Evidence map›Paper›PMID 39290304›Full record

ArticleFrontiers in psychiatry2024

Identification of significant biomarkers for predicting the risk of bipolar disorder with arteriosclerosis based on integrative bioinformatics and machine learning.

Xiabing Zheng, Xiaozhe Zhang, Yaqi Zhang, Cai Chen, Erni Ji

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiabing Zheng *Department of Bipolar Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China.
Xiaozhe Zhang *Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangzhou, China.
Yaqi ZhangDepartment of Geriatrics, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China.
Cai ChenDepartment of Drug Dependence, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China.
Erni JiDepartment of Bipolar Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Increasing evidence has indicated a connection between bipolar disorder (BD) and arteriosclerosis (AS), yet the specific molecular mechanisms remain unclear. This study aims to investigate the hub genes and molecular pathways for BD with AS. Methods: BD-related dataset GSE12649 were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) and key module genes derived from Limma and weighted gene co-expression network analyses (WGCNA) were identified. AS-related genes were sourced from the DisGeNET database, and the overlapping genes between DEGs and AS-related genes were characterized as differentially expressed arteriosclerosis-related genes (DE-ASRGs). The functional enrichment analysis, protein-protein interaction (PPI) network and three machine learning algorithms were performed to explore the hub genes, which were validated with two external validation sets. Additionally, immune infiltration was performed in BD. Results: Overall, 67 DE-ASRGs were found to be overlapping between the DEGs and AS-related genes. Functional enrichment analysis highlighted the cancer pathways between BD and AS. We identified seven candidate hub genes (CTSD, IRF3, NPEPPS, ST6GAL1, HIF1A, SOX9 and CX3CR1). Eventually, two hub genes (CX3CR1 and ST6GAL1) were identified as BD and AS co-biomarkers by using machine learning algorithms. Immune infiltration had revealed the disorder of immunocytes. Discussion: This study identified the hub genes CX3CR1 and ST6GAL1 in BD and AS, providing new insights for further research on the bioinformatic mechanisms of BD with AS and contributing to the diagnosis and prevention of AS in psychiatric clinical practice.

Indexed as

arteriosclerosisbioinformaticsbipolar disorderCX3CR1hub genesST6GAL1

Identifiers

PMID39290304
PMCPMC11405317

What Socratic holds

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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 Socratic graph.