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Abstract

Introduction

Ischemic Stroke (IS) represents the most prevalent subtype of cerebrovascular disease, characterized by complex pathophysiological mechanisms that remain inadequately characterized, particularly concerning mitochondrial dysfunctions. These mitochondrial impairments are increasingly recognized as contributory factors in IS pathogenesis, emphasizing the need for further investigation into the underlying molecular mechanisms involved.

Methods

In this study, we integrated transcriptomic datasets from the Gene Expression Omnibus (GEO) with the comprehensive MitoCarta3.0 mitochondrial proteome inventory to elucidate the role of dysregulated Mitochondrial-Related Genes (MRGs) in IS. We employed an advanced bioinformatics and machine learning pipeline, incorporating differential expression profiling alongside network-based prioritization using CytoHubba. Rigorous feature selection was conducted through LASSO regression, Support Vector Machine (SVM), and Random Forest (RF) algorithms to derive a robust core MRG signature. Our methodology included training and validation cohorts to construct diagnostic models, which were critically evaluated Receiver Operating Characteristic (ROC) curves, nomograms, and calibration analyses.

Results

Our analysis identified a seven-gene signature comprising DNAJA3, ACSL1, HSDL2, ECHDC2, ECHDC3, ALDH2, and PDK4, which demonstrated significant correlation with activated CD8+ T-cell and natural killer cell infiltration. Furthermore, integrative network analyses revealed intricate regulatory interactions among MRGs, microRNAs, and transcription factors. Notably, drug-target predictions indicated Bezafibrate as a promising therapeutic agent for modulating mitochondrial homeostasis in the context of IS.

Discussion

These findings offer a novel framework for ischemic stroke diagnosis and therapy, yet their computational derivation underscores the need for thorough experimental validation of MRGs and drug candidates, along with the integration of diverse clinical data to confirm their real-world applicability.

Conclusion

Our findings underscore mitochondrial dysfunction not only as a critical factor in IS pathogenesis but also as a viable therapeutic target. The identified MRG signature presents potential for clinical application in diagnostic and pharmacological strategies aimed at ameliorating ischemic injury. This study highlights the translational significance of systems biology approaches within cerebrovascular medicine, warranting further mechanistic exploration of mitochondrial-immune interactions in stroke pathology.

© 2025 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
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References

  1. Shao Z. Dou S. Zhu J. Wang H. Xu D. Wang C. Cheng B. Bai B. The role of mitophagy in ischemic stroke. Front Neurol 2020 11 608610 10.3389/fneur.2020.608610 33424757
    [Google Scholar]
  2. Ding Q. Liu S. Yao Y. Liu H. Cai T. Han L. Global, regional, and national burden of ischemic stroke, 1990–2019. Neurology 2022 98 3 e279 e290 10.1212/WNL.0000000000013115 34911748
    [Google Scholar]
