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Volume 31, Issue 42
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Aims

The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms.

Background

Glaucoma has afflicted many patients for many years, with excessive pressure in the eye continuously damaging the nervous system and leading to severe blindness. An effective molecular diagnostic method is currently lacking.

Objective

The present study attempted to reveal the molecular mechanism and gene regulatory network of hub genes in glaucoma, followed by an attempt to reveal the drug-gene-disease network regulated by hub genes.

Methods

A microarray sequencing dataset (GSE9944) was obtained through the Gene Expression Omnibus database. The differentially expressed genes in Glaucoma were identified. Based on these genes, we constructed three machine learning models for feature training, Random Forest model (RF), Least absolute shrinkage and selection operator regression model (LASSO), and Support Vector Machines model (SVM). Meanwhile, Weighted Gene Co-Expression Network Analysis (WGCNA) was performed for GSE9944 expression profiles to identify Glaucoma-related genes. The overlapping genes in the four groups were considered as hub genes of Glaucoma. Based on these genes, we also constructed a molecular diagnostic model of Glaucoma. In this study, we also performed molecular docking analysis to explore the gene-drug network targeting hub genes. In addition, we evaluated the immune cell infiltration landscape in Glaucoma samples and normal samples by applying CIBERSORT method.

Results

8 hub genes were determined: ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158 and GSN. The diagnostic model showed excellent diagnostic performance (area under the curve=1). GSN might positively regulate T cell CD4 naïve as well as negatively regulate T cell regulation (Tregs). In addition, we constructed gene-drug networks in an attempt to explore novel therapeutic agents for Glaucoma.

Conclusion

Our results systematically determined 8 hub genes and established a molecular diagnostic model that allowed the diagnosis of Glaucoma. Our study provided a basis for future systematic studies of Glaucoma pathogenesis.

© 2024 Bentham Science Publishers
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2024-02-14
2024-11-14

