Evidence map›Paper›PMID 36304471›Full record

ArticleFrontiers in immunology2022

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data.

Hongying Zhao, Siwen Zhang, Xiangzhe Yin, Caiyu Zhang, Lixia Wang, Kailai Liu, Haotian Xu, Wangyang Liu, Lin Bo, Shihua Lin and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 42% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

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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

15 authors at 1 institution in 1 country.

Hongying ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Siwen ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xiangzhe YinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Caiyu ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Lixia WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Kailai LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Haotian XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Wangyang LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Lin BoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Shihua LinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Ke FengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Lin LinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Meiting FeiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Shangwei NingCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Li WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.

Indexed as

Breast NeoplasmsRNA, Long NoncodingBiomarkers, TumorDNA Copy Number VariationsFemaleGene Expression Regulation, NeoplasticHumansPrognosisRNA, MessengerBiomarkers, TumorRNA, Long NoncodingRNA, Messengerbreast cancer subtypecopy number variationenhancerlncRNAprognostic marker

Identifiers

PMID36304471
PMCPMC9592759
OpenAlexW4304165727

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.