Abstract
目的
Graves眼病(Graves’ ophthalmopathy，GO)是一种多因素疾病，其非编码RNA相互作用的机制及炎症细胞浸润模式尚不完全清楚。本研究旨在构建GO的竞争性内源RNA(competing endogenous RNA，ceRNA)网络并明确眼眶组织中炎症细胞浸润模式以进一步探究GO的发病机制。
方法
使用GEO2R工具鉴定差异表达基因(differentially expressed genes，DEGs)，对DEGs进行京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes，KEGG)和基因本体分析，并从RNA相互作用数据库中提取RNA的相互作用关系；使用STRING数据库鉴定蛋白质之间的相互作用，并通过Cytoscape进行可视化；基于StarBase、miRcode和DIANA LncBase Experimental v.2数据库中相互作用的非编码RNA来构建ceRNA网络；采用CIBERSORT算法和R语言检测并描述GO中免疫细胞的浸润模式。
结果
共获得114个DEGs，通过KEGG和基因本体分析明确121条通路。使用Cytoscape中的cytoHubba从蛋白质-蛋白质相互作用中提取了4个Hub基因(SRSF6、DDX5、HNRNPC和HNRNPM)，共提取104个节点和142条连接，构建出1个ceRNA网络(MALAT1-MIR21-DDX5)。免疫细胞分析结果显示：GO中CD8+T细胞和CD4+静止型记忆T细胞的比例分别上调和下调。CD4+静止型记忆T细胞的比例与MALAT1、MIR21和DDX5的表达呈正相关(均P<0.05)。
结论
成功构建出GO眼眶组织内的ceRNA调节网络(MALAT1-MIR21-DDX5)，明确在GO中CD4+静止型记忆T细胞的比例下调且与ceRNA组成成分存在正向调节关系，进一步揭示了GO的发病机制。
Keywords: Graves眼病, 生物信息学分析, 竞争性内源RNA, 免疫细胞
Abstract
Objective
Graves’ ophthalmopathy (GO) is a multifactorial disease, and the mechanism of non coding RNA interactions and inflammatory cell infiltration patterns are not fully understood. This study aims to construct a competing endogenous RNA (ceRNA) network for this disease and clarify the infiltration patterns of inflammatory cells in orbital tissue to further explore the pathogenesis of GO.
Methods
The differentially expressed genes were identified using the GEO2R analysis tool. The Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology analysis were used to analyze differential genes. RNA interaction relationships were extracted from the RNA interactome database. Protein-protein interactions were identified using the STRING database and were visualized using Cytoscape. StarBase, miRcode, and DIANA-LncBase Experimental v.2 were used to construct ceRNA networks together with their interacted non-coding RNA. The CIBERSORT algorithm was used to detect the patterns of infiltrating immune cells in GO using R software.
Results
A total of 114 differentially expressed genes for GO and 121 pathways were detected using both the KEGG and gene ontology enrichment analysis. Four hub genes (SRSF6, DDX5, HNRNPC,and HNRNPM) were extracted from protein-protein interaction using cytoHubba in Cytoscape， 104 nodes and 142 edges were extracted, and a ceRNA network was identified (MALAT1-MIR21-DDX5). The results of immune cell analysis showed that in GO, the proportions of CD8+ T cells and CD4+ memory resting T cells were upregulated and downregulated, respectively. The proportion of CD4 memory resting T cells was positively correlated with the expression of MALAT1, MIR21, and DDX5.
Conclusion
This study has constructed a ceRNA regulatory network (MALAT1-MIR21-DDX5) in GO orbital tissue, clarifying the downregulation of the proportion of CD4+ stationary memory T cells and their positive regulatory relationship with ceRNA components, further revealing the pathogenesis of GO.
