Skip to main content
NCBI home page
Journal of Southern Medical University logoLink to Journal of Southern Medical University
. 2021 Jan 20;41(1):39–46. [Article in Chinese] doi: 10.12122/j.issn.1673-4254.2021.01.05

博莱霉素诱导小鼠肺纤维化中差异表达的mRNA及其与LncRNA的共表达网络关系

Differential mRNA expression in C57BL/6 mice with bleomycin-induced pulmonary fibrosis and its association with LncRNA co-expression network

Xuefei YU 1,2, Li LI 2, Linxin ZHENG 2, Weifeng LI 2,*
PMCID: PMC7867477  PMID: 33509751

Abstract

Objective

To study the changes in mRNA and long non-coding RNA (lncRNA) expression profiles in a mouse model of bleomycin-induced lung fibrosis and identify lung fibrosis-related mRNA for coding-noncoding coexpression (CNC) bioinformatics analysis of the differential lncRNAs.

Methods

Lung fibrosis was induced by intratracheal injection of bleomycin in 10 C57BL/6 mice and another 10 mice with intratracheal injection of saline served as the control group. Lung tissues were harvested from the mice at 14 days after the injections and lung fibrosis was assessed using Masson and HE staining. LncRNA chip technology was used to screen the differentially expressed mRNAs and lncRNAs in mice with lung fibrosis, and GO and KEGG pathway analyses of the differential mRNAs were performed using NCBI database and UCSC database to identify possible fibrosis-related mRNAs, which were validated by qRT-PCR to construct a coding and non-coding co- expression network with the differential lncRNAs.

Results

Compared with the control mice, the mice with intratracheal injection of bleomycin showed obvious lung fibrosis. The results of gene chip analysis showed that 127 mRNAs were upregulated and 184 mRNAs were down-regulated in the model group as compared with the control group. GO and pathway analysis suggested that the differentially expressed genes participated mainly in immune response, cell differentiation, and cytoskeletons; the involved signal pathways were associated mainly with cytokine and cytokine receptor interaction and chemokine signal transduction. Bioinformatics analysis identified a significant coexpression network between the fibrosisrelated mRNA and the differentially expressed lncRNA.

Conclusions

In mice with lung fibrosis, the differential expressions of fibrosis-related mRNAs in the lung tissues are closely correlated with the co- expressions of a large number of differential lncRNAs, which points to a new direction for investigation of the pathogenesis of pulmonary fibrosis.

Keywords: bleomycin, pulmonary fibrosis, mRNA, long non-coding RNA, co-expression analysis

特发性肺纤维化是一种病因不明、慢性、进行性、纤维化性间质性肺疾病，缺乏有效治疗方法，预后极差[1-2]。其病理特征主要是肺泡上皮损伤，过量的细胞外基质沉积等，这些病理改变引起肺组织正常结构破坏，气体交换受阻，进而引起心脏和其他重要脏器病变[3]。随着对特发性肺纤维化的病理生理进程的研究不断深入，越来越多的发病理生理机制被提出，包括细胞老化、氧化损伤、内质网应激、间质转化、细胞可塑性、微小RNA、外泌体等，但其发病机制仍未被完全阐述[4-6]。近年来，LncRNA由于其广泛参与各种生命活动的特性在众多疾病的发病机制中的作用受到广泛关注[7]。有研究表明，LncRNA的异常表达可导致肿瘤等疾病[8-9]。然而，肺纤维化中LncRNA的表达和作用机制仍然未知。因此在本研究中，我们使用博来霉素诱导肺纤维化小鼠模型，采用基因芯片检测博来霉素组和对照组肺组织中LncRNA与mRNA的表达，为探讨其在肺纤维化发生发展中的作用提供前期基础。

1. 材料和方法

1.1. 实验分组与模型建立

20只6周龄的无特定病原体级别C57BL/6小鼠从南方医科大学实验动物中心购买，体质量18.9~23.8 g。由中国人民解放军南部战区总医院实验动物中心进行饲养，经动物伦理委员会许可，严格遵守《实验动物管理条例》进行所有实验操作。将20只小鼠随机地平均分成2个组(雌雄各半)：分别为对照组、模型组，10只/组。将各组小鼠麻醉成功后固定于小鼠手术操作台，纵行切开颈前皮肤暴露气管，朝模型组小鼠向心端气管内注入100 μL博来霉素溶液(3 mg/kg)，对照组小鼠注入等量的0.9%的氯化钠注射液。注射后旋即令小鼠头朝上且直立旋转以使药液在其肺内分布均匀; 造模之后的第14天处死小鼠，解剖并收集小鼠肺组织标本。

1.2. 苏木精-伊红(HE)染色与马松三色染色

采用10%水合氯醛对各组小鼠进行腹腔注射麻醉后，用眼科剪剪断腹主动脉放血以处死小鼠，暴露胸部，以眼科镊夹住小鼠肺主支气管，小心分离出肺组织，用滤纸吸干将肺组织表面残留液体，尽快置于4%多聚甲醛中浸泡，在固定48 h后，经常规脱水、石蜡包埋、2 µm厚度切片后，常规进行HE与Masson染色。

