Skip to main content

Advertisement

SpringerLink
Log in

Novel gene signatures predicting and immune infiltration analysis in Parkinson’s disease: based on combining random forest with artificial neural network

  • Original Article
  • Published:
Neurological Sciences Aims and scope Submit manuscript

Abstract

Background

Parkinson’s disease (PD) ranks as the second most prevalent neurodegenerative disorder globally, and its incidence is rapidly rising. The diagnosis of PD relies on clinical characteristics. Although current treatments aim to alleviate symptoms, they do not effectively halt the disease’s progression. Early detection and intervention hold immense importance. This study aimed to establish a new PD diagnostic model.

Methods

Data from a public database were adopted for the construction and validation of a PD diagnostic model with random forest and artificial neural network models. The CIBERSORT platform was applied for the evaluation of immune cell infiltration in PD. Quantitative real-time PCR was performed to verify the accuracy and reliability of the bioinformatics analysis results.

Results

Leveraging existing gene expression data from the Gene Expression Omnibus (GEO) database, we sifted through differentially expressed genes (DEGs) in PD and identified 30 crucial genes through a random forest classifier. Furthermore, we successfully designed a novel PD diagnostic model using an artificial neural network and verified its diagnostic efficacy using publicly available datasets. Our research also suggests that mast cells may play a significant role in the onset and progression of PD.

Conclusion

This work developed a new PD diagnostic model with machine learning techniques and suggested the immune cells as a potential target for PD therapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

All data relevant to the study are included in the article or are uploaded as Supplementary Information. The data supporting the findings of this study are available from the corresponding author upon reasonable request.

References

  1. (2014) Alzheimer’s disease facts and figures. Alzheimer’s Dementia: J Alzheimer’s Assoc 10:e47-92.https://doi.org/10.1016/j.jalz.2014.02.001

  2. Tysnes OB, Storstein A (2017) Epidemiology of Parkinson’s disease. J Neural Transm (Vienna, Austria: 1996) 124:901–905. https://doi.org/10.1007/s00702-017-1686-y

    Article  Google Scholar 

  3. Yang W et al (2020) Current and projected future economic burden of Parkinson’s disease in the U.S. NPJ Parkinson’s Dis 6:15. https://doi.org/10.1038/s41531-020-0117-1

    Article  Google Scholar 

  4. Jankovic J, Tan EK (2020) Parkinson’s disease: etiopathogenesis and treatment. J Neurol Neurosurg Psychiatry 91:795–808. https://doi.org/10.1136/jnnp-2019-322338

    Article  PubMed  Google Scholar 

  5. Bendor JT, Logan TP, Edwards RH (2013) The function of α-synuclein. Neuron 79:1044–1066. https://doi.org/10.1016/j.neuron.2013.09.004

    Article  CAS  PubMed  Google Scholar 

  6. Killinger BA, Kordower JH (2019) Spreading of alpha-synuclein - relevant or epiphenomenon? J Neurochem 150:605–611. https://doi.org/10.1111/jnc.14779

    Article  CAS  PubMed  Google Scholar 

  7. Dickson DW et al (2009) Neuropathological assessment of Parkinson’s disease: refining the diagnostic criteria. Lancet Neurol 8:1150–1157. https://doi.org/10.1016/s1474-4422(09)70238-8

    Article  CAS  PubMed  Google Scholar 

  8. Iwai A et al (1995) The precursor protein of non-A beta component of Alzheimer’s disease amyloid is a presynaptic protein of the central nervous system. Neuron 14:467–475. https://doi.org/10.1016/0896-6273(95)90302-x

    Article  CAS  PubMed  Google Scholar 

  9. Ahmadi SA et al (2020) Analyzing the co-localization of substantia nigra hyper-echogenicities and iron accumulation in Parkinson’s disease: a multi-modal atlas study with transcranial ultrasound and MRI. NeuroImage Clin 26:102185. https://doi.org/10.1016/j.nicl.2020.102185

    Article  PubMed  PubMed Central  Google Scholar 

  10. Obeso JA et al (2017) Past, present, and future of Parkinson’s disease: a special essay on the 200th Anniversary of the Shaking Palsy. Mov Disord: Off J Mov Disord Soc 32:1264–1310. https://doi.org/10.1002/mds.27115

    Article  CAS  Google Scholar 

  11. Del Tredici K, Braak H (2012) Lewy pathology and neurodegeneration in premotor Parkinson’s disease. Mov Disord: Off J Mov Disord Soc 27:597–607. https://doi.org/10.1002/mds.24921

    Article  Google Scholar 

  12. Lenka A, Padmakumar C, Pal PK (2017) Treatment of older Parkinson’s disease. Int Rev Neurobiol 132:381–405. https://doi.org/10.1016/bs.irn.2017.01.005

