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Lung cancer epigenetics: From knowledge to applications
Abstract
Lung cancer is the leading cause of cancer-related mortality worldwide. Advances in our understanding of the genomics of lung cancer have led to substantial progress in the treatment of specific molecular subsets. Immunotherapy also emerges as a major breakthrough in lung cancer treatment. However, challenges remain as a consensual approach for early lung cancer detection remains elusive while primary or secondary drug resistance eventually leads to treatment failure in all patients with advanced disease. Furthermore, a large portion of patients are still treated with conventional chemotherapy that is only modestly effective. The last two decades have seen exponential developments in the epigenetic understanding of lung cancer. Epigenetic alterations in DNA methylation, non-coding RNA expression, chromatin modeling and post transcriptional regulators are key events in each step of lung cancer pathogenesis. Here, we review the central role epigenetic disruptions play in lung cancer carcinogenesis and the acquisition of cancerous phenotype and aggressive behavior as well as in the resistance to therapy. Epigenetic disruptions could represent reliable biomarkers for lung cancer risk assessment, early diagnosis, prognosis stratification, molecular classification and prediction of treatment efficacy. The therapeutic potential of epigenetics targeted drugs in combination with chemotherapy, targeted therapy and/or immunotherapy is currently being intensively investigated. We suggest that integration of tissue-derived or circulating epigenetic biomarkers and epidrugs in clinical trial design will translate epigenetic knowledge of lung cancer into the clinic and improve lung cancer patient outcomes.
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Metadata
- Authors
- Duruisseaux, M., & Esteller, M.
- Venue
- Seminars in Cancer Biology
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 51
- Pages
- 116-128
- ISSN
- 1044-579X (ISSN-L)
- OpenAlex
- W2754970987
- Cited by
- 283
- References
- 248
- FWCI
- 7.38
- 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
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.