1.3. 基因芯片筛选差异表达

mRNA与LncRNA TRIzol法提取各组肺组织RNA，采用Arraystar小鼠LncRNA芯片V3.0芯片检测分析。使用Agilent Feature Extraction软件(v11.0.1.1)获得芯片图，并读值，得到原始数据。使用GeneSpring GX v12.1软件(Agilent Technologies)对原始数据进行Quantile标准化和随后的数据处理。原始数据标准化后经过筛选高质量探针(某探针在6个样品中至少有3个被标记为Present或Marginal)进行进一步分析。两个样品间差异表达LncRNA或差异表达mRNAs通过Fold Change筛选。以倍数变化(FC)>2且P < 0.05认为存在差异表达。

1.4. 对差异mRNA进行生物信息学分析

利用David 6.7在线平台对差异表达的基因作基因本体论(GO)分析和全基因组及代谢途径数据库(KEGG)通路分析。根据最新KEGG数据库，分析差异表达mRNAs的信号通路。

1.5. qRT-PCR验证纤维化相关mRNA与LncRNA表达

针对每一个需要测量的基因和管家基因，选择确定表达该基因的cDNA模板进行PCR反应，配置反应体系(2×Master Mix 5 μL，10 μmol/L的PCR特异引物F 0.5 µL，10 μmol/L的PCR特异引物R 0.5 μL，cDNA 2 μL加水至总体积为10 μL)，引物序列见表 1，轻弹管底将溶液混合，5000 r/min短暂离心，设置PCR反应：95 ℃，10 min; 40个PCR循环(95 ℃，10 s; 60 ℃，60(收集荧光))。PCR产物与100 bp DNA Ladder在2%琼脂糖凝胶电泳，溴化乙锭染色，检测PCR产物是否为单一特异性扩增条带。将PCR产物进行10倍梯度稀释：设定PCR产物浓度为1，分别稀释为1×10-1，1×10-2，1×10-3，1× 10-4，1×10-5，1×10-6，1×10-7，1×10-8，1×10-9，这几个梯度浓度的DNA。将所有cDNA样品分别配置Realtime PCR反应体系。将8 μL混合液加到384-PCR板对应的每个孔中。再加入对应的2 μL cDNA。小心粘上Sealing Film封口膜，并短暂离心混合。在设置PCR程序前将准备好的PCR板放在冰上。将上述384-PCR板置于Realtime PCR仪上进行PCR反应。所有的指标均按以下程序进行：95 ℃，10 min; 40个PCR循环(95 ℃，10 s; 60 ℃，60(s收集荧光))。为了建立PCR产物的熔解曲线，扩增反应结束后，按(95 ℃，10 s; 60 ℃，60 s; 95 ℃，15 s); 并从60 ℃缓慢加热到99 ℃(仪器自动进行-Ramp Rate为0.05 ℃/s)。各样品的目的基因和管家基因分别进行Realtime PCR反应。根据绘制的梯度稀释DNA标准曲线，各样品目的基因和管家基因的浓度结果直接由机器生成。每个样品的目的基因浓度除以其管家基因的浓度，即为此样品此基因的校正后的相对含量。

1.

qRT-PCR使用的引物列表

Primers used for qRT-PCR

Gene Primer sequence 5'-3' Annealing temperature (℃) (product length) (bp)
F: Forward primer; R: Revise primer.
β-actin (M) F:5' GTACCACCATGTACCCAGGC 3'
R :5' AACGCAGCTCAGTAACAGTCC 3'		 60 247
Cd177 F:5' CTGCGGTCTTCAGTTAGATGCT 3'
R :5' CAGTTCTGTGCTCCGTGACTTT 3'		 60 233
Bcl2l15 F:5' TTGGCCGCCTTCGAATACT 3'
R :5' TGGAGCCTTACGGACCACATA 3'		 60 227
Ccl6 F:5' AGAAGATCGTCGCTATAACCCT 3'
R :5' AAGCAGCAGTCTGAAGAAGTGT 3'		 60 69
Ndnf F:5' CCTGGTATTTGACATCTTGGTAG 3'
R :5' GGGTCCTTGAAGTGAGTGGTA 3'		 60 187
Ccl8 F:5' GCTGCTCATAGCTGTCCCTGT 3'
R :5' CTGCAGAATTTGAGACTTCTGG 3'		 60 259
Krtap4-1 F:5' TCTCAGAAACCCACCCAGAATC 3'
R :5' AGAGCAGACAGAGCCACAACAA 3'		 60 70
NR_040597 F:5' AGAAAGATGATTTTGAAAGCCAT 3'
R:5' CCCTCTTCTGAAGCCTCCATAT 3'		 60 90
AK085645 F:5' CCCACGCTTTCACCTATTTCT 3'
R:5' TTTAACAGTCACTTGGCCTCC 3'		 60 76
AK086961 F:5' TTGGAACACAGAAAGTTGATT 3'
R:5' CCAGTGTGATAAAGACATTCAATA 3'		 60 93
Open in a new tab

