Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization DOI Creative Commons

Tong Ni,

Yu Sun,

Zefeng Li

и другие.

Advanced Science, Год журнала: 2024, Номер 12(2)

Опубликована: Ноя. 20, 2024

Abstract Schizophrenia (SCZ) is a complex psychiatric disorder presenting challenges for characterization. The current study aimed to identify and evaluate disease‐responsive essential genes (DREGs) enhance the molecular characterization of SCZ. RNA‐sequencing data from PsychENCODE (536 SCZ patients, 832 controls) peripheral blood transcriptome 144 recruited subjects (59 6 non‐SCZ 79 are analyzed. Shared differential expression obtained using three algorithms. Support vector machine (SVM)‐based recursive feature elimination employed DREGs. biological relevance these DREGs examined through protein–protein interaction network, pathway enrichment, polygenic scoring, brain tissue expression. Key validated in animal models. A DREGs‐based machine‐learning model developed its performance assessed multiple datasets. analysis identified 184 forming an interconnected network involved synaptic plasticity, inflammation, neuronal development, neurotransmission. exhibited distinct SCZ‐related regions Their genetic contributions comparable genome‐wide risk scores. DREG‐based SVM demonstrated high (AUC 85% characterization, 79% specificity). These findings provide new insights into mechanisms underlying emphasize potential improving

Язык: Английский

Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization DOI Creative Commons

Tong Ni,

Yu Sun,

Zefeng Li

и другие.

Advanced Science, Год журнала: 2024, Номер 12(2)

Опубликована: Ноя. 20, 2024

Abstract Schizophrenia (SCZ) is a complex psychiatric disorder presenting challenges for characterization. The current study aimed to identify and evaluate disease‐responsive essential genes (DREGs) enhance the molecular characterization of SCZ. RNA‐sequencing data from PsychENCODE (536 SCZ patients, 832 controls) peripheral blood transcriptome 144 recruited subjects (59 6 non‐SCZ 79 are analyzed. Shared differential expression obtained using three algorithms. Support vector machine (SVM)‐based recursive feature elimination employed DREGs. biological relevance these DREGs examined through protein–protein interaction network, pathway enrichment, polygenic scoring, brain tissue expression. Key validated in animal models. A DREGs‐based machine‐learning model developed its performance assessed multiple datasets. analysis identified 184 forming an interconnected network involved synaptic plasticity, inflammation, neuronal development, neurotransmission. exhibited distinct SCZ‐related regions Their genetic contributions comparable genome‐wide risk scores. DREG‐based SVM demonstrated high (AUC 85% characterization, 79% specificity). These findings provide new insights into mechanisms underlying emphasize potential improving

Язык: Английский

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