71 results about "Survival prognosis" patented technology

The invention provides methods for determining a prognosis for survival for a cancer patient. One method involves (a) measuring a level of a TUCAN in a neoplastic cell-containing sample from the cancer patient, and (b) comparing the level of TUCAN in the sample to a reference level of TUCAN, wherein a low level of TUCAN in the sample correlates with increased survival of the patient. Another method involves (a) measuring a level of TUCAN in a neoplastic cell-containing sample from the cancer patient, and (b) classifying the patient as belonging to either a first or second group of patients, wherein the first group of patients having low levels of TUCAN is classified as having an increased likelihood of survival compared to the second group of patients having high levels of TUCAN.

A method for the prognosis of overall survival or prediction of therapeutic outcome for a patient suffering from epithelial ovarian cancer (EOC), comprising: a. providing a sample from the patient, b. determining the expression level of microRNA family lethal-7b (let-7b) in the sample; c. using the expression level of the let-7b to obtain the prognosis of overall survival or prediction of therapeutic outcome for the patient.

The invention relates to a weight gene co-expression network, and specifically relates to a method for mining radiotherapy specific genes of colorectal cancer by using a weight gene co-expression network. The method for mining radiotherapy specific genes of colorectal cancer by using a weight gene co-expression network provides a new way to mine the specific response genes of colorectal cancer byusing the weight gene co-expression network, and provides a new basis for predicting the survival prognosis of patients with colorectal cancer. The radiotherapy of the target gene discovered by the method of the present invention can improve the survival prognosis of patients with colorectal cancer to some extent, can reduce the mortality of the patients with colorectal cancer, can solve the actual clinical problems, and can provide a better choice for the majority of patients.

The invention discloses a model for evaluating the prognosis of patients with Paget's disease and invasive ductal carcinoma, which comprises a line graph for evaluating the specific survival probability of the patients after five years and a prognosis risk score formula of the patients with Paget's disease and invasive ductal carcinoma. The line graph is the only survival prediction model established for the PD-IDC crowd for the first time, and the PD-IDC model can more accurately predict the survival prognosis of the special crowd; according to the model, the prediction efficiency is better than the prediction efficiency of the other two (AJCC staging and NPI grading) ; secondly, the PD-IDC line graph is concise and easy to understand, the operation for a clinician and a patient is convenient, and the model can predict the specific survival probability of breast cancer for 5 years and 10 years; meanwhile, the patient score calculated according to the risk score formula in the model can clearly distinguish high-risk and low-risk people so as to assist clinicians to formulate high-efficiency treatment schemes.

The invention relates to method of genetic analysis for the prediction of treatment sensitivity and survival prognosis of patients with brain tumors, especially oligodendroglial tumors. The invention provides a method for producing a classification scheme for oligodendroglial tumors comprising the steps of a) providing a plurality of reference samples, said reference samples comprising cell samples from a plurality of reference subjects suffering from oligodendroglial tumors; b) providing reference profiles by establishing a gene expression profile for each of said reference samples individually; c) clustering said individual reference profiles according to similarity; and d) assigning an oligodendroglial tumor class to each cluster.

The invention relates to a biomarker combination for molecular typing and / or prognosis prediction of the muscle-invasive bladder cancer, and a screening method and application thereof. The biomarker combination comprises the following genes: FGF10, TP53INP1, DDR2, MYC, CDC73, IGF1, PLA2G1B, SKI, FN1, EGFR, PPARG, PDGFRA, PDGFD, GAS6, PDGFC, FNTB and CCNB1. Through non-negative matrix factorizationclustering analysis based on transcription data of the biomarker combination, the muscle-invasive bladder cancer can be classified to respectively correspond to different expression characteristic spectrums. The classification corresponds to significantly different overall survival statuses, thus being used for survival prognosis assessment. The transcription data analysis method adopted has theadvantages that the number of biomarker combinations is small, the analysis steps are simple, the requirements of large sample analysis are met, the requirements for the calculation ability are low, and the method is applicable to standardized transcription data; the transcription data can be a transcriptomic data sub set or a set of genetic transcription data for individual detection.

