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image of Investigation of LncRNA Expression Profiles and Analysis of Immune-Related lncRNA-miRNA-mRNA Networks in Neovascular Age-Related Macular Degeneration

Abstract

Introduction

Age-related Macular Degeneration (AMD) is a predominant cause of blindness in the elderly. The present study is the first to investigate the alteration of lncRNAs and mRNAs in neovascular AMD.

Methods

Nine patients with neovascular AMD were included in the study. The control group comprised seven patients with epiretinal membranes. RNA sequencing was performed to obtain the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs). Then, the DElncRNA-DEmRNA co-expression network, ceRNA network, and immune-related ceRNA subnetwork were constructed. Functional annotation of DEmRNAs between the two groups and DEmRNAs in networks was conducted. The immune cell distribution in neovascular AMD was also evaluated. Real-time qPCR (RT-qPCR) was used to validate the expression levels of key markers.

Results

A total of 342 DEmRNAs and 157 DElncRNAs were obtained in neovascular AMD. Functional annotation indicated that these DEmRNAs significantly enriched immune system-related processes, such as positive regulation of B cell activation, immunoglobulin receptor binding, complement activation, and classical pathway. The DElncRNA-DEmRNA co-expression network, including 185 DElncRNA-DEmRNA co-expression pairs, and the ceRNA (DElncRNA-miRNA-DEmRNA) network, containing 45 lncRNA-miRNA pairs and 73 miRNA-mRNA pairs, were constructed. The immune-related ceRNA subnetwork, including 2 lncRNAs, 5 miRNAs, and 3 mRNAs, was constructed. In addition, the distribution of immune cells was slightly different between the neovascular AMD group and the control group. RT-qPCR validation indicated the consistency between the RT-qPCR results and RNA sequencing results.

Conclusion

In conclusion, STC1, S100A1, MEG3, MEG3-hsa-miR-608-S100A1, and MEG3-hsa-miR-130b-3p/hsa-miR-149-3p-STC1 may be related to the occurrence and development of neovascular AMD.

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2025-02-12
2025-07-07
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References

