Identifying the Role of Oligodendrocyte Genes in the Diagnosis of Alzheimer's Disease through Machine Learning and Bioinformatics Analysis

Chen      Yan; Li      Chen; Yao      Yinhui; Shang      Yazhen

Abstract

Background: Due to the heterogeneity of Alzheimer's disease (AD), the underlying pathogenic mechanisms have not been fully elucidated. Oligodendrocyte (OL) damage and myelin degeneration are prevalent features of AD pathology. When oligodendrocytes are subjected to amyloid-beta (Aβ) toxicity, this damage compromises the structural integrity of myelin and results in a reduction of myelin-associated proteins. Consequently, the impairment of myelin integrity leads to a slowdown or cessation of nerve signal transmission, ultimately contributing to cognitive dysfunction and the progression of AD. Consequently, elucidating the relationship between oligodendrocytes and AD from the perspective of oligodendrocytes is instrumental in advancing our understanding of the pathogenesis of AD.

Objective: Here, an attempt is made in this study to identify oligodendrocyte-related biomarkers of AD.

Methods: AD datasets were obtained from the Gene Expression Omnibus database and used for consensus clustering to identify subclasses. Hub genes were identified through differentially expressed genes (DEGs) analysis and oligodendrocyte gene set enrichment. Immune infiltration analysis was conducted using the CIBERSORT method. Signature genes were identified using machine learning algorithms and logistic regression. A diagnostic nomogram for predicting AD was developed and validated using external datasets and an AD model. A small molecular compound was identified using the eXtreme Sum algorithm.

Results: 46 genes were found to be significantly correlated with AD progression by examining the overlap between DEGs and oligodendrocyte genes. Two subclasses of AD, Cluster A, and Cluster B, were identified, and 9 signature genes were identified using a machine learning algorithm to construct a nomogram. Enrichment analysis showed that 9 genes are involved in apoptosis and neuronal development. Immune infiltration analysis found differences in immune cell presence between AD patients and controls. External datasets and RT-qPCR verification showed variation in signature genes between AD patients and controls. Five small molecular compounds were predicted.

Conclusion: It was found that 9 oligodendrocyte genes can be used to create a diagnostic tool for AD, which could help in developing new treatments.

Keywords: Alzheimer's disease, oligodendrocytes, bioinformatics, machine learning, nomogram, immune infiltration.

[1]
Cuevas PEG, Davidson PM, Mejilla JL, Rodney TW. Reminiscence therapy for older adults with Alzheimer’s disease: A literature review. Int J Ment Health Nurs  2020; 29(3): 364-71.
 [http://dx.doi.org/10.1111/inm.12692] [PMID: 31984570]

[2]
Masters CL, Beyreuther K. Science, medicine, and the future: Alzheimer’s disease. BMJ  1998; 316(7129): 446-8.
 [http://dx.doi.org/10.1136/bmj.316.7129.446] [PMID: 9492674]

[3]
Song T, Chen Y, Li C, et al. Identification of molecular correlations of GSDMD with Pyroptosis in Alzheimer’s disease. Comb Chem High Throughput Screen  2024; 27(14): 2125-39.
 [http://dx.doi.org/10.2174/0113862073285497240226061936] [PMID: 39099451]

[4]
Scheltens P, De Strooper B, Kivipelto M, et al. Alzheimer’s disease. Lancet  2021; 397(10284): 1577-90.
 [http://dx.doi.org/10.1016/S0140-6736(20)32205-4] [PMID: 33667416]

[5]
Esquerda-Canals G, Montoliu-Gaya L, Güell-Bosch J, Villegas S. Mouse models of Alzheimer’s disease. J Alzheimers Dis  2017; 57(4): 1171-83.
 [http://dx.doi.org/10.3233/JAD-170045] [PMID: 28304309]

[6]
Yao Y, Liu Q, Ding S, Chen Y, Song T, Shang Y. Scutellaria baicalensis Georgi stems and leaves flavonoids promote neuroregeneration and ameliorate memory loss in rats through cAMP-PKA-CREB signaling pathway based on network pharmacology and bioinformatics analysis. Heliyon  2024; 10(6): e27161.
 [http://dx.doi.org/10.1016/j.heliyon.2024.e27161] [PMID: 38533079]

[7]
Zhang H, Liu Q, Ding S, Li H, Shang YZ. Flavonoids from stems and leaves of Scutellaria Baicalensis Georgi improve composited aβ-induced Alzheimer’s disease model rats’ memory and neuroplasticity disorders. Comb Chem High Throughput Screen  2023; 26(8): 1519-32.
 [http://dx.doi.org/10.2174/1386207325666221003092627] [PMID: 36200197]

