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Physiol. Genomics 15: 65-74, 2003. First published August 5, 2003; doi:10.1152/physiolgenomics.00181.2002
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Received 17 December 2002; accepted in final form 24 July 2003.
Physiological Genomics 15:65-74 (2003)
1094-8341/03 $5.00 © 2003 American Physiological Society

Identification of new genes differentially expressed in coronary artery disease by expression profiling

Stephen R. Archacki 1,2,3,4, George Angheloiu 4, Xiao-Li Tian 2,3,4, Fen Lai Tan 3, Nick DiPaola 4, Gong-Qing Shen 2,3,4, Christine Moravec 1,4, Stephen Ellis 4, Eric J. Topol 2,3,4 and Qing Wang 1,2,3,4

1 Department of Biological, Geological, and Environmental Sciences, Cleveland State University, Cleveland 44115
2 Center for Molecular Genetics, Cleveland Clinic Foundation, Cleveland, Ohio 44195
3 Department of Molecular Cardiology, Cleveland Clinic Foundation, Cleveland, Ohio 44195
4 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio 44195


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 DISCLOSURES
 References
 
Genetic factors increase the risk to coronary artery disease (CAD). To date, a limited number of genes that potentially contribute to development of CAD have been identified. In this study, we have performed large-scale gene expression analysis of ~12,000 human genes in nine severely atherosclerotic and six nonatherosclerotic human coronary arteries using oligonucleotide microarrays. Fifty-six genes showed differential expression in atherosclerotic coronary artery tissues; expression of 55 genes was increased in atherosclerotic coronary arteries, whereas only one gene, GST, encoding a reducing agent, showed downregulated expression. The expression data of selected genes were validated by quantitative RT-PCR analysis as well as immunostaining. The associations of 49 genes with CAD appear to be novel, and they include genes encoding ICAM-2, PIM-2, ECGF1, fusin, B cell activator (BL34, GOS8), Rho GTPase activating protein-4, retinoic acid receptor responder, ß2-arrestin, membrane aminopeptidase, cathepsins K and H, MIR-7, TNF-{alpha}-induced protein 2 (B94), and flavocytochrome 558. In conclusion, we have identified 56 genes whose expression is associated with CAD, and 49 of them may represent new genes linked to CAD.

array; atherosclerosis; coronary artery disease/myocardial infarction; gene expression; sudden death


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 DISCLOSURES
 References
 
CORONARY ARTERY DISEASE (CAD) and myocardial infarction (MI) are the leading causes of death in the United States and other Western countries. An estimated 7.5 million living Americans have experienced MI, and 12.6 million have CAD (1). Approximately 1.1 million Americans developed MI in 1999, and the mortality rate was more than 45%.

CAD is a multifactorial disorder influenced by both genetic and environmental factors and their interactions. Genetic-epidemiological and twin studies strongly suggest that genetic factors contribute to the development of atherosclerosis (9). Case-control studies have been used to identify genes that are associated with increased risks to CAD and MI. These genes include the thrombospondin (TSP) genes (TSP-1, TSP-2, and TSP-4) (43), LTA gene encoding lymphotoxin-{alpha} (29), connexin 37 (52), and the genes encoding plasminogen-activator inhibitor type 1, stromelysin-1 (52), apolipoprotein E (ApoE), Lp(a) or Apo(a), Apo AI, ApoCIII, ApoIV, fibrinogen, tissue-type plasminogen activator (TPA), plasminogen activator inhibitor (PAI-1), von Willebrand factor (VWR), platelet glycoprotein IIIa, lipoprotein lipase, cholesterol ester hydrolase, cholesterol ester transfer protein (CETP), factor V, factor VII, angiotensin converting enzyme, angiotensinogen, and endothelial nitric oxide synthase (49).

Various biochemical methods including Northern blot, Western blot, and immunostaining have also been employed to identify genes that are associated with CAD. Previous studies have implicated the upregulation of specific genes in atherosclerotic tissue including chitotriosidase (4), lumican (27), VCAM-1 (10), ICAM-1 (10) matrix metalloproteinase-9 (MMP-9) (42), and osteopontin (24), as well as the downregulation of endothelial nitric oxide synthase (25). Recently, subtraction suppression hybridization (SSH) was employed to identify genes that are differentially expressed in stable and ruptured atherosclerotic plaques (a major cause of thromboembolic complications) in human coronary arteries. Only a limited number of genes associated with plaque rupture were identified by SSH: the perilipin gene was found to be upregulated, and genes for ß-actin, fibronectin, and immunoglobulin-{lambda} chain were downregulated (8). DNA microarray technology was also performed on arteries such as common, internal, and external iliac artery, aorta, popliteal artery, posterior tibial artery, and tibiofibular trunk to define a group of 75 novel genes expressed in atherosclerotic plaques; however, the coronary arteries, the target organ of CAD, were not investigated (12).

