Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Identification and verification of PTPN3 as a novel biomarker in predicting cancer prognosis, immunity, and immunotherapeutic efficacy

BACKGROUND

The importance of protein tyrosine phosphatase non-receptor type 3 (PTPN3) in controlling multifaceted tumor cell behaviors throughout cancer development has received widespread attention. Nevertheless, little is known about the biological roles of PTPN3 in drug sensitivity, immunotherapeutic effectiveness, tumor immune microenvironment, and cancer prognosis.

METHODS

The Cancer Genome Atlas (TCGA) database's RNAseq data were used to examine the expression of PTPN3 in 33 different cancer types. In addition, immunohistochemistry (IHC) was performed to validate the expression of PTPN3 across various cancer types within our clinical cohorts. The features of PTPN3 alterations were demonstrated throughout the cBioPortal database. This study focused on examining the prognostic and clinicopathological importance of PTPN3 through the acquisition of clinical data from the TCGA database. The investigation of PTPN3's probable role in the tumor immune microenvironment was demonstrated by the application of CIBERSORT, ESTIMATE algorithms, and the TISIDB database. Using Spearman's rank correlation coefficient, the relationships between PTPN3 expression and tumor mutation burden (TMB) and microsatellite instability (MSI) were evaluated. To further investigate the putative biological activities and downstream pathways of PTPN3 in various cancers in humans, Gene Set Enrichment Analysis (GSEA) was carried out. In addition, an examination was conducted to explore the associations between PTPN3 and the effectiveness of PD-1/PD-L1 inhibitors, utilizing data extracted from the GEO database.

RESULTS

PTPN3 was abnormally expressed in multiple cancer types and was also strictly associated with the prognosis of cancer patients. IHC was used to investigate and confirm the various expression levels of PTPN3 in various malignancies, including breast cancer, lung cancer, sarcoma, and kidney renal clear cell carcinoma in our clinical cohorts. There is a high correlation between the levels of PTPN3 expression in different cancers and infiltrating immune cells, including mast cells, B cells, regulatory T cells, CD8 + T cells, macrophages, and dendritic cells. Infiltrating immune cells, such as regulatory T cells, CD8 + T cells, macrophages, B cells, dendritic cells, and mast cells, are strongly correlated with PTPN3 expression levels in various tumors. The expression of PTPN3 exhibited a substantial correlation with many immune-related biomolecules and the expression of TMB and MSI in multiple types of cancer. In addition, PTPN3 has demonstrated promise in predicting the therapeutic benefits of PD-1/PD-L1 inhibitors and the susceptibility to anti-cancer medications in the treatment of clinical cancer.

CONCLUSIONS

Our findings highlight the importance of PTPN3 as a prognostic biomarker and predictor of immunotherapy success in various forms of cancer. Furthermore, PTPN3 appears to have an important role in modifying the tumor immune microenvironment, highlighting its potential as a promising biomarker for prognosis prediction, immunotherapeutic efficacy evaluation, and identification of immune-related characteristics in diverse cancer types.

Oxidative DNA Damage and Zinc Status in Patients With Rheumatoid Arthritis in Duhok, Iraq