1.6. 共表达CNC网络信息学分析

根据前述基因GO、Pathway分析的纤维化相关的条目，同时参考肺纤维文献报道的相关基因，挑选代表性的mRNAs，并比对在本芯片中他们的差异变化，观察P值。将挑选出得代表性得mRNAs，和所有差异LncRNA进行CNC共表达分析。通过计算皮尔逊相关系数对上述mRNAs和所有差异LncRNA的表达相关性进行分析，筛选皮尔逊相关系数≥0.98，P值≤0.05，fdr≤0.1的结果作为相互关系对。根据上述皮尔逊相关系数结果，利用Cytoscape绘图绘制mRNA-LncRNA关系对。

1.7. 统计学分析

采用SPSS22.0对所有的实验结果进行统计学的分析处理。所有数据均进行正态性检验，符合正态性检验后以均数±标准差表示。样本符合正态分布、方差齐时，采用两组独立样本的t检验法进行分析，当P < 0.05时，即认为差异有统计学意义; 样本符合正态分布、但方差不齐时，采用两组独立样本的t'检验，当P < 0.05时，认为差异有统计学意义。

2. 结果

2.1. 模型鉴定

HE染色示对照组小鼠肺组织的完整而清晰，肺泡壁薄且较连续，肺泡无明显的炎症细胞浸润; 模型组：小鼠肺组织出现弥漫性实变，肺泡壁增厚明显，肺泡间隔破坏，肺泡中可见大量炎症细胞浸润，较多成纤维细胞出现在肺间质(图 1)。Masson染色示对照组小鼠肺组织结构清晰，未见明显炎症细胞浸润及上皮下的蓝色胶原沉积; 模型组小鼠肺组织结构紊乱，肺间质可见炎症细胞浸润，上皮下的蓝色胶原沉积明显增加(图 2)。证实肺纤维化模型构建成功，可用于进一步实验。

1.

1

Open in a new tab

小鼠肺组织病理染色

Histopathological examination of lung tissues of the mice (HE staining, original magnification: ×200). A: Control group. B: Model group.

2.

2

Open in a new tab

小鼠肺组织病理染色

Masson staining of lung tissues of the mice (×200). A: Control group. B: Model group.

2.2. 芯片筛选差异mRNA结果

将6个合格样本同时进行高通量mRNA芯片杂交后的微点阵图显示，共检测出16 403条mRNA，进行聚类分析与比较后，发现差异表达的共311条mRNAs，用于辅助LncRNA的研究(表 2)。

2.

部分差异表达mRNAs

List of some of the differentially expressed mRNAs in mice with lung fibrosis

SeqID P Regulation Fold change Model (raw) Normal (raw) Model (normalized) Normal (normalized)
NM_026862 0.0211 up 17.4872 346.0901 22.9401 8.4079 4.2797
NM_009139 0.0291 up 11.1071 928.6594 87.0080 9.8018 6.3284
NM_001204203 0.0023 up 6.3793 443.9770 67.7496 8.7451 6.0716
NM_001204201 0.0117 up 10.1578 467.7274 46.4265 8.8158 5.4712
NM_001276413 0.0141 up 9.3872 105.8674 11.4205 6.4472 3.2165
NM_001031851 0.0233 down 7.2631 79.6036 452.74988 5.9421 8.8027
NM_001081206 0.0085 down 3.7192 31.0679 112.8070 4.9226 6.8176
NM_010322 0.0081 down 2.8527 287.7628 728.6592 7.9909 9.5032
NM_011435 0.0021 down 3.3624 311.6001 1023.5387 8.2489 9.9985
NM_011561 0.0099 down 2.9914 74.8061 217.2233 6.1629 7.7438
Open in a new tab

2.3. 聚类分析肺纤维中差异表达mRNA的信号通路

依据上述得到的差异表达mRNA，使用GO聚类分析在肺纤维组中差异表的mRNA参与了免疫应答及调节、信号传导、细胞增殖及分化、细胞粘附和迁移等生物过程，KEGG通路分析显示在上调的mRNA主要涉及细胞黏附分子、ECM受体相互作用、DNA复制、补体途径等通路，下调的mRNA主要涉及氧化磷酸化、氨基酸、甘油酯代谢通路(图 3、4)。

3.

3

Open in a new tab

GO分析差异表达基因的生物学过程

Enrichment of mRNAs associated with biological processes. A: Up-regulated mRNAs. B: Down-regulated mRNAs.

4.

4

Open in a new tab

KEGG通路富集分析

KEGG pathway analysis. A: Up-regulated mRNAs. B: down-regulated mRNAs.

