Evidence map›Paper›PMID 31131513›Full record

ArticleFEBS open bio2019

Network analysis of aging acceleration reveals systematic properties of 11 types of cancers.

Xiaoqiong Xia, Mengyu Zhou, Hao Yan, Sijia Li, Xianzheng Sha, Yin Wang

Abstract read
In one paragraph

Article in FEBS open bio, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xiaoqiong XiaDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China.
Mengyu ZhouDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China.
Hao YanDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China.
Sijia LiDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China.
Xianzheng ShaDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China.
Yin WangDepartment of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancers are known to be associated with accelerated aging, but to date, there has been a paucity of systematic and in-depth studies of the correlation between aging and cancer. DNA methylation (DNAm) profiles can be used as aging markers and utilized to construct aging predictors. In this study, we downloaded 333 paired samples of DNAm, expression and mutation profiles encompassing 11 types of tissues from The Cancer Genome Atlas public access portal. The DNAm aging scores were calculated using the Support Vector Machine regression model. The DNAm aging scores of cancers revealed significant aging acceleration compared to adjacent normal tissues. Aging acceleration-associated mutation modules and expression modules were identified in 11 types of cancers. In addition, we constructed bipartite networks of mutations and expression, and the differential expression modules related to aging-associated mutations were selected in 11 types of cancers using the expression quantitative trait locus method. The results of enrichment analyses also identified common functions across cancers and cancer-specific characteristics of aging acceleration. The aging acceleration interaction network across cancers suggested a core status of thyroid carcinoma and neck squamous cell carcinoma in the aging process. In summary, we have identified correlations between aging and cancers and revealed insights into the biological functions of the modules in aging and cancers.

Indexed as

AgingCpG IslandsDNA MethylationEpigenesis, GeneticGene Regulatory NetworksHumansMutationNeoplasmsQuantitative Trait LociSupport Vector Machineaccelerated agingcancerDNA methylationnetwork analysispan-cancer

Identifiers

PMID31131513
PMCPMC6609580

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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.