Background In patients with rheumatoid arthritis, oxidative DNA damage is increased by deficient zinc levels as well as increasing disease activity. However, the relationship between zinc levels, disease activity, and oxidative DNA damage remains unclear. In this study, we investigated serum zinc levels and disease activity and their association with 8-hydroxy-2-deoxyguanosine (8-OHdG). Methodology This case-control study was conducted among rheumatoid arthritis patients (n = 264) and healthy individuals (n = 192). Oxidative DNA damage was assessed by measuring serum 8-OHdG using enzyme-linked immunosorbent assay. Colorimetry was used to measure serum zinc levels. Disease activity was assessed using the Disease Activity Score-28 (DAS-28) score. Results Significantly higher 8-OHdG levels (p < 0.00) were found in the test group compared to the control group. Moreover, significantly lower serum zinc levels (p < 0.001) were noted in patients with rheumatoid arthritis compared to the control group. In addition, higher 8-OHdG levels were found in patients with low serum zinc levels compared to those with normal mean serum zinc levels. Lower levels of DNA oxidative damage were found in patients with moderate and low disease activity compared to those with high disease activity. A significant negative correlation was noted between serum zinc levels and DAS-28 scores and oxidative DNA damage marker (r = - 0.30, p = 0.038 and r = - 0.26, p = 0.043, respectively), while a significant positive correlation was observed between body mass index and 8-OHdG (r = 0.22, p = 0.02) in healthy individuals. Conclusions High serum 8-OHdG levels and high disease activity with low mean serum zinc levels may indicate a high degree of oxidative DNA damage in patients with rheumatoid arthritis.

Artificial intelligence in rheumatoid arthritis: potential applications and future implications

The widespread adoption of digital health records, coupled with the rise of advanced diagnostic testing, has resulted in an explosion of patient data, comparable in scope to genomic datasets. This vast information repository offers significant potential for improving patient outcomes and decision-making, provided one can extract meaningful insights from it. This is where artificial intelligence (AI) tools like machine learning (ML) and deep learning come into play, helping us leverage these enormous datasets to predict outcomes and make informed decisions. AI models can be trained to analyze and interpret patient data, including physician notes, laboratory testing, and imaging, to aid in the management of patients with rheumatic diseases. As one of the most common autoimmune diseases, rheumatoid arthritis (RA) has attracted considerable attention, particularly concerning the evolution of diagnostic techniques and therapeutic interventions. Our aim is to underscore those areas where AI, according to recent research, demonstrates promising potential to enhance the management of patients with RA.

Decoding Applications of Artificial Intelligence in Rheumatology

Artificial intelligence (AI) is not a newcomer in medicine. It has been employed for image analysis, disease diagnosis, drug discovery, and improving overall patient care. ChatGPT (Chat Generative Pre-trained Transformer, Inc., Delaware) has renewed interest and enthusiasm in artificial intelligence. Algorithms, machine learning, deep learning, and data analysis are some of the complex terminologies often encountered when health professionals try to learn AI. In this article, we try to review the practical applications of artificial intelligence in vernacular language in the fields of medicine and rheumatology in particular. From the standpoint of the everyday physician, we have endeavored to encapsulate the influence of AI on the cutting edge of medical practice and the potential revolutionary shift in the realm of rheumatology.

Identification of the candidate genes of diagnosing rheumatoid arthritis using the single-cell sequencing technology and T cell subclusters analysis of patients with rheumatoid arthritis

Objectives: This study aims to analyze the heterogeneity among different cell types in peripheral blood mononuclear cells (PBMC) in rheumatoid arthritis (RA) patients and to analyze T cell subsets to obtain key genes that may lead to RA.

Materials and methods: The sequencing data of 10,483 cells were obtained from the GEO data platform. The data were filtered and normalized initially and, then, principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (TSNE) cluster analysis were performed using the Seurat package in R language to group the cells, thereby obtaining the T cells. The T cells were subjected to subcluster analysis. The differentially expressed genes (DEGs) in T cell subclusters were obtained, and the hub genes were determined by Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network construction. Finally, the hub genes were validated using other datasets in the GEO data platform.

Results: The PBMC of RA patients were mainly divided into T cells, natural killer (NK) cells, B cells, and monocyte cells. The number of T cells was 4,483, which were further divided into seven clusters. The pseudotime trajectory analysis showed that the differentiation of T cells developed from cluster 0 and cluster 1 to cluster 5 and cluster 6. Through GO, KEGG and PPI analysis, the hub genes were identified. After validation by external data sets, nine genes were identified as candidate genes highly associated with the occurrence of RA, including CD8A, CCL5, GZMB, NKG7, PRF1, GZMH, CCR7, GZMK, and GZMA.