  3. Pu L. Wang L. Zhang R. Zhao T. Jiang Y. Han L. Projected Global trends in ischemic stroke incidence, deaths and disability-adjusted life years from 2020 to 2030. Stroke 2023 54 5 1330 1339 10.1161/STROKEAHA.122.040073 37094034
    [Google Scholar]
  4. Carinci M. Vezzani B. Patergnani S. Ludewig P. Lessmann K. Magnus T. Casetta I. Pugliatti M. Pinton P. Giorgi C. Different roles of mitochondria in cell death and inflammation: Focusing on mitochondrial quality control in ischemic stroke and reperfusion. Biomedicines 2021 9 2 169 10.3390/biomedicines9020169 33572080
    [Google Scholar]
  5. R P. Subramanian S. Ramanathan K. Alhalaiqa F. Assessing the impact of real-time visual feedback during treadmill training on walking improvement in stroke patients. Int J Exp Res Rev 2024 39 Spl Volume 180 189 10.52756/ijerr.2024.v39spl.014
    [Google Scholar]
  6. Xiong Y. Wakhloo A.K. Fisher M. Advances in acute ischemic stroke therapy. Circ Res 2022 130 8 1230 1251 10.1161/CIRCRESAHA.121.319948 35420919
    [Google Scholar]
  7. Liu F. Lu J. Manaenko A. Tang J. Hu Q. Mitochondria in ischemic stroke: New insight and implications. Aging Dis 2018 9 5 924 937 10.14336/AD.2017.1126 30271667
    [Google Scholar]
  8. Norat P. Soldozy S. Sokolowski J.D. Gorick C.M. Kumar J.S. Chae Y. Yağmurlu K. Prada F. Walker M. Levitt M.R. Price R.J. Tvrdik P. Kalani M.Y.S. Mitochondrial dysfunction in neurological disorders: Exploring mitochondrial transplantation. NPJ. Regen. Med. 2020 5 1 22 10.1038/s41536‑020‑00107‑x 33298971
    [Google Scholar]
  9. López-Otín C. Blasco M.A. Partridge L. Serrano M. Kroemer G. Hallmarks of aging: An expanding universe. Cell 2023 186 2 243 278 10.1016/j.cell.2022.11.001 36599349
    [Google Scholar]
  10. Kislin M. Sword J. Fomitcheva I.V. Croom D. Pryazhnikov E. Lihavainen E. Toptunov D. Rauvala H. Ribeiro A.S. Khiroug L. Kirov S.A. Reversible disruption of neuronal mitochondria by ischemic and traumatic injury revealed by quantitative two-photon imaging in the neocortex of anesthetized mice. J. Neurosci. 2017 37 2 333 348 10.1523/JNEUROSCI.1510‑16.2016 28077713
    [Google Scholar]
  11. Li Q. Zhang T. Wang J. Zhang Z. Zhai Y. Yang G.Y. Sun X. Rapamycin attenuates mitochondrial dysfunction via activation of mitophagy in experimental ischemic stroke. Biochem. Biophys. Res. Commun. 2014 444 2 182 188 10.1016/j.bbrc.2014.01.032 24440703
    [Google Scholar]
  12. Gao L. Liu F. Hou P.P. Manaenko A. Xiao Z.P. Wang F. Xu T.L. Hu Q. Neurons release injured mitochondria as “Help-Me” signaling after ischemic stroke. Front. Aging. Neurosci. 2022 14 785761 10.3389/fnagi.2022.785761 35309888
    [Google Scholar]
  13. Tang Y. Guo H. Chen L. Wang X. Chen Q. Gou L. Liu X. Wang X. Development and validation of a prognostic model for mitophagy-related genes in colon adenocarcinoma: A study based on TCGA and GEO databases. PLoS One 2023 18 4 e0284089 10.1371/journal.pone.0284089 37023088
    [Google Scholar]
  14. Pham H.N. Pham L. Sato K. Bioinformatic analysis identified novel candidate genes with the potentials for diagnostic blood testing of primary biliary cholangitis. PLoS One 2023 18 10 e0292998 10.1371/journal.pone.0292998 37844121
    [Google Scholar]