Keywords: Graves’ ophthalmopathy, bioinformatics analysis, competing endogenous RNA, immune cells
Graves眼病(Graves’ ophthalmolpathy，GO)是一种器官特异性自身免疫性疾病，也是发病率最高的成人眼眶疾病[1]。CD34+眼眶成纤维细胞可在转化生长因子β(transforming growth factor β，TGF-β)和过氧化物酶体增殖物激活受体γ(peroxisome proliferator-activated receptor γ，PPAR-γ)的影响下分别分化为肌成纤维细胞和脂肪细胞，从而参与GO的发病[2-3]。在GO的指南中，维持甲状腺功能正常和戒烟是控制GO进展的2种重要方式[4]。此外，遗传调控也被认为是GO发病机制中的一个重要因素。研究[5-8]表明非编码RNA可以调节GO中的蛋白质表达和CD34+眼眶成纤维细胞功能。
非编码RNA是一种不编码蛋白质但参与蛋白质生产和修饰过程的RNA。根据非编码RNA的长度是否大于200 nt，可分为长链非编码RNA(long non-coding RNA，lncRNA)和短链非编码RNA，后者包括miRNA。近年来，许多miRNA被认为参与了GO发病的调控过程，包括眼外肌肉的水肿和纤维化、脂肪生成和GO的其他病理过程。例如，miR-130a可降低腺苷一磷酸激活的蛋白激酶的活性，促进脂肪组织在眼眶中积聚[9]。MiR-146a可下调透明质酸和一型胶原蛋白的分泌，从而参与眼外肌水肿和纤维化改变[10]。一项RNA测序研究[11]表明：lncRNA可能参与成纤维细胞的细胞外基质的合成，该研究共鉴定出809个差异表达的lncRNA，并认为这些lncRNA参与了GO发病过程中某些重要生物学过程的调控。
GO中不同的非编码RNA之间具有相互调控作用，从而形成广泛的调控网络。根据竞争性内源RNA(competing endogenous RNA，ceRNA)假说，非编码RNA可以与mRNA竞争miRNA的结合位点，从而调节蛋白质的表达和修饰[12]。在眼科领域，有关ceRNA的调控机制尚不完全清楚。Ning等[13]发现ceRNA参与青光眼的发病机制，LINC01518可下调TGF-β1的表达，而该过程可被has-miR-216b-5p抑制。研究[14]表明lncRNA TRPM2-AS充当miR-497的ceRNA，可上调视网膜母细胞瘤中WEE1蛋白的表达。关于ceRNA在GO中的作用研究较少，有关该疾病中非编码RNA之间的复杂调控关系有待确定。
在GO患者的眼眶内，B细胞分泌的某些自身免疫性抗体如促甲状腺素受体抗体水平增加。这些抗体不仅诱导眼眶成纤维细胞功能障碍并促进其分泌免疫因子，还可促进眼眶内其他免疫细胞的积聚、增殖和分化，引起多种类型的免疫细胞浸润眼眶组织，如T细胞、B细胞和单核细胞[3]。然而这些免疫细胞的比例和分布仍不清楚。
本研究旨在分析GO患者和健康对照之间的差异表达mRNA和非编码RNA，并据此分析参与GO的功能通路和蛋白质相互作用网络；通过构建不同RNA之间的相互作用来预测GO的ceRNA，并分析免疫细胞在GO患者眼眶中的浸润模式。
1. 资料与方法
1.1. 基因芯片数据和差异表达分析
从基因表达综合(gene expression omnibus，GEO)数据库(https//www.ncbi.nlm.nih.gov/geo/)中下载相关的基因芯片数据集(GSE58331和GSE105149)。其中，从GSE105149获得4个GO样本和7个健康对照样本，从GSE58331获得8个GO样本和7个健康对照样本，分别分为GO组和对照组。GEO2R分析工具(https//www.ncbi.nlm.nih.gov/geo/geo2r/)用于分析差异表达基因(differentially expressed genes，DEGs)。设置DEGs的标准为P<0.05和|log2[倍数变化(fold change，FC)]|>1；分别定义log2(FC)>1和log2(FC)<1的DEGs为上调DEGs和下调DEGs。使用GraphPad Prism 9绘制相应的火山图。
1.2. 功能富集分析
对DEGs进行京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes，KEGG)分析和基因本体分析。使用org.Hs.eg.db包获得每个DEG的Entrez ID，随后使用R语言(R 4.0.1)的clusterProfiler包来确定DEGs的富集通路[15-16]。从比较毒理学基因组学数据库(comparative toxicogenomics database，CTD)中下载GO的相关基因，通过与DEGs对比，确定二者共同的基因[17]。
1.3. DEGs之间交互关系的检测
在RNA相互作用体数据库中搜索并提取RNA相互作用关系[18]。在搜索结果中，设置ID 1和ID 2的物种参数为人类，并使用Cytoscape 3.7.2构建关系网络。
1.4. 蛋白质-蛋白质相互作用分析
设置综合评分≥0.4的标准后，使用STRING数据库11.0 (https://version-11-0.string-db.org/)获取蛋白质-蛋白质相互作用，其数据来源为RNA相互作用网络中提取的mRNA。使用Cytoscape 3.7.2对获取的结果进行可视化。设置最大聚类中心度(maximal clique centrality，MCC)、最大邻域分量(maximum neighborhood component，MNC)、边缘过化分量(edge percolated component，EPC)、最大邻域分量密度(density of maximum neighborhood component，DMNC)和等级算法中排名前10的基因为Hub基因后，使用CytoHubba提取Hub基因。
