Regular aspirin use and gene expression profiles in prostate cancer patients

Stopsack, Konrad H.; Ebot, Ericka M.; Downer, Mary K.; Gerke, Travis; Rider, Jennifer R.; Mucci, Lorelei A.

doi:10.1007/s10552-018-1049-5

Cited by 5 publications

(6 citation statements)

References 40 publications

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“…Gene expression counts from bulk RNAseq are the input data. The four steps in the workflow are: (1) quality control; (2) internal normalization; (3) correction for batch effects; (4) PCA and dimension reduction ( Figure 2).…”

Section: Spectra Workflowmentioning

confidence: 99%

“…For this reason, gene expression studies are gaining momentum in new fields, such as genomic epidemiology. [1][2][3][4][5][6][7][8][9][10][11][12][13] The need to incorporate transcriptomes in multivariable models alongside other risk factors brings new demands for techniques designed with this in mind.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, gene expression studies are gaining momentum in genomic epidemiology. [1][2][3][4][5] The need to incorporate transcriptome data in multivariable models alongside other risk factors brings new demands for approaches to describe transcriptomes.…”

Section: Introductionmentioning

confidence: 99%

“…Transcriptomes represent the combined effects of inherited, somatic, and epigenetic variation and can provide insight into genetic and environmental risk factors. As a result, gene expression studies are gaining momentum in genomic epidemiology (Allott et al, 2020; López et al, 2019; Stopsack et al, 2018; Sweeney et al, 2014; Zhang et al, 2018). The need to incorporate transcriptome data in multivariable models alongside other risk factors brings new demands for approaches to describe transcriptomes.…”

Section: Introductionmentioning

confidence: 99%

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Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach

Griffin

Hanson

Avery

et al. 2020

Preprint

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Cancers are highly heterogeneous diseases and large molecular datasets are increasingly part of describing an individuals unique experience. Gene expression is particularly attractive because it captures both genetic and environmental consequences. Our new approach, SPECTRA, provides a framework of agnostic multi-gene linear equations to calculate variables tuned to the needs of genomic epidemiology studies. SPECTRA variables are not supervised to an outcome. They are quantitative, linearly uncorrelated variables that retain integrity to the original data and cumulatively explain the majority of the global population variance. Together these variables represent a deep dive into the transcriptome, including both large and small sources of variance. The latter is often over-looked, but holds potential for the identification of smaller groups of individuals with large effects and important for developing precision strategies. Each SPECTRA variable is a quantitative tissue phenotype that can be considered a phenotypic outcome providing new avenues to explore disease risk. Also, as a set of SPECTRA variables, they are ideal for modeling alongside other variables as predictors for any clinical outcome of interest. We demonstrate the flexibility of SPECTRA variables for multiple endpoints using RNA sequencing from 767 myeloma patients in the CoMMpass study. Quantitative transcriptome SPECTRA variables enhance the tools researchers have available for incorporating expression in studies to advance precision screening, prevention, intervention, and survival.

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Section: Spectra Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach

Griffin

Hanson

Avery

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Transcriptomes represent the combined effects of inherited, somatic, and epigenetic variation and can provide insight into genetic and environmental risk factors. Gene expression studies are gaining momentum in genomic epidemiology (1)(2)(3)(4)(5). The need to incorporate transcriptome data in multivariable models with other risk factors, and for improved representation of disease complexity for insight into effects of multiple risk factors, brings new demands for approaches to describe transcriptomes.…”

Section: Introductionmentioning

confidence: 99%

Supplemental Figure S4 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes

Griffin¹,

Hanson²,

Avery³

et al. 2023

Preprint

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<p>Kaplan-Meier curves of overall survival by spectra and UAMS risk-groups in the CoMMpass data. CoMMpass patients were assigned to high and low risk separately using the OS Spectra score and the UAMS score (See Methods, Comparison to the UAMS risk score). Blue and orange lines: both methods agree on patient's risk category. Purple line: Spectra correctly identified high-risk patients (poorer survival) that are misclassified as low risk by UAMS. Green line: Spectra correctly identified low-risk patients (better survival) that are misclassified as high risk by UAMS.</p>

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Table S5 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes

Griffin¹,

Hanson²,

Avery³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Regular aspirin use and gene expression profiles in prostate cancer patients

Cited by 5 publications

References 40 publications

Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach

Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach

Supplemental Figure S4 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes

Table S5 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes

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