This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen and verify the diagnostic markers of OA. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in OA tissues, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. A total of 458 DEGs were screened in this study. GRB10 and E2F3 (AUC = 0.962) were identified as diagnostic markers of OA. Immune cell infiltration analysis found that resting mast cells, T regulatory cells, CD4 memory resting T cells, activated NK cells, and eosinophils may be involved in the OA process. In addition, GRB10 was correlated with NK resting cells, naive CD4 + T cells, and M1 macrophages, while E2F3 was correlated with resting mast cells. In conclusion, GRB10 and E2F3 can be used as diagnostic markers of osteoarthritis, and immune cell infiltration plays an important role in the occurrence and progression of OA.

Osteoarthritis (OA) is a chronic degenerative disease of the bone and joints. Immune-related genes and immune cell infiltration are important in OA development. We analyzed immune-related genes and immune infiltrates to identify OA diagnostic markers. The datasets GSE51588, GSE55235, GSE55457, GSE82107, and GSE114007 were downloaded from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and differentially expressed immune-related genes (DEIRGs), and functional correlation analysis was conducted. Second, CIBERSORT was used to evaluate infiltration of immune cells in OA tissue. Finally, the least absolute shrinkage and selection operator logistic regression algorithm and support vector machine-recurrent feature elimination algorithm were used to screen and verify diagnostic markers of OA. A total of 711 DEGs and 270 DEIRGs were identified in this study. Functional enrichment analysis showed that the DEGs and DEIRGs are closely related to cellular calcium ion homeostasis, ion channel complexes, chemokine signaling pathways, and JAK-STAT signaling pathways. Differential analysis of immune cell infiltration showed that M1 macrophage infiltration was increased but that mast cell and neutrophil infiltration were decreased in OA samples. The machine learning algorithm cross-identified 15 biomarkers (BTC, PSMD8, TLR3, IL7, APOD, CIITA, IFIH1, CDC42, FGF9, TNFAIP3, CX3CR1, ERAP2, SEMA3D, MPO, and plasma cells). According to pass validation, all 15 biomarkers had high diagnostic efficacy (AUC > 0.7), and the diagnostic efficiency was higher when the 15 biomarkers were fitted into one variable (AUC = 0.758). We developed 15 biomarkers for OA diagnosis. The findings provide a new understanding of the molecular mechanism of OA from the perspective of immunology.

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein‐protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM‐receptor interaction, and phosphatidylinositol 3‐kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

Intervertebral disc degeneration (IDD) is the most common chronic skeletal muscle degeneration disease. Although the underlying mechanisms remain unclear, nucleus pulposus (NP) autophagy, senescence, and apoptosis are known to play a critical role in this process. Previous studies suggest that bromodomain‐containing protein 4 (BRD4) promotes senescent and apoptotic effects in several age‐related degenerative diseases. It is not known, however, if BRD4 inhibition is protective in IDD. In this study, we explored whether BRD4 influenced IDD. In human clinical specimens, the BRD4 level was markedly increased with the increasing Pfirrmann grade. At the cellular level, BRD4 inhibition prevented IL‐1β‐induced senescence and apoptosis of NP cells and activated autophagy via the AMPK/mTOR/ULK1 signaling pathway. Inhibition of autophagy by 3‐methyladenine (3‐MA) partially reversed the antisenescence and antiapoptotic effects of BRD4. In vivo, BRD4 inhibition attenuated IDD. Taken together, the results of this study showed that BRD4 inhibition reduced NP cell senescence and apoptosis by induced autophagy, which ultimately alleviated IDD. Therefore, BRD4 may serve as a novel potential therapeutic target for the treatment of IDD.

Neuropathic pain is a serious clinical problem to be solved. This study is aimed at investigating protein kinase A (PKA) expression in neuropathic pain and its possible mechanisms of involvement. A neuropathic pain-related gene expression dataset was downloaded from Gene Expression Omnibus, and differentially expressed genes were screened using the R software. cytoHubba was used to screen for hub genes. A spared nerve injury (SNI) rat model was established, and the paw withdrawal threshold was determined using von Frey filaments. Western blotting and immunofluorescence were used to detect the expression and cellular localization, respectively, of key proteins in the spinal cord. Western blot, ELISA, and TUNEL assays were used to detect cell signal transduction, inflammation, and apoptosis, respectively. Pka was identified as a key gene involved in neuropathic pain. After SNI, mechanical allodynia occurred, PKA expression in the spinal cord increased, the p38MAPK pathway was activated, and spinal cord inflammation and apoptosis occurred in rats. PKA colocalized with neurons, astrocytes, and microglia, and apoptotic cells were mainly neurons. Intrathecal injection of a PKA inhibitor not only relieved mechanical hyperalgesia, inflammatory reaction, and apoptosis in SNI rats but also inhibited p38MAPK pathway activation. However, intrathecal injection of a p38MAPK inhibitor attenuated mechanical hyperalgesia, inflammation, and apoptosis, but did not affect PKA expression. In conclusion, PKA is involved in neuropathic pain by activating the p38MAPK pathway to mediate spinal cord cell apoptosis.