86 results about "Prognostic model" patented technology

The invention discloses a prognosis evaluation method and system for II-III stage colorectal cancer patients, and relates to the technical field of wisdom medicines.The prognosis evaluation method comprises the steps that variables related to survival prognosis of the patients are screened, potential variables influencing RFS and OS of the patients are obtained, and prognosis outcomes of the colorectal cancer patients are evaluated by evaluating relevance between various indexes and prognosis outcomes of the colorectal cancer patients and correcting confounding factors; screening out indexes having important value for the model; the screened indexes and machine learning algorithms are utilized to construct RFS and OS prognosis models of the CRC patients, the RFS and OS prognosis models are used for performing prognosis evaluation on the CRC patients in the II-III stages, and the machine learning algorithms comprise six types of LR, RF, XGB, SVC, MLP and GNB. By integrating clinical data and a machine learning technology, an efficient and accurate tool is provided for prognosis evaluation of the II-III stage colorectal cancer patient, and a scientific basis is provided for making clinical decision support and an individualized treatment scheme.

The invention discloses a lung adenocarcinoma prognosis risk prediction model and a construction method and application thereof, and belongs to the technical field of biomedical detection. The lung adenocarcinoma (LUAD) prognosis model is successfully constructed by systematically analyzing the immune infiltration related gene (CAFRG) of tumor-associated fibroblasts (CAF), and the application value of the lung adenocarcinoma (LUAD) prognosis model in prediction of patient prognosis is verified. The CAFRG risk score is negatively related to the immune microenvironment score and is remarkably related to immune checkpoint gene expression, which suggests that a high-risk patient may have the characteristic of immune escape. Furthermore, based on TIDE tool analysis, the patients in the low-risk group have more active T cell immune response. The risk score is also closely related to the sensitivity of antitumor drugs, especially Doramapimod. Therefore, a new theoretical basis is provided for personalized treatment of lung adenocarcinoma, and a new direction is provided for future clinical research.

The invention provides a lung adenocarcinoma prognostic marker and a model, constructs a lung adenocarcinoma prognostic model based on mRNA and lncRNA related to programmed cell death (PCD), and synthesizes bioinformatics analysis and experiments to explore the biological effect of the lncRNA model in lung adenocarcinoma. The prognosis model covers all PCD pathways and various molecules, has stable prognosis prediction accuracy, and can provide a potential molecular target for personalized treatment. According to the method disclosed by the invention, the PCD-related mRNA and lncRNA are integrated to construct a lung adenocarcinoma diagnosis model suitable for Chinese population, so that the identification capability of tumor patient classification is enhanced from multiple aspects, the accuracy and reliability of prognosis prediction are improved, powerful support can be provided for promoting personalized treatment strategies of lung adenocarcinoma patients, and the method is suitable for popularization and application.

A hybrid liver cancer prognosis model, prognosis system and medium, the model takes independent influence factors as input and outputs hybrid liver cancer postoperative risk scores, the hybrid liver cancer postoperative risk scores are used for dividing risk levels, and the risk levels are used for hybrid liver cancer prognosis evaluation. And / or is used for postoperative adjuvant chemotherapy decision-making of mixed liver cancer. The hybrid liver cancer prognosis model is jointly constructed by combining the traditional clinical pathological characteristics and immune microenvironment indexes, the key effect of the tumor immune microenvironment in CHC prognosis is indicated, and the constructed hybrid liver cancer prognosis model has good CHC prognosis prediction performance; in addition, decisions of postoperative adjuvant chemotherapy can be provided for patients with different risk levels, precise treatment of the CHC patients is achieved, and long-term survival prognosis of the CHC patients is improved.

The invention belongs to the technical field of prognosis models, and particularly relates to a diffuse large B-cell lymphoma risk prognosis model based on lactic acid metabolism related genes. The model construction comprises the following steps: S1, data acquisition and processing; s2, constructing a prognosis model; s3, verifying and evaluating the prognosis model; and S4, application of a prognosis model. GSE10846 and GSE87371 data sets are used for screening lactic acid metabolism related genes (LMRGs), a prognosis risk model is constructed and verified, and association of the prognosis risk model with clinical pathological characteristics, immune characteristics and treatment response is disclosed.