To summarize, only a small number of genes that potentially contribute to the pathogenesis of CAD have been identified. No microarray analysis has been performed on human coronary arteries from patients with CAD. Therefore, in this study we used oligonucleotide microarrays with ~12,000 unique genes to identify genes that are differentially expressed in atherosclerotic human coronary arteries.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 DISCLOSURES
 References
 
Tissue Sampling of Human Coronary Arteries
Coronary arteries were obtained from explanted hearts through the Cleveland Clinic Heart Transplant Program and unmatched or rejected donor hearts from Lifebanc of Northeast Ohio (Table 1). Informed consent was obtained from all participants according to the standards established by the Cleveland Clinic Foundation Review Board on Human Subjects. Coronary artery segments measuring 5 mm in length were harvested immediately after heart extraction and cleaned of adjacent adipose and myocardial tissue. The adventitial layer remained intact. The coronary arteries were harvested immediately upon explantation of the heart in the surgical suite and snap-frozen in liquid nitrogen and stored at -80°C as previously described until used (22).


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  		Table 1. Patient characteristics

 
We selected a segment of the proximal lateral anterior descending (LAD) with obvious intraluminal plaque on inspection. The presence or absence of atherosclerosis was determined by a cardiovascular pathologist’s diagnosis made upon gross examination of transverse coronary sections visually and microscopically. The atherosclerotic coronary arteries had an intraluminal stenosis of >75%, all of which had similar complexities (stage IV according to the American Heart Association classification), and non-CAD tissues had no detectable atherosclerosis.

RNA Isolation and Oligonucleotide Arrays
Total RNA was isolated from the coronary arteries using TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. RNA quality was confirmed visually on a 1% denaturing agarose gel, and RNA concentration was measured using a spectrophotometer. Double-stranded complementary DNA (ds-cDNA) was synthesized from 15 µg of total RNA using the SuperScript Choice System (Invitrogen) with an HPLC-purified oligo-dT primer containing a T7 RNA polymerase promoter (Genset, La Jolla, CA) as instructed by the manufacturer. In vitro transcription was performed with 1 µg of ds-cDNA using the Enzo BioArray RNA transcript labeling kit (Enzo Diagnostics, Farmingdale, NY). Fragmentation of biotinylated cRNA (20 µg), hybridization, washing, and staining were performed following the instructions by Affymetrix.

Data Extraction and Statistical Analysis
The Human Genome U95A Array (Affymetrix) was used. Each array contains ~12,000 unique genes. One microarray was used for each human coronary artery after the quality and integrity of the RNA/cRNA sample was validated with a test array. To make comparisons across microarrays, data sets on the arrays were normalized to a targeted total fluorescence of 300 representing total cRNA hybridized on the array.

We used GeneSpring 4.2 (Silicogenetics) for data analysis. Nine atherosclerotic coronary arteries (group D) were compared with six normal arteries (group N). All samples were treated as one group of replicates. We used the distribution of all genes in the 50th percentile to generate the median value for group comparisons, and all raw data with a score less than zero were set to zero. The algorithm to generate a list of genes that showed a statistically significant difference (P < 0.05) between the two groups was described previously (40). Genes were further filtered by an absolute call: present (P) or marginally present (M) in at least six of nine samples for the diseased group or four of six samples for the normal group, for upregulated genes and downregulated genes, respectively. The final list consisted of 56 genes.

Quantitative RT-PCR
Quantitative real-time polymerase chain reaction (quantitative RT-PCR) was used to validate our microarray findings. Reverse transcription was performed with 5 µg of total RNA from all 15 samples used for microarray analysis using the SuperScript Choice System (Invitrogen). Quantitative RT-PCR was performed in ABI Prism 7700 Sequence Detection System using SYBR Green (Applied Biosystems) following the manufacturer’s instructions with the following standard PCR conditions (94°C 10 min, and 94°C 30 s, 58°C 1 min, 72°C 45 s for 35 cycles, then 10-min extension at 72°C). Relative expression values were calculated as previously described (54). Primers were designed based on sequences from the GenBank and to span exon-intron junctions to prevent amplification of genomic DNA. The gene ß2-microglobulin was used as an internal control.

