Marker for predicting bladder cancer chemosensitivity and application
A marker and sensitivity technology, applied in the biological field, can solve the problems of patients and medical workers suffering from chemotherapy resistance, no solution, and the inability to guarantee the accuracy of predicting patients' chemotherapy sensitivity, so as to improve the clinical treatment effect , good application value and prospect effect
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Embodiment 1
[0016] Select 20 cases of gemcitabine-resistant bladder cancer chemotherapy-resistant patients and gemcitabine-sensitive bladder cancer chemotherapy-sensitive patients, and perform quantitative PCR on the cancer and adjacent tissues of each patient to detect the abnormal expression of SOCS1 in chemotherapy-resistant patients in the previous study. , CYLD, HMGB1 and Ciap-1 gene levels, real-time quantitative PCR reaction using ProQPCR SuperMix
[0017] (Biochain) kit, each group set up 3 duplicate wells, using the cDNA of 40 patients' cancer tissues and para-cancer tissues as templates, and GADPH as an internal reference, to detect SOCS1, CYLD, and HMGB1 in cancer and para-cancer tissues of each patient. And the expression level of Ciap-1, and the ratio of the relative content of cancer and paracancerous tissue (O△Ct) as the expression difference ratio of the gene in patients, the experimental data is carried out through the IQ5Multicolor Real-time PCR Detection System (Hercules...
Embodiment 2
[0027] Example 2 Application of Biomarkers in Predicting Patients with Bladder Cancer Chemotherapy Resistance
[0028] A total of 80 patients with bladder cancer were selected, and real-time quantitative PCR (q-PCR) was used to detect the relative levels of bladder cancer Ciap-1 and SOCS1D mRNA in cancer and adjacent tissues (OCt). O△Ct) was used as the expression difference ratio of the gene in patients to predict the patient's resistance to gemcitabine.
[0029] Real-time quantitative PCR was used to detect the difference ratio of Ciap-1 and SOCS1 gene expression in cancer or paracancerous tissues of patients. ROC curve analysis showed that the areas under the ROC line of Ciap-1 and SOCS1 were 0.8214 and 0.8731, respectively. The predicted area under the curve is 0.9641, the efficiency is significantly higher than the joint prediction of a single gene, and the prediction efficiency is significantly improved. The specific data is as follows figure 1 and shown in Table 1.
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