Background: The association between obesity and musculoskeletal chronic pain has attracted much attention these days; however, the causal relationship between them is uncertain. Hence, this study performed a Mendelian randomization (MR) analysis to investigate the causal effects of body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) on knee pain, hip pain, and back pain.Materials and methods: The summary data for obesity and musculoskeletal chronic pain came from the genome-wide association study datasets. Significant and independent (p < 5 × 10⁻⁸; r² < 0.001, kb = 10,000) single-nucleotide polymorphisms were extracted for MR analysis. The inverse variance weighted (IVW) and other methods were used for MR analysis, while sensitivity analyses were conducted to test the reliability and stability.Results: The positive causal effects of BMI on knee pain (odds ratio (OR) = 1.049; 95% CI: 1.034 to 1.063; p = 9.88 × 10⁻¹²), hip pain (OR = 1.034; 95% CI: 1.024 to 1.044; p = 1.38 × 10⁻¹²), and back pain (OR = 1.022; 95% CI: 1.007 to 1.038; p = 0.004) were observed. WC and HC were also positively associated with knee pain (WC: OR = 1.057; 95% CI: 1.041 to 1.072; p = 1.54 × 10⁻¹³; HC: OR = 1.034; 95% CI: 1.017 to 1.052; p = 1.32 × 10⁻⁴) and hip pain (WC: OR = 1.031; 95% CI: 1.020 to 1.042; p = 2.61 × 10⁻⁸; HC: OR = 1.027; 95% CI: 1.018 to 1.035; p = 5.48 × 10⁻¹⁰) but not back pain. No causal relationship was found between WHR and musculoskeletal chronic pain. The results were robust according to sensitivity tests.Conclusions: This study revealed that BMI was positively related to knee, hip, and back pain and that WC and HC were positively associated with knee and hip pain, while WHR was not related to any type of musculoskeletal chronic pain.

Osteosarcoma (OS) is the most prevalent secondary sarcoma associated with retinoblastoma (RB). However, the molecular mechanisms driving the interactions between these two diseases remain incompletely understood. This study aims to explore the transcriptomic commonalities and molecular pathways shared by RB and OS, and to identify biomarkers that predict OS prognosis effectively. RNA sequences and patient information for OS and RB were obtained from the University of California Santa Cruz (UCSC) Xena and Gene Expression Omnibus databases. When RB and OS were first identified, a common gene expression profile was discovered. Weighted Gene Co-expression Network Analysis (WGCNA) revealed co-expression networks associated with OS after immunotyping patients. To evaluate the genes shared by RB and OS, univariate and multivariate Cox regression analysis were then carried out. Three machine learning methods were used to pick key genes, and risk models were created and verified. Next, medications that target independent prognostic genes were found using the Cellminer database. The comparison of differential gene expression between OS and RB revealed 1216 genes, primarily linked to the activation and proliferation of immune cells. WGCNA identified 12 modules related to OS immunotyping, with the grey module showing a strong correlation with the immune-inflamed phenotype. This module intersected with differential genes from RB, producing 65 RB-associated OS Immune-inflamed Genes (ROIGs). Analysis identified 6 hub genes for model construction through univariate Cox regression and three machine learning techniques. A risk model based on these hub genes was established, demonstrating significant prognostic value for OS. Genes shared between OS and RB contribute to the progression of both cancers through multiple pathways. The ROIGs risk score model independently predicts the overall survival of OS patients. Additionally, this study highlights genes with potential as therapeutic targets or biomarkers for clinical use.