[8]
Zhang X, Wang R, Hu D, et al. Oligodendroglial glycolytic stress triggers inflammasome activation and neuropathology in Alzheimer’s disease. Sci Adv  2020; 6(49): eabb8680.
 [http://dx.doi.org/10.1126/sciadv.abb8680] [PMID: 33277246]

[9]
Zou P, Wu C, Liu TCY, Duan R, Yang L. Oligodendrocyte progenitor cells in Alzheimer’s disease: from physiology to pathology. Transl Neurodegener  2023; 12(1): 52.
 [http://dx.doi.org/10.1186/s40035-023-00385-7] [PMID: 37964328]

[10]
Sadick J S, O'dea M R, Hasel P, Dykstra T, Faustin A, Liddelow S A. Astrocytes and oligodendrocytes undergo subtype-specific transcriptional changes in alzheimer's disease . Neuron  2022; 110(11): 1788-805.
 [http://dx.doi.org/10.1016/j.neuron.2022.03.008]

[11]
Ossenkoppele R, van der Kant R, Hansson O. Tau biomarkers in Alzheimer’s disease: Towards implementation in clinical practice and trials. Lancet Neurol  2022; 21(8): 726-34.
 [http://dx.doi.org/10.1016/S1474-4422(22)00168-5] [PMID: 35643092]

[12]
DeFlitch L, Gonzalez-Fernandez E, Crawley I, Kang SH. Age and Alzheimer’s disease-related Oligodendrocyte changes in Hippocampal Subregions. Front Cell Neurosci  2022; 16: 847097.
 [http://dx.doi.org/10.3389/fncel.2022.847097] [PMID: 35465615]

[13]
Zhang P, Kishimoto Y, Grammatikakis I, et al. Senolytic therapy alleviates Aβ-associated oligodendrocyte progenitor cell senescence and cognitive deficits in an Alzheimer’s disease model. Nat Neurosci  2019; 22(5): 719-28.
 [http://dx.doi.org/10.1038/s41593-019-0372-9] [PMID: 30936558]

[14]
Lane CA, Hardy J, Schott JM. Alzheimer’s disease. Eur J Neurol  2018; 25(1): 59-70.
 [http://dx.doi.org/10.1111/ene.13439] [PMID: 28872215]

[15]
Maes OC, Schipper HM, Chertkow HM, Wang E. Methodology for discovery of Alzheimer’s disease blood-based biomarkers. J Gerontol: Series A  2009; 64A(6): 636-45.
 [http://dx.doi.org/10.1093/gerona/glp045] [PMID: 19366883]

[16]
Naughton BJ, Duncan FJ, Murrey DA, et al. Blood genome-wide transcriptional profiles reflect broad molecular impairments and strong blood-brain links in Alzheimer’s disease. J Alzheimers Dis  2014; 43(1): 93-108.
 [http://dx.doi.org/10.3233/JAD-140606] [PMID: 25079797]

[17]
Niculescu AB, Le-Niculescu H, Roseberry K, et al. Blood biomarkers for memory: Toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs. Mol Psych  2020; 25(8): 1651-72.
 [http://dx.doi.org/10.1038/s41380-019-0602-2] [PMID: 31792364]

[18]
Xu M, Zhou H, Hu P, et al. Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning. Front Immunol  2023; 14: 1084531.
 [http://dx.doi.org/10.3389/fimmu.2023.1084531] [PMID: 36911691]

[19]
Ye L, Liang R, Liu X, Li J, Yue J, Zhang X. Frailty and sarcopenia: A bibliometric analysis of their association and potential targets for intervention. Ageing Res Rev  2023; 92: 102111.
 [http://dx.doi.org/10.1016/j.arr.2023.102111] [PMID: 38031836]

[20]
Lian P, Cai X, Wang C, et al. Identification of metabolism-related subtypes and feature genes in Alzheimer’s disease. J Transl Med  2023; 21(1): 628.
 [http://dx.doi.org/10.1186/s12967-023-04324-y] [PMID: 37715200]

[21]
Yao Y, Zhao J, Zhou X, Hu J, Wang Y. Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction. Bioengineered  2021; 12(1): 2734-49.
 [http://dx.doi.org/10.1080/21655979.2021.1938498] [PMID: 34130601]

