journal article·2025-01Open access

Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization

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

Advanced Science· Volume 12· Issue 2

Abstract

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Genetic Associations and Epidemiology · Bioinformatics and Genomic Networks · Machine Learning in Bioinformatics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Key Laboratory of National Health Commission for Forensic Sciences Xi'an Jiaotong University Health Science Center Xi'an 710061 China

Institute of Neuroscience Bio‐evidence Sciences Academy Xi'an Jiaotong University Health Science Center Xi'an 712046 China

Department of Endocrinology and Metabolism Qilu Hospital of Shandong University Ji'nan 250000 China

Oujiang 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 China

Department of Ultrasound the Second Affiliated Hospital Xi'an Jiaotong University Xi'an 710004 China

Department of Immunology and Pathogenic Biology College of Basic Medicine Xi'an Jiaotong University Health Science Center Xi'an 710061 China

College of Forensic Medicine Hebei Key Laboratory of Forensic Medicine Hebei Medical University Shijiazhuang 050017 China

Department of Psychiatry Li Ka Shing Faculty of Medicine the University of Hong Kong Hong Kong SAR 999077 China

Department of Forensic Medicine School of Forensic Medicine Kunming Medical University Kunming 650500 China

Department of Genetics, School of Medicine & Forensics Shanxi Medical University Taiyuan 030009 China

Behavioral Health Services and Psychiatry Research Henry Ford Health Detroit MI 48202 USA

Department of Psychiatry Michigan State University East Lansing MI 48824 USA

Funding

National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities

References (50)

Show source-provided references
1. 10.1093/schbul/sbad12010.1093/schbul/sbad120 ↗
2. 10.1038/nature1359510.1038/nature13595 ↗
3. 10.1038/nature0818510.1038/nature08185 ↗
4. 10.12688/f1000research.18491.110.12688/f1000research.18491.1 ↗
5. 10.1016/j.biopsych.2020.03.00810.1016/j.biopsych.2020.03.008 ↗
6. 10.1038/s41380-019-0601-310.1038/s41380-019-0601-3 ↗
7. 10.1093/bib/bbz04910.1093/bib/bbz049 ↗
8. 10.1001/jamapsychiatry.2020.019610.1001/jamapsychiatry.2020.0196 ↗
9. 10.1016/j.biopsych.2014.07.01110.1016/j.biopsych.2014.07.011 ↗
10. 10.1038/nature0818610.1038/nature08186 ↗
11. 10.1186/s13148-020-00842-410.1186/s13148-020-00842-4 ↗
12. 10.3390/ijms2207336410.3390/ijms22073364 ↗
13. 10.1016/j.cmpb.2021.10659010.1016/j.cmpb.2021.106590 ↗
14. 10.1111/cns.1431610.1111/cns.14316 ↗
15. 10.1016/j.csbj.2022.09.01410.1016/j.csbj.2022.09.014 ↗
16. 10.1155/2017/786050610.1155/2017/7860506 ↗
17. 10.1038/nrdp.2015.6710.1038/nrdp.2015.67 ↗
18. 10.1016/j.phrs.2019.10433810.1016/j.phrs.2019.104338 ↗
19. 10.1038/s41467-017-02593-y10.1038/s41467-017-02593-y ↗
20. 10.1016/j.biopsych.2015.02.02610.1016/j.biopsych.2015.02.026 ↗
21. 10.2147/NDT.S20501410.2147/NDT.S205014 ↗
22. 10.1002/ajmg.b.3046510.1002/ajmg.b.30465 ↗
23. 10.3390/cells1202024510.3390/cells12020245 ↗
24. 10.1016/j.jad.2024.01.09810.1016/j.jad.2024.01.098 ↗
25. 10.1038/s41390-022-02260-z10.1038/s41390-022-02260-z ↗
26. 10.1093/cercor/bhad28110.1093/cercor/bhad281 ↗
27. 10.15252/embj.20138757910.15252/embj.201387579 ↗
28. 10.1038/srep2995010.1038/srep29950 ↗
29. 10.1016/j.jbc.2021.10142710.1016/j.jbc.2021.101427 ↗
30. 10.1038/s41380-022-01437-610.1038/s41380-022-01437-6 ↗
31. 10.3389/fimmu.2023.113441210.3389/fimmu.2023.1134412 ↗
32. 10.1093/neuonc/noad14310.1093/neuonc/noad143 ↗
33. 10.1126/science.adg513610.1126/science.adg5136 ↗
34. 10.1007/s11831-021-09547-010.1007/s11831-021-09547-0 ↗
35. 10.1007/s10462-021-09975-110.1007/s10462-021-09975-1 ↗
36. Wang D. · ScienceScience · 2018 · 362
37. 10.1038/s41380-021-01420-710.1038/s41380-021-01420-7 ↗
38. 10.1038/s41380-022-01781-710.1038/s41380-022-01781-7 ↗
39. 10.1038/s41593-024-01636-010.1038/s41593-024-01636-0 ↗
40. 10.1093/bioinformatics/btx37310.1093/bioinformatics/btx373 ↗
41. 10.1093/bioinformatics/bty56010.1093/bioinformatics/bty560 ↗
42. 10.1038/nmeth.331710.1038/nmeth.3317 ↗
43. 10.1093/nar/gkv00710.1093/nar/gkv007 ↗
44. 10.1186/s13059-014-0550-810.1186/s13059-014-0550-8 ↗
45. Dai Z. · F1000Res · 2014 · 3 · 95
46. 10.1089/omi.2011.011810.1089/omi.2011.0118 ↗
47. 10.1023/A:101248730279710.1023/A:1012487302797 ↗
48. 10.1038/s41586-022-04434-510.1038/s41586-022-04434-5 ↗
49. 10.1093/gigascience/giz08210.1093/gigascience/giz082 ↗
50. 10.1038/s41596-020-0353-110.1038/s41596-020-0353-1 ↗