Conclusion: Based on single-cell sequencing analysis, we identified nine candidate genes for diagnosing RA, and validated their diagnostic value for RA patients. Our findings may provide new sights for the diagnosis and treatment of RA.

Role of the granzyme family in rheumatoid arthritis: Current Insights and future perspectives

Rheumatoid arthritis (RA) is a complex autoimmune disease characterized by chronic inflammation that affects synovial tissues of multiple joints. Granzymes (Gzms) are serine proteases that are released into the immune synapse between cytotoxic lymphocytes and target cells. They enter target cells with the help of perforin to induce programmed cell death in inflammatory and tumor cells. Gzms may have a connection with RA. First, increased levels of Gzms have been found in the serum (GzmB), plasma (GzmA, GzmB), synovial fluid (GzmB, GzmM), and synovial tissue (GzmK) of patients with RA. Moreover, Gzms may contribute to inflammation by degrading the extracellular matrix and promoting cytokine release. They are thought to be involved in RA pathogenesis and have the potential to be used as biomarkers for RA diagnosis, although their exact role is yet to be fully elucidated. The purpose of this review was to summarize the current knowledge regarding the possible role of the granzyme family in RA, with the aim of providing a reference for future research on the mechanisms of RA and the development of new therapies.

Identification of biomarkers associated with CD8+ T cells in rheumatoid arthritis and their pan-cancer analysis

Introduction

Rheumatoid arthritis (RA), a prevailing chronic progressive autoimmune disease, seriously affects the patient's quality of life. However, there is still a lack of precise treatment and management methods in clinical practice. Previous studies showed that CD8+ T cells take a lead in the progression of RA.

Methods

Genes closely related to CD8+T cells in RA were identified through multiple RA datasets, CIBERSORT, and WGCNA algorithms. Further machine learning analysis were performed to identify CD8+T cell-related genes most closely related to RA. In addition, the relationship between these three key genes and 33 cancer species was also explored in this study.

Results

In this study, 10 genes were identified to be closely related to CD8+T cells in RA. Machine learning analysis identified 3 CD8+T cell-related genes most closely related to RA: CD8A, GZMA, and PRF1.

Discussion

Our research aims to provide new ideas for the clinical treatment of 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.

Functional characterization of FBXL7 as a novel player in human cancers

F-box and leucine-rich repeat protein 7 (FBXL7), an F-box protein responsible for substrate recognition by the SKP1-Cullin-1-F-box (SCF) ubiquitin ligases, plays an emerging role in the regulation of tumorigenesis and tumor progression. FBXL7 promotes polyubiquitylation and degradation of diverse substrates and is involved in many biological processes, including apoptosis, cell proliferation, cell migration and invasion, tumor metastasis, DNA damage, glucose metabolism, planar cell polarity, and drug resistance. In this review, we summarize the downstream substrates and upstream regulators of FBXL7. We then discuss its role in tumorigenesis and tumor progression as either an oncoprotein or a tumor suppressor, and further describe its aberrant expression and association with patient survival in human cancers. Finally, we provide future perspectives on validating FBXL7 as a cancer biomarker for diagnosis and prognosis and/or as a potential therapeutic target for anticancer treatment.