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References

  1. Glaucoma. Am. Fam. Physician.20231073Online36920818
     [Google Scholar]
  2. ChakrabartiA. MohanN. NazmN. MehtaR. EdwardD. Newer advances in medical management of glaucoma.Indian J. Ophthalmol.20227061920193010.4103/ijo.IJO_2239_2135647957
     [Google Scholar]
  3. AldaasK. ChallaP. WeberD.J. FleischmanD. Infections and glaucoma.Surv. Ophthalmol.202267363765810.1016/j.survophthal.2021.08.00934487741
     [Google Scholar]
  4. JavittG.H. VollebregtE.R. Regulation of molecular diagnostics.Annu. Rev. Genomics Hum. Genet.202223165367310.1146/annurev‑genom‑121521‑01041636044907
     [Google Scholar]
  5. XiongT. LvX.S. WuG.J. GuoY.X. LiuC. HouF.X. WangJ.K. FuY.F. LiuF.Q. Single-cell sequencing analysis and multiple machine learning methods identified G0S2 and HPSE as novel biomarkers for abdominal aortic aneurysm.Front. Immunol.20221390730910.3389/fimmu.2022.90730935769488
     [Google Scholar]
  6. HanH. ChenY. YangH. ChengW. ZhangS. LiuY. LiuQ. LiuD. YangG. LiK. Identification and verification of diagnostic biomarkers for glomerular injury in diabetic nephropathy based on machine learning algorithms.Front. Endocrinol.20221387696010.3389/fendo.2022.87696035663304
     [Google Scholar]
  7. ChenY. LiaoR. YaoY. WangQ. FuL. Machine learning to identify immune-related biomarkers of rheumatoid arthritis based on WGCNA network.Clin. Rheumatol.20224141057106810.1007/s10067‑021‑05960‑934767108
     [Google Scholar]
  8. HuL. ChenM. DaiH. WangH. YangW. A metabolism-related gene signature predicts the prognosis of breast cancer patients: Combined analysis of high-throughput sequencing and gene chip data sets.Oncologie202224480382210.32604/oncologie.2022.026419
     [Google Scholar]
  9. ChenY. HuangL. WeiZ. LiuX. ChenL. WangB. Development and validation of a nomogram model to predict the prognosis of intrahepatic cholangiocarcinoma.Oncologie202224232934010.32604/oncologie.2022.022521
     [Google Scholar]
  10. EraslanG. AvsecŽ. GagneurJ. TheisF.J. Deep learning: New computational modelling techniques for genomics.Nat. Rev. Genet.201920738940310.1038/s41576‑019‑0122‑630971806
     [Google Scholar]
  11. GuptaR. SrivastavaD. SahuM. TiwariS. AmbastaR.K. KumarP. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery.Mol. Divers.20212531315136010.1007/s11030‑021‑10217‑333844136
     [Google Scholar]
  12. AlabiR.O. MäkitieA.A. PirinenM. ElmusratiM. LeivoI. AlmangushA. Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer.Int. J. Med. Inform.202114510431310.1016/j.ijmedinf.2020.10431333142259
     [Google Scholar]
  13. ChenX. LiT.H. ZhaoY. WangC.C. ZhuC.C. Deep-belief network for predicting potential miRNA-disease associations.Brief. Bioinform.2021223bbaa18610.1093/bib/bbaa18634020550
     [Google Scholar]
  14. HaJ. ParkC. ParkC. ParkS. IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization.J. Biomed. Inform.202010210335810.1016/j.jbi.2019.10335831857202
     [Google Scholar]
  15. HaJ. ParkS. NCMD: Node2vec-based neural collaborative filtering for predicting MiRNA-disease association.IEEE/ACM Trans. Comput. Biol. Bioinform.20232021257126810.1109/TCBB.2022.3191972
     [Google Scholar]
  16. HaJ. MDMF: Predicting miRNA–disease association based on matrix factorization with disease similarity constraint.J. Pers. Med.202212688510.3390/jpm1206088535743670
     [Google Scholar]
  17. HaJ. SMAP: Similarity-based matrix factorization framework for inferring miRNA-disease association.Knowl. Base. Syst.202326311029510.1016/j.knosys.2023.110295
     [Google Scholar]
  18. ShenW. SongZ. ZhongX. HuangM. ShenD. GaoP. QianX. WangM. HeX. WangT. LiS. SongX. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform.iMeta202213e3610.1002/imt2.36
     [Google Scholar]
  19. LeekJ.T. JohnsonW.E. ParkerH.S. JaffeA.E. StoreyJ.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments.Bioinformatics201228688288310.1093/bioinformatics/bts03422257669
     [Google Scholar]
  20. RitchieM.E. PhipsonB. WuD. HuY. LawC.W. ShiW. SmythG.K. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res.2015437e4710.1093/nar/gkv00725605792
     [Google Scholar]
  21. YuG. WangL.G. HanY. HeQ.Y. clusterProfiler: An R package for comparing biological themes among gene clusters.OMICS201216528428710.1089/omi.2011.011822455463
     [Google Scholar]
  22. CortesC. VapnikV. Support-vector networks.Mach. Learn.199520327329710.1007/BF00994018
     [Google Scholar]
  23. Sidey-GibbonsJ.A.M. Sidey-GibbonsC.J. Machine learning in medicine: A practical introduction.BMC Med. Res. Methodol.20191916410.1186/s12874‑019‑0681‑430890124
