12 results about "Early hcc" patented technology

The application belongs to the technical field of medical detection, and discloses a biomarker for diagnosing hepatocellular carcinoma and application thereof. The biomarker comprises a combination of coding nucleic acids of alpha-1-microglobulin, vitronectin, apolipoprotein A1, fibrinogen gamma chain, fibrinogen alpha chain and albumin; and the coding nucleic acids comprise mRNA or cDNA. The application purifies CD147 positive extracellular vesicles from plasma, and performs PCR detection on mRNA of hepatocellular carcinoma biomarkers carried in the extracellular vesicles. A HCC Score diagnosis model is constructed based on detection results of the biomarkers, and has high diagnostic sensitivity and specificity for hepatocellular carcinoma, has great potential in screening of early hepatocellular carcinoma, and has important practical significance for early diagnosis of hepatocellular carcinoma.

The application provides a three-differentiated methylation marker composition for detecting liver cancer / early liver cancer and application thereof, and belongs to the technical field of biological detection. The kit contains three DNA methylation markers, and the three-marker composition is significantly related to liver cancer. Through the combined results of the methylation levels of the three markers, the doctor can make a judgment on whether early liver cancer occurs. More than 70% of the samples used in the development of the kit reagent are early liver cancer samples (stage I or stage II), which is expected to improve the discovery rate of early liver cancer in the high-risk population of liver cancer in China, realize early discovery and early treatment, reduce the incidence and mortality of primary hepatocellular carcinoma, and reduce the pressure on individuals and social medical insurance.

The invention provides a marker composition for liver cancer detection, a liver cancer detection kit and a liver cancer detection device, and relates to the technical field of biology, and the marker composition comprises a DNA methylation marker positioned on at least one of a DAB2IP gene, a TSPYL5 gene and a ZNF783 gene. The marker composition has good sensitivity and specificity in liver cancer detection, and provides a new strategy and approach for screening and diagnosis of early liver cancer.

The present invention discloses a plasma exosome protein marker VCAM1, a kit and its application for the diagnosis of hepatocellular carcinoma, which belongs to the field of molecular marker technology. The technical problem to be solved is how to improve the sensitivity and / or specificity of hepatocellular carcinoma diagnosis. The present invention provides the use of biomarkers and / or substances for detecting the biomarkers in the diagnosis of hepatocellular carcinoma or in the preparation of products for the diagnosis of hepatocellular carcinoma; the biomarker is a plasma exosome VCAM1 protein or a combination of plasma exosome VCAM1 protein and plasma AFP. The biomarker VCAM1 protein and the detection method of the present invention have the characteristics of high sensitivity and high specificity. Their application in the diagnosis of clinical hepatocellular carcinoma patients can improve the diagnosis rate of early liver cancer, screen out subclinical liver cancer or occult liver cancer from the population, and help improve the cure rate of liver cancer and reduce the mortality rate.

The invention relates to the technical field of medical imaging omics analysis, and discloses an early-stage liver cancer postoperative recurrence prediction model and method based on imaging omics phenotypes, and the method comprises the following steps: data collection and preprocessing, feature screening and standardization, model construction, model verification and risk layering. The data acquisition and preprocessing is to obtain preoperative enhanced CT image data and clinical information of an early-stage hepatocellular carcinoma (HCC) patient meeting the agralan standard, and the clinical information comprises serum alpha fetoprotein (AFP) level and tumor number. According to the image omics phenotype-based early liver cancer postoperative recurrence prediction model and method, pre-operation and post-operation dual models are constructed by integrating pre-operation enhanced CT image omics characteristics (including first-order statistics, texture and wavelet characteristics), clinical parameters (such as serum alpha fetoprotein and tumor number) and post-operation pathological variables (such as microvascular invasion and satellite nodules).

The invention belongs to the technical field of medical detection, and discloses a biomarker for diagnosing hepatocellular carcinoma and application of the biomarker. The biomarker comprises a combination of a coding nucleic acid of alpha-1-microglobulin, a coding nucleic acid of vitronectin, a coding nucleic acid of apolipoprotein A1, a coding nucleic acid of a fibrinogen gamma chain, a coding nucleic acid of a fibrinogen alpha chain and a coding nucleic acid of albumin; the coding nucleic acid comprises mRNA or cDNA. According to the invention, CD147 positive extracellular vesicles are purified from plasma, PCR detection is carried out on mRNA of an internally loaded hepatocellular carcinoma biomarker, and an HCC Score diagnosis model constructed by taking a detection result of the biomarker as a characteristic has relatively high diagnosis sensitivity and specificity on hepatocellular carcinoma, has great potential in early screening of liver cancer, and can be used for diagnosis of hepatocellular carcinoma. And the kit has important practical significance on early diagnosis of hepatocellular carcinoma.

The invention relates to the technical field of gene diagnosis, and particularly discloses a liver cancer diagnostic kit based on a GBP2 gene and a detection method, the kit comprises an RNA extraction component used for extracting total RNA from a liver cancer or para-carcinoma tissue sample to obtain an RNA sample; the reverse transcription component is used for carrying out reverse transcription reaction on the RNA sample to obtain cDNA (complementary deoxyribonucleic acid); carrying out RT-qPCR detection on the components; the fluorescent dye premix liquid is used for carrying out real-time quantitative PCR amplification on the cDNA to obtain a GBP2 gene expression Ct (GBP2) value; the internal reference gene detection component comprises a GAPDH primer pair and is used for obtaining an internal reference Ct (GAPDH) value; the components are interpreted, delta Ct is obtained through calculation, a preset threshold value is compared with delta Ct, and the GBP2 expression level is obtained through judgment; the GBP2 is used as a liver cancer specific diagnosis marker, the detection rate of early liver cancer can be improved by detecting the expression level (such as mRNA or protein level) of the GBP2, and the gene is particularly suitable for AFP negative or low expression patients and has higher sensitivity and specificity.

