Device and method for identifying and evaluating tumor progression
A technology for tumors and patients, applied in biochemical equipment and methods, biochemical cleaning devices, enzymology/microbiology devices, etc., can solve problems such as cancer progression or difficult prognosis
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[0210] 1. A device for identifying biological indicators capable of evaluating tumor progression, said device comprising:
[0211] 1) a clinical feature module capable of providing clinical features of a patient suffering from the tumor, the clinical features comprising the patient's tumor stage and / or the patient's survival time;
[0212] 2) a biological indicator module, which can provide at least one biological indicator derived from the patient;
[0213] 3) a correlation judging module, which can determine the correlation between the at least one biological indicator of each patient and the clinical characteristics of the corresponding patient; and
[0214] 4) An identification module, which can identify the biological indicators determined to be related to the clinical features in module 3) as being able to evaluate the progress of the tumor.
[0215] 2. A device for identifying biological indicators capable of evaluating tumor progression, said device comprising a compu...
Embodiment 1
[0304] Example 1 Patient and Tumor Sample Data Sources
[0305] Most of the genomic and clinical datasets of BLCA patients used in this application were downloaded from "NCI GDC DataPortal Legacy Archive". Among them, the clinical information of BLCA patients comes from TCGA-BLCA clinical files. The obtained BLCA patient RNA-seq dataset contained 419 samples, including 400 tumor samples and 19 normal samples. All gene expression values were normalized.
[0306] Somatic mutation data at TCGA level 2 using the Mutation Annotation Format (MAF file). TCGA level 3 methylation data was downloaded from "jhu-usc_BLCA.HumanMethylation450". Correlation data between mRNA expression and DNA methylation at TCGA level 4 came from Broad GDAC Firehose. Copy number variation (CNV) data for TCGA Level 4 was downloaded from BroadGDAC Firehose.
[0307] The following discrete indices are used to represent the level of amplification and deletion of CNVs: severe deletion=-2; deletion=1; no c...
Embodiment 2
[0310] Example 2 Screening key genes based on survival rate analysis
[0311] Use survival analysis to study the relationship between survival status and different potential influencing factors (eg, key genes).
[0312] experimental method:
[0313] Cox proportional hazards regression
[0314] Univariate and multivariate Cox proportional hazards regression models were applied to identify key genes that may affect the survival of BLCA patients. The expression values of individual genes in all BLCA samples were first normalized according to their z-scores. Genes expressed only in less than 20 samples were removed.
[0315] In univariate Cox proportional hazards regression, gene expression value was used as the only predictor variable; whereas in multivariate Cox proportional hazards regression, age, sex, tumor stage, and gene expression value were all used as predictor variables. p-values were adjusted using the 'Benjamini & Hochberg' method.
[0316] Thresholds for sta...
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