1.5. CeRNA网络的构建
从starBase(https://rnasysu.com/encori/)、miRcode(http://www.mircode.org/)和DIANA-LncBase(https://diana.e-ce.uth.gr/lncbasev3)获取lncRNA-miRNA之间的相互作用，其中lncRNA-miRNA对至少被2个数据库收录。从starBase和miRcode中获得miRNA-mRNA对，其中miRNA-mRNA对至少被2个数据库收录。使用Cytoscape 3.7.2从提取的lncRNA miRNA对和miRNA mRNA对中构建ceRNA网络。
1.6. 免疫细胞浸润分析
使用CIBERSORT算法计算GSE58331中22种免疫细胞的比率，分析眼眶组织中浸润的各种免疫细胞的比例[19]。Corrplot包用于计算免疫细胞之间的相关性。
1.7. 统计学处理
采用Wilcoxon符号秩和检验比较GO组和对照组之间各种免疫细胞之间的比值差异，通过Pearson相关分析用于检测细胞比例和基因之间的关系。P<0.05为差异具有统计学意义。
2. 结 果
2.1. DEGs分析
将GEO2R分析得到的数据按照|log2FC|降序排序。去除重复符号和空值后，保留22 188个基因。根据|log2(FC)|>1和P<0.05的标准，在GSE105149中鉴定出536个基因(下调基因455个，上调基因81个)，其中15个非编码RNA(下调14个，上调1个)(图1A、1C)；在GSE58331中鉴定出3 445个基因(下调2 536个，上调909个)，其中58个非编码RNA(下调34个，上调24个)(图1B、1D)。GES5831中GO和对照组表达上调和下调的基因以及非编码RNA见图1E、1F。维恩图(图1G、1H)显示：GSE105149和GSE58331有15个上调基因和99个下调基因相同。
图1.
GO中差异表达的基因图谱
Figure 1 Gene map of differential expression in GO
A-D: Volcanic map shows DEGs and non coding RNAs in GSE105149 (A and C) and GSE58331 (B and D). E and F: Heat map shows DEGs and non-coding RNAs between GO and control in GSE58331. G and H: Number of upregulated and downregulated genes shared between these 2 datasets. GO: Graves’ ophthalmolpathy; DEGs: Differentially expressed genes.
2.2. DEGs的功能富集
将微阵列获得的编号转化为基因名，共获得323个DEGs。KEGG数据库表明：7条富集通路中最显著的3条是内质网蛋白质加工、唾液分泌和碳水化合物消化吸收，分别富含15、10和6个基因(图2A)。从CTD中共提取1 779个基因，发现来自微阵列的DEGs和CTD之间有25个共同基因(图2B)。基因本体分析共提取121条通路，包括62个生物过程(biological process，BP)、22个细胞成分(cellular component，CC)和36个分子功能(molecular function，MF)。最显著的通路分别是内质网蛋白质折叠、亚细胞核和BP、CC和MF中的α-淀粉酶活性(图2C)。在R软件中共提取198条通路，其中最显著的是内质网中的蛋白质折叠、局灶性黏附和BB、CC和BP中的二硫化物氧化还原酶活性(图2D)。
图2.
基因富集分析
Figure 2 Gene enrichment analysis
A: Kyoto encyclopedia of genes and genomes (KEGG); B: Common genes of microarray shown in the Venn diagram; C: Gene ontology analysis; D: Relationship between common genes and the results of gene ontology enrichment analysis. DEGs: Differentially expressed genes; CTD: Comparative toxicogenomics database.
2.3. DEGs之间的相互作用
从RNA相互作用体数据库中共提取569 750个相互作用对，提取134个节点和302个连接(图3A)。在134个节点中，包含124个mRNA、8个lncRNA和4个miRNA(表1)。LncRNA和microRNA及其相互作用如图3B和3C所示。相互作用网络显示lncRNA、肺腺癌转移相关转录子1(metastasis-associated lung adenocarcinoma tran 1，MALAT1)和NEAT1之间的相互作用(MALAT1与自身相互作用)，但lncRNA与miRNA之间未见相互关系。非编码RNA与mRNA存在相互作用，如MALAT1、HNRNPM、HNRNPC和DDX5(图3D-3G)。这些非编码RNA及其靶mRNA可能形成一个复杂的调控网络。
图3.