     [Google Scholar]
  24. SimonN. FriedmanJ. HastieT. TibshiraniR. Regularization paths for Cox’s proportional hazards model via coordinate descent.J. Stat. Softw.201139511310.18637/jss.v039.i0527065756
     [Google Scholar]
  25. IshwaranH. LuM. KogalurU.B. randomForestSRC: Variable Importance (VIMP) with Subsampling Inference Vignette.Available from: https://ishwaran.org/vignettes/rfsrc-subsample.pdf 2021
     [Google Scholar]
  26. LangfelderP. HorvathS. WGCNA: An R package for weighted correlation network analysis.BMC Bioinformat.20089155910.1186/1471‑2105‑9‑55919114008
     [Google Scholar]
  27. HeY. GeJ. Tombran-TinkJ. Mitochondrial defects and dysfunction in calcium regulation in glaucomatous trabecular meshwork cells.Invest. Ophthalmol. Vis. Sci.200849114912492210.1167/iovs.08‑219218614807
     [Google Scholar]
  28. ChenB. KhodadoustM.S. LiuC.L. NewmanA.M. AlizadehA.A. Profiling tumor infiltrating immune cells with CIBERSORT.Methods Mol. Biol.2018171124325910.1007/978‑1‑4939‑7493‑1_1229344893
     [Google Scholar]
  29. El-HachemN. AutoDock and AutoDockTools for protein-ligand docking: Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) as a case study.Methods Mol. Biol.20171598391403
     [Google Scholar]
  30. SterlingT. IrwinJ.J. ZINC 15 – Ligand discovery for everyone.J. Chem. Inf. Model.201555112324233710.1021/acs.jcim.5b0055926479676
     [Google Scholar]
  31. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.201031245546110.1002/jcc.2133419499576
     [Google Scholar]
  32. SeeligerD. de GrootB.L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina.J. Comput. Aided Mol. Des.201024541742210.1007/s10822‑010‑9352‑620401516
     [Google Scholar]
  33. ReimersM. CareyV.J. Bioconductor: An open source framework for bioinformatics and computational biology.Methods Enzymol.200641111913410.1016/S0076‑6879(06)11008‑316939789
     [Google Scholar]
  34. HarrisA. GuidoboniG. SieskyB. MathewS. VerticchioV.A.C. RoweL. ArcieroJ. Ocular blood flow as a clinical observation: Value, limitations and data analysis.Prog. Retin. Eye Res.20207810084110.1016/j.preteyeres.2020.10084131987983
     [Google Scholar]
  35. HeY. LeungK.W. ZhuoY.H. GeJ. Pro370Leu mutant myocilin impairs mitochondrial functions in human trabecular meshwork cells.Mol. Vis.20091581582519390644
     [Google Scholar]
  36. SaracalogluA. DemiryürekS. OkumusS. OztuzcuS. BozgeyikI. CoskunE. AksoyU. KayduE. ErbagciI. GürlerB. AlasehirliB. DemiryürekA.T. Toward novel diagnostics for primary open-angle glaucoma? an association study of polymorphic variation in ras homolog family member (A, B, C, D) Genes RHOA, RHOB, RHOC, and RHOD.OMICS201620529029510.1089/omi.2016.003127195967
     [Google Scholar]
  37. PotrčM. VolkM. de RosaM. PižemJ. TeranN. JakličH. MaverA. Drnovšek-OlupB. BollatiM. VogelnikK. HočevarA. GornikA. PfeiferV. PeterlinB. HawlinaM. FakinA. Clinical and histopathological features of gelsolin amyloidosis associated with a novel GSN variant p.Glu580Lys.Int. J. Mol. Sci.2021223108410.3390/ijms2203108433499149
     [Google Scholar]
  38. LiuM. PiH. XiY. WangL. TianL. ChenM. XieJ. DengP. ZhangT. ZhouC. LiangY. ZhangL. HeM. LuY. ChenC. YuZ. ZhouZ. KIF5A-dependent axonal transport deficiency disrupts autophagic flux in trimethyltin chloride-induced neurotoxicity.Autophagy202117490392410.1080/15548627.2020.173944432160081
     [Google Scholar]
  39. Asare-WereheneM. CommunalL. CarmonaE. HanY. SongY.S. BurgerD. Mes-MassonA.M. TsangB.K. Plasma gelsolin inhibits CD8+ T-cell function and regulates glutathione production to confer chemoresistance in ovarian cancer.Cancer Res.202080183959397110.1158/0008‑5472.CAN‑20‑078832641415
     [Google Scholar]
  40. YangX. ZengQ. GöktasE. GopalK. Al-AswadL. BlumbergD.M. CioffiG.A. LiebmannJ.M. TezelG. T-lymphocyte subset distribution and activity in patients with glaucoma.Invest. Ophthalmol. Vis. Sci.201960487788810.1167/iovs.18‑2612930821813
     [Google Scholar]
/content/journals/cmc/10.2174/0109298673283658231130104550
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Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models
Curr. Med. Chem. 31, 7059 (2024); https://doi.org/10.2174/0109298673283658231130104550
/content/journals/cmc/10.2174/0109298673283658231130104550
/content/journals/cmc/10.2174/0109298673283658231130104550
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  • Article Type:     Research Article
Keyword(s): diagnostic model; Glaucoma; LASSO regression model; random forest model; support vector machines model; WGCNA

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