  15. Zhang K. Yang K. Yu G. Shi B. Development of a novel mitochondrial dysfunction-related Alzheimer's disease diagnostic model using bioinformatics and machine learning. Curr. Alzheimer. Res. 2024 10.2174/0115672050353736241218054012 39757625
    [Google Scholar]
  16. Barrett T. Wilhite S.E. Ledoux P. Evangelista C. Kim I.F. Tomashevsky M. Marshall K.A. Phillippy K.H. Sherman P.M. Holko M. Yefanov A. Lee H. Zhang N. Robertson C.L. Serova N. Davis S. Soboleva A. NCBI GEO: archive for functional genomics data sets--update. Nucleic. Acids. Res. 2013 41 Database issue D991 D995 23193258
    [Google Scholar]
  17. Barr T.L. Conley Y. Ding J. Dillman A. Warach S. Singleton A. Matarin M. Genomic biomarkers and cellular pathways of ischemic stroke by RNA gene expression profiling. Neurology 2010 75 11 1009 1014 10.1212/WNL.0b013e3181f2b37f 20837969
    [Google Scholar]
  18. Stamova B. Jickling G.C. Ander B.P. Zhan X. Liu D. Turner R. Ho C. Khoury J.C. Bushnell C. Pancioli A. Jauch E.C. Broderick J.P. Sharp F.R. Gene expression in peripheral immune cells following cardioembolic stroke is sexually dimorphic. PLoS One 2014 9 7 e102550 10.1371/journal.pone.0102550 25036109
    [Google Scholar]
  19. Gautier L. Cope L. Bolstad B.M. Irizarry R.A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004 20 3 307 315 10.1093/bioinformatics/btg405 14960456
    [Google Scholar]
  20. Leek J.T. Johnson W.E. Parker H.S. Jaffe A.E. Storey J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012 28 6 882 883 10.1093/bioinformatics/bts034 22257669
    [Google Scholar]
  21. Rath S. Sharma R. Gupta R. Ast T. Chan C. Durham T.J. Goodman R.P. Grabarek Z. Haas M.E. Hung W.H.W. Joshi P.R. Jourdain A.A. Kim S.H. Kotrys A.V. Lam S.S. McCoy J.G. Meisel J.D. Miranda M. Panda A. Patgiri A. Rogers R. Sadre S. Shah H. Skinner O.S. To T.L. Walker M.A. Wang H. Ward P.S. Wengrod J. Yuan C.C. Calvo S.E. Mootha V.K. MitoCarta3.0: An updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic. Acids. Res. 2021 49 D1 D1541 D1547 10.1093/nar/gkaa1011 33174596
    [Google Scholar]
  22. Zhao W. Fang H. Wang T. Yao C. Identification of mitochondria-related biomarkers in childhood allergic asthma. BMC. Med. Genomics. 2024 17 1 141 10.1186/s12920‑024‑01901‑y 38783263
    [Google Scholar]
  23. Li S. Li X. Jiang S. Wang C. Hu Y. Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches. BMC. Med. Genomics. 2024 17 1 120 10.1186/s12920‑024‑01891‑x 38702721
    [Google Scholar]
  24. Peng C. Zhang Y. Lang X. Zhang Y. Role of mitochondrial metabolic disorder and immune infiltration in diabetic cardiomyopathy: new insights from bioinformatics analysis. J. Transl. Med. 2023 21 1 66 10.1186/s12967‑023‑03928‑8 36726122
    [Google Scholar]
  25. Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic. Acids. Res. 2015 43 7 e47 10.1093/nar/gkv007 25605792
    [Google Scholar]
  26. Gustavsson E.K. Zhang D. Reynolds R.H. Garcia-Ruiz S. Ryten M. ggtranscript : An R package for the visualization and interpretation of transcript isoforms using ggplot2. Bioinformatics 2022 38 15 3844 3846 10.1093/bioinformatics/btac409 35751589
    [Google Scholar]
  27. Yu B. Tao D. Heatmap regression via randomized rounding. IEEE Trans Pattern Anal Mach Intell 2022 44 11 8276 8289 10.1109/TPAMI.2021.3103980 34379589
