Front. Genet. Frontiers in Genetics Front. Genet. 1664-8021 Frontiers Media S.A. 703860 10.3389/fgene.2021.703860 Genetics Review Non-Coding RNA Networks in Pulmonary Hypertension Zang et al. NcRNA Networks in PH Zang Hongbin 1 Zhang Qiongyu 2 Li Xiaodong 1 * Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, China Department of Neurology, Shengjing Hospital of China Medical University, Shenyang, China

Edited by: Shaveta Kanoria, Wadsworth Center, United States

Reviewed by: Yuan Zhou, Peking University, China

Hugo E. Verdejo, Pontificia Universidad Católica de Chile, Chile

Hui Zhang, University of Colorado, United States

 *Correspondence: Xiaodong Li, Licardio@163.com

This article was submitted to RNA, a section of the journal Frontiers in Genetics

 30 11 2021 2021 12 703860 30 04 2021 08 11 2021 Copyright © 2021 Zang, Zhang and Li. 2021 Zang, Zhang and Li

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Non-coding RNAs (ncRNAs) are involved in various cellular processes. There are several ncRNA classes, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). The detailed roles of these molecules in pulmonary hypertension (PH) remain unclear. We systematically collected and reviewed reports describing the functions of ncRNAs (miRNAs, lncRNAs, and circRNAs) in PH through database retrieval and manual literature reading. The characteristics of identified articles, especially the experimental methods, were carefully reviewed. Furthermore, regulatory networks were constructed using ncRNAs and their interacting RNAs or genes. These data were extracted from studies on pulmonary arterial smooth muscle cells, pulmonary artery endothelial cells, and pulmonary artery fibroblasts. We included 14 lncRNAs, 1 circRNA, 74 miRNAs, and 110 mRNAs in the constructed networks. Using these networks, herein, we describe the current knowledge on the role of ncRNAs in PH. Moreover, these networks actively provide an improved understanding of the roles of ncRNAs in PH. The results of this study are crucial for the clinical application of ncRNAs.

 pulmonary hypertension long non-coding RNA circular RNA microRNA network 1 Introduction

Pulmonary hypertension (PH) is a serious disease characterized by progressively increased pulmonary vascular resistance and pulmonary artery pressure; the diagnostic criterion is mean pulmonary artery pressure ≥25 mmHg (Galiè et al., 2016; Weber et al., 2018). The increased pulmonary artery pressure in PH results from changes in the structure and function of the vessel wall, which is induced by abnormal pulmonary cell proliferation, apoptosis, and migration (Bourgeois et al., 2018a). Patients with PH may experience dyspnea, fatigue, syncope, chest pain, and/or edema of the legs and ankles. The causes of PH can be broadly classified as primary and secondary causes. To date, ion channels, vasoactive substances, immune factors, and genetic factors are known to be involved in the pathogenesis of PH (Chelladurai et al., 2016; Veith et al., 2016; Bourgeois et al., 2018b).

Recently, many non-coding RNAs (ncRNAs) have been recognized as important regulators in the development of PH. Most human genes (>95%) do not produce proteins but ncRNA molecules. Among them, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) are the most widely studied. MiRNAs are small ncRNAs containing 21–22 nucleotides, which post-transcriptionally regulate gene expression (Wakiyama and Yokoyama. 2014). LncRNAs, which have more than 200 nucleotides, are transcribed from intergenic or intragenic regions. They can bind to proteins, RNA, or DNA to execute regulatory roles (Botti et al., 2017). CircRNAs are a novel class of ncRNAs with a closed loop structure, making them highly stable and capable of interacting with proteins or RNA (Di et al., 2019). NcRNAs have been identified to regulate multiple steps of gene expression. However, because of the large quantity and diverse mechanisms, it is difficult to comprehensively understand the roles of ncRNAs.

NcRNA-based therapeutics have emerged for several diseases, including PH. An effective ncRNA-based strategy demands a thorough understanding of the diverse and context-dependent regulatory relationships of ncRNAs. The regulation of gene expression by ncRNAs is frequently cell specific, suggesting that not only expression level, but also activity or bioavailability contribute to the biofunction of ncRNAs (Correia de Sousa et al., 2019). Thus, in this article, we reviewed the published literature to search for functional miRNAs, lncRNAs, and circRNAs in PH. Next, we constructed networks of validated ncRNAs and their interacting RNAs or genes to investigate the role of ncRNAs in PH.

 2 Screening of Articles 2.1 Criteria for Study Selection

A literature search was performed in PubMed with the query listed in Table 1; we identified 602 articles. In addition, we also reviewed other public databases, including the Human microRNA Disease Database v3.2, miRWalk 2.0, and LncRNADisease v2.0, to identify validated functional ncRNAs in PH. Studies were selected when the following criteria were met: 1) the study reported pathogenic roles of miRNAs, lncRNAs, and/or circRNAs in PH; 2) mechanistic studies were performed in pulmonary arterial smooth muscle cells (PASMCs), pulmonary artery endothelial cells (PAECs), and/or pulmonary artery fibroblasts (PAFs); and 3) the relationships between ncRNAs and their interacting RNAs or genes were experimentally identified via luciferase reporter assay, western blot, and/or qPCR. Using these criteria returned 140 qualified articles (Figure 1).

Query for searching articles from PubMed.

Query Number of articles
(“rna, untranslated"[MeSH Terms] or “non-coding RNA” or “ncRNA” or “noncoding RNA” or “RNA, Long Noncoding"[Mesh] or “long non-coding RNA” or “lncRNA” or “long intergenic non-coding RNA” or “lincRNA” or “RNA, Circular"[Mesh] or “circRNA” or “circular RNA” or “MicroRNAs"[Mesh] or “microRNA” OR “miRNA”) and (“PAH” or “pulmonary hypertension” or “pulmonary artery hypertension") 602

Steps of the data analysis used to build the ncRNA networks. NcRNAs: non-coding RNAs.

