Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization
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) to enhance the molecular characterization of SCZ. RNA‐sequencing data from PsychENCODE (536 SCZ patie...
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| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202407628 |
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| author | Tong Ni Yu Sun Zefeng Li Tao Tan Wei Han Miao Li Li Zhu Jing Xiao Huiying Wang Wenpei Zhang Yitian Ma Biao Wang Di Wen Teng Chen Justin Tubbs Xiaofeng Zeng Jiangwei Yan Hongsheng Gui Pak Sham Fanglin Guan |
| author_facet | Tong Ni Yu Sun Zefeng Li Tao Tan Wei Han Miao Li Li Zhu Jing Xiao Huiying Wang Wenpei Zhang Yitian Ma Biao Wang Di Wen Teng Chen Justin Tubbs Xiaofeng Zeng Jiangwei Yan Hongsheng Gui Pak Sham Fanglin Guan |
| author_sort | Tong Ni |
| collection | DOAJ |
| description | 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) to enhance the molecular characterization of SCZ. RNA‐sequencing data from PsychENCODE (536 SCZ patients, 832 controls) and peripheral blood transcriptome data from 144 recruited subjects (59 SCZ patients, 6 non‐SCZ psychiatric patients, 79 controls) are analyzed. Shared differential expression genes are obtained using three algorithms. Support vector machine (SVM)‐based recursive feature elimination is employed to identify DREGs. The biological relevance of these DREGs is examined through protein–protein interaction network, pathway enrichment, polygenic scoring, and brain tissue expression. Key DREGs are validated in SCZ animal models. A DREGs‐based machine‐learning model for SCZ characterization is developed and its performance is assessed using multiple datasets. The analysis identified 184 DREGs forming an interconnected network involved in synaptic plasticity, inflammation, neuronal development, and neurotransmission. DREGs exhibited distinct expression in SCZ‐related brain regions and animal models. Their genetic contributions are comparable to genome‐wide polygenic risk scores. The DREG‐based SVM model demonstrated high performance (AUC 85% for SCZ characterization, 79% for specificity). These findings provide new insights into the molecular mechanisms underlying SCZ and emphasize the potential of DREGs in improving SCZ characterization. |
| format | Article |
| id | doaj-art-3a43e20c26b8490fa70d2a788dc5b90b |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-3a43e20c26b8490fa70d2a788dc5b90b2025-01-13T15:29:43ZengWileyAdvanced Science2198-38442025-01-01122n/an/a10.1002/advs.202407628Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia CharacterizationTong Ni0Yu Sun1Zefeng Li2Tao Tan3Wei Han4Miao Li5Li Zhu6Jing Xiao7Huiying Wang8Wenpei Zhang9Yitian Ma10Biao Wang11Di Wen12Teng Chen13Justin Tubbs14Xiaofeng Zeng15Jiangwei Yan16Hongsheng Gui17Pak Sham18Fanglin Guan19Key Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaDepartment of Endocrinology and Metabolism Qilu Hospital of Shandong University Ji'nan 250000 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Key Laboratory of Alzheimer's Disease of Zhejiang Province Institute of Aging Wenzhou Medical University Wenzhou 325603 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaDepartment of Ultrasound the Second Affiliated Hospital Xi'an Jiaotong University Xi'an 710004 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaDepartment of Immunology and Pathogenic Biology College of Basic Medicine Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaCollege of Forensic Medicine Hebei Key Laboratory of Forensic Medicine Hebei Medical University Shijiazhuang 050017 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaDepartment of Psychiatry Li Ka Shing Faculty of Medicine the University of Hong Kong Hong Kong SAR 999077 ChinaDepartment of Forensic Medicine School of Forensic Medicine Kunming Medical University Kunming 650500 ChinaDepartment of Genetics, School of Medicine & Forensics Shanxi Medical University Taiyuan 030009 ChinaBehavioral Health Services and Psychiatry Research Henry Ford Health Detroit MI 48202 USADepartment of Psychiatry Li Ka Shing Faculty of Medicine the University of Hong Kong Hong Kong SAR 999077 ChinaKey Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 ChinaAbstract Schizophrenia (SCZ) is a complex psychiatric disorder presenting challenges for characterization. The current study aimed to identify and evaluate disease‐responsive essential genes (DREGs) to enhance the molecular characterization of SCZ. RNA‐sequencing data from PsychENCODE (536 SCZ patients, 832 controls) and peripheral blood transcriptome data from 144 recruited subjects (59 SCZ patients, 6 non‐SCZ psychiatric patients, 79 controls) are analyzed. Shared differential expression genes are obtained using three algorithms. Support vector machine (SVM)‐based recursive feature elimination is employed to identify DREGs. The biological relevance of these DREGs is examined through protein–protein interaction network, pathway enrichment, polygenic scoring, and brain tissue expression. Key DREGs are validated in SCZ animal models. A DREGs‐based machine‐learning model for SCZ characterization is developed and its performance is assessed using multiple datasets. The analysis identified 184 DREGs forming an interconnected network involved in synaptic plasticity, inflammation, neuronal development, and neurotransmission. DREGs exhibited distinct expression in SCZ‐related brain regions and animal models. Their genetic contributions are comparable to genome‐wide polygenic risk scores. The DREG‐based SVM model demonstrated high performance (AUC 85% for SCZ characterization, 79% for specificity). These findings provide new insights into the molecular mechanisms underlying SCZ and emphasize the potential of DREGs in improving SCZ characterization.https://doi.org/10.1002/advs.202407628characterizationmachine learningmolecular signaturesschizophreniatranscriptome |
| spellingShingle | Tong Ni Yu Sun Zefeng Li Tao Tan Wei Han Miao Li Li Zhu Jing Xiao Huiying Wang Wenpei Zhang Yitian Ma Biao Wang Di Wen Teng Chen Justin Tubbs Xiaofeng Zeng Jiangwei Yan Hongsheng Gui Pak Sham Fanglin Guan Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization Advanced Science characterization machine learning molecular signatures schizophrenia transcriptome |
| title | Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization |
| title_full | Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization |
| title_fullStr | Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization |
| title_full_unstemmed | Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization |
| title_short | Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization |
| title_sort | integrated transcriptome analysis reveals novel molecular signatures for schizophrenia characterization |
| topic | characterization machine learning molecular signatures schizophrenia transcriptome |
| url | https://doi.org/10.1002/advs.202407628 |
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