ABSTRACT: Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 779 genes in microglial cells from 36 mice, which had been consecutively operated on within a defined short period of time
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 8,644 genes in 20 pancreatic cancer cases, which had been consecutively operated.
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 8,644 genes in 50 non-small cell lung cancer (NSCLC) cases, which had been consecutively operated on within a defined short period of ti
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 3100 capture probes, covering all human, mouse and rat microRNAs annotated in miRBase 18.0, in 8 melanoma samples divided into 4 groups (Control, ALA, US, and SDT group). In the study presented here, a consecutively operated, well-defined cohort of 8 melanoma cases was used to acquire miRNA expression profiles with Exiqon arrays based on miRBase 18.0.
Project description:Interactions between stromal cell-derived factor-1M-NM-1 (SDF-1M-NM-1) and its cognate receptor CXCR4 are crucial for the recruitment of mesenchymal stem cells (MSCs) from bone marrow (BM) reservoirs to damaged tissues for repair during alarm situations. MicroRNAs are differentially expressed in stem cell niches, suggesting a specialized role in stem cell regulation. Here, we gain insight into the molecular mechanisms involved in regulating SDF-1M-NM-1. Individualized outcome prediction classifiers were successfully constructed through expression profiling of microRNAs (in all organisms as annotated in Sanger miRBase Release 11.0 (http://microrna.sanger.ac.uk))in one burned murine skin tissue compared to normal skin tissue,which had 57 upregulated microRNAs and 28 down-regulated microRNAs In the study presented here, a consecutively operated, well-defined cohort of 2 cases, was used to acquire expression profiles of microRNAs(in all organisms as annotated in Sanger miRBase Release 11.0 (http://microrna.sanger.ac.uk)), leading to the successful construction of supervised .
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 8,644 genes in 50 non-small cell lung cancer (NSCLC) cases, which had been consecutively operated on within a defined short period of time and followed up more than five years. The resultant classifier of NSCLCs yielded 82% accuracy for forecasting survival or death five years after surgery of a given patient. In addition, since two major histologic classes may differ in terms of outcome-related expression signatures, histologic type-specific outcome classifiers were also constructed. The resultant highly predictive classifiers, designed specifically for non-squamous cell carcinomas, showed a prediction accuracy of more than 90% independent of disease stage. In addition to the presence of heterogeneities in adenocarcinomas, our unsupervised hierarchical clustering analysis revealed for the first time the existence of clinicopathologically relevant subclasses of squamous cell carcinomas with marked differences in their invasive growth and prognosis. This finding clearly suggests that NSCLCs comprise distinct subclasses with considerable heterogeneities even within one histologic type. Overall, these findings should advance not only our understanding of the biology of lung cancer but also our ability to individualize post-operative therapies based on the predicted outcome. Keywords: cell type comparison and prognosis prediction
Project description:Interactions between stromal cell-derived factor-1M-NM-1 (SDF-1M-NM-1) and its cognate receptor CXCR4 are crucial for the recruitment of mesenchymal stem cells (MSCs) from bone marrow (BM) reservoirs to damaged tissues for repair during alarm situations. MicroRNAs are differentially expressed in stem cell niches, suggesting a specialized role in stem cell regulation. Here, we gain insight into the molecular mechanisms involved in regulating SDF-1M-NM-1. Individualized outcome prediction classifiers were successfully constructed through expression profiling of microRNAs (in all organisms as annotated in Sanger miRBase Release 11.0 (http://microrna.sanger.ac.uk))in one burned murine skin tissue compared to normal skin tissue,which had 57 upregulated microRNAs and 28 down-regulated microRNAs. In the study presented here, a consecutively operated, well-defined cohort of 2 cases, was used to acquire expression profiles of microRNAs (in all organisms as annotated in Sanger miRBase Release 11.0 (http://microrna.sanger.ac.uk)), leading to the successful construction of supervised. The most recent version of the array (v.11.0 - hsa, mmu & rno array) contains more than 1700 capture probes, covering all microRNAs annotated in miRBase 11.0, as well as all viral microRNAs, related to these species.
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of ncRNAs in 165 CRC cases.