- Research article
- Open Access
Bioinformatic screening of human ESTs for differentially expressed genes in normal and tumor tissues
- Abdel Aouacheria†1, 3Email author,
- Vincent Navratil†1,
- Audrey Barthelaix2,
- Dominique Mouchiroud1 and
- Christian Gautier1
© Aouacheria et al; licensee BioMed Central Ltd. 2006
- Received: 14 October 2005
- Accepted: 26 April 2006
- Published: 26 April 2006
Owing to the explosion of information generated by human genomics, analysis of publicly available databases can help identify potential candidate genes relevant to the cancerous phenotype. The aim of this study was to scan for such genes by whole-genome in silico subtraction using Expressed Sequence Tag (EST) data.
Genes differentially expressed in normal versus tumor tissues were identified using a computer-based differential display strategy. Bcl-xL, an anti-apoptotic member of the Bcl-2 family, was selected for confirmation by western blot analysis.
Our genome-wide expression analysis identified a set of genes whose differential expression may be attributed to the genetic alterations associated with tumor formation and malignant growth. We propose complete lists of genes that may serve as targets for projects seeking novel candidates for cancer diagnosis and therapy. Our validation result showed increased protein levels of Bcl-xL in two different liver cancer specimens compared to normal liver. Notably, our EST-based data mining procedure indicated that most of the changes in gene expression observed in cancer cells corresponded to gene inactivation patterns. Chromosomes and chromosomal regions most frequently associated with aberrant expression changes in cancer libraries were also determined.
Through the description of several candidates (including genes encoding extracellular matrix and ribosomal components, cytoskeletal proteins, apoptotic regulators, and novel tissue-specific biomarkers), our study illustrates the utility of in silico transcriptomics to identify tumor cell signatures, tumor-related genes and chromosomal regions frequently associated with aberrant expression in cancer.
- Differential Expression Profile
- Sage Data
- Cancer Candidate Gene
- Digital Differential Display
- Data Mining Procedure
Large-scale transcriptome analysis of genes that are differently expressed in tumor tissues compared to their normal counterparts is an important route to the identification of candidates that could play a role in human malignancies. A number of techniques, ranging from differential display and nucleic acid subtraction to serial analysis of gene expression, expression microarrays and gene chips, have been used to the discovery of such aberrantly expressed cancer-related genes . The well-established differential screening technology, that allows for the simultaneous comparison of multiple gene expression levels between two samples differing in tissue type and pathological state, has been the more extensively applied. This simple and powerful method could be performed either experimentally or, since late 1999, digitally using expression databases. The computer-based differential display methodology, also referred to as 'in silico subtraction' or 'electronic northern' [2–7], could identify transcripts preferentially expressed or repressed in the tumor context by comparing cancerous libraries (present in publicly available databases) against the remaining libraries. Strikingly, only few attempts were made to apply in silico transcriptomics to genome-wide and multi-tissue screening of cancer genes [8–10]. Thus, given the continuous expansion of the EST databases, both in terms of sequence and source diversity, updated and independent transcriptomic analyses are permanently needed.
In this study, we mined EST libraries for genes differentially expressed in normal and tumor tissues by using a novel computational approach, with the assumption that both the up- and down-regulated pools might contain genes involved in tumorigenesis. This strategy identified differential expression profiles and cancer candidate genes which may be useful in future cancer research. Higher expression of the anti-apoptotic protein Bcl-xL in liver cancer specimens compared to normal liver was confirmed by immunoblot analysis. Strikingly, we found that most cancer-associated changes in gene expression corresponded to genes that were actually downregulated or repressed. The chromosomes and chromosomal regions most frequently associated with aberrant expression changes in tumor versus normal cells were also determined. This analysis suggests that, although genes differentially expressed in cancerous libraries are distributed throughout the genome, chromosomal 'hot spots' of candidate genes could be identified.
Identification of differentially expressed genes between normal and cancer tissues
Overview of the EST-based data mining strategy. Screening for differentially expressed genes between normal and cancer tissues. EST counts in each analytical step. Total number of EST clusters in each class (upregulated, downregulated, tumor-specific or absent in tumors) was determined after Bonferroni corrected exact Fisher test.