2.4. 筛选并验证纤维化相关mRNA

根据第一部分基因GO、Pathway分析的相关通路的条目，同时参考肺纤维文献报道的相关基因，挑选代表性的mRNAs，并比对在本芯片中他们的差异变化，观察P值，qRT-PCR进一步验证纤维化相关mRNA表达。最终筛选出与芯片结果相一致的包括ccl8，ccl6，CD177，Ndnf，Krtap4-1，Bcl2l15共6个与纤维化相关mRNA(图 5)。

5.

5

Open in a new tab

挑选得6个肺纤维化相关mRNA的microarray与qRT-PCR结果比较

Comparison of expression levels of 6 fibrosis-related mRNAs between microarray and qRT-PCR validation. The qRT-PCR results were consistent with the microarray data.

2.5. CNC网络信息学分析

通过分析上述6个筛选出的mRNA和所有差异LncRNA的CNC共表达关系，构成如下具有高度相关性的CNC共表达网络关系图(图 6)，结果显示，纤维化相关mRNA和大量差异表达的LncRNA存在高度相关性，是一对多的关系。在此图中共有208个结点，其中6个为上述mRNA，其余202个为LncRNA。红色为芯片所有差异表达的LncRNA，绿色为经qPCR验证的差异纤维化相关的mRNA。图中实线表示两个结点内的基因之间存在相关性，虚线则表示两个结点内的基因之间存在负相关性，mRNA和LncRNA对的共表达程度用实线或虚线的长度进行表示。

6.

6

Open in a new tab

6个纤维化相关mRNA和其高度相关的LncRNAs共表达网络图

Co-expression networks of 6 fibrosis-related mRNAs and their highly correlated lncRNAs.

2.6. CNC网络中挑选3个差异表达的LncRNA进行qRT-PCR验证

进一步挑选CNC网络中3个表达差异的LncRNA (NR_040597、AK085645与AK086961)进行qRT-PCR验证，结果显示：与对照组相比，模型组中NR_040597的表达下调，差异具有统计学意义; 与对照组相比，模型组中AK085645的表达下调，差异具有统计学意义; 与对照组相比，模型组中AK086961的表达下调，但差异不具有统计学意义(图 7)。

7.

7

Open in a new tab

qRT-PCR验证挑选的LncRNAs结果

Expression of selected lncRNAs by qRT-PCR. In qRT-PCR validation, a vs control group, P=0.006, t=12.12, n=3; b vs control group, P=0.043, t=4.67, n=3; c vs control group, cP=0.913, t=0.12, n=3.

3. 讨论

LncRNA是长度大于200个核苷酸的非编码RNA，参与广泛的生物过程，从转录到mRNA拼接、RNA衰变和翻译等基因生命周期中几乎每一步都能受到LncRNA的影响[10]。LncRNA具有“一对多”和“多对一”的“枢纽”调节功能，能够在表观遗传调控、转录调控以及转录后调控等众多层面调控基因表达[11-12]。目前研究表明，LncRNA在多个器官纤维化发生发展中发挥着重要作用[13-14]。LncRNA-ATB可通过竞争性结合miR- 425-5p激活活化肝星状细胞，增加胶原I产生来促进HCV诱导的肝纤维发生[15]。心肌细胞中LncRNA GAS5与miR-21相互作用调节抑癌基因磷脂酶和张力蛋白同源物的表达，影响心脏成纤维细胞的增殖[16]。Lnc-TSI通过抑制Smad3的磷酸化阻断TGF-β/Smad3通路活化，从而抑制肾脏纤维化的发生发展[17]。有对人肺纤维化组织样本的研究表明LncRNA-CD99P1与n341773可能通过抑制胶原蛋白的表达影响成纤维细胞的增殖与分化[18]。也有在IL-1β诱导的纤维化细胞模型中研究发现LncRNA-IL7AS与MIR3142HG具有调节纤维化中炎症反应的作用[19]。这表明LncRNA在人肺纤维化中也发挥着一定作用，但目前，LncRNA在肺纤维化疾病中的作用研究较少。因此本研究采用基因芯片检测肺纤维化组和对照组肺组织中LncRNA及mRNA的表达，通过CNC共表达分析计算出与肺纤维化相关mRNA具有相同表达模式的LncRNA，通过这些熟知mRNA的功能，来推导lncRNA的功能，为探讨其在肺纤维化发生发展中的作用提供前期基础[20]。

本研究通过小鼠动物模型进行肺纤维化中差异mRNA/LncRNA的筛选。博来霉素是一种糖肽类抗生素，可引起细胞内单链和双链DNA断裂，进而产生超氧化物和氢氧根自由基，引起炎症反应、肺毒性及肺纤维化[21]。研究表明，气管内注射博来霉素造模更加稳定可靠，且与临床IPF的病理机制更加贴近，因此我们使用气管内注射博来霉素诱导的小鼠肺纤维化模型[22]。研究结果表明模型组小鼠肺组织大面积实变，肺泡间隔增厚，间隔内成纤维细胞增多，肺泡内出血，炎症细胞浸润，和大量蓝色胶原沉积，肺纤维化动物模型复制成功。进一步提取各组肺组织RNA，应用Arraystar小鼠LncRNA芯片V3.0芯片检测筛选mRNA与LncRNA。