Clinical Applications of Artificial Intelligence-An Updated Overview

Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology

Knee Osteoarthritis (ΚΟΑ) is a degenerative joint disease of the knee that results from the progressive loss of cartilage. Due to KOA's multifactorial nature and the poor understanding of its pathophysiology, there is a need for reliable tools that will reduce diagnostic errors made by clinicians. The existence of public databases has facilitated the advent of advanced analytics in KOA research however the heterogeneity of the available data along with the observed high feature dimensionality make this diagnosis task difficult. The objective of the present study is to provide a robust Feature Selection (FS) methodology that could: (i) handle the multidimensional nature of the available datasets and (ii) alleviate the defectiveness of existing feature selection techniques towards the identification of important risk factors which contribute to KOA diagnosis. For this aim, we used multidimensional data obtained from the Osteoarthritis Initiative database for individuals without or with KOA. The proposed fuzzy ensemble feature selection methodology aggregates the results of several FS algorithms (filter, wrapper and embedded ones) based on fuzzy logic. The effectiveness of the proposed methodology was evaluated using an extensive experimental setup that involved multiple competing FS algorithms and several well-known ML models. A 73.55% classification accuracy was achieved by the best performing model (Random Forest classifier) on a group of twenty-one selected risk factors. Explainability analysis was finally performed to quantify the impact of the selected features on the model's output thus enhancing our understanding of the rationale behind the decision-making mechanism of the best model.

Melatonin supplementation in the culture medium rescues impaired glucose metabolism in IVF mice offspring

Increasing evidence suggests that in vitro fertilization (IVF) may be associated with an increased risk of developing obesity and metabolic diseases later in life in the offspring. Notably, the addition of melatonin to culture medium may improve embryo development and prevent cardiovascular dysfunction in IVF adult mice. This study aimed to determine if melatonin supplementation in the culture medium can reverse impaired glucose metabolism in IVF mice offspring and the underlying mechanisms. Blastocysts used for transfer were generated by natural mating (control group) or IVF with or without melatonin (10^-6  M) supplementation (mIVF and IVF group, respectively) in clinical-grade culture media. Here, we first report that IVF decreased hepatic expression of Fbxl7, which was associated with impaired glucose metabolism in mice offspring. Melatonin addition reversed the phenotype by up-regulating the expression of hepatic Fbxl7. In vitro experiments showed that Fbxl7 enhanced the insulin signaling pathway by degrading RhoA through ubiquitination and was up-regulated by transcription factor Foxa2. Specific knockout of Fbxl7 in the liver of adult mice, through tail intravenous injection of recombinant adeno-associated virus, impaired glucose tolerance, while overexpression of hepatic Fbxl7 significantly improved glucose tolerance in adult IVF mice. Thus, the data suggest that Fbxl7 plays an important role in maintaining glucose metabolism of mice, and melatonin supplementation in the culture medium may rescue the long-term risk of metabolic diseases in IVF offspring.

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.

Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

Background

Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with similar clinical phenotypes. This study aimed to determine the mechanistic similarities and differences between OA and RA by integrated analysis of multiple gene expression data sets.

Methods

Microarray data sets of OA and RA were obtained from the Gene Expression Omnibus (GEO). By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein–protein interaction (PPI) network analysis of DEGs were conducted to determine hub genes and pathways. The “Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)” algorithm was employed to evaluate the immune infiltration cells (IICs) profiles in OA and RA. Moreover, mouse models of RA and OA were established, and selected hub genes were verified in synovial tissues with quantitative polymerase chain reaction (qPCR).

Results

A total of 1116 DEGs were identified between OA and RA. GO functional enrichment analysis showed that DEGs were enriched in regulation of cell morphogenesis involved in differentiation, positive regulation of neuron differentiation, nuclear speck, RNA polymerase II transcription factor complex, protein serine/threonine kinase activity and proximal promoter sequence-specific DNA binding. KEGG pathway analysis showed that DEGs were enriched in EGFR tyrosine kinase inhibitor resistance, ubiquitin mediated proteolysis, FoxO signaling pathway and TGF-beta signaling pathway. Immune cell infiltration analysis identified 9 IICs with significantly different distributions between OA and RA samples. qPCR results showed that the expression levels of the hub genes (RPS6, RPS14, RPS25, RPL11, RPL27, SNRPE, EEF2 and RPL19) were significantly increased in OA samples compared to their counterparts in RA samples (P < 0.05).

Conclusion

This large-scale gene analyses provided new insights for disease-associated genes, molecular mechanisms as well as IICs profiles in OA and RA, which may offer a new direction for distinguishing diagnosis and treatment between OA and RA.