Total RNA (Ensembl)
Total ESTs (after clustering)
~ 3.3 106
Total up-regulated EST clusters
Total down-regulated EST clusters
Total EST clusters specific to tumors
Total EST clusters absent in tumors
Cancer candidate gene analysis
Taken together, these results suggest that EST data could be successfully mined to provide digital profiles of differential gene expression at the full genome level between normal and cancerous tissues. Our lists of transcriptional signatures might help to select candidate markers in cancer genetics or potential targets for therapy.
Increased expression of Bcl-xL in liver tumors
Identification of chromosome locations of differential gene expression in cancer
Chromosomal regions of differential gene expression in cancer. (A) Number of hits, i.e. number of genes with differential expression per chromosome, is depicted. 'Up' and 'down' mean chromosomal regions with increased and decreased tumor expression, respectively. '%' represents the percentage of candidate genes against the total number of genes present on the chromosome. (B) Chromosomal regions found to be associated with at least 5 hits in the digital subtraction analysis are shown ('banding'). '%' represents the percentage of hits for a particular chromosomal banding against the total number of genes present in the same banding. Chromosomal bandings marked in bold correspond to previously identified regions associated with either tumor amplicon ('Up' column) or deleted ('Down' column) regions in tumors.
First, our results show that some chromosomes appear to be more active than others (Table 2A), with, for instance, chromosomes 15, 19 and Y being rarely involved in cancer-related gene expression changes compared to chromosomes 4 and 6. As expected from the results of Table 1, most chromosomal regions associated with changes in expression levels actually correspond to gene inactivation patterns in cancer cells (373 up-regulated versus 744 down-regulated hits), striking examples of cancer-associated inactivation of gene expression being chromosome 17 and chromosome 3. While most tissues (14/16) were clearly subject to these cancer-associated gene inactivation patterns (especially lung, eye, colon, prostate and stomach), two tissues (tumor blood and liver) did not follow this trend. Chromosomal regions displaying at least five hits were further listed and this rough analysis was sufficient to detect 11 and 29 regions of clustering of up- and down-regulated genes, respectively (Table 2B). We found previously identified chromosomal regions associated with either tumor amplicon (e.g. 12q13) or deleted (e.g. 11p15) regions in tumors [23–26]. Interestingly, some of the chromosomal locations which were identified show tissue specificity, e.g. 12q13.3 in muscle. Moreover, in some cases, candidate genes could be contiguous or clustered in limited banding intervals. For instance, 19q13 is associated in tumor tissues of placental origin with complete extinction of eight clustered genes, namely pregnancy-specific beta-1-glycoproteins PSG-1, -2, -3, -4, -5, -6, -9 and -11. These genes belonging to the carcinoembryonic antigen family encode the major placental proteins found in maternal circulation during pregnancy [27, 28].
In conclusion, in addition to providing differential expression profiles for individual genes, our EST-based procedure identified discrete regions on specific chromosomes that are enriched in genes deregulated in cancer libraries.
Owing to advances in biotechnology and bioinformatics, researchers can now capture "molecular portraits" of various particular cancers using gene chips or SAGE data. These methods provide information on tens of thousands of genes simultaneously, and some variations in genes might be directly related to the cancer phenotype [1, 29]. As multi-dimensional analysis of EST data is analogous to microarray experiments, we used the virtual differential display methodology to identify genes differentially expressed in normal versus tumor tissues. Our comprehensive approach gives an overview of numerous candidate genes which may be useful as improved biomarkers for diagnosis or as targets for developing novel treatment methods. For instance, EST-based formulation of collagen, integrin or cytokeratin expression profiles may have potential as a diagnostic aid for the detection of both tumor formation and development. Noteworthy, for discovery of tumor-associated molecules, it may be beneficial to use a combination of various digital differential display procedures and experimental data on gene expression. This is illustrated by the identification of prostate-specific Ets factor as a novel marker for breast cancer both computationally [8, 30] (and this study) and experimentally [30–32].