 2.2 General Characteristics of Qualified Articles

When sorted by publication date, we found that the number of eligible articles continuously increased year by year (Figure 2A). The impact factors (IF) of the articles ranged from 0 to 36.13; articles with 3 ≤ IF < 5 accounted for the highest proportion (Figure 2B). Of the 140 qualified articles, 32.14% were studies using human tissues or cells. In studies using experimental animals, rats were the most commonly used, accounting for 26.43% of the total studies (Figure 2C). Moreover, when classified by cell type, 78.42, 15.83, 1.44, and 4.32% of studies were performed in PASMCs, PAECs, PAFs, and both PASMCs and PAECs, respectively (Figure 2D).

Characteristics of the extracted studies. (A) Distribution of the included articles according to the publication year. (B) Distribution of the included articles according to the impact factor. (C) Proportions of different species studied in the included articles. (D) Proportions of different cell types used in the included articles.

 3 Non-coding RNA Networks for Pulmonary Hypertension 3.1 Construction of Non-coding RNA Regulatory Networks

Regulatory networks were constructed using ncRNAs and their interacting RNAs or genes in PASMCs, PAECs, and PAFs. Given ncRNA conservation among species, only human ncRNAs or ncRNAs that were conserved between human and experimental animals were included. If there were contradictory results, the results from higher-impact articles were selected. In addition, some crucial regulatory relationships between protein-coding genes and validated transcription factor–miRNA interactions from TransmiR v2.0 were also described in the networks to present an in-depth explanation on the roles of ncRNAs in PH. The nodes represented interacting molecules, and the edges represented the regulatory connections. Each edge indicated a publication supporting the connection. Square and circular nodes represented ncRNAs and coding RNAs or genes, respectively. Node color was based on the type of molecule (lncRNAs and circRNAs are orange, miRNAs are blue, and coding RNAs or genes are empty). Node sizes represented their degrees (number of edges that directly link to the node). Edges represented the regulatory connections: red edges depicted links indicating repressive action (semicircular arrow heads), and black edges indicated activation (traditional arrow heads). The nodes in this network were involved in cell proliferation, apoptosis, migration, metabolism, endothelial–mesenchymal transition, and extracellular matrix remodeling. The steps used in our approach are shown in Figure 1.

 3.2 General Characteristics of the Constructed Networks

In total, 140 articles describing 14 lncRNAs, 1 circRNA, 74 miRNAs, and 110 mRNAs, were included in our networks. Considering the unique biological characteristics of different cell types, we constructed networks according to cell type. The network of PASMCs contained 13 lncRNAs, 1 circRNA, 69 miRNAs, and 96 mRNAs. The network of PAECs contained 1 lncRNA, 25 miRNAs, and 29 mRNAs. The network of PAFs contained 6 miRNAs and four mRNAs. The networks are shown in Figures 3–5. Detailed network compositions are listed in Table 2.

PH-associated network of ncRNAs and their interacting RNAs or genes in PASMCs. The square and circular nodes represent ncRNAs and coding RNAs or genes, respectively. Node color is based on the type of molecule (lncRNAs and circRNAs are orange, miRNAs are blue, and coding RNAs or genes are empty). Node sizes represent the degrees (the number of edges that directly link to the node). Edges represent the regulatory connections, and each edge indicates a publication. When multiple publications describe one interaction, multiple edges connect the same two nodes. Red edges depict links indicating repressive action (semicircular arrow heads), and black edges are those indicating activation (with traditional arrow heads). The nodes in this network are involved in cell proliferation, apoptosis, migration, and metabolism. PH: pulmonary hypertension; PASMCs: pulmonary artery smooth muscle cells; lncRNAs: long non-coding RNAs; circRNA: circular RNA; miRNA: microRNA.

The PH-associated network of ncRNAs and their interacting RNAs or genes in PAECs. The square and circular nodes represent ncRNAs and coding RNAs or genes, respectively. Node color is based on the type of molecule (lncRNAs are orange, miRNAs are blue, and coding RNAs or genes are empty). Node sizes represent the degrees (number of edges that directly link to the node). Edges represent regulatory connections. Each edge indicates a publication. When multiple publications describe one interaction, multiple edges connect the same two nodes. Red edges depict links indicating repressive action (semicircular arrow heads), and black edges represent those indicating activation (traditional arrow heads). The nodes in this network were primarily involved in proliferation, apoptosis resistance, migration, and endothelial–mesenchymal transition. PAECs: pulmonary artery endothelial cells.

The PH-associated network of ncRNAs and their interacting RNAs or genes in PAFs. The square and circular nodes represent ncRNAs and coding RNAs or genes, respectively. Node color is based on the type of molecule (miRNAs are blue, coding RNAs or genes are empty). Node sizes represent the degrees (number of edges that directly link to the node). Edges represent regulatory connections. Each edge indicates a publication. When multiple publications describe one interaction, multiple edges connect the same two nodes. Red edges depict links indicating repressive action (semicircular arrow heads), and black edges are those indicating activation (traditional arrow heads). The nodes in this network were primarily involved in cell proliferation and extracellular matrix remodeling. PAFs: pulmonary artery fibroblasts.

List of network interactions.

