Objective: This study aimed to use the Surveillance Epidemiology and End Results (SEER) database to investigate the incidence and associated factors of early-onset colorectal cancer (EO-CRC), construct a nomogram based on prognostic-related variables to predict the risk of liver metastasis in EO-CRC, predict the overall survival (OS), and guide individualized treatment, to help manage EO-CRC and improve survival.
Methods: Data regarding patients diagnosed with CRC between 2010 and 2016 were retrieved from the SEER database, and the incidence rates of different age groups, genders, and distant metastases (bone, brain, liver, and lung) after age standardization were analyzed and calculated. We selected patients with EO-CRC for further study and randomly divided them according to a 7:3 ratio for the training and validation cohorts. The validation cohort was used for the internal verification. Logistic regression analyses were used to examine the risk factors of liver metastasis. Multivariate analysis was used to construct a nomogram to predict the risk of liver metastasis in EO-CRC. Cox regression analysis identified statistically significant variables related to prognosis to construct a nomogram to predict the OS of EO-CRC patients. The nomogram’s performance was estimated by the receiver operating characteristic (ROC) curve and calibration curve. The Kaplan-Meier method was used to classify patients into high-risk and low-risk groups according to the optimal cutoff of the prognosis (PI). Risk stratification effectively avoids the survival paradox.
Results: The incidence of CRC decreased annually from 2010-2016 and increased with age, continuing to rise from 35 years old. The incidence of CRC according to gender and distant metastasis is stable, and the incidence in men is higher than in women. The most common distant metastatic organ is the liver. Logistic regression analysis revealed that the grade, N stage, treatment (surgery, radiotherapy, chemotherapy), bone metastasis, CEA, tumor deposits, and perineural invasion were significantly related to liver metastasis of EO-CRC. The optimal cutoff, specificity, and sensitivity of the total score of the risk nomogram for liver metastasis of EO-CRC in the training cohort were -1.627, 0.801, and 0.754, respectively. The validation cohort's optimal cutoff, specificity, and sensitivity were -1.903, 0.763, and 0.763, respectively. ROC curves showed good discrimination in the training cohort (area under the curve [AUC] 0.848) and validation cohort (AUC 0.839). Cox regression analysis revealed that race, primary tumor location, grade, sex, NM stage, primary tumor resection, chemotherapy, tumor size, distant metastasis (bone, liver, lung), CEA, tumor deposits, and perineural invasion were independent prognostic factors for OS in patients with EO-CRC. The 1-, 3-, and 5-year OS AUCs were 0.845, 0.838, and 0.816 in the training cohort and 0.854, 0.839, and 0.815 in the validation cohort, respectively. Using the optimal cutoff of the prognosis, all patients were stratified into high-risk and low-risk groups, and the Kaplan-Meier curve indicated that patients with higher risk had worse survival outcomes. The calibration curves exhibited good consistency between predicted and actual survival rates.
Conclusions: This study analyzes the relevant epidemiological information and clinicopathological and molecular characteristics of EO-CRC and uses a nomogram to stratify the risk of patients with EO-CRC, which will help clinicians manage patients and formulate more precise individualized treatment strategies.