基因相互作用分析
Figure 3 Gene interaction analysis
A: Interactions between genes; B and C: LncRNA and miRNA with their targets, respectively; D-G: Single mRNA and its regulated non coding RNA network.
表1.
非编码RNA及其相互作用
Table 1 Non coding RNAs and their interactions
| 基因 | 类别 | 连接的数目 |
|---|---|---|
| FTX | LncRNA | 14 |
| NEAT1 | LncRNA | 95 |
| MALAT1 | LncRNA | 125 |
| POLR2J4 | LncRNA | 17 |
| PSMA3-AS1 | LncRNA | 13 |
| STAG3L5P-PVRIG2P-PILRB | LncRNA | 14 |
| SNHG17 | LncRNA | 21 |
| NUTM2B-AS1 | LncRNA | 11 |
| MIR612 | MicroRNA | 32 |
| MIR3652 | MicroRNA | 11 |
| MIR21 | MicroRNA | 13 |
| MIR664b | MicroRNA | 12 |
2.4. 蛋白质-蛋白质相互作用分析
使用STRING在线数据库共获得247条连接和87个节点(图4A)。使用cytoHubba提供的5种算法(Degree、DMNC、MCC、EPC和MNC)计算核心基因(表2)。在5种算法中排名前位的核心基因是富含丝氨酸和精氨酸的剪接因子6(serine and arginine rich splicing factor 6，SRSF6)、异质核核糖核蛋白M(heterogeneous nuclear ribonucleoprotein M，HNRNPM)、异质性核核糖核蛋白质C(heterogeneous nuclear ribonucleoprotein C，HNRNPC)和DEAD盒解旋酶5(DEAD-box helicase 5，DDX5)(图4B-4F)。
图4.
蛋白质-蛋白质相互作用
Figure 4 Protein-protein interaction
A: Width of the connecting line represents the overall score, while a wider line indicates a higher score. A warmer color indicates a higher ranking. B-F: Darker the color of a node, the higher its ranking. PPI: Protein-protein interaction; DMNC: Density of maximum neighborhood component; MCC: Maximal clique centrality; EPC: Edge percolated component; MNC: Maximum neighborhood component.
表2.
使用5种算法提取的核心基因
Table 2 Core genes extracted using 5 algorithms
| 等级 | Degree | DMNC | EPC | MCC | MNC |
|---|---|---|---|---|---|
| 1 | HSP90AA1 | DDX42 | HNRNPD | HNRNPA1 | HNRNPA1 |
| 2 | HNRNPD | SRSF4 | HNRNPC | HNRNPC | HNRNPD |
| 3 | HNRNPA1 | HNRNPM | HNRNPA2B1 | HNRNPM | HNRNPC |
| 4 | HNRNPC | HNRNPH1 | HNRNPH1 | HNRNPH1 | HNRNPA2B1 |
| 5 | HNRNPA2B1 | RBM39 | SRSF6 | HNRNPD | DDX5 |
| 6 | SYNCRIP | SRSF6 | HNRNPA1 | HNRNPA2B1 | SYNCRIP |
| 7 | DDX5 | SRSF11 | SYNCRIP | SRSF6 | HSP90AA1 |
| 8 | SRSF6 | SFPQ | DDX5 | DDX5 | HNRNPM |
| 9 | HNRNPM | DDX5 | HNRNPM | SRSF11 | HNRNPH1 |
| 10 | SFPQ | HNRNPC | SFPQ | SRSF4 | SRSF6 |
DMNC: Density of maximum neighborhood component; MCC: Maximal clique centrality; EPC: Edge percolated component; MNC: Maximum neighborhood component.
2.5. ceRNA的构建
用PPI中提取的4个核心基因(SRSF6、DDX5、HNRNPC和HNRNRM)在starBase和miRcode数据库中进行搜索，获得48对共同的miRNA-mRNA对(图5A)。从starBase、miRcode和DIANA-LncBase Experimental v.2中提取8个与4个核心基因相互作用的lncRNA-miRNA对，共获得94对lncRNA-miRNA(图5B)。从3个数据库中删除重复的miRNA后，保留98个miRNA，与DEGs中共同的miRNA为MIR21。根据3个数据库(starBase、miRcode和DIANA-LncBase Experimental v.2)和RNA相互作用的结果，提取出1个ceRNA(MALAT1)，其可以通过MIR21调控DDX5(图5C)。
图5.
