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Cited by 2Alzheimer's disease research and treatmentsAutophagy in Disease and TherapyDementia and Cognitive Impairment Research

Identification of Lipophagy-Related Gene Signature for Diagnosis and Risk Prediction of Alzheimer’s Disease

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Guo, Hongxiu, Zheng, Siyi, Sun, Shangqi, Shi, Xueying, Wang, Xiufeng, Yang, Yang, Ma, Rong, Li, Gang·2025·Biomedicines
DOI 10.3390/biomedicines13020362PMC PMC11853564
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Abstract

Background: Recent research indicates that lipid metabolism and autophagy play crucial roles in the development of Alzheimer’s disease (AD). Investigating the relationship between AD diagnosis and gene expression related to lipid metabolism, autophagy, and lipophagy may improve early diagnosis and the identification of therapeutic targets. Methods: Transcription datasets from AD patients were obtained from the Gene Expression Omnibus (GEO). Genes associated with lipid metabolism, autophagy, and lipophagy were sourced from the Gene Set Enrichment Analysis (GSEA) database and the Human Autophagy Database (HADb). Lipophagy-related hub genes were identified using a combination of Limma analysis, weighted gene co-expression network analysis (WGCNA), and machine learning techniques. Based on these hub genes, we developed an AD risk prediction nomogram and validated its diagnostic accuracy using three external validation datasets. Additionally, the expression levels of the hub genes were assessed through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: Our analysis identified three hub genes—ACBD5, GABARAPL1, and HSPA8—as being associated with AD progression. The nomogram constructed from these hub genes achieved an area under the curve (AUC) value of 0.894 for AD risk prediction, with all validation sets yielding AUC values greater than 0.8, indicating excellent diagnostic efficacy. qRT-PCR results further corroborated the associations between these hub genes and AD development. Conclusions: This study identified and validated three lipophagy-related hub genes and developed a reliable diagnostic model, offering insights into the pathology of AD and facilitating the diagnosis of AD patients.

