The Footprint of CHASERR as a Potential Culprit in Alzheimer's Disease Patients: An In-Silico -Experimental Study

• Published in: Current Alzheimer research Vol. 22; no. 3; p. 205
• Main Authors: Khosroabadi, Zahra, Niazmand, Anoosha, Mousavi, Seyed Reza, Hosseini, Neda, Bagheri, Nastaran, Chitsaz, Ahmad, Salehi, Mansoor
• Format: Journal Article
• Language: English
• Published: United Arab Emirates 01.01.2025
• Subjects: Aged; Alzheimer Disease - blood; Alzheimer Disease - genetics; Computer Simulation; Female; Gene Expression Profiling; Gene Regulatory Networks; Humans; Male; RNA, Long Noncoding - blood; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; pathogenesis; expression; lncRNA; Alzheimer’s disease; regulation; CHASERR
• ISSN: 1875-5828
• DOI: 10.2174/0115672050381537250422075255

Dementia has become a major global cause of death, posing significant health and economic challenges. Alzheimer's disease (AD) is the most common type of dementia. Recent studies have shown that long noncoding RNAs (lncRNAs) play a role in AD development. In this context, the current study conducted a comprehensive meta-analysis of high-throughput Gene Expression Omnibus (GEO) datasets to identify significant lncRNAs that could play a crucial role in the pathogenesis of AD. Three microarray expression profiles of human subjects diagnosed with AD and corresponding healthy controls were obtained from the GEO database. Afterward, the expression profiles from the chosen microarray datasets were combined. A network of differentially expressed genes (DEGs) was visualized, identifying key hub genes. Subsequently, the two significant lncRNAs, identified as and , were chosen based on the number of interactions between hubs and lncRNAs. Blood samples were collected from AD patients as well as from healthy control individuals. Ultimately, the expression levels of and were quantitatively assessed in the blood samples of 50 AD patients and 50 healthy controls using the quantitative Real-Time PCR (q-PCR) technique. Experimental validation showed that was differentially expressed in Alzheimer's disease (AD) patients compared to the control group. In contrast, LINC01003 revealed no significant difference between the AD patients and the control group. This study thoroughly examined the molecular landscape of AD, identifying key differentially expressed genes and highlighting candidate as a potential molecular biomarker for AD patients.