142 results about "Overall survival" patented technology

The invention discloses an application of CD132 in treatment of AML (acute myeloid leukemia). The CD132 is differentially expressed in the AML, the expression level of the CD132 is related to the malignant degree of the AML and the total survival rate of AML patients, and CD132 knock-down can inhibit the proliferation capacity of AML cells, influence the cell cycle of the AML cells and promote cell apoptosis of the AML cells.

Synergistic drug combinations with small molecule PAC‑1 for the treatment of uveal melanoma or cutaneous melanoma. There are currently no targeted drug therapies for mutations associated with uveal melanoma. As no standard therapy or treatment has improved overall survival, up to 50% of patients eventually develop metastatic disease despite basic radiation or surgical treatment. Drug combinations with PAC 1 allow for the use of lower doses of the compound, resulting in cancer cell death in uveal melanoma. Drug combinations with PAC‑1 and the kinase inhibitor entrectinib have shown synergistic effects against uveal melanoma cell lines. Specifically, PAC‑1 and entrectinib have synergistic effects against wild-type and mutant uveal melanoma cell lines (e.g., GNAQ and GNA11).

The invention discloses a hypopharyngeal carcinoma induced chemotherapy prognosis prediction and treatment scheme processing method and device and a medium, and can be applied to the technical field of big data processing. According to the method, after a first CT image, first clinical text information, a first induction chemotherapy regimen selected by a hypopharyngeal carcinoma patient in historical initial treatment and historical multi-modal information of a plurality of hypopharyngeal carcinoma patients in historical initial treatment before induction chemotherapy are obtained, a preset model is trained through the historical multi-modal information, wherein the historical multi-modal information comprises a first CT image, first clinical text information, the first induction chemotherapy regimen selected by the hypopharyngeal carcinoma patients in historical initial treatment and the total lifetime; then, after a corresponding score cut-off value after preset model training is completed is determined, current multi-modal information of a hypopharyngeal cancer patient subjected to current primary treatment before induced chemotherapy is input into the trained preset model to predict a current risk value, and then a target recommendation scheme for induced chemotherapy is determined based on the current risk value and the score cut-off value. Therefore, the determined target recommended regimen is closer to the actual situation, and the matching degree of the recommended chemotherapy regimen and the actual situation is improved.

The invention provides a tumor treatment prognosis evaluation method and system based on machine learning. The method comprises the following steps: S1, analyzing the influence of each immune cell subset of peripheral blood of a patient on tumor treatment responsiveness through a single-factor logistic regression model; s2, based on the total lifetime of the patient, constructing a multi-factor COX risk scale model by using the immune cell subpopulation, and calculating a peripheral blood immune pattern risk score of the patient; and S3, dividing the patients into a high-risk group and a low-risk group according to the median value of the risk score. According to the tumor treatment prognosis evaluation method and system based on machine learning, the peripheral blood immune pattern of the advanced tumor patient at different time points can be evaluated while noninvasive and dynamic material taking is achieved, then the influence of small-dose rhythm chemotherapy on prognosis of the advanced tumor patient is accurately and dynamically predicted, and the method and system are suitable for clinical transformation and popularization and have good application prospects. And a reference value is provided for treatment scheme selection of advanced tumor patients.

This invention relates to talazoparib, or a pharmaceutically acceptable salt thereof, in combination with enzalutamide, or a pharmaceutically acceptable salt thereof, for the treatment of homologous recombination repair (HRR) gene-mutated metastatic castration-resistant prostate cancer and methods of increasing overall survival thereof.

The present invention belongs to the field of biomedicine, and specifically relates to a prognostic model for predicting the overall survival rate of AML patients based on 9 fatty acid metabolism-related genes. The present invention analyzed the gene expression profiles of 354 AML patients from TCGA and VIZOME, determined the prognosis-related genes based on the fatty acid metabolism-related genes of the TCGA dataset using univariate Cox regression analysis and survival analysis, and established a prognostic model with 9 fatty acid-related genes. This model can evaluate the prognosis of AML patients more specifically and sensitively. Then, using a nomogram, the fatty acid metabolism gene prognostic model, age, and cytogenetic risk degree were integrated into a scoring system to more accurately predict the survival of AML patients, with broad clinical application prospects.

