Skip to content
2000
Volume 17, Issue 1
  • ISSN: 1874-4672
  • E-ISSN: 1874-4702
file format text download EPUB
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background

Temporal lobe epilepsy (TLE) and major depressive disorder (MDD) are prevalent and complex neurological disorders that affect individuals globally. Clinical and epidemiological studies indicate a significant comorbidity between TLE and MDD; however, the shared molecular mechanisms underlying this relationship remain unclear. This study aims to explore the common key genes associated with TLE and MDD through a systematic analysis of gene expression profiles, elucidate their underlying molecular pathological mechanisms, and evaluate the potential applications of these genes in diagnostic and therapeutic contexts.

Methods

Brain tissue gene expression data for TLE and MDD were obtained from the GEO database. Differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), functional enrichment, and protein-protein interaction (PPI) network construction were performed to identify shared gene modules. LASSO and random forest (RF) machine learning models were used to select diagnostic candidate genes, validated through ROC curve analysis. Immune infiltration analysis explored the immune involvement of key genes, while single-cell sequencing confirmed gene expression across cell types. Potential therapeutic drugs were identified using a drug database.

Results

A total of 372 DEGs were identified as either up- or down-regulated between TLE and MDD, with WGCNA revealing nine shared gene modules. Seven hub genes, including HTR7 and CDHR2, demonstrated strong ROC performance. Immune infiltration analysis revealed changes in immune cell populations linked to key genes, confirmed by single-cell sequencing. Upadacitinib was identified as a potential therapeutic drug targeting these genes.

Conclusion

This study identified shared gene expression profiles between TLE and MDD, emphasizing immune pathway-related molecular mechanisms. Immune infiltration analysis and single-cell sequencing underscored the significance of immune regulation in their comorbidity, while drug prediction highlights candidates for precision medicine, establishing a foundation for future research and therapeutic strategies.

© 2024 The Author(s).
© 2024 The Author(s). Published by Bentham Science Publishers. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/cmp/10.2174/0118761429380394250217093030
2024-01-01
2025-09-05

  • From This Site

    /content/journals/cmp/10.2174/0118761429380394250217093030
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmp/17/1/CMP-17-E18761429380394.html?itemId=/content/journals/cmp/10.2174/0118761429380394250217093030&mimeType=html&fmt=ahah

