中文版 | English
题名

基于单细胞转录组与迁移学习的阿尔茨海默症判别模型的构建

其他题名
CONSTRUCTION OF ALZHEIMER'S DISEASE DISCRIMINANT MODEL BASED ON SINGLE- CELL TRANSCRIPTOMES AND TRANSFER LEARNING
姓名
姓名拼音
ZHAO Pengfei
学号
12133176
学位类型
硕士
学位专业
0710 生物学
学科门类/专业学位类别
07 理学
导师
康林
导师单位
医学院
论文答辩日期
2024-05-07
论文提交日期
2024-06-18
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

阿尔兹海默症具有极高的发病率和死亡率,已成为严重危害个人、家庭乃至整个社会的严重疾病。近年来,尽管诊断领域已取得显著进展,但由于其真实致病机制尚未完全探明,仍无法开发出治愈药物。疾病发生的本质是基因的异常表达,因此,基于基因水平的特征分析和机制研究是目前和未来科研的重点,也是攻克阿尔兹海默症的关键。

以生信分析为先导,识别关键基因位点,最后结合基础实验验证是机制研究最合理可行的方案。本研究基于iPAGE算法,整合单细胞转录组数据与组织转录组数据,并将迁移学习和机器学习有机的结合起来,用以构建鲁棒的阿尔兹海默症判别模型。相较于传统方法构建的模型,该模型具有特征数量更少、预测结果更准,生物学可解释性更强的特质。

研究结果一方面表明iPAGE在保留数据真实信息方面表现优异,且受检测批次效应的影响较小,因此能够有效解决不同来源数据的差异性和难以合并的问题;迁移学习引入先验知识可以提升目标域的学习能力,单细胞转录组作为大数据经迁移学习可以显著提升小样本组织数据的学习效果。另一方面,特征基因对在不同类型数据中展现出相同的表达模式,提示阿尔兹海默症的病理变化涉及大脑的各个区域和不同类型的细胞,呈现出全脑范围所有细胞的改变;模型中确定的核心基因ELAVL3RPH3ARTN1FAIM2SNAP25为揭示阿尔兹海默症致病机制提供了研究方向和目标,并有望成为潜在的治疗靶点。

关键词
语种
中文
培养类别
独立培养
入学年份
2021
学位授予年份
2024-07
参考文献列表

[1] HIPPIUS H, NEUNDÖRFER G. The discovery of Alzheimer’s disease[J]. Dialogues in Clinical Neuroscience, 2003, 5(1): 101–108.
[2] MAURER K, VOLK S, GERBALDO H. Auguste D and Alzheimer’s disease[J]. THE LANCET, 1997, 349.
[3] ALZHEIMER A. Uber einen eigenartigen schweren Er Krankungsprozeb der Hirnrinde[J]. Neurologisches Centralblatt, 1906, 23: 1129–1136.
[4] SCHÖLL M, LOCKHART S N, SCHONHAUT D R, et al. PET Imaging of Tau Deposition in the Aging Human Brain[J]. Neuron, 2016, 89(5): 971–982.
[5] GRAEBER M B, KÖSEL S, GRASBON-FRODL E, et al. Histopathology and APOE genotype of the first Alzheimer disease patient, Auguste D[J]. Neurogenetics, 1998, 1(3): 223–228.
[6] 2023 Alzheimer’s disease facts and figures[J]. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 2023, 19(4): 1598–1695.
[7] Dementia[EB/OL]. https://www.who.int/news-room/fact-sheets/detail/dementia.
[8] QUERFURTH H W, LAFERLA F M. Alzheimer’s disease[J]. The New England Journal of Medicine, 2010, 362(4): 329–344.
[9] GOEDERT M, SPILLANTINI M G. A century of Alzheimer’s disease[J]. Science (New York, N.Y.), 2006, 314(5800): 777–781.
[10] SELKOE D J, HARDY J. The amyloid hypothesis of Alzheimer’s disease at 25 years[J]. EMBO molecular medicine, 2016, 8(6): 595–608.
