37 results about "Good prognosis" patented technology

The invention discloses a method for screening breast cancer biomarkers based on weighted gene co-expression network analysis and application. High-expression keratin KRT5 and low-expression ubiquitin coupling enzyme UBE2C in breast cancer patients are screened out and are closely related to good prognosis of the high-expression keratin KRT5 and the low-expression ubiquitin coupling enzyme UBE2C, and the KRT5 and the UBE2C are effective molecular markers for breast cancer early-stage diagnosis and prognosis analysis. In-vitro experiments show that the development process of the breast cancer can be effectively inhibited by promoting the expression of the KRT5 gene and inhibiting the expression of the UBE2C gene. According to the invention, early-stage diagnosis and prognosis analysis of breast cancer can be rapidly carried out, a new direction is provided for diagnosis, treatment and prognosis of breast cancer, and development of individualized treatment is promoted.

The invention belongs to the field of biotechnology and medical technology, and discloses application of tumor-associated macrophages with high expression of SLC16A10 in prognosis diagnosis and treatment of colorectal cancer. According to the invention, colorectal cancer single-cell transcriptome sequencing data analysis before and after anti-PD-1 treatment is carried out in the earlier stage; the tumor-associated macrophage subgroup with high expression of the SLC16A10 is enriched in a response group after colorectal cancer anti-PD-1 treatment, and the prognosis of a colorectal cancer patient with high expression of the SLC16A10 is good. Knock-down of the SLC16A10 leads to reduction of expression of the macrophage M1 type marker, and activation and toxicity of co-cultured T cells are reduced. The SLC16A10 promotes T cell activation and weakens immunosuppression on T cells, so that colorectal cancer anti-PD-1 treatment response is caused. The research explains the influence and mechanism of the macrophage SLC16A10 on colorectal cancer anti-PD-1 treatment, and provides a new strategy and theoretical basis for immunotherapy of colorectal cancer.

The invention relates to the technical field of biology, and provides a LanCL1 gene as a target for typing and prognosis of glioma. According to the application of the reagent for detecting LanCL1 gene expression in preparation of products for glioma molecular typing and prognosis prediction, molecular typing of glioma patients can be achieved, LanCL1 high expression is highly related to low malignancy and good prognosis, and clinical risk stratification, treatment decision and treatment assistance are facilitated.

This disclosure presents methods and reagents for cancer prognosis and treatment based on APOE genotyping. Specifically, the presence of the APOE2 genotype is indicative of a poor prognosis for the subject, whereas the presence of the APOE4 genotype is indicative of a good prognosis for the subject. The disclosed methods and reagents are useful for treating cancer and for making medical and clinical decisions on adjuvant therapy, tumor surveillance, and the likelihood of disease progression.

The invention relates to the technical field of medical diagnosis, in particular to application of a VAPA protein detection reagent in preparation of products for diagnosis and prognosis of colon cancer risks. The amino acid sequence of the VAPA protein is shown as SEQ ID NO.1, the diagnosis and prognosis subject is a human, and the detected sample is the tissue of the subject. The low expression of the VAPA protein is related to the occurrence and malignant growth behavior of the colon cancer, and compared with a healthy control, the low expression of the VAPA protein indicates that a subject suffers from the colon cancer or has the risk of suffering from the colon cancer; if the VAPA protein expression in the tissue sample of the colon cancer patient is reduced, judging that the colon cancer patient has poor prognosis; if the VAPA protein expression in the tissue sample of the colon cancer patient is increased, good prognosis is determined. The result shows that the VAPA protein is a potential colon cancer treatment target.

The present invention discloses the application of CERS5 as a biomarker in the preparation of a detection agent for gastric cancer prognosis, belonging to the field of tumor molecular biology. The detection agent is used for detecting the level of CERS5 in samples of gastric cancer patients in the Asian population. The expression level of CerS5 is an independent influencing factor for the poor prognosis of gastric cancer in the Asian population. The present invention also provides the application of CERS5 as a biomarker in constructing a model for evaluating the prognosis of gastric cancer in gastric cancer patients, where the gastric cancer patients are gastric cancer patients in the Asian population. A tissue microarray is constructed from samples of gastric cancer patients in the Asian population; the expression of CERS5 in the tissue microarray is detected by immunohistochemistry; the H scoring system is used to evaluate the expression intensity of CERS5. If the CERS5 level in the sample of a gastric cancer patient > the critical value, it indicates a poor prognosis for the gastric cancer patient; if the CERS5 level in the sample of a gastric cancer patient ≤ the critical value, it indicates a good prognosis for the gastric cancer patient.

