The Efficacy of Different Inflammatory Markers for the Prognosis of Patients with Malignant Tumors.

BACKGROUND

Inflammation is considered essential in cancer progression, as it affects the nutritional status and prognosis of patients. In this study, we aim to analyze the efficacy of various inflammatory markers in predicting prognosis in cancer patients.

METHODS

Patients with malignant tumor were included as primary and validation cohort. Basic clinical information, anthropometric indicators, body composition analysis, and serological indicators were recorded. After proposing the optimal thresholds by time-dependent receiver operating characteristic (ROC), univariate and multivariate Cox regression analyses were performed to analyze the association between inflammatory markers and overall survival (OS). A nomogram was established to develop a scored-inflammatory marker system. Eight inflammatory models based on combinations of inflammatory markers were assessed. Cox regression analysis was used to analyze the relationship of each inflammatory model and mortality of participants. Then, subanalysis of specific tumor types was conducted by Cox regression. Logistic regression models were used to analyze the relationship between different inflammatory models and malnutrition.

RESULTS

Univariate and multivariate Cox regression analyses indicated that pack-years of cigarette smoking, C-reactive protein (CRP), and systemic immune-inflammation index (SII) were related to the OS of cancer patients. A nomogram was constructed to develop a scored-inflammatory marker system. Among the eight inflammatory models, patients in model A had worst prognosis compared with patients in other models. Subanalysis next showed lung cancer, breast cancer and digestive system neoplasms patients in model A suffered the worst prognosis. Logistic regression indicated that model A was also with predictive value for malnutrition.

CONCLUSION

A scored-inflammatory marker system was established to predict the OS of cancer patients. The inflammatory models established in this study can be used to predict prognosis, as well as cancer-related malnutrition. Inflammatory model A suffered the worst OS and was with the predictive efficacy for malnutrition.