[11] BALLATORE C, LEE V M-Y, TROJANOWSKI J Q. Tau-mediated neurodegeneration in Alzheimer’s disease and related disorders[J]. Nature Reviews. Neuroscience, 2007, 8(9): 663–672.
[12] 田金洲, 解恒革, 王鲁宁, 等. 中国阿尔茨海默病痴呆诊疗指南(2020年版)[J]. 中华老年医学杂志, 2021, 40(3): 15.
[13] BRIGGS R, KENNELLY S P, O’NEILL D. Drug treatments in Alzheimer’s disease[J]. Clinical Medicine (London, England), 2016, 16(3): 247–253.
[14] PASSERI E, ELKHOURY K, MORSINK M, et al. Alzheimer’s Disease: Treatment Strategies and Their Limitations[J]. International Journal of Molecular Sciences, 2022, 23(22): 13954.
[15] BLENNOW K, HAMPEL H. CSF markers for incipient Alzheimer’s disease[J]. The Lancet Neurology, 2003, 2(10): 605–613.
[16] JANELIDZE S, TEUNISSEN C E, ZETTERBERG H, et al. Head-to-Head Comparison of 8 Plasma Amyloid-β 42/40 Assays in Alzheimer Disease[J]. JAMA neurology, 2021, 78(11): 1375–1382.
[17] PALMQVIST S, TIDEMAN P, CULLEN N, et al. Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures[J]. Nature Medicine, 2021, 27(6): 1034–1042.
[18] JIANG Y, UHM H, IP F C, et al. A blood-based multi-pathway biomarker assay for early detection and staging of Alzheimer’s disease across ethnic groups[J]. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 2024.
[19] LE GUEN Y, BELLOY M E, GRENIER-BOLEY B, et al. Association of Rare APOE Missense Variants V236E and R251G With Risk of Alzheimer Disease[J]. JAMA neurology, 2022, 79(7): 652–663.
[20] BELLENGUEZ C, KÜÇÜKALI F, JANSEN I E, et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias[J]. Nature Genetics, 2022, 54(4): 412–436.
[21] WEINER M W, VEITCH D P, AISEN P S, et al. The Alzheimer’s Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement[J]. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 2017, 13(5): 561–571.
[22] SKLAVENITIS-PISTOFIDIS R, GETZ G, GHOBRIAL I. Single-cell RNA sequencing: one step closer to the clinic[J]. Nature Medicine, 2021, 27(3): 375–376.
[23] GRIFFITHS J A, SCIALDONE A, MARIONI J C. Using single-cell genomics to understand developmental processes and cell fate decisions[J]. Molecular Systems Biology, 2018, 14(4): e8046.
[24] BRIGGS J A, WEINREB C, WAGNER D E, et al. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution[J]. Science (New York, N.Y.), 2018, 360(6392): eaar5780.
[25] JERBY-ARNON L, SHAH P, CUOCO M S, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade[J]. Cell, 2018, 175(4): 984-997.e24.
[26] KUPPE C, IBRAHIM M M, KRANZ J, et al. Decoding myofibroblast origins in human kidney fibrosis[J]. Nature, 2021, 589(7841): 281–286.
[27] BOSSEL BEN-MOSHE N, HEN-AVIVI S, LEVITIN N, et al. Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells[J]. Nature Communications, 2019, 10(1): 3266.
[28] SHAPIRO E, BIEZUNER T, LINNARSSON S. Single-cell sequencing-based technologies will revolutionize whole-organism science[J]. Nature Reviews. Genetics, 2013, 14(9): 618–630.
[29] KOLODZIEJCZYK A A, KIM J K, SVENSSON V, et al. The technology and biology of single-cell RNA sequencing[J]. Molecular Cell, 2015, 58(4): 610–620.
[30] NAWY T. Single-cell sequencing[J]. Nature Methods, 2014, 11(1): 18.
[31] ZHANG X, LI T, LIU F, et al. Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems[J]. Molecular Cell, 2019, 73(1): 130-142.e5.
