Evidence map›Paper›PMID 36918862›Full record

ArticleBMC medical genomics2023

Identification of ferroptosis related biomarkers and immune infiltration in Parkinson's disease by integrated bioinformatic analysis.

Na Xing, Ziye Dong, Qiaoli Wu, Yufeng Zhang, Pengcheng Kan, Yuan Han, Xiuli Cheng, Yaru Wang, Biao Zhang

Open access · goldFull text read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Na Xing *Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Ziye Dong *Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Qiaoli WuTianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin Huanhu Hospital, Tianjin, China.
Yufeng ZhangClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
Pengcheng KanDepartment of Clinical Laboratory, Tianjin Huanhu Hospital, Tianjin, China.
Yuan HanDepartment of Clinical Laboratory, Tianjin Huanhu Hospital, Tianjin, China.
Xiuli ChengDepartment of Clinical Laboratory, Tianjin Huanhu Hospital, Tianjin, China.
Yaru WangChu Hsien-I Memorial Hospital (Metabolic Diseases Hospital) of Tianjin Medical University, Tianjin, China.
Biao ZhangClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China. zhangbiaohh@nankai.edu.cn.
Tianjin Huanhu Hospital · CNTianjin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasing evidence has indicated that ferroptosis engages in the progression of Parkinson's disease (PD). This study aimed to explore the role of ferroptosis-related genes (FRGs), immune infiltration and immune checkpoint genes (ICGs) in the pathogenesis and development of PD.

methodsThe microarray data of PD patients and healthy controls (HC) from the Gene Expression Omnibus (GEO) database was downloaded. Weighted gene co-expression network analysis (WGCNA) was processed to identify the significant modules related to PD in the GSE18838 dataset. Machine learning algorithms were used to screen the candidate biomarkers based on the intersect between WGCNA, FRGs and differentially expressed genes. Enrichment analysis of GSVA, GSEA, GO, KEGG, and immune infiltration, group comparison of ICGs were also performed. Next, candidate biomarkers were validated in clinical samples by ELISA and receiver operating characteristic curve (ROC) was used to assess diagnose ability.

resultsIn this study, FRGs had correlations with ICGs, immune infiltration. Then, plasma levels of LPIN1 in PD was significantly lower than that in healthy controls, while the expression of TNFAIP3 was higher in PD in comparison with HC. ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750-0.916). Moreover, each biomarker alone could discriminate the PD from HC (LPIN1: AUC = 0.754, 95% CI: 0.659-0.849; TNFAIP3: AUC = 0.754, 95% CI: 0.660-0.849). For detection of early PD from HC, the model of combination maintained diagnostic accuracy with an AUC of 0.831 (95% CI: 0.734-0.927), LPIN1 also performed well in distinguishing the early PD from HC (AUC = 0.817, 95% CI: 0.717-0.917). However, the diagnostic efficacy was relatively poor in distinguishing the early from middle-advanced PD patients.

conclusionThe combination model composed of LPIN1 and TNFAIP3, and each biomarker may serve as an efficient tool for distinguishing PD from HC.

Indexed as

FerroptosisParkinson DiseaseAlgorithmsBiomarkersComputational BiologyHumansPhosphatidate PhosphataseBiomarkersLPIN1 protein, humanPhosphatidate PhosphataseBioinformaticELISAFerroptosisImmune checkpoint geneImmune infiltrationParkinson’s disease

Identifiers

PMID36918862
PMCPMC10012699
OpenAlexW4324142548

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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.