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Abstract Background Osteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes. Methods A proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data. Results The abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA. Conclusion Our study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

BackgroundFor Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments.MethodsWe utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs.ResultsSubtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs. ConclusionsThe findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

BackgroundHead and neck squamous cell carcinoma (HNSCC) is among the most lethal and most prevalent malignant tumors. Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. Therefore, we aimed at identifying a glycolysis-related prognostic model for HNSCC and to analyze its relationship with tumor immune cell infiltrations.MethodsThe mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), while glycolysis-related genes were obtained from the Molecular Signature Database (MSigDB). Bioinformatics analysis included Univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to select optimal prognosis-related genes for constructing glycolysis-related gene prognostic index(GRGPI), as well as a nomogram for overall survival (OS) evaluation. GRGPI was validated using the Gene Expression Omnibus (GEO) database. A predictive nomogram was established based on the stepwise multivariate regression model. The immune status of GRGPI-defined subgroups was analyzed, and high and low immune groups were characterized. Prognostic effects of immune checkpoint inhibitor (ICI) treatment and chemotherapy were investigated by Tumor Immune Dysfunction and Exclusion (TIDE) scores and half inhibitory concentration (IC50) value. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to validate the model by analyzing the mRNA expression levels of the prognostic glycolysis-related genes in HNSCC tissues and adjacent non-tumorous tissues.ResultsFive glycolysis-related genes were used to construct GRGPI. The GRGPI and the nomogram model exhibited robust validity in prognostic prediction. Clinical correlation analysis revealed positive correlations between the risk score used to construct the GRGPI model and the clinical stage. Immune checkpoint analysis revealed that the risk model was associated with immune checkpoint-related biomarkers. Immune microenvironment and immune status analysis exhibited a strong correlation between risk score and infiltrating immune cells. Gene set enrichment analysis (GSEA) pathway enrichment analysis showed typical immune pathways. Furthermore, the GRGPIdel showed excellent predictive performance in ICI treatment and drug sensitivity analysis. RT-qPCR showed that compared with adjacent non-tumorous tissues, the expressions of five genes were significantly up-regulated in HNSCC tissues.ConclusionThe model we constructed can not only be used as an important indicator for predicting the prognosis of patients but also had an important guiding role for clinical treatment.

This study investigated the association between intestinal microbiota abundance and diversity and cluster of differentiation (CD)4+ T cell subpopulations, cytokine levels, and disease activity in rheumatoid arthritis RA. A total of 108 rheumatoid arthritis (RA) patients and 99 healthy control (HC) subjects were recruited. PICRUSt2 was used for functional metagenomic predictions. Absolute counts of peripheral CD4+ T cell subpopulations and cytokine levels were detected by flow cytometry and with a cytokine bead array, respectively. Correlations were analyzed with the Spearman rank correlation test. The results showed that the diversity of intestinal microbiota was decreased in RA patients compared to HCs. At the phylum level, the abundance of Firmicutes, Fusobacteriota, and Bacteroidota was decreased while that of Actinobacteria and Proteobacteria was increased and at the genus level, the abundance of Faecalibacterium, Blautia, and Escherichia-Shigella was increased while that of Bacteroides and Coprococcus was decreased in RA patients compared to HC subjects. The linear discriminant analysis effect size indicated that Bifidobacterium was the most significant genus in RA. The most highly enriched Kyoto Encyclopedia of Genes and Genomes pathway in RA patients was amino acid metabolism. The relative abundance of Megamonas, Monoglobus, and Prevotella was positively correlated with CD4+ T cell counts and cytokine levels; and the relative numbers of regulatory T cells (Tregs) and T helper (Th17)/Treg ratio were negatively correlated with disease activity in RA. These results suggest that dysbiosis of certain bacterial lineages and alterations in gut microbiota metabolism lead to changes in the host immune profile that contribute to RA pathogenesis.

Growing experimental and clinical evidence suggests that a chronic inflammatory response induced by gut microbiome critically contribute to the development of rheumatoid arthritis (RA). Previous studies demonstrated the disturbance of lymphocyte subpopulations in RA patients. The purpose of this study was to explore the characteristics of gut microbiome and the associations between bacterium and lymphocyte subpopulations as well as cytokines in patients with RA. Fecal samples from 205 RA patients and 199 healthy controls (HCs) were collected for bacterial DNA extraction and 16S ribosomal RNA (rRNA) gene sequencing. The levels of peripheral lymphocyte subpopulation such as T, B, CD4+T, CD8+T, NK, T helper 1 (Th1), Th2, Th17, and regulatory T cells (Tregs) of these subjects were detected by flow cytometry combined with standard absolute counting beads. The serum levels of cytokines interleukin-2 (IL-2), IL-4, IL-6, IL-10, IL-17, tumour necrosis factor-α (TNF-α), and interferon-γ (INF-γ) were tested by flow cytometric bead array (CBA). Alpha and beta diversity of gut microbiome were explored by bioinformatics analysis. Spearman rank correlation test was used to explore the relationships between gut microbiome and lymphocyte subsets as well as serum cytokines. The diversity and relative abundance of intestinal microbiota in patients with RA were significantly different from those in HCs. Detailly, the abundant of phylum Proteobacteria in RA patients was more than that in HCs, while Firmicutes was less than in HCs. There was increased relative abundance of genus Clostridium_XlVa as well as genus Blautia, more abundance of Ruminococcus2 in patients with lower levels of T, B, CD4+T, and Tregs. In addition, the relative abundances of Pelagibacterium, Oxalobacter, ClostridiumXlVb, and ClostridiumXVIII were correlated with cytokines. Gut microbiome of RA patients was clearly different from that of HCs. Abnormal bacteria communities are associated with the altered levels of lymphocyte subpopulation and cytokines, which might be one of the pathogenesis of RA.