The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis
Open accessAbstract
Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we investigate the composition of ESCC tumors based on 208,659 single-cell transcriptomes derived from 60 individuals. We identify 8 common expression programs from malignant epithelial cells and discover 42 cell types, including 26 immune cell and 16 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and analyse the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we link the cancer cell transcriptomes to the somatic mutations and identify several markers significantly associated with patients’ survival, which may be relevant to precision care of ESCC patients. These results reveal the immunosuppressive status in the ESCC TME and further our understanding of ESCC.
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
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21492 · Cited by 84,109
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Cancer statistics in China, 2015
Chen, W., Zheng, R., Baade, P. D., Zhang, S., Zeng, H., Bray, F., Jemal, A., Yu, X. Q., & He, J. (2016) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21338 · Cited by 17,038
Self-citationGSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Microenvironmental regulation of tumor progression and metastasis
Quail, D. F., & Joyce, J. A. (2013) · Nature Medicine · doi:10.1038/nm.3394 · Cited by 7,994
Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy
Sharma, P., Hu-Lieskovan, S., Wargo, J. A., & Ribas, A. (2017) · Cell · doi:10.1016/j.cell.2017.01.017 · Cited by 5,588
Cited by
Li, J., Li, L., You, P., Wei, Y., & Xu, B. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.009 · Cited by 55
Immunosuppression and phenotypic plasticity in an atlas of human hepatocholangiocarcinoma
Li, Y., Xun, Z., Long, J., Sun, H., Yang, X., Wang, Y., Wang, Y., Xue, J., Zhang, N., Zhang, J., et al. (2024) · Hepatobiliary Surgery and Nutrition · doi:10.21037/hbsn-23-400 · Cited by 2
Self-citation
Metadata
- Authors
- Zhang, X., Peng, L., Luo, Y., Zhang, S., Pu, Y., Chen, Y., Guo, W., Yao, J., Shao, M., Fan, W., Cui, Q., Xi, Y., Sun, Y., Niu, X., Zhao, X., Chen, L., Wang, Y., Liu, Y., Yang, X., Wang, C., Zhong, C., Tan, W., Wang, J., Wu, C., & Lin, D.
- Venue
- Nature Communications
- Published
- 2021-09-06
- Volume
- 12
- Issue
- 1
- Pages
- 5291-5291
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3196701377
- Cited by
- 363
- References
- 80
- FWCI
- 15.30
- Type
- journal article
- Language
- en
- Open access
- gold
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: Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis. https://app.cochat.ai/papers/10.1038/s41467-021-25539-x
- The paper
- Zhang, X., Peng, L., Luo, Y., Zhang, S., Pu, Y., Chen, Y., Guo, W., Yao, J., Shao, M., Fan, W., Cui, Q., Xi, Y., Sun, Y., Niu, X., Zhao, X., Chen, L., Wang, Y., Liu, Y., Yang, X., . . . Lin, D. (2021). Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis. Nature Communications. https://doi.org/10.1038/s41467-021-25539-x
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.