    Article  CAS  PubMed  Google Scholar 

  13. Braak H, Ghebremedhin E, Rüb U, Bratzke H, Del Tredici K (2004) Stages in the development of Parkinson’s disease-related pathology. Cell Tissue Res 318:121–134. https://doi.org/10.1007/s00441-004-0956-9

    Article  PubMed  Google Scholar 

  14. Fox SH et al (2018) International Parkinson and movement disorder society evidence-based medicine review: update on treatments for the motor symptoms of Parkinson’s disease. Mov Disord: Off J Mov Disord Soc 33:1248–1266. https://doi.org/10.1002/mds.27372

    Article  CAS  Google Scholar 

  15. Reich SG, Savitt JM (2019) Parkinson’s disease. Med Clin North Am 103:337–350. https://doi.org/10.1016/j.mcna.2018.10.014

    Article  PubMed  Google Scholar 

  16. Surmeier DJ, Obeso JA, Halliday GM (2017) Selective neuronal vulnerability in Parkinson disease. Nat Rev Neurosci 18:101–113. https://doi.org/10.1038/nrn.2016.178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Titova N, Padmakumar C, Lewis SJG, Chaudhuri KR (2017) Parkinson’s: a syndrome rather than a disease? J Neural Transm (Vienna, Austria: 1996) 124:907–914. https://doi.org/10.1007/s00702-016-1667-6

    Article  CAS  Google Scholar 

  18. Greenland JC, Williams-Gray CH, Barker RA (2019) The clinical heterogeneity of Parkinson’s disease and its therapeutic implications. Eur J Neurosci 49:328–338. https://doi.org/10.1111/ejn.14094

    Article  PubMed  Google Scholar 

  19. Clark LN, Louis ED (2018) Essential tremor. Handbook Clin Neurol 147:229–239. https://doi.org/10.1016/b978-0-444-63233-3.00015-4

    Article  Google Scholar 

  20. Deng H, Wang P, Jankovic J (2018) The genetics of Parkinson disease. Ageing Res Rev 42:72–85. https://doi.org/10.1016/j.arr.2017.12.007

    Article  CAS  PubMed  Google Scholar 

  21. Blauwendraat C, Nalls MA, Singleton AB (2020) The genetic architecture of Parkinson’s disease. Lancet Neurol 19:170–178. https://doi.org/10.1016/s1474-4422(19)30287-x

    Article  CAS  PubMed  Google Scholar 

  22. Saberi-Karimian M et al (2021) Potential value and impact of data mining and machine learning in clinical diagnostics. Crit Rev Clin Lab Sci 58:275–296. https://doi.org/10.1080/10408363.2020.1857681

    Article  PubMed  Google Scholar 

  23. Tai AMY et al (2019) Machine learning and big data: implications for disease modeling and therapeutic discovery in psychiatry. Artif Intell Med 99:101704. https://doi.org/10.1016/j.artmed.2019.101704

    Article  PubMed  Google Scholar 

  24. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  25. Han S, Kim H, Lee Y-S (2020) Double random forest. Mach Learn 109:1569–1586. https://doi.org/10.1007/s10994-020-05889-1

    Article  Google Scholar 

  26. Thiessen ED (2017) What’s statistical about learning? Insights from modelling statistical learning as a set of memory processes. Phil Trans R Soc London. Series B, Biol Sci 372. https://doi.org/10.1098/rstb.2016.0056

  27. Qi Y (2012) Random forest for bioinformatics. Ensemble Mach Learn: Methods Appl. https://doi.org/10.1007/978-1-4419-9326-7_11

    Article  Google Scholar 

  28. Cohen Y et al (2022) Recent advances at the interface of neuroscience and artificial neural networks. J Neurosci: Off J Soc Neurosci 42:8514–8523. https://doi.org/10.1523/jneurosci.1503-22.2022

    Article  CAS  Google Scholar 

  29. Khan J et al (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7:673–679. https://doi.org/10.1038/89044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Nayarisseri A et al (2021) Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery. Curr Drug Targets 22:631–655. https://doi.org/10.2174/1389450122999210104205732

    Article  CAS  PubMed  Google Scholar 

  31. Durrenberger PF et al (2015) Common mechanisms in neurodegeneration and neuroinflammation: a BrainNet Europe gene expression microarray study. J Neural Transm (Vienna, Austria: 1996) 122:1055–1068. https://doi.org/10.1007/s00702-014-1293-0

    Article  CAS  Google Scholar 

  32. Zheng B et al (2010) PGC-1α, a potential therapeutic target for early intervention in Parkinson’s disease. Sci Transl Med 2:52ra73. https://doi.org/10.1126/scitranslmed.3001059