[32] WANG X, HE Y, ZHANG Q, et al. Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2[J]. Genomics, Proteomics & Bioinformatics, 2021, 19(2): 253–266.
[33] WU F, FAN J, HE Y, et al. Single-cell profiling of tumor heterogeneity and the microenvironment in advanced non-small cell lung cancer[J]. Nature Communications, 2021, 12(1): 2540.
[34] XU K, WANG R, XIE H, et al. Single-cell RNA sequencing reveals cell heterogeneity and transcriptome profile of breast cancer lymph node metastasis[J]. Oncogenesis, 2021, 10(10): 66.
[35] HAQUE A, ENGEL J, TEICHMANN S A, et al. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications[J]. Genome Medicine, 2017, 9(1): 75.
[36] LUQUEZ T, GAUR P, KOSATER I M, et al. Cell type-specific changes identified by single-cell transcriptomics in Alzheimer’s disease[J]. Genome Medicine, 2022, 14(1): 136.
[37] ZHUANG F, QI Z, DUAN K, et al. A Comprehensive Survey on Transfer Learning[J]. Proceedings of the IEEE, 2021, 109(1): 43–76.
[38] WANG J, HORLACHER M, CHENG L, et al. DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning[J]. Bioinformatics (Oxford, England), 2024, 40(2): btae065.
[39] LIN Y, WU T-Y, WAN S, et al. scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning[J]. Nature Biotechnology, Nature Publishing Group, 2022, 40(5): 703–710.
[40] WANG J, AGARWAL D, HUANG M, et al. Data denoising with transfer learning in single-cell transcriptomics[J]. Nature Methods, Nature Publishing Group, 2019, 16(9): 875–878.
[41] GAO Y, CUI Y. Deep transfer learning for reducing health care disparities arising from biomedical data inequality[J]. Nature Communications, Nature Publishing Group, 2020, 11(1): 5131.
[42] SONG Y, ZHU S, ZHANG N, et al. Blood Circulating miRNA Pairs as a Robust Signature for Early Detection of Esophageal Cancer[J]. Frontiers in Oncology, 2021, 11: 723779.
[43] WOZNIAK J M, MILLS R H, OLSON J, et al. Mortality Risk Profiling of Staphylococcus aureus Bacteremia by Multi-omic Serum Analysis Reveals Early Predictive and Pathogenic Signatures[J]. Cell, 2020, 182(5): 1311-1327.e14.
[44] CHENG L, LO L-Y, TANG N L S, et al. CrossNorm: a novel normalization strategy for microarray data in cancers[J]. Scientific Reports, 2016, 6: 18898.
[45] LAZAR C, MEGANCK S, TAMINAU J, et al. Batch effect removal methods for microarray gene expression data integration: a survey[J]. Brief Bioinform, 2013, 14(4): 469–90.
[46] MCHUGH L, SELDON T A, BRANDON R A, et al. A Molecular Host Response Assay to Discriminate Between Sepsis and Infection-Negative Systemic Inflammation in Critically Ill Patients: Discovery and Validation in Independent Cohorts[J]. PLoS Med, 2015, 12(12): e1001916.
[47] SCICLUNA B P, WIEWEL M A, VAN VUGHT L A, et al. Molecular Biomarker to Assist in Diagnosing Abdominal Sepsis upon ICU Admission[J]. Am J Respir Crit Care Med, 2018, 197(8): 1070–1073.
[48] A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium[J]. Nat Biotechnol, 2014, 32(9): 903–14.
[49] LEEK J T, SCHARPF R B, BRAVO H C, et al. Tackling the widespread and critical impact of batch effects in high-throughput data[J]. Nat Rev Genet, 2010, 11(10): 733–9.
[50] WU Q, ZHENG X, LEUNG K-S, et al. meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data[J]. Bioinformatics, 2022, 38(14): 3513–3522.
[51] ZHENG X, LEUNG K-S, WONG M-H, et al. Long non-coding RNA pairs to assist in diagnosing sepsis[J]. BMC Genomics, 2021, 22(1): 275.
[52] LI Q, ZHENG X, XIE J, et al. bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks[J]. Bioinformatics, 2023: btad109.
