AI Article Synopsis

  • The human genome features topologically associating domains (TADs) that facilitate interactions within specific regions, impacting gene expression regulation and potentially linking disruptions to cancer.
  • Researchers identified 35 prognostic TADs in various cancer types through analysis of 1,467 consensus TADs, revealing that many prognostic TADs lacked directly relevant genes while highlighting the significance of regulatory mutations.
  • The study underscores how structural changes in TADs can influence cancer progression, paving the way for advancements in personalized medicine.

Article Abstract

The human genome is organized into topologically associating domains (TADs), which represent contiguous regions with a higher frequency of intra-interactions as opposed to inter-interactions. TADs contribute to gene expression regulation by restricting the interactions between their regulatory elements, and TAD disruption has been associated with cancer. Here, we provide a proof of principle that mutations within TADs can be used to predict the survival of cancer patients. Specifically, we constructed a set of 1467 consensus TADs representing the three-dimensional organization of the human genome and used Cox regression analysis to identify a total of 35 prognostic TADs in different cancer types. Interestingly, only 46% of the 35 prognostic TADs comprised genes with known clinical relevance. Moreover, in the vast majority of such cases, the prognostic value of the TAD was not directly related to the presence/absence of mutations in the gene(s), emphasizing the importance of regulatory mutations. In addition, we found that 34% of the prognostic TADs show strong structural perturbations in the cancer genome, consistent with the widespread, global epigenetic dysregulation often observed in cancer patients. In summary, this study elucidates the mechanisms through which non-coding variants may influence cancer progression and opens new avenues for personalized medicine.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966451PMC
http://dx.doi.org/10.3390/cancers11121886DOI Listing

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