    [Google Scholar]
  28. Jia A. Xu L. Wang Y. Venn diagrams in bioinformatics. Brief. Bioinform. 2021 22 5 bbab108 10.1093/bib/bbab108 33839742
    [Google Scholar]
  29. Yu G. Wang L.G. Han Y. He Q.Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012 16 5 284 287 10.1089/omi.2011.0118 22455463
    [Google Scholar]
  30. Szklarczyk D. Gable A.L. Lyon D. Junge A. Wyder S. Huerta-Cepas J. Simonovic M. Doncheva N.T. Morris J.H. Bork P. Jensen L.J. Mering C. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019 47 D1 D607 D613 10.1093/nar/gky1131 30476243
    [Google Scholar]
  31. Smoot M.E. Ono K. Ruscheinski J. Wang P.L. Ideker T. Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics 2011 27 3 431 432 10.1093/bioinformatics/btq675 21149340
    [Google Scholar]
  32. Patil I. Visualizations with statistical details: The ‘ggstatsplot’ approach. J. Open. Source. Softw. 2021 6 61 3167 10.21105/joss.03167
    [Google Scholar]
  33. Xie Z. Bailey A. Kuleshov M.V. Clarke D.J.B. Evangelista J.E. Jenkins S.L. Lachmann A. Wojciechowicz M.L. Kropiwnicki E. Jagodnik K.M. Jeon M. Ma’ayan A. Gene set knowledge discovery with enrichr. Curr. Protoc. 2021 1 3 e90 10.1002/cpz1.90 33780170
    [Google Scholar]
  34. Kuleshov M.V. Jones M.R. Rouillard A.D. Fernandez N.F. Duan Q. Wang Z. Koplev S. Jenkins S.L. Jagodnik K.M. Lachmann A. McDermott M.G. Monteiro C.D. Gundersen G.W. Ma’ayan A. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic. Acids. Res. 2016 44 W1 W90 W97 10.1093/nar/gkw377 27141961
    [Google Scholar]
  35. Yoo M. Shin J. Kim J. Ryall K.A. Lee K. Lee S. Jeon M. Kang J. Tan A.C. DSigDB: Drug signatures database for gene set analysis. Bioinformatics 2015 31 18 3069 3071 10.1093/bioinformatics/btv313 25990557
    [Google Scholar]
  36. Trott O. Olson A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  37. Anbalagan S. Kanakarajan S. Selvaraj R. Kolappapillai P. In silico molecular docking analysis of flavone and phytol from Vilvam (Aegle marmelos) against human hepatocellular carcinoma (HepG-2) mitochondrial proteins. Int. J. Exp. Res. Rev. 2023 36 405 414 10.52756/ijerr.2023.v36.035
    [Google Scholar]
  38. He Z. Ning N. Zhou Q. Khoshnam S.E. Farzaneh M. Mitochondria as a therapeutic target for ischemic stroke. Free. Radic. Biol. Med. 2020 146 45 58 10.1016/j.freeradbiomed.2019.11.005 31704373
    [Google Scholar]
  39. Ram Bachan S. K R. Srinivasan V. Suganthirababu P. Efficacy of dry needling in enhancing hand function and reducing spasticity in MCA stroke patients: A prospective case report. Int. J. Exp. Res. Rev. 2024 42 162 168 10.52756/ijerr.2024.v42.014
    [Google Scholar]
  40. Umasankar Y. Srinivasan V. Suganthirababu P. Murugaiyan P. Efficacy of proprioceptive neuromuscular facilitation on jaw function in bruxism among post stroke survivor: A case study. Int. J. Exp. Res. Rev. 2024 42 292 297 10.52756/ijerr.2024.v42.025
    [Google Scholar]
  41. Anrather J. Iadecola C. Inflammation and stroke: An overview. Neurotherapeutics 2016 13 4 661 670 10.1007/s13311‑016‑0483‑x 27730544
    [Google Scholar]