[53] WANG R, ZHENG X, WANG J, et al. Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia[J]. Briefings in Bioinformatics, 2022, 23(2): bbac002.
[54] BARRETT T, WILHITE S E, LEDOUX P, et al. NCBI GEO: archive for functional genomics data sets—update[J]. Nucleic Acids Research, 2013, 41(Database issue): D991-995.
[55] OTERO-GARCIA M, MAHAJANI S U, WAKHLOO D, et al. Molecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer’s disease[J]. Neuron, Elsevier, 2022, 0(0).
[56] WANG X, TIAN Y, LI C, et al. Exploring the key ferroptosis-related gene in the peripheral blood of patients with Alzheimer’s disease and its clinical significance[J]. Frontiers in Aging Neuroscience, 2022, 14: 970796.
[57] LIANG W S, DUNCKLEY T, BEACH T G, et al. Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain[J]. Physiological Genomics, 2007, 28(3): 311–322.
[58] LIANG W S, DUNCKLEY T, BEACH T G, et al. Altered neuronal gene expression in brain regions differentially affected by Alzheimer’s disease: a reference data set[J]. Physiological Genomics, 2008, 33(2): 240–256.
[59] LIANG W S, REIMAN E M, VALLA J, et al. Alzheimer’s disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(11): 4441–4446.
[60] READHEAD B, HAURE-MIRANDE J-V, FUNK C C, et al. Multiscale Analysis of Independent Alzheimer’s Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus[J]. Neuron, 2018, 99(1): 64-82.e7.
[61] NARAYANAN M, HUYNH J L, WANG K, et al. Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases[J]. Molecular Systems Biology, 2014, 10: 743.
[62] NATIVIO R, LAN Y, DONAHUE G, et al. An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease[J]. Nature Genetics, 2020, 52(10): 1024–1035.
[63] NATIVIO R, DONAHUE G, BERSON A, et al. Dysregulation of the epigenetic landscape of normal aging in Alzheimer’s disease[J]. Nature Neuroscience, 2018, 21(4): 497–505.
[64] LIU X, YU X, ZACK D J, et al. TiGER: A database for tissue-specific gene expression and regulation[J]. BMC Bioinformatics, 2008, 9(1): 271.
[65] YU X, LIN J, MASUDA T, et al. Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae[J]. Nucleic Acids Research, 2006, 34(3): 917–927.
[66] YU X, LIN J, ZACK D J, et al. Identification of tissue-specific cis-regulatory modules based on interactions between transcription factors[J]. BMC Bioinformatics, 2007, 8(1): 437.
[67] YU X, LIN J, ZACK D J, et al. Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues[J]. Nucleic Acids Research, 2006, 34(17): 4925–4936.
[68] BENSON D A, CAVANAUGH M, CLARK K, et al. GenBank[J]. Nucleic Acids Research, 2013, 41(Database issue): D36-42.
[69] KANG J, TANG Q, HE J, et al. RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility[J]. Nucleic Acids Research, 2022, 50(D1): D326–D332.
[70] SZKLARCZYK D, KIRSCH R, KOUTROULI M, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest[J]. Nucleic Acids Research, 2023, 51(D1): D638–D646.
[71] ASHBURNER M, BALL C A, BLAKE J A, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium[J]. Nature Genetics, 2000, 25(1): 25–29.
[72] GENE ONTOLOGY CONSORTIUM, ALEKSANDER S A, BALHOFF J, et al. The Gene Ontology knowledgebase in 2023[J]. Genetics, 2023, 224(1): iyad031.
[73] KNOX C, WILSON M, KLINGER C M, et al. DrugBank 6.0: the DrugBank Knowledgebase for 2024[J]. Nucleic Acids Research, 2024, 52(D1): D1265–D1275.
[74] TEAM R. R: A language and environment for statistical computing.[J]. MSOR connections, 2014.
[75] TIBSHIRANI R. Regression Shrinkage and Selection via The Lasso: A Retrospective[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2011, 73(3): 273–282.
