Web‐based data warehouse on gene expression in human colorectal cancer

Abstract

Based on biomedical literature databases, we tried a first step for constructing a gene expression "data warehouse" specific to human colorectal cancer (CRC). Results of genome-wide transcriptomic research were available from 12 studies, using various technologies, namely, SAGE, cDNA and oligonucleotide arrays, and adaptor-tagged amplification. Three studies analyzed CRC cell lines and nine studies of human samples. The total number of patients was 144. Out of 982 up- or down-regulated genes, 863 (88%) were found to be differentially expressed in a single study, 88 in two studies, 22 in three studies, 7 in four studies, and only 2 genes in six studies. Eight large-scale proteomics studies were published in CRC, using 2-D-, SDS- or free-flow electrophoresis, involving only 11 patients. Out of 408 differentially expressed proteins, 339 (83%) were found to be differentially expressed only in a single study, 16 in three studies, 10 in four studies, 3 in five, and 1 in eight studies. Confirmation at proteome level of results obtained with large-scale transcriptomics studies was possible in 25%. This proportion was higher (67%) for reproducing proteome results using transcriptomics technologies. Obviously, reproducibility and overlapping between published gene expression results at proteome and transcriptome level are low in human CRC. Thus, the development of standardized processes for collecting samples, storing, retrieving, and querying gene expression data obtained with different technologies is of central importance in translational research.

Meta-analysis of Colorectal Cancer Gene Expression Profiling Studies Identifies Consistently Reported Candidate Biomarkers

“…Meta-analyses have been done previously to determine differentially expressed genes in colorectal cancer (1,9,10). However, these studies and others usually do not consider whether the level of overlap observed is statistically significant.…”

“…Although an abundance of data comparing the expression profiles of cancerous to normal tissue has been generated, to date, no reliable biomarker has resulted. One explanation for this lack of translational success to the clinic has been the inconsistency in the results of independent studies (1,9,10). Explanations for this low overlap include utilization of different tissue resection methods (microdissection, laser capture microdissection, etc.…”

Identification of PSME3 as a Novel Serum Tumor Marker for Colorectal Cancer by Combining Two-dimensional Polyacrylamide Gel Electrophoresis with a Strictly Mass Spectrometry-based Approach for Data Analysis

“…Therefore, genomics-and proteomicsbased approaches have been used to identify new biomarkers for CRC. Particularly 2-DE has been used in many of the proteomics studies (11). Despite a number of limitations, the resolution of 2-DE gels is impressive, rendering this technology still a preferred tool in many proteomics studies (12).…”

Proteomic analysis of differentially expressed serum proteins in lung cancer

“…Despite a number of limitations, the resolution of 2D gels is impressive, rendering this technology still a preferred tool in many proteomics studies [15]. Along with 2DE, two technical methods often applied in the research of serum proteomics include: Surface enhanced laser desorption and ionization time-offlight mass spectrometry (SELDI-TOF-MS) and Matrix assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF-MS).…”