ArticleBMC medical genomics2019
Significant random signatures reveals new biomarker for breast cancer.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data.PLoS computational biology · 2025Article
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- Identification of TAT as a Biomarker Involved in Cell Cycle and DNA Repair in Breast Cancer.Biomolecules · 2024Article
- Removing the association of random gene sets and survival time in cancers with positive random bias using fixed-point gene set.Scientific reports · 2023Article
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Authors and funding
7 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
methodsIn 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.
resultsFirst, 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.
conclusionApplying 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 a new biomarker.
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