[76] FRIEDMAN J, HASTIE T, TIBSHIRANI R. Regularization Paths for Generalized Linear Models via Coordinate Descent[J]. Journal of Statistical Software, 2010, 33(1): 1–22.
[77] ROBIN X, TURCK N, HAINARD A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves[J]. BMC bioinformatics, 2011, 12: 77.
[78] GINESTET C. ggplot2: Elegant Graphics for Data Analysis[J]. Journal of the Royal Statistical Society Series A: Statistics in Society, 2011, 174(1): 245–246.
[79] WU T, HU E, XU S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data[J]. Innovation (Cambridge (Mass.)), 2021, 2(3): 100141.
[80] HAO Y, HAO S, ANDERSEN-NISSEN E, et al. Integrated analysis of multimodal single-cell data[J]. Cell, Elsevier, 2021, 184(13): 3573-3587.e29.
[81] LIU X, ZHENG X, WANG J, et al. A long non‐coding RNA signature for diagnostic prediction of sepsis upon ICU admission[J]. Clinical and Translational Medicine, 2020, 10(3).
[82] CHENG L, NAN C, KANG L, et al. Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis[J]. Journal of Translational Medicine, 2020, 18(1): 217.
[83] LI H, ZHENG X, GAO J, et al. Whole transcriptome analysis reveals non-coding RNA’s competing endogenous gene pairs as novel form of motifs in serous ovarian cancer[J]. Computers in Biology and Medicine, 2022, 148: 105881.
[84] CHENG L, WU H, ZHENG X, et al. GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion[J]. Bioinformatics, 2023, 39(1): btac850.
[85] MOK S C, BONOME T, VATHIPADIEKAL V, et al. A gene signature predictive for outcome in advanced ovarian cancer identifies a survival factor: microfibril-associated glycoprotein 2[J]. Cancer Cell, 2009, 16(6): 521–532.
[86] YEUNG T-L, LEUNG C S, WONG K-K, et al. TGF-β modulates ovarian cancer invasion by upregulating CAF-derived versican in the tumor microenvironment[J]. Cancer Research, 2013, 73(16): 5016–5028.
[87] KING E R, TUNG C S, TSANG Y T M, et al. The anterior gradient homolog 3 (AGR3) gene is associated with differentiation and survival in ovarian cancer[J]. The American Journal of Surgical Pathology, 2011, 35(6): 904–912.
[88] ELGAAEN B V, OLSTAD O K, SANDVIK L, et al. ZNF385B and VEGFA are strongly differentially expressed in serous ovarian carcinomas and correlate with survival[J]. PloS One, 2012, 7(9): e46317.
[89] YEUNG T-L, LEUNG C S, WONG K-K, et al. ELF3 is a negative regulator of epithelial-mesenchymal transition in ovarian cancer cells[J]. Oncotarget, 2017, 8(10): 16951–16963.
[90] YAMAMOTO Y, NING G, HOWITT B E, et al. In vitro and in vivo correlates of physiological and neoplastic human Fallopian tube stem cells[J]. The Journal of Pathology, 2016, 238(4): 519–530.
[91] HENDRIX N D, WU R, KUICK R, et al. Fibroblast growth factor 9 has oncogenic activity and is a downstream target of Wnt signaling in ovarian endometrioid adenocarcinomas[J]. Cancer Research, 2006, 66(3): 1354–1362.
[92] BOWEN N J, WALKER L D, MATYUNINA L V, et al. Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as ovarian cancer initiating cells[J]. BMC medical genomics, 2009, 2: 71.
[93] KOLDE R. pheatmap: Pretty Heatmaps[J]. 2015.
[94] GRAU J, GROSSE I, KEILWAGEN J. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R[J]. Bioinformatics (Oxford, England), 2015, 31(15): 2595–2597.
[95] YANG Y, ZHANG Y, LI S, et al. A robust and generalizable immune-related signature for sepsis diagnostics[J]. Transactions on Computational Biology and Bioinformatics, 2021: 1–1.