IL32: The multifaceted and unconventional cytokine

Interleukin 32 is a unique intracellular cytokine which affects many cellular and physiological functions like cell death and survival, inflammation and response to pathogens. With numerous transcripts, more than one biologically active isoforms, IL32 drives its effect in diverse cellular functions. A cytokine restricted to higher mammals, it is known to fine tune multiple pathways involved in metabolic processes or infection. It modulates the immune response against diverse pathogens like Leishmania, Mycobacterium and HIV. IL32 has been associated with cancers of inflammatory nature too. It also plays an important role in chronic inflammatory diseases like RA, lung and airway disease like COPD. In this review we have discussed about identification and characterization of this non classical cytokine IL32, its structure and function at gene as well as at protein level, isoforms and their diverse functions. Role of IL32 in multiple diseases and particularly mycobacterial disease has been highlighted here. We have also summarised the genetic variants present in the IL32 gene and it's promoter region. Association of these variants, with cellular phenotype, patho-physiological conditions in different disease have also been discussed here.

Identification of Critical Genes and lncRNAs in Osteolysis after Total Hip Arthroplasty and Osteoarthritis by RNA Sequencing

Total hip arthroplasty (THA) is a cost-effective treatment for osteoarthritis (OA), and osteolysis is a common complication of THA. This study was aimed at exploring the relevant molecular biomarkers for osteolysis after THA. We performed RNA sequence to identify and characterize expressed mRNAs and lncRNAs in OA and osteolysis. Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in OA and osteolysis were acquired, as well as shared DEmRNAs/DElncRNAs in OA and osteolysis and osteolysis-specific DEmRNAs/DElncRNAs. Then, shared and osteolysis-specific DElncRNA-DEmRNA coexpression networks were constructed to further investigate the function of DElncRNAs and DEmRNAs in OA and osteolysis. In total, 343 DEmRNAs and 25 DElncRNAs in OA, 908 DEmRNAs and 107 DElncRNAs in osteolysis, and 406 DEmRNAs and 46 DElncRNAs between OA and osteolysis were acquired. A total of 136 shared DEmRNAs and 9 shared DElncRNAs in OA and osteolysis and 736 osteolysis-specific DEmRNAs and 103 osteolysis-specific DElncRNAs were acquired. Then, 128 shared DElncRNA-DEmRNA coexpression pairs and 522 osteolysis-specific DElncRNA-DEmRNA coexpression pairs were identified. The present study highlighted the roles of four interaction pairs, including two shared lncRNA-mRNA interaction pairs in OA and osteolysis (AC111000.4 and AC016831.6), which may function in the immune process of OA and osteolysis by regulating CD8A and CD8B, respectively, and two osteolysis-specific interaction pairs (AC090607.4-FOXO3 and TAL1-ABALON), which may play an important role in osteoclastogenesis.

A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.

Circulating Neutrophil Extracellular Traps Signature for Identifying Organ Involvement and Response to Glucocorticoid in Adult-Onset Still's Disease: A Machine Learning Study