  42. Yang J.L. Mukda S. Chen S.D. Diverse roles of mitochondria in ischemic stroke. Redox. Biol. 2018 16 263 275 10.1016/j.redox.2018.03.002 29549824
    [Google Scholar]
  43. Andrabi S.S. Parvez S. Tabassum H. Ischemic stroke and mitochondria: Mechanisms and targets. Protoplasma 2020 257 2 335 343 10.1007/s00709‑019‑01439‑2 31612315
    [Google Scholar]
  44. Giacomello M. Pyakurel A. Glytsou C. Scorrano L. The cell biology of mitochondrial membrane dynamics. Nat. Rev. Mol. Cell. Biol. 2020 21 4 204 224 10.1038/s41580‑020‑0210‑7 32071438
    [Google Scholar]
  45. Sun K. Jing X. Guo J. Yao X. Guo F. Mitophagy in degenerative joint diseases. Autophagy 2021 17 9 2082 2092 10.1080/15548627.2020.1822097 32967533
    [Google Scholar]
  46. Hong F. Pan S. Guo Y. Xu P. Zhai Y. PPARs as nuclear receptors for nutrient and energy metabolism. Molecules 2019 24 14 2545 10.3390/molecules24142545 31336903
    [Google Scholar]
  47. Su L.J. Ren Y.C. Chen Z. Ma H.F. Zheng F. Li F. Zhang Y.Y. Gong S.S. Kou J.P. Ginsenoside Rb1 improves brain, lung, and intestinal barrier damage in middle cerebral artery occlusion/reperfusion (MCAO/R) mice via the PPARγ signaling pathway. Chin. J. Nat. Med. 2022 20 8 561 571 10.1016/S1875‑5364(22)60204‑8 36031228
    [Google Scholar]
  48. Ruan W. Li J. Xu Y. Wang Y. Zhao F. Yang X. Jiang H. Zhang L. Saavedra J.M. Shi L. Pang T. MALAT1 Up-regulator polydatin protects brain microvascular integrity and ameliorates stroke through C/EBPβ/MALAT1/CREB/PGC-1α/PPARγ pathway. Cell. Mol. Neurobiol. 2019 39 2 265 286 10.1007/s10571‑018‑00646‑4 30607811
    [Google Scholar]
  49. Kulkarni S.S. Salehzadeh F. Fritz T. Zierath J.R. Krook A. Osler M.E. Mitochondrial regulators of fatty acid metabolism reflect metabolic dysfunction in type 2 diabetes mellitus. Metabolism 2012 61 2 175 185 10.1016/j.metabol.2011.06.014 21816445
    [Google Scholar]
  50. Candelario-Jalil E. Dijkhuizen R.M. Magnus T. Neuroinflammation, stroke, blood-brain barrier dysfunction, and imaging modalities. Stroke 2022 53 5 1473 1486 10.1161/STROKEAHA.122.036946 35387495
    [Google Scholar]
  51. Jha M.K. Rahman M.H. Park D.H. Kook H. Lee I.K. Lee W.H. Suk K. Pyruvate dehydrogenase kinase 2 and 4 gene deficiency attenuates nociceptive behaviors in a mouse model of acute inflammatory pain. J. Neurosci. Res. 2016 94 9 837 849 10.1002/jnr.23727 26931482
    [Google Scholar]
  52. Oh C.J. Ha C.M. Choi Y.K. Park S. Choe M.S. Jeoung N.H. Huh Y.H. Kim H.J. Kweon H.S. Lee J. Lee S.J. Jeon J.H. Harris R.A. Park K.G. Lee I.K. Pyruvate dehydrogenase kinase 4 deficiency attenuates cisplatin-induced acute kidney injury. Kidney. Int. 2017 91 4 880 895 10.1016/j.kint.2016.10.011 28040265
    [Google Scholar]
  53. Dou X. Fu Q. Long Q. Liu S. Zou Y. Fu D. Xu Q. Jiang Z. Ren X. Zhang G. Wei X. Li Q. Campisi J. Zhao Y. Sun Y. PDK4-dependent hypercatabolism and lactate production of senescent cells promotes cancer malignancy. Nat. Metab. 2023 5 11 1887 1910 10.1038/s42255‑023‑00912‑w 37903887
    [Google Scholar]
  54. Sun S. He J. Zhang Y. Xiao R. Yan M. Ren Y. Zhu Y. Jin T. Xia Y. Genetic polymorphisms in the ALDH2 gene and the risk of ischemic stroke in a Chinese han population. Oncotarget 2017 8 60 101936 101943 10.18632/oncotarget.21803 29254215
    [Google Scholar]
