Identification of Combined Biomarker for Predicting Alzheimer’s Disease Using Machine Learning
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objectives Alzheimer’s disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.
Abstract
Objectives Alzheimer’s disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.
참고문헌 (33)
- Puthiyedth N, "Identification of differentially expressed genes through integrated study of Alzheimer’s disease affected brain regions", PLoS One, 11, pp.e0152342~ (2016)
- Bradbury R, "Topics in medicinal chemistry" (2007), Springer
- Zhou S, "Identification of 5potential predictive biomarkers for Alzheimer’s disease by integrating the unified test for molecular signatures and weighted gene coexpression network analysis", J Gerontol A Biol Sci Med Sci, 78, pp.653~658 (2023)
- Alzheimer’s Association, "2022 Alzheimer’s disease facts and figures", Alzheimers Dement, 18, pp.700~789 (2022)
- Baumgart M, "Summary of the evidence on modifiable risk factors for cognitive decline and dementia : a population-based perspective", Alzheimers Dement, 11, pp.718~726 (2015)
- Golde TE, "Alzheimer’s disease-the journey of a healthy brain into organ failure", Mol Neurodegener, 17, pp.18~ (2022)
- Tanzi RE, "The genetics of Alzheimer disease", Cold Spring Harb Perspect Med, 2, pp.a006296~ (2012)
- Roberts R, "Classification and epidemiology of MCI", Clin Geriatr Med, 29, pp.753~772 (2013)
- Pendlebury ST, "Prevalence, incidence, and factors associated with pre-stroke and post-stroke dementia : a systematic review and meta-analysis", Lancet Neurol, 8, pp.1006~1018 (2009)
- Serrano-Pozo A, "Neuropathological alterations in Alzheimer disease", Cold Spring Harb Perspect Med, 1, pp.a006189~ (2011)
- Lv M, "Identification of osteoporosis markers through bioinformatic functional analysis of serum proteome", Medicine(Baltimore), 99, pp.e22172~ (2020)
- Ralston SH, "Genetics of osteoporosis", Proc Nutr Soc, 66, pp.158~165 (2007)
- Kim SK, "Osteoporosis risk prediction using machine learning and conventional methods", Annu Int Conf IEEE Eng Med Biol Soc, 2013, pp.188~191 (2013)
- Shim JG, "Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women", Arch Osteoporos, 15, pp.169~ (2020)
- Zheng Z, "Identification of combined biomarkers for predicting the risk of osteoporosis using machine learning", Aging(Albany NY), 14, pp.4270~4280 (2022)
- Ashburner M, "Gene ontology : tool for the unification of biology", Nat Genet, 25, pp.25~29 (2000)
- Huang DW, "The DAVID gene functional classification tool : a novel biological module-centric algorithm to functionally analyze large gene lists", Genome Biol, 8, pp.R183~ (2007)
- Zhang X, "Prediction of chemosensitivity in multiple primary cancer patients using machine learning", Anticancer Res, 41, pp.2419~2429 (2021)
- McLachlan GJ, "Discriminant analysis and statistical pattern recognition" (2004), Wiley-Interscience
- Liu YZ, "A novel pathophysiological mechanism for osteoporosis suggested by an in vivo gene expression study of circulating monocytes", J Biol Chem, 280, pp.29011~29016 (2005)
- Altman NS, "An introduction to kernel and nearest-neighbor nonparametric regression", Am Stat, 46, pp.175~185 (1992)
- Vapnik V, "The nature of statistical learning theory" (2000), Springer
- Breiman L, "Random forests", Mach Learn, 45, pp.5~32 (2001)
- Kawaue T, "Lzts1 controls both neuronal delamination and outer radial glial-like cell generation during mammalian cerebral development", Nat Commun, 10, pp.2780~ (2019)
- Kropp M, "The expression profile of the tumor suppressor gene Lzts1 suggests a role in neuronal development", Dev Dyn, 241, pp.984~994 (2012)
- Willison AG, "The neurodevelopmental spectrum seen with CHD2 variants", Pediatr Investig, 6, pp.147~148 (2022)
- Iwata-Otsubo A, "DOCK3-related neurodevelopmental syndrome : biallelic intragenic deletion of DOCK3 in a boy with developmental delay and hypotonia", Am J Med Genet A, 176, pp.241~245 (2018)
- Markunas CA, "Genetic variants in SLC9A9 are associated with measures of attention-deficit", hyperactivity disorder symptoms in families, 2010, pp.20, 73~81
- Kirtay M, "ATR regulates neuronal activity by modulating presynaptic firing", Nat Commun, 12, pp.4067~ (2021)
- Gerschütz A, "Neuron-specific alterations in signal transduction pathways associated with Alzheimer’s disease", J Alzheimers Dis, 40, pp.135~142 (2014)
- Antonell A, "Altered blood gene expression of tumor-related genes(PRKCB, BECN1, and CDKN2A)in Alzheimer’s disease", Mol Neurobiol, 53, pp.5902~5911 (2016)
- Zhou Z, "Molecular identification of protein kinase C beta in Alzheimer’s disease", Aging(Albany NY), 12, pp.21798~21808 (2020)
- Figueroa RL, "Predicting sample size required for classification performance", BMC Med Inform Decis Mak, 12, pp.8~ (2012)