Silencing BLNK protects against interleukin-1β-induced chondrocyte injury through the NF-κB signaling pathway

Identification of immune microenvironment subtypes and clinical risk biomarkers for osteoarthritis based on a machine learning model

Background

Osteoarthritis (OA) is a degenerative disease with a high incidence worldwide. Most affected patients do not exhibit obvious discomfort symptoms or imaging findings until OA progresses, leading to irreversible destruction of articular cartilage and bone. Therefore, developing new diagnostic biomarkers that can reflect articular cartilage injury is crucial for the early diagnosis of OA. This study aims to explore biomarkers related to the immune microenvironment of OA, providing a new research direction for the early diagnosis and identification of risk factors for OA.

Methods

We screened and downloaded relevant data from the Gene Expression Omnibus (GEO) database, and the immune microenvironment-related genes (Imr-DEGs) were identified using the ImmPort data set by combining weighted coexpression analysis (WGCNA). Functional enrichment of GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to explore the correlation of Imr-DEGs. A random forest machine learning model was constructed to analyze the characteristic genes of OA, and the diagnostic significance was determined by the Receiver Operating Characteristic Curve (ROC) curve, with external datasets used to verify the diagnostic ability. Different immune subtypes of OA were identified by unsupervised clustering, and the function of these subtypes was analyzed by gene set enrichment analysis (GSVA). The Drug-Gene Interaction Database was used to explore the relationship between characteristic genes and drugs.

Results

Single sample gene set enrichment analysis (ssGSEA) revealed that 16 of 28 immune cell subsets in the dataset significantly differed between OA and normal groups. There were 26 Imr-DEGs identified by WGCNA, showing that functional enrichment was related to immune response. Using the random forest machine learning model algorithm, nine characteristic genes were obtained: BLNK (AUC = 0.809), CCL18 (AUC = 0.692), CD74 (AUC = 0.794), CSF1R (AUC = 0.835), RAC2 (AUC = 0.792), INSR (AUC = 0.765), IL11 (AUC = 0.662), IL18 (AUC = 0.699), and TLR7 (AUC = 0.807). A nomogram was constructed to predict the occurrence and development of OA, and the calibration curve confirmed the accuracy of these 9 genes in OA diagnosis.

Conclusion

This study identified characteristic genes related to the immune microenvironment in OA, providing new insight into the risk factors of OA.

Syk/BLNK/NF-κB signaling promotes pancreatic injury induced by tacrolimus and potential protective effect from rapamycin

BACKGROUND

The treatment of tacrolimus-induced post-transplantation diabetes mellitus (PTDM) has become a hot topic to improve the long-term survival of organ transplant patients, however whose pathogenesis has not been fully elucidated. In pancreas, the up-regulation of NF-κB has been reported to stimulate cytokine IL-1β/TNF-α secretion, inducing pancreatic injury, meanwhile other studies have reported the inhibitory effect of rapamycin on NF-κB.

PURPOSE

The aim of this study was to clarify the mechanism of tacrolimus-induced pancreatic injury and to explore the potential effect from small dose of sirolimus.

METHODS

Wistar rats were randomly divided normal control (NC) group, PTDM group, sirolimus intervention (SIR) group. Transcriptomic analysis was used to screen potential mechanism of PTDM. Biochemical index detections were used to test the indicators of pancreatic injury. Pathological staining, immumohistochemical staining, immunofluorescent staining, western blot were used to verify the underlying mechanism.

RESULTS

Compared with NC group, the level of insulin was significant reduction (P < 0.01), inversely the level of glucagon was significantly increase (P < 0.01) in PTDM group. Transcriptomic analysis indicated Syk/BLNK/NF-κB signaling was significantly up-regulated in PTDM group. Pathological staining, immumohistochemical staining, immunofluorescent staining, western blot verified Syk/BLNK/NF-κB and TNF-α/IL-1β were all significantly increased (P < 0.05 or P < 0.01), demonstrating the mechanism of tacrolimus-induced pancreatic injury via Syk/BLNK/NF-κB signaling. In addition, compared with PTDM group, the levels of weight, FPG, AMY, and GSP in SIR group were significant ameliorative (P < 0.05 or P < 0.01), and the expressions of p-NF-κB, TNF-α/IL-1β in SIR group were significantly reduction (P < 0.05 or P < 0.01), showing Syk/BLNK/NF-κB signaling promoted pancreatic injury induced by tacrolimus and potential protective effect from rapamycin reducing NF-κB.

