23 results about "Proteomics methods" patented technology

According to the biomarker for predicting the recurrence risk of the colorectal cancer and the application of the biomarker, a proteomics method is used for analyzing proteins with significant abundance level differences in blood of two groups of people with recurrence and non-recurrence of the colorectal cancer after operative treatment of patients with the colorectal cancer, so that the recurrence risk of the colorectal cancer is predicted, and the recurrence risk of the colorectal cancer is predicted. According to the method, a biomarker capable of being used for predicting the recurrence risk of the colorectal cancer is screened out, and a multi-marker joint detection model is further constructed, so that the recurrence risk of the colorectal cancer can be accurately, noninvasively and efficiently predicted, and clinical requirements are met.

Discussed herein are novel engineered polypeptides which are effective at cutting and tagging H3 histone tails from endogenous histones, facilitating multiplex "cut-and-paste" middle down proteomics with tandem mass tags. This cut-and-paste proteomics approach permits the quantitative analysis of H3 histone modification crosstalk after treatment with different histone deacetylase inhibitors.

The application relates to a high-throughput identification method of lncRNA coding peptides and application thereof, and belongs to the technical field of biology. The method combines a translationomics technology and a protein mass spectrometry technology, takes ribosome footprint sequencing data as a starting point, determines an open reading frame located on an lncRNA gene according to a three-base periodicity rule of a sequencing fragment, further constructs a proteogenomics index after systematic filtration, uses a computational proteomics method to detect an lncRNA translation event, and systematically identifies lncRNA coding peptides on the basis. The method has the characteristics of modularity and strong expandability, can be widely used for various types of proteome data, and can high-throughput identify and quantitatively detect lncRNA coding peptides. The application provides a prediction of molecular typing and prognosis of cancer patients based on an lncRNA coding peptide network model, and provides an important reference basis for precise medication of clinical patients.

The invention discloses a construction method and application of an animal model with delayed embryonic development, and relates to the technical field of animal models.The construction method of the animal model with delayed embryonic development comprises the following steps that S1, an early embryo is obtained; s2, performing contact culture on the early embryo and a development retardation inducer; s3, culturing to obtain a slowly developed embryo; the proteomics analysis method based on non-data-dependent acquisition is used for analyzing an animal model with slow embryonic development. The method comprises the following steps: providing an embryo sample; obtaining a protein expression profile by adopting a non-data-dependent acquisition proteomics method; according to a protein expression profile, identifying differential expression proteins related to the embryonic development retardation; the animal model with the delayed embryonic development is constructed, and an experimental model is provided for researching a molecular mechanism of the delayed embryonic development; a non-data-dependent collection proteomics analysis method is applied to the animal model, and compared with a traditional data-dependent collection method, the method has higher stability.

The application provides a plasma exosome marker and application for early diagnosis of pancreatic cancer, utilizes a metabolomics and proteomics method, analyzes metabolites and proteins with significant differences in exosomes separated from plasma of pancreatic cancer patients, pancreatitis patients and normal persons, screens a series of biomarkers capable of early indicating a pancreatic cancer occurrence risk, and further screens a group of biomarkers to construct a pancreatic cancer diagnosis model, so that the pancreatic cancer diagnosis model can be used for conveniently and efficiently predicting whether an individual is suffering from pancreatic cancer, and meets clinical requirements.

The invention provides methods and systems for detecting a biomarker in a synovial fluid wherein the system also includes a control to ensure that the test sample is indeed synovial fluid. The biomarkers and the control for synovial fluid can be identified using proteomic methods, including but not limited to antibody based methods, such as an enzyme-linked immunosorbant assay (ELISA), a radioimmunoassay (RIA), or a lateral flow immunoassay.

The invention relates to a biomarker for Parkinson's disease diagnosis and application thereof, and belongs to the technical field of biological detection. The biomarkers comprise one or more of NDUFC1 (Non-Dulbecco Fluorescent Receptor 1), CHGA (Chrysanthemum Glycolic Acid), CCL5 and ST3GAL6. The biomarker can play an important role in mechanism research of Parkinson's disease. According to the invention, a group of Parkinson's disease diagnosis biomarkers are found in neuron-derived exosomes and planetary glial cell-derived exosomes of a Parkinson's disease clinical queue by combining machine learning with a proteomics method, and the AUC value reaches 0.967.