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Durrenberger PF et al (2012) Selection of novel reference genes for use in the human central nervous system: a BrainNet Europe Study. Acta Neuropathol 124:893–903. https://doi.org/10.1007/s00401-012-1027-z

    Article  PubMed  Google Scholar 

  34. Huang DW et al (2007) DAVID Bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 35:W169-175. https://doi.org/10.1093/nar/gkm415

    Article  PubMed  PubMed Central  Google Scholar 

  35. Huang DW et al (2007) The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8:R183. https://doi.org/10.1186/gb-2007-8-9-r183

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. Omics: J Integr Biol 16:284–287. https://doi.org/10.1089/omi.2011.0118

    Article  CAS  Google Scholar 

  37. Liu Y, Zhao H (2017) Variable importance-weighted random forests. Quant Biol (Beijing, China) 5:338–351

    Google Scholar 

  38. Beck MW (2018) NeuralNetTools: visualization and analysis tools for neural networks. J Stat Softw 85:1–20. https://doi.org/10.18637/jss.v085.i11

    Article  PubMed  PubMed Central  Google Scholar 

  39. Robin X et al (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12:77. https://doi.org/10.1186/1471-2105-12-77

    Article  PubMed  PubMed Central  Google Scholar 

  40. Michael Friendly YU (2002) Corrgrams: exploratory displays for correlation matrices. Am Stat 56:316–324

    Article  Google Scholar 

  41. Surmeier DJ (2018) Determinants of dopaminergic neuron loss in Parkinson’s disease. FEBS J 285:3657–3668. https://doi.org/10.1111/febs.14607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Blauwendraat C et al (2019) Parkinson’s disease age at onset genome-wide association study: defining heritability, genetic loci, and α-synuclein mechanisms. Mov Disord: Off J Mov Disord Soc 34:866–875. https://doi.org/10.1002/mds.27659

    Article  CAS  Google Scholar 

  43. Chen X, Cao W, Zhuang Y, Chen S, Li X (2021) Integrative analysis of potential biomarkers and immune cell infiltration in Parkinson’s disease. Brain Res Bull 177:53–63. https://doi.org/10.1016/j.brainresbull.2021.09.010

    Article  CAS  PubMed  Google Scholar 

  44. Bae JR, Kim SH (2017) Synapses in neurodegenerative diseases. BMB Rep 50:237–246. https://doi.org/10.5483/bmbrep.2017.50.5.038

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Goedert M (2001) Alpha-synuclein and neurodegenerative diseases. Nat Rev Neurosci 2:492–501. https://doi.org/10.1038/35081564

    Article  CAS  PubMed  Google Scholar 

  46. Burré J (2015) The synaptic function of α-synuclein. J Parkinson’s Dis 5:699–713. https://doi.org/10.3233/jpd-150642

    Article  Google Scholar 

  47. Gitler AD et al (2008) The Parkinson’s disease protein alpha-synuclein disrupts cellular Rab homeostasis. Proc Natl Acad Sci USA 105:145-150.https://doi.org/10.1073/pnas.0710685105

  48. Oorschot DE (1996) Total number of neurons in the neostriatal, pallidal, subthalamic, and substantia nigral nuclei of the rat basal ganglia: a stereological study using the cavalieri and optical disector methods. J Comp Neurol 366:580–599. https://doi.org/10.1002/(sici)1096-9861(19960318)366:4%3c580::Aid-cne3%3e3.0.Co;2-0

    Article  CAS  PubMed  Google Scholar 

  49. Roberts RC, Force M, Kung L (2002) Dopaminergic synapses in the matrix of the ventrolateral striatum after chronic haloperidol treatment. Synapse (New York, N.Y.) 45:78–85. https://doi.org/10.1002/syn.10081

    Article  CAS  PubMed  Google Scholar 

  50. Bridi JC, Hirth F (2018) Mechanisms of α-synuclein induced synaptopathy in Parkinson’s disease. Front Neurosci 12:80. https://doi.org/10.3389/fnins.2018.00080

    Article  PubMed  PubMed Central  Google Scholar 

  51. Busch DJ et al (2014) Acute increase of α-synuclein inhibits synaptic vesicle recycling evoked during intense stimulation. Mol Biol Cell 25:3926–3941. https://doi.org/10.1091/mbc.E14-02-0708

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Janezic S et al (2013) Deficits in dopaminergic transmission precede neuron loss and dysfunction in a new Parkinson model. Proc Natl Acad Sci USAm 110:E4016-4025.https://doi.org/10.1073/pnas.1309143110

  53. Lee H, James WS, Cowley SA (2017) LRRK2 in peripheral and central nervous system innate immunity: its link to Parkinson’s disease. Biochem Soc Trans 45:131–139. https://doi.org/10.1042/bst20160262

