Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

Rychkov, Dmitry; Neely, Jessica; Oskotsky, Tomiko; Yu, Steven; Perlmutter, Noah; Nititham, Joanne; Carvidi, Alexander; Krueger, Melissa A.; Gross, Andrew J.; Criswell, Lindsey A.; Ashouri, Judith F.; Sirota, Marina

doi:10.3389/fimmu.2021.638066

Cited by 30 publications

(19 citation statements)

References 89 publications

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“…An adaptive deep neural network [ 137 ] was able to outperform non-DL methods in predicting DAS28-ESR from demographical and clinical data with an AUC of 0.73 (categorical prediction) and mean standard error of 0.9 (numerical prediction). However, the attempt by Rychkov et al [ 138 ] to predict DAS28 using omics data yielded unsatisfactory results, and their novel RA score showed only a weak ( r = 0.33) correlation with DAS28. The clinical disease activity index (CDAI) [ 139 ] is another scoring system that only uses clinical data and can be calculated more rapidly than DAS28.…”

Section: Artificial Intelligence In Ramentioning

confidence: 99%

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Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

2022

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Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of Sara Momtazmanesh and Ali Nowroozi have contributed equally to this work and share first authorship.

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Section: Artificial Intelligence In Ramentioning

confidence: 99%

“…Table 8 summarizes studies implementing ML and DL for monitoring disease course and predicting prognosis [ 79 , 137 , 138 , 140 , 141 , 146 , 147 , 149 , 157 – 166 ].…”

Section: Artificial Intelligence In Ramentioning

confidence: 99%

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

2022

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“…Lymphocyte antigen 96 (LY96), also known as myeloid differentiation 2 (MD2), is required for the activation of TLR4 by lipopolysaccharide (LPS), which plays an important role in innate immunity and is the first line of defense against microbial infection ( Dou et al, 2013 ). LY96 plays a vital role in inflammation-related and immune-related diseases such as Crohn’s disease ( Yang et al, 2021 ), rheumatoid arthritis ( Rychkov et al, 2021 ), and inflammatory diabetic cardiomyopathy ( Wang et al, 2020a ). Recently, several studies revealed that LY96 was tightly correlated with tumor initiation and progression.…”

Section: Introductionmentioning

confidence: 99%

Pan-Cancer Analysis of the Characteristics of LY96 in Prognosis and Immunotherapy Across Human Cancer

Nie

Peng

et al. 2022

Front. Mol. Biosci.

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Lymphocyte antigen 96 (LY96) is implicated in tumorigenesis by modulating host immunity. However, an integrated pan-cancer analysis of LY96 in prognosis and immunotherapy across human cancers is still lacking. Therefore, we analyzed the LY96 expression and its prognostic role in tumors by multiple databases. We also investigated the correlation between LY96 and copy number, DNA methylation, somatic mutation, microsatellite instability (MSI), tumor mutation burden (TMB), tumor microenvironment (TME), and immune cell infiltration across human cancers. In addition, the biological processes related to LY96 across various tumors and the correlation between LY96 and 50% inhibitive concentration (IC50) of various drugs were investigated. We found that LY96 was differently expressed between tumor and normal tissues and was significantly upregulated in most types of cancers. LY96 was gradually upregulated from stages I to IV in several cancers. Moreover, we found LY96 may play a prognostic role in most cancers, and patients with high or low LY96 expression often show different clinical outcomes. LY96 was also associated with copy number, DNA methylation, somatic mutation, MSI, TMB, TME characteristics, and immune cell infiltration in cancers. LY96 may also regulate classic tumor-associated pathways in several cancers and is related to drug resistance. This article may help to elucidate the role of LY96 in tumorigenesis, which may promote the development of immunotherapy and targeted therapy in cancers.

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“…By bioinformatics analysis, FN1, VEGFA, HGF, SERPINA1, MMP9, PPBP, CD44, FPR2, IGF1, and ITGAM were recognized as the hub genes related to synovial macrophages in RA [ 20 ]. Furthermore, TNFAIP6/TSG6 and HSP90AB1/HSP90 were identified as new biomarkers for RA by cross-tissue transcriptomic analysis leveraging machine learning approaches [ 21 ]. However, there are some shortcomings in these findings.…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of Hub Genes for Predicting Treatment Targets and Immune Landscape in Rheumatoid Arthritis

et al. 2022

BioMed Research International

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Background. Rheumatoid arthritis (RA) is recognized as a chronic inflammatory disease featured by pathological synovial inflammation. Currently, the underlying pathophysiological mechanisms of RA remain unclear. In the study, we attempted to explore the underlying mechanisms of RA and provide potential targets for the therapy of RA via bioinformatics analysis. Methods. We downloaded four microarray datasets (GSE77298, GSE55235, GSE12021, and GSE55457) from the GEO database. Firstly, GSE77298 and GSE55457 were identified DEGs by the “limma” and “sva” packages of R software. Then, we performed GO, KEGG, and GSEA enrichment analyses to further analyze the function of DEGs. Hub genes were screened using LASSO analysis and SVM-RFE analysis. To further explore the differences of the expression of hub genes in healthy control and RA patient synovial tissues, we calculated the ROC curves and AUC. The expression levels of hub genes were verified in synovial tissues of normal and RA rats by qRT-PCR and western blot. Furthermore, the CIBERSORTx was implemented to assess the differences of infiltration in 22 immune cells between normal and RA synovial tissues. We explored the association between hub genes and infiltrating immune cells. Results. CRTAM, CXCL13, and LRRC15 were identified as RA’s potential hub genes by machine learning and LASSO algorithms. In addition, we verified the expression levels of three hub genes in the synovial tissue of normal and RA rats by PCR and western blot. Moreover, immune cell infiltration analysis showed that plasma cells, T follicular helper cells, M0 macrophages, M1 macrophages, and gamma delta T cells may be engaged in the development and progression of RA. Conclusions. In brief, our study identified and validated that three hub genes CRTAM, CXCL13, and LRRC15 might involve in the pathological development of RA, which could provide novel perspectives for the diagnosis and treatment with RA.

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Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

Cited by 30 publications

References 89 publications

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Pan-Cancer Analysis of the Characteristics of LY96 in Prognosis and Immunotherapy Across Human Cancer

Identification and Validation of Hub Genes for Predicting Treatment Targets and Immune Landscape in Rheumatoid Arthritis

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