The presence of FCGR2B promoter or transmembrane region variant alleles leads to reduced serum IL-6 levels in rheumatoid arthritis

Genetic insights into therapeutic targets for gout: evidence from a multi-omics mendelian randomization study

BACKGROUND

Considering that the treatment of gout is poor, we performed a Mendelian randomization (MR) study to identify candidate biomarkers and therapeutic targets for gout.

METHODS

A drug-targeted MR study was performed for gout by integrating the gout genome-wide association studies (GWAS) summary data and cis expression quantitative trait loci of 2,633 druggable genes from multiple cohorts. Summary data-based Mendelian randomization (SMR) analyses based on transcript and protein levels were further implemented to validate the reliability of the identified potential therapeutic targets for gout. Phenome-wide MR (Phe-MR) analysis was conducted in 1403 diseases to investigate incidental side effects of potential therapeutic targets for gout.

RESULTS

Eight potential therapeutic targets (ALDH3B1, FCGR2B, IL2RB, NRBP1, RCE1, SLC7A7, SUMF1, THBS3) for gout were identified in the discovery cohort using MR analysis. Replication analysis and meta-analysis implemented in the replication cohort validated the robustness of the MR findings (P < 0.05). Evidence from the SMR analysis (P < 0.05) further strengthened the reliability of the 8 potential therapeutic targets for gout also revealed that high levels of ALDH3B1 reduced the gout risk possibly modified by the methylation site cg25402137. SMR analysis (P < 0.05) at the protein level added emphasis on the impact of the risk genes NRBP1 and SUMF1 on gout. Phe-MR analysis indicated significant causality between 7 gout causal genes and 45 diseases.

CONCLUSION

This study identified several biomarkers associated with gout risk, providing new insights into the etiology of gout and promising targets for the development of therapeutic agents.

Multi-omics study of key genes, metabolites, and pathways of periodontitis

OBJECTIVE

This study aimed to explore the key genes, metabolites, and pathways that influence periodontitis pathogenesis by integrating transcriptomic and metabolomic studies.

DESIGN

Gingival crevicular fluid samples from periodontitis patients and healthy controls were collected for liquid chromatography/tandem mass-based metabolomics. RNA-seq data for periodontitis and control samples were obtained from the GSE16134 dataset. Differential metabolites and differentially expressed genes (DEGs) between the two groups were then compared. Based on the protein-protein interaction (PPI) network module analysis, key module genes were selected from immune-related DEGs. Correlation and pathway enrichment analyses were performed for differential metabolites and key module genes. A multi-omics integrative analysis was performed using bioinformatic methods to construct a gene-metabolite-pathway network.

RESULTS

From the metabolomics study, 146 differential metabolites were identified, which were mainly enriched in the pathways of purine metabolism and Adenosine triphosphate binding cassette transporters (ABC transporters). The GSE16134 dataset revealed 102 immune-related DEGs (458 upregulated and 264 downregulated genes), 33 of which may play core roles in the key modules of the PPI network and are involved in cytokine-related regulatory pathways. Through a multi-omics integrative analysis, a gene-metabolite-pathway network was constructed, including 28 genes (such as platelet derived growth factor D (PDGFD), neurturin (NRTN), and interleukin 2 receptor, gamma (IL2RG)); 47 metabolites (such as deoxyinosine); and 8 pathways (such as ABC transporters).

CONCLUSION

PDGFD, NRTN, and IL2RG may be potential biomarkers of periodontitis and may affect disease progression by regulating deoxyinosine to participate in the ABC transporter pathway.

Novel Immune-Related Gene-Based Signature Characterizing an Inflamed Microenvironment Predicts Prognosis and Radiotherapy Efficacy in Glioblastoma

Effective treatment of glioblastoma (GBM) remains an open challenge. Given the critical role of the immune microenvironment in the progression of cancers, we aimed to develop an immune-related gene (IRG) signature for predicting prognosis and improving the current treatment paradigm of GBM. Multi-omics data were collected, and various bioinformatics methods, as well as machine learning algorithms, were employed to construct and validate the IRG-based signature and to explore the characteristics of the immune microenvironment of GBM. A five-gene signature (ARPC1B, FCGR2B, NCF2, PLAUR, and S100A11) was identified based on the expression of IRGs, and an effective prognostic risk model was developed. The IRG-based risk model had superior time-dependent prognostic performance compared to well-studied molecular pathology markers. Besides, we found prominent inflamed features in the microenvironment of the high-risk group, including neutrophil infiltration, immune checkpoint expression, and activation of the adaptive immune response, which may be associated with increased hypoxia, epidermal growth factor receptor (EGFR) wild type, and necrosis. Notably, the IRG-based risk model had the potential to predict the effectiveness of radiotherapy. Together, our study offers insights into the immune microenvironment of GBM and provides useful information for clinical management of this desperate disease.

Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BACKGROUND

It is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.

METHODS

Two expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.

RESULTS

1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.

CONCLUSIONS

In summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.