Role of Apg-1 in HSF1 activation and bortezomib sensitivity in myeloma cells

Causal Links Between Bone Diseases and Temporomandibular Disorders

INTRODUCTION AND AIMS

Epidemiological observational studies have explored the link between bone joint-related diseases and temporomandibular disorders (TMD), but inconsistent conclusions have emerged due to various limitations. This study aims to investigate the causal relationship between bone joint-related diseases and TMD using Mendelian randomization (MR).

METHODS

We utilized a two-sample MR design, applying pooled genome-wide association study (GWAS) data from six subtypes of bone and joint diseases and TMD. Primary analysis was conducted using the inverse-variance weighted (IVW) method, complemented by weighted median (WM), weighted mode, and MR-Egger regression to assess causal relationships. Additionally, we performed reverse causality analyses and applied sensitivity analyses, including MR-PRESSO, MR-Egger, Cochran's Q, and leave-one-out methods to evaluate result robustness, explore heterogeneity, and identify potential biases.

RESULTS

MR genetic prediction analyses indicated that bone and joint-related diseases increase the risk of TMD, with specific odds ratios (OR) for ankylosing spondylitis (AS: OR 1.36, 95% CI: 1.04-1.77, P = .026), rheumatoid arthritis (RA: OR 1.08, 95% CI: 1.03-1.13, P = .001), and osteoporosis (OR 1.0751, 95% CI: 1.0047-1.1505, P = .036). Conversely, reverse MR analysis revealed a positive genetic link from TMD to RA (RA: OR 1.12, 95% CI: 1.02-1.23, P = .018). MR-Egger regression showed no influence of horizontal pleiotropy, and MR-PRESSO detected no outliers. The leave-one-out analysis confirmed the results' stability.

CONCLUSIONS

The findings demonstrate a positive, causal association between TMD risk and AS, RA, and osteoporosis. Moreover, TMD patients exhibit an increased risk of developing RA.

CLINICAL RELEVANCE

Understanding these relationships aids in better diagnosis and management of TMD and its association with bone joint diseases, potentially guiding clinical interventions.

Identification of docetaxel-related biomarkers for prostate cancer

Prostate cancer (PCa) which was the second commonly diagnosed malignancy, contributed to the top fifth carcinoma death in men. Nevertheless, the main chemotherapeutic agent docetaxel came to failure due to chemoresistance. Recently, increasing evidence suggested the importance of tumour microenvironment (TME) in PCa. The present study aimed to explore the specific TME in PCa and find biomarkers related to both immune infiltration and docetaxel. The docetaxel-specific genes and differential expression genes comparing PCa with normal control samples were derived using DESeq2 and zinbwave with GSE140440, TCGA and GTEx datasets. Immune-infiltration-related genes were identified using CIBERSORT and co-expression network analysis. Key genes related to both docetaxel and immune infiltrating in PCa, including nine genes, namely ZNF486, IFI6, TMOD2, HSPA4L, ITPR1, LRRC37A7P, APOC1, APOBEC3G, and ITGA2, were determined by overlapping above three gene sets. ITGA2 was then defined as the hub gene for its significant prognostic implications. Further validations conducted on Oncomine, GEO, TISIDB, MSigDB, and The Human Protein Atlas confirmed the docetaxel-specific and immune infiltrating characteristics of ITGA2. To sum up, our findings could provide a better understanding of immune infiltrating and docetaxel-resistance in PCa, mostly, ITGA2 could serve as potential prognosis biomarkers and targets for the combination of docetaxel.