竞争性内源RNA的构建
Figure 5 Construction of competing endogenous RNA
A and B: Construct lncRNA miRNA and miRNA mRNA pairs and visualize them using Cytoscape; C: Construct predictive ceRNA. CeRNA: Competing endogenous RNA.
2.6. GO中浸润的免疫细胞
采用CIBERSORT算法分析22种免疫细胞的组成，结果如图6A所示。GO组和对照组之间的免疫细胞比例差异见图6B、6C。在GO泪腺组织中，CD8+ T细胞的比例高于对照组(P=0.03)，而CD4+ 静止型记忆T细胞的比例低于对照组(P=0.02)，表明CD8+ T细胞和CD4+静止型记忆T细胞在GO泪腺组织中分别上调和下调。免疫细胞类型之间的关系见图6D，其中调节性T细胞和滤泡辅助性T细胞之间相关性最高(R=0.700，P=0.004)。CeRNA的组成与CD4 T细胞的相关性结果显示：CD4+静止型记忆T细胞的比例与MALAT1(R=0.640，P=0.011)、MIR21(R=0.750，P=0.001)和DDX5(R=0.700，P=0.004)呈正相关(图6E-6G)。CD8+ T细胞与ceRNA组成成分之间无显著相关性(均P>0.05)。
图6.
GO中免疫细胞浸润组织的分析
Figure 6 Analysis of immune cell infiltration tissues in GO
A: Proportion of 22 immune cell types in the sample of GSE58331; B and C: Comparison of immune cell ratios between GO and control groups; D: Correlation between immune cell types; E-G: Correlation between CD4 positive stationary memory T cells and ceRNA composition. GO: Graves’ ophthalmolpathy.
3. 讨 论
非编码RNA在其他类型的疾病中有较充分的研究，但关于lncRNA和ceRNA在GO中的类别和机制，以及在GO中免疫细胞比例的研究较少。
本研究使用GEO2R进行DEGs分析，共提取出28个上调和295个下调的共同基因，进一步富集该通路。使用CTD对GO相关基因进行验证后发现，内质网蛋白折叠、黏着斑和钙黏蛋白结合分别是BB、CC和MF中最显著的通路。将mRNA与lncRNA、miRNA的相互作用进行可视化并构建PPI网络。通过5种算法提取出4个核心基因(SRSF6、DDX5、HNRNPC和HNRNRM)。将这4个核心基因及其相互作用的非编码RNA导入3个数据库(starBase、miRcode和DIANA-LncBase Experimental v.2)，获得靶向miRNA。利用至少2个数据库中的共同miRNA构建MALAT1通过MIR21调控DDX5的ceRNA网络。最后，分析免疫细胞在GO组织中的浸润情况，并发现CD8+ T细胞和CD4+ 静止型记忆T细胞的比例在GO和对照组之间存在差异。而且还发现CD4+ 静止型记忆T细胞的比例与MALAT1、MIR21和DDX5的表达呈正相关。
MALAT1作为一种lncRNA最早在与转移相关的非小细胞肺癌中被发现[20]。MALAT1通过与多个mRNA和miRNA位点相互作用参与蛋白质的翻译和翻译后调控[21]。虽然MALAT1被发现与癌症、心血管和神经系统疾病等疾病相关，但其与GO相关的证据并不多。由于眼外纤维化和脂肪生成是GO的主要致病特征，抑制MALAT1可减轻心脏纤维化和脂肪生成的研究提示了同样的过程可能发生在GO中[22-23]。此外，降低MALAT1的表达还可减轻糖尿病视网膜病变中的炎症[24]，这为MALAT1是眼部疾病中可能的调控位点提供了证据。
MiR-21在GO中可促进纤维化，可在眼眶成纤维细胞中促进增殖和分化过程，减少眼眶成纤维细胞凋亡[25]。血小板源性生长因子-BB上调miR的表达，进而下调程序性细胞死亡因子4的表达，导致GO的发生[26]。此外，miR-21与另外4个miRNA(miR-Let7d-5p、miR-96-5p、miR-142-3p和miR-301a-3p)一起组成独立的危险因素，与预后较差相关[5]。最近的研究[27-29]还发现了影响MIR21调控的lncRNA，如MEG3、SNHG1和NKILA。Chu等[30]利用组织微阵列和聚合酶链式反应定量显示miR21和MALAT1在甲状腺癌中均上调，但miR21是否受MALAT1调控尚不清楚。
DDX5是一种包含DEAD-box的RNA解旋酶，可调节细胞周期进展[31]。DDX5与其他激活的转录因子一起定位其靶基因的启动子，并作为共转录因子调节表达过程[32]。DDX5不仅与衰老相关，还在多囊肾病中参与细胞增殖和纤维化的调节[33]。DDX5磷酸化后可能通过将β-catenin转运入细胞核并激活cyclin D1促进上皮-间充质转化[34]。但是DDX5在GO中的作用尚不清楚，需要更多的研究来阐明其机制。
免疫细胞浸润是GO发生、发展的重要因素。为缓解GO的临床症状，临床上用糖皮质激素和免疫抑制剂抑制炎症反应以及免疫细胞的功能和活性。然而，GO中免疫细胞的比例及其与基因的关系尚未得到充分研究。研究[35]表明：细胞毒性T细胞和CD8+T调节性细胞之间，以及T细胞和钙螯合素-1的自身免疫之间存在正相关，但所研究的22种免疫细胞的比例尚不清楚。
本研究受到3个因素的限制。首先，GO是一种器官特异性自身免疫性疾病，泪腺、眼外肌和脂肪组织均有致病性改变。在这些组织中，RNA表达水平和调控ceRNA网络的变化是否相似仍不清楚。例如，泪腺的数据提示miR-21下调，但在脂肪组织成纤维细胞的数据中上调[25]。其次，RNA之间的相互作用关系是基于各种疾病模型和生物学过程的数据，在TAO患者体内可能存在更为复杂的调控过程，而本研究只针对MALAT1-MIR21-DDX5一个网络进行了探究。最后，GO的疾病发展是一个漫长的过程，根据临床活动性评分可分为活动期和静止期。本研究揭示了某一个阶段免疫细胞的比例，但未能动态说明疾病过程中的变化。
综上，本研究成功构建1条ceRNA通路(MALAT1-MIR21-DDX5)，明确在GO中CD8+ T细胞和CD4+静止型记忆T细胞分别上调和下调，且与ceRNA的组成存在相关性。
基金资助
国家自然科学基金(82071006)。
This work was supported by the National Natural Science Foundation of China (82071006).
利益冲突声明
作者声称无任何利益冲突。
作者贡献
曹家敏、陈海燕 资料收集，软件操作，统计分析，论文撰写与修改；谢冰雨、陈艺枝 协助统计分析，论文撰写；熊炜 协助论文修改；李明渊 研究设计和论文修改。所有作者阅读并同意最终的文本。