[22]
Li T, Li W, Guo X, Tan T, Xiang C, Ouyang Z. Unraveling the potential mechanisms of the anti-osteoporotic effects of the Achyranthes bidentata–Dipsacus asper herb pair: A network pharmacology and experimental study. Front Pharmacol  2023; 14: 1242194.
 [http://dx.doi.org/10.3389/fphar.2023.1242194] [PMID: 37849727]

[23]
Liu P, Liu Z, Luo Q, et al. A pan-cancer analysis of potassium channel tetramerization domain containing 12 in human cancer. Sci Rep  2023; 13(1): 13898.
 [http://dx.doi.org/10.1038/s41598-023-41091-8] [PMID: 37626178]

[24]
Qi B, Li Y, Peng Z, et al. Macrophage-Myofibroblast transition as a potential origin for skeletal muscle fibrosis after injury via complement system activation. J Inflamm Res  2024; 17: 1083-94.
 [http://dx.doi.org/10.2147/JIR.S450599] [PMID: 38384372]

[25]
Yao Y, Zhao J, Hu J, Song H, Wang S, Ying W. Identification of potential biomarkers and immune infiltration in pediatric sepsis via multiple-microarray analysis. Eur J Inflamm  2022; 20
 [http://dx.doi.org/10.1177/1721727X221144392]

[26]
Creanza TM, Delre P, Ancona N, Lentini G, Saviano M, Mangiatordi GF. Structure-based prediction of hERG-related cardiotoxicity: A benchmark study. J Chem Inf Model  2021; 61(9): 4758-70.
 [http://dx.doi.org/10.1021/acs.jcim.1c00744] [PMID: 34506150]

[27]
Choi W, Oh JH, Riyahi S, et al. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys  2018; 45(4): 1537-49.
 [http://dx.doi.org/10.1002/mp.12820] [PMID: 29457229]

[28]
Zhang Q, Li J, Weng L. Identification and validation of aging-related genes in Alzheimer’s disease. Front Neurosci  2022; 16: 905722.
 [http://dx.doi.org/10.3389/fnins.2022.905722] [PMID: 35615282]

[29]
Zhang P, Wu X, Wang D, Zhang M, Zhang B, Zhang Z. Unraveling the role of low-density lipoprotein-related genes in lung adenocarcinoma: Insights into tumor microenvironment and clinical prognosis. Environ Toxicol  2024; 39(10): 4479-95.
 [http://dx.doi.org/10.1002/tox.24230] [PMID: 38488684]

[30]
Wang H, Li H, Rong Y, et al. Bioinformatics identification and validation of maternal blood biomarkers and immune cell infiltration in preeclampsia: An observational study. Medicine (Baltimore)  2024; 103(21): e38260.
 [http://dx.doi.org/10.1097/MD.0000000000038260] [PMID: 38788026]

[31]
Chen HL, Liu YH, Tan CH. Age-related genes affecting the immune cell infiltration in ulcerative colitis revealed by weighted correlation network analysis and machine learning. Eur Rev Med Pharmacol Sci  2023; 27(18): 8447-62.
 [http://dx.doi.org/10.26355/eurrev_202309_33768] [PMID: 37782162]

[32]
Zhang M, Li Q, Zhang W, Yang Y, Gu J, Dong Q. Identification and validation of genes associated with copper death in oral squamous cell carcinoma based on machine learning and weighted gene co-expression network analysis. J Stomatol Oral Maxillofac Surg  2023; 124(6): 101561.
 [http://dx.doi.org/10.1016/j.jormas.2023.101561] [PMID: 37451513]

[33]
Inokuchi R, Iwagami M, Sun Y, Sakamoto A, Tamiya N. Machine learning models predicting undertriage in telephone triage. Ann Med  2022; 54(1): 2989-96.
 [http://dx.doi.org/10.1080/07853890.2022.2136402] [PMID: 36286496]

[34]
Wang W, Tian SL, Jin D, et al. The role of bile acid subtypes in the diagnosis of cholangiocarcinoma. Asia Pac J Clin Oncol  2022; 18(2): e163-72.
 [http://dx.doi.org/10.1111/ajco.13588] [PMID: 34161672]

[35]
Feng Y, Miao F, Li Y, Li M, Cao Y. Validating the 2023 FIGO staging system: A nomogram for endometrioid endometrial cancer and adenocarcinoma. Cancer Med  2024; 13(10): e7216.
 [http://dx.doi.org/10.1002/cam4.7216] [PMID: 38752451]