Patients with gastric carcinoma are different in survival prognosis. High-risk patients with gastric carcinoma are patients with gastric carcinoma, the mortality risks of who are high. The invention relates to a prognosis method for the patients with gastric carcinoma. The prognosis method comprises the following steps of: collecting clinical information of the patients with gastric carcinoma to be tested; and, comparing the clinical information of the patients with gastric carcinoma to be tested with preoperative evaluation indexes of the high-risk patients with gastric carcinoma so as to obtain a prognosis result of the patients with gastric carcinoma to be tested, wherein when the clinical information of the patients with gastric carcinoma to be tested accords with standards of the preoperative evaluation indexes of the high-risk patients with gastric carcinoma, the patients with gastric carcinoma to be tested are the high-risk patients with gastric carcinoma; and the preoperative evaluation indexes of the high-risk patients with gastric carcinoma includes the age older than or equal to 60 years, a body mass index of being less than 18.5 kg / m<2>, the blood albumin content of being less than 40 g / L, low cell differentiation degree and the like. In the prognosis method for the patients with gastric carcinoma provided by the invention, required information can be obtained before operation; and thus, prognosis conditions of the patients with gastric carcinoma can be evaluated before operation.

The invention discloses a molecular marker for diagnosing and treating the bladder cancer, namely LncRNA DANCR, and also discloses application of the molecular marker to screening or preparation of reagents used for diagnosing the bladder cancer diagnosing, bladder cancer lymphatic metastasis, bladder cancer prognosis relapse or bladder cancer clinical stages and application of the molecular marker to screening or preparation of drugs used for treating the bladder cancer. It is found that the LncRNA DANCR has high expression in bladder cancer cases with lymphatic metastasis, has negative correlation with overall survival prognosis of a patient and is an independent indicator for diagnosing the bladder cancer and judging bladder cancer development and survival prognosis; silencing of the LncRNA DANCR can restrain in-vitro proliferation, cell migration and invasion and in-vivo tumor formation and lymphatic metastasis of bladder cancer cells. It is verified that the LncRNA DANCR is an important cancerigenic factor of the bladder cancer for the first time, and can be used as a molecular marker for bladder cancer prognosis diagnosis and a new target spot for treatment.

The human HLJ1 tumor suppressor gene is herein defined as regulated by promoter, enhancer, and silencer regions. HLJ1 promoter activity and gene expression are inversely correlated with metastatic ability. HLJ1 is highly expressed, and inducible, in cells with low metastatic ability and expressed to a lesser extent in highly metastatic cells. HLJ1 gene expression suppressed the growth of human lung adenocarcinoma cells in vitro, and inhibited tumor growth in vivo. It also impeded the motility of human adenocarcinoma cells and reduced the anchorage-independent growth capacity and invasiveness of metastatic lung adenocarcinoma cells. The degree to which human lung adenocarcinoma patients express HLJ1 predicts their survival prognosis and their probability of relapse.

The present invention features a method for determining the prognosis for survival of a cancer patient. Methods for measuring the level of NOL3 expression in a cancer cell-containing sample from a patient, and comparing the level of NOL3 expression in the sample to a reference level of NOL3 expression are also included. A higher level of NOL3 relative to the reference level correlates with decreased survival of the patient, and an equivalent or lower level of NOL3 relative to the reference level correlates with increased survival of the patient.