References

  1. BoscoD.B. TianD.S. WuL.J. Neuroimmune interaction in seizures and epilepsy: Focusing on monocyte infiltration.FEBS J.2020287224822483710.1111/febs.1542832473609
     [Google Scholar]
  2. LiuY. ChenQ. JeongH-W. Dopamine signaling regulates hematopoietic stem and progenitor cell function.Blood202213925366810.1182/blood.202201640135737403
     [Google Scholar]
  3. PrinceM. PatelV. SaxenaS. MajM. MaselkoJ. PhillipsM.R. RahmanA. No health without mental health.Lancet2007370959085987710.1016/S0140‑6736(07)61238‑017804063
     [Google Scholar]
  4. KondziellaD. AlvestadS. VaalerA. SonnewaldU. Which clinical and experimental data link temporal lobe epilepsy with depression?J. Neurochem.200710362136215210.1111/j.1471‑4159.2007.04926.x17887964
     [Google Scholar]
  5. ValenteK.D.R. FilhoB.G. Depression and temporal lobe epilepsy represent an epiphenomenon sharing similar neural networks: Clinical and brain structural evidences.Arq. Neuropsiquiatr.201371318319010.1590/S0004‑282X201300030001123563720
     [Google Scholar]
  6. DevinskyO. VezzaniA. O’BrienT.J. JetteN. SchefferI.E. Curtisd.M. PeruccaP. Epilepsy.Nat. Rev. Dis. Primers2018411802410.1038/nrdp.2018.2429722352
     [Google Scholar]
  7. MillerA.H. RaisonC.L. The role of inflammation in depression: From evolutionary imperative to modern treatment target.Nat. Rev. Immunol.2016161223410.1038/nri.2015.526711676
     [Google Scholar]
  8. GuerriniP. Human milk fortifiers.Acta Paediatr.199483s402373910.1111/j.1651‑2227.1994.tb13358.x7841618
     [Google Scholar]
  9. LuoH ZhaoQ WeiW Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer.Sci. Transl. Med.202012524eaax753310.1126/scitranslmed.aax7533
     [Google Scholar]
  10. LangfelderP. HorvathS. WGCNA: An R package for weighted correlation network analysis.BMC Bioinformatics20089155910.1186/1471‑2105‑9‑55919114008
     [Google Scholar]
  11. TokunagaR. NaseemM. LoJ.H. BattaglinF. SoniS. PucciniA. BergerM.D. ZhangW. BabaH. LenzH.J. B cell and B cell-related pathways for novel cancer treatments.Cancer Treat. Rev.201973101910.1016/j.ctrv.2018.12.00130551036
     [Google Scholar]
  12. WurmM.J. RathouzP.J. HanlonB.M. Regularized Ordinal Regression and the ordinalNet R Package.J. Stat. Softw.202199610.18637/jss.v099.i0634512213
     [Google Scholar]
  13. TangF. BarbacioruC. WangY. NordmanE. LeeC. XuN. WangX. BodeauJ. TuchB.B. SiddiquiA. LaoK. SuraniM.A. mRNA-Seq whole-transcriptome analysis of a single cell.Nat. Methods20096537738210.1038/nmeth.131519349980
     [Google Scholar]
  14. ZhangX. LiT. LiuF. ChenY. YaoJ. LiZ. HuangY. WangJ. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems.Mol. Cell2019731130142.e510.1016/j.molcel.2018.10.02030472192
     [Google Scholar]
  15. BarrettT. WilhiteS.E. LedouxP. EvangelistaC. KimI.F. TomashevskyM. MarshallK.A. PhillippyK.H. ShermanP.M. HolkoM. YefanovA. LeeH. ZhangN. RobertsonC.L. SerovaN. DavisS. SobolevaA. NCBI GEO: Archive for functional genomics data sets—update.Nucleic Acids Res.201241D1D991D99510.1093/nar/gks119323193258
     [Google Scholar]
  16. 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]
  17. KanehisaM. GotoS. KEGG: Kyoto encyclopedia of genes and genomes.Nucleic Acids Res.2000281273010.1093/nar/28.1.2710592173
     [Google Scholar]
  18. The Gene Ontology Consortium The Gene Ontology Resource: 20 years and still GOing strong.Nucleic Acids Res.201947D1D330D33810.1093/nar/gky105530395331
     [Google Scholar]
  19. YuG. WangL.G. HanY. HeQ.Y. clusterProfiler: An R package for comparing biological themes among gene clusters.OMICS201216528428710.1089/omi.2011.011822455463
     [Google Scholar]
  20. SzklarczykD. GableA.L. NastouK.C. LyonD. KirschR. PyysaloS. DonchevaN.T. LegeayM. FangT. BorkP. JensenL.J. Meringv.C. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets.Nucl. Acids Res.202149D1D605D61210.1093/nar/gkaa107433237311
     [Google Scholar]
  21. FranzM. RodriguezH. LopesC. ZuberiK. MontojoJ. BaderG.D. MorrisQ. GeneMANIA update 2018.Nucleic Acids Res.201846W1W60W6410.1093/nar/gky31129912392
     [Google Scholar]
  22. YangC. DelcherC. ShenkmanE. RankaS. Machine learning approaches for predicting high cost high need patient expenditures in health care.Biomed. Eng. Online201817Suppl. 113110.1186/s12938‑018‑0568‑330458798