[96] QUINTON R J, DIDOMIZIO A, VITTORIA M A, et al. Whole-genome doubling confers unique genetic vulnerabilities on tumour cells[J]. Nature, 2021, 590(7846): 492–497.
[97] KOMURA K, INAMOTO T, TSUJINO T, et al. Increased BUB1B/BUBR1 expression contributes to aberrant DNA repair activity leading to resistance to DNA-damaging agents[J]. Oncogene, 2021, 40(43): 6210–6222.
[98] SHINDO K, YU J, SUENAGA M, et al. Deleterious Germline Mutations in Patients With Apparently Sporadic Pancreatic Adenocarcinoma[J]. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 2017, 35(30): 3382–3390.
[99] J L, L L, PA A, et al. Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning[J]. Frontiers in genetics, Front Genet, 2022, 13.
[100] CHEN O J, CASTELLSAGUÉ E, MOUSTAFA-KAMAL M, et al. Germline Missense Variants in CDC20 Result in Aberrant Mitotic Progression and Familial Cancer[J]. Cancer Research, 2022, 82(19): 3499–3515.
[101] VILLARROYA-BELTRI C, MALUMBRES M. Mitotic Checkpoint Imbalances in Familial Cancer[J]. Cancer Research, 2022, 82(19): 3432–3434.
[102] BELUR NAGARAJ A, KOVALENKO O, AVELAR R, et al. Mitotic Exit Dysfunction through the Deregulation of APC/C Characterizes Cisplatin-Resistant State in Epithelial Ovarian Cancer[J]. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 2018, 24(18): 4588–4601.
[103] ZOU J, LI Y, LIAO N, et al. Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis[J]. Journal of Ovarian Research, 2022, 15: 30.
[104] URZÚA U, AMPUERO S, ROBY K F, et al. Dysregulation of mitotic machinery genes precedes genome instability during spontaneous pre-malignant transformation of mouse ovarian surface epithelial cells[J]. BMC Genomics, 2016, 17(Suppl 8): 728.
[105] GOU R, ZHENG M, HU Y, et al. Identification and clinical validation of NUSAP1 as a novel prognostic biomarker in ovarian cancer[J]. BMC Cancer, 2022, 22: 690.
[106] ZHAO Y, HE J, LI Y, et al. NUSAP1 potentiates chemoresistance in glioblastoma through its SAP domain to stabilize ATR[J]. Signal Transduction and Targeted Therapy, 2020, 5(1): 44.
[107] YADAV D K, SHARMA A, DUBE P, et al. Identification of crucial hub genes and potential molecular mechanisms in breast cancer by integrated bioinformatics analysis and experimental validation[J]. Computers in Biology and Medicine, 2022, 149: 106036.
[108] ZHENG H, WANG M, ZHANG S, et al. Comprehensive pan-cancer analysis reveals NUSAP1 is a novel predictive biomarker for prognosis and immunotherapy response[J]. International Journal of Biological Sciences, 2023, 19(14): 4689–4708.
[109] SHUKLA S, PATRIC I R P, PATIL V, et al. Methylation silencing of ULK2, an autophagy gene, is essential for astrocyte transformation and tumor growth[J]. The Journal of Biological Chemistry, 2014, 289(32): 22306–22318.
[110] LIANG P, WANG B. An autophagy-independent role of ULK1/ULK2 in mechanotransduction and breast cancer cell migration[J]. Autophagy, 2024: 1–2.
[111] LIANG P, ZHANG J, WU Y, et al. An ULK1/2-PXN mechanotransduction pathway suppresses breast cancer cell migration[J]. EMBO reports, 2023, 24(11): e56850.
[112] LI F, ZHAO C, DIAO Y, et al. MEX3A promotes the malignant progression of ovarian cancer by regulating intron retention in TIMELESS[J]. Cell Death & Disease, 2022, 13(6): 553.
[113] KAMINSKY L A, ARENA R, MYERS J, et al. Updated Reference Standards for Cardiorespiratory Fitness Measured with Cardiopulmonary Exercise Testing: Data from the Fitness Registry and the Importance of Exercise National Database (FRIEND)[J]. Mayo Clinic Proceedings, 2022, 97(2): 285–293.
