211 results about "Gene sets" patented technology

The present invention provides gene sets the expression of which is important in the diagnosis and/or prognosis of breast cancer.

The present invention provides methods using a gene expression profiling analysis (1) to determine whether a human sample is a tumor using a gene set containing nucleic acid sequences of SEQ ID NOS: 1-7, 8-17 or 1-17; (2) to identify whether a tumor tissue is an adenocarcinoma (using a gene set containing nucleic acid sequences of SEQ ID NOS: 15, and 18-21) or a squamous cell carcinoma (using a gene set containing nucleic acid sequences of SEQ ID NOS: 22-27); and (3) to predict the prognosis of survival and metastasis in humans with tumor (using a gene set containing nucleic acid sequences of SEQ ID NOS:19, and 28-42 or SEQ ID NOS: 19, 29, 31, 40, and 42), particularly for those humans who are at the early stage of lung cancer. The gene expression profiling is preferably performed by cDNA microarray-based techniques and/or Real-Time Reverse Transcription-Polymerase Chain Reaction (Real-Time RT-PCR), and analyzed by statistical means.

The present invention provides identification of a thirty-five gene set that predicts the anticancer activity of inhibitors of the RAF/MEK/MAPK pathway, methods of qualifying cancer status in a subject, methods of identifying an anti-tumor response in a subject, methods of monitoring the efficacy of a therapeutic drug in a subject, and methods of identifying an agent useful in the treatment of a cancer based on expression of the thirty-five gene set.

A method for preparing oligonucleotide chip for transgene product detection and use are disclosed. It includes: designing specific oligonucleotide probe and related primer by heterogeneric inserting gene, species internal standard gene, specific boundary sequence in transgene product gene set, fixing probe on glass slide to form transgene product detecting chip, amplifying DNA of plant to be tested by related primmer, marking by fluorescence, and hybridizing with chip. It can be use to acquire variable information.

The invention discloses a preparation method of a liver cancer detection panel. The preparation method comprises the following steps: screening liver cancer information and related genes thereof, thusobtaining a gene set; selecting a gene target zone; designing a panel probe; synthesizing an RNA (Ribonucleic Acid) single-stranded probe, thus obtaining the gene detection panel. The gene detectionpanel designed by the invention can be used for diagnosis, typing and prognosis of liver cancer, has wide gene coverage degree and strong transferability and is suitable for newly-developed detectionplatforms.

The present invention provides gene sets the expression of which is important in the diagnosis and/or prognosis of cancer, in particular of breast cancer.

The present invention provides a number of gene markers whose expression is altered in various gliomas. In particular, by examining the expression these markers, one can accurately classify a glioma as glioblastoma multiforme (GM), anaplastic astrocytoma (AA), anaplastic oligodendroglioma (AO) or oligodendroglioma (OL). The diagnosis may be performed on nucleic acids, for example, using a DNA microarray, or on protein, for example, using immunologic means. Also disclosed are methods of therapy.

The invention discloses gene sets and a scoring model for evaluating a tumor microenvironment, and application of the gene sets, and belongs to the field of biological information. It is proved for the first time that the gastric cancer has remarkable tumor microenvironment typing, and prognosis difference of gastric cancer patients with different typing is remarkable. According to a constructionmethod of the multi-gene scoring model for evaluating the gastric cancer tumor microenvironment, 44 gene sets capable of representing different microenvironment types are screened out, and the prediction efficiency of the tumor microenvironment multi-gene scoring model corresponding to the gene sets is remarkably better than that of other existing models; and the model is not only a prognostic marker of a gastric cancer patient, but also has a quite remarkable prediction effect in pan-cancer. The model provided by the invention not only can be used as a good prognosis biomarker, but also can be used for predicting checkpoint inhibitor immunotherapy response of various tumors by effectively evaluating the tumor microenvironment, and has important significance and clinical application valuefor better screening immunotherapy benefiting people.

The invention discloses a high efficiency turning glycosyl beta-galactosidase gene. The genes are gained by enterobacter cloacae B5 CGMCC No.1401 gene set DNA under PCR amplification. The whole length of gene nucleotide sequence is 3090 basic groups, and coding 1029 amino acid. The recombinase features are that enzyme is quart-subunit albumen; polymer molecular weight are 391kD; mono subunit are 97.8kD; the enzyme to lactose Km value is 0.32mmol/L, Vmax is 210.70umol/(L.min); and it to onitrophenol-beta-D-galactoside Km value is close to 0; its optimum reaction temperature is 35 centigrade degree; the pH value is from 6.5-9.5; it can be stored at least two months at room temperature; oligomerization galactose yield is 55%; receptor substrate specificity of the enzyme turning glycosyl is very wild; it can used to compose multi galactoside compounds. The gained enzyme gene can be used to modify gene to gain new beta-galactoside synthetase, and improve its combined efficiency fundamentally.