  55. Zhang X. Sun A. Ge J. Origin and spread of the ALDH2 Glu504Lys allele. Phenomics 2021 1 5 222 228 10.1007/s43657‑021‑00017‑y 36939783
    [Google Scholar]
  56. Kannangara T.S. Carter A. Xue Y. Dhaliwal J.S. Béïque J.C. Lagace D.C. Excitable adult-generated gabaergic neurons acquire functional innervation in the cortex after stroke. Stem. Cell. Reports. 2018 11 6 1327 1336 10.1016/j.stemcr.2018.10.011 30416050
    [Google Scholar]
  57. Abeysinghe H.C.S. Bokhari L. Quigley A. Choolani M. Chan J. Dusting G.J. Crook J.M. Kobayashi N.R. Roulston C.L. Pre-differentiation of human neural stem cells into GABAergic neurons prior to transplant results in greater repopulation of the damaged brain and accelerates functional recovery after transient ischemic stroke. Stem. Cell. Res. Ther. 2015 6 1 186 10.1186/s13287‑015‑0175‑1 26420220
    [Google Scholar]
  58. Zhang C. Qin F. Li X. Du X. Li T. Identification of novel proteins for lacunar stroke by integrating genome-wide association data and human brain proteomes. BMC. Med. 2022 20 1 211 10.1186/s12916‑022‑02408‑y 35733147
    [Google Scholar]
  59. Muralikrishna Adibhatla R. Hatcher J.F. Phospholipase A2, reactive oxygen species, and lipid peroxidation in cerebral ischemia. Free. Radic. Biol. Med. 2006 40 3 376 387 10.1016/j.freeradbiomed.2005.08.044 16443152
    [Google Scholar]
  60. Andone S. Farczádi L. Imre S. Bălașa R. Fatty acids and lipid paradox-neuroprotective biomarkers in ischemic stroke. Int. J. Mol. Sci. 2022 23 18 10810 10.3390/ijms231810810 36142720
    [Google Scholar]
  61. Sharma N.K. Chuang Key C.C. Civelek M. Wabitsch M. Comeau M.E. Langefeld C.D. Parks J.S. Das S.K. Genetic regulation of Enoyl-CoA hydratase domain-containing 3 in adipose tissue determines insulin sensitivity in african americans and europeans. Diabetes 2019 68 7 1508 1522 10.2337/db18‑1229 31010960
    [Google Scholar]
  62. Huh J.Y. Reilly S.M. Abu-Odeh M. Murphy A.N. Mahata S.K. Zhang J. Cho Y. Seo J.B. Hung C.W. Green C.R. Metallo C.M. Saltiel A.R. TANK-binding kinase 1 regulates the localization of Acyl-CoA synthetase ACSL1 to control hepatic fatty acid oxidation. Cell. Metab. 2020 32 6 1012 1027.e7 10.1016/j.cmet.2020.10.010 33152322
    [Google Scholar]
  63. Zhang H.M. Kuang S. Xiong X. Gao T. Liu C. Guo A.Y. Transcription factor and microRNA co-regulatory loops: Important regulatory motifs in biological processes and diseases. Brief. Bioinform. 2015 16 1 45 58 10.1093/bib/bbt085 24307685
    [Google Scholar]
  64. Feng Y. Li Y. Zhang Y. Zhang B.H. Zhao H. Zhao X. Shi F.D. Jin W.N. Zhang X.A. miR-1224 contributes to ischemic stroke-mediated natural killer cell dysfunction by targeting Sp1 signaling. J. Neuroinflammation. 2021 18 1 133 10.1186/s12974‑021‑02181‑4 34118948
    [Google Scholar]
  65. Deng X. Hou S. Wang Y. Yang H. Wang C. Genetic insights into the relationship between immune cell characteristics and ischemic stroke: A bidirectional Mendelian randomization study. Eur. J. Neurol. 2024 31 5 e16226 10.1111/ene.16226 38323746
    [Google Scholar]
  66. Wu Y. Chen L. Qiu Z. Zhang X. Zhao G. Lu Z. PINK1 protects against dendritic cell dysfunction during sepsis through the regulation of mitochondrial quality control. Mol. Med. 2023 29 1 25 10.1186/s10020‑023‑00618‑5 36809929
