Department of Clinical Laboratory, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
World J Surg Oncol. 2021 Jan 24;19(1):27. doi: 10.1186/s12957-021-02141-5.
The aim of this study was to establish a regression equation model of serum bone metabolism markers. We analyzed the diagnostic value of bone metastases in lung cancer and provided laboratory evidence for the early clinical treatment of bone metastases in lung cancer.
A total of 339 patients with non-metastatic lung cancer, patients with lung cancer with bone metastasis, and patients with benign lung disease who were treated in our hospital from July 2012 to October 2015 were included. A total of 103 patients with lung cancer in the non-metastatic group, 128 patients with lung cancer combined with bone metastasis group, and 108 patients with benign lung diseases who had nontumor and nonbone metabolism-related diseases were selected as the control group. Detection and analysis of type I collagen carboxyl terminal peptide β-special sequence (β-CTX), total type I procollagen amino terminal propeptide (TPINP), N-terminal-mid fragment of osteocalcin (N-MID), parathyroid hormone (PTH), vitamin D (VitD3), alkaline phosphatase (ALP), calcium (CA), phosphorus (P), cytokeratin 19 fragment (F211), and other indicators were performed. Four multiple regression models were established to determine the best diagnostic model for lung cancer with bone metastasis.
Analysis of single indicators of bone metabolism markers in lung cancer was performed, among which F211, β-CTX, TPINP, and ALP were significantly different (P < 0.05). The ROC curve of each indicator was less than 0.712. Based on the multiple regression models, the fourth model was the best and was much better than a single indicator with an AUC of 0.856, a sensitivity of 70.0%, a specificity of 91.0%, a positive predictive value of 82.5%, and a negative predictive value of 72.0%.
Multiple regression models of bone metabolism markers were established. These models can be used to evaluate the progression of lung cancer and provide a basis for the early treatment of bone metastases.
本研究旨在建立血清骨代谢标志物的回归方程模型。我们分析了肺癌骨转移的诊断价值，为肺癌骨转移的早期临床治疗提供了实验室依据。
选取 2012 年 7 月至 2015 年 10 月在我院治疗的非转移性肺癌患者、肺癌合并骨转移患者和良性肺部疾病患者 339 例。非转移性肺癌组 103 例，肺癌合并骨转移组 128 例，良性肺部疾病组 108 例，均无肿瘤及非骨代谢相关疾病。检测和分析 1 型胶原羧基末端肽β特殊序列（β-CTX）、总 1 型前胶原氨基末端前肽（TPINP）、骨钙素 N 端中段片段（N-MID）、甲状旁腺激素（PTH）、维生素 D（VitD3）、碱性磷酸酶（ALP）、钙（CA）、磷（P）、细胞角蛋白 19 片段（F211）等指标。建立四个多元回归模型，确定最佳诊断肺癌伴骨转移的模型。
对肺癌患者骨代谢标志物的单项指标进行分析，其中 F211、β-CTX、TPINP、ALP 差异均有统计学意义（P<0.05）。各指标的 ROC 曲线下面积均小于 0.712。基于多元回归模型，第四模型为最佳模型，明显优于单一指标，AUC 为 0.856，灵敏度为 70.0%，特异度为 91.0%，阳性预测值为 82.5%，阴性预测值为 72.0%。
建立了骨代谢标志物的多元回归模型，这些模型可用于评估肺癌的进展，为骨转移的早期治疗提供依据。
