Integrative analysis of an lncRNA‑associated competing endogenous RNA network in human trabecular meshwork cells under oxidative stress
- Authors:
- Published online on: January 21, 2020 https://doi.org/10.3892/mmr.2020.10955
- Pages: 1606-1614
-
Copyright: © Yao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Glaucoma is the leading cause of irreversible blindness and is characterized by the progressive degeneration of the optic nerve and loss of the visual field (1). A total of 76 million and 111.8 million individuals worldwide are estimated to have glaucoma by 2020 and 2040, respectively (2,3). Increased intraocular pressure (IOP) has been identified as the main risk factor for glaucoma (4). IOP is maintained by the aqueous humor, which is produced by the ciliary epithelium; the aqueous humor enters into the posterior chamber around the lens and iris, and then goes through the trabecular meshwork (TM) to the Schlemm's canal, eventually entering the episcleral venous circulation via the conventional route (5). The TM presents as a porous, sponge-like structure, and is composed of strands or beams of connective tissues that contain a core of collagenous and elastic fibers, and is covered by flat cells (6). Since the TM is an important structure in the aqueous humor outflow tract, it plays a major role in maintaining IOP balance. Variations in IOP are usually a result of changes in the physiological status of the TM (6).
Reactive oxygen species (ROS) is a collective term used to define oxygen radicals such as superoxide and non-radicals such as hydrogen peroxide (H2O2) (7). Excessive ROS can not only cause direct damage to the DNA, protein and lipids, but it can also lead to oxidative stress via the activation of various signaling pathways, such as the mitogen-activated protein kinase (MAPK), PI3K/AKT, NF-κB and p53 signaling pathways (8). These pathways play important roles in determining cell death and survival (8). Although there are multiple hypotheses, oxidative stress has been widely accepted as the pathological mechanism of glaucoma (9). Exposure of eyes to sunlight can result in the acceleration of the generation of ROS due to ultraviolet radiation (7), while also resulting in oxidative stress to the ocular structures (10). Previous research has also demonstrated that oxidative stress can result in pathological alteration of the anterior chamber and TM (10). The TM has shown greater sensitivity to oxidative damage compared to other ocular tissues, possibly due to the lack of an effective antioxidant mechanism in the TM (9). Furthermore, oxidative DNA damage has been shown to be significantly higher in the TM of glaucoma patients (11). It has been hypothesized that repetitive oxidative stress due to in vivo H2O2 can impair TM cell adhesion and cause cell loss, resulting in elevated resistance to aqueous humor outflow (12). Thus, oxidative stress is an important mechanism that regulates TM pathology.
Long non-coding RNAs (lncRNAs) are a group of non-coding transcripts of >200 nucleotides in length (13). lncRNAs form the largest percentage of mammalian non-coding transcriptomes (13). lncRNAs play a significant role in regulating gene expression, protein modification, cell differentiation, immune response and other critical biochemical pathways (14,15). Studies have identified roles for lncRNAs in several neurodegenerative diseases, such as Alzheimer's, Parkinson's and Huntington's disease (16–19). In 2011, a hypothesis was proposed, which suggested that lncRNAs were microRNA (miRNA) sponges that suppressed miRNA interactions with mRNAs and affected translation of protein-coding genes (20). The study referred to the miRNA sponges as competing endogenous RNAs (ceRNAs). Given this lncRNA-miRNA-mRNA regulation network, in the present study a ceRNA network that further describes the interactions of these RNAs was elucidated.
Over the years, there have been extensive studies into the effects of lncRNAs on various types of diseases (19,21). Nevertheless, the effects of the lncRNA-associated ceRNA network on human trabecular meshwork cells (HTMCs) under oxidative stress have not yet been completely described. In this study, the aim was to analyze the effects of a lncRNA-associated ceRNA network on HTMCs under oxidative stress.
Materials and methods
Tissue samples from Gene Expression Omnibus (GEO) database and bioinformatics analysis
The RNA expression data were downloaded from the NCBI GEO database (GSE126170). The series contained three HTMC samples that were treated with 300 µm H2O2 in serum-free medium for 2 h and three control samples that were treated with vehicle. Agilent-078298 human ceRNA array V1.0 4X180K (Agilent Technologies, Inc.) was utilized to perform all the treatments. Approval from an ethics committee was not required as the data were downloaded from the GEO database.
