Evidence map›Paper›PMID 38172670›Full record

ArticleCellular & molecular biology letters2024

The pyroptosis mediated biomarker pattern: an emerging diagnostic approach for Parkinson's disease.

Junhan Liang, Zhirong Wan, Cheng Qian, Madiha Rasheed, Changling Cao, Jingyan Sun, Xuezhe Wang, Zixuan Chen, Yulin Deng

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

Article in Cellular & molecular biology letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
4.0field-weighted citation impact, top 6% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 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.

Junhan LiangBeijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Medical Technology, Beijing Institute of Technology, Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
Zhirong WanDepartment of Neurology, Aerospace Center Hospital, Beijing, 100049, People's Republic of China.
Cheng QianBeijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Medical Technology, Beijing Institute of Technology, Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
Madiha RasheedBeijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Medical Technology, Beijing Institute of Technology, Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
Changling CaoBeijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Medical Technology, Beijing Institute of Technology, Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
Jingyan SunSchool of Life Sciences, Beijing Institute of Technology, Beijing, 100081, People's Republic of China.
Xuezhe WangBeijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Medical Technology, Beijing Institute of Technology, Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
Zixuan ChenBeijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Medical Technology, Beijing Institute of Technology, Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China. zx-chen@bit.edu.cn.ORCID http://orcid.org/0000-0001-9109-4484
Yulin DengBeijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, School of Medical Technology, Beijing Institute of Technology, Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China. deng@bit.deu.cn.
Beijing Institute of Technology · CNAerospace Center Hospital · CN

Funding

National Natural Science Foundation of China 81601114National Natural Science Foundation of China U1532264
6 · The paper itself

Abstract

backgroundParkinson's disease (PD) affects 1% of people over 60, and long-term levodopa treatment can cause side effects. Early diagnosis is of great significance in slowing down the pathological process of PD. Multiple pieces of evidence showed that non-coding RNAs (ncRNAs) could participate in the progression of PD pathology. Pyroptosis is known to be regulated by ncRNAs as a key pathological feature of PD. Therefore, evaluating ncRNAs and pyroptosis-related proteins in serum could be worthy biomarkers for early diagnosis of PD.

methodsNcRNAs and pyroptosis/inflammation mRNA levels were measured with reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Luciferase assays were performed to confirm GSDME as a target of miR-675-5p and HMGB1 as a target of miR-1247-5p. In the serum of healthy controls (n = 106) and PD patients (n = 104), RT-qPCR was utilized to assess miR-675-5p, miR-1247-5p, and two related ncRNAs (circSLC8A1and lncH19) levels. The enzyme-linked immunosorbent assay measured serum levels of pyroptosis-related proteins in controls (n = 54) and PD patients (n = 70).

resultsOur data demonstrated that miR-675-5p and miR-1247-5p significantly changed in PD neuron and animal models. Overexpressed miR-675-5p or downregulated miR-1247-5p could regulate pyroptosis and inflammation in PD neuron models. Using the random forest algorithm, we constructed a classifier based on PD neuron-pyroptosis pathology (four ncRNAs and six proteins) having better predictive power than single biomarkers (AUC = 92%). Additionally, we verified the performance of the classifier in early-stage PD patients (AUC ≥ 88%).

conclusionSerum pyroptosis-related ncRNAs and proteins could serve as reliable, inexpensive, and non-invasive diagnostic biomarkers for PD. LIMITATIONS: All participants were from the same region. Additionally, longitudinal studies in the aged population are required to explore the practical application value of the classifier.

Indexed as

MicroRNAsParkinson DiseaseAgedAnimalsBiomarkersHumansInflammationPyroptosisBiomarkersMicroRNAsMIRN1247 microRNA, humanMIRN675 microRNA, humanBiomarker patternsMachine learningParkinson’s disease

Identifiers

PMID38172670
PMCPMC10765853
OpenAlexW4390536468

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.