The present invention discloses a biomarker for predicting the risk of liver cancer recurrence and its application. The use of a substance for detecting biomarkers in the preparation of a product for predicting the risk of liver cancer recurrence, wherein the biomarker includes one or more of IGFBP4, ORM1, RNASE1, DEFA3, PGM5, FBLN5, DCD, SERPINB1, CORO1A and B3GNT2. The present invention uses a proteomics method to analyze the significant differences in proteins in the blood of patients with liver cancer recurrence and non-recurrence, screens out biomarkers that can be used to predict the risk of liver cancer recurrence, and further constructs a liver cancer recurrence risk prediction model. The biomarker and liver cancer recurrence risk prediction model of the present invention can achieve simple, accurate and rapid diagnosis of liver cancer recurrence risk, improve the diagnostic level, and are suitable for large-scale promotion and application. Since the biomarker is derived from serum, sampling has the advantages of being convenient, immediate and non-invasive, so the present invention has important clinical application value.

The application discloses a pigment and a construction method of an engineering bacterium for producing the pigment. A high-expression protein is identified in a blue jellyfish umbrella epidermis through a proteomics method. It is found through sequence alignment and domain analysis that the protein is similar to a lung-shaped root mouth jellyfish blue pigment protein precursor, has a curled cysteine-rich domain and a ring cake domain, and conforms to the properties of a root mouth jellyfish order specific pigment family. Therefore, the protein is named as a jellyfish blue pigment. A jellyfish blue pigment gene expression vector is constructed, and a round red blastoconidium yeast is reformed through a genetic engineering method to construct an industrial yeast strain for producing the blue pigment. The blue pigment synthesized by the constructed strain is non-toxic and has no side effects, and can be applied to the printing and dyeing industry in the future.

The invention provides a biomarker for predicting whether an individual suffers from colorectal early canceration or not and application of the biomarker, a proteomics method is utilized, and proteins with significant abundance level difference in blood of progression-stage adenoma patients, early-stage colorectal cancer patients and healthy control people in the colorectal cancer tumor process are analyzed, so that the colorectal cancer early canceration can be predicted, and the colorectal cancer early canceration can be predicted. According to the method, a biomarker for predicting the risk of an individual suffering from colorectal early canceration (including progressive adenoma and early colorectal cancer) is screened out, and a multi-marker joint detection model is further constructed, so that the risk of the individual suffering from colorectal early canceration can be accurately, noninvasively and efficiently predicted, and clinical needs are met.

In response to the problems existing in the prior art, the present invention provides a biomarker for predicting the risk of recurrence or metastasis of endometrial cancer and its application. By using proteomics methods, the differences in protein abundance in the blood of patients with recurrent and non-recurrent endometrial cancer after surgical treatment are analyzed to screen out biomarkers that can be used to predict the risk of recurrence of endometrial cancer, and further construct an endometrial cancer recurrence risk prediction model, which can accurately, non-invasively and efficiently predict the risk of recurrence of endometrial cancer, provide a new tool for the management of endometrial cancer patients, and improve patients' treatment and prognosis monitoring.

The present invention provides a biomarker for predicting the risk of recurrence of papillary thyroid carcinoma and its application. By using proteomics methods, by analyzing proteins with significantly different abundance levels in the blood of two groups of people with recurrent and non-recurrent papillary thyroid carcinoma after surgical treatment of papillary thyroid carcinoma, biomarkers that can be used to predict the risk of recurrence of papillary thyroid carcinoma are screened out, and a multi-marker joint detection model is further constructed, which can achieve accurate, non-invasive and efficient prediction of the risk of recurrence of papillary thyroid carcinoma to meet clinical needs.