筛选结果显示，与对照组相比，模型组中存在差异性表达(FC>2.0倍且P < 0.05)的mRNA共311个，其中表达上调的mRNA 127个，表达下调的有184个，GO分析及pathway分析发现发生变化的基因在生物学功能上主要涉及免疫反应、细胞分化、细胞骨架等, 参与的信号通路主要有细胞因子与细胞因子受体相互作用、趋化因子信号转导通路等。上述的GO分析主要富集的功能，与既往肺纤维化方向研究的主要生物进程高度一致，同时也提示了芯片的可靠性[23-24]。根据第一部分基因GO、Pathway分析的纤维化相关的条目，同时参考肺纤维文献报道的相关基因，挑选代表性的mRNAs，并比对在本芯片中他们的差异变化，观察P值，最终筛选出CCL8、CCL6、CD177、Ndnf、Krtap4-1、Bcl2l15共6个与纤维化相关mRNA。为了验证芯片的可靠性，我们通过qRT-PCR进一步验证纤维化相关mRNA的表达。qRTPCR结果显示6个筛选的纤维化相关mRNA基因变化趋势与与芯片结果高度一致，可用于进一步与差异LncRNA进行CNC生物信息学分析。

结果显示，大量的差异LncRNA和纤维化相关的mRNA具有高度的相关性(相关系数≥0.995，FDR ≤0.01)，为一对多的关系，构成复杂的共表达网络，提示有大量LncRNA可能通过这些基因，参与了肺纤维化的调控。目前对lncRNA在肺纤维化中的研究较少，有部分研究揭示了lncRNA可能通过调控蛋白转录，阻断微小RNA，调节炎症因子如IL6的表达等多种机制发挥作用[18, 25]。因此，我们进一步挑选3条共表达网络中的LncRNA，通过qRT-PCR的验证。结果显示肺纤维化过程中NR_040597与AK085645的表达具有差异性，AK086961的表达不具有差异性。在我们构建的CNC共表达网络关系中，AK085645负调控CCL6、NDNF及CCL8;NR_040597负调控CCL8。CCL6、CCL8属于趋化因子家族中的一员，多个研究表明它们可通过促进白细胞和间充质组细胞的迁移，调节血管生成和血管重构等多种机制参与肺纤维化及其他纤维增生紊乱疾病的进程[26-27]。NDNF属于神经元衍生的神经营养因子，有研究表明其通过调节上皮-间质转分化抑制肿瘤细胞的增殖、迁移与进展[28-29]。上皮-间质转分化又是肺纤维化进展过程中的一个重要机制[30-31]。因此我们推测AK085645极有可能通过负调控CCL6、CCL8与NDNF的转录在肺纤维化中可能发挥着潜在作用，下一步我们将在后继的功能和机制研究中进行验证。

综上所述，本研究采用基因芯片方法对小鼠体内的博来霉素诱发肺纤维化模型和对照组中的mRNA及LncRNA表达进行系统、全面的筛选，建立肺纤维化中差异表达的mRNA/LncRNA表达谱，为后续研究提供重要数据。通过CNC共表达分析计算出肺纤维化相关mRNA与差异表达的LncRNA具有极其密切的共表达网络关系，构建出高度相关的mRNA-LncRNA关系对，将为进一步研究lncRNA在肺纤维化发生发展中的作用提供实验基础。

Biography

喻雪飞，硕士，E-mail: yuxuefeiji@163.com

Funding Statement

广州市科技计划项目(201607010310)；广东省自然科学基金(2014A030313596)

Contributor Information

喻 雪飞 (Xuefei YU), Email: yuxuefeiji@163.com.

李 伟峰 (Weifeng LI), Email: lwf980622@126.com.