[36]
Tang Y, Ding C, Xu Q, et al. Prediction nomogram for coronary artery aneurysms at one month in Kawasaki disease. Ital J Pediatr  2023; 49(1): 146.
 [http://dx.doi.org/10.1186/s13052-023-01551-3] [PMID: 37932799]

[37]
Zhang L, Cui Y, Mei J, Zhang Z, Zhang P. Exploring cellular diversity in lung adenocarcinoma epithelium: Advancing prognostic methods and immunotherapeutic strategies. Cell Prolif  2024; e13703.
 [http://dx.doi.org/10.1111/cpr.13703] [PMID: 38946232]

[38]
You W, Ouyang J, Cai Z, Chen Y, Wu X. Comprehensive analyses of immune subtypes of stomach Adenocarcinoma for mRNA vaccination. Front Immunol  2022; 13: 827506.
 [http://dx.doi.org/10.3389/fimmu.2022.827506] [PMID: 35874675]

[39]
Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods  2015; 12(5): 453-7.
 [http://dx.doi.org/10.1038/nmeth.3337] [PMID: 25822800]

[40]
Blalock EM, Geddes JW, Chen KC, Porter NM, Markesbery WR, Landfield PW. Incipient Alzheimer’s disease: Microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc Natl Acad Sci USA  2004; 101(7): 2173-8.
 [http://dx.doi.org/10.1073/pnas.0308512100] [PMID: 14769913]

[41]
Liang WS, Dunckley T, Beach TG, et al. Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol Genom  2007; 28(3): 311-22.
 [http://dx.doi.org/10.1152/physiolgenomics.00208.2006] [PMID: 17077275]

[42]
Liang WS, Reiman EM, Valla J, et al. Alzheimer’s disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc Natl Acad Sci USA  2008; 105(11): 4441-6.
 [http://dx.doi.org/10.1073/pnas.0709259105] [PMID: 18332434]

[43]
Readhead B, Haure-Mirande JV, Funk CC, et al. Multiscale analysis of independent Alzheimer’s cohorts finds disruption of molecular, genetic, and clinical networks by human Herpesvirus. Neuron  2018; 99(1): 64-82.e7.
 [http://dx.doi.org/10.1016/j.neuron.2018.05.023] [PMID: 29937276]

[44]
Liang WS, Dunckley T, Beach TG, et al. Altered neuronal gene expression in brain regions differentially affected by Alzheimer’s disease: A reference data set. Physiol Genom  2008; 33(2): 240-56.
 [http://dx.doi.org/10.1152/physiolgenomics.00242.2007] [PMID: 18270320]

[45]
Webster JA, Gibbs JR, Clarke J, et al. Genetic control of human brain transcript expression in alzheimer disease. Am J Hum Genet  2009; 84(4): 445-58.
 [http://dx.doi.org/10.1016/j.ajhg.2009.03.011] [PMID: 19361613]

[46]
Blalock EM, Buechel HM, Popovic J, Geddes JW, Landfield PW. Microarray analyses of laser-captured hippocampus reveal distinct gray and white matter signatures associated with incipient Alzheimer’s disease. J Chem Neuroanat  2011; 42(2): 118-26.
 [http://dx.doi.org/10.1016/j.jchemneu.2011.06.007] [PMID: 21756998]

[47]
Miller JA, Woltjer RL, Goodenbour JM, Horvath S, Geschwind DH. Genes and pathways underlying regional and cell type changes in Alzheimer’s disease. Genome Med  2013; 5(5): 48.
 [http://dx.doi.org/10.1186/gm452] [PMID: 23705665]

[48]
Yao Z, Dong H, Zhu J, et al. Age-related decline in hippocampal tyrosine phosphatase PTPRO is a mechanistic factor in chemotherapy-related cognitive impairment. JCI Insight  2023; 8(14): e166306.
 [http://dx.doi.org/10.1172/jci.insight.166306] [PMID: 37485875]

[49]
Hokama M, Oka S, Leon J, et al. Altered expression of diabetes-related genes in Alzheimer’s disease brains: The Hisayama study. Cereb Cortex  2014; 24(9): 2476-88.
 [http://dx.doi.org/10.1093/cercor/bht101] [PMID: 23595620]

[50]
Berchtold NC, Cribbs DH, Coleman PD, et al. Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc Natl Acad Sci USA  2008; 105(40): 15605-10.
 [http://dx.doi.org/10.1073/pnas.0806883105] [PMID: 18832152]