Upstream molecule Downstream molecule Interaction type a PMID Reference
ALOX12 RXR-α pos 24963038 Sharma et al. (2014)
ALOX15 RXR-α pos 24963038 Sharma et al. (2014)
ALOX5 RXR-α pos 24963038 Sharma et al. (2014)
APLN miR-424-5p pos 23263626 Kim et al. (2013)
APLN miR-503-5p pos 23263626 Kim et al. (2013)
CASC2 miR-222-3p neg 32206065 Han et al. (2020)
CCND1 miR-17-5p pos 18695042 Yu et al. (2008)
CCND1 miR-19a-3p pos 28090171 Inoue and Fry. (2015)
CCND1 miR-20a-5p Pos 28090171 Inoue and Fry. (2015)
CPS1-IT IL-1β neg 30982984 Zhang et al. (2019b)
CREB1 MCU pos 27648837 Hong et al. (2017)
H19 let-7b-5p neg 30547791 Su et al. (2018)
HDAC4 miR-424-5p neg 29102771 Takagi et al. (2018)
HDAC4 miR-503-5p neg 29102771 Takagi et al. (2018)
HDAC5 miR-424-5p neg 29102771 Takagi et al. (2018)
HDAC5 miR-503-5p neg 29102771 Takagi et al. (2018)
HIF-1α let-7b-3p pos 30628484 Zhang H et al. (2019)
HIF-1α miR-145-5p pos 25129238 Agrawal et al. (2014)
HIF-1α miR-191-5p pos 25119596 Song et al. (2014)
HIF-1α miR-195-5p pos 28862358 Zeng et al. (2018)
HIF-1α miR-19a-3p pos 31682848 Zhao et al. (2019)
HIF-1α miR-205-5p pos 23924028 Gandellini et al. (2014)
HIF-1α miR-210-3p neg 22886504 Gou et al. (2012)
HIF-1α miR-214-3p pos 24011070 el Azzouzi et al. (2013)
HIF-1α miR-223-3p neg 26084306 Meloche et al. (2015a)
HIF-1α miR-27a-3p pos 24517586 Camps et al. (2014)
HIF-1α miR-361-5p pos 29339076 Zhang Y et al. (2018)
HOXA-AS3 HOXA3 pos 30304383 Zhang R et al. (2019)
Hsa_circ_0016070 miR-942-5p neg 31593832 Zhou et al. (2019)
JARID1B MANTIS neg 2,8351900 Leisegang et al. (2017)
let-7a STAT3 neg 32803651 Cheng et al. (2020)
let-7b-3p ACE2 neg 30628484 Zhang Y et al. (2019)
let-7b-5p AT1R neg 30547791 Su et al. (2018)
let-7b-5p ET-1 neg 24978044 Guo et al. (2014)
let-7b-5p TGFBR1 neg 24978044 Guo et al. (2014)
let-7g MYC neg 27889560 Zhang W.-F et al. (2017)
LincRNA-Cox2 let-7a neg 32803651 Cheng et al. (2020)
LncRNA-Ang362 miR-221-3p pos 31313741 Wang et al. (2020)
LncRNA-Ang362 miR-222-3p pos 31313741 Wang et al. (2020)
LRP8 POU5F1 neg 26565914 Bertero et al. (2015)
MALAT1 miR-124-3p neg 31257528 Wang S et al. (2019)
MANTIS BRG1 pos 2,8351900 Leisegang et al. (2017)
MEG3 miR-328-3p neg 31477557 Xing X.-Q et al. (2019)
miR-100-5p MTOR neg 26409044 Wang et al. (2015)
miR-103a-3p HIF-1β neg 26827991 Deng et al. (2016)
miR-107-3p HIF-1β neg 26827991 Deng et al. (2016)
miR-107-3p NOR1 neg 31933977 Chen et al. (2019)
miR-1181 STAT3 neg 30211651 Qian et al. (2018)
miR-124-3p CAMTA1 neg 23853098 Kang K et al. (2013)
miR-124-3p GRB2 neg 28496318 Li Y et al. (2017)
miR-124-3p KLF5 neg 31257528 Wang D et al. (2019)
miR-124-3p NFATC1 neg 23853098 Kang B.-Y et al. (2013)
miR-124-3p PTBP1 neg 23853098 Kang K et al. (2013)
miR-124-3p PTBP1 neg 24122720 Wang et al. (2014)
miR-124-3p PTBP1 neg 2,8971999 Caruso et al. (2017)
miR-124-3p PTBP1 neg 2,8972001 Zhang H et al. (2017)
miR-125a-5p BMPR2 neg 25854878 Huber et al. (2015)
miR-125a-5p MFN1 neg 28593577 Ma et al. (2017)
miR-125a-5p STAT3 neg 29700287 Cai et al. (2018)
miR-1268a CDKN2A(P16) neg 31370272 Lee and Kang. (2019)
miR-1281 HDAC4 neg 29514810 Li et al. (2018)
miR-130a-3p BMPR2 neg 28755990 Li L et al. (2017)
miR-130a-3p CDKN1A(P21) neg 25681685 Brock et al. (2015)
miR-130a-3p LRP8 neg 26565914 Bertero et al. (2015)
miR-130a-3p PPARγ neg 24960162 Bertero et al. (2014)
miR-130a-3p PPARγ neg 26565914 Bertero et al. (2015)
miR-130b-3p LRP8 neg 26565914 Bertero et al. (2015)
miR-130b-3p PPARγ neg 24960162 Bertero et al. (2014)
miR-130b-3p PPARγ neg 26565914 Bertero et al. (2015)
miR-132-3p PTEN neg 30896881 Zeng et al. (2019)
miR-135a-5p TRPC1 neg 30038339 Liu A et al. (2019)
miR-138-5p CREB1 neg 27648837 Hong et al. (2017)
miR-138-5p MCU neg 27648837 Hong et al. (2017)
miR-138-5p MST1 neg 23485012 Li et al. (2013)
miR-138-5p TASK-1 neg 29257242 Liu G et al. (2018)
miR-1-3p SPHK1 neg 29167124 Sysol et al. (2018)
