Original Article
Does selected immunological panel possess the value of predicting the prognosis of early-stage resectable non-small cell lung cancer?
Abstract
Background: The immune status in the tumor micro-environment of lung cancer is highly informative, and expression status of some biomarkers in the tumor micro-environment may have deep connected statistical characteristics which could indicate immune status and predict related clinical outcomes. In this study, a panel of 9 immune biomarkers are selected for their possible predictive value of clinical outcomes in early-stage resectable non-small cell lung cancer (NSCLC).
Methods: We introduced a new way of interpreting the expression of Immunohistochemistry-based biomarkers in predicting the prognosis of patients. Immunohistochemistry were performed on surgical samples from 139 patients with NSCLC. Cox regression and subgroup analysis were conducted for the screening of biomarkers that were significant in survival and prognosis. Afterwards, Principle Component Analysis was conducted on screened biomarkers in order to extract characteristic root, which were utilized by an Artificial Neuron Network (ANN) for the prediction of clinical outcomes.
Results: Six out of 9 biomarkers were considered significant and were selected for the data analysis. The KMO-Bartlett’s Sphericity test was valid (0.658>0.5, P=0.0001). The principle component regression results indicated that the survival was associated with the principle component Z4 (Y=0.316×Z4+2.298, R=0.189, P=0.026). Also, regression-free survival was associated with the principle component Z4 and Z2 (Y=0.314×Z4+0.255×Z2+2.061, R=0.249, P=0.013). Principle components ranking (PCrank) was calculated and after the determination of a cutoff of 0.2, the intergroup comparison of subgrouphigh (PCrank value above 0.2) and subgrouplow (PCrank value below 0.2) was significant in overall survival (OS) (P=0.025). A database search validated our results. Afterwards, the ANN model successfully predicted the clinical outcome, with accuracies of 94.1% and 96.2% respectively in models 1 and 2.
Conclusions: The selected immunological panels have promising potential for predicting the prognosis of resectable NSCLC.
Methods: We introduced a new way of interpreting the expression of Immunohistochemistry-based biomarkers in predicting the prognosis of patients. Immunohistochemistry were performed on surgical samples from 139 patients with NSCLC. Cox regression and subgroup analysis were conducted for the screening of biomarkers that were significant in survival and prognosis. Afterwards, Principle Component Analysis was conducted on screened biomarkers in order to extract characteristic root, which were utilized by an Artificial Neuron Network (ANN) for the prediction of clinical outcomes.
Results: Six out of 9 biomarkers were considered significant and were selected for the data analysis. The KMO-Bartlett’s Sphericity test was valid (0.658>0.5, P=0.0001). The principle component regression results indicated that the survival was associated with the principle component Z4 (Y=0.316×Z4+2.298, R=0.189, P=0.026). Also, regression-free survival was associated with the principle component Z4 and Z2 (Y=0.314×Z4+0.255×Z2+2.061, R=0.249, P=0.013). Principle components ranking (PCrank) was calculated and after the determination of a cutoff of 0.2, the intergroup comparison of subgrouphigh (PCrank value above 0.2) and subgrouplow (PCrank value below 0.2) was significant in overall survival (OS) (P=0.025). A database search validated our results. Afterwards, the ANN model successfully predicted the clinical outcome, with accuracies of 94.1% and 96.2% respectively in models 1 and 2.
Conclusions: The selected immunological panels have promising potential for predicting the prognosis of resectable NSCLC.
