| Objective To investigate the value of regression models of bone metabolism markers for predicting and diagnosing bone metastasis of lung cancer.To provide laboratory evidence for early intervention in the treatment of lung cancer with bone metastases.Methods Non-metastasis lung cancer patients,,lung cancer patients with bone metastasis,benign disease control cases were investigated from July 2012 to October 2015,which including lung cancer without metastasis group(lung cancer without metastasis group,103 cases);lung cancer with bone metastasis group(lung cancer bone metastasis group,128 cases).At the same time,108 patients with no tumor and bone metabolism related diseases were selected as control group(control group).The diagnosis of lung cancer was confirmed by histopathological report,medical imaging evidence or the pathology of the needle sample.Detection and analysis of each group of type Ⅰ collagen carboxy terminal peptide sequence specific beta(beta-CTX),type Ⅰ procollagen(TPINP),N terminal fragment molecule Osteocalcin(N-MID),parathyroid hormone(PTH),vitamin D(VitD3),alkaline phosphatase(ALP),calcium(CA),Phosphorus(P),cytokeratin 19(F211)and other indicators of the differences.To establish a variety of combinatorial models,ROC curve was drawn to predict and diagnose the performance of lung cancer bone metastasis.Results1.The levels of β-CTX,TPINP and ALP in lung cancer bone metastasis group were significantly higher than those in control group(P<0.05).CA was significantly lower than the control group(P<0.05).2.There were significant differences in F211,β-CTX,TPINP and ALP between bone metastasis group and lung cancer without metastasis group(P<0.05).The area of AUC of TPINP was 0.712.TPINP had a sensitivity of 60.9%and a specificity of 78.4%for diagnosis of bone metastasis.3.Establishment of regression model of bone metabolic markers in lung cancer and comparison of diagnostic.There were significant differences(P<0.05)in the results of combination B × T,combination 1,combination 2 and combination 3.The positive predictive value of the combination model 3 was 74.0%and the negative predictive value was 68.0%.4.The AUC of combination 4 was 0.856,the sensitivity was 70.0%,the specificity was 91.0%,the positive predictive value was 82.5%,and the negative predictive value was 72.0%.5.The relationship between bone metabolism markers and metastasis of lung cancer:the number of bone metastases in lung cancer.The levels of ALP,F211,β-CTX,VITD3,TPINP in patients with bone metastases>4 were significantly higher than those in patients with bone metastases≤4.The difference was statistically significant(P<0.05).6.B/T in bone metastasis of lung cancer represents the trend of bone resorption and bone formation and interaction,bone metastasis 0-1 month group(B/T)was significantly higher than other groups,were statistically significant(P<0.05).7.The combination of the three models of lung cancer patients early prediction of bone metastases,the combination of 4 positive predictive accuracy rate of 83.3%;negative predictive accuracy rate of 81.1%;total prediction accuracy of 81.9%.Conclusion1.The levels of β-CTX and TPINP were significantly higher in patients with lung cancer bone metastasis and those without lung cancer metastasis.2.From a single indicator,the AUC of TPINP was 0.712,which was the best for the diagnosis of bone metastasis of lung cancer.In combination model,the AUC of combination 4 was 0.856,which was the best diagnostic characteristic for lung cancer bone metastasis.3.the best diagnostic characteristic of lung cancer was bone metastasis.The AUC of combination 4 was 0.856,The combination model showed that the combination model 4 was the best,the positive predictive accuracy of the lung cancer bone metastasis was 83.3%,the negative predictive accuracy was 81.1%,and the total prediction accuracy was 81.9%. |
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