The invention relates to bioinformatics and biomedicine, in particular to a lung cancer patient immunotherapy prognosis prediction model based on proteomics. Metabonomics detection is carried out on peripheral blood of a lung cancer patient, and a PFS prognosis prediction model combining 16 differential proteins is found. The prediction model training set AUC is 0.9349, the sensitivity is 0.7949, the specificity is 1.0000, the accuracy is 0.8769, the NPV is 0.7647, and the PPV is 1.0000; the test set AUC is 0.8190, the sensitivity is 0.8571, the specificity is 0.7333, the accuracy is 0.7931, the NPV is 0.8462, and the PPV is 0.7500. When the output cut off value is larger than or equal to 0.5, the prognosis of the lung cancer immunotherapy patient is good, the progression-free lifetime is longer than 6 months, the prognosis of the lung cancer immunotherapy patient can be accurately predicted through the prediction model, and clinical guidance is provided. Wherein the 16 differential proteins are A6XNE2, P11226, A0A075B6P5, B6EDE2, Q7Z2U7, P33908, A0A0X9T0I7, A0A2U8J8W0, A0A2U8J8Z2, P0DP08, A0A2U8J9C2, A0A120HF66, Q53HT9, Q0ZCI0, A0A140VK43 and A0A0X9V981. The invention also relates to a method for preparing the protein.

The invention provides a blood diagnosis marker for identifying prostate cancer with good prognosis and metastatic castration-resistant prostate cancer and application of the blood diagnosis marker, and relates to the technical field of prostate cancer risk assessment. The blood diagnosis markers are NCKAP1, ERCC1, ZBTB46, PAQR7, ARMC8, PAQR8, LRRN4, NOG, LRRC47, CDKN2D, CLEC1B, STARD7 and ZSCAN2, and the blood diagnosis markers are used for diagnosis of the blood. The marker can be used for preparing a kit and a risk assessment chip for identifying prostate cancer with good prognosis and metastatic castration-resistant prostate cancer. According to the invention, the defects in the prior art are overcome, and a good and reliable basis is provided for identifying the prostate cancer with good prognosis and the metastatic castration-resistant prostate cancer by determining the blood diagnosis marker.

The invention belongs to the technical field of disease prognosis and molecular biology, and particularly relates to a prognosis analysis marker and a prognosis analysis system for a diffuse large B-cell lymphoma patient and application of the prognosis analysis marker and the prognosis analysis system. According to the invention, a prognosis analysis system based on key amino acids and clinical related indexes is established in DLBCL patients for the first time. The progression-free lifetime of the DLBCL patient can be effectively predicted, and the progression-free lifetime is closely related to clinical characteristics of the patient. Furthermore, in key amino acids related to prognosis, tryptophan and glutamine are key molecules, which proves that tryptophan and glutamine have good prognosis value in DLBCL patients. Meanwhile, according to the technical scheme, the prognosis model is imported into the autonomous AI system for operation, autonomous operation, autonomous detection and autonomous adjustment are achieved, and efficiency is improved. A new DLBCL patient prognosis evaluation strategy is provided, a theoretical basis is laid for individualized management of DLBCL patients, and therefore the method has good practical application value.