  1. Pennington K.L. DeAngelis M.M. Epidemiology of age-related macular degeneration (AMD): Associations with cardiovascular disease phenotypes and lipid factors. Eye Vis. (Lond.) 2016 3 1 34 10.1186/s40662‑016‑0063‑5 28032115
    [Google Scholar]
  2. Bhutto I. Lutty G. Understanding age-related macular degeneration (AMD): Relationships between the photoreceptor/retinal pigment epithelium/Bruch’s membrane/choriocapillaris complex. Mol. Aspects Med. 2012 33 4 295 317 10.1016/j.mam.2012.04.005 22542780
    [Google Scholar]
  3. Murali A. Krishnakumar S. Subramanian A. Parameswaran S. Bruch’s membrane pathology: A mechanistic perspective. Eur. J. Ophthalmol. 2020 30 6 1195 1206 10.1177/1120672120919337 32345040
    [Google Scholar]
  4. Tisi A. Feligioni M. Passacantando M. Ciancaglini M. Maccarone R. The impact of oxidative stress on blood-retinal barrier physiology in age-related macular degeneration. Cells 2021 10 1 64 10.3390/cells10010064 33406612
    [Google Scholar]
  5. Mitchell P. Liew G. Gopinath B. Wong T.Y. Age-related macular degeneration. Lancet 2018 392 10153 1147 1159 10.1016/S0140‑6736(18)31550‑2 30303083
    [Google Scholar]
  6. Thomas C.J. Mirza R.G. Gill M.K. Age-related macular degeneration. Med. Clin. North Am. 2021 105 3 473 491 10.1016/j.mcna.2021.01.003 33926642
    [Google Scholar]
  7. Jia Y. Bailey S.T. Wilson D.J. Tan O. Klein M.L. Flaxel C.J. Potsaid B. Liu J.J. Lu C.D. Kraus M.F. Fujimoto J.G. Huang D. Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration. Ophthalmology 2014 121 7 1435 1444 10.1016/j.ophtha.2014.01.034 24679442
    [Google Scholar]
  8. Kokotas H. Grigoriadou M. Petersen M.B. Age-related macular degeneration: Genetic and clinical findings. cclm 2011 49 4 601 616 10.1515/CCLM.2011.091 21175380
    [Google Scholar]
  9. Lee S. Kim S. Jeon J.S. Microfluidic outer blood–retinal barrier model for inducing wet age-related macular degeneration by hypoxic stress. Lab Chip 2022 22 22 4359 4368 10.1039/D2LC00672C 36254466
    [Google Scholar]
  10. Murphy C. Johnson A.P. Koenekoop R.K. Seiple W. Overbury O. The relationship between cognitive status and known single nucleotide polymorphisms in age-related macular degeneration. Front. Aging Neurosci. 2020 12 586691 10.3389/fnagi.2020.586691 33178008
    [Google Scholar]
  11. Chen X. Jiang C. Qin B. Liu G. Ji J. Sun X. Xu M. Ding S. Zhu M. Huang G. Yan B. Zhao C. LncRNA ZNF503-AS1 promotes RPE differentiation by downregulating ZNF503 expression. Cell Death Dis. 2017 8 9 e3046 10.1038/cddis.2017.382 28880276
    [Google Scholar]
  12. Meola N. Pizzo M. Alfano G. Surace E.M. Banfi S. The long noncoding RNA Vax2os1 controls the cell cycle progression of photoreceptor progenitors in the mouse retina. RNA 2012 18 1 111 123 10.1261/rna.029454.111 22128341
    [Google Scholar]
  13. Xu X.D. Li K.R. Li X.M. Yao J. Qin J. Yan B. Long non-coding RNAs: New players in ocular neovascularization. Mol. Biol. Rep. 2014 41 7 4493 4505 10.1007/s11033‑014‑3320‑5 24623407
    [Google Scholar]
  14. Zhu W. Meng Y.F. Xing Q. Tao J.J. Lu J. Wu Y. Identification of lncRNAs involved in biological regulation in early age-related macular degeneration. Int. J. Nanomedicine 2017 12 7589 7602 10.2147/IJN.S140275 29089757
    [Google Scholar]
  15. Chen X. Sun R. Yang D. Jiang C. Liu Q. LINC00167 regulates RPE differentiation by targeting the miR-203a-3p/SOCS3 axis. Mol. Ther. Nucleic Acids 2020 19 1015 1026 10.1016/j.omtn.2019.12.040 32044724
