A prognostic nomogram integrating carcinoembryonic antigen levels for predicting overall survival in elderly patients with stage II–III colorectal cancer

Abstract

Background With the aging of the population, colorectal surgeons will have to face more elderly colorectal cancer (CRC) patients in the future. We aim to analyze independent risk factors affecting overall survival in elderly (age ≥65 years) patients with stage II–III CRC and construct a nomogram to predict patient survival. Methods A total of 3,016 elderly CRC patients with stage II–III were obtained from the SEER database. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analyses were used to screen independent prognostic factors, and a survival prediction nomogram was constructed based on the results. The consistency index (C-index), decision curve analysis (DCA), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to compare the predictive ability between the nomogram and tumor-node-metastasis (TNM) stage system. All patients were classified into high-risk and low-risk groups based on risk scores calculated by nomogram. The Kaplan-Meier method was used to compare the survival differences between two groups. Results The 3- and 5-year area under the curve (AUC) values of the prediction nomogram model were 76.6% and 74.8%, respectively. The AIC, BIC, and C-index values of the nomogram model were 6,032.502, 15,728.72, and 0.707, respectively, which were better than the TNM staging system. Kaplan-Meier survival analysis showed a significant survival difference between high-risk and low-risk groups (P<0.0001). Conclusions We constructed a prediction nomogram for stage II–III elderly CRC patients by combining pre-treatment carcinoembryonic antigen (CEA) levels, which can accurately predict patient survival. This facilitates clinicians to accurately assess patient prognosis and identify high-risk patients to adopt more aggressive and effective treatment strategies.

Relationships of SIGLEC family-related lncRNAs with clinical prognosis and tumor immune microenvironment in ovarian cancer

“…SIGLEC family-related lncRNAs with prognostic value were selected out based on the screening criteria p < 0.05 using univariate Cox regression analysis. Then, we further narrow the prognostic related SIGLEC family-related lncRNAs using A least absolute shrinkage and selection operator (LASSO) Cox regression and forward stepwise method, where the shrinkage and selection of variables were achieved through a logistic regression model with LASSO penalties, and λ-values were determined through iterative cross-validation 18 . Finally, we used multivariate Cox regression analysis to conduct a risk model and a riskscore for each OC patients were calculated according to the formula:…”