The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Dissecting the Single-Cell Transcriptome Network Underlying Gastric Premalignant Lesions and Early Gastric Cancer
Open accessAbstract
Intestinal-type gastric cancer is preceded by premalignant lesions, including chronic atrophic gastritis and intestinal metaplasia. In this study, we constructed a single-cell atlas for 32,332 high-quality cells from gastric antral mucosa biopsies of patients spanning a cascade of gastric premalignant lesions and early gastric cancer (EGC) using single-cell RNA sequencing. We then constructed a single-cell network underlying cellular and molecular characteristics of gastric epithelial cells across different lesions. We found that gland mucous cells tended to acquire an intestinal-like stem cell phenotype during metaplasia, and we identified OR51E1 as a marker for unique endocrine cells in the early-malignant lesion. We also found that HES6 might mark the pre-goblet cell cluster, potentially aiding identification of metaplasia at the early stage. Finally, we identified a panel of EGC-specific signatures, with clinical implications for the precise diagnosis of EGC. Our study offers unparalleled insights into the human gastric cellulome in premalignant and early-malignant lesions.
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(2008) · Journal of Machine Learning Research · Cited by 33,051
Coussens, L. M., & Werb, Z. (2002) · Nature · doi:10.1038/nature01322 · Cited by 15,111
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Classification and Grading of Gastritis: The Updated Sydney System
Dixon, M. F., Genta, R. M., Yardley, J. H., & Correa, P. (1996) · The American Journal of Surgical Pathology · doi:10.1097/00000478-199610000-00001 · Cited by 5,185
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
The Vienna classification of gastrointestinal epithelial neoplasia
Schlemper, R. J., Riddell, R. H., Kato, Y., Borchard, F., Cooper, H. S., Dawsey, S. M., Dixon, M. F., Fenoglio-Preiser, C. M., Fléjou, J.-F., Geboes, K., et al. (2000) · Gut · doi:10.1136/gut.47.2.251 · Cited by 2,138
A single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750
Cited by
Zou, X., Chen, K., Zou, J., Han, P., Hao, J., & Han, Z. (2020) · Frontiers of Medicine · doi:10.1007/s11684-020-0754-0 · Cited by 2,287
Management of Helicobacter pylori infection: the Maastricht VI/Florence consensus report
Malfertheiner, P., Megraud, F., Rokkas, T., Gisbert, J. P., Liou, J.-M., Schulz, C., Gasbarrini, A., Hunt, R. H., Leja, M., O'Morain, C., et al. (2022) · Gut · doi:10.1136/gutjnl-2022-327745 · Cited by 1,533
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193
Qi, F., Qian, S., Zhang, S., & Zhang, Z. (2020) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2020.03.044 · Cited by 905
“Totally, we curated single cell gene expression matrices of 13 human tissues, including lung[11], liver[12], ileum[13], rectum[13], blood[14], bone marrow[15], skin[16], spleen[17], esophagus[17], colon[18], eye[19], stomach[20] and”
A human cell atlas of fetal gene expression
Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., Zager, M. A., Aldinger, K. A., Blecher-Gonen, R., Zhang, F., et al. (2020) · Science · doi:10.1126/science.aba7721 · Cited by 764
Metadata
- Authors
- Zhang, P., Yang, M., Zhang, Y., Xiao, S., Lai, X., Tan, A., Du, S., & Li, S.
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 27
- Issue
- 6
- Pages
- 1934-1947.e5
- ISSN
- 2211-1247 (ISSN-L)
- OpenAlex
- W2944311410
- Cited by
- 478
- References
- 41
- FWCI
- 12.17
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zhang, P., Yang, M., Zhang, Y., Xiao, S., Lai, X., Tan, A., Du, S., & Li, S. (2019). Dissecting the Single-Cell Transcriptome Network Underlying Gastric Premalignant Lesions and Early Gastric Cancer. Cell Reports. https://doi.org/10.1016/j.celrep.2019.04.052
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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.