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ArticleCombinatorial chemistry & high throughput screening2025

Development and Assessment of a Prediction Model for Alzheimer's Disease Diagnosis Based on Thermoregulation-Related Genes.

Yufu Wu, Jiali Yang, Linlin Guo, Dandan Hao, Wan Zhang, Hu Chen, Lin Pei

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Article in Combinatorial chemistry & high throughput screening, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Yufu WuNeurology Department, Beijing Geriatric Hospital, Beijing, 100095, China.
Jiali YangSchool of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, 050200, China.ORCID 0009-0006-4765-0301
Linlin GuoNeurology Department, Beijing Geriatric Hospital, Beijing, 100095, China.
Dandan HaoNeurology Department, Beijing Geriatric Hospital, Beijing, 100095, China.
Wan ZhangTraditional Chinese Medicine Department, Hospital of Renmin University of China, Beijing, 100872, China.
Hu ChenSchool of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, 050200, China.
Lin PeiNeurology Department, Beijing Geriatric Hospital, Beijing, 100095, China.

Funding

Research and Platform Construction of Key Technologies for Information Management of Traditional Chinese Medicine Resources in Hebei Province Fund 203777120D
6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) is a prevalent neurodegenerative condition among the elderly population and the most common form of dementia, however, we lack potent interventions to arrest its inherent pathogenic vectors. Robust evidence indicates thermoregulatory perturbations during and before the onset of symptoms. Therefore, temperature-regulated biomarkers may offer clues to therapeutic targets during the presymptomatic stage.

objectiveThe purpose of this study is to develop and assess a thermoregulation-related gene prediction model for Alzheimer's Disease diagnosis.

methodsThis study aims to utilize microarray bioinformatic analysis to identify the potential biomarkers of AD by analyzing four microarray datasets (GSE48350, GSE5281, GSE122063, and GSE181279) of AD patients. Furthermore, thermoregulation-associated hub genes were identified, and the expression patterns in the brain were explored. In addition, we explored the infiltration of immune cells with thermoregulation-related hub genes. Diagnostic marker validation was then performed at the single-cell level. Finally, the prediction of targeted drugs was performed based on the hub genes.

resultsThrough the analysis of four datasets pertaining to AD, a total of five genes associated with temperature regulation were identified. Notably, CCK, CXCR4, SLC27A4, and SLC17A6 emerged as diagnostic markers indicative of AD-related brain injury. Furthermore, in the examination of peripheral blood samples from AD patients, SLC27A4 and CXCR4 were identified as pivotal diagnostic indicators. Regrettably, animal experimentation was not pursued to validate the data; rather, an assessment of temperature regulation-related genes was conducted. Future investigations will be undertaken to establish the correlation between these genes and AD pathology.

conclusionOverall, CCK, CXCR4, SLC27A4, and SLC17A6 can be considered pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

Indexed as

Alzheimer DiseaseBody Temperature RegulationBiomarkersComputational BiologyHumansBiomarkersAlzheimer’s diseasediagnostic markerhub genesneurodegenerative conditionpivotal diagnostic indicators.thermoregulation-related genes

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.