References

  • 1.Martinez FJ, Collard HR, Pardo A, et al. Idiopathic pulmonary fibrosis. Nat Rev Dis Primers. 2017;3:17074. doi: 10.1038/nrdp.2017.74. [Martinez FJ, Collard HR, Pardo A, et al. Idiopathic pulmonary fibrosis[J]. Nat Rev Dis Primers, 2017, 3: 17074.] [DOI] [PubMed] [Google Scholar]
  • 2.Wakwaya Y, Brown KK. Idiopathic pulmonary fibrosis: epidemiology, diagnosis andOutcomes. Am J Med Sci. 2019;357(5):359–69. doi: 10.1016/j.amjms.2019.02.013. [Wakwaya Y, Brown KK. Idiopathic pulmonary fibrosis: epidemiology, diagnosis andOutcomes[J]. Am J Med Sci, 2019, 357(5): 359-69.] [DOI] [PubMed] [Google Scholar]
  • 3.Ding Q, Luckhardt T, Hecker L, et al. New insights into the pathogenesis and treatment of idiopathic pulmonary fibrosis. Drugs. 2011;71(8):981–1001. doi: 10.2165/11591490-000000000-00000. [Ding Q, Luckhardt T, Hecker L, et al. New insights into the pathogenesis and treatment of idiopathic pulmonary fibrosis[J]. Drugs, 2011, 71(8): 981-1001.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Richeldi L, Collard H R, Jones M G. Idiopathic pulmonary fibrosis. Lancet. 2017;389(10082):1941–52. doi: 10.1016/S0140-6736(17)30866-8. [Richeldi L, Collard H R, Jones M G. Idiopathic pulmonary fibrosis[J]. Lancet, 2017, 389(10082):1941-52.] [DOI] [PubMed] [Google Scholar]
  • 5.Miao CG, Xiong YY, Zhang GX, et al. MicroRNAs in idiopathic pulmonary fibrosis, new research progress and their pathophysiological implication. Exp Lung Res. 2018;44(3):178–90. doi: 10.1080/01902148.2018.1455927. [Miao CG, Xiong YY, Zhang GX, et al. MicroRNAs in idiopathic pulmonary fibrosis, new research progress and their pathophysiological implication[J]. Exp Lung Res, 2018, 44(3): 178-90.] [DOI] [PubMed] [Google Scholar]
  • 6.Makiguchi T, Yamada M, Yoshioka Y, et al. Serum extracellular vesicular miR-21-5p is a predictor of the prognosis in idiopathic pulmonary fibrosis. Respir Res. 2016;17(1):110. doi: 10.1186/s12931-016-0427-3. [Makiguchi T, Yamada M, Yoshioka Y, et al. Serum extracellular vesicular miR-21-5p is a predictor of the prognosis in idiopathic pulmonary fibrosis[J]. Respir Res, 2016, 17(1): 110.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Beermann J, Piccoli MT, Viereck J, et al. Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches. Physiol Rev. 2016;96(4):1297–325. doi: 10.1152/physrev.00041.2015. [Beermann J, Piccoli MT, Viereck J, et al. Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches[J]. Physiol Rev, 2016, 96(4): 1297-325.] [DOI] [PubMed] [Google Scholar]
  • 8.Wapinski O, Chang HY. Long noncoding RNAs and human disease. Trends Cell Biol. 2011;21(6):354–61. doi: 10.1016/j.tcb.2011.04.001. [Wapinski O, Chang HY. Long noncoding RNAs and human disease[J]. Trends Cell Biol, 2011, 21(6): 354-61.] [DOI] [PubMed] [Google Scholar]
  • 9.Vafadar A, Shabaninejad Z, Movahedpour A, et al. Long non-coding RNAs as epigenetic regulators in cancer. Curr Pharm Des. 2019;25(33):3563–77. doi: 10.2174/1381612825666190830161528. [Vafadar A, Shabaninejad Z, Movahedpour A, et al. Long non-coding RNAs as epigenetic regulators in cancer[J]. Curr Pharm Des, 2019, 25(33): 3563-77.] [DOI] [PubMed] [Google Scholar]
  • 10.Jarroux J, Morillon A, Pinskaya M. History, Discovery, and Classification of lncRNAs. Adv Exp Med Biol. 2017;1008:1–46. doi: 10.1007/978-981-10-5203-3_1. [Jarroux J, Morillon A, Pinskaya M. History, Discovery, and Classification of lncRNAs[J]. Adv Exp Med Biol, 2017, 1008: 1-46.] [DOI] [PubMed] [Google Scholar]
  • 11.Wang K C, Chang H Y. Molecular mechanisms of long noncoding RNAs. Mol Cell. 2011;43(6):904–14. doi: 10.1016/j.molcel.2011.08.018. [Wang K C, Chang H Y. Molecular mechanisms of long noncoding RNAs[J]. Mol Cell, 2011, 43(6): 904-14.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Schaukowitch K, Kim TK. Emerging epigenetic mechanisms of long non-coding RNAs. Neuroscience. 2014;264:25–38. doi: 10.1016/j.neuroscience.2013.12.009. [Schaukowitch K, Kim TK. Emerging epigenetic mechanisms of long non-coding RNAs[J]. Neuroscience, 2014, 264: 25-38.