[114] PAAP D, TAKKEN T. Reference values for cardiopulmonary exercise testing in healthy adults: a systematic review[J]. Expert Review of Cardiovascular Therapy, 2014, 12(12): 1439–1453.
[115] ZHANG M, YANG B, REN T, et al. Dual engine-driven bionic microneedles for early intervention and prolonged treatment of Alzheimer’s disease[J]. Journal of Controlled Release: Official Journal of the Controlled Release Society, 2024, 367: 184–196.
[116] OGAWA Y, KAKUMOTO K, YOSHIDA T, et al. Elavl3 is essential for the maintenance of Purkinje neuron axons[J]. Scientific Reports, Nature Publishing Group, 2018, 8(1): 2722.
[117] MULLIGAN M R, BICKNELL L S. The molecular genetics of nELAVL in brain development and disease[J]. European Journal of Human Genetics, Nature Publishing Group, 2023, 31(11): 1209–1217.
[118] TAN M G K, LEE C, LEE J H, et al. Decreased rabphilin 3A immunoreactivity in Alzheimer’s disease is associated with Aβ burden[J]. Neurochemistry International, 2014, 64: 29–36.
[119] SHI Q, GE Y, HE W, et al. RTN1 and RTN3 protein are differentially associated with senile plaques in Alzheimer’s brains[J]. Scientific Reports, 2017, 7(1): 6145.
[120] HE W, LU Y, QAHWASH I, et al. Reticulon family members modulate BACE1 activity and amyloid-beta peptide generation[J]. Nature Medicine, 2004, 10(9): 959–965.
[121] PLANELLS-FERRER L, URRESTI J, COCCIA E, et al. Fas apoptosis inhibitory molecules: more than death-receptor antagonists in the nervous system[J]. Journal of Neurochemistry, 2016, 139(1): 11–21.
[122] KIVISÄKK P, CARLYLE B C, SWEENEY T, et al. Increased levels of the synaptic proteins PSD-95, SNAP-25, and neurogranin in the cerebrospinal fluid of patients with Alzheimer’s disease[J]. Alzheimer’s Research & Therapy, 2022, 14(1): 58.
[123] NILSSON J, ASHTON N J, BENEDET A L, et al. Quantification of SNAP-25 with mass spectrometry and Simoa: a method comparison in Alzheimer’s disease[J]. Alzheimer’s Research & Therapy, 2022, 14(1): 78.
[124] SALONER R, PAOLILLO E W, WOJTA K J, et al. Sex-specific effects of SNAP-25 genotype on verbal memory and Alzheimer’s disease biomarkers in clinically normal older adults[J]. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 2023, 19(8): 3448–3457.
[125] WOOD H. SNAP25—an early biomarker in AD and CJD[J]. Nature Reviews. Neurology, 2022, 18(10): 575.
[126] HALBGEBAUER S, STEINACKER P, HENGGE S, et al. CSF levels of SNAP-25 are increased early in Creutzfeldt-Jakob and Alzheimer’s disease[J]. Journal of Neurology, Neurosurgery, and Psychiatry, 2022: jnnp-2021-328646.

所在学位评定分委会
生物学
国内图书分类号
R749.1
来源库
人工提交
成果类型学位论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/765867
专题南方科技大学
南方科技大学医学院
推荐引用方式
GB/T 7714
赵鹏飞. 基于单细胞转录组与迁移学习的阿尔茨海默症判别模型的构建[D]. 深圳. 南方科技大学,2024.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可 操作
12133176-赵鹏飞-南方科技大学医(15206KB)----限制开放--请求全文
个性服务
原文链接
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
导出为Excel格式
导出为Csv格式
Altmetrics Score
谷歌学术
谷歌学术中相似的文章
[赵鹏飞]的文章
百度学术
百度学术中相似的文章
[赵鹏飞]的文章
必应学术
必应学术中相似的文章
[赵鹏飞]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
[发表评论/异议/意见]
暂无评论

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。