The invention discloses a tumor feature gene selection method combining mRNA and microRNA expression profile chips. The method is implemented by the following steps of step 1, by using the mRNA and microRNA expression profile chips, detecting expression values of a large amount of genes, by adopting a filtration feature gene selection method, ranking relevance of all the genes, removing a large amount of low-relevance genes, and leaving a small amount of genes that are closely related to tumor classification; step 2, combining mRNA and microRNA feature genes obtained by adopting the filtration feature gene selection method to form a gene pool U; and step 3, by adopting a genetic algorithm, further selecting genes for the gene pool, eliminating redundant genes, and performing a search to obtain an optimal gene set S with optimal features. The method is simple in step and high in result accuracy.

Colon cancer cells in a sample are screened by analyzing the amount of expression of at least 2 or more genes or products thereof selected from the group of genes listed in Tables 1 and 30. As compared to conventional method, patients having colon cancer can be detected with higher accuracy. Colon cancer cells in stool are also screened by analyzing expression of genes selected from the group of genes listed in Table 37.

The invention discloses a detection method of a bot program and belongs to the technical field of information safety, and the method comprises the following steps: extracting antibody genes of a normal program set B, constructing antibody gene sets Agdl, and forming an antibody gene library Agd by the antibody gene sets Agdl of different antibody gene lengths; carrying out feature extraction on a normal program set B' by the antibody gene sets Agdl and constructing a normal program state model; generating detectors by normal program state sets Cb and generating a detector set by the detectors; detecting the bot program by the detector set; and evolving the antibody gene library and the detectors dynamically. The method can not only identify known bot programs but also discover new bot programs or variations of the known bot programs through self-learning and evolvement in a computer environment which changes in real time, thus effectively solving the key issue that a feature code library of computer viruses can not be synchronous with the multistate bot programs.

The invention discloses a novel feature gene selection method based on logistic and relevant information entropy. The method comprises the following steps that a dataset is subjected to logistic regression, a gene variable with great influence on the classification is obtained, a Relief algorithm is used for giving a value on the gene variable, sequencing is carried out, a maximum feature value gene is added to an initial feature gene set, and the relevant information entropy is calculated. The novel feature gene selection method has the advantages that a logistic regression model in the machine study is introduced into the feature gene selection method, and a high-quality gene expression profile is obtained; the correlation between gene variables is measured by the relevant information entropy, redundant genes are deleted, and a feature gene sub set with high classification capability and fewer genes is obtained through searching a feature gene space set.

The invention belongs to the technical field of tumor diagnosis, and particularly relates to identification of esophageal cancer related characteristic pathways of and a construction method of an early diagnosis model. The identification and constructing method includes the steps: expression spectrum preprocessing, differential expression gene extraction, sample cluster analysis, gene cluster analysis, specific gene set functional pathway analysis, comparison of pathway aberration scores, comparative analysis of functional differences, construction of esophageal cancer specific co-expression networks, characteristic selection of genes, prediction of deep learning models, and the like. The method of the invention divides genes into different groups according to expression similarity and functional consistency of the genes, and analyzes the genes in the form of gene collection, thus being able to avoid the disadvantages of high false positive rate, big random error and unstable result inthe traditional method, and also being able to more specifically identify the function significantly associated with esophageal cancer.

The present invention relates to a novel system for predicting a prognosis capable of predicting the prognosis of locally advanced gastric cancer, and more specifically, capable of predicting a clinical result after a resection during gastric cancer surgery through gene set enrichment comparative analysis.

A computer program product, disposed on a non-transitory computer readable media, for analyzing a biological relevance of a candidate gene to a human phenotype is provided. The product includes computer executable process steps operable to control a computer to receive an input phenotype comprised of a plurality of input human traits and at least one input candidate gene; identify a plurality of disease-linked genes by querying disease-linked gene data and identifying genes causally linked to at least one disease; provide values of a semantic similarity metric for a identified gene set with respect to the input phenotype based on a comparison of human traits linked to each gene of the identified gene set and the input human traits, the identified gene set including genes mechanistically related to the input candidate gene that are included in the identified disease-linked genes; and output a statistical measure indicating whether the values of the semantic similarity metric of the genes of the identified gene set with respect to the input phenotype are greater than the values of the semantic similarity metric of others of the identified disease-linked genes with respect to the input phenotype by a statistically significant amount.

Methods of activating a signaling cascade comprising, introducing leptin and/or a cytokine to a receptor complex comprising gp 130, optionally in combination with a compound acting on adenylate cyclase or acting on one or more downstream targets of adenylate cyclase, thereby inducing genes in neuro-endocrine cells or cells of neuro-endocrine origin. Two distinct gene-sets are induced, immediate early response genes (STAT-3, SOCS-3, Metallothionein-II, the serine/threonine kinase Fnk and the rat homologue of MRF-1), and late induced target genes (Pancreatitis Associated Protein I, Squalene Epoxidase, Uridinediphosphate Glucuronyl Transferase and Annexin VIII). Strong co-stimulation with the adenylate cyclase activator forskolin was shown with respect to late induced target genes. Transcripts encoding Leptin Induced Protein I (LIP-I) and Leptin Induced Protein II (LIP-II) were identified; however, no forskolin co-stimulatory effect was observed. It is also demonstrated that leptin modulates in vivo expression of MT-II, Fnk and Pancreatitis Associated Protein I genes.