The invention discloses an intrahepatic cholangiocarcinoma or tumor conventional ultrasonic image auxiliary diagnosis method and system based on a Vision-RWKV framework, and relates to the technical field of medical image auxiliary diagnosis. Comprising the following steps: acquiring image data information which is conventional liver ultrasonic image data of a plurality of existing patients; constructing an intrahepatic cholangiocarcinoma or tumor identification model based on a Vision-RWKV architecture, wherein the model has a linear complexity bidirectional attention mechanism; training an intrahepatic cholangiocarcinoma or tumor recognition model based on a Vision-RWKV framework by adopting the image data information; and inputting a conventional liver ultrasonic image of a patient to be identified into the trained intrahepatic cholangiocarcinoma or tumor identification model to obtain an identification result, wherein the result comprises a lesion type, lesion position calibration and credibility. The method has the advantages that ultrasonic examination is simple, economical and easy to operate, and CT / MRI examination is high in accuracy, so that more and wider crowds are covered and served, missed diagnosis and misdiagnosis are reduced, and patients obtain good prognosis and life quality.

Provided herein are methods for classifying how a subject having a cancer will respond to immunotherapeutic (IT) therapy based on the subject's immunosignature or frameshift signature. Also provided herein are methods for classifying a subject having a cancer as having a good prognosis or a poor prognosis based on the subject's immunosignature or frameshift signature.

The invention relates to bioinformatics and biomedicine, in particular to a lung cancer patient immunotherapy prognosis prediction model based on metabonomics. Metabonomics detection is carried out on peripheral blood of a lung cancer patient, and a PFS prognosis prediction model combining 17 differential metabolites is found. A prediction model training set AUC is 0.9497, the sensitivity is 0.9231, the specificity is 0.8846, the accuracy is 0.9077, the NPV is 0.8846, and the PPV is 0.9231; the test set AUC is 0.8810, the sensitivity is 0.9286, the specificity is 0.7333, the accuracy is 0.8276, the NPV is 0.9167, and the PPV is 0.7647. When the output cut off value is greater than or equal to 0.5, the prognosis of the lung cancer immunotherapy patient is good, and no progression lifetime gt exists; after 6 months, the immunotherapy prognosis of the lung cancer patient can be accurately predicted through the prediction model, and clinical guidance is provided. Wherein the 17 differential metabolites are as follows: Com56newg, Com513newg, Com327 newg, Com527newg, Com206newg, Com521newg, Com176newg, Com383newg, Com95newg, Com520newg, Com22newg, Com416newg, Com24newg, Com427newg, Com167newg, Com117newg and Com367newg. The invention also relates to a method for preparing the differential metabolite.

The invention relates to a cancer prognosis data processing technology, and aims to provide a gastric cancer prognosis method based on apoptosis index, which comprises the following steps: A, acquiring pathological detection data of a gastric cancer pathological tissue sample, and constructing a source database; respectively establishing a regression model Y1 and a prediction model A1 by taking the survival time as a dependent variable; c, based on a nonlinear regression method, taking the whole regression model Y1 as an independent variable of nonlinear regression, establishing a regression model Y2, and calculating a regression coefficient and a random error of the regression model Y2; correcting the prediction model A1 by using a regression coefficient and a random error to obtain a prediction model A2; and D, predicting the survival time by using the prediction model A2 to obtain a prognosis conclusion. The method is simple and easy to implement, has good prognosis accuracy and can be effectively popularized and popularized.

The present invention relates to a method for predicting the survival time of a patient suffering from a solid cancer comprising i) determining in a tumor sample obtained from the patient the gene expression level of at least 7 genes selected from the group consisting of CCR2, CD3D, CD3E, CD3G, CD8A, CXCL10, CXCL11, GZMA, GZMB, GZMK, GZMM, IL15, IRF1, PRF1, STAT1, CD69, ICOS, CXCR3, STAT4, CCL2, and TBX21, ii) comparing every expression level determined at step i) with their predetermined reference value and iii) providing a good prognosis when all expression levels determined at step i) are higher than their predetermined reference values, or providing a bad prognosis when all expression levels determined at step i) are lower than their predetermined reference values or providing an intermediate prognosis when at least one expression level determined value is higher than its predetermined value. The method is also particularly suitable for predicting the responsiveness of the patient to a treatment.