Immunostaining
The cryosections (6 µm) of coronary arteries were fixed with paraformaldehyde (4%) and permeabilized with Triton X-100 (0.2%). The primary antibodies used include the antiserum for MMP-9 (clone 56–2A4 ICN Biomedica), ICAM-2 (Santa Cruz Biotechnology), osteopontin (Calbiochem), PIM-2 (Santa Cruz Biotechnology), CD31 (Becton-Dickinson), and {alpha}-smooth muscle actin (Sigma) (1:250). The secondary antibodies conjugated with either FITC or Cy3 were used for visualization (1:250). The sections were counterstained with 4’,6-diamidino-2-phenylindole (DAPI; Vectashield with OAD; Vector Laboratories, Burlingame, CA). The immunostained slides were visualized with a Zeiss Axiophot fluorescence microscope, and the images were captured with Photometrics SmartCature.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 DISCLOSURES
 References
 
Identification of 56 Genes That Are Differentially Expressed in CAD Coronary Arteries
We profiled nine coronary arteries from CAD patients and six non-CAD arteries using the Affymetrix Human Genome U95A Arrays. The mean age of the CAD group (group D) is 58 ± 7.7 yr and did not have a statistical difference from that of the normal group (group N) (62 ± 10 yr) (P > 0.05) (Table 1). Between the two groups (N vs. D), there were not any statistically significant differences (P > 0.05) in percent of genes expressed (58 ± 17% vs. 54 ± 15%), Q-score (2.28 ± .34 vs. 2.45 ± 48), or background noise (43.68 ± 9.5 vs. 54.9 ± 24.6), respectively. The average mean (SD) difference of the housekeeping genes was not statistically significant (N vs. D) (P > 0.05): ß-actin (17,074 ± 4,259 and 16,081 ± 4,674), GAPDH (8,957 ± 314 vs. 9,341 ± 1,417) and ß2-microglobulin (8,541 ± 2,028 vs. 8,526 ± 2,817), respectively. These results indicate low variability and high reproducibility of our microarray analysis.

Of the ~12,000 genes analyzed by oligonucleotide arrays, we performed two independent analyses using Wilcoxon Mann-Whitney parametric test and the Student’s t-test for genes that were differentially expressed (Fig. 1). Using a P < 0.05, the Wilcoxon ANOVA test generated 420 potentially differentially expressed genes between the two groups. At the same time, we used a Student’s t-test (P < 0.05), which considers both absolute differences between the two groups, to generate a list of 372 genes. Combination of both statistical tests generated a list of 401 genes. From this gene list, 177 genes, or 44%, were upregulated by 1.5-fold, whereas 76 genes, or 19%, were downregulated. Sixty-one genes of the 177 upregulated genes met our criteria of a minimum expression level of 400, the average raw score of ~12,000 genes on each array, whereas 1 gene out of 76, or 1.3%, downregulated genes met this criteria. The final gene list generated by filtering these genes based on the call of present (P) or marginally present (M) consisted of 56 genes associated with CAD.



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  		Fig. 1. Algorithm of statistical protocols used for our comprehensive cut-off points for data mining. All genes were initially filtered by a Welch test and Welch ANOVA that met the critical value of P < 0.05 for both tests. Genes were filtered with a mean average difference of 400 units for the diseased group for upregulated genes and normal group for downregulated genes. Genes were further filtered by an absolute call: present (P) or marginally present (M) in at least six of nine samples for the diseased group or four of six samples for the normal group for upregulated genes and downregulated genes, respectively. The final list consisted of 56 genes.

 
Functional Grouping of 56 Genes Associated with CAD
The 56 genes in the final list were classified into five functional groups (Table 2).


