Evidence map›Paper›PMID 37426641›Full record

ArticleFrontiers in immunology2023

Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning.

Yuchen Liu, Haixu Jiang, Tianlun Kang, Xiaojun Shi, Xiaoping Liu, Chen Li, Xiujuan Hou, Meiling Li

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
4.2field-weighted citation impact, top 5% 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

12 citing papers in PubMed, 17 citations in OpenAlex.

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  8. [Immunological characteristics of patients with anti-synthetase syndrome overlap with rheumatoid arthritis].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2024
    Article
  9. Review
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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

8 authors at 4 institutions in 1 country.

Yuchen LiuSchool of Clinical Medicine, Peking Union Medical College, Beijing, China.
Haixu JiangDepartment of Rheumatology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
Tianlun KangDepartment of Rheumatology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
Xiaojun ShiDepartment of Rheumatology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
Xiaoping LiuDepartment of Rheumatology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
Chen LiPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiujuan HouDepartment of Rheumatology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
Meiling LiDepartment of Rheumatology, Fuyang Hospital of Anhui Medical University, Fuyang, Anhui, China.
Beijing University of Chinese Medicine · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNGuangxi Medical University · CNPeking Union Medical College Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aim: Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers. Methods: We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC). Results: The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979. Conclusion: We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.

Indexed as

Arthritis, RheumatoidBlood PlateletsAlgorithmsHumansMachine LearningReproducibility of Resultsbioinformatics analysisdiagnostic modelmachine learning (ML)plateletrheumatoid arthritis

Identifiers

PMID37426641
PMCPMC10327425
OpenAlexW4381996720

What Socratic holds

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LicenceCC BY
Read underepoch 390

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 Socratic graph.