Preoperative CA19-9 and GGT ratio as a prognostic indicator in ampullary carcinoma

Glutathione‑degrading enzymes in the complex landscape of tumors (Review)

Glutathione (GSH)‑degrading enzymes are essential for starting the first stages of GSH degradation. These enzymes include extracellular γ‑glutamyl transpeptidase (GGT) and intracellular GSH‑specific γ‑glutamylcyclotransferase 1 (ChaC1) and 2. These enzymes are essential for cellular activities, such as immune response, differentiation, proliferation, homeostasis regulation and programmed cell death. Tumor tissue frequently exhibits abnormal expression of GSH‑degrading enzymes, which has a key impact on the development and spread of malignancies. The present review summarizes gene and protein structure, catalytic activity and regulation of GSH‑degrading enzymes, their vital roles in tumor development (including regulation of oxidative and endoplasmic reticulum stress, control of programmed cell death, promotion of inflammation and tumorigenesis and modulation of drug resistance in tumor cells) and potential role as diagnostic biomarkers and therapeutic targets.

Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma

OBJECTIVE

To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it.

METHODS

Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group.

RESULTS

The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model.

CONCLUSION

The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.