The invention provides a group of DNA methylation markers for evaluating survival prognosis risk of coronary heart disease. The marker comprises methylation sites corresponding to the following genes:chr3: 101901234, SEMA3B, CORO2B, SLC39A8, chr7: 27235733, chr10: 3086002, chr2: 164594200, RNASEH1, chr10: 13489731, ABCA3, ZNF444, UBE2E2, DAZAP1, NAT10, DDAH1 and FKBP5. Prognosis and death risks of coronary heart disease patients can be predicted by monitoring changes of methylation levels of the 16 sites. The invention also constructs a model for evaluating the survival prognosis risk of thecoronary heart disease based on the 16 methylation sites, and the accuracy of blood diagnosis and prognosis prediction of patients with the coronary heart disease can be improved through the model. According to the method, the Lasso Cox regression model is used for screening important variables, so that the model dimension is greatly reduced, the detection cost is favorably reduced, and the popularization in clinical application is facilitated.

The invention provides a hepatoma marker OXCT1 and application thereof to hepatoma diagnosis, treatment and prognosis. The inventor discovers that the expression of OXCT1 in the hepatoma tumor in the body is obviously higher than the expression in the normal liver tissue; the cell proliferation can be obviously reduced through endogenous OXCT1 inhibition; in addition, the survival rate of hepatoma patients with higher OXCT1 expression is lower, i.e., the over expression of the OXCT1 definitely indicates the relatively poor survival prognosis. On the basis, the OXCT1 can be the tumor auxiliary diagnosis marker, and can be effective target points for tumor treatment, and can also be survival prognosis indexes of the hepatoma patients.

The invention belongs to the technical field of medical treatment and pyradiomics, and particularly relates to a gene heterogeneity visual quantification method in glioma based on pyradiomics and a system. The method of the invention comprises the steps of segmenting a glioma magnetic resonance image by means of an image segmenting network 3D U-net; performing predictive modeling on the integral glioma IDH (isocitrate dehydrogenase), namely performing high-flux characteristic extraction and characteristic screening on the image, and screening a characteristic combination which is most sensitive and most effective to gene expression; performing heterogeneous modeling on the glioma IDH based on an image block, extracting a multi-dimensional data block of the glioma image, obtaining the IDH expression strength of each data block based on the integral predicting model; and finally forming the IDH distribution visualization and quantitative expression of the whole tumor. The method and thesystem have advantages of more accurately determining the prognosis and radiotherapy and chemotherapy sensitivity of the patient, realizing surgery resection and targeting treatment in heterogeneous atlas navigation, and realizing high clinical value in improving treatment effect of the patient and improving survival prognosis.

The invention belongs to the field of artificial intelligence technology application, and particularly relates to a ferroptosis model construction method and application. The method specifically comprises the following steps: screening out a plurality of ferroptosis regulation molecules with large expression level change by adopting an existing gene expression database and ferroptosis regulation molecules, and typing the gastric cancer by adopting a consensus clustering method; performing gene expression difference analysis on the gastric cancer subtypes to obtain differential expression genes; then screening survival prognosis-related differential genes and regression coefficients thereof from the differential expression genes by adopting single-gene Cox regression analysis, and obtaining survival-related differential genes i of the patient to be analyzed; and counting the expression quantity Expi of the survival-related differential gene i, and constructing a ferroptosis model according to a regression coefficient betai corresponding to the survival-related differential gene i: ferroptosis score = [sigma]*Exp. The ferroptosis score can well predict survival prognosis, chemotherapy drug sensitivity and immunotherapy effect of the gastric cancer patient.

The invention discloses a kit for prognosis and postoperative treatment response prediction of breast cancer. Five miRNAs (miR-205, miR-320a, miR-665, miR-188-5p and miR-198), which are obviously related to the survival prognosis of patients, are discovered by the inventor, wherein two of the miRNAs are protective and the other three miRNAs are dangerous. By virtue of the five miRNAs molecular tags, the patients are classified into patients of high risk and patients of low risk; and overall survivals of the two groups are compared based on survival analysis, discovering that a significant difference exists between the overall survivals of the high-risk group and the low-risk group (p=0.001). The reagent is effective for guiding the clinical treatment of the breast cancer and is capable of conducting appropriate adjuvant therapy on the patients of high risk in prognosis, so that the survival quality of the patients is improved; and meanwhile, unnecessary treatment on the patients of low risk can be avoided.