Adult-onset Still’s disease (AOSD) is an autoinflammatory disease with multisystem involvement. Early identification of patients with severe complications and those refractory to glucocorticoid is crucial to improve therapeutic strategy in AOSD. Exaggerated neutrophil activation and enhanced formation of neutrophil extracellular traps (NETs) in patients with AOSD were found to be closely associated with etiopathogenesis. In this study, we aim to investigate, to our knowledge for the first time, the clinical value of circulating NETs by machine learning to distinguish AOSD patients with organ involvement and refractory to glucocorticoid. Plasma samples were used to measure cell-free DNA, NE-DNA, MPO-DNA, and citH3-DNA complexes from training and validation sets. The training set included 40 AOSD patients and 24 healthy controls (HCs), and the validation set included 26 AOSD patients and 16 HCs. Support vector machines (SVM) were used for modeling and validation of circulating NETs signature for the diagnosis of AOSD and identifying patients refractory to low-dose glucocorticoid treatment. The training set was used to build a model, and the validation set was used to test the predictive capacity of the model. A total of four circulating NETs showed similar trends in different individuals and could distinguish patients with AOSD from HCs by SVM (AUC value: 0.88). Circulating NETs in plasma were closely correlated with systemic score, laboratory tests, and cytokines. Moreover, circulating NETs had the potential to distinguish patients with liver and cardiopulmonary system involvement. Furthermore, the AUC value of combined NETs to identify patients who were refractory to low-dose glucocorticoid was 0.917. In conclusion, circulating NETs signature provide added clinical value in monitoring AOSD patients. It may provide evidence to predict who is prone to be refractory to low-dose glucocorticoid and help to make efficient therapeutic strategy.

Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry

Objective

To use unbiased, data‐driven, principal component (PC) and cluster analysis to identify patient phenotypes of rheumatoid arthritis (RA) that might exhibit distinct trajectories of disease progression, response to treatment, and risk for adverse events.

Methods

Patient demographic, socioeconomic, health, and disease characteristics recorded at entry into a large, single‐center, prospective observational registry cohort, the Brigham and Women’s Rheumatoid Arthritis Sequential Study (BRASS), were harmonized using PC analysis to reduce dimensionality and collinearity. The number of PCs was established by eigenvalue >1, cumulative variance, and interpretability. The resulting PCs were used to cluster patients using a K‐means approach. Longitudinal clinical outcomes were compared between the clusters over 2 years.

Results

Analysis of 142 variables from 1,443 patients identified 41 PCs that accounted for 77% of the cumulative variance in the data set. Cluster analysis distinguished 5 patient clusters: 1) less RA disease activity/multimorbidity, shorter RA duration, lower incidence of comorbidities; 2) less RA disease activity/multimorbidity, longer RA duration, more infections, psychiatric comorbidities, health care utilization; 3) moderate RA disease activity/multimorbidity, more neurologic comorbidity; 4) more RA disease activity/multimorbidity, shorter RA duration, more metabolic comorbidity, higher body mass index; 5) more RA disease activity/multimorbidity, longer RA duration, more hepatic, orthopedic comorbidity and RA‐related surgeries. The clusters exhibited differences in clinical outcomes over 2 years of follow‐up.

Conclusion

Data‐driven analysis of the BRASS registry identified 5 distinct phenotypes of RA. These results illustrate the potential of data‐driven patient profiling as a tool to support personalized medicine in RA. Validation in an independent data set is ongoing.

Inflammatory and Oxidative Stress Markers-Mirror Tools in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic progressive autoimmune disease, associated with significant morbidity, mainly due to progressive damage and consequent disability. Oxidative stress is an important part of RA pathophysiology, as in autoimmune disease the interaction between immune response and endogenous/exogenous antigens subsequently induce the production of reactive oxygen species. The oxidative stress process seems to be positively strongly correlated with inflammation and accelerated joint destruction. We were asking ourselves if the oxidative stress biomarkers are the mirror tools of disease activity, outcome, and inflammation level in a group of RA patients under standard or biological therapy compared to healthy age-matched controls. In order to do this, the oxidative stress damage biomarkers (lipids peroxide and protein carbonyl level), antioxidant defense capacity, and pro-inflammatory status of plasma were quantified. In this study, we took into account the complete picture of RA diseases and assessed, for the first time, the inflammatory level in correlation with the oxidative stress level and antioxidant capacity of RA patients. Our results revealed that protein oxidation through carbonylation is significantly increased in RA groups compared to controls, and both protein carbonyl Pcarb and thiobarbituric acid reactive substance (TBARS) are reliable markers of ROS damage. Therefore, it is unanimous that neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio ( MLR), platelet/lymphocyte ratio (PltLR) correlated with Pcarb, and TBARS can provide a view of the complex phenomenon represented by proteins/lipids damage, key contributors to disease outcome, and an increased awareness should be attributed to these biomarkers.