The invention provides a glioblastoma patient lifetime prediction method and system, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: obtaining and coding a digital full-slice image of a patient; screening high-information-content image blocks based on information entropy; executing a dendritic calculation rule and a structural plasticity rule through internal learnable dendritic neurons by using a dendritic morphological neural calculation model, and extracting global and local features from the image blocks; fusing the features through feature fusion neurons to obtain depth features; and finally, predicting the total lifetime through the output neurons. By simulating hierarchical calculation and plasticity of neuron dendrites, a double-level feature learning model capable of integrating global context and local details of a full-slice image at the same time is constructed, and the technical problems that an existing prediction method is insufficient in feature integration and limited in model calculation structure are effectively solved. And more comprehensive and accurate prediction of the lifetime of the glioblastoma patient is realized.

The invention discloses an HIV (human immunodeficiency virus) combined hepatocellular carcinoma risk stratification model and a construction method thereof, the construction method comprises the following steps: obtaining clinical data of an HIC-HCC patient, the clinical data comprising demographic information, clinical feature information and treatment and outcome information; taking the total lifetime of the patient as an outcome, performing single-factor analysis on the clinical data, and screening out a plurality of risk factors influencing the total lifetime of the patient; performing multi-factor analysis on the plurality of risk factors to obtain independent prognosis factors of the total lifetime of the patient; the independent prognosis factors comprise organ fatigue and multiple tumors; and establishing a risk layering model based on the independent prognosis factors.

The present invention discloses a biomarker for predicting the prognosis of intrahepatic cholangiocarcinoma and the therapeutic effect of pemetinib and its application, and relates to the field of biomedical technology; the biomarker is WDR5. The present invention has found that WDR5 can be used as a biomarker for predicting the prognosis of intrahepatic cholangiocarcinoma with FGFR2 fusion mutation, and the overall survival rate of the group with high expression of WDR5 in cholangiocarcinoma tissue is significantly lower than that of the group with low expression of WDR5. In addition, the present invention has also found that WDR5 can be used as a biomarker for predicting the therapeutic effect of the new targeted drug pemetinib on intrahepatic cholangiocarcinoma with FGFR2 fusion mutation, and the responsiveness of patients with intrahepatic cholangiocarcinoma with FGFR2 fusion mutation and high expression of WDR5 to the drug pemetinib is significantly lower than that of patients with intrahepatic cholangiocarcinoma with FGFR2 fusion mutation and low expression of WDR5.

The invention relates to the technical field of biological medicine, in particular to application of UL16 binding protein 2 (ULBP2) as a colorectal cancer (CRC) marker. Diagnostic performance of ULBP2 expression is analyzed and evaluated through a subject working characteristic (ROC) curve, the result shows that the ULBP2 is remarkably and highly expressed in CRC tissue, the high expression level is related to remarkably shortening of the overall survival (OS) of a patient, and good diagnosis and prognosis values of the ULBP2 are shown. Functional experiment results show that knock-down of the ULBP2 gene significantly reduces the survival rate and proliferation ability of HCT-8 and LoVo cells, and effectively inhibits tumor cell migration, indicating that the ULBP2 inhibitor has good application prospects in development of colorectal cancer treatment drugs. The invention provides a new biomarker and a treatment target for diagnosis, prognosis and treatment of colorectal cancer.

The disclosure belongs to the technical field of medicine. Provided are a CD97 and GSDME interaction inhibitor having anti-tumor activity and an application thereof. A compound screened in the disclosure enhances the susceptibility of NK cells to kill tumor cells by inhibiting the interaction between GSDME and CD97 in tumor cells, so as to induce an immune anti-tumor effect. Moreover, the compound enhances the susceptibility of tumor cells to pyroptosis by blocking the protective effect of CD97 on cleavage of GSDME proteins. Therefore, the compound can be used for treating GSDME and CD97 double-positive malignant tumors, and combined chemotherapy, immunotherapy, etc., to enhance the susceptibility of malignant tumors to chemotherapy and immunotherapy, prolonging the overall survival period of patients with malignant tumors. Specifically provided is a compound having a structure set forth in formula (I), a pharmaceutically acceptable salt, a solvent compound or a deuterated compound thereof.