The invention provides an esophageal squamous carcinoma patient survival risk prediction method based on a convolutional neural network. The method comprises the steps of: collecting M clinical phenotype indexes and survival information of esophageal squamous carcinoma patients as original data; performing research by using a Kaplan-Meier method and a log-rank method to obtain a relationship between clinical phenotype indexes and lifetime information of the esophageal cancer patients; analyzing clinical phenotype indexes influencing survival prognosis of the patients by using Univariate Cox hazard analysis; extracting clinical phenotype indexes with higher correlation with the survival risk of the patients through a Relief feature selection algorithm and Pearson correlation analysis; and finally, constructing a survival risk prediction model of the esophageal squamous carcinoma patients by using a convolutional neural network and using clinical phenotype indexes with higher correlation, and further judging the prognosis survival risk of the patients. According to the method, the postoperative survival condition of the esophageal squamous carcinoma patients is accurately predicted, the prognosis risk prediction capability is improved, and the prognosis risk prediction cost is reduced.

Indicators that can guide clinical decisions in cancer, particularly posttranslational modification of proteins which result in altered prognosis and differential sensitivity to targeted cancer therapy, are provided. In particular, monitoring of phosphorylation of PTEN may be utilized to predict or assess drug response, drug sensitivity, and clinical outcome. Modulation of PTEN phosphorylation may be utilized to alter sensitivity and outcome in cancer patients. Posttranslational modification of PTEN, particularly phosphorylation, is correlated with resistance to targeted cancer therapy, including EGFR inhibitors, and with reduced survival prognosis. Methods and assays for determining phosphorylation of PTEN, particularly Y240 phosphorylation, are provided. Methods for sensitizing tumors to inhibition and targeted therapy by modulating PTEN phosphorylation are provided.

The invention provides a detection method for survival prognosis of glioblastoma. The method is characterized in that the method comprises the following steps: the expression levels of specific expression genes CBX3, BARD1, EGFR, IFRD1, CTSS, STAT1, GUCY1A3 and MOBP in a glioblastoma tissue are detected, survival prognosis of a glioblastoma patient is predicted according to the expression level ofthe detected gene, and the survival time of the glioblastoma patient highly expressing CBX3, BARD1, EGFR, IFRD1, CTSS or / and STAT1 is short; the glioma patient with high expression of GUCY 1A3 or / andMOBP has a long survival time. The invention also provides a prognostic typing method for glioblastoma. The glioblastoma with high expression of CBX3, BARD1, EGFR, IFRD1, CTSS or / and STAT1 in glioblastoma tissues is / are short in prognostic survival time; and the high expression of the GUCY 1A3 or / and MOBP in the cytoma tissue is prognosis, and the survival time is long.

The invention relates to the field of biotechnology, and provides a new application of SAMSN1 protein, especially an application of SAMSN1 protein in preparation of a glioblastoma prognosis evaluation reagent or kit. According to the invention, an immunohistochemical method is adopted to detect the relative expression level of SAMSN1 protein in glioblastoma tissue, so as to determine the survival prognosis of glioblastoma patients. Based on the correlation of the relative expression level of SAMSN1 protein with glioblastoma, the protein can be used as a molecular marker, and detection of the expression level of the protein can guide the prognosis evaluation of glioblastoma.

The present invention provides novel methods and compositions for the diagnosis and treatment of colon cancers. In particular, the present invention provides diagnostics and prognostics for colon (including colon adenocarcinoma) cancer patients, wherein the methods related to measuring miR levels can predict poor survival. The invention also provides methods of identifying inhibitors of tumorigenesis.

The present invention relates to the method of measuring the YKL-40 level and using this measurement as a prognosis for survival of an individual suffering from heart disease caused by atherosclerosis. The method may be used for classification of individuals in order to optimize treatment or monitoring the individuals during the course of or prior to or after treatment. The individual may suffer from any type of cardiovascular disease or disorder. The method also detects and determines whether diagnostically or prognostically significant levels of YKL-40 molecules are present in a biological sample. Furthermore the level of YKL-40 may be used to predict disease relapse.

