Project description:Campare the difference between pairwise NOF and coCAF tissues for three patients patient #603: NOF #603 vs coCAF #603 patient #609: NOF #609 vs coCAF #609 patient #612: NOF #612 vs coCAF #612
Project description:This SuperSeries is composed of the following subset Series: GSE35249: aNOF vs. CAF GSE35250: NOF vs. coCAF 7d GSE35251: NOF vs. iCAF 2d GSE35252: NOF vs. iCAF 4d Refer to individual Series
Project description:Compare the difference between pairwise aNOF and CAF samples for two patients patient #225: aNOF #225 vs CAF #225 patient #248: aNOF #248 vs CAF #248
Project description:We used DNA microarrays (HG-U95Av2 GeneChips) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients. Sample classes include kidney biopsies and PBLs from patients with 1) healthy normal donor kidneys, 2) well-functioning transplants with no clinical evidence of rejection, 3) kidneys undergoing acute rejection, and 4) transplants with renal dysfunction without rejection. Nomenclature for samples is as follows: 1) all sample names include either BX or PBL to indicate that they were derived from biopsies or PBLs respectively, 2) C indicates samples from healthy normal donors, 3) TX indicates samples from patients with well-functioning transplants with no clinical evidence of rejection, 3) AR indicates samples from transplant patients with kidneys undergoing acute rejection, 4) NR indicates samples from transplant patients with renal dysfunction without rejection. Abbreviations used to describe patient samples include the following: BX - Biopsy; PBL- Peripheral Blood Lymphocytes; CsA -Cyclosporine; MMF - Mycophenolate Mofetil; P - Prednisone; FK - Tacrolimus; SRL - Sirolimus; CAD -Cadaveric; LD - Live Donor; Scr - Serum Creatinine; ATN - Acute Tubular Necrosis CNI - Calcineurin Inhibitor; FSGS - Focal Segmental Glomerulosclerosis several array data sets did not pass quality control and were not analyzed. These include AR1PBL, NR4BX, and NR6PBL Keywords = DNA microarrays, gene expression, kidney, rejection, transplant Keywords: other. This dataset is part of the TransQST collection.
Project description:Background: Glioblastoma multiforme (GBM) is the most aggressive and most lethal primary malignant brain tumor, correlated with survival rates of less than one year from the time of diagnosis. Current surgical procedure attempts to remove the bulk of the tumor mass, whereas GBM frequently recurs within 1-3cm from the primary tumor resection site. Molecular mechanisms involved in the recurrence of the tumor are still poorly understood. The aim of the study was to define the molecular signature of GBM surrounding white matter (WM) in order to better understand the molecular mechanisms involved with tumor relapse. Material & Methods: Human GBM tumor bulk and surrounding tissue (1-3cm from the border of the tumor) were obtained from five patients who underwent total tumour resection, while normal white matter was harvested from patients who underwent surgical procedure for nonmalignant pathologies. Samples were processed for hybridization on the Affymetrix Human U133A arrays and data were examined with the GeneSpring analysis software. Results: Gene expression analysis of the samples was done in 2 independent steps. First, molecular profiling comparison of GBM surrounding WM and normal WM resulted in 59 genes differentially expressed between both tissues. Among these, numerous genes expressed by mature neural cells were down-regulated in GBM surrounding WM, while gene products supporting invasion were overexpressed. Moreover, KLRC1, a specific natural killer receptor naturally involved in the activation of antitumoral cells was drastically repressed in GBM surrounding WM, suggesting that the antitumoral immune surveillance is compromised in this tissue. Second, we focused our study on genes specifically regulated in GBM periphery respectively to GBM core. The highest up-regulated gene in GBM surrounding tissue encodes for DTX4, a regulator of NOTCH signalling pathway described for its key role in maintaining neural progenitors in an uncommitted state. Conclusion: This study revealed unique molecular characteristics of GBM surrounding tissue, showing the dysregulation of genes involved in immune surveillance along with genes associated to stemness maintenance. All together, these data may help to understand the molecular mechanisms associated with GBM recurrence This study attempted to define the molecular characteristics of the GBM surrounding tissue. To this end, GBM tumor samples were obtained from 5 patients who underwent total tumor resection. Surrounding tumor mass tissue was retrieved in all cases from not infiltrated white matter sited at 2 cm from the macroscopic tumor border. Furthermore, control white matter biopsies were harvested from patients operated on for deep intracerebral cavernomas. Each sample was hybridized onto Affymetrix human U133 arrays. For each patient, tumor core sample and surrounding tissue were harvested and are identified with the same suffix number. In 2 cases, (patients 3 and 4), two tumor peripheral tissue samples were harvested and are identified with the same number followed by "R" (replicate).
