The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Long Noncoding RNAs in Cardiovascular Diseases
Abstract
In recent year, increasing evidence suggests that noncoding RNAs play important roles in the regulation of tissue homeostasis and pathophysiological conditions. Besides small noncoding RNAs (eg, microRNAs), >200-nucleotide long transcripts, namely long noncoding RNAs (lncRNAs), can interfere with gene expressions and signaling pathways at various stages. In the cardiovascular system, studies have detected and characterized the expression of lncRNAs under normal physiological condition and in disease states. Several lncRNAs are regulated during acute myocardial infarction (eg, Novlnc6 ) and heart failure (eg, Mhrt ), whereas others control hypertrophy, mitochondrial function and apoptosis of cardiomyocytes. In the vascular system, the endothelial-expressed lncRNAs (eg, MALAT1 and Tie-1-AS) can regulate vessel growth and function, whereas the smooth-muscle–expressed lncRNA smooth muscle and endothelial cell–enriched migration/differentiation-associated long noncoding RNA was recently shown to control the contractile phenotype of smooth muscle cells. This review article summarizes the data on lncRNA expressions in mouse and human and highlights identified cardiovascular lncRNAs that might play a role in cardiovascular diseases. Although our understanding of lncRNAs is still in its infancy, these examples may provide helpful insights how lncRNAs interfere with cardiovascular diseases.
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
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Circular RNAs are a large class of animal RNAs with regulatory potency
Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., Maier, L., Mackowiak, S. D., Gregersen, L. H., Munschauer, M., et al. (2013) · Nature · doi:10.1038/nature11928 · Cited by 7,990
Natural RNA circles function as efficient microRNA sponges
Hansen, T. B., Jensen, T. I., Clausen, B. H., Bramsen, J. B., Finsen, B., Damgaard, C. K., & Kjems, J. (2013) · Nature · doi:10.1038/nature11993 · Cited by 7,985
Circular RNAs are abundant, conserved, and associated with ALU repeats
Jeck, W. R., Sorrentino, J. A., Wang, K., Slevin, M. K., Burd, C. E., Liu, J., Marzluff, W. F., & Sharpless, N. E. (2013) · RNA · doi:10.1261/rna.035667.112 · Cited by 4,388
Super-enhancers in the control of cell identity and disease
Hnisz, D., Abraham, B. J., Lee, T. I., Lau, A., Saint-André, V., Sigova, A. A., Hoke, H. A., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.09.053 · Cited by 3,662
Long Noncoding RNA as Modular Scaffold of Histone Modification Complexes
Tsai, M.-C., Manor, O., Wan, Y., Mosammaparast, N., Wang, J. K., Lan, F., Shi, Y., Segal, E., & Chang, H. Y. (2010) · Science · doi:10.1126/science.1192002 · Cited by 3,290 · cited 3×
“From above results, authors proposed a model in which lincRNAs function as cell-type–specific flexible scaffolds to bring protein complexes (eg, epigenetic factors) to initiate transcriptional programs, which was previously proposed by Howard Chang’s group for HOTAIR.18 Of note, even low-expressed lncRNAs may exhibit…”
InfluentialSelective Inhibition of Tumor Oncogenes by Disruption of Super-Enhancers
Lovén, J., Hoke, H. A., Lin, C. Y., Lau, A., Orlando, D. A., Vakoc, C. R., Bradner, J. E., Lee, T. I., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.03.036 · Cited by 2,961
Widespread transcription at neuronal activity-regulated enhancers
Kim, T.-K., Hemberg, M., Gray, J. M., Costa, A. M., Bear, D. M., Wu, J., Harmin, D. A., Laptewicz, M., Barbara-Haley, K., Kuersten, S., et al. (2010) · Nature · doi:10.1038/nature09033 · Cited by 2,450
Circular intronic long noncoding RNAs
Zhang, Y., Zhang, X.-O., Chen, T., Xiang, J.-F., Yin, Q.-F., Xing, Y.-H., Zhu, S., Yang, L., & Chen, L.-L. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.08.017 · Cited by 2,221
Genomewide Association Analysis of Coronary Artery Disease
Samani, N. J., Erdmann, J., Hall, A. S., Hengstenberg, C., Mangino, M., Mayer, B., Dixon, R. J., Meitinger, T., Braund, P., Wichmann, H.-E., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa072366 · Cited by 2,044
Cited by
Diverse Applications of Nanomedicine
Pelaz, B., Alexiou, C., Alvarez-Puebla, R. A., Alves, F., Andrews, A. M., Ashraf, S., Balogh, L. P., Ballerini, L., Bestetti, A., Brendel, C., et al. (2017) · ACS Nano · doi:10.1021/acsnano.6b06040 · Cited by 1,350
Vascular Smooth Muscle Cells and Arterial Stiffening: Relevance in Development, Aging, and Disease
Lacolley, P., Regnault, V., Segers, P., & Laurent, S. (2017) · Physiological Reviews · doi:10.1152/physrev.00003.2017 · Cited by 734
Targeting vascular (endothelial) dysfunction
Daiber, A., Steven, S., Weber, A., Shuvaev, V. V., Muzykantov, V. R., Laher, I., Li, H., Lamas, S., & Münzel, T. (2017) · British Journal of Pharmacology · doi:10.1111/bph.13517 · Cited by 501
Advances in pathogenesis and therapeutic strategies for osteoporosis
Song, S., Guo, Y., Yang, Y., & Fu, D. (2022) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2022.108168 · Cited by 476
The epigenetic landscape related to reactive oxygen species formation in the cardiovascular system
Kietzmann, T., Petry, A., Shvetsova, A., Gerhold, J. M., & Görlach, A. (2017) · British Journal of Pharmacology · doi:10.1111/bph.13792 · Cited by 253
Metadata
- Authors
- Uchida, S., & Dimmeler, S.
- Venue
- Circulation Research
- Published
- 2015-02-13
- Volume
- 116
- Issue
- 4
- Pages
- 737-750
- ISSN
- 0009-7330 (ISSN-L)
- 1524-4571
- OpenAlex
- W2145701907
- Cited by
- 739
- References
- 15
- FWCI
- 23.06
- 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: Long Noncoding RNAs in Cardiovascular Diseases. https://app.cochat.ai/papers/10.1161/circresaha.116.302521
- The paper
- Uchida, S., & Dimmeler, S. (2015). Long Noncoding RNAs in Cardiovascular Diseases. Circulation Research. https://doi.org/10.1161/circresaha.116.302521
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.