Abstract

To analyze the prognostic significance of plasma total cholesterol (TC) and high-density lipoprotein (HDL) in patients with gastric cancer receiving oxaliplatin combination with S-1 (SOX) chemotherapy after radical resection and to establish models of relevant prognostic influencing factors. The clinicopathologic features of 301 patients treated with SOX after radical gastrectomy were retrospectively analyzed. Univariate analysis, multivariate analysis, and the Kaplan-Meier survival curve were used to analyze the prognostic value of TC and HDL in patients undergoing adjuvant SOX chemotherapy after curative gastric surgery. Based on the results of multivariate Cox regression, we constructed nomograms to predict 1 and 3-year CSS (cancer-specific survival time) and DFS (disease-free survival time) in patients with adjuvant chemotherapy after radical gastrectomy. We assessed the model's accuracy using the consistency index (C index) and calibration curve, and the ROC curve and DCA curve were applied to compare it with TNM staging. Multivariate analysis revealed that TC and HDL were independent influencing factors of CSS, while HDL was an independent factor unique to DFS. According to Kaplan-Meier curves, low TC and HDL suggested poor survival (P < 0.001). The relevant prognostic factors from the multivariate study were used to build nomograms for DFS and CSS. Both DFS and CSS had C index and AUC values that were higher than 0.71. The calibration curves demonstrated that the predicted results are consistent with the observed results. The AUC valves for DFS and CSS in our models were higher than TNM staging. The decision curve analysis indicated that net benefits were moderately positive. Significant differences in survival were seen between the high and low-risk groups according to the nomogram risk score. TC and HDL have a certain significance for the prognosis of patients with gastric cancer after radical resection receiving adjuvant SOX chemotherapy. Lower TC and HDL suggested poor DFS and CSS. Both prediction models for CSS and DFS demonstrated good predictive ability and had a higher predictive value than the TNM staging system.

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