6 results about "Methylation analysis" patented technology

This invention discloses a method for constructing a gene methylation diagnostic model for differentiated thyroid cancer (PTC). The method includes the following steps: S1, obtaining a test sample; S2, extracting and storing DNA from the test sample; S3, performing methylation analysis; S4, establishing a diagnostic model based on DNA methylation, where the model shows that patients with cg03596178, cg06033721, cg06688989, cg07209244, cg07485775, cg14484681, cg19979108, and cg20943461 are more likely to experience disease progression and are considered risk factors; S5, cross-validating and evaluating the performance to obtain the gene methylation diagnostic model. This model, by detecting and statistically analyzing specific methylation sites, can differentiate patients with recurrent or metastatic characteristics in differentiated thyroid cancer. The relevant model can assist in the clinical diagnosis and follow-up of thyroid patients.

The application discloses a nucleic acid sequence construction method for cfDNA multiplex gene methylation detection and application thereof. The construction method comprises the following steps: using a methylation-sensitive restriction endonuclease to perform enzyme cutting on to-be-detected cfDNA to obtain enzyme cutting fragments, using a ligase to connect an adapter to a nick end of the enzyme cutting fragments to form an adapter product; and using the adapter product as a template to perform PCR amplification to construct a recombined nucleic acid sequence for target gene methylation site detection. The construction method can be used for cfDNA level methylation qPCR analysis and low-abundance sample quantitative analysis, has small sample demand, has a wide methylation analysis coverage, and has high methylation enrichment efficiency.

This invention discloses a method for constructing a gene methylation prognostic assessment model for differentiated thyroid cancer, characterized by the following steps: S1, obtaining a test sample; S2, extracting and storing DNA from the test sample; S3, performing methylation analysis; S4, constructing a prognostic classification model based on DNA methylation, calculating the risk value of the prognostic model as Risk Score = 0.15411928*cg03190661 - 0.10405129*cg15676916 + 0.06108015; S5, cross-validating to evaluate performance and obtain the gene methylation prognostic assessment model. This model uses the Risk Score to assess the prognosis of differentiated thyroid cancer: low-risk group, normal follow-up is recommended; medium-risk group, follow-up time can be reduced to half; high-risk group, close follow-up is required.

A method is provided for probabilistically assigning a tissue of origin to a nucleic acid in a sample, e.g., DNA in a cell-free fluid sample obtained from a human subject. A hydroxymethylation profile is generated for the sample DNA and then compared across a reference data set of hydroxymethylation profile vectors, where each hydroxymethylation profile vector identifies the hydroxymethylation profile at a specific reference locus, the tissue-specific gene associated with the reference locus, and the tissue with which the gene and reference locus are associated. A tissue of origin can be probabilistically assigned to the sample nucleic acid using the results of the comparison. Other methods of use are also provided.

A method is provided for probabilistically assigning a tissue of origin to a nucleic acid in a sample, e.g., DNA in a cell-free fluid sample obtained from a human subject. A hydroxymethylation profile is generated for the sample DNA and then compared across a reference data set of hydroxymethylation profile vectors, where each hydroxymethylation profile vector identifies the hydroxymethylation profile at a specific reference locus, the tissue-specific gene associated with the reference locus, and the tissue with which the gene and reference locus are associated. A tissue of origin can be probabilistically assigned to the sample nucleic acid using the results of the comparison. Other methods of use are also provided.

Methods and systems for detecting and correcting 3 '-end hypomethylation bias, and for analyzing methyl-seq data to detect or diagnose disease are described. In some examples, the disclosed methods may comprise: determining a methylation state for each sequenced fragment of a plurality of sequenced fragments based on the sequenced fragment data derived from a sample from a subject; determining for each sequenced fragment of the plurality a probability that the methylation state of a sequenced fragment is significantly different from a distribution of methylation states determined for a plurality of sequenced fragments derived from samples from healthy individuals that map to a same genomic interval; determining a disease probability metric for the sample based on the probabilities determined for the plurality of sequenced fragments; and outputting a determination that the subject has the disease based on a comparison of the disease probability metric for the sample to a first predetermined threshold.