Evidence map›Paper›PMID 38267752›Full record

ArticleMolecular neurobiology2024

Exploration of Positive and Negative Schizophrenia Symptom Heterogeneity and Establishment of Symptom-Related miRNA-mRNA Regulatory Network: Based on Transcriptome Sequencing Data.

Mengdi Jin, Mengtong Xie, Lin Dong, Fengyu Xue, Weizhen Li, Lintong Jiang, Junnan Li, Min Zhang, Haideng Song, Qingxing Lu and 1 more

Abstract read
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In one paragraph

Article in Molecular neurobiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Mengdi JinDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Mengtong XieDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Lin DongDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Fengyu XueDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Weizhen LiDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Lintong JiangDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Junnan LiDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Min ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Haideng SongDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Qingxing LuDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China.
Qiong YuDepartment of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China. yuqiong@jlu.edu.cn.ORCID http://orcid.org/0000-0003-1143-1749

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Schizophrenia (SCZ) symptoms can be classified as positive and negative ones, each of which has distinct traits and possibly differences in gene expression and regulation. The co-expression networks linked to PANSS (Positive and Negative Syndrome Scale) scores were identified by weighted gene co-expression network analysis (WGCNA) using the expression profiles of miRNA and mRNA in the peripheral blood of first-episode SCZ patients. The heterogeneity between positive and negative symptoms was demonstrated using gene functional enrichment, gene-medication interaction, and immune cell composition analysis. Then, target gene prediction and correlation analysis of miRNA and mRNA constructed a symptom-related miRNA-mRNA regulatory network, screened regulatory pairs, and predicted binding sites. A total of six mRNA co-expression modules, two miRNA co-expression modules, and ten hub genes were screened to be significantly associated with positive symptoms; five mRNA co-expression modules and eight hub genes were correlated with negative symptoms. Positive symptom-related modules were significantly enriched in axon guidance, actin skeleton regulation, and sphingolipid signaling pathway, while negative symptom-related modules were significantly enriched in adaptive immune response, leukocyte migration, dopaminergic synapses, etc. The development of positive symptoms may have been influenced by potential regulatory pairings such as miR-98-5p-EIF3J, miR-98-5p-SOCS4, let-7b-5p-CLUH, miR-454-3p-GTF2H1, and let-7b-5p-SNX17. Additionally, immune cells were substantially connected with several hub genes for symptoms. Positive and negative symptoms in SCZ individuals were heterogeneous to some extent. miRNAs such as let-7b-5p and miR-98-5p might contribute to the incidence of positive symptoms by targeting mRNAs, while the immune system's role in developing negative symptoms may be more nuanced.

Indexed as

Gene Regulatory NetworksMicroRNAsRNA, MessengerSchizophreniaTranscriptomeFemaleGene Expression ProfilingHumansMaleMicroRNAsRNA, MessengerFirst-episode schizophreniamiRNA-mRNA regulatory networkSymptom heterogeneityTranscriptome sequencing

Identifiers

PMID38267752

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.