    [Google Scholar]
  16. Allingham M.J. Loksztejn A. Cousins S.W. Mettu P.S. Immunological aspects of age-related macular degeneration. Adv. Exp. Med. Biol. 2021 1256 143 189 10.1007/978‑3‑030‑66014‑7_6 33848001
    [Google Scholar]
  17. Liberzon A. Subramanian A. Pinchback R. Thorvaldsdóttir H. Tamayo P. Mesirov J.P. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011 27 12 1739 1740 10.1093/bioinformatics/btr260 21546393
    [Google Scholar]
  18. Love M.I. Huber W. Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014 15 12 550 10.1186/s13059‑014‑0550‑8 25516281
    [Google Scholar]
  19. Garcia-Moreno A. López-Domínguez R. Villatoro-García J.A. Ramirez-Mena A. Aparicio-Puerta E. Hackenberg M. Pascual-Montano A. Carmona-Saez P. Functional enrichment analysis of regulatory elements. Biomedicines 2022 10 3 590 10.3390/biomedicines10030590 35327392
    [Google Scholar]
  20. Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005 102 43 15545 15550 10.1073/pnas.0506580102 16199517
    [Google Scholar]
  21. Zhao S. Zhong Y. Shen F. Cheng X. Qing X. Liu J. Comprehensive exosomal microRNA profile and construction of competing endogenous RNA network in autism spectrum disorder: A pilot study. Biomol Biomed. 2024 24 2 292 301 10.17305/bb.2023.9552 37865919
    [Google Scholar]
  22. Shannon P. Markiel A. Ozier O. Baliga N.S. Wang J.T. Ramage D. Amin N. Schwikowski B. Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 13 11 2498 2504 10.1101/gr.1239303 14597658
    [Google Scholar]
  23. Sticht C. De La Torre C. Parveen A. Gretz N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One 2018 13 10 e0206239 10.1371/journal.pone.0206239 30335862
    [Google Scholar]
  24. Li J.H. Liu S. Zhou H. Qu L.H. Yang J.H. starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014 42 D1 D92 D97 10.1093/nar/gkt1248 24297251
    [Google Scholar]
  25. Bhattacharya S. Dunn P. Thomas C.G. Smith B. Schaefer H. Chen J. Hu Z. Zalocusky K.A. Shankar R.D. Shen-Orr S.S. Thomson E. Wiser J. Butte A.J. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. Sci. Data 2018 5 1 180015 10.1038/sdata.2018.15 29485622
    [Google Scholar]
  26. Newman A.M. Liu C.L. Green M.R. Gentles A.J. Feng W. Xu Y. Hoang C.D. Diehn M. Alizadeh A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 2015 12 5 453 457 10.1038/nmeth.3337 25822800
    [Google Scholar]
  27. Agarwal V. Bell G.W. Nam J.W. Bartel D.P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 2015 4 e05005 10.7554/eLife.05005 26267216
    [Google Scholar]
  28. Blasiak J. Hyttinen J.M.T. Szczepanska J. Pawlowska E. Kaarniranta K. Potential of long non-coding RNAs in age-related macular degeneration. Int. J. Mol. Sci. 2021 22 17 9178 10.3390/ijms22179178 34502084
    [Google Scholar]
  29. Wang Z. Gerstein M. Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009 10 1 57 63 10.1038/nrg2484 19015660
    [Google Scholar]
  30. Mortazavi A. Williams B.A. McCue K. Schaeffer L. Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 2008 5 7 621 628 10.1038/nmeth.1226 18516045
    [Google Scholar]
  31. Nagalakshmi U. Wang Z. Waern K. Shou C. Raha D. Gerstein M. Snyder M. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008 320 5881 1344 1349 10.1126/science.1158441 18451266
    [Google Scholar]
