ArticleBMC bioinformatics2018
A comparative study of survival models for breast cancer prognostication revisited: the benefits of multi-gene models.
Article in BMC bioinformatics, 2018. 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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Who cites it
4 citing papers in PubMed.
- Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.Frontiers in oncology · 2022Article
- Expression Profile and Prognostic Value of Wnt Signaling Pathway Molecules in Colorectal Cancer.Biomedicines · 2021Article
- An Immune-Gene-Based Classifier Predicts Prognosis in Patients With Cervical Squamous Cell Carcinoma.Frontiers in molecular biosciences · 2021Article
- The Expression Profile and Prognostic Significance of Metallothionein Genes in Colorectal Cancer.International journal of molecular sciences · 2019Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundThe development of clinical -omic biomarkers for predicting patient prognosis has mostly focused on multi-gene models. However, several studies have described significant weaknesses of multi-gene biomarkers. Indeed, some high-profile reports have even indicated that multi-gene biomarkers fail to consistently outperform simple single-gene ones. Given the continual improvements in -omics technologies and the availability of larger, better-powered datasets, we revisited this "single-gene hypothesis" using new techniques and datasets.
resultsBy deeply sampling the population of available gene sets, we compare the intrinsic properties of single-gene biomarkers to multi-gene biomarkers in twelve different partitions of a large breast cancer meta-dataset. We show that simple multi-gene models consistently outperformed single-gene biomarkers in all twelve partitions. We found 270 multi-gene biomarkers (one per ~11,111 sampled) that always made better predictions than the best single-gene model.
conclusionsThe single-gene hypothesis for breast cancer does not appear to retain its validity in the face of improved statistical models, lower-noise genomic technology and better-powered patient cohorts. These results highlight that it is critical to revisit older hypotheses in the light of newer techniques and datasets.
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