     [Google Scholar]
  23. EllisK. KerrJ. GodboleS. LanckrietG. WingD. MarshallS. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.Physiol. Meas.201435112191220310.1088/0967‑3334/35/11/219125340969
     [Google Scholar]
  24. AlderdenJ. PepperG.A. WilsonA. WhitneyJ.D. RichardsonS. ButcherR. JoY. CumminsM.R. Predicting pressure injury in critical care patients: A machine-learning model.Am. J. Crit. Care201827646146810.4037/ajcc201852530385537
     [Google Scholar]
  25. LiY.R. MengK. YangG. LiuB.H. LiC.Q. ZhangJ.Y. ZhangX.M. Diagnostic genes and immune infiltration analysis of colorectal cancer determined by LASSO and SVM machine learning methods: A bioinformatics analysis.J. Gastrointest. Oncol.20221331188120310.21037/jgo‑22‑53635837194
     [Google Scholar]
  26. PanX. JinX. WangJ. HuQ. DaiB. Placenta inflammation is closely associated with gestational diabetes mellitus.Am. J. Transl. Res.20211354068407934149999
     [Google Scholar]
  27. 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]
  28. ZamaniM. Alizadeh-TabariS. SinghS. LoombaR. Meta‐analysis: Prevalence of, and risk factors for, non‐alcoholic fatty liver disease in patients with inflammatory bowel disease.Aliment. Pharmacol. Ther.202255889490710.1111/apt.1687935274325
     [Google Scholar]
  29. WishartD.S. KnoxC. GuoA.C. ShrivastavaS. HassanaliM. StothardP. ChangZ. WoolseyJ. DrugBank: A comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res.20063490001D668D67210.1093/nar/gkj06716381955
     [Google Scholar]
  30. GulyaevaN.V. Stress-associated molecular and cellular hippocampal mechanisms common for epilepsy and comorbid depressive disorders.Biochemistry202186664165610.1134/S000629792106003134225588
     [Google Scholar]
  31. TombiniM. AssenzaG. RicciL. LanzoneJ. BoscarinoM. VicoC. MagliozziA. LazzaroD.V. Temporal lobe epilepsy and alzheimer’s disease: From preclinical to clinical evidence of a strong association.J. Alzheimers Dis. Rep.20215124326110.3233/ADR‑20028634113782
     [Google Scholar]
  32. VezzaniA. FrenchJ. BartfaiT. BaramT.Z. The role of inflammation in epilepsy.Nat. Rev. Neurol.201171314010.1038/nrneurol.2010.17821135885
     [Google Scholar]
  33. MaguireJ. Mechanisms of psychiatric comorbidities in epilepsy.Curr. Top. Behav. Neurosci.20205510714410.1007/7854_2020_19233723802
     [Google Scholar]
  34. WangJ. QiuS. XuY. LiuZ. WenX. HuX. ZhangR. LiM. WangW. HuangR. Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy.Clin. Neurophysiol.201412591744175610.1016/j.clinph.2013.12.12024686109
     [Google Scholar]
  35. LöscherW. KlitgaardH. TwymanR.E. SchmidtD. New avenues for anti-epileptic drug discovery and development.Nat. Rev. Drug Discov.2013121075777610.1038/nrd412624052047
     [Google Scholar]
  36. PaolicelliR.C. BishtK. TremblayM.Ã. Fractalkine regulation of microglial physiology and consequences on the brain and behavior.Front. Cell. Neurosci.2014812910.3389/fncel.2014.0012924860431
     [Google Scholar]
  37. FriedmanA. KauferD. HeinemannU. Blood–brain barrier breakdown-inducing astrocytic transformation: Novel targets for the prevention of epilepsy.Epilepsy Res.2009852-314214910.1016/j.eplepsyres.2009.03.00519362806
     [Google Scholar]
  38. WetheringtonJ. SerranoG. DingledineR. Astrocytes in the epileptic brain.Neuron200858216817810.1016/j.neuron.2008.04.00218439402
     [Google Scholar]
  39. AvoliM. Curtisd.M. GABAergic synchronization in the limbic system and its role in the generation of epileptiform activity.Prog. Neurobiol.201195210413210.1016/j.pneurobio.2011.07.00321802488
     [Google Scholar]
  40. EngelJ.Jr Biomarkers in epilepsy: Introduction.Biomarkers Med.20115553754410.2217/bmm.11.6222003902
     [Google Scholar]
  41. VezzaniA. AronicaE. MazaratiA. PittmanQ.J. Epilepsy and brain inflammation.Exp. Neurol.2013244112110.1016/j.expneurol.2011.09.03321985866
     [Google Scholar]
  42. NajmI. LalD. VanegasA.M. CendesF. Lopes-CendesI. PalminiA. PaglioliE. SarnatH.B. WalshC.A. WiebeS. AronicaE. BaulacS. CorasR. KobowK. CrossJ.H. GarbelliR. HolthausenH. RösslerK. ThomM. El-OstaA. LeeJ.H. MiyataH. GuerriniR. PiaoY.S. ZhouD. BlümckeI. The ILAE consensus classification of focal cortical dysplasia: An update proposed by an ad hoc task force of the ILAE diagnostic methods commission.Epilepsia20226381899191910.1111/epi.1730135706131
