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| Paper IPM / Biological Sciences / 15861 |
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Background: In 2012, Venet et al. proposed that at least in the case of breast cancer, most published signatures are
not significantly more associated with outcome than randomly generated signatures. They suggested that nominal
p-value is not a good estimator to show the significance of a signature. Therefore, one can reasonably postulate that
some information might be present in such significant random signatures.
Methods: In this research, first we show that, using an empirical p-value, these published signatures are more
significant than their nominal p-values. In other words, the proposed empirical p-value can be considered as a
complimentary criterion for nominal p-value to distinguish random signatures from significant ones. Secondly, we
develop a novel computational method to extract information that are embedded within significant random
signatures. In our method, a score is assigned to each gene based on the number of times it appears in significant
random signatures. Then, these scores are diffused through a protein-protein interaction network and a permutation
procedure is used to determine the genes with significant scores. The genes with significant scores are considered as
the set of significant genes.
Results: First, we applied our method on the breast cancer dataset NKI to achieve a set of significant genes in breast
cancer considering significant random signatures. Secondly, prognostic performance of the computed set of
significant genes is evaluated using DMFS and RFS datasets. We have observed that the top ranked genes from this
set can successfully separate patients with poor prognosis from those with good prognosis. Finally, we investigated
the expression pattern of TAT, the first gene reported in our set, in malignant breast cancer vs. adjacent normal tissue
and mammospheres.
Conclusion: Applying the method, we found a set of significant genes in breast cancer, including TAT, a gene that
has never been reported as an important gene in breast cancer. Our results show that the expression of TAT is
repressed in tumors suggesting that this gene could act as a tumor suppressor in breast cancer and could be used as
anewbiomarker.
Keywords: Random signature, Network diffusion, Biomarker, Breast cancer, TAT (Tyrosine Aminotransferase)
https://doi.org/10.1186/s12920-019-0609-1
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