Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease

Cao, Wenyuan; Ji, Zhangge; Zhu, Shoulian; Wang, Mei; Sun, Runming

doi:10.1186/s12920-023-01775-6

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…The study successfully establishes a risk score model based on eight epigenetic-related genes, demonstrating its efficacy in predicting CRC patient outcomes in both training and validation sets, and further integrates it with clinical characteristics to enhance prognostic predictions and suggest targeted therapeutic approaches. In 3 , the authors focus on identifying key genes associated with Alzheimer's disease (AD) by analyzing microarray datasets to pinpoint differentially expressed genes (DEGs), employing bioinformatics tools for Gene Ontology and pathway enrichment, and constructing a protein-protein interaction network to isolate hub genes, whose predictive value was further validated through principal component analysis and histological examination of an AD mouse model. In 4 , the authors delve into personalized therapy strategies for liver hepatocellular carcinoma (LIHC) patients by analyzing gene expression profiles and inflammation-related phenotypes to identify characteristic genes and lncRNAs linked to LIHC prognosis, subsequently developing a machine learning-based prognostic model, the Inf-PR model, which demonstrates superior predictive accuracy over traditional prognostic factors and existing models through ten-fold cross-validation.…”

Section: Introductionmentioning

confidence: 99%

Integrating Data Mining, Deep Learning, and GeneOntology Analysis for Gene Expression-BasedDisease Diagnosis Systems

Babichev,

Liakh,

Škvor

2024

Preprint

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The manuscript details the outcomes of a comprehensive study on the application of cluster-bicluster analysis, gene ontology analysis, and convolutional neural network (CNN) for diagnosing cancer and Alzheimer’s disease using gene expression data derived from both DNA microarray experiments and mRNA sequencing. It outlines a conceptual framework and provides a block diagram of the stepwise procedure for analyzing gene expression data, aiming to enhance the accuracy and objectivity of disease diagnosis. The research methodology involves initial gene ontology analysis, followed by the application of the Self Organizing Tree Algorithm (SOTA) for clustering gene expression profiles, an ensemble algorithm for data biclustering, and CNN for sample classification. Bayesian optimization method was employed to determine the optimal hyperparameters for all models. The analysis of simulation results demonstrates the high efficacy of the proposed approach. Specifically, for Alzheimer’s data, the number of genes analyzed was reduced from 44,662 to 4,004. Subsequent cluster-bicluster analysis divided this data into two subsets containing 1,158 and 2,846 genes, respectively. Classification accuracy for samples within these subsets reached 89.8% and 91.8%. In cancer data analysis, the gene count was reduced from 60,660 to 10,422, with 3,955 and 6,467 genes in the first and second clusters, respectively. The classification accuracy for these subsets was 97.4% and 97.6%, respectively. To our mind, the implementation of this model promises to significantly improve the efficacy of early diagnosis systems for complex diseases.

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Section: Introductionmentioning

confidence: 99%

Integrating Data Mining, Deep Learning, and GeneOntology Analysis for Gene Expression-BasedDisease Diagnosis Systems

Babichev,

Liakh,

Škvor

2024

Preprint

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Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer’s Disease Cohorts

Wang,

Li,

et al. 2024

Journal of Alzheimer’s Disease

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Background: Blood biomarkers are crucial for the diagnosis and therapy of Alzheimer’s disease (AD). Energy metabolism disturbances are closely related to AD. However, research on blood biomarkers related to energy metabolism is still insufficient. Objective: This study aims to explore the diagnostic and therapeutic significance of energy metabolism-related genes in AD. Methods: AD cohorts were obtained from GEO database and single center. Machine learning algorithms were used to identify key genes. GSEA was used for functional analysis. Six algorithms were utilized to establish and evaluate diagnostic models. Key gene-related drugs were screened through network pharmacology. Results: We identified 4 energy metabolism genes, NDUFA1, MECOM, RPL26, and RPS27. These genes have been confirmed to be closely related to multiple energy metabolic pathways and different types of T cell immune infiltration. Additionally, the transcription factors INSM2 and 4 lncRNAs were involved in regulating 4 genes. Further analysis showed that all biomarkers were downregulated in the AD cohorts and not affected by aging and gender. More importantly, we constructed a diagnostic prediction model of 4 biomarkers, which has been validated by various algorithms for its diagnostic performance. Furthermore, we found that valproic acid mainly interacted with these biomarkers through hydrogen bonding, salt bonding, and hydrophobic interaction. Conclusions: We constructed a predictive model based on 4 energy metabolism genes, which may be helpful for the diagnosis of AD. The 4 validated genes could serve as promising blood biomarkers for AD. Their interaction with valproic acid may play a crucial role in the therapy of AD.

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Metabonomics and Transcriptomics Analyses Reveal the Underlying HPA-Axis-Related Mechanisms of Lethality in Larimichthys polyactis Exposed to Underwater Noise Pollution

Jiang,

Zhang,

et al. 2024

IJMS

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The problem of marine noise pollution has a long history. Strong noise (>120 dB re 1 µPa) will affects the growth, development, physiological responses, and behaviors of fish, and also can induce the stress response, posing a mortal threat. Although many studies have reported that underwater noise may affect the survival of fish by disturbing their nervous system and endocrine system, the underlying causes of death due to noise stimulation remain unknown. Therefore, in this study, we used the underwater noise stress models to conduct underwater strong noise (50–125 dB re 1 µPa, 10–22,000 Hz) stress experiments on small yellow croaker for 10 min (short-term noise stress) and 6 days (long-term noise stress). A total of 150 fishes (body weight: 40–60 g; body length: 12–14 cm) were used in this study. Omics (metabolomics and transcriptomics) studies and quantitative analyses of important genes (HPA (hypothalamic–pituitary–adrenal)-axis functional genes) were performed to reveal genetic and metabolic changes in the important tissues associated with the HPA axis (brain, heart, and adrenal gland). Finally, we found that the strong noise pollution can significantly interfere with the expression of HPA-axis functional genes (including corticotropin releasing hormone (CRH), corticotropin releasing hormone receptor 2 (CRHR2), and arginine vasotocin (AVT)), and long-term stimulation can further induce metabolic disorders of the functional tissues (brain, heart, and adrenal gland), posing a lethal threat. Meanwhile, we also found that there were two kinds of death processes, direct death and chronic death, and both were closely related to the duration of stimulation and the regulation of the HPA axis.

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Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease

Cited by 3 publications

References 41 publications

Integrating Data Mining, Deep Learning, and GeneOntology Analysis for Gene Expression-BasedDisease Diagnosis Systems

Integrating Data Mining, Deep Learning, and GeneOntology Analysis for Gene Expression-BasedDisease Diagnosis Systems

Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer’s Disease Cohorts

Metabonomics and Transcriptomics Analyses Reveal the Underlying HPA-Axis-Related Mechanisms of Lethality in Larimichthys polyactis Exposed to Underwater Noise Pollution

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