ArticleScientific reports2021
Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Transcriptomic Evidence of Immune-Tumor Uncoupling Defines a High-Risk State in Uterine Corpus Endometrial Carcinoma.International journal of molecular sciences · 2026Article
- Defining hypoxia in cancer: A landmark evaluation of hypoxia gene expression signatures.Cell genomics · 2025Article
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- Gene signatures for cancer research: A 25-year retrospective and future avenues.PLoS computational biology · 2024Article
- A triple hormone receptor ER, AR, and VDR signature is a robust prognosis predictor in breast cancer.Breast cancer research : BCR · 2024Article
- High NTRK2 protein expression levels may be associated with poorer prognosis of breast cancer patients.The Journal of international medical research · 2024Article
- Article
- Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer.Scientific reports · 2023Article
- Non-coding RNA's prevalence as biomarkers for prognostic, diagnostic, and clinical utility in breast cancer.Functional & integrative genomics · 2023Review
- Removing the association of random gene sets and survival time in cancers with positive random bias using fixed-point gene set.Scientific reports · 2023Article
- Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data.Scientific reports · 2023Article
- The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer.International journal of molecular sciences · 2023Review
- Identification of a gene expression signature associated with breast cancer survival and risk that improves clinical genomic platforms.Bioinformatics advances · 2023Article
- Severe testing with high-dimensional omics data for enhancing biomedical scientific discovery.NPJ systems biology and applications · 2022Article
- Article
- ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin.NPJ breast cancer · 2022Article
- Prognostic modeling of predictive maintenance with survival analysis for mobile work equipment.Scientific reports · 2022Article
- Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma.Frontiers in bioinformatics · 2022Article
- Identification of therapeutically potential targets and their ligands for the treatment of OSCC.Frontiers in oncology · 2022Article
- Are There Limits in Explainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures.Cancers · 2021Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large.
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