Haemagglutinin mutations and glycosylation changes shaped the 2012/13 influenza A(H3N2) epidemic, Houston, Texas.

BACKGROUND

Accurate prognosis prediction is crucial for managing non-small cell lung cancer (NSCLC). Tumor heterogeneity limits the predictive power of traditional tumor lymph node metastasis (TNM) staging alone. The interplay between inflammation, immunity, and nutrition in the tumor microenvironment offers a potential source of novel prognostic biomarkers.

METHODS

This multicenter retrospective study enrolled 473 stage I-IIIA NSCLC patients who underwent radical resection. An inflammatory-immune-nutritional integrated scoring system (IINS) was developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression from preoperative hematological parameters. The model was validated internally (n = 142) and externally using the NHANES database (n = 134). A prognostic nomogram incorporating IINS and clinicopathological factors was constructed and evaluated using time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

RESULTS

The IINS was formulated based on platelet-to-lymphocyte ratio (PLR), systemic inflammatory response index (SIRI), and prognostic nutritional index (PNI). Patients were stratified into high- and low-IINS groups using an optimal cutoff of 4.9. Multivariate analysis identified IINS as an independent prognostic factor for both overall survival (OS) (HR = 1.63, 95% CI [1.17-2.26], P = 0.004) and disease-free survival (DFS) (HR = 1.41, 95% CI [1.02-1.97], P = 0.039). The nomogram demonstrated good predictive accuracy, with 1-, 3-, and 5-year area under the curves (AUCs) for OS ranging from 0.695 to 0.730 in the validation cohort. DCA confirmed the nomogram's superior clinical utility compared to the TNM staging system.

CONCLUSIONS

The novel IINS, integrating inflammatory, immune, and nutritional status, is a robust independent prognostic indicator for resected NSCLC. The developed nomogram provides a user-friendly and accurate tool for predicting postoperative survival, potentially aiding in risk stratification and personalized adjuvant therapy decisions.