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

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

MaLiAmPi enables generalizable and taxonomy-independent microbiome features from technically diverse 16S-based microbiome studies

For studies using microbiome data, the ability to robustly combine data from technically and biologically distinct microbiome studies is a crucial means of supporting more robust and clinically relevant inferences. Formidable technical challenges arise when attempting to combine data from technically diverse 16S rRNA gene variable region amplicon sequencing (16S) studies. Closed operational taxonomic units and taxonomy are criticized as being heavily dependent upon reference sets and with limited precision relative to the underlying biology. Phylogenetic placement has been demonstrated to be a promising taxonomy-free manner of harmonizing microbiome data, but it has lacked a validated count-based feature suitable for use in machine learning and association studies. Here we introduce a phylogenetic-placement-based, taxonomy-independent, compositional feature of microbiota: phylotypes. Phylotypes were predictive of clinical outcomes such as obesity or pre-term birth on technically diverse independent validation sets harmonized post hoc. Thus, phylotypes enable the rigorous cross-validation of 16S-based clinical prognostic models and associative microbiome studies.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Identification of immunogenic cell death-related signature on prognosis and immunotherapy in kidney renal clear cell carcinoma

Background

Immunogenic cell death (ICD) is considered a particular cell death modality of regulated cell death (RCD) and plays a significant role in various cancers. The connection between kidney renal clear cell carcinoma (KIRC) and ICD remains to be thoroughly explored.

Methods

We conducted a variety of bioinformatics analyses using R software, including cluster analysis, prognostic analysis, enrichment analysis and immune infiltration analysis. In addition, we performed Quantitative Real-time PCR to evaluate RNA levels of specific ICD genes. The proliferation was measured through Cell Counting Kit-8 (CCK-8) assay and colony-formation assay in RCC cell lines.

Results

We determined two ICD subtypes through consensus clustering analysis. The two subtypes showed significantly different clinical outcomes, genomic alterations and tumor immune microenvironment. Moreover, we constructed the ICD prognostic signature based on TF, FOXP3, LY96, SLC7A11, HSP90AA1, UCN, IFNB1 and TLR3 and calculated the risk score for each patient. Kaplan-Meier survival analysis and ROC curve demonstrated that patients in the high-risk group had significantly poorer prognosis compared with the low-risk group. We then validated the signature through external cohort and further evaluated the relation between the signature and clinical features, tumor immune microenvironment and immunotherapy response. Given its critical role in ICD, we conducted further analysis on LY96. Our results indicated that downregulation of LY96 inhibited the proliferation ability of RCC cells.

Conclusions

Our research revealed the underlying function of ICD in KIRC and screened out a potential biomarker, which provided a novel insight into individualized immunotherapy in KIRC.

A survey of artificial intelligence in rheumatoid arthritis

The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.

HSP90β promotes osteoclastogenesis by dual-activation of cholesterol synthesis and NF-κB signaling

Heat shock protein 90β (Hsp90β, encoded by Hsp90ab1 gene) is the most abundant proteins in the cells and contributes to variety of biological processes including metabolism, cell growth and neural functions. However, genetic evidences showing Hsp90β in vivo functions using tissue specific knockout mice are still lacking. Here, we showed that Hsp90β exerted paralogue-specific role in osteoclastogenesis. Using myeloid-specific Hsp90ab1 knockout mice, we provided the first genetic evidence showing the in vivo function of Hsp90β. Hsp90β binds to Ikkβ and reduces its ubiquitylation and proteasomal degradation, thus leading to activated NF-κB signaling. Meanwhile, Hsp90β increases cholesterol biosynthesis by activating Srebp2. Both pathways promote osteoclastogenic genes expression. Genetic deletion of Hsp90ab1 in osteoclast or pharmacological inhibition of Hsp90β alleviates bone loss in ovariectomy-induced mice. Therefore, Hsp90β is a promising druggable target for the treatment of osteoporosis.

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

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.

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

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 patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis

Background

Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment.

Methods

GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457.

Results

In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait.

Conclusions

CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.

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

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.

Construction of a ceRNA Network Related to Rheumatoid Arthritis

(1) Background: Rheumatoid arthritis (RA) is a common systemic autoimmune disease affecting many people and has an unclear and complicated physiological mechanism. The competing endogenous RNA (ceRNA) network plays an essential role in the development and occurrence of various human physiological processes. This study aimed to construct a ceRNA network related to RA. (2) Methods: We explored the GEO database for peripheral blood mononuclear cell (PBMC) samples and then analyzed the RNA of 52 samples (without treatment) to obtain lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs), which can be differentially expressed with statistical significance in the progression of RA. Next, a ceRNA network was constructed, based on the DELs, DEMs, and DEGs. At the same time, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were used to validate the possible function of the ceRNA network. (3) Results: Through our analysis, 389 DELs, 247 DEMs, and 1081 DEGs were screened. After this, a ceRNA network was constructed for further statistical comparisons, including 16 lncRNAs, 1 miRNA, and 15 mRNAs. According to the GO and KEGG analysis, the ceRNA network was mainly enriched in the mTOR pathway, the dopaminergic system, and the Wnt signaling pathway. (4) Conclusions: The novel ceRNA network related to RA that we constructed offers novel insights into and targets for the underlying molecular mechanisms of the mTOR pathway, the dopaminergic system, and the Wnt signaling pathway (both classic and nonclassic pathways) that affect the level of the genetic regulator, which might offer novel ways to treat RA.

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

INTRODUCTION

Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA.

AREAS COVERED

In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed.

EXPERT OPINION

ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.