The present invention belongs to the field of artificial intelligence and proteomics, and specifically relates to a method for identifying a gastric cancer proteomic typing framework based on deep learning feature extraction, the method comprising: obtaining a protein expression spectrum to be identified, and preprocessing the protein expression spectrum; inputting the preprocessed protein expression spectrum into a trained autoencoder, extracting nodes of the bottleneck layer of the autoencoder, and using the nodes to perform consistency clustering to obtain clustering labels of the protein expression spectrum to be identified; training a classifier according to the clustering labels, predicting unknown label samples, and completing gastric cancer proteomic typing framework identification; the present invention uses a deep learning autoencoder to perform consistency clustering on feature extraction of stage II and stage III gastric cancer patients from multiple centers, and obtains molecular subtypes with significant survival differences, and the subtypes are characterized by good prognosis, chemotherapy benefit, and poor prognosis, chemotherapy ineffectiveness.

The invention relates to a sepsis diagnosis and treatment scheme recommendation method based on reinforcement learning and clinical knowledge, and the method comprises the steps: firstly modeling an antibiotic combination use problem in a sepsis treatment process into a Markov decision process, and constructing a model through DQN; secondly, deep reinforcement learning based on a value function is combined with sepsis clinical data and medical guideline related content, clinical real data are referred to in a reward function, SOFA scoring knowledge and clinical guideline content are fused, and the model is guided to conduct strategy recommendation in the direction of reasonably shortening antibiotic medication duration and ensuring good prognosis of a patient. According to the constructed model, patient demographic characteristics, basic vital signs, microbial culture results and antibiotic use data serve as model input, four reward functions are constructed in combination with clinical priori knowledge, clinical data and medical guide guidance, the medical interpretability and clinical consistency of SAI-DQN are enhanced, and the method is suitable for clinical application and popularization. And the SAI-DQN is utilized to provide personalized antibiotic combined treatment suggestions for the sepsis patients.

The invention discloses a lung adenocarcinoma source meningeal carcinoma prognosis prediction method and device, electronic equipment and a storage medium, and belongs to the field of intelligent prediction methods.The method comprises the steps that genetic information of a cerebrospinal fluid sample of a lung adenocarcinoma source meningeal carcinoma patient is obtained; performing feature extraction on the genetic information to obtain genetic features and content data thereof, the genetic features being IL-6 and / or TNF-alpha; and performing classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample is a sample with good prognosis or poor prognosis, thereby realizing a more accurate prognosis prediction effect of the lung adenocarcinoma source meningeal carcinoma disease, and providing a basis for subsequent clinical diagnosis and treatment.

The invention relates to the technical field of prognosis evaluation of cancers, in particular to application of an IgA < + > plasma cell marker in prognosis evaluation of HNSCC. The expression level of the IgA + plasma cell marker can reflect the prognosis condition of the HNSCC, high expression of S100A10, MANF, SPCS3, SSR4, ALG5, KDELR1, CYBA or SEC61G is in positive correlation with poor prognosis of the HNSCC (poor prognosis), and high expression of JCHAIN, GNG7, SSR3, CD38, IRF4 or FAM30A is in negative correlation with poor prognosis of the HNSCC (good prognosis). Therefore, an HNSCC prognosis risk prediction model is established, and the problem that HNSCC prognosis evaluation is inaccurate or low in efficiency is solved.

The invention relates to a molecular marker for diagnosis, prognosis evaluation and treatment of cardiac fibrosis, and belongs to the technical field of biological medicines. The invention provides a molecular marker capable of diagnosing cardiac fibrosis, evaluating the severity of cardiac fibrosis, evaluating the prognosis of cardiovascular diseases accompanied with cardiac fibrosis or evaluating the prognosis of acute myocardial infarction. The molecular marker comprises a hepatocyte growth factor. Research finds that the HGF is remarkably increased in plasma of a patient with cardiac fibrosis, and the plasma HGF level of a patient with aortic valve stenosis is also remarkably increased before an operation, so that the HGF can be used for diagnosing cardiac fibrosis and evaluating the severity of cardiac fibrosis. Researches find that timely transient increase of the HGF in plasma of a patient with acute myocardial infarction is significantly related to good prognosis, and continuous low-level increase, continuous high-level increase, recurrence and delayed increase of the HGF are significantly related to poor prognosis, so that the HGF can be used for evaluating prognosis of acute myocardial infarction.