Analysis of differentially expressed genes
The limma package in the R software (version 3.5.2; www.R-project.org) (22) was used to identify differentially expressed lncRNAs and mRNAs with |log2 Fold Change (FC)| thresholds >1.0 and adjusted P<0.05 in the treatment and control groups. The volcano map was obtained using the pheatmap package in the R software (version 3.5.2; www.R-project.org). The Encyclopedia of DNA Elements (version 38; http://www.encodeproject.org/) and Ensembl (version 96; htps://www.ensembl.org/) were used in order to define and annotate the differentially expressed RNAs (mRNAs and lncRNAs).
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis
In order to explore the underlying biological functions and processes of these differentially expressed genes, the GO database (http://www.geneontology.org), KEGG (http://www.kegg.jp/), and the clusterProfilerGOpackage and clusterProfilerKEGG packages in R software (version 3.5.2; www.R-project.org) (23) were used to perform functional enrichment analysis. Records with P<0.05 and enrichment >2.0 were defined as ‘conserved’.
Construction of the lncRNA-miRNA-mRNA ceRNA networks
The miRNA targets of lncRNAs were predicted using the mircode database (version 11; http://www.mircode.org/) (24) and mRNA targets of miRNAs using miRTarBase (version 7.0; http://mirtarbase.mbc.nctu.edu.tw/) (25), miRDB (version 5.0, http://mirdb.org/) (26) and TargetScan (version 7.2; http://www.targetscan.org/) (27). Targeted mRNAs were cross-matched with differentially expressed mRNAs. The lncRNAs, miRNAs and mRNAs with |log2FC| >1.0 and P<0.05 were recorded. Finally, a lncRNA-associated ceRNA network was constructed and visualized using Cytoscape v3.6.1 (28).
Results
Differential expression of lncRNAs induced by oxidative stress in HTMCs
A total of 70 differentially expressed lncRNAs were discovered from HTMCs treated with 300 µm H2O2 in serum-free medium for 2 h and HTMCs treated with vehicle and having |log2FC| >1 and adjusted P<0.05 (Table SI). Of these differentially expressed lncRNAs, 24 were part of the ceRNA network that was constructed using the following steps (Table I). The volcano map of differentially expressed lncRNAs and mRNAs was obtained from the limma package and pheatmap package in the R software (Fig. 1A). The heatmap of differentially expressed lncRNAs showed clear differences in expression between the two groups (Fig. 1B).
GO enrichment and KEGG pathway analysis of differentially expressed mRNAs
As shown in the heatmap in Fig. 2, a total of 518 differentially expressed mRNAs were identified. In order to explore the functions and signaling pathways of these genes GO and KEGG analyses were carried out. A total of 6 GO enrichment terms were listed (Fig. 3A) and the functions that were the most enriched were receptor ligand activity (GO: 0048018), protein heterodimerization activity (GO: 0046982) and growth factor activity (GO: 0008083) in 32, 21 and 20 genes, respectively. As shown in the bubble chart in Fig. 3B, 10 KEGG pathways were also identified. Among the KEGG pathways, cytokine-cytokine receptor interaction, transcriptional misregulation in cancer and the MAPK signaling pathway ranked top 3, and were implicated in 22, 19 and 19 genes, respectively.
Construction of ceRNA network
To further understand how lncRNAs and miRNAs regulate mRNA in HTMCs under oxidative stress, an lncRNA-miRNA-mRNA network was designed. Using mircode, 24 miRNAs that interacted with 24 lncRNAs were predicted (Table II). It was found that the lncRNA, nuclear enriched abundant transcript 1 (NEAT1), was downregulated the most and was predicted to interact with 21 miRNAs. A total of 1,228 mRNA targets of these 24 miRNAs were predicted using miRDB, miRTarBase and TargetScan databases. miRNA targets of mRNAs that were not included in the 518 differentially expressed mRNAs were discarded and 40 mutual mRNAs were preserved (Fig. 4A; Table III). Among the mRNAs that were preserved, the expressions of 27 mRNAs, including ABL2, activity regulated cytoskeleton associated protein, fos proto-oncogene, AP-1 transcription factor subunit and phorbol-12-myristate-13-acetate-induced protein 1 were upregulated, and that of 13 mRNAs, including dachshund family transcription factor 1, KIAA1147, membrane associated guanylate kinase, WW and PDZ domain containing 1, and SOX4, were downregulated (Table SII). Moreover, the GO enrichment analysis of mRNAs that were a part of the ceRNA network demonstrated the role of transcription factor activity, RNA polymerase II transcription factor binding, core promoter sequence-specific DNA binding and pattern binding (Fig. 4B). Finally, based on these lncRNA-miRNA and miRNA-mRNA interactions, a ceRNA network containing 24 lncRNAs, 24 miRNAs and 40 mRNAs was constructed and visualized using Cytoscape v3.7.0 (Fig. 5).
