Exploration of biomarkers in osteoarthritis based on bioinformatics

Receptor tyrosine kinase C-kit promotes a destructive phenotype of FLS in osteoarthritis via intracellular EMT signaling

BACKGROUND

Chronic inflammation, mainly derived from fibroblast-like synoviocytes (FLSs), plays a central role in the pathomechanism of osteoarthritis (OA). Recently, epithelial-mesenchymal transition (EMT) signaling was found to be activated in OA-derived FLSs with a pro-inflammatory phenotype. However, the role of EMT signaling in regulating FLS function and OA-related inflammation remains unknown.

METHODS

The synovium of OA patients were evaluated for EMT and inflammation markers. The FLSs with activated EMT signaling were co-cultured with chondrocytes (chond). Gene expression of OA synovial samples were analyzed. The role of receptor tyrosine kinase C-kit was investigated in OA-FLSs and an OA rat model. The downstream pathways driven by C-kit were explored in OA-FLSs.

RESULTS

EMT marker N-cadherin (N-CDH) was upregulated in 40.0% of the OA samples. These N-CDH+ OA samples showed higher expression of pro-inflammatory factors. In co-culture, FLSs derived from N-CDH+ OA samples induced a typical degenerative phenotype of chonds and stimulated their production of matrix degrading enzymes. C-kit was significantly upregulated and spatially co-localized with N-CDH in N-CDH+ OA samples. In OA-FLSs, C-kit activated intracellular EMT signaling and induced destructive features of OA-FLSs. In OA rat model, C-kit largely promoted synovial inflammation and cartilage destruction, whereas knocking-down C-kit significantly restored the health of OA joints. Using GSK3β S9A mutant, we demonstrated that C-kit drives EMT signaling in OA-FLS by promoting phosphorylation of GSK3β and nuclear retention of the EMT transcription factor Snail.

CONCLUSION

C-kit drives EMT signaling in OA-FLSs and promotes a destructive FLS phenotype, leading to synovial inflammation and cartilage destruction.

Identification of essential genes and immune cell infiltration in rheumatoid arthritis by bioinformatics analysis

Rheumatoid arthritis (RA) is a common autoimmune disease that can lead to severe joint damage and disability. And early diagnosis and treatment of RA can avert or substantially slow the progression of joint damage in up to 90% of patients, thereby preventing irreversible disability. Previous research indicated that 50% of the risk for the development of RA is attributable to genetic factors, but the pathogenesis is not well understood. Thus, it is urgent to identify biomarkers to arrest RA before joints are irreversibly damaged. Here, we first use the Robust Rank Aggregation method (RRA) to identify the differentially expressed genes (DEGs) between RA and normal samples by integrating four public RA patients' mRNA expression data. Subsequently, these DEGs were used as the input for the weighted gene co-expression network analysis (WGCNA) approach to identify RA-related modules. The function enrichment analysis suggested that the RA-related modules were significantly enriched in immune-related actions. Then the hub genes were defined as the candidate genes. Our analysis showed that the expression levels of candidate genes were significantly associated with the RA immune microenvironment. And the results indicated that the expression of the candidate genes can use as predictors for RA. We hope that our method can provide a more convenient approach for the early diagnosis of RA.

A novel pyroptosis-related indicator of immune infiltration features and prognosis in breast cancer

Breast cancer is the most common malignancy in women, and there is evidence for the dual role of cell pyroptosis in tumor development. However, little is known about the relationship between cell pyroptosis and breast cancer and its prognostic value. We aimed to construct a prognostic model using cell-pyroptosis-related genes to provide innovative insights into the prognosis and treatment of breast cancer. We screened candidate genes for pyroptosis using public databases and identified 10 cell pyroptosis signature genes with the random forest method. Finally, a nomogram for predicting 1-, 3-, and 5-year survival probabilities was constructed. The differences in immune cell distributions between survival periods were similar across the breast cancer datasets. The 10 identified key pyroptosis factors showed a significant correlation with Her2, tumor–node–metastasis (TNM) stage, and survival of breast cancer. The risk scores correlated positively with the infiltration features of naive B cells, CD8+ T cells, atpdelnd mast cells, while they correlated negatively with those of M0 macrophages and dendritic cells. In conclusion, our findings confirm that cell pyroptosis is closely associated with breast cancer. Importantly, the prognostic complex values generated from the 10 cell-pyroptosis-related genes based on various clinical features may provide an important basis for future studies on the prognosis of breast cancer.