The present invention provides a prognostic scoring system for hepatocellular carcinoma based on PUS family genes and its application. The input variables of the prognostic scoring system for hepatocellular carcinoma based on PUS family genes include the expression status coefficient, Gini coefficient and integrated risk coefficient of the PUS family genes in the dataset; the expression status coefficient is determined according to the relationship between the mRNA expression level of the PUS family genes and the average measured value. The prognostic scoring system for hepatocellular carcinoma based on PUS family genes associates the importance of 5 HCC-related PUS family genes with the prognosis and clinical characteristics of HCC patients. In different HCC datasets, there are significant differences in overall survival rates among patients with different scores. The higher the score, the lower the overall survival rate. The prognostic scoring system for hepatocellular carcinoma can be effectively used for the evaluation of the prognosis of HCC patients and has certain specificity and sensitivity in clinical practice.

The invention discloses a lung adenocarcinoma prognosis and immune infiltration related prediction model. By deeply mining data in TCGA and GEO databases, a prognosis model based on eight pyroptosis related genes is constructed, and compared with a traditional method, the five-year survival prognosis of a lung adenocarcinoma patient can be more accurately predicted. A verification result shows that the model has relatively high accuracy (AUC = 0.687) in prediction of the total survival of one year, and through Kaplan-Meier analysis and Log-Rank inspection, survival differences of patients in high and low risk groups in a training set and a verification set are remarkably distinguished. The research method and the model have strong universality, provide important reference for research and application of a pyroptosis related mechanism in other cancer types, promote the progress of the oncology field in the research related to the pyroptosis mechanism, and have wide clinical application value.

The invention belongs to the technical field of biological medicine, and particularly relates to a marker for diagnosis or / and prognosis analysis of lung cancer and application of the marker. Researches find that acetylation modification of lysine residues at the 375th site of JMJD6 protein is related to lung cancer, the acetylation modification level of the JMJD6 K375 site in cancer tissue of a lung cancer patient is remarkably lower than that of paracancerous normal tissue, and the acetylation modification level of the JMJD6 K375 site is remarkably related to prognosis of the lung cancer patient. Patients with increased acetylation modification level of the K375 site of the JMJD6 have better total lifetime than patients with low acetylation modification level of the K375 site of the JMJD6; therefore, JMJD6 K375 site acetylation modification can be used for auxiliary diagnosis and prognosis prediction of lung cancer, also can be used as a lung cancer drug action target, is used for screening or preparing drugs for treating lung cancer and / or improving prognosis of lung cancer patients, and has important significance for clinical auxiliary diagnosis and prognosis prediction of lung cancer.

The present disclosure relates to methods of treating or inhibiting the growth of a tumor, wherein the methods include selecting a subject with cancer and administering to the subject in need thereof a therapeutically effective amount of a cancer vaccine (e.g., mRNA vaccine against a tumor) in combination with a bispecific antigen-binding molecule comprising a first antigen-binding domain that binds specifically CD28 and a second antigen-binding domain that binds specifically to a tumor-associated antigen (TAA). The combination therapy demonstrates increased anti-tumor efficacy, increased duration of tumor control and / or increased overall survival, as compared to a subject administered the cancer vaccine as monotherapy.

This application relates generally to lung cancer-related biomarkers, such as aggressive lung cancer-related molecules, which can be used to predict clinical outcomes, such as patient overall survival and / or metastasis-free survival, and methods of using the same to diagnose, prognose, monitor, and treat lung cancer, such as lung adenocarcinoma, in a subject

The present invention relates to the field of molecular biology, and in particular to a biomarker related to liver cancer and its application. The present invention first discovered that the expression of CYSTM1 gene is upregulated in liver cancer, and the total survival of liver cancer patients with high expression of CYSTM1 gene is significantly reduced, suggesting that detecting the expression level of CYSTM1 gene can become one of the auxiliary diagnostic indicators for liver cancer diagnosis, and high expression of CYSTM1 gene has high accuracy as a liver cancer diagnostic factor. Moreover, the present invention also discovered that inhibiting the expression of CYSTM1 gene can effectively inhibit the proliferation and cloning of liver cancer, and overexpressing CYSTM1 gene can significantly enhance the proliferation and cloning of liver cancer. Therefore, interfering with the expression of CYSTM1 gene can become a new way to prevent or treat liver cancer, providing a new idea for the development of related drugs and clinical treatment in the later stage.