Discussion
lncRNAs can act as ceRNA and suppress miRNA function, thus preventing miRNAs from binding to and interacting with target mRNAs (20). In the present analysis, 24 dysregulated lncRNAs that have miRNA targets were identified. Among these, NEAT1 was downregulated the most, and was predicted to interact with 21 miRNAs. NEAT1 plays an important role in the formation of nuclear structure (29). A previous study suggested that the downregulation of NEAT1 lowers cell viability in hepatocellular carcinoma and esophageal squamous cell carcinoma by serving as a ceRNA and influencing the expression of miR-129-5p (30,31). Another study showed that NEAT1 regulates the miR-107-mediated expression of cyclin-dependent kinase 6 in laryngeal squamous cell cancer (32). A number of studies have demonstrated that the expression of NEAT1 can be activated by NF-κB and p53, and activate PI3K/AKT pathways (33–35). However, the function of NEAT1 in the pathogenesis of glaucoma has not yet been investigated. Apart from directly participating in biological processes, lncRNAs play an important role in various signal pathways, such as NF-κB, PI3K/AKT, Notch and Wnt/β-catenin (36–40). Multiple types of NF-κB-associated lncRNAs, including NEAT1, have been reported to interact with different sites in the NF-κB/IκB complex in order to orchestrate opposite effects. For example, the interaction of NF-κB with NF-κB interacting lncRNA (NKILA) can result in the binding and inhibition of the phosphorylation site of IκB, thus suppressing NF-κB activation (37). NKILA can, in turn, be upregulated by NF-κB (37). p50-associated cyclooxygenase-2 extragenic RNA enhances NF-κB signaling by combining with free p50 to decrease the concentration of p50-p50 heterodimers and increase the concentration of p65-p50 heterodimers (40). lncRNA AK023948 was found to stabilize p85 by interacting with p85 and DExH-box helicase 9, as well as elevating AKT activity in the PI3K/AKT pathway (38).
miRNAs, such as miR-107, miR-137, miR-22 and miR-590, have been reported to regulate several biochemical pathways in glaucoma (41). HTMCs injured by oxidative stress can release miR-107, miR-149, miR-21 and miR-450 into the aqueous humor, which then travels via the uveoscleral pathway to the peripapillary retina, thus resulting in the transmission of damaging signals to the optic nerve (42). miR-27a was demonstrated to play a critical role in cell proliferation, differentiation and apoptosis by regulating f-box and WD repeat domain containing 7 (43). The upregulation of miR-24 can be a limitation in the activation of transforming growth factor β (TGFβ)1 under mechanical stress, which can in turn contribute to the pathogenesis of glaucoma (44). Additionally, miR-23-27-24 gene clusters can enhance angiogenesis (45) and inhibit neuronal apoptosis by suppressing the expression of apoptotic peptidase activating factor 1 (46). These miRNAs were detected in the present analysis, suggesting that miRNAs in the ceRNA network play an important role in the pathological changes associated with glaucoma.
The functional enrichment of differentially expressed mRNAs was mainly associated with the activities of cytokines and cytokine receptors. Previously, it was demonstrated that the TGFβ pathway plays an important role in the regulation of aqueous humor by promoting the expression of genes related to cell contractility and senescence of TM (47). These processes are coordinated via numerous signaling molecules and crosstalk communication between various pathways, including MAPK, JNKs and Rho GTPase signaling pathways (47). Other pathways, such as the brain-derived neurotrophic factor, JNK, PI3K/AKT, PTEN, Bcl-2, caspase and calcium-calpain pathways have been shown to participate in the pathological process of glaucoma (48). Consistent with previous studies, the pathway analysis that was performed also demonstrated an enrichment of these cytokine-cytokine receptor activities and the MAPK pathway, thus indicating the effect of these signaling pathways in the progression of glaucoma.