Project description:The Eol1 cell line has been derived from a patient with chronic eosiniphilic leukemia. Eol1 cells express the FIP1L1-PDGFRalpha oncogene. Inhibition of FIP1L1-PDGFRalpha with imatinib mesylate (Glivec) blocks proliferation and survival of the cells. We performed microarray expression analysis to identify genes specifically regulated by FIP1L1-PDGFRalpha using imatinib-treated cells as baseline. The list of regulated genes was consistent with the activation of STAT trancription factors by FIP1L1-PDGFRA. Keywords: 4 hours treatment with Glivec Eol1 cells were cultured in presence or absence of Glivec for 4 hours before RNA extraction and hybridization on Affymetrix microarray.
Project description:Surgical samples have long been used as important subjects for cancer research. In accordance with an increase of neoadjuvant therapy, biopsy samples have recently become imperative for cancer transcriptome. On the other hand, both biopsy and surgical samples are available for expression profiling for predicting clinical outcome by adjuvant therapy; however, it is still unclear whether surgical sample expression profiles are useful for the prediction by the use of biopsy samples because little has been done about comparative gene expression profiling between the two kinds of samples. When gene expression profiles were compared between biopsy and surgical samples, artificially induced epithelial-mesenchymal transition (aiEMT) was found in the surgical samples. This study will evoke the fundamental misinterpretation including underestimation of the prognostic evaluation power of markers by overestimation of EMT in past cancer research, and will furnish some advice for the near future as follows: 1) Understanding how long the tissues were under an ischemic condition; 2) Prevalence of biopsy samples for in vivo expression profiling with low biases on basic and clinical research; and 3) Checking cancer cell contents and normal- or necrotic-tissue contamination in biopsy samples for prevalence. We used microarrays to compare gene expression profiles between 20 biopsy (BPY) and 20 surgical (OPE) samples derived from the cancerous portion of the esophagus of 20 esophageal cancer patients. One biopsy sample and one surgical sample was obtained from each patient; these sample pairs have the same number.
Project description:Lymph node involvement is the most important prognostic factor in breast cancer, but little is known about the underlying molecular changes. First, to identify a molecular signature associated with nodal metastasis, gene expression analysis was performed on a homogeneous group of 96 primary breast tumors, balanced for lymph node involvement. Each tumor was diagnosed as a poorly differentiated, estrogen positive, her2-neu negative invasive ductal cancer. (Affymetrix Human U133 Plus 2.0 microarray chips). A model, including 241 genes was built and validated on an internal and external dataset performed with Affymetrix technology. All samples used for validation had the same characteristics as the initial tumors. The area under the ROC curve (AUC) for the internal dataset was 0.646 and 0.651 for the external datasets. Thus, the molecular profile of a breast tumor reveals information about lymph node involvement, even in a homogeneous group of tumors. However, an AUC of 0.65 indicates only a weak correlation. Our model includes multiple kinases, apoptosis related and zinc ion binding genes. Pathway analysis using the Molecular Signatures Database revealed relevant gene sets (BAF57, Van 't Veer). Next, miRNA profiling was performed on 82/96 tumors using Human MiRNA microarray chips (Illumina). Eight miRNAs were significantly differentially expressed according to lymph node status at a significance level of 0.05, without correcting for multiple testing. The analysis of the inverse correlation between a miRNA and its computationally predicted targets point to general deregulation of the miRNA machinery potentially responsible for lymph node invasion. In conclusion, our results provide evidence that lymph node involvement in breast cancer is not a random process. Gene expression profiling: A training set of 96 patients and an independent internal dataset of 20 patients balanced for lymph node involvement was selected from the multidisciplinary breast centre database miRNA profiling not provided in this Series.