Background: Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the spine and sacroiliac joints. Recent genetic studies suggest certain plasma proteins may play a causal role in AS development. This study aims to identify and characterize these proteins using Mendelian randomization (MR) and colocalization analyses.Methods: Plasma protein data were obtained from recent publications in Nature Genetics, integrating data from five previous GWAS datasets, including 738 cis-pQTLs for 734 plasma proteins. GWAS summary data for AS were sourced from IGAS and other European cohorts. MR analyses were conducted using “TwoSampleMR” to assess causal links between plasma protein levels and AS. Colocalization analysis was performed with the coloc R package to identify shared genetic variants. Sensitivity analyses and protein-protein interaction (PPI) network analyses were conducted to validate findings and explore therapeutic targets. We performed Phenome-wide association study (PheWAS) to examine the potential side effects of drug protein on AS treatment.Results: After FDR correction, eight significant proteins were identified: IL7R, TYMP, IL12B, CCL8, TNFAIP6, IL18R1, IL23R, and ERAP1. Elevated levels of IL7R, IL12B, CCL8, IL18R1, IL23R, and ERAP1 increased AS risk, whereas elevated TYMP and TNFAIP6 levels decreased AS risk. Colocalization analysis indicated that IL23R, IL7R, and TYMP likely share causal variants with AS. PPI network analysis identified IL23R and IL7R as potential new therapeutic targets.Conclusions: This study identified eight plasma proteins with significant associations with AS risk, suggesting IL23R, IL7R, and TYMP as promising therapeutic targets. Further research is needed to explore underlying mechanisms and potential for drug repurposing.

The inflammation/immune response pathway is considered a key contributor to the development of Langerhans cell histiocytosis (LCH) bone metastasis. However, the dynamic changes in the immune microenvironment of LCH bone metastasis and critical regulators are still unclear. Expression profiling by arrays of GSE16395, GSE35340, and GSE122476 was applied to detect the immune microenvironment changes in the development of LCH bone metastasis. The single‐cell high‐throughput sequencing of GSE133704, involved in LCH bone lesions, was analyzed. The online database Metascape and gene set variation analysis (GSVA) algorithms were used to detect the gene function of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein–protein interaction (PPI) network of hub regulators was constructed by the STRING database. In these results, key immune cells, such as Tem cells, NK T cells, CD8(+) T cells, and Th1 cells, were identified in LCH bone metastasis. These genes, which include LAG3, TSPAN5, LPAR5, VEGFA, CXCL16, CD74, and MARCKS, may significantly correlate with the cellular infiltration of B cells, aDCs, pDCs, cytotoxic cells, T cells, CD8+ T cells, T helper cells, and Tcm cells. In conclusion, our study constructed an atlas of the immune microenvironment of LCH bone metastasis. Genes including LAG3, TSPAN5, LPAR5, VEGFA, CXCL16, CD74, and MARCKS may be involved in the development of LCH bone metastasis. The hub gene‐immune cell interactive map may be a potential prognostic biomarker for the progression of LCH bone metastasis and synergetic targets for immunotherapy in LCH patients.

Increasing evidence suggests that immune-inflammatory responses are closely associated with osteonecrosis (ON). However, the specific inflammatory regulators involved in this pathogenesis remain unclear. Using a bidirectional Mendelian randomization (MR) investigation, we methodically investigated circulating inflammatory proteins that are causally linked to osteonecrosis. The genetic tools for 91 inflammatory circulating proteins were from a genome-wide association study with 14,824 participants, most of whom were from Europe. The ON summary statistics were acquired from the FinnGen database. The main strategy for drawing conclusions about causality was the inverse-variance weighted (IVW) method. The final results were supported by a number of sensitivity studies, including MR-Egger, weighted median, simple mode, weighted mode, and linkage disequilibrium score regression (LDSC). A link between CDCP1 and a higher risk of osteonecrosis has been revealed by genetic data (IVW OR = 1.23, 95% CI = 1.01–1.50, P = .037). Increased concentrations of CSF1 (IVW OR = 0.72, 95% CI = 0.54–0.96, P = .026), IL-10RB (IVW OR = 0.84, 95% CI = 0.699–0.999, P = .049), and MCP-4 (IVW OR = 0.81, 95% CI = 0.67–0.99, P = .038) were associated with a reduced risk of ON, while ON did not significantly affect these proteins. Only IL-18 may be a protective factor against osteonecrosis, according to reverse MR data (IVW OR = 0.97, 95% CI = 0.94–0.999, P = .042). This MR study investigated the causal associations between 91 inflammatory cytokines and osteonecrosis in detail overall. We discovered causal connections between reverse magnetic resonance imaging (reverse MR) and CDCP1, CSF1, MCP-4, and IL-18. These elements might contribute significantly to the pathophysiology of ON and might present fresh treatment options for the illness.