The invention provides a non-functional pancreatic neuroendocrine tumor prognostic marker and application thereof. The prognostic marker is one or more of GNAO1, INA and VCAN. Wherein the high expression of GNAO1 and INA is related to good prognosis, and the high expression of VCAN is related to poor prognosis. Three prognostic related proteins are combined into a multivariable prognostic marker, and the multivariable prognostic marker has excellent performance in the aspect of distinguishing high-risk patients from low-risk patients and has a good calibration effect in the aspect of estimating absolute risks, and the prediction error rate is low.

The present invention relates to a method for assessing disease severity or a clinical stage of, or predicting prognosis for chemotherapy in, dogs suffering from lymphoma. According to the present invention, in dogs suffering from lymphoma, it has been confirmed that CD34-positive expression is associated with a higher risk of cranial mediastinal lymph node metastasis and fever compared to CD34-negative expression; and since the CD34-positive expression is classifiable as a clinical disease within the WHO clinical substages, CD34 positivity can serve as an indicator of poor prognosis. In addition, it has been confirmed that MHC class II (MHCII)-positive expression is associated with a higher risk of fever and adverse effects from chemotherapy compared to MHCII-negative expression, and thus can serve as an indicator of favorable prognosis. Accordingly, the present invention may contribute not only to preserving companion dog health and reducing medical expenses for pet owners, but also to improving the quality of veterinary consultations.

The present invention refers to a mathematical calculation with the technical purpose of predicting the risk of recurrence / progression of prostate cancer through automated computer-aided processing. The said mathematical calculation provides the specific formula that better predicts patient prognosis, specifically the risk of biochemical recurrence (BCR). The said formula, herein termed ProstARK, integrates measurement of the tissue expression of a biomarker, along with clinical variables such as age, tumor stage, and Prostate Specific Antigen (PSA) serum levels. The herein disclosed mathematical processing may be handled on a local or remote computer or a device developing computing the said formula from inputted biomarker measurement and clinical variables. ProstARK may be advantageously integrated into the clinics for accurate risk assessment and improvement of patient management. A specifically defined cut-off may be employed to define high and low-risk patients. High-risk patients might benefit from active treatment instead of active surveillance. Moreover, the low-risk patients that do not meet this criterion have a favorable prognosis and could avoid unnecessary interventions.

The present application relates to the technical field of medical diagnosis, and particularly relates to the use of a detection reagent of CROT protein in a product for prognosis of nasopharyngeal carcinoma risk, wherein the amino acid sequence of the CROT protein is shown as SEQ ID NO. 1. The subject for prognosis of nasopharyngeal carcinoma risk is a human, and the sample for detection is a tissue of the subject. The high expression of the CROT protein is related to the occurrence and malignant growth behavior of nasopharyngeal carcinoma, and compared with a healthy control, the high expression of the CROT protein indicates that the subject has nasopharyngeal carcinoma or has the risk of nasopharyngeal carcinoma; the expression of the CROT protein in a tissue of a nasopharyngeal carcinoma patient is increased, and it is determined as a poor prognosis; the expression of the CROT protein in a tissue of a nasopharyngeal carcinoma patient is decreased, and it is determined as a good prognosis.

The invention discloses a biomarker for predicting the curative effect of lung squamous cell carcinoma immune combined chemotherapy and application of the biomarker. The biomarker comprises any one of hsa-miR-610, hsa-miR-4536-5p, hsa-miR-204-5p, hsa-miR-5196-3p, hsa-miR-6513-5p or hsa-miR-143-3p or a combination of at least two of the hsa-miR-610, the hsa-miR-4536-5p, the hsa-miR-204-5p, the hsa-miR-5196-3p, the hsa-miR-6513-5p and the According to the invention, expression difference of plasma exosome miRNA in patients with good prognosis and poor prognosis of advanced lung squamous cell carcinoma on immune combined chemotherapy is analyzed through small RNA sequencing, modeling is carried out, a biomarker which can be used for prediction of curative effect of lung squamous cell carcinoma immune combined chemotherapy and screening of beneficial people is screened, and prediction efficiency of the combined model is verified at the same time. A non-invasive detection solution is provided aiming at how to more conveniently and accurately distinguish the immune combined chemotherapy benefit people and the benefit-deficient people.

