ArticleNPJ genomic medicine2021
Estimating the predictive power of silent mutations on cancer classification and prognosis.
Article in NPJ genomic medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
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- Assessing the reliability of point mutation as data augmentation for deep learning with genomic data.BMC bioinformatics · 2024Article
- Differential Synonymous Codon Selection in the B56 Gene Family of PP2A Regulatory Subunits.International journal of molecular sciences · 2023Article
- Hairy cell leukaemia with unusual BRAF mutations.Journal of cellular and molecular medicine · 2023Article
- Synonymous Variants: Necessary Nuance in Our Understanding of Cancer Drivers and Treatment Outcomes.Journal of the National Cancer Institute · 2022Review
- IL22RA1/JAK/STAT Signaling Acts As a Cancer Target Through Pan-Cancer Analysis.Frontiers in immunology · 2022Article
- FASN, SCD1 and ANXA9 gene polymorphism as genetic predictors of the fatty acid profile of sheep milk.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.