miR-140-5p DNMT1 neg 27021683 Zhang and Xu. (2016)
miR-140-5p SMURF1 neg 27214554 Rothman et al. (2016)
miR-140-5p TNF-α neg 30367500 Zhu et al. (2019b)
miR-143-3p ABCA1 neg 30195228 Yue et al. (2018)
miR-141-5p RHOA neg 32559140 Lei et al. (2020)
miR-145-5p ABCA1 neg 30195228 Yue et al. (2018)
miR-150-5p HIF-1α neg 28715868 Chen M et al. (2017)
miR-150-5p NFATC3 neg 30551428 Li et al. (2019)
miR-15a-5p VEGF neg 31894295 Zhang et al. (2020)
miR-17-5p BMPR2 neg 19390056 Brock et al. (2009)
miR-17-5p CDKN1A(P21) neg 30305109 Liu J. J et al. (2018)
miR-17-5p MFN2 neg 27640178 Lu et al. (2016)
miR-17-5p PAI-1 neg 29644896 Chen K.-H et al. (2018)
miR-17-5p PDLIM5 neg 25647182 Chen et al. (2015)
miR-17-5p PHD2 neg 27919930 Chen et al. (2016)
miR-17-5p PTEN neg 30305109 Liu G et al. (2018)
miR-182-3p MYADM neg 32373233 Sun et al. (2020)
miR-190a-5p KCNQ5 neg 24446351 Li et al. (2014)
miR-190a-5p KLF15 neg 30538440 Jiang et al. (2018)
miR-191-5p BMPR2 neg 31119161 Zhang Z et al. (2019)
miR-193-3p ALOX12 neg 24963038 Sharma et al. (2014)
miR-193-3p ALOX15 neg 24963038 Sharma et al. (2014)
miR-193-3p ALOX5 neg 24963038 Sharma et al. (2014)
miR-193-3p IGF1R neg 24963038 Sharma et al. (2014)
miR-195-5p SMAD7 neg 28862358 Zeng et al. (2018)
miR-199a-5p SMAD3 neg 27038547 Liu H et al. (2016)
miR-199b-5p GSK3B neg 27188753 Wu et al. (2016)
miR-19a-3p PAI-1 neg 29644896 Chen T et al. (2018)
miR-19a-3p PTEN neg 31682848 Zhao et al. (2019)
miR-19b-3p PAI-1 neg 29644896 Chen K.-H et al. (2018)
miR-200c-3p MAP2 neg 29044995 Yuan et al. (2017)
miR-200c-3p ZEB1 neg 29044995 Yuan et al. (2017)
miR-203a-3p FGF2 neg 30575929 Wang et al. (2018)
miR-204-5p ATG7 neg 31542480 Liu H.-M et al. (2019)
miR-204-5p BRD4 neg 26224795 Meloche et al. (2015a)
miR-204-5p FOXM1 neg 29290032 Bourgeois et al. (2018b)
miR-204-5p RUNX2 neg 27149112 Ruffenach et al. (2016)
miR-204-5p SHP2 neg 21321078 Courboulin et al. (2011)
miR-204-5p TGFBR2 neg 29196166 Yu et al. (2018)
miR-205-5p MICAL2 neg 30853343 Tao et al. (2019)
miR-206 Notch3 neg 23071643 Jalali et al. (2012)
miR-206 HIF-1α neg 23628900 Yue et al. (2013)
miR-20a-5p BMPR2 neg 19390056 Brock et al. (2009)
miR-20a-5p PAI-1 neg 29644896 Chen T et al. (2018)
miR-20a-5p PDLIM5 neg 25647182 Chen et al. (2015)
miR-20a-5p PHD2 neg 27919930 Chen et al. (2016)
miR-210-3p E2F3 neg 22886504 Gou et al. (2012)
miR-210-3p ISCU1/2 neg 25825391 White et al. (2015)
miR-210-3p MKP-1 neg 25044272 Jin et al. (2015)
miR-214-3p ARHGEF12 neg 31373336 Xing Y et al. (2019)
miR-214-3p CCNL2 neg 27381447 Liu Y et al. (2016)
miR-214-3p LMOD1 neg 27144530 Sahoo et al. (2016)
miR-214-3p MEF2C neg 27144530 Sahoo et al. (2016)
miR-214-3p PTEN neg 28684904 Liu et al. (2017)
miR-21-5p DDAH1 neg 24895913 Iannone et al. (2014)
miR-21-5p PDCD4 neg 28522568 Green et al. (2017)
miR-21-5p PTEN neg 26208095 Green et al. (2015)
miR-21-5p RHOB neg 22371328 Parikh et al. (2012)
miR-221-3p AXIN2 neg 28694128 Nie et al. (2019)
miR-222-3p ING5 neg 32206065 Han et al. (2020)
miR-222-3p CDKN1B(P27) neg 28854428 Xu et al. (2017)
miR-222-3p TIMP3 neg 28854428 Xu et al. (2017)
miR-223-3p ITGB3 neg 30507047 Liu et al. (2019a)
miR-223-3p MLC2 neg 27121304 Zeng et al. (2016)
miR-223-3p PARP1 neg 26084306 Meloche et al. (2015b)
miR-223-3p RHOB neg 27121304 Zeng et al. (2016)
miR-23a-3p BMPR2 neg 29864909 Zhang X et al. (2018)
miR-26b-5p CCND1 neg 2,7322082 Wang P et al. (2016)
miR-26b-5p CTGF neg 2,7322082 Wang R et al. (2016)
miR-26b-5p CTGF neg 28816418 Zhou et al. (2018)
miR-27a-3p PPARγ neg 24244514 Kang B.-Y et al. (2013)
miR-27a-3p PPARγ neg 28484848 Xie et al. (2017)
miR-27a-3p SMAD5 neg 31004656 Liu et al. (2019b)
miR-27b-3p PPARγ neg 25795136 Bi et al. (2015)
miR-27b-3p PPARγ neg 28484848 Xie et al. (2017)
miR-29b-3p KCNA5 neg 31553627 Babicheva et al. (2020)
miR-301a-3p LRP8 neg 26565914 Bertero et al. (2015)
miR-301a-3p PPARγ neg 24960162 Bertero et al. (2014)