General limitations of EST-based strategies, which have been abundantly discussed elsewhere [4, 33, 34], include poor sequencing depth of the libraries, uncertainty concerning the origin of the samples, and differences in library sizes. In addition, analysis of tumor-related differential expression patterns of individual transcripts may have specific drawbacks. For example, cancer cells often proliferate more rapidly than adjacent normal cells and it is possible that, in some cases, the observed changes in transcript abundance may reflect a response to increased proliferation rather than transformation per se. One related problem is that many cell types are often pooled together during the preparation of EST libraries. Given that most cancers start as growths of single cells, the lack of cell-type specific libraries is a major limiting factor of the method. Lastly, the determined variations in transcript expression may not correlate with similar variations in the abundance of the encoded protein, highlighting the need to experimentally test the computer-based predictions either by western blotting or immunohistochemistry. Our validation result showing that Bcl-xL protein expression was markedly increased in hepatocellular carcinoma and liver adenocarcinoma suggests that this Bcl-2 family member represent a potential marker for progression of a subset of liver cancers. Analysis of Bcl-xL immunoreactivity in more liver cancer specimens is needed to enhance the reliability of this finding. However, as it correlates with previous results [35–37], and in view of the pro-survival effect of Bcl-xL, we hypothesize that Bcl-xL overexpression could confer specific protection from death to several types of liver cancer cells compared to their healthy counterparts. If true, modulation of Bcl-xL expression level and/or activity might represent an interesting strategy to optimize the efficacy of chemotherapeutic agents in this particular tissue, as liver cancer represents a significant source of morbidity and mortality worldwide .
Aside from the proposal of potential diagnosis markers and targets for future cancer research, a more theoretical perspective of our study is the identification of critical factors that could influence differential gene expression levels in normal versus cancer cells, including genomic landscape features, e.g. levels of polymorphisms, chromosome breakpoints, gene density, GC content and chromatin methylation status. In this regard, although we cannot rule out the possibility of unidentified biases in our data mining procedure, our result showing a higher frequency of gene inactivation patterns in tumor tissues is intriguing, and sheds light on the importance of understanding the molecular mechanisms of negative gene regulation in cancer.
The final outcomes of the present work are identification of chromosomal regions frequently associated with aberrant expression in cancer libraries, description of differential expression profiles, and listing of cancer candidate genes (e.g. Bcl-xL) which may be useful as tissue-specific biomarkers for cancer diagnosis or as targets for anticancer research.
We have used an EST-based pipeline to scan for differential gene expression levels between normal and tumor states. Human ESTs from dbEST  (October 2004 release) were first extracted using the ACNUC sequence retrieval system . ESTs were classified according to their UNIGENE library features  (October 2004). For each EST in dbEST, we extracted the accession code of the EST and the tissue or organ from which the EST library has been made. The tissue type was retrieved from the line containing 'Tissue_type', 'Tissue description', 'Organ' or 'Keyword'. This parsing approach stored no data when the tissue information did not appear in these fields, or in case of typographical errors or ambiguous aliases. ESTs that were labeled as coming from an unspecified tissue (e.g. 'mixed', 'pooled organs', 'cell line') or from a mixture of specified tissues, were discarded. The eVOC ontology  (October 2004) for anatomical sites and pathology types was then used to classify the libraries through a number of criteria such as tissue origin and pathological context including tumor state. This well-accepted hierarchical vocabulary provided us with a mean to determine when a specific tissue was part of an organ and when a specific label was part of the 'tumoral' state. A total of 5135 'tumor' and 2503 'normal' (i.e. non-pathological) libraries were catalogued. Our approach to EST clustering used the human genome as a reliable guide. ENSEMBL RNAs  annotated on human genome assembly (release 16.3) were used as a backbone for the clustering of dbEST sequences using MEGABLAST (alignment length = 100 bp and similarity = 95%) . In order to avoid paralogous false positive assignation, only best EST hit matches were subsequently selected. RNA clustering of ESTs in both normal and tumor tissues was the starting point for digital differential analysis of gene expression.