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  1. [1]Scheltens P. De Strooper B. Kivipelto M. Holstege H. Chételat G. Teunissen C. Cummings J. van der Flier W. Alzheimer’s disease Lancet 2021 397 1577 1590 10.1016/S0140-6736(20)32205-4 33667416 PMC8354300 · DOI · Paper page
  2. [3]Rhodius-Meester H.F.M. Tijms B.M. Lemstra A.W. Prins N.D. Pijnenburg Y.A.L. Bouwman F. Scheltens P. van der Flier W.M. Survival in memory clinic cohort is short, even in young-onset dementia J. Neurol. Neurosurg. Psychiatry 2019 90 726 728 10.1136/jnnp-2018-318820 30045942 PMC6581097 · DOI
  3. [6]Menon N.K. Dhopeshwarkar G.A. Essential fatty acid deficiency and brain development Prog. Lipid Res. 1982 21 309 326 10.1016/0163-7827(82)90013-3 6763224 · DOI
  4. [7]Barupal D.K. Baillie R. Fan S. Saykin A.J. Meikle P.J. Arnold M. Nho K. Fiehn O. Kaddurah-Daouk R. Sets of coregulated serum lipids are associated with Alzheimer’s disease pathophysiology Alzheimer’s Dement 2019 11 619 627 10.1016/j.dadm.2019.07.002 31517024 PMC6732667 · DOI
  5. [8]Kunkle B.W. Grenier-Boley B. Sims R. Bis J.C. Damotte V. Naj A.C. Boland A. Vronskaya M. van der Lee S.J. Amlie-Wolf A. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing Nat. Genet. 2019 51 414 430 10.1038/s41588-019-0358-2 30820047 PMC6463297 · DOI · Paper page
  6. [9]Kao Y.C. Ho P.C. Tu Y.K. Jou I.M. Tsai K.J. Lipids and Alzheimer’s Disease Int. J. Mol. Sci. 2020 21 1505 10.3390/ijms21041505 32098382 PMC7073164 · DOI · Paper page
  7. [12]Thorwald M.A. Silva J. Head E. Finch C.E. Amyloid futures in the expanding pathology of brain aging and dementia Alzheimer’s Dement 2023 19 2605 2617 10.1002/alz.12896 36536382 PMC10271937 · DOI
  8. [13]Laval T. Ouimet M. A role for lipophagy in atherosclerosis Nat. Rev. Cardiol. 2023 20 431 432 10.1038/s41569-023-00885-z 37161064 PMC10169197 · DOI
  9. [14]Luo R. Su L.Y. Li G. Yang J. Liu Q. Yang L.X. Zhang D.F. Zhou H. Xu M. Fan Y. Activation of PPARA-mediated autophagy reduces Alzheimer disease-like pathology and cognitive decline in a murine model Autophagy 2020 16 52 69 10.1080/15548627.2019.1596488 30898012 PMC6984507 · DOI · Paper page
  10. [15]Li Q. Zhao Y. Guo H. Li Q. Yan C. Li Y. He S. Wang N. Wang Q. Impaired lipophagy induced-microglial lipid droplets accumulation contributes to the buildup of TREM1 in diabetes-associated cognitive impairment Autophagy 2023 19 2639 2656 10.1080/15548627.2023.2213984 37204119 PMC10472854 · DOI
  11. [16]Xu Y. Propson N.E. Du S. Xiong W. Zheng H. Autophagy deficiency modulates microglial lipid homeostasis and aggravates tau pathology and spreading Proc. Natl. Acad. Sci. USA 2021 118 e2023418118 10.1073/pnas.2023418118 34187889 PMC8271658 · DOI · Paper page
  12. [18]Zhang Y. Miao Y. Tan J. Chen F. Lei P. Zhang Q. Identification of mitochondrial related signature associated with immune microenvironment in Alzheimer’s disease J. Transl. Med. 2023 21 458 10.1186/s12967-023-04254-9 37434203 PMC10334674 · DOI
  13. [19]Wang B. Fu C. Wei Y. Xu B. Yang R. Li C. Qiu M. Yin Y. Qin D. Ferroptosis-related biomarkers for Alzheimer’s disease: Identification by bioinformatic analysis in hippocampus Front. Cell. Neurosci. 2022 16 1023947 10.3389/fncel.2022.1023947 36467613 PMC9709107 · DOI
  14. [20]Wang Y. Wan X. Du S. Integrated analysis revealing a novel stemness-metabolism-related gene signature for predicting prognosis and immunotherapy response in hepatocellular carcinoma Front. Immunol. 2023 14 1100100 10.3389/fimmu.2023.1100100 37622118 PMC10445950 · DOI
  15. [21]Chen B. Xie K. Zhang J. Yang L. Zhou H. Zhang L. Peng R. Comprehensive analysis of mitochondrial dysfunction and necroptosis in intracranial aneurysms from the perspective of predictive, preventative, and personalized medicine Apoptosis 2023 28 1452 1468 10.1007/s10495-023-01865-x 37410216 PMC10425526 · DOI
  16. [22]Tang G. Qi L. Sun Z. Liu J. Lv Z. Chen L. Huang B. Zhu S. Liu Y. Li Y. Evaluation and analysis of incidence and risk factors of lower extremity venous thrombosis after urologic surgeries: A prospective two-center cohort study using LASSO-logistic regression Int. J. Surg. 2021 89 105948 10.1016/j.ijsu.2021.105948 33892158 · DOI
  17. [23]Liu F. Huang Y. Liu F. Wang H. Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning Front. Immunol. 2023 14 1126647 10.3389/fimmu.2023.1126647 36969166 PMC10033585 · DOI
