevalent in sporadic MSI tumors with advancing age[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile RAS mutation rates remained relatively stable across age groups in our cohort, this contrasts with the recognized role of RAS-driven pathways in aging and tumorigenesis. These findings suggest that while downstream signaling may influence age-related tumor biology, RAS mutational frequency itself may not be age-dependent in real-world settings[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSurgical intervention rates declined with age, consistent with prior findings indicating that older adults are more likely to undergo conservative procedures without significantly worse outcomes[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, the use of adjuvant chemotherapy decreased with age, reflecting a broader trend toward treatment de-intensification in elderly patients.\u003c/p\u003e\u003cp\u003eIn our cohort, younger patients were more likely to receive oxaliplatin-based adjuvant regimens (e.g., XELOX), whereas older adults more often received capecitabine monotherapy. Although clinical trials have demonstrated that fit elderly patients can derive meaningful benefit from adjuvant therapy, its real-world utilization remains limited likely due to concerns about tolerability, comorbidity, and life expectancy[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile all patients received first-line chemotherapy, subsequent treatment lines were progressively less utilized in older age groups, reflecting prior findings where therapy was often de-escalated beyond the first line, particularly among elderly patients or those with higher comorbidity burdens[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study demonstrated a stepwise decline in OS with advancing age, with markedly lower 5-year OS among older patients compared to their younger counterparts. This age-dependent survival gap is well-documented in large population-based datasets, including SEER analyses, where older adults consistently exhibit inferior relative survival\u0026mdash;even after adjusting for tumor stage, site, and histology suggesting that factors such as frailty, comorbidity, and under-treatment contribute to poorer outcomes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Notably, in metastatic CRC subgroups, younger patients often present with more aggressive disease yet may paradoxically show improved survival, particularly in early-stage diagnosis, possibly due to better performance status and treatment tolerance[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, real-world data confirm that elderly patients not only present with worse postoperative outcomes but also experience higher early mortality and reduced access to optimal therapy, further compounding the survival disparity[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our findings reinforce the need for age-sensitive treatment planning and underscore the complex interplay between biology, treatment intensity, and survivorship across the CRC continuum.\u003c/p\u003e\u003cp\u003eIn our cohort, PFS declined progressively with age, from 14.4 months in first-line to 3.6 months in third/fourth-line therapy among older adults. This trend reflects reduced treatment durability and highlights real-world challenges in managing older mCRC patients. Although existing literature focuses primarily on regimen-specific outcomes in refractory settings, meaningful parallels exist. Yang et al., and Wu et al., reported median PFS ranging from 3.0 to 6.3 months with chemo targeted agent combinations closely matching the later-line PFS seen in our older subgroup[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Ciardiello et al., similarly reported a median PFS of 4.0 months with biologic rechallenge, with notably better outcomes in patients without liver metastases[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. While differences in study designs and populations exist, our findings underscore that advancing age is independently associated with diminished PFS across treatment lines, likely reflecting the combined effects of frailty, comorbidities, and treatment de-intensification. These results support the relevance of PFS not only as a measure of therapeutic efficacy, but also as a surrogate marker of physiological resilience in age-stratified mCRC care.\u003c/p\u003e\u003cp\u003eIn our multivariable analysis, older age (\u0026ge;\u0026thinsp;65) remained an independent predictor of worse OS, consistent with findings from large-scale cohorts such as Liu et al., who reported a 48% increase in CRC-specific mortality among patients\u0026thinsp;\u0026ge;\u0026thinsp;80 years, emphasizing both age and treatment underutilization as key contributors to poor outcomes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Poor performance status (ECOG\u0026thinsp;\u0026ge;\u0026thinsp;2) and absence of surgery were also independently associated with decreased OS, aligning with Kam et al., who identified ECOG status and extent of metastatic spread as pivotal survival determinants[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Right-sided tumor location in our cohort conferred worse prognosis, echoing Kamran et al., where left-sided primaries demonstrated superior OS across RAS subtypes, even after biologic therapy[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. RAS and BRAF mutations were associated with poorer survival in our analysis. This is consistent with evidence highlighting RAS mutations as markers of resistance and aggressive tumor biology, and the well-established poor prognosis of BRAF-mutant CRC, despite recent advances in targeted therapy[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Collectively, these findings reinforce the multifactorial nature of CRC prognosis, where age, tumor biology, and treatment accessibility interplay to shape survival outcomes.