| Summary: | Lianghua Guo,1 Bin Song,1 Jianhong Xiao,1 Hui Lin,1 Junhua Chen,1 Xianghua Su2 1Department of Respiratory Medicine, Mindong Hospital of Fujian Medical University, Fuan City, 355000, People’s Republic of China; 2Department of Neurosurgery, Mindong Hospital of Fujian Medical University, Fuan City, 355000, People’s Republic of ChinaCorrespondence: Xianghua SuDepartment of Respiratory Medicine, Mindong Hospital of Fujian Medical University, No. 89 Heshan Road, Fuan City, 355000, Fujian Province, People’s Republic of ChinaEmail Suxianghua505@126.comBackground: Survival in non-small cell lung cancer (NSCLC) remains poor. Early detection of NSCLC is of great significance to provide a chance to improve survival.Aim: We constructed predictive models to evaluate the predictive value of four tumor biomarkers by including serum human epididymis protein 4 (HE4), carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCCA), and cytokeratin 19 fragment (CY21-1) on detecting NSCLC in a Chinese elderly population.Methods: A total of 363 patients with NSCLC and 433 subjects without cancer (healthy control group) were admitted to the respiratory department in our hospital. We assessed serum levels of these four tumor biomarkers in the two groups and then the predictive value of predictive models was evaluated.Results: Serum median values of HE4 (143.3 pmol/L), CEA (4.60 ng/mL), SCCA (1.52 ng/mL), and CY21-1 (5.36 ng/mL) in patients with NSCLC were significantly higher than the healthy control group, respectively (all P< 0.05). A multivariate logistic regression model showed that HE4 (OR=2.10, 95% CI=1.22– 4.42, P=0.013), CEA (OR=2.30, 95% CI=1.44– 4.53, P=0.004), SCCA (OR=2.20, 95% CI=1.29– 4.55, P=0.011), and CY21-1 (OR=2.60, 95% CI=1.56– 6.25, P< 0.001) were significantly and independently associated with increased risk of NSCLC on admission after confounding factors were corrected. Importantly, the ROC curve suggested HE4 had a good value on predicting NSCLC in the Chinese elderly population. Additionally, the predictive model (CEA+SCCA+CY21-1+HE4) had better idea capability to detecting the existence of NSCLC (AUC=0.954, 95% CI=0.927– 0.999, P< 0.001).Conclusion: Our study showed that several lung cancer-related biomarkers were used to build prediction models, which has good value for early prediction of NSCLC.Keywords: NSCLC, HE4, CEA, SCCA, CY21-1, Chinese elderly population, predictive value
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