原文网址
http://xbyxb.csu.edu.cn/xbwk/fileup/PDF/2023081185.pdf
参考文献
- 1. Smith TJ, Hegedüs L. Graves’ disease[J]. N Engl J Med, 2016, 375(16): 1552-1565. 10.1056/nejmra1510030. [DOI] [PubMed] [Google Scholar]
- 2. Shih SR, Liao SL, Shih CW, et al. Fibroblast growth factor receptor inhibitors reduce adipogenesis of orbital fibroblasts and enhance myofibroblastic differentiation in Graves’ orbitopathy[J]. Ocul Immunol Inflamm, 2021, 29(1): 193-202. 10.1080/09273948.2019.1672196. [DOI] [PubMed] [Google Scholar]
- 3. Smith TJ, JAMJL Janssen. Insulin-like growth factor-I receptor and thyroid-associated ophthalmopathy[J]. Endocr Rev, 2019, 40(1): 236-267. 10.1210/er.2018-00066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. 中华医学会眼科学分会眼整形眼眶病学组, 中华医学会内分泌学分会甲状腺学组 . 中国甲状腺相关眼病诊断和治疗指南(2022年)[J]. 中华眼科杂志, 2022, 58(9): 646-668. 10.3760/cma.j.cn112142-20220421-00201. [DOI] [Google Scholar]; Oculoplastic and Orbital Disease Group of Chinese Ophthalmological Society of Chinese Medical Association, Thyroid Group of Chinese Society of Endocrinology of Chinese Medical Association . Chinese guideline on the diagnosis and treatment of thyroid-associated ophthalmopathy (2022)[J]. Chinese Journal of Ophthalmology, 2022, 58(9): 646-668. 10.3760/cma.j.cn112142-20220421-00201. [DOI] [PubMed] [Google Scholar]
- 5. Martínez-Hernández R, Sampedro-Núñez M, Serrano-Somavilla A, et al. A microRNA signature for evaluation of risk and severity of autoimmune thyroid diseases[J]. J Clin Endocrinol Metab, 2018, 103(3): 1139-1150. 10.1210/jc.2017-02318. [DOI] [PubMed] [Google Scholar]
- 6. Hu ZJ, He JF, Li KJ, et al. Decreased microRNA-146a in CD4+ T cells promote ocular inflammation in thyroid-associated ophthalmopathy by targeting NUMB[J]. Eur Rev Med Pharmacol Sci, 2017, 21(8): 1803-1809. [PubMed] [Google Scholar]
- 7. Shen LY, Huang FJ, Ye L, et al. Circulating microRNA predicts insensitivity to glucocorticoid therapy in Graves’ ophthalmopathy[J]. Endocrine, 2015, 49(2): 445-456. 10.1007/s12020-014-0487-4. [DOI] [PubMed] [Google Scholar]
- 8. Wang N, Chen FE, Long ZW. Mechanism of microRNA-146a/Notch2 signaling regulating IL-6 in Graves ophthalmopathy[J]. Cell Physiol Biochem, 2017, 41(4): 1285-1297. 10.1159/000464430. [DOI] [PubMed] [Google Scholar]
- 9. Hammond CL, Roztocil E, Gonzalez MO, et al. microRNA-130a is elevated in thyroid eye disease and increases lipid accumulation in fibroblasts through the suppression of AMPK[J]. Invest Ophthalmol Vis Sci, 2021, 62(1): 29. 10.1167/iovs.62.1.29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Liu W, Ma C, Li HY, et al. microRNA-146a downregulates the production of hyaluronic acid and collagen I in Graves’ ophthalmopathy orbital fibroblasts[J]. Exp Ther Med, 2020, 20(5): 1. 10.3892/etm.2020.9165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Wu LQ, Li L, Liang Y, et al. Identification of differentially expressed long non-coding RNAs and mRNAs in orbital adipose/connective tissue of thyroid-associated ophthalmopathy[J]. Genomics, 2021, 113(1): 440-449. 10.1016/j.ygeno.2020.09.001. [DOI] [PubMed] [Google Scholar]
