Evidence map›Paper›PMID 38882525›Full record

ArticleFrontiers in aging neuroscience2024

Bioinformatics gene analysis for potential biomarkers and therapeutic targets of Parkinson's disease based on neutrophil extracellular traps.

Qiang Wang, Youquan Gu, Jun Chen, Xiaoyan Liu, Chen Xie, Xueping Wang

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Article in Frontiers in aging neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qiang WangDepartment of Neurology, The First Hospital of Lanzhou University, Lanzhou, China.
Youquan GuDepartment of Neurology, The First Hospital of Lanzhou University, Lanzhou, China.
Jun ChenDepartment of Neurology, The First Hospital of Lanzhou University, Lanzhou, China.
Xiaoyan LiuChengdu shi Longquanyi qu Diyi Renmin Yiyuan: The First People's Hospital of Longquanyi District, Longquanyi District, Chengdu, China.
Chen XieDepartment of Neurology, The First Hospital of Lanzhou University, Lanzhou, China.
Xueping WangDepartment of Neurology, The First Hospital of Lanzhou University, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Neutrophil extracellular traps (NETs) provide key innate immune mechanisms, and studies have shown innate immunity and adaptive immunity are directly linked to Parkinson's disease (PD) pathology. However, limited research has been conducted on NETs in the context of PD. Methods: A differential analysis was implemented to acquire differentially expressed genes (DEGs) between PD and control as well as between high- and low-score groups determined by a gene set variation analysis (GSVA). Then, the genes within the critical module, obtained through a weighted gene co-expression network analysis (WGCNA), were intersected with the DEGs to identify the overlapping genes. Then, five kinds of algorithms in the protein-protein interaction (PPI) were performed to identify potential biomarkers. Subsequently, a nomogram for forecasting PD probability was created. An enrichment analysis and an immune infiltration analysis were performed on the identified biomarkers. qRT-PCR was performed to validate the expression trends of three biomarkers. Results: We revealed 798 DEGs between PD and control groups as well as 168 DEGs between high- and low-score groups obtained by differential analyses. The pink module containing 926 genes was identified as the critical module. According to the intersection of these gene sets, a total of 43 overlapping genes were screened out. Furthermore, GPR78, CADM3, and CACNA1E were confirmed as biomarkers. Moreover, we found that biomarkers mainly participated in pathways, such as the 'hydrogen peroxide catabolic process', and 'cell cycle'; five kinds of differential immune cells between PD and control groups were identified. Finally, the qRT-PCR analysis demonstrated the up-regulation of GPR78, CADM3, and CACNA1E in the PD group. Discussion: Our study authenticated GPR78, CADM3, and CACNA1E as the biomarkers associated with PD. These findings provide an original reference for the diagnosis and treatment of PD.

Indexed as

bioinformaticsCACNA1ECADM3GPR 78neutrophil extracellular trapsParkinson’s disease

Identifiers

PMID38882525
PMCPMC11178047

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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.