Decoding the genetic comorbidity network of Alzheimer's disease

被引:0
|
作者
Zhang, Xueli [1 ,2 ,3 ]
Li, Dantong [1 ,4 ]
Ye, Siting [5 ,6 ]
Liu, Shunming [2 ]
Ma, Shuo [7 ,8 ]
Li, Min [1 ]
Peng, Qiliang [9 ,10 ]
Hu, Lianting [4 ]
Shang, Xianwen [2 ,11 ]
He, Mingguang [2 ,11 ]
Zhang, Lei [12 ,13 ,14 ]
机构
[1] Southern Med Univ, Guangdong Prov Peoples Hosp, Med Res Inst, Guangdong Acad Med Sci, Guangzhou 510080, Peoples R China
[2] Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Guangdong Eye Inst,Dept Ophthalmol, Guangzhou 510080, Peoples R China
[3] Guangdong Prov Key Lab Artificial Intelligence Med, Guangzhou 510080, Peoples R China
[4] Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Med Big Data Ctr, Guangzhou 510080, Peoples R China
[5] Guangzhou Univ Chinese Med, Affiliated Hosp 2, Dept Ultrasound, Guangzhou 510120, Peoples R China
[6] Guangzhou Univ Chinese Med, Affiliated Hosp 2, Dept Orthopaed, Guangzhou 510120, Peoples R China
[7] Guangzhou Med Univ, Clin Data Ctr, Guangzhou Women & Childrens Med Ctr, Guangzhou, Peoples R China
[8] Johnson & Johnson Med Shanghai Device Co, Dept Ethicon Minimally Invas Procedures & Adv Ener, Shanghai, Peoples R China
[9] Soochow Univ, Affiliated Hosp 2, Dept Radiotherapy & Oncol, Suzhou, Peoples R China
[10] Soochow Univ, Inst Radiotherapy & Oncol, Suzhou, Peoples R China
[11] Hong Kong Polytech Univ, Expt Ophthalmol, Hong Kong, Peoples R China
[12] Nanjing Med Univ, Childrens Hosp, Clin Med Res Ctr, Nanjing, Peoples R China
[13] Monash Univ, Fac Med Nursing & Hlth Sci, Sch Translat Med, Melbourne, Vic, Australia
[14] Alfred Hlth, Melbourne Sexual Hlth Ctr, Artificial Intelligence & Modelling Epidemiol Prog, Melbourne, Vic, Australia
来源
BIODATA MINING | 2024年 / 17卷 / 01期
基金
中国国家自然科学基金;
关键词
DEMENTIA;
D O I
10.1186/s13040-024-00394-w
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Alzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers. Graphical AbstractStudy pipeline. The present study has focused on the comorbidities associated with Alzheimer's disease (AD) by constructing a landscape of these comorbidities at various levels, including diseases, genetics, and pathways.1. The study findings reveal novel and significant pathways that contribute to the etiology of AD and its comorbidities.2. By exploring pleiotropic genes and reported biomarkers of AD comorbidities, the study has identified several potential diagnostic biomarker candidates for AD.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Decoding metabolic signatures in Alzheimer’s disease: a mitochondrial perspective
    Daniele Bano
    Dan Ehninger
    Giacinto Bagetta
    Cell Death Discovery, 9
  • [42] Recent Advances: Decoding Alzheimer's Disease With Stem Cells
    Fang, Yi
    Gao, Ting
    Zhang, Baorong
    Pu, Jiali
    FRONTIERS IN AGING NEUROSCIENCE, 2018, 10
  • [43] Decoding molecular mechanisms: brain aging and Alzheimer's disease
    Hayat, Mahnoor
    Syed, Rafay Ali
    Qaiser, Hammad
    Uzair, Mohammad
    Al-Regaiey, Khalid
    Khallaf, Roaa
    Albassam, Lubna Abdullah Mohammed
    Kaleem, Imdad
    Wang, Xueyi
    Wang, Ran
    Bhatti, Mehwish S.
    Bashir, Shahid
    NEURAL REGENERATION RESEARCH, 2025, 20 (08) : 2279 - 2299
  • [44] Alzheimer's Disease Variant Portal: A Catalog of Genetic Findings for Alzheimer's Disease
    Kuksa, Pavel P.
    Liu, Chia-Lun
    Fu, Wei
    Qu, Liming
    Zhao, Yi
    Katanic, Zivadin
    Clark, Kaylyn
    Kuzma, Amanda B.
    Ho, Pei-Chuan
    Tzeng, Kai-Teh
    Valladares, Otto
    Chou, Shin-Yi
    Naj, Adam C.
    Schellenberg, Gerard D.
    Wang, Li-San
    Leung, Yuk Yee
    JOURNAL OF ALZHEIMERS DISEASE, 2022, 86 (01) : 461 - 477
  • [45] Decoding the non-coding RNAs in Alzheimer’s disease
    Nicole Schonrock
    Jürgen Götz
    Cellular and Molecular Life Sciences, 2012, 69 : 3543 - 3559
  • [46] Selenotranscriptome network in Alzheimer's disease
    Cardoso, B. R.
    Day, K.
    PROCEEDINGS OF THE NUTRITION SOCIETY, 2024, 83 (OCE1)
  • [47] Genetic Determinants of Disease Progression in Alzheimer's Disease
    Wang, Xingbin
    Lopez, Oscar L.
    Sweet, Robert A.
    Becker, James T.
    DeKosky, Steven T.
    Barmada, Mahmud M.
    Demirci, F. Yesim
    Kamboh, M. Ilyas
    JOURNAL OF ALZHEIMERS DISEASE, 2015, 43 (02) : 649 - 655
  • [48] From genetic correlations of Alzheimer’s disease to classification with artificial neural network models
    Claudia Cava
    Salvatore D’Antona
    Francesca Maselli
    Isabella Castiglioni
    Danilo Porro
    Functional & Integrative Genomics, 2023, 23
  • [49] From genetic correlations of Alzheimer's disease to classification with artificial neural network models
    Cava, Claudia
    D'Antona, Salvatore
    Maselli, Francesca
    Castiglioni, Isabella
    Porro, Danilo
    FUNCTIONAL & INTEGRATIVE GENOMICS, 2023, 23 (04)
  • [50] An imaging and genetic-based deep learning network for Alzheimer's disease diagnosis
    Li, Yuhan
    Niu, Donghao
    Qi, Keying
    Liang, Dong
    Long, Xiaojing
    FRONTIERS IN AGING NEUROSCIENCE, 2025, 17