miR-301a-3p PPARγ neg 26565914 Bertero et al. (2015)
miR-301b-3p LRP8 neg 26565914 Bertero et al. (2015)
miR-301b-3p PPARγ neg 24960162 Bertero et al. (2014)
miR-301b-3p PPARγ neg 26565914 Bertero et al. (2015)
miR-30a-5p YKL-40 neg 31115541 Tan et al. (2019)
miR-30c-5p PDGFRβ neg 25882492 Xing et al. (2015)
miR-328-3p CaV1.2 neg 22392900 Guo et al. (2012)
miR-328-3p IGF1R neg 22392900 Guo et al. (2012)
miR-328-3p IGF1R neg 31477557 Xing X.-Q et al. (2019)
miR-328-3p PIM-1 neg 27448984 Qian et al. (2016)
miR-339-5p FRS2 neg 28947594 Chen J et al. (2017)
miR-34-5p PDGFRα neg 27302634 Wang P et al. (2016)
miR-34a-3p MIEF1 neg 29431643 Chen K.-H et al. (2018)
miR-34a-3p MIEF2 neg 29431643 Chen T et al. (2018)
miR-361-5p ABCA1 neg 29339076 Zhang Y et al. (2018)
miR-424-5p FGF2 neg 23263626 Kim et al. (2013)
miR-424-5p FGF2 neg 24960162 Bertero et al. (2014)
miR-424-5p FGFR1 neg 23263626 Kim et al. (2013)
miR-424-5p RICTOR neg 29102771 Takagi et al. (2018)
miR-449a-5p MYC neg 30715622 Zhang et al. (2019a)
miR-454-3p LRP8 neg 26565914 Bertero et al. (2015)
miR-454-3p PPARγ neg 26565914 Bertero et al. (2015)
miR-4632-3p CJUN neg 28701355 Qian et al. (2017)
miR-495-3p VEZF1 neg 31030195 Fu et al. (2019)
miR-497-5p CDKN2B(P15) neg 31370272 Lee and Kang. (2019)
miR-503-5p FGF2 neg 23263626 Kim et al. (2013)
miR-503-5p FGF2 neg 24960162 Bertero et al. (2014)
miR-503-5p FGFR1 neg 23263626 Kim et al. (2013)
miR-503-5p RICTOR neg 29102771 Takagi et al. (2018)
miR-637 CDK6 neg 27794186 Sang et al. (2016)
miR-665 CDKN1A(P21) neg 31370272 Lee and Kang. (2019)
miR-760 TLR4 neg 30226538 Yang et al. (2018)
miR-92b-3p USP28 neg 30149918 Hao et al. (2018)
miR-942-5p CCND1 neg 31593832 Zhou et al. (2019)
miR-96-5p 5-HT1B neg 25871906 Wallace et al. (2015)
miR-98-5p ALK1 neg 31322216 Li et al. (2019)
miR-98-5p ET-1 neg 26098770 Kang et al. (2016)
MYC miR-19a-3p pos 17943719 Schulte et al. (2008)
MYC miR-19b-3p pos 17943719 Schulte et al. (2008)
MYC miR-34a-3p neg 18066065 Chang et al. (2008)
NFATC3 miR-23a-3p pos 19574461 Lin et al. (2009)
NF-κB miR-130a-3p pos 28755990 Li Q et al. (2017)
NF-κB miR-210-3p pos 25341039 Liu et al. (2014)
NF-κB miR-27a-3p pos 28484848 Xie et al. (2017)
NF-κB miR-27b-3p pos 28484848 Xie et al. (2017)
PARP-1 STAT3 pos 24270264 Meloche et al. (2014)
PAXIP1-AS1 PXN pos 30450722 Jandl et al. (2019)
POU5F1 miR-130a-3p pos 24960162 Bertero et al. (2014)
POU5F1 miR-130a-3p pos 26565914 Bertero et al. (2015)
POU5F1 miR-130b-3p pos 24960162 Bertero et al. (2014)
POU5F1 miR-130b-3p pos 26565914 Bertero et al. (2015)
POU5F1 miR-301a-3p pos 24960162 Bertero et al. (2014)
POU5F1 miR-301a-3p pos 26565914 Bertero et al. (2015)
POU5F1 miR-301b-3p pos 24960162 Bertero et al. (2014)
POU5F1 miR-301b-3p pos 26565914 Bertero et al. (2015)
POU5F1 miR-454-3p pos 26565914 Bertero et al. (2015)
PPARγ APLN pos 24960162 Bertero et al. (2014)
PPARγ miR-204-5p pos 24960162 Bertero et al. (2014)
PPARγ miR-21-5p neg 26208095 Green et al. (2015)
PPARγ miR-21-5p neg 28522568 Green et al. (2017)
PPARγ miR-27a-3p neg 24244514 Kang K et al. (2013)
PPARγ miR-98-5p pos 26098770 Kang et al. (2016)
PPARγ POU5F1 neg 26565914 Bertero et al. (2015)
RUNX2 HIF-1α pos 27149112 Ruffenach et al. (2016)
RXR-α miR-193-3p neg 24963038 Sharma et al. (2014)
SMILR miR-141-5p neg 32559140 Lei et al. (2020)
STAT3 miR-17-5p pos 19390056 Brock et al. (2009)
STAT3 miR-204-5p neg 23975026 Xu et al. (2013)
STAT3 miR-20a-5p pos 19390056 Brock et al. (2009)
STAT3 miR-34a-3p neg 24642471 Rokavec et al. (2014)
TGF-β1 miR-143-3p pos 2,6311719 Deng et al. (2015)
TGF-β1 miR-199a-5p pos 20705240 Davis et al. (2010)
TGF-β1 miR-21-5p pos 20705240 Davis et al. (2010)
TUG1 miR-328-3p neg 31679623 Wang D et al. (2019)
TYKRIL PDGFRβ pos 32634060 Zehendner et al. (2020)
UCA1 ING5 neg 30353369 Zhu et al. (2019a)
ZEB1 miR-200c-3p neg 18829540 Bracken et al. (2008)

