International journal of general medicine · 2021
The Prognostic Value of Biomarkers on Detecting Non-Small Cell Lung Cancer in a Chinese Elderly Population.
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Guo Lianghua, Song Bin, Xiao Jianhong, Lin Hui, Chen Junhua, Su Xianghua
Аннотация
BACKGROUND: 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.
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.