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Gene & Protein in Disease Identification of new genes
biomarkers, and therapeutic targets. The involvement of Nat Commun. 2020;11:69.
AI in cancer research is multifaceted. It integrates clinical doi: 10.1038/s41467-019-13803-0
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with different cancer types. These biomarkers could be
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tumor responses to therapy treatments, aiding in the of mutational cancer driver genes. Nat Rev Cancer.
development of better therapeutic options. AI is used to 2020;20:555-572.
analyze genomic sequencing data to identify novel cancer- doi: 10.1038/s41568-020-0290-x
associated genes. Deep learning models can detect subtle 5. Nielsen F, van Overeem Hansen T, Sørensen C. Hereditary
patterns and mutations that might be missed by traditional breast and ovarian cancer: New genes in confined pathways.
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aiding researchers in understanding the functional
significance of specific mutations. They assist in identifying doi: 10.1038/nrc.2016.72
potential drug targets, accelerating drug discovery by 6. Sosinsky A, Ambrose J, Cross W, et al. Insights for precision
pinpointing genes or proteins that play a crucial role in oncology from the integration of genomic and clinical
cancer development. These tools can analyze complex data of 13,880 tumors from the 100,000 Genomes Cancer
biological pathways, revealing interconnected networks Programme. Nat Med. 2024;30:279-289.
of genes and proteins involved in cancer. This holistic doi: 10.1038/s41591-023-02682-0
view aids in understanding underlying mechanisms
and potential therapeutic therapy treatment. AI can also 7. Koh G, Degasperi A, Zou X, Momen S, Nik-Zainal S.
suggest existing drugs for repurposing based on their Mutational signatures: Emerging concepts, caveats and
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doi: 10.1038/s41568-021-00377-7
Despite their relevance, AI-based findings should
be experimentally validated before clinical translation. 8. Saito Y, Koya J, Araki M, et al. Landscape and function of
Collaboration among researchers, access to comprehensive multiple mutations within individual oncogenes. Nature.
databases, advanced technologies, and integration of 2020;582:95-99.
multiple approaches can lead to the identification of new doi: 10.1038/s41586-020-2175-2
genes involved in cancer. These findings pave the way for 9. Terekhanova NV, Karpova A, Liang WW, et al. Epigenetic
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Nature. 2023;623:432-441.
Conflict of interest doi: 10.1038/s41586-023-06682-5
Amancio Carnero is an Associate Editor of this journal. 10. Eifert C, Powers RS. From cancer genomes to oncogenic
drivers, tumour dependencies and therapeutic targets. Nat
Further disclosure Rev Cancer. 2012;12:572-578.
There are tens of thousands of works describing techniques doi: 10.1038/nrc3299
to identify new genes involved in cancer. It would have 11. Katti A, Diaz BJ, Caragine CM, Sanjana NE, Dow LE.
been impossible to cite all of them, even citations of only CRISPR in cancer biology and therapy. Nat Rev Cancer.
some landmark works using every technique. Therefore, 2022;22:259-279.
we only suggest further reading on the same topic and the doi: 10.1038/s41568-022-00441-w
references listed in this editorial for a better understanding.
12. Paczkowska M, Barenboim J, Sintupisut N, et al. Integrative
References pathway enrichment analysis of multivariate omics data. Nat
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pathways to discover cancer driver genes with Moonlight. doi: 10.1038/s41467-019-13983-9
Volume 4 Issue 1 (2025) 2 doi: 10.36922/gpd.2892

