A Computational Approach to Discriminate AMD/ non-AMD Patients Based on Retina Tissue Gene Expression Profile Machine Learning approaches to discriminate AMD/non-AMD samples
Journal of Ophthalmic and Optometric Sciences,
Vol. 5 No. 2 (2021),
14 Azar 2022
,
Page 6-20
https://doi.org/10.22037/joos.v5i2.38925
Abstract
Background: Age-related macular degeneration (AMD) is the progressive degenerative disease of the macula and the main cause of blindness in older adults. Various risk factors have been associated with disease progression among different individuals.AMD is affected by different risk factors such as aging, genetic susceptibility, environmental risk factors and lifestyle. Since the etiology of AMD is not fully known, it would be essential to identify disease risk factors and novel predictive risk factors to detect AMD at an early stage.
Material and Methods: The expression data were obtained from the Gene Expression Omnibus database. Samples were quantile normalized, and log2 transformed. Furthermore, outlier samples were removed by hierarchical clustering. R limma was used to run a linear model and identify differentially expressed genes (DEGs). As a result, 33 genes were discovered with a q-value less than 0.05 and a |log (FC)|≥0.7. With a machine learning (ML) approach, DEGs were applied to discriminate between the case and control samples. Furthermore, FeatureSelect is used to extract the most effective separator genes. Nine genes were identified as the best disease discriminator genes through 11 feature selection algorithms.
Results: The gene set found in the study distinguishes healthy samples from patient samples with an accuracy of 87.5 %. We found DEF119B, UBD, and GRP to be three novel potential AMD candidate biomarkers using ML models and feature selection.
Conclusion: Machine learning can be beneficial in diagnosing, preventing and treating diseases, especially in diseases such as AMD that do not have a clear etiology.
- Gene Expression
- Machine Learning
- AMD
- Gene Selection
- Feature Selection
How to Cite
References
Fleckenstein M, Keenan TDL, Guymer RH, Chakravarthy U, Schmitz-Valckenberg S, Klaver CC, et al. Age-related macular degeneration. Nat Rev Dis Primers. 2021;7(1):31.
Tan W, Zou J, Yoshida S, Jiang B, Zhou Y. The Role of Inflammation in Age-Related Macular Degeneration. Int J Biol Sci. 2020;16(15):2989-3001.
Heesterbeek TJ, Lorés-Motta L, Hoyng CB, Lechanteur YTE, den Hollander AI. Risk factors for progression of age-related macular degeneration. Ophthalmic Physiol Opt. 2020;40(2):140-70.
Ambati J, Fowler BJ. Mechanisms of age-related macular degeneration. Neuron. 2012;75(1):26-39.
Somasundaran S, Constable IJ, Mellough CB, Carvalho LS. Retinal pigment epithelium and age-related macular degeneration: A review of major disease mechanisms. Clin Exp Ophthalmol. 2020;48(8):1043-56.
Stahl A. The Diagnosis and Treatment of Age-Related Macular Degeneration. Dtsch Arztebl Int. 2020;117(29-30):513-20.
Tsuchida M, Fukushima T, Nasuda S, Masoudi-Nejad A, Ishikawa G, Nakamura T, et al. Dissection of rye chromosome 1R in common wheat. Genes & Genetic Systems. 2008;83(1):43-53.
Kouhsar M, Azimzadeh Jamalkandi S, Moeini A, Masoudi-Nejad A. Detection of novel biomarkers for early detection of Non-Muscle-Invasive Bladder Cancer using Competing Endogenous RNA network analysis. Scientific Reports. 2019;9(1):8434.
Najafi A, Bidkhori G, Bozorgmehr JH, Koch I, Masoudi-Nejad A. Genome scale modeling in systems biology: algorithms and resources. Curr Genomics. 2014;15(2):130-59.
Motieghader H, Kouhsar M, Najafi A, Sadeghi B, Masoudi-Nejad A. mRNA-miRNA bipartite network reconstruction to predict prognostic module biomarkers in colorectal cancer stage differentiation. Mol Biosyst. 2017;13(10):2168-80.
Alaei S, Sadeghi B, Najafi A, Masoudi-Nejad A. LncRNA and mRNA integration network reconstruction reveals novel key regulators in esophageal squamous-cell carcinoma. Genomics. 2019;111(1):76-89. Epub 20180106.
