Lung cancer accounts for one-quarter of all cancer deaths around the world. The identification of novel biomarkers from blood to differentiate tumors from normal tissue and to predict tumor behavior and patients’ survival is of great importance in clinical practice. We aimed to establish a nomogram with patients’ characteristics and all available hematological biomarkers for lung cancer patients.
All indexes were cataloged according to clinical significance. Principle component analysis (PCA) was used to reduce the dimensions. Each component was transformed into categorical variables based on recognized cut-off values from receiver operating characteristic (ROC) curve. Kaplan-Meier analysis with log-rank test was used to evaluate the prognostic value of each component. Multivariate analysis was used to determine the promising prognostic biomarkers. Five components were entered into a predictive nomogram. The model was subjected to bootstrap internal validation and to external validation with a separate cohort from Shandong Cancer Hospital. The predictive accuracy and discriminative ability were measured by concordance index (C index) and risk group stratification.
Two hundred and fourty-eight patients were retrospectively analyzed in this study, with 134 in the Discovery Group and 114 in the Validation Group. Forty-seven indexes were sorted into 8 subgroups, and 20 principle components were extracted for further survival analysis. Via cox regression analysis, five components were significant and entered into predictive nomograms. The calibration curves for probability of 3-, and 5-year overall survival (OS) showed optimal agreement between nomogram prediction and actual observation. The new scoring system according to nomogram allowed significant distinction between survival curves within respective tumor-node-metastasis (TNM) subgroups.
A nomogram based on the clinical indexes was established for survival prediction of lung cancer patients, which can be used for treatment therapy selection and clinical care option. PCA makes big data analysis feasible.
Clinical trial identification
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All authors have declared no conflicts of interest.