The present invention relates to a treatment of cancer. Here, the inventors discovered that flavin-containing monooxygenase 4 (FM04) expression was increased in lung tumor samples compared with lung healthy tissue. They found that this phenotype was a common feature in lung adenocarcinoma promoted also by translocation of EML4 / ALK as well as by oncogenic KRAS or EGFR using dedicated mouse models. Moreover, FMO4 was strongly increased in human lung tumors compared with healthy lung tissue, and FMO4 expression was inversely correlated with overall survival in patients with lung adenocarcinoma. Importantly, FMO4 protected lung cancer cells against reactive oxidative species-induced apoptosis. Finally, FMO4 loss of function decreased oncogenic KRAS-induced human bronchoalveolar cells transformation in vitro and in vivo. Thus, the present invention relates to an FMO inhibitor for use in the treatment of cancer in a subject in need thereof.

The application discloses that 5-oxoproline can be used as a biomarker for diagnosing chemotherapy-induced cardiomyopathy, and for the first time provides a blood metabolic marker for assisting in predicting and early diagnosing chemotherapy drug-induced cardiomyopathy, which has high accuracy and diagnostic reference value. By accurately determining the content of the small molecule metabolic marker 5-oxoproline in blood, the diagnosis of early myocardial injury induced by chemotherapy drugs will no longer completely rely on conventional non-specific myocardial injury markers and imaging methods, but will rely on the content change of metabolic products in the blood of patients as an objective index, thus advancing the time window for discovering subclinical myocardial injury, significantly improving the early diagnosis detection rate, simplifying the diagnosis process, providing an opportunity for early intervention, helping to prevent or reverse the further aggravation of chemotherapy-induced cardiomyopathy, and thus improving the overall survival prognosis of tumor patients.

The disclosure relates to methods, compositions, and kits for detecting and analyzing surface marker displaying agents (SMDAs). The disclosure further relates to methods for detecting, diagnosing, monitoring response to treatment or recurrence, or predicting overall survival, progression-free survival or duration of response in a subject suspected of having colorectal cancer (CRC) comprising performing one or more assays on at least one biological sample from the subject to detect one or more specific populations of EVs.

The present invention relates to a biomarker composition for predicting the prognosis of cancer malignancy induced by exposure to microplastics and use thereof, and more particularly, it was confirmed that the expression level of CD44, E-cadherin, N-cadherin, PD-L1, NPAS2, NR1D1, DNMT1, SLC7A2, PCDH7 and CLDN7 was changed in cancer cell lines and animal models treated with polystyrene microspheres which are the one type of microplastic for 4 weeks, and malignancy was induced due to an increase in proliferation, migration and invasion of cancer cells by the change in the expression level of the gene, and 5-year overall survival rates in gastric cancer patients decreased and thus the genes may be provided as a biomarker composition for predicting the prognosis of cancer malignancies by exposure to microplastics.

The invention belongs to the technical field of biomedicine and molecular diagnosis, and particularly relates to a combined marker and a risk scoring model for prognosis prediction of lung adenocarcinoma based on lactylation related genes as well as a construction method and application of the combined marker and the risk scoring model, and the combined marker comprises the following 12 genes: ANGPTL4, ITGA6, SOD1, CCL20, FKBP3, DECR1, SEMA3C, TRIM28, VEGFC, TNNC2, CRTAC1 and HGF. On the basis of sequencing data of large-scale lung adenocarcinoma samples, a group of lactylation related genes closely related to the total lifetime are systematically screened out, and a risk scoring model is constructed. According to the model, survival risk layering can be accurately carried out on the lung adenocarcinoma patient, and reliable reference is provided for clinical treatment strategy formulation and individualized management. In addition, the lactylation related genes are newly found, and the lactylation process in the lung adenocarcinoma can be understood.

The present invention discloses a method for prognostic evaluation of the survival period of lung adenocarcinoma patients, a computer device, and a storage medium. The prognostic index is calculated by the following formula: Prognostic index = β1 * TNM stage + β2 * ERSS; where β1 and β2 are weight coefficients, and ERSS is the endoplasmic reticulum stress score. The present invention evaluates the correlation between endoplasmic reticulum stress-related genes and the prognosis of lung adenocarcinoma patients according to a machine learning method, establishes an endoplasmic reticulum stress score, and combines the endoplasmic reticulum stress score with the TNM stage to establish a survival prediction model. Experiments show that the survival prediction model of the present invention has a good predictive effect on the overall survival period, and solves the problem that the TNM stage alone cannot accurately evaluate the survival period of patients.

