{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16"],"submitter":["Tian X"],"pubmed_abstract":["<h4>Introduction</h4>Preeclampsia (PE) and depressive disorder (DD) exhibit clinical comorbidity, yet the molecular mechanisms underlying this association remain poorly understood.<h4>Methods</h4>Differential expression analysis of placental and peripheral blood transcriptomes was performed to identify PE-associated secretory protein genes. A depression-related coexpression network was constructed to obtain DD-related genes. Protein-protein interaction integration and functional enrichment analyses were then applied to identify shared regulatory pathways. Machine learning algorithms were applied to select core diagnostic genes, followed by validation in independent cohorts. A nomogram model was developed, and gene set enrichment, immune cell infiltration analysis, transcription factor regu"],"journal":["Frontiers in psychiatry"],"pagination":["1596995"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12706674"],"repository":["biostudies-literature"],"pubmed_title":["Exploring the molecular mechanisms of phthalates in the comorbidity of preeclampsia and depression by integrating multiple datasets."],"pmcid":["PMC12706674"],"pubmed_authors":["Liu Y","Gu X","Tian X","Zhang Y","Ma Y","Yuan Y"],"additional_accession":[]},"is_claimable":false,"name":"Exploring the molecular mechanisms of phthalates in the comorbidity of preeclampsia and depression by integrating multiple datasets.","description":"<h4>Introduction</h4>Preeclampsia (PE) and depressive disorder (DD) exhibit clinical comorbidity, yet the molecular mechanisms underlying this association remain poorly understood.<h4>Methods</h4>Differential expression analysis of placental and peripheral blood transcriptomes was performed to identify PE-associated secretory protein genes. A depression-related coexpression network was constructed to obtain DD-related genes. Protein-protein interaction integration and functional enrichment analyses were then applied to identify shared regulatory pathways. Machine learning algorithms were applied to select core diagnostic genes, followed by validation in independent cohorts. A nomogram model was developed, and gene set enrichment, immune cell infiltration analysis, transcription factor regu","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-05-26T17:06:39.787Z","creation":"2026-05-25T03:12:05.494Z"},"accession":"S-EPMC12706674","cross_references":{"pubmed":["41409330"],"doi":["10.3389/fpsyt.2025.1596995"]}}