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The ability to accurately distinguish between aggressive and indolent cancers is fundamental to predicting patient risk and can guide critical treatment decisions
The ability to accurately distinguish between aggressive and indolent cancers is fundamental to predicting patient risk and can guide critical treatment decisions
Both undertreatment and overtreatment of cancer have been identified as significant sources of patient mortality, highlighting the urgent need to improve our ability to accurately identify patients with the most aggressive malignancies
Current risk prediction relies heavily on histopathological and radiological assessment of disease states
Image credit: https://doi.
Image credit: https://doi.
Recently, researchers at Yale University School of Medicine published an article titled "Genome-wide identification and analysis of prognostic features in human cancers" in the journal Cell Reports, which established a rich resource for prognostic biomarker analysis.
Clinical decision-making in cancer relies on an accurate assessment of a patient's risk
The researchers identified more than 100,000 important prognostic biomarkers and demonstrated that these genomic signatures can predict patient outcomes in clinically ill-defined settings
Conversely, the strongest adverse biomarkers represent widely expressed cell cycle and genes, and accordingly, nearly all treatments targeting these features have failed clinical trials
Genome-wide identification and analysis of prognostic features in human cancers
Genome-wide identification and analysis of prognostic features in human cancersImage credit: https://doi.
Image credit: https://doi.
In an ideal biomarker discovery study, patients in each cohort would receive uniform treatment, thereby minimizing one potential source of interpatient variability
Additionally, some cancer types in TCGA include fewer than 100 patients, which may preclude comprehensive biomarker identification
references
Joan C.
Joan C.
Smith et al.
Genome-wide identification and analysis of prognostic features in human cancers.
Cell Rep.
2022 Mar 29;38(13):110569.
doi: 10.
1016/j.
celrep.
2022.
110569.
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