Computer-based differential display procedure
The cDNA libraries were categorized into non-normalized or normalized/subtracted libraries by screening for the appropriate keywords in the original annotation of the respective dbEST entries (in the 'Keyword' and 'Library treatment' fields). All libraries for which none of the keywords were found were defined as being non-normalized. After removal of normalized and subtracted EST libraries, we created pools of equivalent EST libraries, i.e. libraries derived from the same tissue type and state (normal and tumor). Differential screening analysis was accomplished for a considered tissue when both normal and tumor pools displayed at least 10,000 ESTs. A total of 15 distinct paired tissue pools (blood, bone, brain, colon, eye, liver, lung, lymph, mammary gland, muscle, placenta, prostate, skin, stomach, testis) representing approximately 1.5 million ESTs were therefore retained for the whole genome screening. Differential screening was performed for each tissue type individually using EST counts from tumors and corresponding normal counterparts. The relative expression of one particular gene in a tissue was characterized by the ratio of the number of ESTs matching this gene to the total number of ESTs sequenced in the respective tissue. As such 'gene expression profiles' were derived from 'normal' and 'tumor' libraries, it was possible to build 2 × 2 contingency tables and then to apply the Fisher exact test against the null hypothesis that there was no association between a particular gene and the tumoral state. A p-value was determined for statistical significance and, because multiple tests were performed, a Bonferroni correction was applied on each pairs in order to reduce the false positive rate and to perform candidate gene sorting. Statistically significant hits showing at least 10-fold differences were compiled. Four classes of genes were defined, namely (i) genes displaying significantly higher expression levels in tumor tissues ('up-regulated' genes); (ii) genes displaying significantly lower expression levels in tumor tissues ('down-regulated' genes); (iii) genes expressed in tumor but not in normal tissues ('tumor-specific' genes); (iv) genes absent in the tumor types compared to normal tissues. Apart from the genes displaying absolute differences between normal and tumor condition, a ratio based on EST abundance in both conditions was computed to estimate the expression fold change for up- and down-regulated genes. Cytogenetic map position of the hits was inferred using ENSEMBL data (release 16.3). The pattern of expression of the differentially expressed transcripts (n = 1190, as determined by the EST analysis) in normal tissues was independently assessed by comparison to SAGE results obtained on the SAGE Genie website  and processed as previously described . A total of 141 (non-tumoral) libraries containing more than 20,000 tags were partitioned into 19 normal tissues. The expression pattern of 13,435 transcripts was determined. Eight tissues (blood, brain, colon, liver, lung, mammary gland, placenta and prostate) were unambiguously mapped to the tissue terms used in the EST data mining procedure. From this sample, we queried as to which candidate transcripts associated with differential expression in a particular tissue (on the basis of the EST predictions) was expressed in the corresponding normal tissue (according to the SAGE data). Information on differential expression was also gained from reference to primary literature. As this effort corresponded to a manual task particularly unfitted to the large number of candidate genes presented here, we limited the analysis to the "up-regulated" and "down-regulated" lists related to the liver and breast tissues. We found that 54.2% (for liver) and 41.7% (for breast) of the annotated candidates identified through our computer-based screen were consistent with previously published data (see Additional files 1 and 3). The differential display procedure and other analytical steps were developed with R . Expression and genomic data were stored in a local PostgreSQL database (GeMCore)  using PERL and Java script.
Western blot analysis
Nitrocellulose membrane was from Euromedex (Souffelweyersheim, France). The membrane was immunoblotted with anti-human Bcl-xL antibody (1:1 000 dilution, BD Pharmingen), and then with anti-mouse IgG antibody conjugated to horseradish peroxidase (1:5 000 dilution, Dako). Protein bands were revealed using enhanced chemiluminescence kit (ECL, Amersham). The membrane was stripped according to manufacturer's instructions and reprobed with anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) monoclonal antibody (1:1 000 dilution) and with anti-alpha-tubulin (1:1000 dilution, Sigma) to correct for differences in protein loading.
VN is supported by a grant from INRA. AA is recipient of a fellowship from the ARC. The authors wish to thank Sandy Jacquier for critical reading of the manuscript, Dr. Pierre Colas at Aptanomics for providing the anti-Bcl-xL antibody and Dr. Cyrile Lamigeon for the anti-GAPDH antibody. We are grateful to Dr. Marie Semon for sharing the SAGE data.
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