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  		Table 2. Classification of differentially expressed genes in five groups based on their molecular activity

 
Altered expression of inflammation genes in CAD tissues.
This group includes six genes encoding the major histocompatibility complexes (MHCs), six genes encoding immunoglobulins, complement component 2 and 4b, retinoic acid receptor responder, MIR-7, HEM45, CD37 antigen, T-cell receptor ß-chain, lymphocyte-specific protein 1, human Ia invariant {gamma}-chain, tapasin, 17 kDa/15 kDa interferon-stimulated protein, galectin-9, and tumor necrosis factor-{alpha}-induced protein-2 (B94). In the diseased coronaries, all the genes encoding immunoglobulins showed the highest fold changes in expression; consistent with the hypotheses that infection may play a role in the pathogenesis of CAD or an immune response to athero-antigens is driving this response (10).

Altered expression of cell necrosis/apoptosis/proliferation genes in CAD tissues.
This group include genes encoding interferon stimulatory factor-3{gamma}, XBP1 (TREB5), B144 NK cell triggering receptor (LST1), STAT-91, DOC-2 (mitogen-responsive phosphoprotein), a hypothetical gene with homology to perlecan, arrestin-ß2, platelet-derived endothelial cell growth factor (ECGF1), chemokine G-coupled receptor (fusin), B-cell activator gene (BL34, GOS8), fusin, Rho GTPase activating protein-4, and PIM-2.

Altered expression of cell migration/adhesion and matrix degradation genes in CAD tissues.
Genes in this group include the lumican, VCAM-1 and its precursor, ICAM-2, osteopontin precursor, membrane alanine aminopeptidase (IGF1R), MMP-9, and cathepsin H and K genes.

Altered expression of lipid transfer/oxidation/metabolism genes in CAD tissues.
This group includes genes for steroidogenic acute regulatory protein, butyrophilin (BTF4), glutathione-S-transferase (GST), flavocytochrome 588, and chitotriosidase.

Altered expression of genes with unknown functions in CAD tissues.
This group includes three expressed sequence tags (EST) and two genes predicted to encode a small inducible cytokine and a homocysteine-inducible protein.

To summarize, our microarray analysis has defined 56 genes that are differentially expressed in CAD tissues. These genes can be classified into five functional groups. It is interesting that among the 56 genes associated with CAD, only one gene, GST, was downregulated, whereas the rest of genes were upregulated.

Clustering of Differentially Expressed Genes
The 56 differentially expressed genes in CAD were grouped using a hierarchical clustering method referred to as a gene tree. The resultant gene tree recapitulates the two distinct study populations, i.e., those with CAD compared with nondiseased arteries. The gene tree program clusters them in subgroups with similar genetic profiles (Fig. 2).



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  		Fig. 2. Hierarchical clustering of 15 coronary arteries and 56 genes. The gene tree of nine diseased coronary arteries (D) and six normal coronary arteries (N) recapitulates the distinction between the two groups based on their similarity in gene expression. The trust shows fold changes with upregulation [up to 5x (in red)] and downregulation [starting at 0 (in blue)].

 
Validation of Changes in Gene Expression by Quantitative RT-PCR
To validate our microarray data, quantitative RT-PCR was performed using SYBR Green for 13 genes (Table 3). Consistent changes were observed between quantitative RT-PCR and microarrays for all 13 genes examined. The only gene downregulated on our microarray, GST, also showed decreased expression by quantitative RT-PCR. The other 12 genes showed increased expression by microarray analysis were also found to be upregulated by quantitative RT-PCR. These data provide validation of our gene expression patterns identified by microarrays.


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  		Table 3. Confirmation of microarray analysis with quantitative real-time PCR

 
Validation of Changes in Gene Expression by Immunostaining
To further validate the data from the microarray analysis, immunostaining was performed for four proteins encoded by the genes differentially expressed in CAD, including osteopontin (OPN), MMP-9, ICAM-2, and PIM-2 oncogene. The four coronary arteries from CAD patients are different from those used for the microarray analysis. Representative immunostaining images are shown in Figs. 3 and 4. OPN (green signal) was expressed diffusely in the media and fibrous tissue of the coronary artery and colocalized with a smooth muscle cell-specific marker, the monoclonal anti-{alpha} smooth muscle actin (red signal) (Fig. 3A). OPN showed stronger staining in the CAD coronary arteries than normal tissues (Fig. 3A), consistent with our microarray results.