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Zheng W, Chen C, Chen S, et al. Integrated analysis of long non- coding RNAs and mRNAs associated with peritendinous fibrosis. JAdv Res. 2019;15:49–58. doi: 10.1016/j.jare.2018.08.001. [Zheng W, Chen C, Chen S, et al. Integrated analysis of long non- coding RNAs and mRNAs associated with peritendinous fibrosis[J]. JAdv Res, 2019, 15: 49-58.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Riaz F, Li DM. Non-coding RNA associated competitive endogenous RNAregulatory network: novel therapeutic approach in liver fibrosis. Curr Gene Ther. 2019;19(5):305–17. doi: 10.2174/1566523219666191107113046. [Riaz F, Li DM. Non-coding RNA associated competitive endogenous RNAregulatory network: novel therapeutic approach in liver fibrosis[J]. Curr Gene Ther, 2019, 19(5): 305-17.] [DOI] [PubMed] [Google Scholar]
  • 15.Tao H, Zhang JG, Qin RH, et al. LncRNA GAS5 controls cardiac fibroblast activation and fibrosis by targeting miR-21 via PTEN/ MMP-2 signaling pathway. Toxicology. 2017;386:11–8. doi: 10.1016/j.tox.2017.05.007. [Tao H, Zhang JG, Qin RH, et al. LncRNA GAS5 controls cardiac fibroblast activation and fibrosis by targeting miR-21 via PTEN/ MMP-2 signaling pathway[J]. Toxicology, 2017, 386: 11-8.] [DOI] [PubMed] [Google Scholar]
  • 16.Fu N, Niu XM, Wang Y, et al. Role of LncRNA-activated by transforming growth factor beta in the progression of hepatitis C virus-related liver fibrosis. Discov Med. 2016;22(119):29–42. [Fu N, Niu XM, Wang Y, et al. Role of LncRNA-activated by transforming growth factor beta in the progression of hepatitis C virus-related liver fibrosis[J]. Discov Med, 2016, 22(119): 29-42.] [PubMed] [Google Scholar]
  • 17.Wang P, Luo ML, Song EW, et al. Long noncoding RNA lnc-TSI inhibits renal fibrogenesis by negatively regulating the TGF-β/ Smad3 pathway. Sci Transl Med. 2018;10(462):eaat2039. doi: 10.1126/scitranslmed.aat2039. [Wang P, Luo ML, Song EW, et al. Long noncoding RNA lnc-TSI inhibits renal fibrogenesis by negatively regulating the TGF-β/ Smad3 pathway[J]. Sci Transl Med, 2018, 10(462): eaat2039.] [DOI] [PubMed] [Google Scholar]
  • 18.Huang CQ, Yang Y, Liu L. Interaction of long noncoding RNAs and microRNAs in the pathogenesis of idiopathic pulmonary fibrosis. Physiol Genomics. 2015;47(10):463–9. doi: 10.1152/physiolgenomics.00064.2015. [Huang CQ, Yang Y, Liu L. Interaction of long noncoding RNAs and microRNAs in the pathogenesis of idiopathic pulmonary fibrosis[J]. Physiol Genomics, 2015, 47(10): 463-9.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Hadjicharalambous MR, Roux BT, Feghali-Bostwick CA, et al. Long Non-coding RNAs are central regulators of the IL-1β-induced inflammatory response in normal and idiopathic pulmonary lung fibroblasts. Front Immunol. 2018;11(9):2906. doi: 10.3389/fimmu.2018.02906. [Hadjicharalambous MR, Roux BT, Feghali-Bostwick CA, et al. Long Non-coding RNAs are central regulators of the IL-1β-induced inflammatory response in normal and idiopathic pulmonary lung fibroblasts[J]. Front Immunol, 2018, 11(9): 2906.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Zhang WY, Dong R, Diao S, et al. Differential long noncoding RNA/ mRNA expression profiling and functional network analysis during osteogenic differentiation of human bone marrow mesenchymal stem cells. Stem Cell Res Ther. 2017;8(1):30. doi: 10.1186/s13287-017-0485-6. [Zhang WY, Dong R, Diao S, et al. Differential long noncoding RNA/ mRNA expression profiling and functional network analysis during osteogenic differentiation of human bone marrow mesenchymal stem cells[J]. Stem Cell Res Ther, 2017, 8(1): 30.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Della Latta V, Cecchettini A, Del Ry S, et al. Bleomycin in the setting of lung fibrosis induction: From biological mechanisms to counteractions. Pharmacol Res. 2015;97:122–30. doi: 10.1016/j.phrs.2015.04.012. [Della Latta V, Cecchettini A, Del Ry S, et al. Bleomycin in the setting of lung fibrosis induction: From biological mechanisms to counteractions[J]. Pharmacol Res, 2015, 97: 122-30.] [DOI] [PubMed] [Google Scholar]
  • 22.李 伟峰, 胡 玉洁, 袁 伟锋, et al. 气管内滴入与雾化博莱霉素致小鼠肺纤维化模型的比较研究. http://www.j-smu.com:81/CN/Y2012/V32/I02/221. 南方医科大学学报. 2012;32(2):221–5. [李伟峰, 胡玉洁, 袁伟锋, 等.气管内滴入与雾化博莱霉素致小鼠肺纤维化模型的比较研究[J].南方医科大学学报, 2012, 32(2): 221-5.] [PubMed] [Google Scholar]