The present invention provides markers, kits, systems and applications for screening or diagnosing early-stage liver cancer. The markers include any one of AK2, DCTN2, DKK4, IFNGR1, TIMM10 or Galectin4 proteins. The marker group includes any combination of AK2, DCTN2, DKK4, IFNGR1, TIMM10 or Galectin4 proteins. The method for constructing the model includes the following steps: S1, determining the training set, test set samples and validation set samples, all of which include healthy person samples, liver cirrhosis patient samples and liver cancer patient samples; S2, determining the protein biomarker / biomarker group and component weights; S3, training a classification algorithm using the training set and test set samples; S4, using the Youden index as the best threshold for distinguishing liver cancer and non-liver cancer individuals, and using the Youden index as the best threshold for distinguishing liver cirrhosis and very early-stage liver cancer individuals; S5, using the validation set samples to evaluate and optimize the performance of the classification algorithm model. The markers provided by the present invention can efficiently and sensitively screen or diagnose early-stage liver cancer.

The invention belongs to the technical field of medical diagnosis, and particularly relates to a method for constructing an early HCC prediction model based on machine learning and serum miRNA. A large-scale serum miRNA chip data set and an analysis process driven by machine learning are utilized, feature selection and stacking integrated modeling are combined in the process, a public serum miRNA chip data set is utilized to identify a biomarker of the early hepatocellular carcinoma, and a high-performance early hepatocellular carcinoma diagnosis model is developed. As a non-invasive tool, the model has strong clinical transformation potential in early HCC screening. Subsequently, a stem-loop reverse transcription combined quantitative polymerase chain reaction technology is used for quantifying a serum miRNA spectrum type, an optimal HCC detection model XGBoostPLRglm is developed, and the provided serum miRNA characteristic is an effective tool for improving early hepatocellular carcinoma detection and possibly contributes to improvement of prognosis evaluation of hepatocellular carcinoma.

The invention discloses a liver / mitochondria dual-targeting carboxylesterase fluorescent probe as well as a preparation method and application thereof, and belongs to the technical field of medicines. The probe is a compound LDM-CA, has the liver and mitochondria dual-targeting characteristic, and shows high selectivity, sensitivity and strong binding affinity to CEs. In-vitro cell experiments show that the LDM-CA has excellent mitochondrial targeting ability in the HCC cells and specific fluorescence opening response to CEs in the cells, and can clearly distinguish the HCC cells from normal liver cells or non-liver cancer cells. In-vivo imaging of a mouse model shows that no matter in-tumor injection or intravenous injection, the LDM-CA can effectively visualize the HCC tumor and draw the edge of the tumor, has the prospect of serving as a powerful molecular tool for early HCC diagnosis and image-guided surgery, and also provides a valuable means for studying the action of mitochondrial CEs activity in HCC pathogenesis at the subcellular level.

The invention discloses a preparation method and application of an MCM complex for early-stage liver cancer prognosis judgment, and relates to the technical field of proteomics and clinical detection.The preparation method comprises the steps that para-carcinoma tissue of an early-stage hepatocellular carcinoma patient is obtained; performing protein extraction and enzymolysis on the para-carcinoma tissue to obtain a peptide fragment mixture; carrying out quantitative analysis on the peptide fragment mixture by adopting a mass spectrum technology in a data-dependent acquisition mode to obtain relative abundance of six proteins, namely MCM2, MCM3, MCM4, MCM5, MCM6 and MCM7; standardizing the relative abundance and substituting the standardized relative abundance into a preset discrimination formula to calculate a combined score; according to a comparison result of the combined score and a threshold value 3.659, the postoperative recurrence and metastasis risk level of the patient is judged; by means of the technical scheme, it is revealed that the MCM2-7 protein expression combination in the para-carcinoma tissue can serve as an independent prognostic marker for postoperative recurrence and metastasis of early hepatocellular carcinoma (BCLC 0 / A stage), and the limitation of traditional prognostic evaluation depending on tumor tissue is broken through.

A marker, kit, and system for screening or diagnosis of early-stage liver cancer, and a use. The marker comprises any one of AK2, DCTN2, DKK4, IFNGR1, TIMM10 or Galectin 4 protein, and a biomarker panel comprises any combination of AK2, DCTN2, DKK4, IFNGR1, TIMM10 or Galectin 4 protein. A model construction method comprises the following steps: S1, determining a training set, test set samples, and verification set samples, which each comprises healthy individual samples, liver cirrhosis patient samples, and liver cancer patient samples; S2, determining a protein biomarker / biomarker panel and a component weight; S3, using the training set and the test set samples to train a classification algorithm; S4, taking a Youden's index as an optimal threshold for distinguishing liver cancer and non-liver cancer individuals, and taking a Youden's index as an optimal threshold for distinguishing liver cirrhosis and extremely-early-stage liver cancer individuals; and S5, using the verification set samples to evaluate and optimize the performance of a classification algorithm model. The marker can achieve efficient and sensitive screening or diagnosis of early-stage liver cancer.