- 12. Salmena L, Poliseno L, Tay Y, et al. A ceRNA hypothesis: the Rosetta stone of a hidden RNA language?[J]. Cell, 2011, 146(3): 353-358. 10.1016/j.cell.2011.07.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Kong N, Bao YL, Zhao HX, et al. Long noncoding RNA LINC01518 modulates proliferation and migration in TGF-β1-treated human tenon capsule fibroblast cells through the regulation of hsa-miR-216b-5p[J]. Neuromolecular Med, 2022, 24(2): 88-96. 10.1007/s12017-021-08662-2. [DOI] [PubMed] [Google Scholar]
- 14. Li AP, Yang JP, Zhang T, et al. Long noncoding RNA TRPM2-AS promotes the growth, migration, and invasion of retinoblastoma via miR-497/WEE1 axis[J]. Front Pharmacol, 2021, 12: 592822. 10.3389/fphar.2021.592822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Yu GC, Wang LG, Han YY, et al. Profiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287. 10.1089/omi.2011.0118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Zhang C, Zheng Y, Li X, et al. Genome-wide mutation profiling and related risk signature for prognosis of papillary renal cell carcinoma[J]. Ann Transl Med, 2019, 7(18): 427. 10.21037/atm.2019.08.113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Davis AP, Wiegers TC, Wiegers J, et al. CTD Anatomy: analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies[J]. Curr Res Toxicol, 2021, 2: 128-139. 10.1016/j.crtox.2021.03.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Lin YQ, Liu TY, Cui TY, et al. RNAInter in 2020: RNA interactome repository with increased coverage and annotation[J]. Nucleic Acids Res, 2020, 48(D1): D189-D197. 10.1093/nar/gkz804. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles[J]. Nat Methods, 2015, 12(5): 453-457. 10.1038/nmeth.3337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Ji P, Diederichs S, Wang WB, et al. MALAT-1, a novel noncoding RNA, and thymosin beta4 predict metastasis and survival in early-stage non-small cell lung cancer[J]. Oncogene, 2003, 22(39): 8031-8041. 10.1038/sj.onc.1206928. [DOI] [PubMed] [Google Scholar]
- 21. Zhang XJ, Hamblin MH, Yin KJ. The long noncoding RNA Malat1: its physiological and pathophysiological functions[J]. RNA Biol, 2017, 14(12): 1705-1714. 10.1080/15476286.2017.1358347. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Che H, Wang YQ, Li H, et al. Melatonin alleviates cardiac fibrosis via inhibiting lncRNA MALAT1/miR-141-mediated NLRP3 inflammasome and TGF-β1/Smads signaling in diabetic cardiomyopathy[J]. FASEB J, 2020, 34(4): 5282-5298. 10.1096/fj.201902692R. [DOI] [PubMed] [Google Scholar]
- 23. Han J, Shen L, Zhan Z, et al. The long noncoding RNA MALAT1 modulates adipose loss in cancer-associated cachexia by suppressing adipogenesis through PPAR-γ[J]. Nutr Metab (Lond), 2021, 18(1): 27. 10.1186/s12986-021-00557-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Wang Y, Wang L, Guo H, et al. Knockdown of MALAT1 attenuates high-glucose-induced angiogenesis and inflammation via endoplasmic reticulum stress in human retinal vascular endothelial cells[J]. Biomedecine Pharmacother, 2020, 124: 109699. 10.1016/j.biopha.2019.109699. [DOI] [PubMed] [Google Scholar]
