{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Juaiti M"],"funding":["National Natural Science Foundation of China","Hunan Provincial Natural Science Foundation"],"pagination":["e29587"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11040037"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(8)"],"pubmed_abstract":["<h4>Background</h4>Pulmonary arterial hypertension (PAH) represents a substantial global risk to human health. This study aims to identify diagnostic biomarkers for PAH and assess their association with the immune microenvironment through the utilization of sophisticated bioinformatics techniques.<h4>Methods</h4>Based on two microarray datasets, differentially expressed genes (DEGs) were detected, and hub genes underwent a sequence of machine learning analyses. After pathways associated with PAH were assessed by gene enrichment analysis, the identified genes were validated using external datasets and confirmed in a monocrotaline (MCT)-induced rat model. In addition, three algorithms were employed to estimate the proportions of various immune cell types, and the link between hub genes and i"],"journal":["Heliyon"],"pubmed_title":["Integrated bioinformatics analysis and experimental animal models identify a robust biomarker and its correlation with the immune microenvironment in pulmonary arterial hypertension."],"pmcid":["PMC11040037"],"funding_grant_id":["82070055"],"pubmed_authors":["Juaiti M","Feng Y","Zha L","Yu Z","Tang Y","Liang B"],"additional_accession":[]},"is_claimable":false,"name":"Integrated bioinformatics analysis and experimental animal models identify a robust biomarker and its correlation with the immune microenvironment in pulmonary arterial hypertension.","description":"<h4>Background</h4>Pulmonary arterial hypertension (PAH) represents a substantial global risk to human health. This study aims to identify diagnostic biomarkers for PAH and assess their association with the immune microenvironment through the utilization of sophisticated bioinformatics techniques.<h4>Methods</h4>Based on two microarray datasets, differentially expressed genes (DEGs) were detected, and hub genes underwent a sequence of machine learning analyses. After pathways associated with PAH were assessed by gene enrichment analysis, the identified genes were validated using external datasets and confirmed in a monocrotaline (MCT)-induced rat model. In addition, three algorithms were employed to estimate the proportions of various immune cell types, and the link between hub genes and i","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2026-07-01T03:22:00.959Z","creation":"2026-07-01T03:12:29.48Z"},"accession":"S-EPMC11040037","cross_references":{"pubmed":["38660271"],"doi":["10.1016/j.heliyon.2024.e29587"]}}