    [Google Scholar]
  67. Beloborodova N. Bairamov I. Olenin A. Shubina V. Teplova V. Fedotcheva N. Effect of phenolic acids of microbial origin on production of reactive oxygen species in mitochondria and neutrophils. J. Biomed. Sci. 2012 19 1 89 10.1186/1423‑0127‑19‑89 23061754
    [Google Scholar]
  68. Lee S. Kim J. You J.S. Hyun Y.M. Kim J.Y. Lee J.E. Ischemic stroke outcome after promoting CD4+CD25+ Treg cell migration through CCR4 overexpression in a tMCAO animal model. Sci. Rep. 2024 14 1 10201 10.1038/s41598‑024‑60358‑2 38702399
    [Google Scholar]
  69. Wang H. Wang Z. Wu Q. Yuan Y. Cao W. Zhang X. Regulatory T. Regulatory T cells in ischemic stroke. CNS. Neurosci. Ther. 2021 27 6 643 651 10.1111/cns.13611 33470530
    [Google Scholar]
  70. Xiong X.Y. Liu L. Yang Q.W. Functions and mechanisms of microglia/macrophages in neuroinflammation and neurogenesis after stroke. Prog. Neurobiol. 2016 142 23 44 10.1016/j.pneurobio.2016.05.001 27166859
    [Google Scholar]
  71. Rolfes L. Ruck T. David C. Mencl S. Bock S. Schmidt M. Strecker J.K. Pfeuffer S. Mecklenbeck A.S. Gross C. Gliem M. Minnerup J. Schuhmann M.K. Kleinschnitz C. Meuth S.G. Natural killer cells are present in Rag1−/− mice and promote tissue damage during the acute phase of ischemic stroke. Transl. Stroke. Res. 2022 13 1 197 211 10.1007/s12975‑021‑00923‑3 34105078
    [Google Scholar]
  72. Shi K. Tian D.C. Li Z.G. Ducruet A.F. Lawton M.T. Shi F.D. Global brain inflammation in stroke. Lancet. Neurol. 2019 18 11 1058 1066 10.1016/S1474‑4422(19)30078‑X 31296369
    [Google Scholar]
  73. Stuckey S.M. Ong L.K. Collins-Praino L.E. Turner R.J. Neuroinflammation as a key driver of secondary neurodegeneration following stroke? Int. J. Mol. Sci. 2021 22 23 13101 10.3390/ijms222313101 34884906
    [Google Scholar]
  74. Llovera G. Benakis C. Enzmann G. Cai R. Arzberger T. Ghasemigharagoz A. Mao X. Malik R. Lazarevic I. Liebscher S. Ertürk A. Meissner L. Vivien D. Haffner C. Plesnila N. Montaner J. Engelhardt B. Liesz A. The choroid plexus is a key cerebral invasion route for T cells after stroke. Acta. Neuropathol. 2017 134 6 851 868 10.1007/s00401‑017‑1758‑y 28762187
    [Google Scholar]
  75. Palacios E.H. Weiss A. Function of the Src-family kinases, Lck and Fyn, in T-cell development and activation. Oncogene 2004 23 48 7990 8000 10.1038/sj.onc.1208074 15489916
    [Google Scholar]
  76. Maida C.D. Norrito R.L. Daidone M. Tuttolomondo A. Pinto A. Neuroinflammatory mechanisms in ischemic stroke: Focus on cardioembolic stroke, background, and therapeutic approaches. Int. J. Mol. Sci. 2020 21 18 6454 10.3390/ijms21186454 32899616
    [Google Scholar]
  77. Vivier E. Tomasello E. Baratin M. Walzer T. Ugolini S. Functions of natural killer cells. Nat. Immunol. 2008 9 5 503 510 10.1038/ni1582 18425107
    [Google Scholar]
  78. Gan Y. Liu Q. Wu W. Yin J.X. Bai X.F. Shen R. Wang Y. Chen J. La Cava A. Poursine-Laurent J. Yokoyama W. Shi F.D. Ischemic neurons recruit natural killer cells that accelerate brain infarction. Proc. Natl. Acad. Sci. USA. 2014 111 7 2704 2709 10.1073/pnas.1315943111 24550298
    [Google Scholar]
