Colon cancer profiling
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE2138 | GEO | 2005/08/26
SECONDARY ACCESSION(S): PRJNA91759
REPOSITORIES: GEO
Similar Datasets
Project description:Expression profile was obtained for GeneChip probe sets among colon cancer specimens with or without the methylation of MLH1 promoter.
2007-08-29 | E-GEOD-2138 | biostudies-arrayexpress
Project description:Provides a set of enriched normal colon epithelial cells to use as a baseline for disease of the colon We present a dependable expression reference data set for non-neoplastic colonic epithelial cells. An enriched population of fresh colon epithelial cells were obtained from non-neoplastic, colectomy specimens and analyzed using Affymetrix GeneChip Exon 1.0 ST arrays. The findings from this study represent the first comprehensive expression profile for non-neoplastic colonic epithelial cells reported. For demonstration purposes, we have compared the data derived from these cells to a publically available set of tumor and normal matched colon data. This analysis allowed an assessment of global gene expression alterations and demonstrated that adjacent normal tissues, with a high degree of cellular heterogeneity, are not always representative of normal cells for comparison to tumors which arise from the colon epithelium. We also examined alternative splicing events in tumors compared to normal colon epithelial cells. Our analysis of splice variants on a genome-wide scale illustrate that this is a very labor intensive procedure, requiring vigilant examination of the data. It is projected that the contribution of this set of data derived from pure colonic epithelial cells will enhance studies in colon-related disease and offer a vital baseline for studies aimed at elucidating the mechanisms of alternative splicing.
2009-12-10 | GSE19163 | GEO
Project description:Background. Most colorectal cancers (CRC) arise in a progression through adenoma to carcinoma phenotypes as a consequence of altered genetic information. Clinical progression of CRC may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence. Methodology/Principal Findings. Studies were performed on normal mucosa, adenoma, and CRC samples obtained during surgery or colonoscopy. Collections of cryostat sections prepared from the tissue samples were evaluated by a pathologist to control the relative cell type content. RNA was isolated from 105 macro- and 40 microdissected specimens. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data were generated using two normalization algorithms: MAS5 and GCRMA with LVS. The data were evaluated using pair-wise comparisons and data decomposition into SVD modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Based on a consensus of the results obtained by two tissue handling procedures, two normalization algorithms, and two probe set sorting criteria, we identified six KEGG signaling and metabolic pathways (cell cycle, DNA replication, p53 signaling pathway, purine metabolism, pyrimidine metabolism, and RNA polymerase) that are significantly altered in both macro- and microdissected tumor samples compared to normal colon. On the other hand, pathways altered between benign and malignant tumors were identified only in the macrodissected tissues. Conclusion/Significance. Multidirectional analyses of microarray data allow the identification of essential signaling alterations underlying CRC development. Although the proposed strategy is computationally complex and labor–intensive, it may reduce the number of false results. Gene expression profiles of 40 tissue samples representing distant full-thickness normal colon, normal colon mucosa dissected from tumor, adenoma, and carcinoma. Gene expression profiles of 105 tissue samples representing colon carcinomas, adenocarcinomas, and normal colon samples.
2010-08-01 | E-GEOD-20916 | biostudies-arrayexpress
Project description:Samples were taken from surgically resected tumor specimens from patients with proximal colon cancer. The expression profiles were determined using the Affymetrix GeneChip Human Exon 1.0 ST Array version 2. APC gene mutation status was determined using Sanger sequencing. A classifier for APC mutation status was trained using these expression data.
2016-03-02 | GSE63624 | GEO
Project description:Biopsies from the descending colon were obtained colonoscopically from 46 Crohn's disease patients (33 from areas without inflammation and 18 from inflamed areas). Gene profiling analyses were performed using Human Genome U133 Plus 2.0 GeneChip Array. Hybridization data were analyzed with dChip software.
2006-11-06 | E-TABM-118 | biostudies-arrayexpress
Project description:Using single molecule ultra-deep bisulfite sequencing we characterized the CpG methylation landscape from –938 to –337 bp upstream of the MLH1 transcriptional start site position 0, from 30 hematopoietic colony forming cell clones (CFC) either expressing or not expressing MLH1. We identify a correlation between MLH1 promoter methylation and loss of MLH1 expression. Additionally, using the CpG site methylation frequencies obtained in this study we are able to generate a classification algorithm capable of sorting the expressing and non-expressing CFC. Thus, as previously described for many tumor cell types, MLH1 promoter methylation in hematopoietic stem cell clones correlates with the loss of MLH1 expression, a critically important gene in the mismatch repair pathway.