  18. [24]Liang Z. Hu X. Lin R. Tang Z. Ye Z. Mao R. Chen W. Zhou Y. Identification of shared gene signatures and molecular mechanisms between chronic kidney disease and ulcerative colitis Front. Immunol. 2023 14 1078310 10.3389/fimmu.2023.1078310 36860851 PMC9970095 · DOI
  19. [25]Liu C. Zhou Y. Zhou Y. Tang X. Tang L. Wang J. Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning Comput. Biol. Med. 2023 152 106388 10.1016/j.compbiomed.2022.106388 36470144 · DOI
  20. [26]Yu E. Zhang M. Xu G. Liu X. Yan J. Consensus cluster analysis of apoptosis-related genes in patients with osteoarthritis and their correlation with immune cell infiltration Front. Immunol. 2023 14 1202758 10.3389/fimmu.2023.1202758 37860011 PMC10582959 · DOI
  21. [27]Xu M. Zhou H. Hu P. Pan Y. Wang S. Liu L. Liu X. Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning Front. Immunol. 2023 14 1084531 10.3389/fimmu.2023.1084531 36911691 PMC9992203 · DOI
  22. [28]Merlo S. Spampinato S. Canonico P.L. Copani A. Sortino M.A. Alzheimer’s disease: Brain expression of a metabolic disorder? Trends Endocrinol. Metab. 2010 21 537 544 10.1016/j.tem.2010.05.005 20541952 · DOI
  23. [29]Horton P. Next-generation bioinformatics: Connecting bases to genes, networks and disease Brief. Bioinform. 2014 15 137 10.1093/bib/bbt090 24637213 PMC3956072 · DOI
  24. [30]Punzo F. Mientjes E.J. Rohe C.F. Scianguetta S. Amendola G. Oostra B.A. Bertoli-Avella A.M. Perrotta S. A mutation in the acyl-coenzyme A binding domain-containing protein 5 gene (ACBD5) identified in autosomal dominant thrombocytopenia J. Thromb. Haemost. 2010 8 2085 2087 10.1111/j.1538-7836.2010.03979.x 20626622 · DOI
  25. [31]Granadeiro L. Zarralanga V.E. Rosa R. Franquinho F. Lamas S. Brites P. Ataxia with giant axonopathy in Acbd5-deficient mice halted by adeno-associated virus gene therapy Brain 2023 147 1457 1473 10.1093/brain/awad407 38066620 · DOI · Paper page
  26. [32]Costello J.L. Castro I.G. Hacker C. Schrader T.A. Metz J. Zeuschner D. Azadi A.S. Godinho L.F. Costina V. Findeisen P. ACBD5 and VAPB mediate membrane associations between peroxisomes and the ER J. Cell Biol. 2017 216 331 342 10.1083/jcb.201607055 28108524 PMC5294785 · DOI
  27. [33]Hua R. Cheng D. Coyaud É. Freeman S. Di Pietro E. Wang Y. Vissa A. Yip C.M. Fairn G.D. Braverman N. VAPs and ACBD5 tether peroxisomes to the ER for peroxisome maintenance and lipid homeostasis J. Cell Biol. 2017 216 367 377 10.1083/jcb.201608128 28108526 PMC5294787 · DOI · Paper page
  28. [35]Keulers T.G. Libregts S.F. Beaumont J.E.J. Savelkouls K.G. Bussink J. Duimel H. Dubois L. Zonneveld M.I. López-Iglesias C. Bezstarosti K. Secretion of pro-angiogenic extracellular vesicles during hypoxia is dependent on the autophagy-related protein GABARAPL1 J Extracell. Vesicles 2021 10 e12166 10.1002/jev2.12166 34859607 PMC8640512 · DOI
  29. [36]Li Y. Zhang Y. Wang L. Wang P. Xue Y. Li X. Qiao X. Zhang X. Xu T. Liu G. Autophagy impairment mediated by S-nitrosation of ATG4B leads to neurotoxicity in response to hyperglycemia Autophagy 2017 13 1145 1160 10.1080/15548627.2017.1320467 28633005 PMC5529069 · DOI
  30. [37]Mei T. Li Y. Orduña Dolado A. Li Z. Andersson R. Berliocchi L. Rasmussen L.J. Pooled analysis of frontal lobe transcriptomic data identifies key mitophagy gene changes in Alzheimer’s disease brain Front. Aging Neurosci. 2023 15 1101216 10.3389/fnagi.2023.1101216 37358952 PMC10288858 · DOI
  31. [38]Vianello C. Salluzzo M. Anni D. Boriero D. Buffelli M. Carboni L. Increased Expression of Autophagy-Related Genes in Alzheimer’s Disease-Type 2 Diabetes Mellitus Comorbidity Models in Cells Int. J. Environ. Res. Public Health 2023 20 4540 10.3390/ijerph20054540 36901549 PMC10002426 · DOI
  32. [39]Chakrama F.Z. Seguin-Py S. Le Grand J.N. Fraichard A. Delage-Mourroux R. Despouy G. Perez V. Jouvenot M. Boyer-Guittaut M. GABARAPL1 (GEC1) associates with autophagic vesicles Autophagy 2010 6 495 505 10.4161/auto.6.4.11819 20404487 · DOI
  33. [41]Stricher F. Macri C. Ruff M. Muller S. HSPA8/HSC70 chaperone protein: Structure, function, and chemical targeting Autophagy 2013 9 1937 1954 10.4161/auto.26448 24121476 · DOI
