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      Development and validation of a four-microRNA signature for placenta accreta spectrum: an integrated competing endogenous RNA network analysis

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          Abstract

          Background

          Placenta accreta spectrum (PAS) is a major cause of maternal morbidity and mortality in modern obstetrics, however, few studies have explored the underlying molecular mechanisms and biomarkers. In this study, we aimed to elucidate the regulatory RNA network contributing to PAS, comprising long non-coding (lnc), micro (mi), and messenger (m) RNAs, and identify biomarkers for the prediction of intraoperative blood volume loss.

          Methods

          Using RNA sequencing, we compared mRNA, lncRNA, and miRNA expression profiles between five PAS and five normal placental tissues. Furthermore, the miRNA expression profiles in maternal plasma samples from ten PAS and ten control participants were assessed. The data and clinical information were analyzed using R language and GraphPad Prism 7 software.

          Results

          Upon comparing PAS and control placentas, we identified 8,806 lncRNAs, 128 miRNAs, and 1,788 mRNAs that were differentially expressed. Based on a lasso regression analysis and correlation predictions, we developed a competing endogenous (ce) RNA network comprising 20 lncRNAs, 4 miRNAs, and 19 mRNAs. This network implicated a reduced angiogenesis pathway in PAS, and correlation analyses indicated that two miRNAs (hsa-miR‐490-3p and hsa-miR-133a-3p) were positively correlated to operation-related blood volume loss.

          Conclusions

          We identified a ceRNA regulatory mechanism in PAS, and two miRNAs that may potentially serve as biomarkers of PAS prognosis.

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          Most cited references53

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            GSVA: gene set variation analysis for microarray and RNA-Seq data

            Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)
            Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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              MicroRNAs: genomics, biogenesis, mechanism, and function.

              David Bartel (2004)
              MicroRNAs (miRNAs) are endogenous approximately 22 nt RNAs that can play important regulatory roles in animals and plants by targeting mRNAs for cleavage or translational repression. Although they escaped notice until relatively recently, miRNAs comprise one of the more abundant classes of gene regulatory molecules in multicellular organisms and likely influence the output of many protein-coding genes.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Ann Transl Med
                Journal ID (iso-abbrev): Ann Transl Med
                Journal ID (publisher-id): ATM
                Title: Annals of Translational Medicine
                Publisher: AME Publishing Company
                ISSN (Print): 2305-5839
                ISSN (Electronic): 2305-5847
                Publication date (Print and electronic): August 2020
                Publication date (Print): August 2020
                Volume: 8
                Issue: 15
                Electronic Location Identifier: 919
                Affiliations
                [1 ]Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University , Shenyang, China;
                [2 ]Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Benxi, China
                Author notes

                Contributions: (I) Conception and design: C Liu, C Qiao, T Yang; (II) Administrative support: C Liu, C Qiao; (III) Provision of study materials or patients: C Liu; (IV) Collection and assembly of data: N Li, T Yang; (V) Data analysis and interpretation: T Yang, R Hou; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

                Correspondence to: Chong Qiao; Caixia Liu. Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, No.36 SanHao Street, HePing Area, Shenyang, China. Email: qiaochong2002@ 123456hotmail.com ; liucxshimen@ 123456163.com .
                Article
                Publisher ID: atm-08-15-919
                DOI: 10.21037/atm-20-1150
                PMC ID: 7475428
                PubMed ID: 32953719
                SO-VID: 097b2838-08fd-4071-88e5-586f906e1b04
                Copyright © 2020 Annals of Translational Medicine. All rights reserved.
                License:

                Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

                History
                Date received : 01 February 2020
                Date accepted : 10 July 2020
                Categories
                Subject: Original Article

                Keywords: placenta accreta spectrum (pas),competing endogenous rna network,angiogenesis,biomarkers,prognosis
                Data availability:
                Keywords: placenta accreta spectrum (pas), competing endogenous rna network, angiogenesis, biomarkers, prognosis

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