The majority of lncRNAs target more than one miRNA, and miRNAs target a number of mRNAs as well (Tables II and III). For example, lncRNAs such as NEAT1, AC005532, AC006026, AC024560, AC025171, AC073072, AP006216, C21orf91-OT1 and small nucleolar RNA host gene 3 all interact with more than 6 miRNAs (Table II), implying the complex interaction between these genes in biological processes. There is a similar relationship between miRNAs and mRNAs, such as miR-125a-5p, miR-17-5p, miR-20b-5p, miR-27a-3p, miR-301b-3p and miR-429, thus creating a comprehensive network of lncRNA-miRNA-mRNA. By establishing the ceRNA network, associated genes were linked together.
The most crucial metabolic process in the body is the utilization of molecular oxygen and the formation of adenosine triphosphate. ROS are generated from the metabolic pathways, such as the tricarboxylic acid cycle and the respiratory chain. Oxidative stress occurs once ROS overloads in the tissue (7). H2O2 was used to mimic the oxidative stress condition in glaucoma (12). The interplay of lncRNAs and signaling pathways has been partly demonstrated in previous studies. When oxidative stress occurs, PI3K/Akt, NF-κB and p53 signaling pathways are directly activated by ROS, thus causing damage to ocular structures (7,9). The ROS regulate lncRNAs, and some lncRNAs influence these pathways (33–40). A total of 70 differentially expressed lncRNAs were obtained, of which 24 participate in the lncRNA-miRNA-mRNA interaction network. As a ‘sponge’ of miRNA, lncRNAs play an important role in this network.
A total of 24 lncRNAs, 24 miRNAs and 40 mRNAs made up the ceRNA network. As stated before, the functions and associations of some remarkable RNAs have been discussed. lncRNA could not only have effects on various signaling pathways, but also combine with miRNAs to regulate the expression of mRNA, which might lead to morphotype or property changes of TM under oxidative stress. By constructing the ceRNA network, it was possible to clearly demonstrate the complex association between these RNAs and different signaling pathways. Thus, this contributed to the understanding of their interactions and the pathological processes of TM under oxidative stress. Once a typical association to explain pathogenesis is discovered, a new treatment theory for glaucoma could be found.
There were also limitations in the present analysis. Due to the value of HTMCs, the sample size was not large. This also limits the ability to carry out cell experiments to confirm lncRNA function and lncRNA-miRNA-mRNA interactions. There are plans to conduct further experiments, providing that HTMCs are available, in order to explore the role of lncRNA-associated ceRNA networks in glaucoma.
To conclude, the differential expression profiles of lncRNAs were demonstrated, and a lncRNA-associated ceRNA network was constructed in HTMCs under oxidative stress. This could contribute to finding a new pathological mechanism or a potential therapeutic target for glaucoma.
Supplementary Material
Supporting Data
Acknowledgements
Not applicable.
Funding
No funding was received.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
YY, KY and HZ conceived and designed the study. KY, YY, PJ, CD and FL contributed analysis tools and carried out the data analysis. YY, KY and CD wrote the paper. KY, YY and FL reviewed and edited the manuscript. All authors read and approved the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
|
lncRNA |
long non-coding RNA |
|
miRNA |
microRNA |
|
ceRNA |
competing endogenous RNA |
|
HTMC |
human trabecular meshwork cell |
|
IOP |
intraocular pressure |
|
TM |
trabecular meshwork |
|
ROS |
reactive oxygen species |
|
MAPK |
mitogen-activated protein kinase |
|
GO |
Gene Ontology |
|
KEGG |
Kyoto Encyclopedia of Genes and Genomes |
References
|
Quigley HA: Glaucoma. Lancet. 377:1367–1377. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Quigley HA and Broman AT: The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 90:262–267. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Tham YC, Li X, Wong TY, Quigley HA, Aung T and Cheng CY: Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology. 121:2081–2090. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Weinreb RN, Aung T and Medeiros FA: The pathophysiology and treatment of glaucoma: A review. JAMA. 311:1901–1911. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