The invention provides a biomarker for predicting the recurrence or metastasis risk of bladder cancer and application of the biomarker. According to the application of the substance for detecting the biomarker in preparing the product for predicting the recurrence or metastasis risk of the bladder cancer, through a proteomics method, the significant difference of proteins in blood of patients with recurrence and non-recurrence of the bladder cancer is analyzed, the biomarker for predicting the recurrence risk of the bladder cancer is screened out, and the application of the biomarker in predicting the recurrence or metastasis risk of the bladder cancer is realized. And a bladder cancer recurrence risk prediction model is further constructed. The biomarker and the bladder cancer recurrence risk prediction model can realize simple, accurate and rapid diagnosis of the bladder cancer recurrence risk, improve the diagnosis level, and are suitable for large-scale popularization and application. The biomarker is derived from serum and has the advantages of convenience, instantaneity and non-invasiveness in sampling, so that the biomarker has an important clinical application value.

The present invention provides a biomarker for predicting the risk of bladder cancer recurrence or metastasis and its application. The use of a substance for detecting biomarkers in the preparation of a product for predicting the risk of bladder cancer recurrence or metastasis, by using a proteomics method to analyze the significant differences in proteins in the blood of patients with and without bladder cancer recurrence, screened out biomarkers that can be used to predict the risk of bladder cancer recurrence, and further constructed a bladder cancer recurrence risk prediction model. The biomarker and bladder cancer recurrence risk prediction model of the present invention can achieve a simple, accurate and rapid diagnosis of the risk of bladder cancer recurrence, improve the diagnostic level, and are suitable for large-scale promotion and application. Since the biomarker is derived from serum, sampling has the advantages of being convenient, immediate and non-invasive, so the present invention has important clinical application value.

The invention discloses a biomarker for predicting the recurrence risk of liver cancer and application of the biomarker. The invention relates to application of a substance for detecting a biomarker in preparation of a product for predicting the recurrence risk of liver cancer. The biomarker comprises one or more of IGFBP4, ORM1, RNASE1, DEFA3, PGM5, FBLN5, DCD, SERPINB1, CORO1A and B3GNT2. According to the invention, through a proteomics method, significant differences of proteins in blood of patients with liver cancer recurrence and non-recurrence are analyzed, a biomarker for predicting the liver cancer recurrence risk is screened out, and a liver cancer recurrence risk prediction model is further constructed. According to the biomarker and the liver cancer recurrence risk prediction model, the liver cancer recurrence risk can be simply, accurately and rapidly diagnosed, the diagnosis level is improved, and the biomarker and the liver cancer recurrence risk prediction model are suitable for large-scale popularization and application. The biomarker is derived from serum and has the advantages of convenience, instantaneity and non-invasiveness in sampling, so that the biomarker has an important clinical application value.

The invention relates to the field of biomedicine, in particular to a BON sensitized cell model as well as a preparation method and application thereof. The invention provides an application of a BON sensitized cell model, which is characterized in that paeoniflorin is used for intervening the BON sensitized cell model, and a small molecule target of paeoniflorin is screened by a chemical proteome method of a limited enzyme digestion method; the problems that an existing BON cell line model is not sensitized or sensitization operation is complex, sensitization sensitivity is low, and stability and specificity are poor are solved. The BON-1 cells are treated with isovaleric acid, the sensitization state in intestinal diseases is simulated, and a reliable cell model is provided and can be used for studying the intestinal diseases; isovaleric acid and paeoniflorin have a synergistic effect, so that compared with unsensitized cells, the sensitivity and the specificity are higher; target screening is carried out by adopting a novel chemical proteomics method of coupling a limited enzyme digestion method with a mass spectrum (LiP-MS) technology, chemical probe preparation is not needed, and the method has the characteristics that the operation is simple and convenient, and a target sequence can be finely positioned.

This application relates to the field of chemical proteomics, and particularly to a quantitative chemical proteomics method for thiocylation modification sites. It employs aldehyde and alkyne-containing probes to chemically selectively label the proteome of biological samples with thiocylation modification. Quantitative chemical proteomics techniques are then used to quantitatively analyze the chemically labeled thiocylation modification sites. This method enables the identification and quantification of all thiocylation modification sites in biological samples, providing an analytical tool for studying the effects of thiocylation modification on protein structure and function. It facilitates research into the molecular mechanisms of thiocylation modification in related diseases, achieving high coverage, high reproducibility, and high accuracy in target screening, and providing corresponding technical support for subsequent drug development.

