Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies

Xu, Jinling; Zhou, Hui; Cheng, Yangyang; Xiang, Guangda

doi:10.1007/s13167-022-00289-y

Cited by 11 publications

(12 citation statements)

References 75 publications

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“…Studies suggest that hsa-miR-8060 could be a biomarker for diagnosing H1N1 virus infectious diseases ( 56 ). Hsa-miR-6890-5p regulates dehydrogenase/reductase 9 (DHRS9), a significant biomarker for atherosclerosis ( 57 ). Hsa-miR-5003-3p exhibits a correlation with both ICU admission and blood endophenotypes, thereby exerting regulatory influence over the host response to COVID-19 ( 58 ).…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression

Zhang,

Jin,

Zhang

et al. 2024

Front. Immunol.

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BackgroundCoronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses.MethodsSequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes.ResultsThe analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified.ConclusionThis research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression

Zhang,

Jin,

Zhang

et al. 2024

Front. Immunol.

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“…In terms of differences of proteoforms for different individuals, individualized selection of drug targets and treatment methods can improve therapeutic effects, reduce side effects, and help physicians to conduct health risk assessment and make more cost-effective targeted prevention ( 113 , 114 ). In addition, proteoformics can promote 3P medicine to better conduct artificial intelligence/machine learning to promote technological innovation and more convenient treatment for personalized patients ( 115 , 116 , 117 ). Proteoforms as a drug target can potentially stimulate innovation in drug development.…”

Section: Outlook and Expert Recommendationsmentioning

confidence: 99%

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics

Su,

Yang,

Sun

et al. 2024

Molecular & Cellular Proteomics

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“…The development of these bioinformatics technologies provides an important tool and platform for the research of atherosclerosis, helps to reveal its pathogenesis, the discovery of diagnostic markers, and the identification of therapeutic targets, and further promotes the progress of disease diagnosis and treatment. [10] However, the relationship between CASP1 gene and atherosclerosis is still unclear. Therefore, this paper intends to use bioinformatics technology to mine core genes between atherosclerosis and normal tissues, and conduct enrichment analysis and pathway analysis.…”

Section: The Research Was Funded By the 2021 Youth Medical Science An...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High expression of CASP1 induces atherosclerosis

Li,

Du,

Meng

et al. 2024

Medicine

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Atherosclerosis is a chronic, progressive vascular disease. The relationship between CASP1 gene expression and atherosclerosis remains unclear. The atherosclerosis dataset GSE132651 and GSE202625 profiles were downloaded from gene expression omnibus. Differentially expressed genes (DEGs) were screened. The construction and analysis of protein–protein interaction network, functional enrichment analysis, gene set enrichment analysis, and Comparative Toxicogenomics Database analysis were performed. Gene expression heatmap was drawn. TargetScan was used to screen miRNAs that regulate central DEG. 47 DEGs were identified. According to gene ontology analysis, they were mainly enriched in the regulation of stimulus response, response to organic matter, extracellular region, extracellular region, and the same protein binding. Kyoto Encyclopedia of Gene and Genome analysis results showed that the target cells were mainly enriched in the PI3K-Akt signaling pathway, Ras signaling pathway, and PPAR signaling pathway. In the enrichment project of Metascape, vascular development, regulation of body fluid levels, and positive regulation of cell motility can be seen in the gene ontology enrichment project. Eleven core genes (CASP1, NLRP3, MRC1, IRS1, PPARG, APOE, IL13, FGF2, CCR2, ICAM1, HIF1A) were obtained. IRS1, PPARG, APOE, FGF2, CCR2, and HIF1A genes are identified as core genes. Gene expression heatmap showed that CASP1 was highly expressed in atherosclerosis samples and low expressed in normal samples. NLRP3, MRC1, IRS1, PPARG, APOE, IL13, FGF2, CCR2, ICAM1, HIF1A were low expressed in atherosclerosis samples. CTD analysis showed that 5 genes (CASP1, NLRP3, CCR2, ICAM1, HIF1A) were found to be associated with pneumonia, inflammation, cardiac enlargement, and tumor invasiveness. CASP1 gene is highly expressed in atherosclerosis. The higher the CASP1 gene, the worse the prognosis.

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Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies

Cited by 11 publications

References 75 publications

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics

High expression of CASP1 induces atherosclerosis

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