McDougal DH and Gamlin PD: Autonomic control of the eye. Compr Physiol. 5:439–473. 2015.PubMed/NCBI | |
|
Braunger BM, Fuchshofer R and Tamm ER: The aqueous humor outflow pathways in glaucoma: A unifying concept of disease mechanisms and causative treatment. Eur J Pharm Biopharm. 95:173–181. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Bayir H: Reactive oxygen species. Crit Care Med. 33 (12 Suppl):S498–S501. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Martindale JL and Holbrook NJ: Cellular response to oxidative stress: Signaling for suicide and survival. J Cell Physiol. 192:1–15. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Izzotti A, Sacca SC, Longobardi M and Cartiglia C: Sensitivity of ocular anterior chamber tissues to oxidative damage and its relevance to the pathogenesis of glaucoma. Invest Ophthalmol Vis Sci. 50:5251–5258. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Sacca SC, Gandolfi S, Bagnis A, Manni G, Damonte G, Traverso CE and Izzotti A: From DNA damage to functional changes of the trabecular meshwork in aging and glaucoma. Ageing Res Rev. 29:26–41. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Izzotti A, Sacca SC, Cartiglia C and De Flora S: Oxidative deoxyribonucleic acid damage in the eyes of glaucoma patients. Am J Med. 114:638–646. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Zhou L, Li Y and Yue BY: Oxidative stress affects cytoskeletal structure and cell-matrix interactions in cells from an ocular tissue: The trabecular meshwork. J Cell Physiol. 180:182–189. 1999. View Article : Google Scholar : PubMed/NCBI | |
|
Mercer TR, Dinger ME and Mattick JS: Long non-coding RNAs: Insights into functions. Nature reviews. Genetics. 10:155–159. 2009.PubMed/NCBI | |
|
Caley DP, Pink RC, Trujillano D and Carter DR: Long noncoding RNAs, chromatin, and development. ScientificWorldJournal. 10:90–102. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Akhade VS, Pal D and Kanduri C: Long noncoding RNA: Genome organization and mechanism of action. Adv Exp Med Biol. 1008:47–74. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Zhou X and Xu J: Identification of Alzheimer's disease-associated long noncoding RNAs. Neurobiol Aging. 36:2925–2931. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Gstir R, Schafferer S, Scheideler M, Misslinger M, Griehl M, Daschil N, Humpel C, Obermair GJ, Schmuckermair C, Striessnig J, et al: Generation of a neuro-specific microarray reveals novel differentially expressed noncoding RNAs in mouse models for neurodegenerative diseases. RNA. 20:1929–1943. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Xie Y, Hayden MR and Xu B: BDNF overexpression in the forebrain rescues Huntington's disease phenotypes in YAC128 mice. J Neurosci. 30:14708–14718. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Wan P, Su W and Zhuo Y: The role of long noncoding RNAs in neurodegenerative diseases. Mol Neurobiol. 54:2012–2021. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Salmena L, Poliseno L, Tay Y, Kats L and Pandolfi PP: A ceRNA hypothesis: The Rosetta Stone of a hidden RNA language? Cell. 146:353–358. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Huarte M: The emerging role of lncRNAs in cancer. Nat Med. 21:1253–1261. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI | |
|
Yu G, Wang LG, Han Y and He QY: clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS. 16:284–287. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Jeggari A, Marks DS and Larsson E: miRcode: A map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics. 28:2062–2063. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, et al: miRTarBase update 2018: A resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 46:D296–D302. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Wong N and Wang X: miRDB: An online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 43((Database Issue)): D146–D152. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Agarwal V, Bell GW, Nam JW and Bartel DP: Predicting effective microRNA target sites in mammalian mRNAs. Elife. 4:e050052015. View Article : Google Scholar : | |
|
