Abstract:
BACKGROUND:Prognostic signatures are vital to precision medicine. However, development of somatic mutation prognostic signatures for cancers remains a challenge. In this study we developed a novel method for discovering somatic mutation based prognostic signatures. RESULTS:Somatic mutation and clinical data for lung adenocarcinoma (LUAD) and colorectal adenocarcinoma (COAD) from The Cancer Genome Atlas (TCGA) were randomly divided into training (n = 328 for LUAD and 286 for COAD) and validation (n = 167 for LUAD and 141 for COAD) datasets. A novel method of using the log2 ratio of the tumor mutation frequency to the paired normal mutation frequency is computed for each patient and missense mutation. The missense mutation ratios were mean aggregated into gene-level somatic mutation profiles. The somatic mutations were assessed using univariate Cox analysis on the LUAD and COAD training sets separately. Stepwise multivariate Cox analysis resulted in a final gene prognostic signature for LUAD and COAD. Performance was compared to gene prognostic signatures generated using the same pipeline but with different somatic mutation profile representations based on tumor mutation frequency, binary calls, and gene-gene network normalization. Signature high-risk LUAD and COAD cases had worse overall survival compared to the signature low-risk cases in the validation set (log-rank test p-value = 0.0101 for LUAD and 0.0314 for COAD) using mutation tumor frequency ratio (MFR) profiles, while all other methods, including gene-gene network normalization, have statistically insignificant stratification (log-rank test p-value ≥0.05). Most of the genes in the final gene signatures using MFR profiles are cancer-related based on network and literature analysis. CONCLUSIONS:We demonstrated the robustness of MFR profiles and its potential to be a powerful prognostic tool in cancer. The results are robust according to validation testing and the selected genes are biologically relevant.
journal_name
BMC Med Genomicsjournal_title
BMC medical genomicsauthors
Menor M,Zhu Y,Wang Y,Zhang J,Jiang B,Deng Ydoi
10.1186/s12920-018-0454-7subject
Has Abstractpub_date
2019-01-31 00:00:00pages
24issue
Suppl 1issn
1755-8794pii
10.1186/s12920-018-0454-7journal_volume
12pub_type
杂志文章abstract:BACKGROUND:The noninvasive prenatal diagnosis procedures that are currently used to detect genetic diseases do not achieve desirable levels of sensitivity and specificity. Recently, fetal methylated DNA biomarkers in maternal peripheral blood have been explored for the noninvasive prenatal detection of genetic disorder...
journal_title:BMC medical genomics
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abstract:BACKGROUND:A molecular characterization of Alzheimer's Disease (AD) is the key to the identification of altered gene sets that lead to AD progression. We rely on the assumption that candidate marker genes for a given disease belong to specific pathogenic pathways, and we aim at unveiling those pathways stable across ti...
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abstract:BACKGROUND:The purpose of this paper is to describe the data collection efforts and validation of PhenX measures in the Personalized Medicine Research Project (PMRP) cohort. METHODS:Thirty-six measures were chosen from the PhenX Toolkit within the following domains: demographics; anthropometrics; alcohol, tobacco and ...
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更新日期:2009-07-23 00:00:00
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pub_type: 杂志文章
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更新日期:2011-02-04 00:00:00
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更新日期:2011-04-18 00:00:00
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更新日期:2020-01-03 00:00:00
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pub_type: 杂志文章
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更新日期:2014-06-03 00:00:00
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更新日期:2008-04-25 00:00:00
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pub_type: 杂志文章,多中心研究
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