[51]
Berchtold NC, Coleman PD, Cribbs DH, Rogers J, Gillen DL, Cotman CW. Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer’s disease. Neurobiol Aging  2013; 34(6): 1653-61.
 [http://dx.doi.org/10.1016/j.neurobiolaging.2012.11.024] [PMID: 23273601]

[52]
Cribbs DH, Berchtold NC, Perreau V, et al. Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: A microarray study. J Neuroinflammation  2012; 9(1): 643.
 [http://dx.doi.org/10.1186/1742-2094-9-179] [PMID: 22824372]

[53]
Astarita G, Jung KM, Berchtold NC, et al. Deficient liver biosynthesis of docosahexaenoic acid correlates with cognitive impairment in Alzheimer’s disease. PLoS One  2010; 5(9): e12538.
 [http://dx.doi.org/10.1371/journal.pone.0012538] [PMID: 20838618]

[54]
Blair LJ, Nordhues BA, Hill SE, et al. Accelerated neurodegeneration through chaperone-mediated oligomerization of tau. J Clin Invest  2013; 123(10): 4158-69.
 [http://dx.doi.org/10.1172/JCI69003] [PMID: 23999428]

[55]
Sárvári M, Hrabovszky E, Kalló I, et al. Menopause leads to elevated expression of macrophage-associated genes in the aging frontal cortex: Rat and human studies identify strikingly similar changes. J Neuroinflammation  2012; 9(1): 773.
 [http://dx.doi.org/10.1186/1742-2094-9-264] [PMID: 23206327]

[56]
Nitsche A, Arnold C, Ueberham U, et al. Alzheimer-related genes show accelerated evolution. Mol Psych  2021; 26(10): 5790-6.
 [http://dx.doi.org/10.1038/s41380-020-0680-1] [PMID: 32203153]

[57]
Xu P, Wu Z, Peng Y, et al. Neuroprotection of triptolide against amyloid-beta1-42-induced toxicity via the Akt/mTOR/p70S6K-mediated autophagy pathway An Acad Bras Cienc  2022; 94(2): e20210938.

[58]
Rocha DJP, Santos CS, Pacheco LGC. Bacterial reference genes for gene expression studies by RT-qPCR: Survey and analysis. Antonie van Leeuwenhoek  2015; 108(3): 685-93.
 [http://dx.doi.org/10.1007/s10482-015-0524-1] [PMID: 26149127]

[59]
Hajeri S, Vidalakis G, Yokomi RK. Detection of viroids using RT-qPCR. Methods Mol Biol  2022; 2316: 153-62.
 [http://dx.doi.org/10.1007/978-1-0716-1464-8_14] [PMID: 34845693]

[60]
Bloom GS. Amyloid-β and Tau. JAMA Neurol  2014; 71(4): 505-8.
 [http://dx.doi.org/10.1001/jamaneurol.2013.5847] [PMID: 24493463]

[61]
Zhang H, Wei W, Zhao M, et al. Interaction between Aβ and Tau in the pathogenesis of Alzheimer’s disease. Int J Biol Sci  2021; 17(9): 2181-92.
 [http://dx.doi.org/10.7150/ijbs.57078] [PMID: 34239348]

[62]
Guo T, Zhang D, Zeng Y, Huang TY, Xu H, Zhao Y. Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer’s disease. Mol Neurodegener  2020; 15(1): 40.
 [http://dx.doi.org/10.1186/s13024-020-00391-7] [PMID: 32677986]

[63]
Duara R, Barker W. Heterogeneity in Alzheimer’s disease diagnosis and progression rates: Implications for therapeutic trials. Neurotherapeutics  2022; 19(1): 8-25.
 [http://dx.doi.org/10.1007/s13311-022-01185-z] [PMID: 35084721]

[64]
Cano A, Turowski P, Ettcheto M, et al. Nanomedicine-based technologies and novel biomarkers for the diagnosis and treatment of Alzheimer’s disease: From current to future challenges. J Nanobiotechnology  2021; 19(1): 122.
 [http://dx.doi.org/10.1186/s12951-021-00864-x] [PMID: 33926475]

[65]
Muralidar S, Ambi SV, Sekaran S, Thirumalai D, Palaniappan B. Role of tau protein in Alzheimer’s disease: The prime pathological player. Int J Biol Macromol  2020; 163: 1599-617.
 [http://dx.doi.org/10.1016/j.ijbiomac.2020.07.327] [PMID: 32784025]