Newman AM, Gallo NB, Hancox LS, Miller NJ, Radeke CM, Maloney MA, et al. Systems-level analysis of age-related macular degeneration reveals global biomarkers and phenotype-specific functional networks. Genome Med. 2012;4(2):16.
Liang G, Ma W, Luo Y, Yin J, Hao L, Zhong J. Identification of differentially expressed and methylated genes and construction of a co-expression network in age-related macular degeneration. Ann Transl Med. 2022;10(4):223.
Rinsky B, Beykin G, Grunin M, Amer R, Khateb S, Tiosano L, et al. Analysis of the Aqueous Humor Proteome in Patients With Age-Related Macular Degeneration. Invest Ophthalmol Vis Sci. 2021;62(10):18.
Abedi Z, MotieGhader H, Maghsoudloo M, Sheikh Beig Goharrizi MA, Shojaei A. Novel Potential Drugs for Therapy of Age-Related Macular Degeneration Using Protein-Protein Interaction Network (PPI) Analysis. Journal of Ophthalmic and Optometric Sciences. 2019;3(4):11-23.
Hooshmand SA, Zarei Ghobadi M, Hooshmand SE, Azimzadeh Jamalkandi S, Alavi SM, Masoudi-Nejad A. A multimodal deep learning-based drug repurposing approach for treatment of COVID-19. Mol Divers. 2021;25(3):1717-30.
Masoudi-Sobhanzadeh Y, Omidi Y, Amanlou M, Masoudi-Nejad A. DrugR+: A comprehensive relational database for drug repurposing, combination therapy, and replacement therapy. Comput Biol Med. 2019;109:254-62.
Abbasi K, Razzaghi P, Poso A, Ghanbari-Ara S, Masoudi-Nejad A. Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives. Curr Med Chem. 2021;28(11):2100-13.
Maghsoudloo M, Azimzadeh Jamalkandi S, Najafi A, Masoudi-Nejad A. An efficient hybrid feature selection method to identify potential biomarkers in common chronic lung inflammatory diseases. Genomics. 2020;112(5):3284-93.
Cheung R, Chun J, Sheidow T, Motolko M, Malvankar-Mehta MS. Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis. Eye. 2022;36(5):994-1004.
Ganjdanesh A, Zhang J, Chew EY, Ding Y, Huang H, Chen W. LONGL-Net: temporal correlation structure guided deep learning model to predict longitudinal age-related macular degeneration severity. PNAS Nexus. 2022;1(1):pgab003.
Newman AM, Gallo NB, Hancox LS, Miller NJ, Radeke CM, Maloney MA, et al. Systems-level analysis of age-related macular degeneration reveals global biomarkers and phenotype-specific functional networks. Genome Med. 2012;4(2):16-.
Maghsoudloo M, Azimzadeh Jamalkandi S, Najafi A, Masoudi-Nejad A. Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis. Molecular Medicine. 2020;26(1):9.
Mortezaei Z, Lanjanian H, Masoudi-Nejad A. Candidate novel long noncoding RNAs, MicroRNAs and putative drugs for Parkinson's disease using a robust and efficient genome-wide association study. Genomics. 2017;109(3-4):158-64.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
Hosseini SS, Abedi Z, Maghsoudloo M, Sheikh Beig Goharrizi MA, Shojaei A. Investigation of genes associated with primary open-angle glaucoma (POAG) using expression profile analysis. Journal of Ophthalmic and Optometric Sciences. 2019;3(3):37-54.
Maghsoudloo, M. and M.H. Noroozizadeh, A gene selection approach for Diabetic retinopathy microarray data classification using Ant Colony Optimization. Journal of Ophthalmic and Optometric Sciences. Volume, 2019. 3(4).
Masoudi-Sobhanzadeh Y, Motieghader H, Masoudi-Nejad A. FeatureSelect: A software for feature selection based on machine learning approaches. BMC Bioinformatics. 2019;20(1). doi: 10.1186/s12859-019-2754-0.
Abbasi K, Poso A, Ghasemi J, Amanlou M, Masoudi-Nejad A. Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug Discovery. Journal of Chemical Information and Modeling. 2019;59(11):4528-39.
Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Research. 2009;37(suppl_2):W305-W11.
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14(1):128.
Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Research. 2019;48(D1):D845-D55.
Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Research. 2016;45(D1):D833-D9.