CONCLUSION

Syk/BLNK/NF-κB signaling promotes pancreatic injury induced by tacrolimus and rapamycin has a potentially protective effect by down-regulating NF-κB. Further validation and clinical studies are needed in the future.

A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis

Introduction

Aortic aneurysms (AA) are prevalent worldwide with a notable absence of drug therapies. Thus, identifying potential drug targets is of utmost importance. AA often presents in the elderly, coupled with consistently raised serum inflammatory markers. Given that ageing and inflammation are pivotal processes linked to the evolution of AA, we have identified key genes involved in the inflammaging process of AA development through various bioinformatics methods, thereby providing potential molecular targets for further investigation.

Methods

The transcriptome data of AA was procured from the datasets GSE140947, GSE7084, and GSE47472, sourced from the NCBI GEO database, whilst gene data of ageing and inflammation were obtained from the GeneCards Database. To identify key genes, differentially expressed analysis using the "Limma" package and WGCNA were implemented. Protein-protein intersection (PPI) analysis and machine learning (ML) algorithms were employed for the screening of potential biomarkers, followed by an assessment of the diagnostic value. Following the acquisition of the hub inflammaging and AA-related differentially expressed genes (IADEGs), the TFs-mRNAs-miRNAs regulatory network was established. The CIBERSORT algorithm was utilized to investigate immune cell infiltration in AA. The correlation of hub IADEGs with infiltrating immunocytes was also evaluated. Lastly, wet laboratory experiments were carried out to confirm the expression of hub IADEGs.

Results

342 and 715 AA-related DEGs (ADEGs) recognized from GSE140947 and GSE7084 datasets were procured by intersecting the results of "Limma" and WGCNA analyses. After 83 IADEGs were obtained, PPI analysis and ML algorithms pinpointed 7 and 5 hub IADEGs candidates respectively, and 6 of them demonstrated a high diagnostic value. Immune cell infiltration outcomes unveiled immune dysregulation in AA. In the wet laboratory experiments, 3 hub IADEGs, including BLNK, HLA-DRA, and HLA-DQB1, finally exhibited an expression trend in line with the bioinformatics analysis result.

Discussion

Our research identified three genes - BLNK, HLA-DRA, and HLA-DQB1- that play a significant role in promoting the development of AA through inflammaging, providing novel insights into the future understanding and therapeutic intervention of AA.

Bioinformatics gene analysis of potential biomarkers and therapeutic targets of osteoarthritis associated myelodysplastic syndrome

Objective: Osteoarthritis (OA) and Myelodysplastic syndrome (MDS) are diseases caused by the same immune disorder with unclear etiology and many similarities in clinical manifestations; however, the specific mechanisms between osteoarthritis and myelodysplastic syndrome are unclear.

Methods: The expression profile microarrays of osteoarthritis and myelodysplastic syndrome were searched in the GEO database, the intersection of their differential genes was taken, Venn diagrams were constructed to find common pathogenic genes, bioinformatics analysis signaling pathway analysis was performed on the obtained genes, and protein-protein interaction networks were constructed to find hub genes in order to establish diagnostic models for each disease and explore the immune infiltration of hub genes.

Results: 52 co-pathogenic genes were screened for association with immune regulation, immune response, and inflammation. The mean area under the receiver operating characteristic (ROC) for all 10 genes used for co-causal diagnosis ranged from 0.71–0.81. Immune cell infiltration analysis in the myelodysplastic syndrome subgroup showed that the relative numbers of Macrophages M1, B cells memory, and T cells CD4 memory resting in the myelodysplastic syndrome group were significantly different from the normal group, however, in the osteoarthritis subgroup the relative numbers of Mast cells resting in the osteoarthritis subgroup was significantly different from the normal group.

Conclusion: There are common pathogenic genes in osteoarthritis and myelodysplastic syndrome, which in turn mediate differential alterations in related signaling pathways and immune cells, affecting the high prevalence of osteoarthritis and myelodysplastic syndrome and the two disease phenomena.

Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis

Background

Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment.

Methods

GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457.

Results

In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait.

Conclusions

CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.