The invention relates to the technical field of biomedicine, in particular to a liver cancer molecular subtype typing and prognosis model construction method based on glycometabolism and lactic acid metabolism related genes and application thereof. According to the invention, the typing and prognosis model of liver cancer subtypes is constructed for the first time on the basis of a coordinated regulation network of a glycometabolism gene and a lactic acid metabolism gene, the constructed liver cancer subtype subtypes comprise Cluster1 and Cluster2, and the total survival rates of different subtypes have significant difference; the clinical characteristics T.stage and TNM.stage of different subtypes are obviously different from each other; 12 kinds of immune cells have significant differences among different subtypes; iPS immune scores of different subtypes are significantly different; the TIDE scores of different subtypes have significant score differences; the genes with the highest mutation frequency in different subtypes comprise TP53, CTNNB1 and TTN. CLM scores of the prognosis model constructed on the basis of the collaborative regulation network of the glycometabolism genes and the lactic acid metabolism genes have no significant difference in different patient states; the 17 immune cells have significant differences between high and low risk groups. And a new way is provided for individualized treatment strategy formulation and survival outcome improvement of HCC.

The invention discloses a prognosis evaluation method and system for diffuse large B-cell lymphoma. According to the method, firstly, a gene module closely related to lipid metabolism is screened from DLBCL transcriptome data through weighted gene co-expression network analysis (WGCNA), and then eight key prognosis genes including FNDC1, IL22RA2, C15orf48, OMD, MFAP2, BC017398, CXCL6 and TNFAIP6 are identified from the module by adopting multi-step regression analysis (single factor Cox, LASSO and multi-factor Cox). A risk scoring model is constructed based on the expression levels and regression coefficients of the genes, and DLBCL patients can be divided into a high-risk group and a low-risk group with significant survival differences. The risk score and the clinical pathological factors are further integrated to construct a column graph, and individualized survival probability prediction can be achieved. The invention further provides a corresponding prognosis evaluation system, electronic equipment and a storage medium. An independent data set verifies that the prognosis model has excellent prediction performance and clinical practical value.

The invention discloses a colorectal cancer metabolism-related prognosis model construction method based on multiple omics. By integrating multiple omics data, the defect is overcome, a new view angle and method are provided, the accuracy and practicability of the prognosis model are effectively improved, and the blank in the prior art is filled. Meanwhile, the method provided by the invention is not only suitable for prognosis prediction of colorectal cancer patients, but also can be popularized and applied to other types of cancers. As the metabolism-related gene plays an important role in various cancers, the technical scheme provided by the invention has relatively high universality and popularization value, and can provide reference for prognosis prediction and personalized treatment of other cancers. Finally, the prognosis model provided by the invention can be used as a clinical decision support tool to help doctors to make more scientific and accurate decisions in diagnosis and treatment processes. Through early recognition of high-risk patients and implementation of targeted intervention measures, prognosis of the patients can be significantly improved, and the risk of relapse and metastasis can be reduced.

The invention belongs to the technical field of biological information, and provides an ampulla cancer patient prognosis model based on machine learning and a construction method thereof. According to the method, clinical information of an ampulla cancer patient is obtained through a cancer statistical database, clinical characteristics independently related to survival of the patient are analyzed and positioned through single-factor and multi-factor COX, a prognosis model of the ampulla cancer is established through multiple machine learning algorithms according to the clinical characteristics, and the method can be used for survival prediction of the patient. The method has the advantages of being rapid in prediction, high in accuracy and easy and convenient to use, gene determination does not need to be specially conducted on tissue or blood sampling of the patient, accurate survival rate judgment can be achieved only through clinical information, a clinician can make a personalized treatment scheme of the patient according to the prognosis state, and therefore the survival rate of the patient is increased.

The invention discloses construction and application of a lung cancer brain metastasis prognosis model based on a plasma miRNA combined marker. A prognosis model of lung cancer brain metastasis is constructed based on plasma miRNA combined markers, the plasma miRNA combined markers comprise miR-31-5p, miR-219-5p, miR-1-3p, miR-130a-5p, miR-452-3p, miR-598-3p, miR-9-5p, miR-1246, miR-196a-5p and miR-210-3p, the prognosis model obtains a miRNA expression index according to the expression quantity of each plasma miRNA, and a predicted survival rate is obtained according to the miRNA expression index and clinical information and serves as a prognosis prediction result; the prognosis model is used for prognosis prediction of lung cancer brain metastasis. The prediction model and the prognosis prediction system both have high sensitivity and accuracy.

