Kit for predicating lung cancer risk for high-risk groups among China urban population on basis of CT (computed tomography) images and biomarker spectrums
A technology of biomarkers and kits, applied in the field of medical biology, can solve the problems that the high-risk risk model of the Chinese population has not been reported, and achieve the effect of simple expression level
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Embodiment 1
[0032] The composition of kit of the present invention:
[0033] Composed of biomarkers and their enzyme-labeled antibodies, carbonate buffer solution with a pH value of 9.6, phosphate buffer solution with a pH value of 7.4, serum protein dilution, stop solution, tetramethylbenzidine substrate solution, and normal people Serum and positive control serum composition.
[0034] The biomarkers include gastrin-releasing propeptide, carcinoembryonic antigen, cytokeratin 19 fragments and squamous cell carcinoma antigen; Add 2.93 grams of sodium bicarbonate to 1L of distilled water and obtain; the phosphate buffer solution with a pH value of 7.4 is 0.2 grams of potassium dihydrogen phosphate, 2.9 grams of disodium hydrogen phosphate dodecahydrate, 8.0 grams of sodium chloride, 0.2 gram potassium chloride, 0.5mL Tween-20 is added to 1L distilled water and obtains; The described serum protein dilution is 0.1 gram bovine serum, goat serum or rabbit serum protein is added to 100mL phosph...
Embodiment 2
[0050] The present invention collects 454 cases of urban population with clinically determined high-risk factors for lung cancer, and finally diagnoses 285 cases of lung cancer through pathology, and samples the serum of 111 cases of lung cancer patients through a randomized method; at the same time, in the serum bank of 169 non-lung cancer patients, The serum of 129 normal persons was sampled by random method. Four biomarkers in serum were detected by chemiluminescent microparticle immunoassay, and clinical information such as patient age, smoking history, nodule diameter, spiculation, and gender were collected at the same time. The sensitivity and specificity of the obtained models are shown in Table 1. The ROC curve of the lung cancer risk prediction model for high-risk groups based on CT images and biomarker profiles is as follows: figure 1 shown.
[0051] Table 1 The effectiveness of the lung cancer risk prediction model based on CT images and biomarker profiles for Chi...
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