The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Correlation of Peripheral-Blood Gene Expression With the Extent of Coronary Artery Stenosis
Abstract
Background— The molecular pathophysiology of coronary artery disease (CAD) includes cytokine release and a localized inflammatory response within the vessel wall. The extent to which CAD and its severity is reflected by gene expression in circulating cells is unknown. Methods and Results— From an initial coronary catheterization cohort we identified 41 patients, comprising 27 cases with angiographically significant CAD and 14 controls without coronary stenosis. Whole-genome microarray analysis performed on peripheral-blood mononuclear cells yielded 526 genes with >1.3-fold differential expression ( P <0.05) between cases and controls. Real-time polymerase chain reaction on 106 genes (the 50 most significant microarray genes and 56 additional literature genes) in an independent subset of 95 patients (63 cases, 32 controls) from the same cohort yielded 14 genes ( P <0.05) that independently discriminated CAD state in a multivariable analysis that included clinical and demographic factors. From an independent second catheterization cohort, 215 patients were selected for real-time polymerase chain reaction–based replication. A case:control subset of 107 patients (86 cases, 21 controls) replicated 11 of the 14 multivariably significant genes from the first cohort. An analysis of the 14 genes in the entire set of 215 patients demonstrated that gene expression was proportional to maximal coronary artery stenosis ( P <0.001 by ANOVA). Conclusions— Gene expression in peripheral-blood cells reflects the presence and extent of CAD in patients undergoing angiography.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
Inflammation, Atherosclerosis, and Coronary Artery Disease
Hansson, G. K. (2005) · New England Journal of Medicine · doi:10.1056/nejmra043430 · Cited by 8,773
Inflammation in atherosclerosis
Libby, P. (2002) · Nature · doi:10.1038/nature01323 · Cited by 8,315
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Inflammation and Atherosclerosis
Libby, P., Ridker, P. M., & Maseri, A. (2002) · Circulation · doi:10.1161/hc0902.104353 · Cited by 7,724
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
C-Reactive Protein Levels and Outcomes after Statin Therapy
Ridker, P. M., Cannon, C. P., Morrow, D., Rifai, N., Rose, L. M., McCabe, C. H., Pfeffer, M. A., & Braunwald, E. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa042378 · Cited by 2,292
Cited by
Biological interplay between proteoglycans and their innate immune receptors in inflammation
Frey, H., Schroeder, N., Manon‐Jensen, T., Iozzo, R. V., & Schaefer, L. (2013) · The FEBS Journal · doi:10.1111/febs.12145 · Cited by 274
Versican—A Critical Extracellular Matrix Regulator of Immunity and Inflammation
Wight, T. N., Kang, I., Evanko, S. P., Harten, I. A., Chang, M. Y., Pearce, O. M. T., Allen, C. E., & Frevert, C. W. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00512 · Cited by 268
A Systems Biology Framework Identifies Molecular Underpinnings of Coronary Heart Disease
Huan, T., Zhang, B., Wang, Z., Joehanes, R., Zhu, J., Johnson, A. D., Ying, S., Munson, P. J., Raghavachari, N., Wang, R., et al. (2013) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.112.300112 · Cited by 167
Immunomodulatory Role of the Extracellular Matrix Within the Liver Disease Microenvironment
McQuitty, C. E., Williams, R., Chokshi, S., & Urbani, L. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.574276 · Cited by 146
Metadata
- Authors
- Wingrove, J. A., Daniels, S. E., Sehnert, A. J., Tingley, W., Elashoff, M. R., Rosenberg, S., Buellesfeld, L., Grube, E., Newby, L. K., Ginsburg, G. S., & Kraus, W. E.
- Venue
- Circulation: Cardiovascular Genetics
- Published
- 2008-10
- Volume
- 1
- Issue
- 1
- Pages
- 31-38
- ISSN
- 1942-325X (ISSN-L)
- 1942-3268
- OpenAlex
- W2548946619
- Cited by
- 196
- References
- 13
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Correlation of Peripheral-Blood Gene Expression With the Extent of Coronary Artery Stenosis. https://app.cochat.ai/papers/10.1161/circgenetics.108.782730
- The paper
- Wingrove, J. A., Daniels, S. E., Sehnert, A. J., Tingley, W., Elashoff, M. R., Rosenberg, S., Buellesfeld, L., Grube, E., Newby, L. K., Ginsburg, G. S., & Kraus, W. E. (2008). Correlation of Peripheral-Blood Gene Expression With the Extent of Coronary Artery Stenosis. Circulation: Cardiovascular Genetics. https://doi.org/10.1161/circgenetics.108.782730
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
