Evidence map›Paper›PMID 38755717›Full record

ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2024

Screening of key immune

Yiming Huang, Aimin Wang, Fenglin Wang, Yaqi Xu, Wenjing Zhang, Fuyan Shi, Suzhen Wang

Abstract read
In one paragraph

Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 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

7 authors.

Yiming HuangHealth Statistics Teaching and Research Office, School of Public Health, Shandong Second Medical University, Weifang Shandong 261053, China. hym033098@163.com.
Aimin WangHealth Statistics Teaching and Research Office, School of Public Health, Shandong Second Medical University, Weifang Shandong 261053, China.
Fenglin WangHealth Statistics Teaching and Research Office, School of Public Health, Shandong Second Medical University, Weifang Shandong 261053, China.
Yaqi XuHealth Statistics Teaching and Research Office, School of Public Health, Shandong Second Medical University, Weifang Shandong 261053, China.
Wenjing ZhangHealth Statistics Teaching and Research Office, School of Public Health, Shandong Second Medical University, Weifang Shandong 261053, China.
Fuyan ShiHealth Statistics Teaching and Research Office, School of Public Health, Shandong Second Medical University, Weifang Shandong 261053, China. shifuyan@wfmc.edu.cn.
Suzhen WangHealth Statistics Teaching and Research Office, School of Public Health, Shandong Second Medical University, Weifang Shandong 261053, China.

Funding

the National Natural Science Foundation 81803337, 81872719 and 82003560the National Statistical Science Research Project 2018LY79the Natural Science Foundation of Shandong Province ZR2020MH340 and ZR2019MH034the Weifang Medical College Doctoral Startup Fund Project 2017BSQD51
6 · The paper itself

Abstract

objectivesAbnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis (WGCNA) and machine learning.

methodsThis study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson's disease. Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson's disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn.

resultsA total of 218 immune genes related to Parkinson's disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4

conclusionsThis study identifies the Parkinson's disease immune-related key gene

Indexed as

Gene Regulatory NetworksMachine LearningParkinson DiseaseComputational BiologyDatabases, GeneticGene Expression ProfilingGene OntologyHumansOligonucleotide Array Sequence AnalysisSignal TransductionANK1 geneimmunitymachine learningParkinson’s diseaseweighted gene co-expression network analysis

Identifiers

PMID38755717
PMCPMC11103055

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.