Aramillo Irizar P, Schäuble S, Esser D, Groth M, Frahm C, Priebe S, Baumgart M, Hartmann N, Marthandan S, Menzel U, Müller J, Schmidt S, Ast V, Caliebe A, König R, Krawczak M, Ristow M, Schuster S, Cellerino A, Diekmann S, Englert C, Hemmerich P, Sühnel J, Guthke R, Witte OW, Platzer M, Ruppin E, Kaleta C. Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly. Nat Commun. 2018 Jan 30;9(1):327. PubMed.

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  1. This is a very interesting paper that provides evidence for a concept that is intuitive, but that I don’t think has been thoroughly explored: cancer—a disease of dysregulated cellular proliferation—has an opposite (transcriptomic) profile to late-onset neurodegenerative diseases—diseases characterized by cell death. Of course, humans are complex systems and so the existence of these balances makes complete sense. This is elegantly shown in this paper. I would note, however, that the data sets are not particularly large (531 samples in five age points), and so it is difficult to generalize the findings and place them in the context of human biology. It would have been interesting to break down the group of degenerative aging-associated diseases and test for which of them these results hold true. For people studying neurodegeneration, it is interesting to see transcriptomic profiles that are negatively associated with late-onset neurodegenerative disease; these may provide novel therapeutic targets in the future.

    View all comments by Jose Bras

  2. Thank you for your comment concerning our work. Please note that this data set covers four species and four tissues (thereof 86 samples from humans). Thus, the data set might appear small compared to other studies, but we find this antagonism across species and tissues which strongly supports its generality (at least in vertebrates).

    Moreover, we have tested the disease antagonism in gene-expression data from several previously published human-aging studies. Among them, one study in brain covered 55 samples and another one in blood (marked as "cross-cohort" in Figure 3 of our manuscript) comprised approximately 15,000 participants. For the gene-expression analysis we report the associations between individual diseases and the aging data sets in Figure 3. For the genetic analyses, the breakdown into individual diseases can be found in Supplementary Data 1 (sheet "GWAS Cancer vs. DAD").

    Indeed it would also be interesting to look into specific sub-phenotypes of diseases such as late-onset AD, if corresponding expression data is available.

    View all comments by Christoph Kaleta

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