The present invention discloses a molecular marker for head and neck squamous cell carcinoma and its application. The molecular marker contains SMG6 protein. The present invention also provides the application of the molecular marker for head and neck squamous cell carcinoma in auxiliary diagnosis products, prognosis prediction models, as a target in screening anti-head and neck squamous cell carcinoma drugs, and as a head and neck squamous cell carcinoma tumor inhibitor. The present invention has developed a group of molecular markers for predictive prognosis diagnosis of HNSCC. The present invention analyzes the prognostic significance of SMG6 protein in HNSCC and finds that increased expression of SMG6 protein is positively correlated with a good prognosis of HNSCC patients. The present invention confirms through in vivo and in vitro experiments that SMG6 protein plays a role in inhibiting tumors in HNSCC.

The invention generally relates to methods and compositions for the prediction of therapeutic efficacy of cancer treat- ments and the prognosis of cancer. The invention discloses markers that are associated with favorable and unfavorable outcomes, re- spectively, in certain cancer treatments and are useful as prognostic markers for cancer. Methods involving these markers are dis- closed for predicting cancer therapy benefit and prognosing clinical outcome for cancer patients.

The application provides a non-functional pancreatic neuroendocrine tumor prognostic marker and application thereof, the prognostic marker is one or more of GNAO1, INA and VCAN;Wherein GNAO1 and INA high expression is related to good prognosis, and VCAN high expression is related to poor prognosis.Three prognostic related proteins are combined into a multivariate prognostic marker, the multivariate prognostic marker is excellent in distinguishing high-risk patients and low-risk patients, and shows good calibration effect in estimating absolute risk, and the prediction error rate is low.

The present invention discloses a method, device, electronic device and storage medium for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma, belonging to the field of intelligent prediction methods. The method comprises: obtaining genetic information of a cerebrospinal fluid sample from a patient with meningeal carcinoma derived from lung adenocarcinoma; performing feature extraction on the genetic information to obtain genetic characteristics and content data thereof, wherein the genetic characteristics are IL-6 and / or TNF-α; performing classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample has a good prognosis or a poor prognosis. The present invention achieves a more accurate prognosis prediction effect for meningeal carcinoma derived from lung adenocarcinoma, providing a basis for subsequent clinical diagnosis and treatment.

The invention belongs to the technical field of biology, and particularly relates to application of lymphotoxin beta in preparation of a medicine for treating triple-negative breast cancer, triple-negative breast cancer patients in a high immune infiltration group are screened out by adopting unsupervised clustering analysis, and remarkable good prognosis is shown. Five key genes (LTB, IRF8, IDO1, ITM2A and LCP1) for determining prognosis of triple negative breast cancer patients in a high immune infiltration group are screened out, and a corresponding triple negative breast cancer prognosis prediction model is constructed; through research on immune infiltration key gene lymphotoxin beta (LTB), LTB expression is screened out to be a key gene for determining prognosis of TNBC patients, related biological functions of LTB participating in enhancement of killing activity of T cells are clarified, a key role of immune infiltration in prognosis of the TNBC patients is clarified, and an important basis and a potential target are provided for improvement of TNBC immunotherapy effects.

The present invention belongs to the field of biotechnology, and specifically relates to a group of urine protein markers and their use in preparing a product for predicting the prognosis of non-small cell lung cancer. The present invention screens out for the first time from the urine of patients with non-small cell lung cancer a group of urine proteins closely related to the postoperative progression of non-small cell lung cancer: AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein, and constructs a prognosis prediction model based on these six urine proteins. The constructed prognosis prediction model has been verified to have high sensitivity and specificity for predicting the prognosis of patients after surgery of non-small cell lung cancer, reaching 84.5% and 81.2% respectively. It has a good prognosis prediction effect for non-small cell lung cancer, provides reliable guidance for clinical targeted treatment, and is of great significance for improving the prognosis and survival of patients with non-small cell lung cancer.