  32. Cloonan N. Forrest A.R.R. Kolle G. Gardiner B.B.A. Faulkner G.J. Brown M.K. Taylor D.F. Steptoe A.L. Wani S. Bethel G. Robertson A.J. Perkins A.C. Bruce S.J. Lee C.C. Ranade S.S. Peckham H.E. Manning J.M. McKernan K.J. Grimmond S.M. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 2008 5 7 613 619 10.1038/nmeth.1223 18516046
    [Google Scholar]
  33. Behnke V. Wolf A. Langmann T. The role of lymphocytes and phagocytes in age-related macular degeneration (AMD). Cell. Mol. Life Sci. 2020 77 5 781 788 10.1007/s00018‑019‑03419‑4 31897541
    [Google Scholar]
  34. Penfoldi P.L. Provis J.M. Furby J.H. Gatenby P.A. Billson F.A. Autoantibodies to retinal astrocytes associated with age-related macular degeneration. Graefes Arch. Clin. Exp. Ophthalmol. 1990 228 3 270 274 10.1007/BF00920033 2193850
    [Google Scholar]
  35. Patel N. Ohbayashi M. Nugent A.K. Ramchand K. Toda M. Chau K.Y. Bunce C. Webster A. Bird A.C. Ono S.J. Chong V. Circulating anti‐retinal antibodies as immune markers in age‐related macular degeneration. Immunology 2005 115 3 422 430 10.1111/j.1365‑2567.2005.02173.x 15946260
    [Google Scholar]
  36. Reynolds R. Hartnett M.E. Atkinson J.P. Giclas P.C. Rosner B. Seddon J.M. Plasma complement components and activation fragments: Associations with age-related macular degeneration genotypes and phenotypes. Invest. Ophthalmol. Vis. Sci. 2009 50 12 5818 5827 10.1167/iovs.09‑3928 19661236
    [Google Scholar]
  37. Park Y.G. Park Y.S. Kim I.B. Complement system and potential therapeutics in age-related macular degeneration. Int. J. Mol. Sci. 2021 22 13 6851 10.3390/ijms22136851 34202223
    [Google Scholar]
  38. Westberg J.A. Serlachius M. Lankila P. Penkowa M. Hidalgo J. Andersson L.C. Hypoxic preconditioning induces neuroprotective stanniocalcin-1 in brain via IL-6 signaling. Stroke 2007 38 3 1025 1030 10.1161/01.STR.0000258113.67252.fa 17272771
    [Google Scholar]
  39. Wang Y. Huang L. Abdelrahim M. Cai Q. Truong A. Bick R. Poindexter B. Sheikh-Hamad D. Stanniocalcin-1 suppresses superoxide generation in macrophages through induction of mitochondrial UCP2. J. Leukoc. Biol. 2009 86 4 981 988 10.1189/jlb.0708454 19602668
    [Google Scholar]
  40. Bishop A. Cartwright J.E. Whitley G.S. Stanniocalcin-1 in the female reproductive system and pregnancy. Hum. Reprod. Update 2021 27 6 1098 1114 10.1093/humupd/dmab028 34432025
    [Google Scholar]
  41. Wang K. Liu Y. Li S. Zhao N. Qin F. Tao Y. Song Z. Unveiling the therapeutic potential and mechanisms of stanniocalcin-1 in retinal degeneration. Surv. Ophthalmol. 2024 39270826
    [Google Scholar]
  42. Kim S.J. Ko J.H. Yun J.H. Kim J.A. Kim T.E. Lee H.J. Kim S.H. Park K.H. Oh J.Y. Stanniocalcin-1 protects retinal ganglion cells by inhibiting apoptosis and oxidative damage. PLoS One 2013 8 5 e63749 10.1371/journal.pone.0063749 23667669
    [Google Scholar]
  43. Roddy G.W. Rosa R.H. Jr Oh J.Y. Ylostalo J.H. Bartosh T.J. Jr Choi H. Lee R.H. Yasumura D. Ahern K. Nielsen G. Matthes M.T. LaVail M.M. Prockop D.J. Stanniocalcin-1 rescued photoreceptor degeneration in two rat models of inherited retinal degeneration. Mol Ther. 2012 20 4 788 797 10.1038/mt.2011.308 22294148
    [Google Scholar]
  44. Roddy G.W. Yasumura D. Matthes M.T. Alavi M.V. Boye S.L. Rosa R.H. Jr Fautsch M.P. Hauswirth W.W. LaVail M.M. Long-term photoreceptor rescue in two rodent models of retinitis pigmentosa by adeno-associated virus delivery of Stanniocalcin-1. Exp. Eye Res. 2017 165 175 181 10.1016/j.exer.2017.09.011 28974356