pos: positive interaction, neg: negative interaction.

 3.3 Functional Enrichment Analysis

We performed gene ontology (GO) biological process term analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the database for Annotation, Visualization, and Integration Discovery (DAVID). The calculation process was dependent on a hypergeometric test, using a statistical significance threshold of p < 0.05 with a false discovery rate (FDR) correction. These analyses provided a general overview of the biological roles of the included ncRNAs. In addition, we performed cell type-specific functional enrichment analyses. However, owing to the lack of sufficient molecules, the enrichment analysis in PAFs could not be conducted. GO analysis and KEGG pathway enrichment in PASMCs and PAECs revealed several PH-associated terms, such as positive regulation of smooth muscle cell proliferation, positive regulation of endothelial cell proliferation, HIF-1 signaling pathway, and MAPK signaling pathway. The top 10 enriched GO biological process terms and KEGG pathways are shown in Figure 6.

Functional analyses of the ncRNAs. (A,B). The top 10 enriched GO biological process terms and KEGG pathways in all cell types. (C,D) The top 10 enriched GO biological process terms and KEGG pathways in PASMCs. (E,F) The top 10 enriched GO biological process terms and KEGG pathways in PAECs. Node sizes indicate the number of genes enriched in functional clusters. Node colors are related to q values. GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.

 3.4 Key Non-coding RNA Subnetworks

We built three networks according to the cell types. Here, we discuss several important subnetworks, along with their components and interactions, to improve understanding of the roles of ncRNAs in PH. Subnetworks with more than five nodes were regarded as key subnetworks.

3.4.1 The Hsa_circ_0016070/miR-942-5p/CCND1 Subnetwork

CircRNAs are associated with various cardiovascular diseases. Hsa_circ_0016070 was the only circRNA included in our networks. This circRNA is located at chr1: 203595914-203702528, strand: +, promotes cell proliferation by mediating cell cycle progression, and is increased in PH patients (Zhou et al., 2019). CCND1 is an important regulator of the cell cycle. It interacts with cyclin-dependent kinase 4 (CDK4) to form the cyclin D1–CKD4 complex, which then inactivates retinoblastoma (Rb) protein and induces G0 progression to S phase (Matsushime et al., 1991). The subnetwork showed that hsa_circ_0016070 overexpression induced CCND1 expression by buffering miR-942-5p (Zhou et al., 2019). In addition, according to our network, CCND1 could induce the expression of miR-17-5p, miR-19a-3p, and miR-20a-5p, subsequently regulating the biological activities of PASMCs (Figure 7A). Given the considerable number of identified circRNAs, there should be other PH-related circRNAs. A microarray expression profile in thromboembolic pulmonary hypertension patients indicated that hsa_circ_0002062 and hsa_circ_0022342 might be the key circRNAs for the development of chronic thromboembolic pulmonary hypertension (Miao et al., 2017). However, this finding has not been verified by more reliable experimental methods.

Several key ncRNA subnetworks. (A) The hsa_circ_0016070/miR-942-5p/CCND1 subnetwork. (B) The TUG1/MEG3/miR-328-3p/miR-193-3p subnetwork. (C) The CASC2/UCA1/miR-222-3p subnetwork. (D) The MALAT1/miR-124-3p subnetwork.

 3.4.2 The TUG1/MEG3/miR-328-3p/miR-193-3p Subnetwork

Regulatory relationships are indicated by the connection lines in the subnetwork. According to this subnetwork (Figure 7B), both TUG1 and MEG3 can function as competing endogenous RNAs (ceRNAs) that sequester miR-328-3p. In the original studies, the TUG1/miR-328-3p and MEG3/miR-328-3p axes were identified (Wang D et al., 2019; Xing X.-Q et al., 2019). IGF1 is reported to inhibit PASMCs apoptosis and activate elastin in PASMCs. Thus, upregulating IGF1R via the TUG1/miR-328-3p and MEG3/miR-328-3p axes can induce PH by amplifying the pathogenic role of IGF1 (Wang S et al., 2019; Xing Y et al., 2019). Calcium voltage-gated channel subunit alpha1 C (CaV1.2), which contributes to vasoconstriction, is also a target gene of miR-328-3p in PASMCs (Guo et al., 2012), indicating that the TUG1/miR-328-3p and MEG3/miR-328-3p axes are involved in regulating pulmonary artery contraction and dilation. In addition, miR-328-3p can inhibit PASMC proliferation by targeting PIM-1 (Qian et al., 2016). Available data show that miR-193-3p has a shared target gene, IGF1R, with miR-328-3p, but no strong regulatory connection with miR-328-3p or TUG1 or MEG3. Thus, downregulation of miR-193-3p contributes to IGF1R overexpression as well. In addition, miR-193-3p is capable of negatively regulating multiple lipoxygenases, including ALOX5, ALOX12, and ALOX15. These lipoxygenases cause abnormal lipid metabolism, which not only directly accelerates the development of PH, but also induces the increasement of RXR-α. Moreover, miR-193-3p can be downregulated by RXR-α, which directly binds to the miR-193 promoter. Therefore, a feedback loop, which dramatically enhances abnormal miR-193-3p expression forms (Sharma et al., 2014).

 3.4.3 The CASC2/UCA1/miR-222-3p Subnetwork

LncRNA CASC2 is downregulated in hypoxia-induced PASMCs. As a ceRNA of miR-222-3p, CASC2 reduces the expression of ING5, which is a target gene of miR-222-3p, ultimately promoting PASMC proliferation and migration (Han et al., 2020). P27 and TIMP3 are two additional target genes of miR-222-3p (Xu et al., 2017). P27, a member of the Cip/Kip family of cyclin-dependent kinase inhibitors, negatively regulates cell proliferation (Toyoshima and Hunter, 1994). Meanwhile, TIMP3 is a member of the TIMP family, which regulates cell proliferation, apoptosis, and migration via both MMP-dependent or MMP-independent pathways (Zhou et al., 2015). The present subnetwork links CACS2 to P27 and TIMP3 via miR-222-3p, further elaborating the mechanisms of PH (Figure 7C).