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.

This invention discloses a method for constructing a prognostic model for acute myeloid leukemia (AML) based on ferroptosis-related genes. The method involves acquiring gene expression data from AML patients and healthy samples to screen for differentially expressed ferroptosis-related genes (DEGs) associated with AML. Using univariate Cox proportional hazards regression analysis, LASSO regression analysis, and multivariate Cox proportional hazards regression analysis, key genes ACSF2, SLC7A11, DNAJB6, and SOCS1 are selected from these DEGs. A prognostic risk model is constructed based on the expression levels of these key genes and their corresponding multivariate Cox regression coefficients. The risk score formula is: Risk Score = 0.534 × ACSF2 expression value - 0.453 × DNAJB6 expression value + 0.194 × SLC7A11 expression value + 0.308 × SOCS1 expression value. The prognostic model constructed in this invention has high predictive accuracy and reliability, effectively stratifying the risk and assessing the prognosis of AML patients, providing an important reference for the clinical treatment of AML.

The invention relates to the technical field of biology, in particular to 5-hydroxymethylcytosine and application of a prognosis model of 5-hydroxymethylcytosine in nasopharynx cancer prognosis evaluation. According to the method, the prognosis scoring model capable of layering the outcome of the patient is constructed, and the model shows strong distinguishing performance in a training set and a verification set. When it is integrated into a prognostic model together with tumor staging and EBV status, the 5hmC score results in higher calibration accuracy and net clinical benefit in decision curve analysis. According to the findings, a 5hmC prognosis model is determined as a noninvasive marker rich in biological information, can be used for prognosis risk layering of NPC patients, and has potential significance for individualized management and improvement of outcome prediction.

The invention belongs to the technical field of biological medicine, and particularly relates to application of GLUT5 serving as a colorectal cancer biomarker in development of an in-vitro detection kit, a prognosis model and an inhibitor. The GLUT5 is highly expressed in colorectal cancer tumor-related fibroblasts (CAFs), the expression level of the GLUT5 is remarkably related to poor prognosis of a patient, and the GLUT5 can be used as a molecular basis for auxiliary diagnosis and an independent prognosis factor. In mechanism, the GLUT5 promotes tumor progression by mediating fructose metabolism reprogramming of the CAFs. A full-chain solution from early diagnosis and precise prognosis to targeted therapy is provided for colorectal cancer, and the clinical transformation value is remarkable.

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 discloses an intratumor microbial marker group for gastrointestinal tumor prognosis, a prognosis model and application. The biomarker group comprises the following components: Granulicella, Thermodesuftator, Gaetbus, Formosa, Kaistobacter, Candidate Saccharimonas, Dactyloccopsis, Schwartzia, Candidate Kuenia, Sphingosinusus, Microvirgula, Simple, Doria, Halothiobacillus, Rhodobacter, and Oceaniterimus, and is characterized in that the biomarker group comprises the following components: the Granulicella, the Thermodesuftator, the Gaetbus, the Formosa, the Kaistobacter, the Candidate Saccharimonas, the Dactyloccopsis, the Schwartzia, the Candidate Saccharimonas, the The model constructed by the method can accurately predict prognosis, drug response and immunotherapy.

The disclosure belongs to the field of genetic testing and biomedicine, relating to a method for constructing a prognostic model of hepatoma and an application thereof, comprising 1) obtaining and identifying fibroblasts with high FAP expression; 2) obtaining and identifying TAMs; 3) analyzing co-localization between fibroblasts with high FAP expression obtained and the TAMs obtained previously; 4) communicating and analyzing the fibroblasts with high FAP expression after the localization in the Step 3) with TAMs to obtain CCC ligand-receptor genes; 5) screening the CCC ligand-receptor genes obtained previously based on machine learning to obtain key CCC ligand-receptor genes; and 6) constructing a prognostic model of hepatoma according to the key CCC ligand-receptor genes obtained in the Step 5). The present disclosure provides a method for constructing a prognostic model of hepatoma that can be applied to auxiliary judgment of the prognosis of hepatoma patients and an application thereof.

