Evidence map›Paper›PMID 36059547›Full record

ArticleFrontiers in immunology2022

MX2: Identification and systematic mechanistic analysis of a novel immune-related biomarker for systemic lupus erythematosus.

Xiang-Wen Meng, Zhi-Luo Cheng, Zhi-Yuan Lu, Ya-Nan Tan, Xiao-Yi Jia, Min Zhang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
10.4field-weighted citation impact, top 1% 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

24 citing papers in PubMed, 44 citations in OpenAlex.

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  15. Biomarkers for systemic lupus erythematosus: A scoping review.Immunity, inflammation and disease · 2024
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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

6 authors at 2 institutions in 1 country.

Xiang-Wen MengSchool of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
Zhi-Luo ChengSchool of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
Zhi-Yuan LuSchool of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
Ya-Nan TanSchool of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
Xiao-Yi JiaSchool of Pharmacy, Anhui University of Chinese Medicine, Hefei, China.
Min ZhangDepartment of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Anhui University of Traditional Chinese Medicine · CNUniversity of Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease that involves multiple organs. However, the current SLE-related biomarkers still lack sufficient sensitivity, specificity and predictive power for clinical application. Thus, it is significant to explore new immune-related biomarkers for SLE diagnosis and development. Methods: We obtained seven SLE gene expression profile microarrays (GSE121239/11907/81622/65391/100163/45291/49454) from the GEO database. First, differentially expressed genes (DEGs) were screened using GEO2R, and SLE biomarkers were screened by performing WGCNA, Random Forest, SVM-REF, correlation with SLEDAI and differential gene analysis. Receiver operating characteristic curves (ROCs) and AUC values were used to determine the clinical value. The expression level of the biomarker was verified by RT‒qPCR. Subsequently, functional enrichment analysis was utilized to identify biomarker-associated pathways. ssGSEA, CIBERSORT, xCell and ImmuCellAI algorithms were applied to calculate the sample immune cell infiltration abundance. Single-cell data were analyzed for gene expression specificity in immune cells. Finally, the transcriptional regulatory network of the biomarker was constructed, and the corresponding therapeutic drugs were predicted. Results: Multiple algorithms were screened together for a unique marker gene, MX2, and expression analysis of multiple datasets revealed that MX2 was highly expressed in SLE compared to the normal group (all P < 0.05), with the same trend validated by RT‒qPCR (P = 0.026). Functional enrichment analysis identified the main pathway of MX2 promotion in SLE as the NOD-like receptor signaling pathway (NES=2.492, P < 0.001, etc.). Immuno-infiltration analysis showed that MX2 was closely associated with neutrophils, and single-cell and transcriptomic data revealed that MX2 was specifically expressed in neutrophils. The NOD-like receptor signaling pathway was also remarkably correlated with neutrophils (r >0.3, P < 0.001, etc.). Most of the MX2-related interacting proteins were associated with SLE, and potential transcription factors of MX2 and its related genes were also significantly associated with the immune response. Conclusion: Our study found that MX2 can serve as an immune-related biomarker for predicting the diagnosis and disease activity of SLE. It activates the NOD-like receptor signaling pathway and promotes neutrophil infiltration to aggravate SLE.

Indexed as

Lupus Erythematosus, SystemicBiomarkersGene Regulatory NetworksHumansMyxovirus Resistance ProteinsNLR ProteinsTranscriptomeBiomarkersMX2 protein, humanMyxovirus Resistance ProteinsNLR Proteinsbiomarkerimmune infiltrationmachine learningMX2systemic lupus erythematosus

Identifiers

PMID36059547
PMCPMC9433551
OpenAlexW4292408676

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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.