  34. [42]Ho P.W. Leung C.T. Liu H. Pang S.Y. Lam C.S. Xian J. Li L. Kung M.H. Ramsden D.B. Ho S.L. Age-dependent accumulation of oligomeric SNCA/α-synuclein from impaired degradation in mutant LRRK2 knockin mouse model of Parkinson disease: Role for therapeutic activation of chaperone-mediated autophagy (CMA) Autophagy 2020 16 347 370 10.1080/15548627.2019.1603545 30983487 PMC6984454 · DOI
  35. [43]Xu X. Sun Y. Cen X. Shan B. Zhao Q. Xie T. Wang Z. Hou T. Xue Y. Zhang M. Metformin activates chaperone-mediated autophagy and improves disease pathologies in an Alzheimer disease mouse model Protein Cell 2021 12 769 787 10.1007/s13238-021-00858-3 34291435 PMC8464644 · DOI · Paper page
  36. [44]Fontaine S.N. Zheng D. Sabbagh J.J. Martin M.D. Chaput D. Darling A. Trotter J.H. Stothert A.R. Nordhues B.A. Lussier A. DnaJ/Hsc70 chaperone complexes control the extracellular release of neurodegenerative-associated proteins EMBO J. 2016 35 1537 1549 10.15252/embj.201593489 27261198 PMC4946142 · DOI
  37. [45]Kaushik S. Cuervo A.M. Degradation of lipid droplet-associated proteins by chaperone-mediated autophagy facilitates lipolysis Nat. Cell Biol. 2015 17 759 770 10.1038/ncb3166 25961502 PMC4449813 · DOI
  38. [46]Lan Z.Q. Ge Z.Y. Lv S.K. Zhao B. Li C.X. The regulatory role of lipophagy in central nervous system diseases Cell Death Discov. 2023 9 229 10.1038/s41420-023-01504-z 37414782 PMC10326259 · DOI · Paper page
  39. [47]Galasko D. Active immunization in Alzheimer’s disease Nat. Aging 2021 1 493 495 10.1038/s43587-021-00076-w 37117835 · DOI
  40. [48]Dai L. Shen Y. Insights into T-cell dysfunction in Alzheimer’s disease Aging Cell 2021 20 e13511 10.1111/acel.13511 34725916 PMC8672785 · DOI
  41. [49]Xu H. Jia J. Single-Cell RNA Sequencing of Peripheral Blood Reveals Immune Cell Signatures in Alzheimer’s Disease Front. Immunol. 2021 12 645666 10.3389/fimmu.2021.645666 34447367 PMC8382575 · DOI · Paper page
  42. [50]Ribeiro F.C. Cozachenco D. Heimfarth L. Fortuna J.T.S. de Freitas G.B. de Sousa J.M. Alves-Leon S.V. Leite R.E.P. Suemoto C.K. Grinberg L.T. Synaptic proteasome is inhibited in Alzheimer’s disease models and associates with memory impairment in mice Commun. Biol. 2023 6 1127 10.1038/s42003-023-05511-9 37935829 PMC10630330 · DOI
  43. [51]Nakamura T. Lipton S.A. ‘SNO’-Storms Compromise Protein Activity and Mitochondrial Metabolism in Neurodegenerative Disorders Trends Endocrinol. Metab. 2017 28 879 892 10.1016/j.tem.2017.10.004 29097102 PMC5701818 · DOI
  44. [53]Rousseaux M.W. Revelli J.P. Vazquez-Velez G.E. Kim J.Y. Craigen E. Gonzales K. Beckinghausen J. Zoghbi H.Y. Depleting Trim28 in adult mice is well tolerated and reduces levels of α-synuclein and tau eLife 2018 7 e36768 10.7554/eLife.36768 29863470 PMC5993537 · DOI
  45. [54]Nguyen H.D. Kim W.K. Huong Vu G. Molecular mechanisms implicated in protein changes in the Alzheimer’s disease human hippocampus Mech. Ageing Dev. 2024 219 111930 10.1016/j.mad.2024.111930 38554950 · DOI
  46. [55]Sun J. Li L. Xiong L. Chen F. She L. Tang H. Zeng Y. Duan Y. Li L. Wang W. Parthenolide alleviates cognitive dysfunction and neurotoxicity via regulation of AMPK/GSK3β(Ser9)/Nrf2 signaling pathway Biomed. Pharmacother. 2023 169 115909 10.1016/j.biopha.2023.115909 37992573 · DOI

Cited by

Sorted by citations

Authors
Guo, H., Zheng, S., Sun, S., Shi, X., Wang, X., Yang, Y., Ma, R., & Li, G.
Affiliations
  • Wuhan Union Hospital (CN)
  • Binzhou Medical University (CN)
  • Huazhong University of Science and Technology (CN)
Corresponding author
  • Ma, Rong
  • Li, Gang
ORCID
Venue
Biomedicines
Publisher
MDPI AG
Published
2025-02-05
Volume
13
Issue
2
Pages
362
ISSN
2227-9059 (ISSN-L)
DOI
10.3390/biomedicines13020362
PMC
PMC11853564
OpenAlex
W4407151070
Cited by
2
References
9
Citations by year
2026: 2
FWCI
1.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Biomedicines (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
National Natural Science Foundation of China (82271230)
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Epidemiology › Autophagy in Disease and Therapy
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords
Alzheimer's disease, lipophagy, autophagy, lipid metabolism, hub genes, GABARAPL1, Hsc70, risk prediction nomogram, early diagnosis, gene expression signatures

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