The invention relates to the field of biomedicine, and discloses a pancreatic cancer dependency gene score prognosis model construction method based on multi-algorithm optimization, which can utilize a machine learning algorithm to identify biomarkers related to pancreatic cancer prognosis and survival, construct a prognosis model and improve the prognosis accuracy. Comprising the following steps: identifying the activity of pancreatic cancer malignant cells and pancreatic cancer dependent genes; identifying a PADG subgroup by consistent clustering; constructing a prognosis model; performing prognosis gene correlation analysis; the invention relates to machine learning prognosis model immunotherapy assessment based on pancreatic cancer dependent gene scoring. The kit has the advantage that the risk and prognosis of the pancreatic cancer patient receiving immunotherapy can be accurately predicted. The treatment effect is improved, and the lifetime of a patient is prolonged. Discovery of a new treatment target is facilitated, and development of the pancreatic cancer treatment field is promoted.

The invention relates to the technical field of medical prognosis evaluation, in particular to a sex-specific advanced young non-small cell lung cancer prognosis model construction method, which collects clinical data of young metastatic non-small cell lung cancer patients, including sex, age, body mass index, D-2 polymer level, gene mutation state, metastasis site, tumor marker and hematology index. Data are divided into male and female queues, male specific prognostic factors (body mass index, gene mutation state and liver metastasis state) and female specific prognostic factors (D-2 polymer level and gene mutation state) are identified through Cox proportional risk regression analysis, and male and female column map prognostic models are respectively constructed based on the factors. All prognosis factors are converted into risk scores, the corresponding relation between the total score and the survival probability is established, finally, the model can output individualized survival probability prediction results of patients of different genders, and important reference is provided for clinical treatment and prognosis evaluation.

The invention discloses a hepatocellular carcinoma patient prognosis combined prediction model and a construction method and application thereof. The construction method comprises the following steps: acquiring baseline clinical indexes and serum N-carbohydrate chain characteristic data of a patient; key clinical indexes and key glycomics markers are screened out through feature engineering; and fusing the screened features into a joint feature vector, training by using a Cox regression model, and constructing a joint prediction model capable of outputting a risk score and a corresponding risk hierarchical threshold. The invention further relates to an evaluation system based on the model, a storage medium and a kit applying the model. According to the method, the functional serum glycomics characteristics are introduced into prognosis prediction of the specific treatment scene for the first time, the defects that an existing prediction method is single in index and limited in precision are overcome through multi-dimensional information integration, more accurate individualized risk layering can be achieved, a reliable tool is provided for clinical treatment decision making, and the method has important clinical application value.

A disease prognosis model is provided that can be trained to determine a clinical outcome of a disease based on longitudinal data including health records for each point in time in a sequence of points in time. The disease prognosis model may include a recurrent neural network trained to extract, from each health record, a set of features representing local dependencies present within the health record. The disease prognosis model may include spatio-temporal attention trained to determine an importance of each feature in the set of features at each point in time in the sequence of points in time. The disease prognosis model may include a feed-forward neural network trained to determine the clinical outcome of the disease based on the importance of each feature in the set of features at each point in time in the sequence of points in time. A trained disease prognosis model may be applied to determine the clinical outcome of the disease for one or more patients.

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 acute myelogenous leukemia disulfide death gene related prognosis risk model construction method, which comprises the following steps: constructing a disulfide death gene related prognosis feature preliminary model: taking a TCGA-LAML data set as a training set, taking GSE12417 and GSE71014 queues as a verification set, combining a plurality of known disulfide death genes, and constructing the disulfide death gene related prognosis feature preliminary model; and determining a new disulfide death gene data set. And based on a stacking strategy, fitting the preliminary model of the prognosis characteristics related to the disulfide death with a plurality of gene models to obtain an expanded stacking model, and taking the expanded stacking model as a final prognosis model related to the disulfide death of the acute myelogenous leukemia patient. And in combination with a process strategy of data input-feature screening-machine learning training-risk output, the constructed double-sulfur death gene related prognosis features are expanded, and the prediction efficiency of the model is improved.

The invention provides a construction method of a tumor microenvironment specific gene analysis and prognosis prediction model, and belongs to the technical field of molecular biology. The construction method of the tumor microenvironment specific gene analysis and prognosis prediction model comprises the steps of sample collection and pretreatment, division of a training set and a verification set, division of a tumor environment risk group, screening of specific genes, verification of a prognosis model, construction of an evaluation risk prediction model, and verification and evaluation of the model. Drug target analysis and protein expression verification. According to the invention, a prognosis prediction model of the child brain tumor microenvironment specific gene is established, and the research method is verified by a plurality of methods, so that the potential application value of CASP10 and other genes in brain tumor treatment is found, a new thought and direction are provided for research and development and personalized treatment of child brain tumor treatment drugs in the future, and the development and development of the child brain tumor treatment drugs are promoted. The future research should further discuss the application prospects of the key genes in brain tumor treatment.