UCA1 is the other lncRNA in this subnetwork and is highly expressed in hypoxia-induced PASMCs. Studies indicate that UCA1 does not interact with miR-222-3p, but directly inhibits ING5 by competing with ING5 mRNA for hnRNP I, which binds to ING5 mRNA and enhances its translation. Thus, UCA1 overexpression results in the downregulation of ING5 mRNA expression (Zhu T.-T. et al., 2019). The same regulatory pattern has been found between UCA1 and P27 in breast tumor studies (Huang et al., 2014). This interaction may also work in PH and partly contributes to P27 downregulation (Figure 7C).

 3.4.4 The MALAT1/miR-124-3p Subnetwork

LncRNA MALAT1, located at 11q13, is an 8.5-kb molecule that was identified by Ji et al. in a cancer study (Ji et al., 2003). Emerging evidence indicates that MALAT1 plays important roles in various diseases, including PH. Wang et al. reported that MALAT1 is highly expressed in pulmonary artery tissues and PASMCs from patients with PH. MALAT1 controls PASMC proliferation and migration by binding to miR-124-3p, which directly targets KLF5 (Wang D et al., 2019). Kang et al. showed that miR-124-3p also targets three regulators of the NFAT pathway, including NFATc1, CAMTA1, and PTBP1 (Kang B.-Y et al., 2013). The downregulation of miR-124-3p induces PASMC proliferation and reverses the differentiated PASMC phenotype by activating the NFAT pathway. In addition to its role in PASMCs, miR-124-3p also regulates the biological behaviors of PAH endothelial cells (PAH ECs) and PAFs. Studies have confirmed the role of the miR-124-3p/PTBP1 axis in PAH ECs and PAFs (Caruso et al., 2017; Wang et al., 2014; Zhang H et al., 2017). Downregulating miR-124-3p activates PTBP1 expression, which promotes aerobic glycolysis by increasing the PKM2/PKM1 ratio, subsequently inducing PAH EC and PAF proliferation (Anastasiou et al., 2012). Li et al. reported another target of miR-124-3p, GRB2, which enhanced the proliferation of multiple human cells (Li L et al., 2017; Figure 7D).

 3.4.5 Subnetworks of the miR-130/301 Family

There are complicated relationships between the miR-130/301 family and other functional molecules associated with the pathogenesis of PH. In the present study, we found that subnetworks of the miR-130/301 family were involved in multiple biological behaviors, such as proliferation, apoptosis, and migration in PASMCs, PAECs, and PAFs. In addition, these subnetworks also mediated the crosstalk of these pulmonary artery cells.

In PASMCs, the miR-130/301 family is involved in many regulatory axes. Among them, the POU5F1/miR-130/301 family/PPARγ axis, which regulates the expression of miR-204-5p and miR-21-5p, is the most explicitly elaborated axis. According to our studies, the identified target genes of the two miRNAs in PASMCs include BRD4, FOXM1, PSCD4, PTEN, RUNX2, and SHP2, which control cell proliferation, apoptosis, differentiation, and mitochondrial function (Courboulin et al., 2011; Meloche et al., 2015a; Green et al., 2015, 2017; Ruffenach et al., 2016; Liu et al., 2017; Bourgeois et al., 2018a). In addition to the miR-130/301 family, miR-27a/b-3p, which is regulated by NF-κB (Xie et al., 2017), can also act as an upstream controller of PPARγ in PASMCs. Interestingly, the subnetwork analysis indicates that the miR-130/301 family indirectly promotes HIF-1α expression by sustaining the RUNX2 level (Ruffenach et al., 2016). Conversely, HIF-1α induces the expression of miR-27a-3p, which depresses the level of PPARγ (Camps et al., 2014). Thus, a feedback loop with PPARγ and HIF-1α forms. This loop leads to a persistent pathological status. Moreover, as a crucial pathogenic molecule for PH, HIF-1α can function through several miRNAs, including miR-145-5p, miR-19a-3p, miR-195-5p, miR-210-3p, miR-223-3p, and miR-361-5p, to regulate the expression of downstream proteins, eventually causing abnormal cellular behaviors (Agrawal et al., 2014; Gou et al., 2012; Meloche et al., 2015b; Zeng et al., 2018; Zhang X et al., 2018, Zhang H et al., 2019; Zhao et al., 2019; Figure 8A).

The miR-130/301 family subnetworks in (A) PASMCs, (B) PAECs, and (C) PAFs. This miRNA family was involved in multiple processes, such as cell proliferation, apoptosis, migration, endothelial contraction, and matrix remodeling.

In PAECs, the miR-130/301 family also plays an important role. The POU5F1/miR-130/301 family/PPARγ axis indirectly regulates the expression of ET-1 and FGF2 via miR-98-5p and miR-424/503-5p, respectively (Kim et al., 2013; Zhang Y et al., 2018). The roles of ET-1 and FGF2 in PH are well established. ET-1 is synthesized primarily in endothelial cells and mediates pulmonary artery cell proliferation, migration, and constriction through two distinct G protein-coupled receptors: ETA and ETB (Clozel, 2016). Previous studies suggest that excessive FGF2 expression promotes PAEC proliferation by activating ERK1/2 and inhibits apoptosis by inducing BCL2 and BCL-xL activity (Tu et al., 2011). Furthermore, miR-130a-3p controls the level of BMPR2, which triggers idiopathic pulmonary artery hypertension (IPAH) and is involved in the development of other types of PH (Li Q et al., 2017). Considering that miRNAs from the same family have a homologous seed region sequence, other members from the miR-130/301 family may also regulate BMPR2 expression. The transcription of miR-130a-3p is controlled by NF-κB in PAECs. Thus, NF-κB and BMPR2 are linked by miR-130a-3p. In addition, miR-17a-5p, miR-20a-5p, and miR-125a-5p also mediate BMPR2 expression. Besides, two members from the miR-17-92 family, miR-17a-5p and miR-20a-5p, link STAT3 to BMPR2 (Brock et al., 2009; Huber et al., 2015; Figure 8B).

