Biliary atresia potential molecular subtype and recognition method of core gene of biliary atresia potential molecular subtype

Abstract

The invention relates to a biliary atresia potential molecular subtype and core gene identification method, which comprises the following steps: based on a relative expression rank relationship among genes in a sample, mining potential gene pairs with subtype identification capability, and identifying and verifying the molecular subtype through clustering analysis. And then analyzing and identifying gene modules related to BA subtypes by using a weighted gene co-expression network, and screening core genes related to the subtypes. The biliary tract atresia subtype key gene identification method gets rid of limitation of gene expression absolute values, overcomes the disadvantage that expression values among different laboratories are incomparable, eliminates the influence of group difference on results to the greatest extent, and can be applied to single individual samples by the biliary tract atresia subtype key genes identified by the biliary tract atresia subtype key gene identification method.

Description

technical field [0001] The present application relates to the technical field of biliary atresia gene identification, in particular to a method for identifying potential molecular subtypes of biliary atresia and their core genes. Background technique [0002] Biliary atresia (BA) is a common progressive obstructive biliary disease in neonates, with clinical manifestations of obstructive jaundice. If children with BA are not treated in time, they are prone to develop cholestatic cirrhosis, portal vein High pressure, eventually leading to liver failure and death. At present, researchers have proposed many theories to explain the etiology of BA, including genetic defects, viral infection, abnormal immune morphology, and gene polymorphisms. The complex pathogenic factors of BA suggest that different molecular subtypes may exist in BA. [0003] At present, molecular subtyping of diseases based on high-throughput data is mainly based on the similarity of gene expression levels i...

AI Technical Summary

Problems solved by technology

Mining potential molecular subtypes through unsupervised clustering methods, such as non-negative matrix factorization (NMF), hierarchical clustering and latent factor analysis (LF), but these methods are often based on more complex quantitative statistical models, and at the same time Since the detection of gene chips is greatly affected by experimental conditions, reagent batch numbers, experimental operators and operating procedures, there are systematic deviations in samples tested by different laboratories and samples tested by different batches of the same laboratory. After identifying the prognostic markers in a set of samples and estimating the threshold for predicting prognosis, the threshold cannot be directly applied to samples detected by other laboratories using the same gene chip platform, and needs to be retrained, so it is difficult to use different laboratories The detected data directly verifies a set of signs, which is more difficult to apply to the individual judgment of a single sample

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