101 results about "Overall survival" patented technology

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 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 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 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 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.

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.

The invention relates to the technical field of medical artificial intelligence prediction, and discloses a method for predicting the total lifetime of bladder cancer through a multi-modal fusion deep learning model. The method includes quality control and normalized preprocessing by collecting clinical, image and genetic data of a patient. Then multi-modal features are extracted, and high-value features are screened out through feature importance evaluation to form an enhanced feature set; a feature interaction network is adopted to learn a complex dependency relationship among different modal features, and deep fusion is realized to generate integrated feature representation. And finally, constructing a depth prediction model based on the representation, and capturing a mapping relation between the depth prediction model and the total lifetime. Through the deep fusion and feature screening technology, multi-modal information is integrated, the defect that a traditional method neglects interaction between modals is overcome, and the accuracy and reliability of predicting the total lifetime of the bladder cancer patient are improved.

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.

Disclosed herein is a soluble protein signature from the tumor microenvironment that can predict overall survival post-surgery in pancreatic adenocarcinoma. The disclosed protein signatures provide a precision approach to surgical therapy for patients with pancreatic cancer. Also disclosed herein is a soluble protein signature from the tumor microenvironment that can diagnose pancreatic ductal adenocarcinoma (PDAC). Also disclosed herein are proteins that can be used to accurately normalize protein levels in a pancreatic sample.

The present application relates to the application of IFIT1 in the prognosis evaluation of low adhesion type gastric cancer patients. The present application firstly finds that the expression level of IFIT1 and the level of neutrophil infiltration can more accurately reflect the immune characteristics of the microenvironment of low adhesion type gastric cancer tissue and the high and low risk of overall survival of gastric cancer patients after operation, and IFIT1 in TANs is an effective biomarker for the prognosis of this type of gastric cancer. Based on this, a detection kit for low adhesion type gastric cancer patients is provided, which can judge the prognosis of low adhesion type gastric cancer patients and the effect of immunotherapy by detecting the expression amount of IFIT1 in the microenvironment of gastric cancer. If the expression amount of IFIT1 is up-regulated, it indicates that the prognosis of low adhesion type gastric cancer patients and the effect of immunotherapy are not good. The kit can reflect the immune characteristics of the microenvironment of low adhesion type gastric cancer tissue and the risk of overall survival after operation, and is helpful for guiding the decision-making of patient immunotherapy and implementing precision treatment in clinical practice.

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 application belongs to the field of biological pharmacy, and particularly relates to application of a Caerin polypeptide combined with a dendritic cell vaccine in preparation of a drug for treating breast cancer. The application finds that F1 / F3 significantly inhibits proliferation of 4T-1 cells and induces cell death in vitro, significantly inhibits growth of a primary tumor, reduces lung metastasis, and prolongs overall survival in vivo, and inoculation of a DC vaccine loaded with a tumor-related antigen can reduce tumor growth and enhance T cell activation in draining and non-draining lymph nodes. Compared with a vaccine loaded with a whole antigen (DCV1), a DC vaccine (DCV2) combined with F1 / F3 induced 'programmed death antigen' exhibits stronger anti-tumor activity, although TNF-alpha and IL-12 secretion thereof is lower. Therefore, the application combines the F1 / F3 polypeptide with the DCV2 to prepare a drug for treating breast cancer, effectively improves the treatment effect on the tumor, and highlights the importance of a combined immunotherapy strategy.

Identification of effective targets alleviating the programmed cell removal (PrCR) of tumor cells by macrophages is of very high interest. The present inventors have identified that the cyclin-dependent kinase inhibitor p21 protein is a strong regulator of the macrophage-mediated PrCR. Also, they showed that the adoptive transfer of p21 overexpressing monocytes induces macrophage PrCR and transition from an anti-inflammatory to a pro-inflammatory phenotype in vivo, delays cancer progression and increases significantly the overall survival of mice engrafted with cancer cells. The present invention therefore concerns therapeutic compositions comprising monocytes that over-express the cyclin-dependent kinase inhibitor p21 protein, and their use for treating mammals suffering from cancer, especially leukemia.

Provided herein are method to prevent or treat cancer cachexia by administering a combination of a JAK inhibitor and a TLR4 inhibitor. Cancer cachexia is a severe condition characterized by weight loss and muscle wasting. By administering an effective amount of a JAK inhibitor and a TLR4 inhibitor to a subject, the subject has improved appetite, weight gain, and enhanced overall survival. The method can include additional therapeutic agents, such as chemotherapy, immunotherapy or surgery.