  • 23.Zhou Y, He L, Liu XD, et al. Integrated analysis of lncRNA and mRNA transcriptomes reveals new regulators of ubiquitination and the immune response in silica-induced pulmonary fibrosis. Biomed Res Int. 2019;2019:6305065. doi: 10.1155/2019/6305065. [Zhou Y, He L, Liu XD, et al. Integrated analysis of lncRNA and mRNA transcriptomes reveals new regulators of ubiquitination and the immune response in silica-induced pulmonary fibrosis[J]. Biomed Res Int, 2019, 2019: 6305065.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Wang LJ, Huang W, Zhang LM, et al. Molecular pathogenesis involved in human idiopathic pulmonary fibrosis based on an integrated microRNA-mRNA interaction network. Mol Med Rep. 2018;18(5):4365–73. doi: 10.3892/mmr.2018.9456. [Wang LJ, Huang W, Zhang LM, et al. Molecular pathogenesis involved in human idiopathic pulmonary fibrosis based on an integrated microRNA-mRNA interaction network[J]. Mol Med Rep, 2018, 18(5): 4365-73.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Hadjicharalambous MR, Lindsay MA. Idiopathic pulmonary fibrosis: pathogenesis and the emerging role of long non-coding RNAs. Int J Mol Sci. 2020;21(2):E524. doi: 10.3390/ijms21020524. [Hadjicharalambous MR, Lindsay MA. Idiopathic pulmonary fibrosis: pathogenesis and the emerging role of long non-coding RNAs[J]. Int J Mol Sci, 2020, 21(2): E524.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Lee JU, Cheong HS, Shim EY, et al. Gene profile of fibroblasts identify relation of CCL8 with idiopathic pulmonary fibrosis. Respir Res. 2017;18(1):3. doi: 10.1186/s12931-016-0493-6. [Lee JU, Cheong HS, Shim EY, et al. Gene profile of fibroblasts identify relation of CCL8 with idiopathic pulmonary fibrosis[J]. Respir Res, 2017, 18(1): 3.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Belperio JA, Dy M, Burdick MD, et al. Interaction of IL-13 and C10 in the pathogenesis of bleomycin-induced pulmonary fibrosis. Am J Respir Cell Mol Biol. 2002;27(4):419–27. doi: 10.1165/rcmb.2002-0009OC. [Belperio JA, Dy M, Burdick MD, et al. Interaction of IL-13 and C10 in the pathogenesis of bleomycin-induced pulmonary fibrosis[J]. Am J Respir Cell Mol Biol, 2002, 27(4): 419-27.] [DOI] [PubMed] [Google Scholar]
  • 28.Xia Lingling, Shi, et al. NDNF inhibits the migration and invasion of human renal cancer cells through epithelial-mesenchymal transition. Oncol Lett. 2019;17(3):2969–75. doi: 10.3892/ol.2019.9937. [Lingling, Xia, Shi, et al. NDNF inhibits the migration and invasion of human renal cancer cells through epithelial-mesenchymal transition.[J]. Oncol Lett, 2019, 17(3): 2969-75.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Ohashi K, Enomoto T, Joki Y, et al. Neuron-derived neurotrophic factor functions as a novel modulator that enhances endothelial cell function and revascularization processes. J Biol Chem. 2014;289(20):14132–44. doi: 10.1074/jbc.M114.555789. [Ohashi K, Enomoto T, Joki Y, et al. Neuron-derived neurotrophic factor functions as a novel modulator that enhances endothelial cell function and revascularization processes[J]. J Biol Chem, 2014, 289 (20): 14132-44.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.杨 静, 胡 华钟, 张 书勤, et al. 脐带间充质干细胞来源的外泌体通过抑制上皮间质转化缓解肺纤维化. http://www.j-smu.com:81/CN/Y2020/V40/I07/988. 南方医科大学学报. 2020;40(7):988–94. doi: 10.12122/j.issn.1673-4254.2020.07.11. [杨静, 胡华钟, 张书勤, 等.脐带间充质干细胞来源的外泌体通过抑制上皮间质转化缓解肺纤维化[J].南方医科大学学报, 2020, 40(7): 988-94.] [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Ohbayashi M, Kubota S, Kawase A, et al. Involvement of epithelialmesenchymal transition in methotrexate-induced pulmonary fibrosis. J Toxicol Sci. 2014;39(2):319–30. doi: 10.2131/jts.39.319. [Ohbayashi M, Kubota S, Kawase A, et al. Involvement of epithelialmesenchymal transition in methotrexate-induced pulmonary fibrosis[J]. J Toxicol Sci, 2014, 39(2): 319-30.] [DOI] [PubMed] [Google Scholar]

Articles from Journal of Southern Medical University are provided here courtesy of Editorial Department of Journal of Southern Medical University

RESOURCES