Exosomal mRNA Signatures as Predictive Biomarkers for Risk and Age of Onset in Alzheimer's Disease

被引:3
作者
Bolivar, Daniel A. [1 ]
Mosquera-Heredia, Maria I. [2 ]
Vidal, Oscar M. [2 ]
Barcelo, Ernesto [3 ,4 ,5 ]
Allegri, Ricardo [6 ]
Morales, Luis C. [2 ]
Silvera-Redondo, Carlos [2 ]
Arcos-Burgos, Mauricio [7 ]
Garavito-Galofre, Pilar [2 ]
Velez, Jorge I. [1 ]
机构
[1] Univ Norte, Dept Ind Engn, Barranquilla 081007, Colombia
[2] Univ Norte, Dept Med, Barranquilla 081007, Colombia
[3] Inst Colombiano Neuropedag, Barranquilla 080020, Colombia
[4] Univ Costa, Dept Hlth Sci, Barranquilla 080002, Colombia
[5] Univ Costa, Grp Int Invest Neuroconductual GIINCO, Barranquilla 080002, Colombia
[6] Inst Neurol Res FLENI, Montaneses 2325,C1428AQK, Buenos Aires, Argentina
[7] Univ Antioquia, Fac Med, Dept Psiquiatria, Grp Invest Psiquiatria GIPSI,Inst Invest Med, Medellin 050010, Colombia
关键词
Alzheimer's disease; exosomes; mRNA; machine learning; personalized medicine; EXPRESSION; DIAGNOSIS;
D O I
10.3390/ijms252212293
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory loss. While the precise causes of AD remain unclear, emerging evidence suggests that messenger RNA (mRNA) dysregulation contributes to AD pathology and risk. This study examined exosomal mRNA expression profiles of 15 individuals diagnosed with AD and 15 healthy controls from Barranquilla, Colombia. Utilizing advanced bioinformatics and machine learning (ML) techniques, we identified differentially expressed mRNAs and assessed their predictive power for AD diagnosis and AD age of onset (ADAOO). Our results showed that ENST00000331581 (CADM1) and ENST00000382258 (TNFRSF19) were significantly upregulated in AD patients. Key predictors for AD diagnosis included ENST00000311550 (GABRB3), ENST00000278765 (GGTLC1), ENST00000331581 (CADM1), ENST00000372572 (FOXJ3), and ENST00000636358 (ACY1), achieving > 90% accuracy in both training and testing datasets. For ADAOO, ENST00000340552 (LIMK2) expression correlated with a delay of similar to 12.6 years, while ENST00000304677 (RNASE6), ENST00000640218 (HNRNPU), ENST00000602017 (PPP5D1), ENST00000224950 (STN1), and ENST00000322088 (PPP2R1A) emerged as the most important predictors. ENST00000304677 (RNASE6) and ENST00000602017 (PPP5D1) showed promising predictive accuracy in unseen data. These findings suggest that mRNA expression profiles may serve as effective biomarkers for AD diagnosis and ADAOO, providing a cost-efficient and minimally invasive tool for early detection and monitoring. Further research is needed to validate these results in larger, diverse cohorts and explore the biological roles of the identified mRNAs in AD pathogenesis.
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页数:21
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