    [Google Scholar]
  45. Rosa R.H. Jr Xie W. Zhao M. Tsai S.H. Roddy G.W. Su M.G. Potts L.B. Hein T.W. Kuo L. Intravitreal administration of stanniocalcin-1 rescues photoreceptor degeneration with reduced oxidative stress and inflammation in a porcine model of retinitis pigmentosa. Am. J. Ophthalmol. 2022 239 230 243 10.1016/j.ajo.2022.03.014 35307380
    [Google Scholar]
  46. Hong G. Li T. Zhao H. Zeng Z. Zhai J. Li X. Luo X. Diagnostic value and mechanism of plasma S100A1 protein in acute ischemic stroke: A prospective and observational study. PeerJ 2023 11 e14440 10.7717/peerj.14440 36643631
    [Google Scholar]
  47. Yu J. Lu Y. Li Y. Xiao L. Xing Y. Li Y. Wu L. Role of S100A1 in hypoxia-induced inflammatory response in cardiomyocytes via TLR4/ROS/NF-κB pathway. J. Pharm. Pharmacol. 2015 67 9 1240 1250 10.1111/jphp.12415 25880347
    [Google Scholar]
  48. Bai Y. Guo N. Xu Z. Chen Y. Zhang W. Chen Q. Bi Z. S100A1 expression is increased in spinal cord injury and promotes inflammation, oxidative stress and apoptosis of PC12 cells induced by LPS via ERK signaling. Mol. Med. Rep. 2022 27 2 30 10.3892/mmr.2022.12917 36524376
    [Google Scholar]
  49. Most P. Lerchenmüller C. Rengo G. Mahlmann A. Ritterhoff J. Rohde D. Goodman C. Busch C.J. Laube F. Heissenberg J. Pleger S.T. Weiss N. Katus H.A. Koch W.J. Peppel K. S100A1 deficiency impairs postischemic angiogenesis via compromised proangiogenic endothelial cell function and nitric oxide synthase regulation. Circ. Res. 2013 112 1 66 78 10.1161/CIRCRESAHA.112.275156 23048072
    [Google Scholar]
  50. Qiu G.Z. Tian W. Fu H.T. Li C.P. Liu B. Long noncoding RNA-MEG3 is involved in diabetes mellitus-related microvascular dysfunction. Biochem. Biophys. Res. Commun. 2016 471 1 135 141 10.1016/j.bbrc.2016.01.164 26845358
    [Google Scholar]
  51. Tong P. Peng Q.H. Gu L.M. Xie W.W. Li W.J. LncRNA-MEG3 alleviates high glucose induced inflammation and apoptosis of retina epithelial cells via regulating miR-34a/SIRT1 axis. Exp. Mol. Pathol. 2019 107 102 109 10.1016/j.yexmp.2018.12.003 30529346
    [Google Scholar]
  52. Luo R. Jin H. Li L. Hu Y.X. Xiao F. Long noncoding RNA MEG3 inhibits apoptosis of retinal pigment epithelium cells induced by high glucose via the miR-93/Nrf2 axis. Am. J. Pathol. 2020 190 9 1813 1822 10.1016/j.ajpath.2020.05.008 32473920
    [Google Scholar]
  53. Chen G. Qian H.M. Chen J. Wang J. Guan J.T. Chi Z.L. Whole transcriptome sequencing identifies key circRNAs, lncRNAs, and miRNAs regulating neurogenesis in developing mouse retina. BMC Genomics 2021 22 1 779 10.1186/s12864‑021‑08078‑z 34717547
    [Google Scholar]
  54. He Y. Dan Y. Gao X. Huang L. Lv H. Chen J. DNMT1-mediated lncRNA MEG3 methylation accelerates endothelial-mesenchymal transition in diabetic retinopathy through the PI3K/Akt/mTOR signaling pathway. Am. J. Physiol. Endocrinol. Metab. 2021 320 3 E598 E608 10.1152/ajpendo.00089.2020 33284093
    [Google Scholar]
  55. Sun H.J. Zhang F.F. Xiao Q. Xu J. Zhu L.J. lncRNA MEG3, acting as a ceRNA, modulates RPE differentiation through the miR-7-5p/Pax6 axis. Biochem. Genet. 2021 59 6 1617 1630 10.1007/s10528‑021‑10072‑9 34018078
    [Google Scholar]
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  • Article Type:
    Research Article
Keywords: Age-related macular degeneration ; immune ; ceRNA ; lncRNA ; neovascular
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