Prognostic significance of preoperative C-reactive protein-triglyceride-glucose index in long-term outcomes after radical gastrectomy for gastric cancer

Abstract

BACKGROUND

Gastric cancer, a globally prevalent malignant tumor, continues to exhibit high incidence and mortality rates. Although radical gastrectomy remains the primary treatment for this disease, postoperative complications frequently arise, negatively impacting short-term recovery and significantly reducing patients’ quality of life. In this context, accurately predicting the risk of postoperative recurrence and metastasis, coupled with targeted interventions, could substantially improve patient outcomes. The C-reactive protein-triglyceride-glucose index (CTI), a composite biomarker that integrates metabolic disturbances and systemic inflammation, has garnered increasing attention in oncology. The prognostic nutritional index (PNI), a composite measure based on serum albumin and peripheral blood lymphocyte count, is used to evaluate both the nutritional status and systemic immune function of patients. In recent years, both the CTI and PNI have demonstrated significant prognostic value in predicting tumor outcomes, assessing treatment responses, and formulating personalized treatment strategies.

AIM

To investigate whether the combined inflammation and insulin resistance marker, the CTI, can serve as a prognostic indicator for patients undergoing radical gastrectomy for gastric cancer. Additionally, it seeks to develop a predictive model by incorporating the PNI alongside CTI.

METHODS

This retrospective study included a total of 300 patients who underwent radical gastrectomy. The patients were classified into high and low CTI groups based on their CTI index. Cox proportional hazards regression analysis was performed to identify independent prognostic factors influencing overall survival (OS) and disease-free survival (DFS), and two nomogram models were developed.

RESULTS

Of the included patients, 131 had a high CTI and 169 had a low CTI. The DFS period of the low CTI group was significantly longer than that of the high CTI group. The number of postoperative adjuvant treatments, T stage, N stage, CTI, and PNI were identified as independent prognostic factors for DFS. The hazard ratio for CTI was 2.07 (95% confidence interval: 1.36-3.17, P < 0.001). In terms of OS, the OS period of the low CTI group was significantly longer than that of the high CTI group. Whether adjuvant treatment was administered, T stage, CTI, and PNI were independent prognostic factors for OS. The hazard ratio for CTI was 2.47 (95% confidence interval: 1.44-4.23, P = 0.001). The nomogram models for OS and DFS further emphasized the importance of CTI as a key predictor of patient prognosis.

CONCLUSION

CTI is a long-term prognostic indicator for the outcome of radical gastrectomy for gastric cancer. Patients with lower CTI values have a better prognosis. The prediction models constructed by combining CTI with PNI has good predictive ability for DFS and OS after radical gastrectomy.

Keywords: C-reactive protein triglyceride glucose index; Prognostic nutritional index; Gastric cancer; Prognostic model; Radical gastrectomy

Core Tip: Gastric cancer is the third leading cause of cancer-related mortality worldwide. Radical surgery remains the primary treatment for early-stage gastric cancer, but the overall survival rate post-surgery remains relatively low. Identifying key prognostic factors is essential to optimize treatment strategies. The C-reactive protein-triglyceride-glucose (CTI) index, which integrates both inflammatory and metabolic markers, has garnered increasing attention in cancer research, particularly in gastric cancer prognosis. However, few studies have explored the impact of CTI on cancer patient outcomes. Therefore, we conducted a retrospective clinical study to examine the influence of CTI on the prognosis of patients undergoing radical gastrectomy for gastric cancer and developed a corresponding predictive model. Additionally, we incorporated the prognostic nutritional index coefficient to improve the predictive accuracy of the model.