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Sunwoo H, Dinger ME, Wilusz JE, Amaral PP, Mattick JS and Spector DL: MEN epsilon/beta nuclear-retained non-coding RNAs are up-regulated upon muscle differentiation and are essential components of paraspeckles. Genome Res. 19:347–359. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Fang L, Sun J, Pan Z, Song Y, Zhong L, Zhang Y, Liu Y, Zheng X and Huang P: Long non-coding RNA NEAT1 promotes hepatocellular carcinoma cell proliferation through the regulation of miR-129-5p-VCP-IκB. Am J Physiol Gastrointest Liver Physiol. 313:G150–G156. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Li Y, Chen D, Gao X, Li X and Shi G: LncRNA NEAT1 regulates cell viability and invasion in esophageal squamous cell carcinoma through the miR-129/CTBP2 axis. Dis Markers. 2017:53146492017. View Article : Google Scholar : PubMed/NCBI | |
|
Wang P, Wu T, Zhou H, Jin Q, He G, Yu H, Xuan L, Wang X, Tian L, Sun Y, et al: Long noncoding RNA NEAT1 promotes laryngeal squamous cell cancer through regulating miR-107/CDK6 pathway. J Exp Clin Cancer Res. 35:222016. View Article : Google Scholar : PubMed/NCBI | |
|
Cheng N and Guo Y: Long noncoding RNA NEAT1 promotes nasopharyngeal carcinoma progression through regulation of miR-124/NF-κB pathway. Onco Targets Ther. 10:5843–5853. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Fuschi P, Carrara M, Voellenkle C, Garcia-Manteiga JM, Righini P, Maimone B, Sangalli E, Villa F, Specchia C, Picozza M, et al: Central role of the p53 pathway in the noncoding-RNA response to oxidative stress. Aging (Albany NY). 9:2559–2586. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Xu H, Li J and Zhou ZG: NEAT1 promotes cell proliferation in multiple myeloma by activating PI3K/AKT pathway. Eur Rev Med Pharmacol Sci. 22:6403–6411. 2018.PubMed/NCBI | |
|
Zhang A, Xu M and Mo YY: Role of the lncRNA-p53 regulatory network in cancer. J Mol Cell Biol. 6:181–191. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Liu B, Sun L, Liu Q, Gong C, Yao Y, Lv X, Lin L, Yao H, Su F, Li D, et al: A cytoplasmic NF-κB interacting long noncoding RNA blocks IκB phosphorylation and suppresses breast cancer metastasis. Cancer Cell. 27:370–381. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Koirala P, Huang J, Ho TT, Wu F, Ding X and Mo YY: LncRNA AK023948 is a positive regulator of AKT. Nat Commun. 8:144222017. View Article : Google Scholar : PubMed/NCBI | |
|
Trimarchi T, Bilal E, Ntziachristos P, Fabbri G, Dalla-Favera R, Tsirigos A and Aifantis I: Genome-wide mapping and characterization of Notch-regulated long noncoding RNAs in acute leukemia. Cell. 158:593–606. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Peng WX, Koirala P and Mo YY: LncRNA-mediated regulation of cell signaling in cancer. Oncogene. 36:5661–5667. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Romano GL, Platania CB, Forte S, Salomone S, Drago F and Bucolo C: MicroRNA target prediction in glaucoma. Prog Brain Res. 220:217–240. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Izzotti A, Ceccaroli C, Longobardi MG, Micale RT, Pulliero A, La Maestra S and Saccà SC: Molecular damage in glaucoma: From anterior to posterior eye segment. The MicroRNA role. Microrna. 4:3–17. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Wang L, Ye X, Liu Y, Wei W and Wang Z: Aberrant regulation of FBW7 in cancer. Oncotarget. 5:2000–2015. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Luna C, Li G, Qiu J, Epstein DL and Gonzalez P: MicroRNA-24 regulates the processing of latent TGFβ1 during cyclic mechanical stress in human trabecular meshwork cells through direct targeting of FURIN. J Cell Physiol. 226:1407–1414. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Zhou Q, Gallagher R, Ufret-Vincenty R, Li X, Olson EN and Wang S: Regulation of angiogenesis and choroidal neovascularization by members of microRNA-23~27~24 clusters. Proc Natl Acad Sci USA. 108:8287–8292. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Chen Q, Xu J, Li L, Li H, Mao S, Zhang F, Zen K, Zhang CY and Zhang Q: MicroRNA-23a/b and microRNA-27a/b suppress Apaf-1 protein and alleviate hypoxia-induced neuronal apoptosis. Cell Death Dis. 5:e11322014. View Article : Google Scholar : PubMed/NCBI | |
|
Pervan CL: Smad-independent TGF-β2 signaling pathways in human trabecular meshwork cells. Exp Eye Res. 158:137–145. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Gauthier AC and Liu J: Epigenetics and signaling pathways in glaucoma. Biomed Res Int. 2017:57123412017. View Article : Google Scholar : PubMed/NCBI |
