The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
DNA methylation markers for diagnosis and prognosis of common cancers
Open accessAbstract
Significance: The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving diagnosis and prognosis. We evaluated the utility of DNA methylation profiles for differentiating tumors and normal tissues for four common cancers (lung, breast, colon, and liver) and found that they could differentiate cancerous tissue from normal tissue with >95% accuracy. This signature also correctly identified 19 of 20 breast cancer metastases and 29 of 30 colorectal cancer metastases to the liver. We report that methylation patterns can predict the prognosis and survival, with good correlation between differential methylation of CpG sites and expression of cancer-associated genes. Their findings demonstrate the utility of methylation biomarkers for the molecular characterization, diagnosis, and prognosis of cancer.
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy
Paez, J. G., Jänne, P. A., Lee, J. C., Tracy, S., Greulich, H., Gabriel, S., Herman, P., Kaye, F. J., Lindeman, N., Boggon, T. J., et al. (2004) · Science · doi:10.1126/science.1099314 · Cited by 9,368
Jaenisch, R., & Bird, A. (2003) · Nature Genetics · doi:10.1038/ng1089 · Cited by 6,325
Epigenetics in human disease and prospects for epigenetic therapy
Egger, G., Liang, G., Aparicio, A., & Jones, P. A. (2004) · Nature · doi:10.1038/nature02625 · Cited by 3,232
Identification and characterization of essential genes in the human genome
Wang, T., Birsoy, K., Hughes, N. W., Krupczak, K. M., Post, Y., Wei, J. J., Lander, E. S., & Sabatini, D. M. (2015) · Science · doi:10.1126/science.aac7041 · Cited by 1,617
Cited by
Current and future perspectives of liquid biopsies in genomics-driven oncology
Heitzer, E., Haque, I. S., Roberts, C. E. S., & Speicher, M. R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0071-5 · Cited by 1,557
Liquid biopsy enters the clinic — implementation issues and future challenges
Ignatiadis, M., Sledge, G. W., & Jeffrey, S. S. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-00457-x · Cited by 1,191
Principles of DNA methylation and their implications for biology and medicine
Dor, Y., & Cedar, H. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31268-6 · Cited by 640
Universal cancer screening: revolutionary, rational, and realizable
Ahlquist, D. A. (2018) · npj Precision Oncology · doi:10.1038/s41698-018-0066-x · Cited by 135
Development of genome-derived tumor type prediction to inform clinical cancer care
Penson, A., Camacho, N., Zheng, Y., Varghese, A. M., Al-Ahmadie, H., Razavi, P., Chandarlapaty, S., Vallejo, C. E., Vakiani, E., Gilewski, T., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2019.3985 · Cited by 115
Metadata
- Authors
- Hao, X., Luo, H., Krawczyk, M., Wei, W., Wang, W., Wang, J., Flagg, K., Hou, J., Zhang, H., Yi, S., Jafari, M., Lin, D., Chung, C., Caughey, B. A., Li, G., Dhar, D., Shi, W., Zheng, L., Hou, R., Zhu, J., Zhao, L., Fu, X., Zhang, E., Zhang, C., Zhu, J.-K., Karin, M., Xu, R.-H., & Zhang, K.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2017-06-26
- Volume
- 114
- Issue
- 28
- Pages
- 7414-7419
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2663574681
- Cited by
- 458
- References
- 8
- FWCI
- 15.14
- Type
- journal article
- Language
- en
- Open access
- green
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: DNA methylation markers for diagnosis and prognosis of common cancers. https://app.cochat.ai/papers/10.1073/pnas.1703577114
- The paper
- Hao, X., Luo, H., Krawczyk, M., Wei, W., Wang, W., Wang, J., Flagg, K., Hou, J., Zhang, H., Yi, S., Jafari, M., Lin, D., Chung, C., Caughey, B. A., Li, G., Dhar, D., Shi, W., Zheng, L., Hou, R., . . . Zhang, K. (2017). DNA methylation markers for diagnosis and prognosis of common cancers. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1703577114
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.