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  		Fig. 3. Protein expression of osteopontin (A) and ICAM-2 (B) in normal and CAD coronary arteries. A: immunostaining images showing colocalization of osteopontin (green) and smooth muscle {alpha}-actin (red) and increased expression of osteopontin in CAD coronary arteries. B: immunostaining images showing colocalization of ICAM-2 (green) and endothelial cell-specific marker CD31 (red) and increased expression of ICAM-2 in CAD coronary arteries. L, lumen; M, medial layer; E, endothelial cell layer; F, fibrous tissue; CAD, coronary artery disease.

 


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  		Fig. 4. Protein expression of matrix metalloproteinase-9 (MMP-9; A) and PIM-2 (B) in normal and CAD coronary arteries. A: immunostaining images showing increased expression of MMP-9 (red) in CAD coronary arteries. B: immunostaining images showing increased expression of PIM-2 (green) in CAD coronary arteries. L, lumen; M, medial layer. Note that coimmunostaining with CD31 and smooth muscle {alpha}-actin was not performed because the available antibodies are monoclonal (MMP-9 and PIM-2 antibodies) (e.g., the same anti-mouse secondary antibody will not be able to distinguish the primary antibodies for PIM-2 or CD31).

 
Immunostaining signal for ICAM-2 (green) colocalized with the signal for CD31 (red), an endothelial cell-specific marker (Fig. 3B). ICAM-2 expression is also increased in CAD tissues.

MMP-9 has been shown to be expressed in macrophages (42). As shown in Fig. 4A, the immunostaining signal for MMP-9 (red) was very strong and diffusively distributed throughout the media in coronary arteries with atherosclerosis, whereas very little staining was detected in normal tissues.

PIM-2 appeared to be expressed widely in the media layer of coronary arteries (green) (Fig. 4B). PIM-2 expression was increased in the CAD coronary arteries compared with arteries without the disease.

Taken together, the immunostaining data illustrate the distribution and expression patterns of osteopontin, ICAM-2, MMP-9, and PIM-2 in the normal and diseased coronary arteries. Increased expression of these four proteins was observed in the diseased tissues compared with the normal ones. These data further validate results from our microarray array analysis and correlate the increased expression of the mRNA of these genes with increased protein expression.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
DISCLOSURES
 References
 
This is a large-scale study in which microarray analysis has been used to profile gene expression patterns using intact, human atherosclerotic and nonatherosclerotic human coronary arteries. We have identified 56 genes that are differentially expressed in patients with CAD, and 49 genes were not previously linked to CAD. Interestingly, 55 genes are upregulated and one gene, GST, is downregulated in CAD tissues (Table 2). Previous studies have implicated increased expression of chitotriosidase (4), lumican (27), VCAM-1 (10), ICAM-1 (10), MMP-9 (42), and OPN (24) in atherosclerosis. These results are confirmed by our microarray analysis (Table 3). This suggests that the microarray technology is a powerful tool for identification of gene expression patterns associated with CAD.

In the clustering of the nine diseased and six nondiseased human coronary arteries, the gene tree identifies two distinct gene expression patterns: the diseased coronary arteries are clustered as a group, and the nondiseased arteries segregate as an independent group (Fig. 2). Of interest to note is among the patients with CAD, samples D8 and D9 clustered together. These two samples had the most significant occlusion (90% in D8 and 100% in D9) of the left main artery adjacent to the proximal LAD revealed by left heart catheterization.

The majority of genes associated with CAD identified in this study represent new links to CAD processes. Our microarray analysis identified 49 new genes that were not previously shown to be associated with CAD (Table 3). Some of these genes include the following: retinoic acid responder binding (RAR) protein, butyrophilin, steroidogenic acute regulatory protein (STAR), PIM-2, STAT-91, and cathepsins K and H. The RAR gene is regulated by vitamin A signaling pathways and has been shown to upregulate the expression of the scavenger receptor CD36 through RAR in the human monocytic cell line THP-1 (51). Butyrophilin is a structural component of the human milk fat globule and have receptor functions that mediate the transfer of lipid (26). Both cathepsins K and H are lysosomal proteases that are involved in protein degradation. Cathepsin K is a collagenase that cleaves both type I and type II collagens at their helical domains (53). Cathepsin H has been shown to be upregulated 30-fold in aortic abdominal aneurysms compared with normal aortas (45). STAR is another novel gene identified. It has been shown that in humans and genetically manipulated mice, STAR is required and is the rate-limiting step in steroidogenesis for the transfer of cholesterol into the mitochondria (38). Two more novel genes include PIM-2, which is a "proviral integration site of murine" leukemia virus, is present in mitogenically stimulated hematopoietic cells (3), and STAT-91, which is a signal transducer and activator of transcription in hematopoietic cells (35). GST is the only gene whose expression is reduced in CAD tissues, and it is a reducing enzyme (17).

