Application of reagent for detecting expression quantity of immunity related genes, reagent kit and head and neck cancer prognosis risk predicting device
A technology related to gene expression and immunity, which is applied in the application fields of head and neck cancer prognosis risk prediction devices and reagents, can solve the problems of genes, reagents, kits, etc. that are rare in head and neck cancer and lack the prognosis risk of head and neck cancer, and achieve the prediction of prognosis risk , good specificity and sensitivity, good predictive effect
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Embodiment 1
[0057] Build a predictive model
[0058] 1. Discover the immune-related genes of head and neck cancer prognosis: use the data set in the public high-throughput TCGA database as the development data set, including 500 head and neck cancer patient samples, of which 499 patients have complete clinical prognosis information. Immune-related genes were obtained from the ImmPort database, a total of 1073 genes. Among them, 915 immune-related genes were measured in the CIT microarray dataset, and their expression was significantly different among different head and neck cancer patients (Median Absolute Deviation, MAD>0.5). Then resampling was carried out, and in 1000 resamplings, if the variable was selected more than 800 times, it entered the final model to obtain 81 candidate genes.
[0059] 2. Construction of immune-related genes to predict the prognosis of head and neck cancer: According to the patient's prognosis information, the LASSO Cox model was used to reduce the 81 immune-...
experiment example 1
[0071] Validation using samples of known clinical diagnosis results (1 GEO independent data set, 270 patients, of which: 1 case with survival time ≥ 2 years and < 3 years, 152 cases with survival time ≥ 3 years and < 5 years, survival time There are 117 cases with time ≥ 5 years) The prediction model in Example 1 proves that it can significantly predict the prognosis of patients with head and neck cancer. Among them, the expression levels of each gene in the samples used were high-throughput expression values, and the cutoff value of the ROC curve dividing the immune high-risk group and low-risk group was 0.106.
[0072] predictive power (see figure 2 ) as follows: 2-year AUC=0.759 in the training group (TCGA), 3-year AUC=0.782, 5-year AUC=0.732, 2-year specificity 67.5%, 3-year specificity 71%, 5-year specificity 71.8%, 2-year Sensitivity 72.6%, 3-year sensitivity 72.5%, 5-year sensitivity 69.2% ( figure 2 Middle B); In the verification group (GEO), 2-year AUC=0.578, 3-ye...
experiment example 2
[0074] A training cohort (TCGA HNSCC) and a validation cohort (GSE65858) were used for testing in a total of 770 patient samples. Such as figure 2As shown, the test effect in the TCGA data set shows that IRGS is related to OS, and the survival probability of low immune risk patients (IRGS Risk Score value 0.106) times (HR=3.69, 95%CI=2.73-4.98, P figure 2 Middle C); the test effect in the head and neck cancer patients of the GSE65858 (validation) data set also shows that IRGS is related to OS, and the survival probability of low immune risk patients (Risk Score value of IRGS 0.106) 1.84 times the survival probability (HR=1.84, 95%CI=1.21-2.81, P figure 2 Middle F).
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