Evidence map›Paper›PMID 30859354›Full record

ArticleFunctional & integrative genomics2019

Regulatory network reconstruction of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets.

Kan He, Wen-Xing Li, Daogang Guan, Mengting Gong, Shoudong Ye, Zekun Fang, Jing-Fei Huang, Aiping Lu

Abstract read
PubMed Publisher
In one paragraph

Article in Functional & integrative genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.8field-weighted citation impact, top 17% 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

18 citing papers in PubMed, 29 citations in OpenAlex.

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  16. Novel Multiple miRNA-Based Signatures for Predicting Overall Survival and Recurrence-Free Survival of Colorectal Cancer Patients.Medical science monitor : international medical journal of experimental and clinical research · 2019
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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

8 authors at 5 institutions in 2 countries.

Kan HeDepartment of Biostatistics, School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China. hekan_803@163.com.
Wen-Xing LiState Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China.
Daogang GuanSchool of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Kowloon Tong, Hong Kong, China.
Mengting GongCenter for Stem Cell and Translational Medicine, School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China.
Shoudong YeDepartment of Biostatistics, School of Life Sciences, Anhui University, Hefei, 230601, Anhui, China.
Zekun FangHorticulture & Landscape College, Southwest University, Beipei, Chongqing, 400715, China.
Jing-Fei HuangState Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. huangjf@mail.kiz.ac.cn.
Aiping LuSchool of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Kowloon Tong, Hong Kong, China. aipinglu@hkbu.edu.hk.
Anhui University · CNHong Kong Baptist University · HKKunming Institute of Zoology · CNSouthwest University · CNUniversity of Chinese Academy of Sciences · CN

Funding

Anhui University 10117700023Hong Kong Baptist University Strategic Development Fund (SDF) SDF15-0324-P02(b)Hong Kong Scholars Program 2016 XJ2016062Key University Science Research Project of Anhui Province KJ2017A021National Basic Research Program of China 2013CB835100Natural Science Foundation Project of Anhui Province 1508085QC63
6 · The paper itself

Abstract

Although many of the genetic loci associated with breast cancer risk have been reported, there is a lack of systematic analysis of regulatory networks composed of different miRNAs and mRNAs on survival analysis in breast cancer. To reconstruct the microRNAs-genes regulatory network in breast cancer, we employed the expression data from The Cancer Genome Atlas (TCGA) related to five essential miRNAs including miR-21, miR-22, miR-210, miR-221, and miR-222, and their associated functional genomics data from the GEO database. Then, we performed an integration analysis to identify the essential target factors and interactions for the next survival analysis in breast cancer. Based on the results of our integrated analysis, we have identified significant common regulatory signatures including differentially expressed genes, enriched pathways, and transcriptional regulation such as interferon regulatory factors (IRFs) and signal transducer and activator of transcription 1 (STAT1). Finally, a reconstructed regulatory network of five miRNAs and 34 target factors was established and then applied to survival analysis in breast cancer. When we used expression data for individual miRNAs, only miR-21 and miR-22 were significantly associated with a survival change. However, we identified 45 significant miRNA-gene pairs that predict overall survival in breast cancer out of 170 one-on-one interactions in our reconstructed network covering all of five miRNAs, and several essential factors such as PSMB9, HLA-C, RARRES3, UBE2L6, and NMI. In our study, we reconstructed regulatory network of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets. These results may provide new insights into regulatory network-based precision medicine for breast cancer.

Indexed as

Gene Regulatory NetworksBreast NeoplasmsCarcinomaFemaleGene Expression Regulation, NeoplasticHumansMicroRNAsNeoplasm InvasivenessRNA, MessengerSurvival AnalysisMicroRNAsRNA, MessengerBreast cancerMicroRNAsNetworkPathwaysSurvival analysis

Identifiers

PMID30859354
OpenAlexW2921567945

What OpenQuestion holds

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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.