     [Google Scholar]
  43. DevinskyO. VezzaniA. NajjarS. LanerolleD.N.C. RogawskiM.A. Glia and epilepsy: Excitability and inflammation.Trends Neurosci.201336317418410.1016/j.tins.2012.11.00823298414
     [Google Scholar]
  44. MillerA.H. HaroonE. RaisonC.L. FelgerJ.C. Cytokine targets in the brain: Impact on neurotransmitters and neurocircuits.Depress. Anxiety201330429730610.1002/da.2208423468190
     [Google Scholar]
  45. AkiraS. TLR Signaling.Curr. Top. Microbiol. Immunol.200631111610.1007/3‑540‑32636‑7_117048703
     [Google Scholar]
  46. KooJ.W. DumanR.S. IL-1β is an essential mediator of the antineurogenic and anhedonic effects of stress.Proc. Natl. Acad. Sci. USA2008105275175610.1073/pnas.070809210518178625
     [Google Scholar]
  47. DantzerR. O’ConnorJ.C. FreundG.G. JohnsonR.W. KelleyK.W. From inflammation to sickness and depression: When the immune system subjugates the brain.Nat. Rev. Neurosci.200891465610.1038/nrn229718073775
     [Google Scholar]
  48. HabbasS. SantelloM. BeckerD. StubbeH. ZappiaG. LiaudetN. KlausF.R. KolliasG. FontanaA. PryceC.R. SuterT. VolterraA. Neuroinflammatory TNFα impairs memory via astrocyte signaling.Cell201516371730174110.1016/j.cell.2015.11.02326686654
     [Google Scholar]
  49. MullenS.A. BerkovicS.F. Genetic generalized epilepsies.Epilepsia20185961148115310.1111/epi.1404229741207
     [Google Scholar]
  50. PeruccaP. BahloM. BerkovicS.F. The genetics of epilepsy.Annu. Rev. Genomics Hum. Genet.202021120523010.1146/annurev‑genom‑120219‑07493732339036
     [Google Scholar]
  51. ThomasD. HaganJ. 5-HT7 Receptors.Curr. Drug Targets CNS Neurol. Disord.200431819010.2174/156800704348263314965246
     [Google Scholar]
  52. HedlundP.B. The 5-HT7 receptor and disorders of the nervous system: An overview.Psychopharmacology2009206334535410.1007/s00213‑009‑1626‑019649616
     [Google Scholar]
  53. GellynckE. HeyninckK. AndressenK.W. HaegemanG. LevyF.O. VanhoenackerP. CraenenbroeckV.K. The serotonin 5-HT7 receptors: Two decades of research.Exp. Brain Res.2013230455556810.1007/s00221‑013‑3694‑y24042216
     [Google Scholar]
  54. LeopoldoM. LacivitaE. BerardiF. PerroneR. HedlundP.B. Serotonin 5-HT7 receptor agents: Structure-activity relationships and potential therapeutic applications in central nervous system disorders.Pharmacol. Ther.2011129212014810.1016/j.pharmthera.2010.08.01320923682
     [Google Scholar]
  55. DuangkumphaK. StollT. PhetcharaburaninJ. YongvanitP. ThananR. TechasenA. NamwatN. KhuntikeoN. ChamadolN. RoytrakulS. MulvennaJ. MohamedA. ShahA.K. HillM.M. LoilomeW. Discovery and qualification of serum protein biomarker candidates for cholangiocarcinoma diagnosis.J. Proteome Res.20191893305331610.1021/acs.jproteome.9b0024231310545
     [Google Scholar]
  56. DuanW. ZhangY.P. HouZ. HuangC. ZhuH. ZhangC.Q. YinQ. Novel insights into neun: From neuronal marker to splicing regulator.Mol. Neurobiol.20165331637164710.1007/s12035‑015‑9122‑525680637
     [Google Scholar]
  57. ZhangS. JiangC. JiangL. ChenH. HuangJ. GaoX. XiaZ. TranL.J. ZhangJ. ChiH. YangG. TianG. Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay.Tum. Vir. Res.20231620027110.1016/j.tvr.2023.20027137774952
     [Google Scholar]
  58. UrbinaM. ArroyoR. LimaL. 5-HT7 receptors and tryptophan hydroxylase in lymphocytes of rats: Mitogen activation, physical restraint or treatment with reserpine.Neuroimmunomodulation201421524024910.1159/00035714824603678
     [Google Scholar]
/content/journals/cmp/10.2174/0118761429380394250217093030
Loading
Exploring the Immune-related Molecular Mechanisms Underlying the Comorbidity of Temporal Lobe Epilepsy and Major Depressive Disorder through Integrated Data Set Analysis
Curr Mol Pharmacol 17, E18761429380394 (2024); https://doi.org/10.2174/0118761429380394250217093030
/content/journals/cmp/10.2174/0118761429380394250217093030
/content/journals/cmp/10.2174/0118761429380394250217093030
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Bioinformatics; Immune-related; Machine learning; Major depressive disorder; Single-cell; Temporal lobe epilepsy

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test