\u003c/p\u003e\u003cp\u003eThis study represents one of the most comprehensive single-institution, real-world analyses of colorectal cancer across distinct age groups, integrating detailed clinicopathologic, molecular, treatment, and survival data. Stratification by four age tiers allowed nuanced exploration of age-related heterogeneity. Standardized data collection, adherence to STROBE guidelines, and multivariable modeling enhance the methodological rigor and internal validity.\u003c/p\u003e\u003cp\u003eHowever, the retrospective design introduces inherent limitations, including the potential for selection bias and residual confounding. Important geriatric domains such as frailty, cognitive function, and social support were not captured, which could have refined treatment interpretation in older adults. Additionally, the absence of comorbidity indices and patient-reported outcomes limits insight into patient-centered decision-making. Finally, as a single-center study conducted in a tertiary cancer facility, generalizability to broader or community-based populations may be limited.\u003c/p\u003e\u003cp\u003eOur findings highlight the need for age-adapted, biology-informed CRC care. The disproportionate rate of metastatic and aggressive disease in younger patients suggests the need for earlier detection, intensified therapy, and molecular profiling. Conversely, the under-treatment and poorer survival observed in older adults call for greater integration of geriatric assessment tools and individualized therapeutic goals, rather than chronological age alone, to guide care. Molecular variations across age, particularly in BRAF and RAS mutations, support more personalized treatment strategies in different age strata.\u003c/p\u003e\u003cp\u003eProspective studies should incorporate molecular profiling, frailty indices, and quality-of-life measures to better define optimal treatment algorithms across age groups. The role of comorbidity-adjusted endpoints, real-time treatment tolerability metrics, and longitudinal geriatric assessments should be explored in elderly CRC cohorts. In younger patients, research should focus on screening strategies, hereditary syndromes, and response to intensified systemic therapies. Multicenter and population-based collaborations will be critical to validate our findings and shape age-stratified clinical guidelines.\u003c/p\u003e\u003cp\u003eIn conclusion, this age-stratified analysis underscores the clinical and biological heterogeneity of colorectal cancer across the lifespan. Younger patients often present with advanced, aggressive disease but demonstrate superior survival, likely due to better functional status and treatment intensity. In contrast, older patients, even when diagnosed earlier, experience poorer outcomes, driven by under-treatment, frailty, and adverse tumor biology. These findings advocate for a paradigm shift toward precision, age-sensitive oncology, where treatment decisions are tailored not only to tumor characteristics but also to patient age, function, and preferences. Such a shift is essential for equitable, effective CRC care across all age groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of KAMC, Makkah, Saudi Arabia (IRB no. 21-796). The need for informed consent was waived because de-identified data was used. All procedures were performed in accordance with the principles outlined in the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent \u0026nbsp;for publication :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Access and Responsibility\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmad Tashkandi had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research concept and protocol were developed by Hosam Alghanmi. Data collection was carried out by the co-authors\u0026nbsp;M. H. Elsafty, A. Refaat ,\u0026nbsp;A. H. Almatari, and\u0026nbsp;Ruqayya Azher. Hosam Alghanmi and\u0026nbsp;E. Tashkandi were responsible for data cleaning and editing. Statistical analysis and the drafting of the results section were completed by D.K Mohrojy. The manuscript writing was divided among contributors, with Hosam Alghanmi composing the introduction, Khalid writing the methods section, and both Hosam Alghanmi and Emad contributing to the discussion. Emad also handled the language review and final manuscript editing. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Cancer Center at King Abdullah Medical City (KAMC), Makkah, for providing access to the clinical database and institutional support essential to the conduct of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eR. L. Siegel Mph \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Colorectal cancer statistics, 2023,\u0026rdquo; \u003cem\u003eCA. Cancer J. Clin.