The invention relates to an artificial intelligence pathological diagnosis method for renal clear cell carcinoma based on deep learning. The method comprises the following steps: S1, data acquisition;S2, pathological microscopic image processing; S3, modular image feature information extraction; S4, machine deep learning and diagnosis model construction; S5, diagnosis efficiency verification of the artificial intelligence diagnosis model: taking the artificial intelligence diagnosis model constructed by the image data in the training set as a diagnosis classifier, inputting the feature information data extracted by the test set for prediction, and evaluating the diagnosis efficiency of the artificial intelligence diagnosis model through a subject working feature curve; and S6, predictionefficiency research of survival prognosis of patients with renal clear cell carcinoma. The invention further provides a renal clear cell carcinoma artificial intelligence pathological diagnosis modelbased on deep learning. The method can effectively predict the survival prognosis of patients with renal clear cell carcinoma, can achieve the effect that cannot be achieved by traditional film reading diagnosis of pathologists, and can provide effective guidance opinions for judging whether the patients with renal clear cell carcinoma continue to be treated or not after operations.

The invention discloses application of an anti-PDCD4 (Programmed Cell Death 4) antibody in preparation of a detection reagent for predicting personalized medicine sensitivity of paclitaxel or a derivative drug of paclitaxel. The variation of protein abundance of whole cells in tumor cells treated by paclitaxel is analyzed by using a quantitative proteomics technology, the results shows that paclitaxel reduces the expression level of PDCD4 and the sensitivity of the tumor cells to paclitaxel is in positive correlation with the expression level of PDCD4 in cells. PDCD4 in the tumor cells is subject to gene silencing or overexpression and the result further proves that the sensitivity of the cells to paclitaxel is in positive correlation with the expression level of PDCD4 in the cells. Meanwhile, researches on survival prognosis of clinical lung cancer patients shows that under the same paclitaxel combined adjuvant chemotherapy, the survival prognosis of patients with low expression level of PDCD4 is poor and the survival time of patients with high expression level of PDCD4 is long. Based on the research results, the invention provides a reference index and a detection and application method for guiding personalized medicine of paclitaxel or the derivative drug (such as docetaxel) of the paclitaxel.

The invention relates to a bladder cancer pathomics intelligent diagnosis method based on machine learning and a prognosis model thereof, and the method is characterized in that the method comprises the following steps: S1, preforming data acquisition; S2, processing a microscopic pathology image; S3, performing bladder cancer pathological image feature extraction; S4, constructing and inspectinga bladder cancer automatic pathological diagnosis model based on machine learning; and S5, constructing and checking a bladder cancer survival prognosis prediction model based on machine learning. Thebladder cancer pathomics intelligent diagnosis method has the advantages that the bladder cancer pathomics intelligent diagnosis method is constructed based on pathological section microscopic imagemachine learning, effective automatic pathological diagnosis can be achieved, pathological diagnosis is expected to be further promoted to be developed to the efficient and accurate field, and the current situation that domestic pathology physicians are in shortage is relieved; the postoperative survival condition of the bladder cancer patient can be efficiently and accurately predicted, and important guidance opinions are provided for clinical decisions of clinicians.

The present invention relates to a method for determining the survival prognosis of patients suffering from non-small cell lung cancer. More specifically, the present invention provides methods which measure kinase activity by studying phosphorylation levels in response to a kinase inhibitor and profiles in samples obtained from patients diagnosed with non-small cell lung cancer. The present invention also provides methods for predicting the response of a patient diagnosed with non-small cell lung cancer to a medicament.

The present invention provides novel methods and compositions for the diagnosis and treatment of colon cancers. In particular, the present invention provides diagnostics and prognostics for colon (including colon adenocarcinoma) cancer patients, wherein the methods related to measuring miR levels can predict poor survival. The invention also provides methods of identifying inhibitors of tumorigenesis.