The present application relates to the application of long-chain non-coding RNA HILAR inhibitor in the preparation of drugs for preventing or / and treating lung adenocarcinoma metastasis. The present application provides specific intervention means for HILAR, and verifies the effectiveness of inhibiting lung adenocarcinoma metastasis: after the expression of HILAR is targeted and silenced in vitro by small interfering RNA technology, the invasion ability of lung adenocarcinoma cells is significantly inhibited, and the metastasis phenotype is obviously reversed; more importantly, through the delivery of short hairpin RNA by adeno-associated virus vector, the formation and development of lung metastasis are effectively reduced in a lung adenocarcinoma metastasis mouse model, and the in vivo imaging shows that the tumor luminescence signal intensity of the experimental group is continuously lower than that of the control group, and the overall survival condition of the animal is improved.

The present disclosure relates to methods for determining whether a subject with cancer, specifically metastatic colorectal cancer, will respond to treatment using bevacizumab and a fluoropyrimidine chemotherapeutic agent and / or predicting overall survival. The disclosure also relates to methods for selecting a treatment for cancer in a subject and compounds used in said treatment for cancer in said subject. The disclosure also relates to kits that can be used for these methods.

The invention discloses a method for predicting hepatitis B related liver cancer risk based on intestinal flora by using a machine learning model, which is a non-invasive, convenient and feasible means by acquiring clinical and excrement samples of a patient for analysis. An excrement sample is sequenced to obtain an intestinal flora biomarker, screening is performed by setting a detection standard, intestinal flora characteristics, including bacteroides, lactobacillus, actinomycetes, erysipelothrix and Rosella, closely associated with the hepatitis B-associated liver cancer are found, a hepatitis B-associated liver cancer risk prediction model is constructed through a machine learning model, and the risk prediction model is used for predicting the risk of the hepatitis B-associated liver cancer. The defects of a traditional statistical analysis method and artificial analysis bias errors are avoided, the intestinal flora characteristics have higher prediction value and accuracy compared with traditional clinical characteristics, the method can be used for assisting early screening and early diagnosis of hepatitis B related liver cancer patients, and the overall survival prognosis of the patients is improved. The invention also provides a prediction device which is used for realizing hepatitis B related liver cancer risk prediction.

A method of predicting overall viability of a patient by a machine learning based prediction system includes receiving, by the prediction system, a plurality of input modalities corresponding to the patient, the prediction system including a processor and a memory, the input modalities belonging to different types from each other; generating, by the prediction system, a plurality of intermediate features based on the plurality of input modalities, each input modality of the plurality of input modalities corresponding to one or more features of the plurality of intermediate features; and determining, by the prediction system, a survivability score corresponding to an overall survivability of the patient based on a fusion of the plurality of intermediate features.

The invention provides an application of an immune related gene in prognosis prediction of melanoma. On the basis of sequencing data of large-scale melanoma samples, a group of immune-related genes closely related to the total lifetime are systematically screened out, and a risk scoring model is constructed. According to the model, survival risk layering can be accurately carried out on melanoma patients, and reliable reference is provided for clinical treatment strategy formulation and individualized management.

The treatment for pediatric solid tumor patients traditionally consists of a combination of chemotherapy, surgery, and radiation therapy. Targeted therapies, designed specifically for individual tumors, allow a more focused approach to treatment aimed at improving overall survival and diminishing side effects. This invention is directed to antibodies against CD99 and methods of use thereof.

The invention discloses a three-level lymphatic structure gene subtype prediction device for image information entropy habitat analysis. According to a gene expression matrix of a three-level lymphatic structure TLS, a glioblastoma GBM sample is divided into two GBM TLS gene subtypes with significantly different overall lifetime; on the basis of a tumor region-of-interest ROI in T2WI image data paired with a glioblastoma GBM sample, the tumor region-of-interest ROI is divided into tumor habitat sub-regions through information entropy related features, and corresponding radiomics features and important radiomics features are screened in a dimensionality reduction manner to establish a corresponding classification prediction model. And the imaging prediction of the two GBM-TLS gene subtypes is realized. According to the method, the blank that the GBM-TLS gene subtype lacks noninvasive imaging biomarker prediction indexes at present is filled up, and the technical limitation that the tumor spatial heterogeneity cannot be objectively reflected by traditional image omics feature extraction based on the whole tumor region of interest is overcome.