7 results about "CpG site" patented technology

The embodiments of the present application disclose a tumor marker, a kit and a use method for judging early hepatocellular carcinoma. The kit of the embodiments of the present application takes the first DNA shown as SEQ ID NO. 1 and / or the second DNA shown as SEQ ID NO. 2 as a judgment marker, judges whether the ex vivo sample is a positive sample of early hepatocellular carcinoma by judging the methylation degree of the CpG site in the first DNA and / or the second DNA in the ex vivo sample, thereby prompting whether the organism derived from the ex vivo sample has early hepatocellular carcinoma lesion. The kit of the embodiments of the present application has good specificity and sensitivity for the positive judgment of early hepatocellular carcinoma based on the specific tumor marker, and can be applied to the screening and judgment of early hepatocellular carcinoma.

The application relates to the field of biomedical detection technology, in particular to a bladder cancer postoperative recurrence monitoring method based on a ctDNA methylation spectrum, which comprises the following steps: collecting postoperative patient cell-free plasma and extracting circulating tumor DNA; performing bisulfite conversion treatment on the DNA; performing targeted amplification using a multiplex PCR primer group designed for a group of predetermined genomic methylation regions related to recurrence, constructing a sequencing library; performing high-throughput sequencing on the library, obtaining methylation level data of a plurality of CpG sites to form a sample methylation spectrum data matrix; the core of the application is that tumor heterogeneity is overcome through multi-marker combination detection, and a machine learning model is used to integrate multidimensional data to realize precise risk stratification, which has the advantages of non-invasiveness, high sensitivity, high specificity and quantifiable output, and provides an effective tool for individualized follow-up management of bladder cancer postoperation.

The present invention relates to a method of establishing a biomarker for a first phenotype of interest of a CHO cell, the method comprising the steps of: (a) determining methylation values of CpG sites within genomic DNA obtained from a population of CHO cells that are a representation of the first phenotype of interest and are part of the training samples, (b) defining a set of specific CpG sites having reliable methylation values in the training samples of step (a); and (c) performing a penalized regression using the methylation values of step (b) as input and phenotype of interest correlated to the training samples as dependent variable, by applying a penalized regression model; thereby obtaining the specific CpG sites with corresponding weighting factors and intercept of the linear model equation as parameters defining the biomarker for the first phenotype of interest.

This invention relates to a method for constructing a biomarker for a first target phenotype of CHO cells, the method comprising the steps of: (a) determining the methylation values ​​of all CpG sites in the genomic DNA of a CHO cell population; the CHO cell population representing the first target phenotype and being part of a training sample; (b) identifying a set of specific CpG sites from the training sample of step (a), the CpG sites having reliable methylation values; and (c) applying a penalized regression model to perform penalized regression, using the methylation values ​​obtained in step (a) as input and the target phenotype associated with the training sample as the dependent variable; thereby obtaining specific CpG sites with corresponding weight coefficients and the intercept of the linear model equation, which are used as parameters to define the biomarker of the first target phenotype.

Embodiments of the present disclosure provide methods, systems, computing devices and media for determining methylation levels of target CpG sites of tumor tissue. The method comprises: obtaining a first methylation measurement of a mixed tumor tissue sample and a second methylation measurement of a paired cancer adjacent tissue sample, the mixed tumor tissue sample comprising tumor tissue and cancer adjacent tissue; determining a distribution of reads comprising target CpG sites in the mixed tumor tissue sample originating from the tumor tissue or the cancer adjacent tissue based on the first methylation measurement and a third methylation measurement of a reference cancer adjacent tissue sample; and determining a methylation level of the target CpG sites of the tumor tissue in the mixed tumor tissue sample based on the determined distribution, the first methylation measurement and the second methylation measurement. In this way, the accuracy of the methylation level measurement can be improved, thereby effectively avoiding the problem of missed screening of early screening markers and false positive of tissue provenance markers.

This invention relates to a composition, kit, nucleic acid chip, and method for diagnosing liver cancer by detecting the methylation level of CpG sites in one or more genes selected from FAM110A, FAR1, VIM, LDHB, LIPE, INAFM1, ATL1, CELF6, MTHFD2, PAK1, NXPE3, SLC25A36, and VANGL2. This invention enables accurate and rapid diagnosis of liver cancer, and also allows for early diagnosis.

A method for discriminating a cancer state is provided. A first dataset is obtained for a plurality of subjects having a first cancer state. Each subject has a plurality of nucleic acid methylation fragments with methylation patterns comprising CpG site methylation states. An autoencoder including an encoder and decoder is trained by evaluating the error in the autoencoder reconstruction of the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the first dataset. A second dataset is obtained for a plurality of subjects having a second cancer state. A plurality of features is identified by inputting the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the second dataset into the trained autoencoder and computing a score determined by the autoencoder reconstruction of the methylation pattern. The plurality of features is used to train a supervised model that discriminates a cancer state.