A prognostic nomogram of non-small cell lung cancer based on tumor marker inflammatory nutrition score.

BACKGROUND

Patients diagnosed with non-small cell lung cancer (NSCLC) usually have a poor prognosis, so it is critical to identify effective biomarkers for prognosis prediction. The aim of this study is to establish a nomogram to evaluate the prognostic significance of blood markers in patients with NSCLC and provide reference for clinical work.

METHODS

A total of 486 patients with NSCLC who were admitted to hospital from January 2009 to December 2019 were retrospectively analyzed. The cohort was divided into a training set (n=340) and a validation set (n=146). Eleven blood indicators were selected as prognostic parameters by the least absolute shrinkage and selection operator (LASSO) model to establish tumor marker inflammatory nutrition (TMIN) score. Univariate and multivariate regression analyses were performed to establish a TMIN-nomogram model for predicting overall survival (OS) and progression-free survival (PFS). Receiver operating characteristic (ROC) survival curve, calibration curve and clinical decision curve analysis (DCA) were used to evaluate the predictive performance of the TMIN-nomogram model.

RESULTS

The TMIN score were constructed for 11 of the most valuable prognostic variables, including white blood cells (WBCs), neutrophils (N), platelets (PLT), albumin (ALB), globulin (GLB), prealbumin (PAB), carcinoembryonic antigen (CEA), cytokeratin fragment 21-1 (CYFRA21-1), fibrinogen (FIB), platelet/lymphocyte ratio (PLR), and lymphocyte/monocyte ratio (LMR), and patients were divided into low-risk and high-risk groups using optimal cutovers. The TMIN score showed good predictive value for both OS and PFS. In addition, The TMIN score and sex, smoke, pathological classification, American Joint Committee on Cancer stage (AJCC stage), tumor diameter and Eastern Cooperative Oncology Group-performance status (ECOG-PS) and other clinical indicators showed a strong correlation. Univariate and multivariate analyses confirmed that TMIN score was an independent risk factor for OS and PFS in NSCLC patients. It is worth noting that the TMIN nomogram model of OS and PFS based on multivariate analysis combined with TMIN score has very good prognostic value for NSCLC patients.

CONCLUSIONS

TMIN is a promising predictor for PFS and OS in NSCLC patients. The TMIN-nomogram prediction model can be used as an effective tool for the comprehensive prognosis evaluation of NSCLC patients.