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Sweeney MD, Sagare AP, Zlokovic BV (2018) Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat Rev Neurol 14:133–150. https://doi.org/10.1038/nrneurol.2017.188

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Yu X, Yao JY, He J, Tian JW (2015) Protection of MPTP-induced neuroinflammation and neurodegeneration by rotigotine-loaded microspheres. Life Sci 124:136–143. https://doi.org/10.1016/j.lfs.2015.01.014

    Article  CAS  PubMed  Google Scholar 

  56. Kempuraj D et al (2019) Mast cell proteases activate astrocytes and glia-neurons and release interleukin-33 by activating p38 and ERK1/2 MAPKs and NF-κB. Mol Neurobiol 56:1681–1693. https://doi.org/10.1007/s12035-018-1177-7

    Article  CAS  PubMed  Google Scholar 

  57. Bergot AS et al (2014) HPV16-E7 expression in squamous epithelium creates a local immune suppressive environment via CCL2- and CCL5- mediated recruitment of mast cells. PLoS Pathog 10:e1004466. https://doi.org/10.1371/journal.ppat.1004466

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hong GU, Kim NG, Jeoung D, Ro JY (2013) Anti-CD40 Ab- or 8-oxo-dG-enhanced Treg cells reduce development of experimental autoimmune encephalomyelitis via down-regulating migration and activation of mast cells. J Neuroimmunol 260:60–73. https://doi.org/10.1016/j.jneuroim.2013.04.002

    Article  CAS  PubMed  Google Scholar 

  59. Hong GU, Cho JW, Kim SY, Shin JH, Ro JY (2018) Inflammatory mediators resulting from transglutaminase 2 expressed in mast cells contribute to the development of Parkinson’s disease in a mouse model. Toxicol Appl Pharmacol 358:10–22. https://doi.org/10.1016/j.taap.2018.09.003

    Article  CAS  PubMed  Google Scholar 

  60. Zhang C, Jiang H, Wang P, Liu H, Sun X (2017) Transcription factor NF-kappa B represses ANT1 transcription and leads to mitochondrial dysfunctions. Sci Rep 7:44708. https://doi.org/10.1038/srep44708

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Brochard V et al (2009) Infiltration of CD4+ lymphocytes into the brain contributes to neurodegeneration in a mouse model of Parkinson disease. J Clin Investig 119:182–192. https://doi.org/10.1172/jci36470

    Article  CAS  PubMed  Google Scholar 

  62. Skaper SD, Facci L, Giusti P (2014) Mast cells, glia and neuroinflammation: partners in crime? Immunology 141:314–327. https://doi.org/10.1111/imm.12170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kempuraj D et al (2018) Cross-talk between glia, neurons and mast cells in neuroinflammation associated with Parkinson’s disease. J Neuroimmune Pharmacol: Off J Soc NeuroImmune Pharmacol 13:100–112. https://doi.org/10.1007/s11481-017-9766-1

    Article  Google Scholar 

  64. Hendriksen E, van Bergeijk D, Oosting RS, Redegeld FA (2017) Mast cells in neuroinflammation and brain disorders. Neurosci Biobehav Rev 79:119–133. https://doi.org/10.1016/j.neubiorev.2017.05.001

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This work is funded by Scientific Research Project of Hunan Provincial Health Commission (D202303077877), and this work was supported by the China Postdoctoral Science Foundation (No.2022M713535), the Provincial Natural Science Foundation of Hunan (No.2023JJ41005).

Author information

Authors and Affiliations

  1. Department of Critical Care Medicine, National Clinical Research Center for Genetic Disorders, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China

    Shucai Xie

  2. Department of Medicine Oncology, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), Changde, China

    Pei Peng

  3. Department of Orthopedics, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), Changde, China

    Xingcheng Dong

  4. Department of Neurology, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), No. 818 Renmin Road, Changde, 415000, Hunan, China

    Junxing Yuan & Ji Liang

Authors
  1. Shucai Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pei Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xingcheng Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junxing Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ji Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Shucai Xie designed the study. Pei Peng and Xingcheng Dong collated the data. Ji Liang and Shucai Xie carried out data analyses and produced the initial draft of the manuscript. Shucai Xie and Ji Liang contributed to drafting the manuscript. Shucai Xie and Junxing Yuan conducted experiments. All authors have read and approved the final submitted manuscript.

Corresponding author

Correspondence to Ji Liang.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 50 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, S., Peng, P., Dong, X. et al. Novel gene signatures predicting and immune infiltration analysis in Parkinson’s disease: based on combining random forest with artificial neural network. Neurol Sci 45, 2681–2696 (2024). https://doi.org/10.1007/s10072-023-07299-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10072-023-07299-2

Keywords

Search

Navigation