The invention discloses an application of TPD52 as a myasthenia gravis urine diagnostic marker, which detects and finds the difference condition of urine proteins between myasthenia gravis and a healthy control group through a high-throughput proteomics method. The TPD52 protein in the urine can be used for detecting the myasthenia gravis and healthy people, further screening is carried out, and clinical samples verify that the TPD52 protein in the urine can be used for remarkably distinguishing the myasthenia gravis from the healthy people, so that the TPD52 protein can be used as a urine diagnosis marker and is used for noninvasive detection of the myasthenia gravis, and a new way is provided for clinical early diagnosis.

The invention relates to a method for proteomic analysis and a substrate prepared for the method. The substrate is prepared with immobilized antibodies using the electrospray deposition method, where a planar conductive plate is used as the substrate, onto which ions of immunoglobulin-binding protein are deposited by electrospray, then at least one antibody with affinity for the target antigen is dispensed onto the substrate with immobilized immunoglobulin-binding protein, which is anchored via a non-covalent affinity interaction with the immobilized immunoglobulin-binding protein. Subsequently, a sample containing the antigen is dispensed onto the substrate with the immobilized immunoglobulin-binding protein and bound antibody, and the prepared substrate is incubated. After incubation of the substrate, sample residues are removed, an analyte with an antigen is chemically reduced or eluted from the substrate and cleaved with trypsin, an analyte with an antigen is desalted, and the sample is analysed using a proteomic method.

The invention discloses an isothiocyanate target identification method based on a proteolysis-assisted cyclization reaction, and relates to the technical field of chemical proteomics, and the method specifically comprises the following steps: incubating an isothiocyanate-containing chemical probe with a biological sample to obtain an isothiocyanate-labeled protein; then carrying out protease hydrolysis digestion on the isothiocyanate labeled protein to obtain an isothiocyanate modified peptide; coupling the isothiocyanate modified peptide with a biotinylation reagent through a click chemical reaction to obtain a cysteine adduct; and finally, analyzing the cysteine adduct through liquid chromatography-tandem mass spectrometry, and analyzing in combination with a calculation tool to obtain a cysteine adduct site and a corresponding protein target. According to the isothiocyanate target identification method based on the proteolysis-assisted cyclization reaction, a chemical proteomics method based on activity and affinity is developed through the proteolysis-assisted cyclization reaction, and the target of ITCs can be identified on the site level.

The invention discloses a vaginal flora protein marker group related to cervical high-level squamous intraepithelial lesions and application thereof, and adopts a metaproteomics method to construct vaginal flora protein markers for cervical HSIL diagnosis for the first time; the marker combination not only contributes to early recognition of cervical HSIL patients and optimization of clinical intervention strategies, but also provides an important basis for deep understanding of functional mechanisms of vaginal flora in cervical precancerous lesions and development of novel micro-ecological regulation therapy.

The invention provides methods and systems for detecting a biomarker in a synovial fluid wherein the system also includes a control to ensure that the test sample is indeed synovial fluid. The biomarkers and the control for synovial fluid can be identified using proteomic methods, including but not limited to antibody based methods, such as an enzyme-linked immunosorbant assay (ELISA), a radioimmunoassay (RIA), or a lateral flow immunoassay.

The present invention provides a biomarker for predicting the risk of recurrence of colorectal cancer and its application. By using proteomics methods, by analyzing proteins with significantly different abundance levels in the blood of two groups of people with colorectal cancer recurrence and those without colorectal cancer recurrence after surgical treatment, biomarkers that can be used to predict the risk of recurrence of colorectal cancer are screened out, and a multi-marker combined detection model is further constructed, which can achieve accurate, non-invasive and efficient prediction of the risk of recurrence of colorectal cancer to meet clinical needs.