[66]
Sun X, Li L, Dong QX, et al. Rutin prevents tau pathology and neuroinflammation in a mouse model of Alzheimer’s disease. J Neuroinflammation  2021; 18(1): 131.
 [http://dx.doi.org/10.1186/s12974-021-02182-3] [PMID: 34116706]

[67]
van der Kant R, Goldstein LSB, Ossenkoppele R. Amyloid-β-independent regulators of tau pathology in alzheimer disease. Nat Rev Neurosci  2020; 21(1): 21-35.
 [http://dx.doi.org/10.1038/s41583-019-0240-3] [PMID: 31780819]

[68]
Nasrabady SE, Rizvi B, Goldman JE, Brickman AM. White matter changes in Alzheimer’s disease: A focus on myelin and oligodendrocytes. Acta Neuropathol Commun  2018; 6(1): 22.
 [http://dx.doi.org/10.1186/s40478-018-0515-3] [PMID: 29499767]

[69]
Depp C, Sun T, Sasmita AO, et al. Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease. Nature  2023; 618(7964): 349-57.
 [http://dx.doi.org/10.1038/s41586-023-06120-6] [PMID: 37258678]

[70]
Kempuraj D, Thangavel R, Natteru P A, et al. Neuroinflammation induces neurodegeneration. J Neurol Neurosurg Spine  2016; 1(1)

[71]
Shen L, Yang A, Chen X, et al. Proteomic profiling of cerebrum mitochondria, myelin sheath, and synaptosome revealed mitochondrial damage and synaptic impairments in association with 3 × Tg-AD mice model. Cell Mol Neurobiol  2022; 42(6): 1745-63.
 [http://dx.doi.org/10.1007/s10571-021-01052-z] [PMID: 33560469]

[72]
Lai Y, Lin P, Lin F, et al. Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning. Front Immunol  2022; 13: 1046410.
 [http://dx.doi.org/10.3389/fimmu.2022.1046410] [PMID: 36569892]

[73]
Li J, Zhang Y, Lu T, et al. Identification of diagnostic genes for both Alzheimer’s disease and metabolic syndrome by the machine learning algorithm. Front Immunol  2022; 13: 1037318.
 [http://dx.doi.org/10.3389/fimmu.2022.1037318] [PMID: 36405716]

[74]
Yamada D, Kawabe K, Tosa I, et al. Inhibition of the glutamine transporter SNAT1 confers neuroprotection in mice by modulating the mTOR-autophagy system. Commun Biol  2019; 2(1): 346.
 [http://dx.doi.org/10.1038/s42003-019-0582-4] [PMID: 31552299]

[75]
Tan J, Xu Y, Han F, Ye X. Genetical modification on adipose-derived stem cells facilitates facial nerve regeneration. Aging (Albany NY)  2019; 11(3): 908-20.
 [http://dx.doi.org/10.18632/aging.101790] [PMID: 30728320]

[76]
Chong MS, Goh LK, Lim WS, et al. Gene expression profiling of peripheral blood leukocytes shows consistent longitudinal downregulation of TOMM40 and upregulation of KIR2DL5A, PLOD1, and SLC2A8 among fast progressors in early Alzheimer’s disease. J Alzheimers Dis  2013; 34(2): 399-405.
 [http://dx.doi.org/10.3233/JAD-121621] [PMID: 23234877]

[77]
Lagisetty Y, Bourquard T, Al-Ramahi I, et al. Identification of risk genes for Alzheimer’s disease by gene embedding. Cell Genom  2022; 2(9): 100162.
 [http://dx.doi.org/10.1016/j.xgen.2022.100162] [PMID: 36268052]

[78]
Kim SH, Noh MY, Kim HJ, et al. A therapeutic strategy for Alzheimer’s disease focused on immune-inflammatory modulation. Dement Neurocog Disord  2019; 18(2): 33-46.
 [http://dx.doi.org/10.12779/dnd.2019.18.2.33] [PMID: 31297134]

[79]
Wang L, Sato H, Zhao S, Tooyama I. Deposition of lactoferrin in fibrillar-type senile plaques in the brains of transgenic mouse models of Alzheimer’s disease. Neurosci Lett  2010; 481(3): 164-7.
 [http://dx.doi.org/10.1016/j.neulet.2010.06.079] [PMID: 20599473]

Cite As

Current Alzheimer Research

Identifying the Role of Oligodendrocyte Genes in the Diagnosis of Alzheimer's Disease through Machine Learning and Bioinformatics Analysis

Abstract