Prognostic value of dynamic changes of pre- and post-operative tumor markers in colorectal cancer

Male Aged, 80 and over Adult CA-19-9 Antigen Middle Aged Prognosis Carcinoembryonic Antigen Survival Rate Nomograms CA-125 Antigen Preoperative Period Biomarkers, Tumor Humans Female Postoperative Period Colorectal Neoplasms Retrospective Studies Aged
DOI: 10.1007/s12094-024-03429-0 Publication Date: 2024-03-07T21:12:48Z
ABSTRACT
Colorectal cancer (CRC) prognosis assessment is vital for personalized treatment plans. This study investigates the prognostic value of dynamic changes of tumor markers CEA, CA19-9, CA125, and AFP before and after surgery and constructs prediction models based on these indicators.A retrospective clinical study of 2599 CRC patients who underwent radical surgery was conducted. Patients were randomly divided into training (70%) and validation (30%) datasets. Univariate and multivariate Cox regression analyses identified independent prognostic factors, and nomograms were constructed.A total of 2599 CRC patients were included in the study. Patients were divided into training (70%, n = 1819) and validation (30%, n = 780) sets. Univariate and multivariate Cox regression analyses identified age, total number of resected lymph nodes, T stage, N stage, the preoperative and postoperative changes in the levels of CEA, CA19-9, and CA125 as independent prognostic factors. When their postoperative levels are normal, patients with elevated preoperative levels have significantly worse overall survival. However, when the postoperative levels of CEA/CA19-9/CA125 are elevated, whether their preoperative levels are elevated or not has no significance for prognosis. Two nomogram models were developed, and Model I, which included CEA, CA19-9, and CA125 groups, demonstrated the best performance in both training and validation sets.This study highlights the significant predictive value of dynamic changes in tumor markers CEA, CA19-9, and CA125 before and after CRC surgery. Incorporating these markers into a nomogram prediction model improves prognostic accuracy, enabling clinicians to better assess patients' conditions and develop personalized treatment plans.
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