The invention discloses a method of an AAV different treatment regimen prognosis prediction model based on an AI technology. The method comprises the following steps: S1, collecting kidney survival prognosis condition data of existing ANCA related nephritis patients after treatment by adopting different treatment regimens; S2, constructing a prognosis prediction model of each treatment regimen ina treatment regimen set by adopting a Cox regression analysis method and an XGBoost machine learning algorithm, training the constructed prognosis prediction model of each treatment regimen by adopting the kidney survival prognosis condition data after the ANCA related nephritis patients are treated by adopting different treatment regimens to obtain a high-quality Cox regression model and a high-quality XGBoost model for effective prognosis prediction capable of judging the kidney survival prognosis condition of each treatment regimen; S3, respectively inputting the medical characteristic dataof a to-be-evaluated patient into the high-quality Cox regression model and the high-quality XGBoost model of each treatment regimen in the treatment regimen set; and S4, selecting a proper treatmentregimen according to the kidney survival probability of each treatment regimen in the treatment regimen set and the reference value of whether the kidney of each treatment regimen in the treatment regimen set survives and the prognosis risk. According to the invention, the purpose of guiding a doctor to adopt an ACNC related nephritis treatment regimen with the lowest prognosis risk is achieved.

The present invention relates to the technical fields of molecular biology and biomedicine, and particularly relates to a molecular marker for preventing, diagnosing and treating tongue squamous cellcarcinoma, and an application thereof. The disclosed molecular marker for preventing, diagnosing and treating the tongue squamous cell carcinoma is LncRNA DANCR. The molecular marker is applied in screening or preparing reagents for diagnosing tongue squamous cell carcinoma and screening or preparing medicines for treating tongue squamous cell carcinoma. The LncRNA DANCR is found to be highly expressed in tongue squamous cell carcinoma tissues and an independent indicator for diagnosing tongue squamous cell carcinoma and judging progression and survival prognosis of tongue squamous cell carcinoma. Besides, silencing LncRNA DANCR can inhibit proliferation, cell migration and invasion of tongue squamous cell carcinoma in vitro. The LncRNA DANCR is confirmed for the first time as an importantoncogenic factor for tongue squamous cell carcinoma and can be used as the molecular marker for tongue squamous cell carcinoma diagnosis and a new target for treatment.

The invention belongs to the technical field of biomedicine, and particularly relates to a glioma malignant progress and survival prognosis detection molecular marker, a temozolomide drug resistance detection target and application. An m6A methylation reader HNRNPC which is highly expressed in glioma tissue and closely related to the tumor occurrence and development process is screened out by a bioinformatics method, further analysis finds that the expression level of the m6A methylation reader HNRNPC is closely related to the survival rate and prognosis of patients, and through clinical masstumor tissue sample verification, the m6A methylation reader HNRNPC can be used as a molecular marker and an independent survival prognosis factor for glioma malignant progress. Meanwhile, it is foundthat knockout of the HNRNPC can increase medicine sensitivity of glioma cells to temozolomide. A new diagnosis, treatment and prognosis predicting target is provided for glioma treatment, and a new molecular target can be provided for improving temozolomide drug resistance.

The present invention provides a pharmaceutical composition comprising the following active ingredients: 1) amlodipine or a pharmaceutically acceptable salt thereof, 2) pioglitazone or a pharmaceutically acceptable salt thereof, and 3) rosuvastatin or a pharmaceutically acceptable salt thereof. The present invention also provides use of the pharmaceutical composition in preparing a medicament for treating hypertension or metabolic syndrome. The pharmaceutical composition of the present invention can treat hypertension or metabolic syndrome, while effectively controlling the incidence of associated cardiovascular diseases and more potently improving survival prognosis in hypertensive patients. When blood pressure is lowered to desired level, the risk factors such as cardiovascular diseases are rectified, metabolic disorders and prognosis of patients are improved, and survival rate of hypertensive patients is raised.