Many lines of evidence support the notion that inflammation plays a critical role in atherosclerotic lesion pathology. Histological analysis of unstable or ruptured atherosclerotic plaques from patients with acute coronary syndromes identified inflammatory cells including T cells, monocytes, and macrophages. Later studies showed that atherosclerosis was associated with the presence of inflammatory proteins (CRP, cytokines, chemokines) and increased expression of adhesion molecules (ICAM-1, VCAM-1, E-selectin, P-selectin) as inflammatory responses from vascular cells (31, 33). Our study emphasizes the importance of inflammation in CAD. The largest group of CAD-associated genes identified in this study consists of genes involved in inflammation (Table 2). It is, however, important to note that the association of many genes in this group (e.g., genes encoding T-cell receptor ß-chain, 17 kDa/15 kDa interferon-stimulated protein, ICAM-2, CD37 antigen, tapasin, MIR-7, retinoic acid receptor responder, B94, etc.) is novel.

The immunostaining results support our microarray analysis showing not only the upregulation of the mRNA expression, but also increased protein expression. Four proteins demonstrated upregulation in atherosclerotic arteries include osteopontin, MMP-9, ICAM-2, and PIM-2. One component of atherosclerosis is smooth muscle proliferation followed by connective tissue matrix deposition. Osteopontin, a protein secreted from smooth muscle cells, showed an upregulation in the atherosclerotic arteries and reflects the dynamic process of smooth muscle recruitment and proliferation. MMP-9 is secreted from macrophages, and participates in the structural remodeling of the artery as occurs with inflammation. Both proteins were found to be upregulated in the medial layer of the artery which is a site of structural remodeling, lipid deposition, and recruitment of inflammatory cells. PIM-2, an oncogene expressed by hematopoietic cells, also showed upregulation in the diseased arteries, although its definitive function has not been established. ICAM-2 is a protein expressed in endothelial cells as shown in Fig. 3B and showed an increased expression in CAD; however, its precise function in endothelial cells and in atherogenesis remains to be established.

We compared our gene list with another list of 75 genes connected to atherogenesis and identified by a separate DNA microarray analysis and found that the expression of the gene for immunoglobulin-{lambda} (VJC) was upregulated in both studies. Surprisingly, other genes in our list do not match any genes identified by Hiltunen et al. (12). This discrepancy may be explained by different tissue samples used in the two studies, coronary arteries in our study compared with common, internal and external iliac artery, aorta, popliteal artery, posterior tibial artery, and tibiofibular trunk used in the other study.

It is important to note that microarray technology does have its limitations as previously described (5). Furthermore, our study is a genetic profile of the CAD coronary artery tissues at a single time point (a more advanced atherosclerotic stage), and may provide only limited information for the continuous process of atherogenesis. Another limitation of the present study is the small sample size due to difficulties in obtaining human coronary artery tissues. In addition, the majority of tissues used were male tissues as 8/9 CAD and 5/6 normal tissues are male-derived samples. Future studies with an increased sample size, female samples in particular, will further validate our results.

In conclusion, this study represents a large-scale microarray analysis to study gene expression patterns of atherosclerosis using human coronary arteries. We identified 56 genes that are potentially involved in the pathogenesis of CAD or can be used as biomarkers for diagnosis of CAD. Our study also identified 49 genes that are not previously linked to atherosclerotic human coronary arteries. Further analysis is required to determine the exact role of each gene, if any, in development of CAD.


   DISCLOSURES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 DISCLOSURES
References
 
This work was supported in part by the Cleveland Clinic Foundation Lerner Research Institute Seed Grant and a Doris Duke Charitable Foundation Innovation in Clinical Research Award (to Q. Wang and E. J. Topol).


   FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: Q. Wang, Center for Molecular Genetics, ND4-38, Lerner Research Institute, The Cleveland Clinic Foundation, 9500 Euclid Ave., Cleveland, OH 44195 (E-mail: wangq2{at}ccf.org).

10.1152/physiolgenomics.00181.2002.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 DISCLOSURES
 References
 

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