In PAFs, activation of the miR-130/301 family can induce cell proliferation and extracellular matrix remodeling by inhibiting PPARγ and LRP8. Meanwhile, matrix remodeling can activate POU5F1, which subsequently promotes miR-130/301 family expression (Bertero et al., 2015). Thus, a positive feedback circuit is activated that dramatically accelerates the development of PH (Figure 8C).

The roles of the miR-130/301 family in different pulmonary artery cell types are not independent. Rather, the miR-130/301 family contributes to crosstalk between these cells. Extracellular matrix remodeling, which can be induced by overexpression of the miR-130/301 family, promotes proliferation and contraction of pulmonary artery cells viamiR-130/301 family-dependent and -independent pathways. The remodeled extracellular matrix can activate the POU5F1/miR-130/301 family/PPARγ axis in PASMCs, PAECs, and PAFs, subsequently regulating downstream molecules such as miR-204-5p, miR-424-5p, miR-503-5p, and FGF2 (Bertero et al., 2015). Upregulating miR-424-5p and miR-503-5p or inhibiting FGF2 in PAECs can repress PASMC and PAF proliferation induced by conditioned media from PAECs, indicating that these molecules are involved in the crosstalk among different pulmonary vascular cells. The remodeled extracellular matrix can also induce the expression of the proliferative miRNA, miR-27a/b-3p, in PACEs, and PASMCs, as well as the expression of the vasoconstrictor ET-1, and the inflammatory cytokine IL-6 in PACEs (Bertero et al., 2014; Bertero et al., 2015; Figure 9).

Contribution of the miR-130/301 family to the crosstalk between various pulmonary artery cells. The remodeled extracellular matrix induces proliferation and contraction in pulmonary artery cells via miR-130/301 family-dependent and -independent pathways. Meanwhile, matrix remodeling can be induced by overexpression of the miR-130/301 family.

 4 Environmental Factors in Pulmonary Hypertension

Noncoding RNA interference is an important epigenetic mechanism. Recent evidence has identified the roles of epigenetic changes in the development of PH. These changes link the pathogenic genes of PH and environmental factors such as hypoxia, virus infection, and air pollution (Gamen et al., 2016). For example, BMPR2 is a transmembrane serine/threonine kinase receptor, which is essential for vascular homeostasis. Although mutations in the BMPR2 gene account for a considerable portion of patients with familial pulmonary artery hypertension (FPAH), only 20–30% of carriers with mutations in this gene suffer from PH, indicating that other factors contribute to the onset of the disease (Orriols et al., 2017; Zhao et al., 2019). According to our network, hypoxia can induce the expression of several miRNAs by HIF-1α, such as, miR-145-5p, miR-19a-3p, miR-191-5p, miR-214-3p, and miR-27a-3p (Agrawal et al., 2014; Camps et al., 2014; el Azzouzi et al., 2013; Song et al., 2014; Zhao et al., 2019). Among them, miR-191-5p can increase cell proliferation, impair apoptosis, and induce phenotypic alteration through inhibiting BMPR2 expression, subsequently contributing to vascular remodeling (Song et al., 2014). Therefore, the HIF-1α/miR-191-5p/BMPR2 axis reveals the connection between hypoxia and BMPR2 expression and partially explains the incomplete penetrance of BMPR2 mutations in FPAH.

 5 Potential Applications of Non-coding RNAs

Ultimately, studies on molecular mechanisms aim to inform clinical practices. NcRNAs are potential diagnostic biomarkers for PH. For example, circRNAs are not easily degraded, making them ideal serum biomarkers. Zhang et al. reported hsa_circ_0068481 overexpression in the serum from patients with IPAH. Furthermore, hsa_circ_0068481 expression is significantly correlated with 6-min walk distance, N-terminal pro-B-type natriuretic peptide, H2S, pulmonary hypertension risk stratification, right heart failure, and survival rate (Zhang et al., 2019a). However, because of the absence of an associated molecular mechanism, this circRNA was not included in our networks. NcRNAs may also act as potential therapeutic targets for PH. For example, Rothman et al. identified downregulation of miR-140-5p in a rat PH model. In vitro, miR-140-5p mimics suppressed PASMC proliferation and migration. In vivo, miR-140-5p mimics prevented the progression of established PH in rats (Rothman et al., 2016). The results are encouraging. However, ncRNA therapy is far from being applied in clinical settings, since a ncRNA may have diverse biofunctions. This means that when used as therapeutic agent, a ncRNA may cause adverse effects, some of which may even be life-threatening. In our opinion, carefully selected ncRNA targets and well-designed action sites can be helpful to avoid such adverse effects. These measures require a comprehensive and in-depth understanding of the mechanisms of ncRNAs in diseases. In this study, we constructed networks to demonstrate the current findings on ncRNAs from studies performed in PH patients and animal models. However, shortcomings of these studies, including the paucity of human data, sex bias, and heterogeneity of animal models, limit the translation of these findings into applications for human disease. Therefore, further studies should be performed to confirm these findings in different animal models and patient cohorts of PH. Additionally, large, well-designed, and unbiased clinical studies are required to illuminate further application of ncRNAs.

 6 Conclusion

The roles of ncRNAs in PH remained unclear. In this study, we performed an extensive literature search and adopted uniform and strict criteria for the selection of each article to avoid biased outcomes. The ncRNA networks were constructed by assembling ncRNAs and their interacting RNAs or genes from included articles. These networks provide a better understanding of the roles of ncRNAs in PH and can be helpful in elucidating the potential clinical applications of ncRNAs.

 Author Contributions

Conceptualization, HZ; writing—original draft preparation HZ and QZ; writing—review and editing, HZ and XL. All authors have read and agreed to the published version of the article.

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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