  79. Ross A.M. Lee C.S. Brewer M. Peripheral immune response and infection in first-time and recurrent ischemic stroke or transient ischemic attack. J. Neurosci. Nurs. 2014 46 4 199 206 10.1097/JNN.0000000000000061 24875290
    [Google Scholar]
  80. Prass K. Meisel C. Höflich C. Braun J. Halle E. Wolf T. Ruscher K. Victorov I.V. Priller J. Dirnagl U. Volk H.D. Meisel A. Stroke-induced immunodeficiency promotes spontaneous bacterial infections and is mediated by sympathetic activation reversal by poststroke T helper cell type 1-like immunostimulation. J. Exp. Med. 2003 198 5 725 736 10.1084/jem.20021098 12939340
    [Google Scholar]
  81. Chen W. Chen Y. Wu L. Gao Y. Zhu H. Li Y. Ji X. Wang Z. Wang W. Han L. Zhu B. Wang H. Xu M. Identification of cell death-related biomarkers and immune infiltration in ischemic stroke between male and female patients. Front. Immunol. 2023 14 1164742 10.3389/fimmu.2023.1164742 37435058
    [Google Scholar]
  82. Zhao X. Liang Q. Li H. Jing Z. Pei D. Single-cell RNA sequencing and multiple bioinformatics methods to identify the immunity and ferroptosis-related biomarkers of SARS-CoV-2 infections to ischemic stroke. Aging (Albany NY) 2023 15 16 8237 8257 10.18632/aging.204966 37606960
    [Google Scholar]
  83. Lu Y. Fujioka H. Wang W. Zhu X. Bezafibrate confers neuroprotection in the 5xFAD mouse model of Alzheimer’s disease. Biochim. Biophys. Acta. Mol. Basis. Dis. 2023 1869 8 166841 10.1016/j.bbadis.2023.166841 37558011
    [Google Scholar]
  84. Brondani M. Roginski A.C. Ribeiro R.T. de Medeiros M.P. Hoffmann C.I.H. Wajner M. Leipnitz G. Seminotti B. Mitochondrial dysfunction, oxidative stress, ER stress and mitochondria-ER crosstalk alterations in a chemical rat model of Huntington’s disease: Potential benefits of bezafibrate. Toxicol. Lett. 2023 381 48 59 10.1016/j.toxlet.2023.04.011 37116597
    [Google Scholar]
  85. Tanne D. Benderly M. Goldbourt U. Boyko V. Brunner D. Graff E. Reicher-Reiss H. Shotan A. Mandelzweig L. Behar S. A prospective study of plasma fibrinogen levels and the risk of stroke among participants in the bezafibrate infarction prevention study. Am. J. Med. 2001 111 6 457 463 10.1016/S0002‑9343(01)00914‑7 11690571
    [Google Scholar]
  86. Tanne D. Koren-Morag N. Graff E. Goldbourt U. Blood lipids and first-ever ischemic stroke/transient ischemic attack in the Bezafibrate Infarction Prevention (BIP) Registry: high triglycerides constitute an independent risk factor. Circulation 2001 104 24 2892 2897 10.1161/hc4901.100384 11739302
    [Google Scholar]
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Investigating the Mechanisms of Mitochondrial Dysfunction in Ischemic Stroke and Predicting Therapeutics Through Machine Learning and Integrated Bioinformatics
Bentham Science Publishers ; https://doi.org/10.2174/0109298673371220250809113736
/content/journals/cmc/10.2174/0109298673371220250809113736
/content/journals/cmc/10.2174/0109298673371220250809113736
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  • Article Type:     Research Article
Keywords: machine learning ; GEO ; Ischemic stroke ; mitochondrial dysfunction ; translational approach ; homeostasis

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