Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA
Open accessAbstract
With accumulating research on the interconnections among different types of genomic regulations, researchers have found that multidimensional genomic studies outperform one-dimensional studies in multiple aspects. Among many sources of multidimensional genomic data, The Cancer Genome Atlas (TCGA) provides the public with comprehensive profiling data on >30 cancer types, making it an ideal test bed for conducting and comparing different analyses. In this article, the analysis goal is to apply several existing methods and associate multidimensional genomic measurements with cancer outcomes in particular prognosis, with special focus on the predictive power of genomic signatures. We exploit clinical data and four types of genomic measurement including mRNA gene expression, DNA methylation, microRNA and copy number alterations for breast invasive carcinoma, glioblastoma multiforme, acute myeloid leukemia and lung squamous cell carcinoma collected by TCGA. To accommodate the high dimensionality, we extract important features using Principal Component Analysis, Partial Least Squares and Least Absolute Shrinkage and Selection Operator (Lasso), which are representative of dimension reduction and variable selection techniques and have been extensively adopted, and fit Cox survival models with combined important features. We calibrate the predictive power of each type of genomic measurement for the prognosis of four cancer types and find that the results vary across cancers. Our analysis also suggests that for most of the cancers in our study and the adopted methods, there is no substantial improvement in prediction when adding other genomic measurement after gene expression and clinical covariates have been included in the model. This is consistent with the findings that molecular features measured at the transcription level affect clinical outcomes more directly than those measured at the DNA/epigenetic level.
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LASSO has also been employed for cancer prognosis.
“LASSO has also been employed for cancer prognosis”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Zhao et al., 2015
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- Authors
- Zhao, Q., Shi, X., Xie, Y., Huang, J., Shia, B., & Ma, S.
- Affiliations
- Yale University (US)
- Shanghai University of Finance and Economics (CN)
- The University of Texas Southwestern Medical Center (US)
- University of Iowa (US)
- Fu Jen Catholic University (TW)
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-03-13
- Volume
- 16
- Issue
- 2
- Pages
- 291-303
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- PMC
- PMC4375393
- OpenAlex
- W2030678916
- Cited by
- 154
- References
- 46
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 12 · 2023: 6 · 2022: 11 · 2021: 17 · 2020: 19 · 2019: 13 · 2018: 12 · 2017: 14 · 2016: 23 · 2015: 9 · 2014: 4
- FWCI
- 6.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Briefings in Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (CA165923, CA152301, R03CA182984, CA182984, R01CA152301, R01CA142774, CA142774, R21CA165923)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- cancer prognosis, genomic signatures, gene expression, DNA methylation, microRNA, copy number alterations, invasive breast cancer, glioblastoma, acute myeloid leukemia, lung squamous cell carcinoma, dimensionality reduction, feature selection, TCGA
- MeSH terms
- Brain Neoplasms, Breast Neoplasms, Carcinoma, Squamous Cell, Female, Glioblastoma, Humans, Lung Neoplasms, Male, Neoplasms, Prognosis, Leukemia, Myeloid, Acute, Proportional Hazards Models, Least-Squares Analysis, Computational Biology, Genomics, Principal Component Analysis, Databases, Genetic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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