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Functional & Integrative Genomics

, Volume 19, Issue 4, pp 645–658 | Cite as

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

  • Kan HeEmail author
  • Wen-Xing Li
  • Daogang Guan
  • Mengting Gong
  • Shoudong Ye
  • Zekun Fang
  • Jing-Fei HuangEmail author
  • Aiping LuEmail author
Original Article
  • 242 Downloads

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.

Keywords

MicroRNAs Network Breast cancer Pathways Survival analysis 

Abbreviations

TCGA

The Cancer Genome Atlas

IRFs

interferon regulatory factors

STAT1

signal transducer and activator of transcription 1

miRNAs

microRNAs

mRNAs

messenger RNAs

RPMM

reads per million mapped

RMA

Robust Multichip Average

GSEA

gene set enrichment analysis

NES

normalized enrichment score

HR

hazard ratio

ER

estrogen receptor

DEGs

differentially expressed genes

TFBSs

transcription factor binding sites

bHLH

the basic helix-loop-helix

DBD

DNA-binding domain

PSMB9

proteasome subunit beta 9

HLA-C

human leukocyte antigen C

RARRES3

retinoic acid receptor responder 3

UBE2L6

ubiquitin conjugating enzyme E2 L6

NMI

N-myc interactor

Notes

Acknowledgments

Many thanks to the National Postdoctoral office and the Hongkong Scholars Association.

Authors’ contributions

KH, JFH, and APL designed the study. WXL, DGG, MTG, SDY, and ZF performed the experiments and/or data analysis. KH, WXL, DGG, and APL wrote the paper with input from all authors.

Funding

We acknowledge financial support from the Natural Science Foundation Project of Anhui Province (1508085QC63), and Key University Science Research Project of Anhui Province (KJ2017A021), and the Scientific Research Foundation and Academic & Technology Leaders Introduction Project (the 211 Project) of Anhui University (10117700023). Our work was also supported by the Hong Kong Scholars Program 2016 (XJ2016062) and National Basic Research Program of China (Grant No. 2013CB835100). Financial support by the Hong Kong Baptist University Strategic Development Fund (SDF) (SDF15-0324-P02(b) to A.L.) should also be acknowledged.

Compliance with ethical standards

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

Not applicable.

Competing interests

The authors declare that they have no conflicts of interest.

Supplementary material

10142_2019_670_Fig8_ESM.png (7 kb)
Figure S1

Expression value distributions of the normalized datasets. The boxplots show the expression value distributions of the normalized GEO datasets (GSE17508, GSE19777, GSE19777, and GSE52674) that were selected for our integrated analysis associated with the five miRNAs. The red box represents case samples and the green box represents control samples. (PNG 7 kb)

10142_2019_670_MOESM1_ESM.tif (19 kb)
High resolution image (TIF 18 kb)
10142_2019_670_MOESM2_ESM.pdf (691 kb)
Figure S2 Survival analysis of individual miRNAs based on subtypes. It showed the survival analysis of each miRNA using the profiles of patient subtypes including ER, PR, and HER2 positive or negative. (PDF 690 kb)
10142_2019_670_Fig9_ESM.png (5.7 mb)
Figure S3

Transcriptional regulation of STAT1 among the five miRNAs. It shows the transcriptional regulation of STAT1 among the five miRNAs, including miR-21 knockdown, miR-210 overexpression, miR-22 knockout, miR-221 knockdown, and miR-222 knockdown. (PNG 5866 kb)

10142_2019_670_MOESM3_ESM.tif (1 mb)
High resolution image (TIF 1026 kb)
10142_2019_670_MOESM4_ESM.pdf (694 kb)
Figure S4 Survival analysis of 45 significant miRNA-gene interactions. Survival analysis of 45 significant miRNA-gene interactions. The red curves represent overall survival under low expression of both miRNAs and genes. The green curves represent overall survival under low expression of miRNAs but high expression of genes. The blue curves represent overall survival under high expression of miRNAs but low expression of genes. The purple curves represent overall survival under high expression of both miRNAs and genes. (PDF 694 kb)
10142_2019_670_MOESM5_ESM.xls (332 kb)
Table S1 Differentially expressed genes in response to the regulation of the five miRNAs. Significantly upregulated or downregulated genes in response to the regulation of the five miRNAs, including miR-21 knockdown, miR-210 overexpression, miR-22 knockout, miR-221 knockdown, and miR-222 knockdown. (XLS 332 kb)
10142_2019_670_MOESM6_ESM.xls (32 kb)
Table S2 KEGG pathway enrichment analysis for the five miRNAs. Details of the significantly enriched KEGG pathways associated with the regulation of the five miRNAs, including miR-21 knockdown, miR-210 overexpression, miR-22 knockout, miR-221 knockdown, and miR-222 knockdown. (XLS 31 kb)
10142_2019_670_MOESM7_ESM.xls (50 kb)
Table S3 Transcription factors significantly related to the five mRNAs. Transcription factors significant related to the regulation of the five miRNAs, including miR-21 knockdown, miR-210 overexpression, miR-22 knockout, miR-221 knockdown, and miR-222 knockdown. (XLS 50 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biostatistics, School of Life SciencesAnhui UniversityHefeiChina
  2. 2.School of Chinese MedicineHong Kong Baptist UniversityKowloon TongChina
  3. 3.Center for Stem Cell and Translational Medicine, School of Life SciencesAnhui UniversityHefeiChina
  4. 4.State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of ZoologyChinese Academy of SciencesKunmingChina
  5. 5.Kunming College of Life ScienceUniversity of Chinese Academy of SciencesKunmingChina
  6. 6.Horticulture & Landscape CollegeSouthwest UniversityChongqingChina

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