2017-02-09 | GSE73868 | GEO
Project description:Background. Most colorectal cancers (CRC) arise in a progression through adenoma to carcinoma phenotypes as a consequence of altered genetic information. Clinical progression of CRC may occur in parallel with distinctive signaling alterations. We designed multidirectional analyses integrating microarray-based data with biostatistics and bioinformatics to elucidate the signaling and metabolic alterations underlying CRC development in the adenoma-carcinoma sequence. Methodology/Principal Findings. Studies were performed on normal mucosa, adenoma, and CRC samples obtained during surgery or colonoscopy. Collections of cryostat sections prepared from the tissue samples were evaluated by a pathologist to control the relative cell type content. RNA was isolated from 105 macro- and 40 microdissected specimens. The measurements were done using Affymetrix GeneChip HG-U133plus2, and probe set data were generated using two normalization algorithms: MAS5 and GCRMA with LVS. The data were evaluated using pair-wise comparisons and data decomposition into SVD modes. The method selected for the functional analysis used the Kolmogorov-Smirnov test. Based on a consensus of the results obtained by two tissue handling procedures, two normalization algorithms, and two probe set sorting criteria, we identified six KEGG signaling and metabolic pathways (cell cycle, DNA replication, p53 signaling pathway, purine metabolism, pyrimidine metabolism, and RNA polymerase) that are significantly altered in both macro- and microdissected tumor samples compared to normal colon. On the other hand, pathways altered between benign and malignant tumors were identified only in the macrodissected tissues. Conclusion/Significance. Multidirectional analyses of microarray data allow the identification of essential signaling alterations underlying CRC development. Although the proposed strategy is computationally complex and labor–intensive, it may reduce the number of false results.
2010-08-01 | GSE20916 | GEO
Project description:Constitutional MLH1 epimutations are a rare cause of Lynch syndrome. Low methylation levels (≤10%) have been occasionally described. The aim of this study was the identification of patients with low levels of epigenetic mosaicism in MLH1 gene. Eighteen patients with MLH1 hypermethylated tumors and undetectable methylation in blood as assessed by Methylation-Specific Multiplex Ligation-Dependent Probe Amplification were included (MS-MLPA). Highly sensitive MS-Melting Curve Analysis (MS-MCA) at MLH1 promoter was used to screen for epigenetic mosaicism. Constitutional methylation was confirmed by other methods. Mutational analysis of hereditary cancer genes including MLH1 was performed. MS-MCA analysis identified one case (5.6%) with low levels of methylation (1-2%) in blood DNA. The patient had developed 3 gastrointestinal tumors at ages 22, 24 and 25, sharing MLH1 promoter hypermethylation and loss of heterozygosity associated with c.655A allele. The presence of low MLH1 methylation levels was confirmed by clonal bisulfite sequencing, evidencing the association with c.-93G allele (in phase with c.655G). The extension of the hypermethylated region overlaps with the reported in constitutional MLH1 epimutation carriers. No rare germline variants were identified. The use of highly sensitive techniques such as MS-MCA has demonstrated to be useful for the detection of low MLH1 methylation levels in blood.
2019-05-22 | GSE131541 | GEO
Project description:Purpose: The molecular pathology of colon cancer is still unclear. In this study, we performed a proteomic study of colon tissues from the patients with or without HIV infection. Experimental design: Colon tissues from the patients with or without HIV infection were obtained. The proteins were digested, and labeled by TMT kit. The differential proteins were selected by PCA analyzed. The differential proteins were analyzed by GO database, and STRING software. Results: A total of 720, 730, 35, 5, and 21 differentially expressed proteins were identified in No_HIV_C to No_HIV_adjacent, HIV_C to HIV_A, HIV_C to No_HIV_C, HIV_A to No_HIV_A, and HIV to No_HIV, respectively.
2023-07-14 | PXD043821 | iProX
Project description:Samples were taken from surgically resected tumor specimens from patients with proximal colon cancer. The expression profiles were determined using the Affymetrix GeneChip Human Exon 1.0 ST Array version 2. APC gene mutation status was determined using Sanger sequencing. A classifier for APC mutation status was trained using these expression data. 52 microsatellite stable (MSS) proximal colon cancers samples were analyzed. 17 samples were APC wild-type and 35 had APC protein-truncating mutations.
2016-03-02 | E-GEOD-63624 | biostudies-arrayexpress