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

Meng, Xiangwen; Cheng, Zhi-Luo; Lǚ, Zhiyuan; Tan, Yanan; Jia, Xiaoyi; Zhang, Min

doi:10.3389/fimmu.2022.978851

Cited by 16 publications

(8 citation statements)

References 69 publications

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“…This could explain why we found different predictors than previous studies. The number of studies using multiple algorithms to identify key genes or biomarkers is growing, and we believe that this will become a new trend (Meng et al 2022;Zhang et al 2022a). Actually, it remains challenging to identify predictors for disease outcomes in blood, and further laboratory and clinical researches are needed to validate our results.…”

Section: Discussionmentioning

confidence: 99%

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Qin

Liu

et al. 2023

Funct Integr Genomics

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Neurological prognostication after cardiac arrest (CA) is important to avoid pursuing futile treatments for poor outcome and inappropriate withdrawal of life-sustaining treatment for good outcome. To predict neurological outcome after CA through biomarkers in peripheral blood mononuclear cells, four datasets were downloaded from the Gene Expression Omnibus database. GSE29546 and GSE74198 were used as training datasets, while GSE92696 and GSE34643 were used as verification datasets. The intersection of differentially expressed genes and hub genes from multiscale embedded gene co-expression network analysis (MEGENA) was utilized in the machine learning screening. Key genes were identified using support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF). The results were validated using receiver operating characteristic curve analysis. An mRNA-miRNA network was constructed. The distribution of immune cells was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Five biomarkers were identified as predictors for neurological outcome after CA, with an area under the curve (AUC) greater than 0.7: CASP8 and FADD-like apoptosis regulator (CFLAR), human protein kinase X (PRKX), miR-483-5p, let-7a-5p, and let-7c-5p. Interestingly, the combination of CFLAR minus PRKX showed an even higher AUC of 0.814. The mRNA-miRNA network consisted of 30 nodes and 76 edges. Statistical differences were found in immune cell distribution, including neutrophils, NK cells active, NK cells resting, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, B cells memory, and mast cells resting between individuals with good and poor neurological outcome after CA. In conclusion, our study identified novel predictors for neurological outcome after CA. Further clinical and laboratory studies are needed to validate our findings.

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Section: Discussionmentioning

confidence: 99%

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Qin

Liu

et al. 2023

Funct Integr Genomics

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“…In a recent study, MX2 was found to be an immune-related biomarker for SLE, which can be used for predicting the diagnosis and disease activity of SLE. MX2 activates the NOD-like receptor signaling pathway, promotes neutrophil in ltration, and may worsen the condition of SLE [14]. Another study identi ed 28 hub genes, such as RPS7, RPL19, RPS17, and RPS19, playing important roles in the occurrence, development, and prognosis of SLE [15].…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes in Systemic Lupus Erythematosus through integrated bioinformatics

Yan,

Zhang,

Zheng

et al. 2023

Preprint

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Background：Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with unclear mechanisms, limiting treatment options. Our study identifies potential core genes of SLE and their clinical applicability. Method：Using differential expression and weighted gene co-expression network analysis (WGCNA), we identified novel susceptibility modules and associated core genes. Examination of these genes through KEGG and GO analyses revealed their roles. Diagnostic performance of the core genes was evaluated using column line plot models and Receiver Operating Characteristic (ROC) curves. We also assessed the correlation between core genes and immune infiltration and used Mendelian randomization to determine the causal effect of GYPB on SLE. Results：The gene co-expression network constructed through WGCNA identified 144 key genes associated with SLE. The column line graph model demonstrated strong predictive power for SLE risk, with its diagnostic effectiveness validated by the ROC curve. A causal relationship was established between the top five core genes and immune cell infiltration in SLE. A negative correlation was observed between the gene GYPB and SLE, suggesting that GYPB might serve as a potential therapeutic target. Conclusion：This investigation provides new insights into SLE molecular mechanisms and potential therapeutic avenues.

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“…136 For extrarenal flares, 59 63 66 101 106 113 137-148 approximately half of the reports used genetic or genetic expression datasets. Novel biomarkers predicting SLE flares or increased disease activity include MX2 106 and M1 143 gene expression and a nine-protein combination (PHACTR2, GOT2, L-selectin, CMC4, MAP2K1, CMPK2, ECPAS, SRA1 and STAT2). 101 The AUC of the best models in these reports ranged from 0.70 to 0.99.…”

Section: Lupus Science and Medicinementioning

confidence: 99%

Systemic lupus in the era of machine learning medicine

Zhan,

Buhler,

Chen

et al. 2024

Lupus Sci Med

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Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

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

Cited by 16 publications

References 69 publications

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Identification of Key Genes in Systemic Lupus Erythematosus through integrated bioinformatics

Systemic lupus in the era of machine learning medicine

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