This invention discloses a method for constructing a gene methylation prognostic assessment model for differentiated thyroid cancer, characterized by the following steps: S1, obtaining a test sample; S2, extracting and storing DNA from the test sample; S3, performing methylation analysis; S4, constructing a prognostic classification model based on DNA methylation, calculating the risk value of the prognostic model as Risk Score = 0.15411928*cg03190661 - 0.10405129*cg15676916 + 0.06108015; S5, cross-validating to evaluate performance and obtain the gene methylation prognostic assessment model. This model uses the Risk Score to assess the prognosis of differentiated thyroid cancer: low-risk group, normal follow-up is recommended; medium-risk group, follow-up time can be reduced to half; high-risk group, close follow-up is required.

This invention discloses a prognostic model and construction method for cervical squamous cell carcinoma, belonging to the fields of bioinformatics and oncology. The construction method is based on a set of genes related to programmed cell death, integrating multi-omics data of cervical squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the Cancer Genome Characterization Project (CGCI). After standardized preprocessing, differential expression analysis, bootstrap resampling combined with univariate Cox regression for initial screening, consensus screening using multiple machine learning algorithms, and finally, multivariate Cox proportional hazards regression analysis to identify three core independent prognostic genes and construct a risk scoring model. This model has undergone multi-dimensional validation and optimization, demonstrating robust predictive performance. The method of this invention is standardized and highly reproducible, and the constructed model has high accuracy and strong generalization ability, providing a reliable tool for individualized prognostic assessment and clinical decision-making for cervical squamous cell carcinoma patients.

The invention relates to the technical field of image analysis, in particular to a glioma layering method and system based on medical image-biological multi-omics fusion, and the method comprises the steps: obtaining a data set, respectively obtaining image, pathology, gene, transcription, proteomics and high-throughput sequencing data, and constructing an omics model; the omics model comprises a radiomics prognosis model, a pathomics prognosis model, a genomics prognosis model, a transcriptomics prognosis model and a proteomics prognosis model; fusing the prognosis models to obtain a multi-omics fusion model; and respectively carrying out transcriptomics analysis, proteomics analysis, whole exome sequencing analysis and immune correlation analysis on the risk layering result of the multi-omics fusion model, and carrying out model verification. By integrating image, pathology, gene, transcription, proteomics and high-throughput sequencing data, biological mechanisms and pathways are deeply understood, and more specific guidance and prediction are provided for treatment and prognosis evaluation of patients.

The present invention discloses a prognostic model of lysosome-related genes for hepatocellular carcinoma, a construction method and an application thereof, wherein the lysosome-related genes include: ATG10, ATP6V0B, HMOX1, RAB7A, SQSTM1, TMEM41B, TP53, FTH1, RNF19B, RAMP3, TPP1, AP1M2, CLTA and DNAJC6. The present invention uses 14 lysosome-related genes to establish a prognostic model and related application products that can predict the overall survival rate of hepatocellular carcinoma patients, and verifies that the model has good predictive performance and has potential clinical application value in individualized hepatocellular carcinoma prognosis evaluation.

This invention relates to the field of medical imaging assessment technology, specifically a prognostic assessment system for liver cancer portal vein tumor thrombosis radiotherapy that integrates multiple MRI parameters. The system includes modules for MRI multi-parameter acquisition, data preprocessing, feature extraction, fusion assessment, dynamic updating, result output, and data storage. The system acquires MRI structural, functional, and temporal parameters. After preprocessing such as adaptive noise reduction, it extracts core features using an attention mechanism combined with CNN. An attention fusion algorithm integrates MRI features, radiotherapy dose, and clinical parameters. An improved algorithm is used to construct a prognostic model, which is dynamically optimized through transfer learning. The system outputs visualized prognostic results, treatment recommendations, and abnormal warnings, and the data is stored with encryption. This system addresses the problems of low assessment accuracy, lack of dynamic adjustment, and poor practicality in existing technologies, improving assessment accuracy and timeliness, providing reliable support for individualized radiotherapy decisions, and balancing data security with clinical research needs.