\u003c/em\u003e, vol. 73, no. 3, pp. 233\u0026ndash;254, May 2023, doi: 10.3322/CAAC.21772.\u003c/li\u003e\n \u003cli\u003eG. Patel and P. Patil, \u0026ldquo;Worrisome Trends in Young-Onset Colorectal Cancer: Now Is the Time for Action,\u0026rdquo; \u003cem\u003eIndian J. Surg. 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Kamran \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Primary tumor sidedness is an independent prognostic marker for survival in metastatic colorectal cancer: Results from a large retrospective cohort with mutational analysis,\u0026rdquo; \u003cem\u003eCancer Med.\u003c/em\u003e, vol. 7, no. 7, pp. 2934\u0026ndash;2942, Jul. 2018, doi: 10.1002/CAM4.1558,.\u003c/li\u003e\n \u003cli\u003eI. Nakayama, T. Hirota, and E. Shinozaki, \u0026ldquo;BRAF Mutation in Colorectal Cancers: From Prognostic Marker to Targetable Mutation,\u0026rdquo; \u003cem\u003eCancers (Basel).\u003c/em\u003e, vol. 12, no. 11, p. 3236, Nov. 2020, doi: 10.3390/CANCERS12113236.\u003c/li\u003e\n \u003cli\u003eS. Yermekova, M. Orazgaliyeva, T. Goncharova, F. Rakhimbekova, D. Kaidarova, and O. Shatkovskaya, \u0026ldquo;Characteristic Mutational Damages in Gastric and Colorectal Adenocarcinomas,\u0026rdquo; \u003cem\u003eAsian Pac. J. Cancer Prev.\u003c/em\u003e, vol. 24, no. 11, p. 3939, 2023, doi: 10.31557/APJCP.2023.24.11.3939.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Age-stratified analysis, Early-onset CRC, Elderly, Survival outcomes, Treatment disparities, Saudi Arabia","lastPublishedDoi":"10.21203/rs.3.rs-7088094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7088094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eColorectal cancer (CRC) exhibits significant age-related heterogeneity in tumor biology, clinical presentation, and treatment response. However, real-world, age-stratified data from the Middle East remain limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study of 761 patients with histologically confirmed colorectal adenocarcinoma treated at a tertiary cancer center in Saudi Arabia between 2015 and 2021. Patients were stratified into four age groups (\u0026le;\u0026thinsp;40, 41\u0026ndash;50, 51\u0026ndash;64, \u0026ge;\u0026thinsp;65 years). Clinicopathologic features, treatment patterns, and survival outcomes were compared using Kaplan\u0026ndash;Meier and Cox regression analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eYounger patients (\u0026le;\u0026thinsp;40) were more likely to present with metastatic disease (61.4%), rectal primaries (57.6%), mucinous/signet ring histology, and peritoneal spread. Older patients (\u0026ge;\u0026thinsp;65) exhibited a higher prevalence of right-sided tumors (37.3%), BRAF mutations (9.7%), and functional impairment. Treatment intensity declined significantly with age, with older adults receiving fewer surgeries, adjuvant therapies, and later-line systemic regimens. Despite more aggressive disease at diagnosis, younger patients achieved superior median overall survival (38.2 vs. 24.8 months) and progression-free survival across all therapy lines. In multivariable analysis, age\u0026thinsp;\u0026ge;\u0026thinsp;65, ECOG\u0026thinsp;\u0026ge;\u0026thinsp;2, stage IV disease, right-sided location, absence of surgery, and BRAF mutation independently predicted worse survival.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study highlights stark age-related disparities in CRC presentation, molecular profile, treatment delivery, and outcomes. Younger patients benefit from intensive therapy despite biologically aggressive disease, whereas older adults remain under-treated and experience poorer survival. These findings support the need for age-adapted, biology-informed CRC care and underscore the importance of integrating geriatric and molecular assessment into clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Age-Stratified Insights in Colorectal Cancer: A Four-Tier Analysis of Presentation, Treatment, and Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 13:01:40","doi":"10.21203/rs.3.rs-7088094/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-27T18:07:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T01:37:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61285321927208173605028910348969025249","date":"2025-08-21T09:17:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T19:54:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125149683041372381807257720900151563271","date":"2025-08-19T11:52:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48088545724217634745760854940654557888","date":"2025-08-11T14:26:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T14:10:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-17T01:12:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T04:42:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T04:41:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-07-10T02:12:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9302059d-783e-4f2e-a3d2-4b15c4583aeb","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-19T13:01:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 13:01:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7088094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7088094","identity":"rs-7088094","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}