31 results about "Gene subset" patented technology

The present invention provides gene expression information useful for predicting whether a cancer patient is likely to have a beneficial response to treatment with chemotherapy, comprising measuring, in a biological sample comprising a breast tumor sample obtained from the patient, the expression levels of gene subsets to obtain a risk score associated with a likelihood of a beneficial response to chemotherapy, wherein the score comprises at least one of the following variables: (i) Recurrence Score, (ii) ESRI Group Score; (iii) Invasion Group Score; (iv) Proliferation Group Score; and (v) the expression level of the RNA transcript of at least one of MYBL2 and SCUBE2, or the corresponding expression product. The invention further comprises a molecular assay-based algorithm to calculate the likelihood that the patient will have a beneficial response to chemotherapy based on the risk score.

Transcriptomics data from tumor tissue of patients diagnosed with metastatic breast cancer are clustered and associated with overall survival of the patients. A subset of genes from one of the clusterassociated with poor outcome are used to generate a survival prediction model predicting a survival time based on expression levels of a plurality of genes. Using such generated survival prediction model, a survival time of a patient diagnosed with metastatic breast cancer can be predicted and a treatment regimen can be updated or generated based on the survival time.

The present invention provides algorithm-based molecular assays that involve measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The present invention also provides methods of obtaining a quantitative score for a patient with kidney cancer based on measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The genes may be grouped into functional gene subsets for calculating the quantitative score and the gene subsets may be weighted according to their contribution to cancer recurrence.

The invention discloses a tumor key gene identification method based on particle swarm optimization and a marking criterion. The method comprises the steps that one gene marking criterion is defined to acquire gene classification ability information so as to filter genes with a low correlation to a tumor category; and the Metropolis criterion is utilized to improve a particle swarm optimization (PSO) algorithm in combination with the gene classification ability information so as to realize identification of tumor key genes. The new method for gene classification information and PSO improvement overcomes the defect that a traditional method for identifying tumor key genes based on PSO is prone to fall into a locally optimal solution, gene subsets in a smaller number and with a high correlation to the tumor category can be selected, and therefore the method is beneficial for improving subsequent tumor identification.

The present invention relates to a tumor-specific gene identification method based on PLS multi-perturbation integrated gene selection. Aiming at the characteristics of tumor microarray data, different perturbation mechanisms are introduced to provide an analysis framework for multi-perturbation integrated gene selection; using PLS multi-gene measurement method, a new PLS-based integrated gene selection method was developed under this framework. On the one hand, the method of the present invention is based on the overall effect of subsets, and can quickly identify genes with differential expression, and at the same time identify genes with weak differential expression signals; on the other hand, the method of the present invention is based on multiple perturbation mechanisms , which can identify a series of different gene subsets with small length and strong discriminative ability. Therefore, the method of the present invention can identify a series of different gene subsets and weakly differentially expressed genes, through which the specific expression patterns of tumor genes can be more comprehensively understood.

Methods of identifying a mammal having an increased likelihood of recurrence of breast cancer includes identifying in a breast tissue sample of the mammal expression of at least two genes selected from the group consisting of Hs.125867 (EVL), Hs.591847 (NAT1), Hs.208124 (ESR1), Hs.26225 (GABRP), Hs.408614 (ST8SIA1), Hs.480819 (TBC1D9), Hs.504115 (TRIM29), Hs.523468 (SCUBE2), Hs.532082 (IL6ST), Hs.592121 (RABEP1), Hs.79136 (SLC39A6), Hs.82128 (TPBG), Hs.95243 (TCEAL1), Hs.95612 (DSC2), Hs.654961 (FUT8), Hs.1594 (CENPA), Hs.184339 (MELK), Hs.26010 (PFKP), Hs.592049 (PLK1), Hs.370834 (ATAD2), Hs.437638 (XBP1), Hs.444118 (MCM6), Hs.469649 (BUB1), Hs.470477 (PTP4A2), Hs.473583 (YBX1), Hs.480938 (LRBA), Hs.524134 (GATA3), Hs.531668 (CX3CL1), Hs.532824 (MAPRE2), Hs.591314 (GMPS), Hs.83758 (CKS2) and Hs.99962 (SLC43A3) and subsets of the genes.

The present invention divides malignant tumor patient data into clinical factors and pre-radiotherapy gene expression profile data, namely gene sets; constructs a regression model of clinical factors based on whether the clinical factors are used or not, and calculates the clinical factors PS of each sample; Two independent samples t-test test was performed as the group, and the differentially expressed gene subset with P<0.1 was screened out; PS was included as a fixed independent variable, and the expression level of one of the remaining genes in the gene subset was included as an independent variable each time. For the regression model with radiosensitivity or tolerance as the outcome, the area under the curve AUC was calculated, and the gene expression with the largest AUC was selected to be included in the regression model, until the AUC of the regression model reached the maximum and the cycle ended to form a multiple regression model; genes included in the multiple regression model It constitutes a radiosensitivity marker gene; based on the multiple regression model, the dependent variable or its inverse is obtained as the radiotherapy sensitivity score of the patient, and a certain cutoff value is used to predict whether the patient is radiosensitive or not.

The present invention provides algorithm-based molecular assays that involve measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The present invention also provides methods of obtaining a quantitative score for a patient with kidney cancer based on measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The genes may be grouped into functional gene subsets for calculating the quantitative score and the gene subsets may be weighted according to their contribution to cancer recurrence.