Applied machine learning and artificial intelligence in rheumatology

Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning and deep learning. We provide an overview of current machine learning applications in rheumatology, mainly supervised learning methods for e-diagnosis, disease detection and medical image analysis. In the future, machine learning will be likely to assist rheumatologists in predicting the course of the disease and identifying important disease factors. Even more interestingly, machine learning will probably be able to make treatment propositions and estimate their expected benefit (e.g. by reinforcement learning). Thus, in future, shared decision-making will not only include the patient’s opinion and the rheumatologist’s empirical and evidence-based experience, but it will also be influenced by machine-learned evidence.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Omics Approaches in Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma, which represents 80% of pancreatic cancers, is mainly diagnosed when treatment with curative intent is not possible. Consequently, the overall five-year survival rate is extremely dismal—around 5% to 7%. In addition, pancreatic cancer is expected to become the second leading cause of cancer-related death by 2030. Therefore, advances in screening, prevention and treatment are urgently needed. Fortunately, a wide range of approaches could help shed light in this area. Beyond the use of cytological or histological samples focusing in diagnosis, a plethora of new approaches are currently being used for a deeper characterization of pancreatic ductal adenocarcinoma, including genetic, epigenetic, and/or proteo-transcriptomic techniques. Accordingly, the development of new analytical technologies using body fluids (blood, bile, urine, etc.) to analyze tumor derived molecules has become a priority in pancreatic ductal adenocarcinoma due to the hard accessibility to tumor samples. These types of technologies will lead us to improve the outcome of pancreatic ductal adenocarcinoma patients.

Matrix remodeling associated 7 promotes differentiation of bone marrow mesenchymal stem cells toward osteoblasts

The matrix remodeling associated 7 (MXRA7) gene had been ill-studied and its biology remained to be discovered. Inspired by our previous findings and public datasets concerning MXRA7, we hypothesized that the MXRA7 gene might be involved in bone marrow mesenchymal stem cells (BMSCs) functions related to bone formation, which was checked by utilizing in vivo or in vitro methodologies. Micro-computed tomography of MXRA7-deficient mice demonstrated retarded osteogenesis, which was reflected by shorter femurs, lower bone mass in both trabecular and cortical bones compared with wild-type (WT) mice. Histology confirmed the osteopenia-like feature including thinner growth plates in MXRA7-deficient femurs. Immunofluorescence revealed less osteoblasts in MXRA7-deficient femurs. Polymerase chain reaction or western blot analysis showed that when WT BMSCs were induced to differentiate toward osteoblasts or adipocytes in culture, MXRA7 messenger RNA or protein levels were significantly increased alongside osteoblasts induction, but decreased upon adipocytes induction. Cultured MXRA7-deficient BMSCs showed decreased osteogenesis upon osteogenic differentiation induction as reflected by decreased calcium deposition or lower expression of genes responsible for osteogenesis. When recombinant MXRA7 proteins were supplemented in a culture of MXRA7-deficient BMSCs, osteogenesis or gene expression was fully restored. Upon osteoblast induction, the level of active β-catenin or phospho-extracellular signal-regulated kinase in MXRA7-deficient BMSCs was decreased compared with that in WT BMSCs, and these impairments could be rescued by recombinant MXRA7 proteins. In adipogenesis induction settings, the potency of MXRA7-deficient BMSCs to differentiate into adipocytes was increased over the WT ones. In conclusion, this study demonstrated that MXRA7 influences bone formation via regulating the balance between osteogenesis and adipogenesis in BMSCs.

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)_OS = 2.2, p-value < 0.001), ANXA2 (HR_OS = 2.1, p-value < 0.001), and LAMC2 (HR_DFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.