- 25. Tong BD, Xiao MY, Zeng JX, et al. MiRNA-21 promotes fibrosis in orbital fibroblasts from thyroid-associated ophthalmopathy[J]. Mol Vis, 2015, 21: 324-334. [PMC free article] [PubMed] [Google Scholar]
- 26. Lee JY, Yun M, Paik JS, et al. PDGF-BB enhances the proliferation of cells in human orbital fibroblasts by suppressing PDCD4 expression via up-regulation of microRNA-21[J]. Invest Ophthalmol Vis Sci, 2016, 57(3): 908. 10.1167/iovs.15-18157. [DOI] [PubMed] [Google Scholar]
- 27. Jia HY, Zhang K, Lu WJ, et al. LncRNA MEG3 influences the proliferation and apoptosis of psoriasis epidermal cells by targeting miR-21/caspase-8[J]. BMC Mol Cell Biol, 2019, 20(1): 46. 10.1186/s12860-019-0229-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Wang JO, Zhu Z, Qiu H, et al. LncRNA NKILA inhibits the proliferation and promotes the apoptosis of CSCC cells by downregulating miRNA-21[J]. J Cell Physiol, 2020, 235(11): 7863-7869. 10.1002/jcp.29439. [DOI] [PubMed] [Google Scholar]
- 29. Li WD, Dong XS, He CJ, et al. LncRNA SNHG1 contributes to sorafenib resistance by activating the Akt pathway and is positively regulated by miR-21 in hepatocellular carcinoma cells[J]. J Exp Clin Cancer Res, 2019, 38(1): 183. 10.1186/s13046-019-1177-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Chu YH, Hardin H, Schneider DF, et al. microRNA-21 and long non-coding RNA MALAT1 are overexpressed markers in medullary thyroid carcinoma[J]. Exp Mol Pathol, 2017, 103(2): 229-236. 10.1016/j.yexmp.2017.10.002. [DOI] [PubMed] [Google Scholar]
- 31. Zhang L, Li XG. DEAD-box RNA helicases in cell cycle control and clinical therapy[J]. Cells, 2021, 10(6): 1540. 10.3390/cells10061540. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Iyer RS, Nicol SM, Quinlan PR, et al. The RNA helicase/transcriptional co-regulator, p68 (DDX5), stimulates expression of oncogenic protein kinase, Polo-like kinase-1 (PLK1), and is associated with elevated PLK1 levels in human breast cancers[J]. Cell Cycle, 2014, 13(9): 1413-1423. 10.4161/cc.28415. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Zhang L, Li LX, Zhou JX, et al. RNA helicase p68 inhibits the transcription and post-transcription of Pkd1 in ADPKD[J]. Theranostics, 2020, 10(18): 8281-8297. 10.7150/thno.47315. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Yang LQ, Lin CR, Liu ZR. P68 RNA helicase mediates PDGF-induced epithelial mesenchymal transition by displacing axin from β-catenin[J]. Cell, 2006, 127(1): 139-155. 10.1016/j.cell.2006.08.036. [DOI] [PubMed] [Google Scholar]
- 35. Al-Ansari F, Lahooti H, Stokes L, et al. Correlation between thyroidal and peripheral blood total T cells, CD8+ T cells, and CD8+ T-regulatory cells and T-cell reactivity to calsequestrin and collagen XIII in patients with Graves’ ophthalmopathy[J]. Endocr Res, 2018, 43(4): 264-274. 10.1080/07435800.2018.1470639. [DOI] [PubMed] [Google Scholar]