The International journal of biological markers · 2024
Application of a nomogram from coagulation-related biomarkers and C1q and total bile acids in distinguishing advanced and early-stage lung cancer.
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Long Tingting, Zhu Xinyu, Tang Dongling, Li Huan, Zhang Pingan
Аннотация
BACKGROUND: This study aimed to establish a nomogram to distinguish advanced- and early-stage lung cancer based on coagulation-related biomarkers and liver-related biomarkers.
METHODS: A total of 306 patients with lung cancer and 172 patients with benign pulmonary disease were enrolled. Subgroup analyses based on histologic type, clinical stage, and neoplasm metastasis status were carried out and multivariable logistic regression analysis was applied. Furthermore, a nomogram model was developed and validated with bootstrap resampling.
RESULTS: The concentrations of complement C1q, fibrinogen, and D-dimers, fibronectin, inorganic phosphate, and prealbumin were significantly changed in lung cancer patients compared to benign pulmonary disease patients. Multiple regression analysis based on subgroup analysis of clinical stage showed that compared with early-stage lung cancer, female (P < 0.001), asymptomatic admission (P = 0.001), and total bile acids (P = 0.011) were negatively related to advanced lung cancer, while C1q (P = 0.038), fibrinogen (P < 0.001), and D-dimers (P = 0.001) were positively related. A nomogram model based on gender, symptom, and the levels of total bile acids, C1q, fibrinogen, and D-dimers was constructed for distinguishing advanced lung cancer and early-stage lung cancer, with an area under the receiver operating characteristic curve of 0.919. The calibration curve for this nomogram revealed good predictive accuracy (P-Hosmer-Lemeshow = 0.697) between the predicted probability and the actual probability.
CONCLUSIONS: We developed a nomogram based on gender, symptom, and the levels of fibrinogen, D-dimers, total bile acids, and C1q that can individually distinguish early- and advanced-stage lung cancer.
METHODS: A total of 306 patients with lung cancer and 172 patients with benign pulmonary disease were enrolled. Subgroup analyses based on histologic type, clinical stage, and neoplasm metastasis status were carried out and multivariable logistic regression analysis was applied. Furthermore, a nomogram model was developed and validated with bootstrap resampling.
RESULTS: The concentrations of complement C1q, fibrinogen, and D-dimers, fibronectin, inorganic phosphate, and prealbumin were significantly changed in lung cancer patients compared to benign pulmonary disease patients. Multiple regression analysis based on subgroup analysis of clinical stage showed that compared with early-stage lung cancer, female (P < 0.001), asymptomatic admission (P = 0.001), and total bile acids (P = 0.011) were negatively related to advanced lung cancer, while C1q (P = 0.038), fibrinogen (P < 0.001), and D-dimers (P = 0.001) were positively related. A nomogram model based on gender, symptom, and the levels of total bile acids, C1q, fibrinogen, and D-dimers was constructed for distinguishing advanced lung cancer and early-stage lung cancer, with an area under the receiver operating characteristic curve of 0.919. The calibration curve for this nomogram revealed good predictive accuracy (P-Hosmer-Lemeshow = 0.697) between the predicted probability and the actual probability.
CONCLUSIONS: We developed a nomogram based on gender, symptom, and the levels of fibrinogen, D-dimers, total bile acids, and C1q that can individually distinguish early- and advanced-stage lung cancer.