Ahmadi H, Ahmadi A, Azimzadeh-Jamalkandi S, Shoorehdeli MA, Salehzadeh-Yazdi A, Bidkhori G, et al. HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens. Genomics. 2013;101(2):94-100. E
Masoudi-Nejad A, Wang E. Cancer modeling and network biology: accelerating toward personalized medicine. Semin Cancer Biol. 2015;30:1-3.
Abd-Elnaby M, Alfonse M, Roushdy M. Classification of breast cancer using microarray gene expression data: A survey. Journal of Biomedical Informatics. 2021;117:103764.
Mahendran N, Durai Raj Vincent PM, Srinivasan K, Chang C-Y. Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions. Frontiers in Genetics. 2020;11.
Yan Q, Jiang Y, Huang H, Swaroop A, Chew EY, Weeks DE, et al. Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk. Transl Vis Sci Technol. 2021;10(2):29.
Yan Q, Weeks DE, Xin H, Swaroop A, Chew EY, Huang H, et al. Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression. Nat Mach Intell. 2020;2(2):141-50.
Masoudi-Nejad A, Goto S, Jauregui R, Ito M, Kawashima S, Moriya Y, et al. EGENES: Transcriptome-Based Plant Database of Genes with Metabolic Pathway Information and Expressed Sequence Tag Indices in KEGG. Plant Physiology. 2007;144(2):857-66.
Darabi M, Izadi-Darbandi A, Masoudi-Nejad A, Naghavi MR, Nemat-Zadeh G. Bioinformatics study of the 3-hydroxy-3-methylglotaryl-coenzyme A reductase (HMGR) gene in Gramineae. Mol Biol Rep. 2012;39(9):8925-35.
Du Y, Kong N, Zhang J. Genetic Mechanism Revealed of Age-Related Macular Degeneration Based on Fusion of Statistics and Machine Learning Method. Frontiers in Genetics. 2021;12.
Oca AI, Pérez-Sala Á, Pariente A, Ochoa R, Velilla S, Peláez R, et al. Predictive Biomarkers of Age-Related Macular Degeneration Response to Anti-VEGF Treatment. J Pers Med. 2021;11(12). Epub 20211208.
Mah MM, Roverato N, Groettrup M. Regulation of Interferon Induction by the Ubiquitin-Like Modifier FAT10. Biomolecules. 2020;10(6).
Boehm AN, Bialas J, Catone N, Sacristan-Reviriego A, van der Spuy J, Groettrup M, et al. The ubiquitin-like modifier FAT10 inhibits retinal PDE6 activity and mediates its proteasomal degradation. Journal of Biological Chemistry. 2020;295(42):14402-18.
Radhakrishnan Y, Hamil KG, Yenugu S, Young SL, French FS, Hall SH. Identification, characterization, and evolution of a primate beta-defensin gene cluster. Genes Immun. 2005;6(3):203-10.
Cai H, Fields M, Hoshino R, Del Priore L. Effects of Aging and Anatomic Location on Gene Expression in Human Retina. Frontiers in Aging Neuroscience. 2012;4.
Whitmore SS, Wagner AH, DeLuca AP, Drack AV, Stone EM, Tucker BA, et al. Transcriptomic analysis across nasal, temporal, and macular regions of human neural retina and RPE/choroid by RNA-Seq. Exp Eye Res. 2014;129:93-106.
Roesler R, Kent P, Luft T, Schwartsmann G, Merali Z. Gastrin-releasing peptide receptor signaling in the integration of stress and memory. Neurobiol Learn Mem. 2014;112:44-52.
Ischia J, Patel O, Bolton D, Shulkes A, Baldwin GS. Expression and function of gastrin-releasing peptide (GRP) in normal and cancerous urological tissues. BJU Int. 2014;113 Suppl 2:40-7.
Petronilho F, Danielski LG, Roesler R, Schwartsmann G, Dal-Pizzol F. Gastrin-releasing peptide as a molecular target for inflammatory diseases: an update. Inflamm Allergy Drug Targets. 2013;12(3):172-7.
Chen YJ, Yeung L, Sun CC, Huang CC, Chen KS, Lu YH. Age-Related Macular Degeneration in Chronic Kidney Disease: A Meta-Analysis of Observational Studies. Am J Nephrol. 2018;48(4):278-91.
Roesler R, Luft T, Schwartsmann G. Targeting the gastrin-releasing peptide receptor pathway to treat cognitive dysfunctionassociated with Alzheimer's Disease. Dement Neuropsychol. 2007;1(2):118-23.
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