Evidence map›Paper›PMID 31074374›Full record

ArticleBMC bioinformatics2019

Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data.

Yang Hao, Daixi Li, Yong Xu, Jian Ouyang, Yongkun Wang, Yuqi Zhang, Baoguo Li, Lu Xie, Guangrong Qin

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed
4.6field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

85 citing papers in PubMed, 132 citations in OpenAlex.

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25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Yang HaoSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Daixi LiSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. dxli75@126.com.
Yong XuSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Jian OuyangShanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China.
Yongkun WangSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Yuqi ZhangSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Baoguo LiSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Lu XieShanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China. luxiex2017@outlook.com.
Guangrong QinShanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, 201203, China. guangrong.qin@systemsbiology.org.
University of Shanghai for Science and Technology · CNShanghai Academy of Science & Technology · CNShanghai Center For Bioinformation Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression.

resultsThrough systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets.

conclusionsOur study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors.

Indexed as

DNA MethylationGene Expression Regulation, NeoplasticBiomarkers, TumorComputational BiologyGene Expression ProfilingHumansLipid MetabolismMutationNeoplasmsTranscriptomeTumor MicroenvironmentBiomarkers, TumorLipid metabolismMultiple omics analysisPan-cancerTumor immune micro-environment

Identifiers

PMID31074374
PMCPMC6509864
OpenAlexW2942514872

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LicenceCC BY
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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.