Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Mechanisms of Atherosclerotic Cerebral Infarction

Zhang, Benzhuo; Huang, Wei; Yi, Mingquan; Xing, Chunxu

doi:10.1155/2022/3912697

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…And the identification of novel molecular subtypes was beneficial to make a more effective treatment strategy 48 , 49 . In 2019, Marios K. Georgakis et al 50 found genetic predisposition to higher circulating levels of monocyte chemoattractant protein-1 was associated with higher risk of stroke. Besides, associations were also found with etiologic stroke subtypes, specifically large-artery stroke and cardioembolic stroke.…”

Section: Discussionmentioning

confidence: 99%

Identification of significant m6A regulators and immune microenvironment characterization in ischemic stroke

Zhao,

Song,

et al. 2024

Sci Rep

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The role of m6A modification in the regulation of the immune microenvironment (IME) of ischemic stroke (IS) is barely known. Thus, we aim to investigate the impact of m6A modification on the IME of IS and its diagnostic value in IS. We comprehensively assessed the m6A modification patterns, the relationship between these modification patterns and the characteristics of the IME. The m6A modification patterns of individual IS sample were quantified by m6Ascore. The performance of m6A phenotype-related genes as potential biomarkers was evaluated by the area under the receiver operating characteristic curve. Experimental validation was also performed by qRT-PCR. Six dysregulated m6A regulators were identified and a classification model consisting of four key m6A regulators (METLL3, RBMX, RBM15B, YTDHF3) could distinguish IS and healthy control samples well. METTL3 and YTHDF3 are closely related to circulating neutrophil abundance. Two distinct m6A modification patterns were determined which differed in immunocyte abundance. We also identified six m6A phenotype-related genes (APOBEC3A, PTMA, FCGR3A, LOC440926, LOC649946, and FTH1L11), and further explored their biological function. Among them, APOBEC3A, FCGR3A, and FTH1L11 were positively associated with neutrophil abundance. APOBEC3A and FCGR3A were stable diagnostic m6A-associated genes in both the discovery and validation cohorts. This study reveals that m6A modification plays a non-negligible role in the formation of a diversified and complex IME in IS. The m6A phenotype-related genes could be diagnostic biomarkers of IS.

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Section: Discussionmentioning

confidence: 99%

Identification of significant m6A regulators and immune microenvironment characterization in ischemic stroke

Zhao,

Song,

et al. 2024

Sci Rep

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“…The non-coding LINC00588 and the BIK gene are gender-specific since they are mostly expressed in the testis and prostate, but the dataset is balanced between male and female subjects that eliminate the sex bias, so their changes are most likely explained from differential expression between responders and not responders within the male group. Even though most of the selected genes have a notable expression in the pancreas, thyroid, liver, and stomach, no previous association has been made between these markers and weight loss except one study identifying AP2B1 as a potential marker for eosinophilic gastroenteritis ( Zhang et al 2022 ), linking it thus indirectly to weight loss. These genes are associated with completely different molecular functions and pathways while their independent predictive potential is small with univariate statistical analysis showing marginal significance or no significant changes between responders and nonresponders ( Fig.…”

Section: Discussionmentioning

confidence: 99%

MEvA-X: a hybrid multiobjective evolutionary tool using an XGBoost classifier for biomarkers discovery on biomedical datasets

Panagiotopoulos

Korfiati²,

Theofilatos

et al. 2023

Bioinformatics

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Motivation Biomarker discovery is one of the most frequent pursuits in bioinformatics and is crucial for precision medicine, disease prognosis, and drug discovery. A common challenge of biomarker discovery applications is the low ratio of samples over features for the selection of a reliable not-redundant subset of features, but despite the development of efficient tree-based classification methods, such as the extreme gradient boosting (XGBoost), this limitation is still relevant. Moreover, existing approaches for optimizing XGBoost do not deal effectively with the class imbalance nature of the biomarker discovery problems, and the presence of multiple conflicting objectives, since they focus on the training of a single-objective model. In the current work, we introduce MEvA-X, a novel hybrid ensemble for feature selection and classification, combining a niche-based multi-objective evolutionary algorithm (EA) with the XGBoost classifier. MEvA-X deploys a multi-objective EA to optimize the hyper-parameters of the classifier and perform feature selection, identifying a set of Pareto-optimal solutions and optimizing multiple objectives, including classification and model simplicity metrics. Results The performance of the MEvA-X tool was benchmarked using one omics dataset coming from a microarray gene expression experiment, and one clinical questionnaire-based dataset combined with demographic information. MEvA-X tool outperformed the state-of-the-art methods in the balanced categorization of classes, creating multiple low-complexity models and identifying important non-redundant biomarkers. The best-performing run of MEvA-X for the prediction of weight loss using gene expression data yields a small set of blood circulatory markers which are sufficient for this precision nutrition application but need further validation. Availability https://github.com/PanKonstantinos/MEvA-X. Supplementary information Supplementary data are available at Bioinformatics online.

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Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Mechanisms of Atherosclerotic Cerebral Infarction

Cited by 2 publications

References 30 publications

Identification of significant m6A regulators and immune microenvironment characterization in ischemic stroke

Identification of significant m6A regulators and immune microenvironment characterization in ischemic stroke

MEvA-X: a hybrid multiobjective evolutionary tool using an XGBoost classifier for biomarkers discovery on biomedical datasets

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