Role of NCF2 as a potential prognostic factor and immune infiltration indicator in hepatocellular carcinoma

Targeted serum proteome profiling reveals nicotinamide adenine dinucleotide phosphate (NADPH)-related biomarkers to discriminate linear IgA bullous disorder from dermatitis herpetiformis

Linear IgA bullous dermatosis (LABD) and dermatitis herpetiformis (DH) represent the major subtypes of IgA mediated autoimmune bullous disorders. We sought to understand the disease etiology by using serum proteomics. We assessed 92 organ damage biomarkers in LAB, DH, and healthy controls using the Olink high-throughput proteomics. The positive proteomic serum biomarkers were used to correlate with clinical features and HLA type. Targeted proteomic analysis of IgA deposition bullous disorders vs. controls showed elevated biomarkers. Further clustering and enrichment analyses identified distinct clusters between LABD and DH, highlighting the involvement of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. Comparative analysis revealed biomarkers with distinction between LABD and DH and validated in the skin lesion. Finally, qualitative correlation analysis with DEPs suggested six biomarkers (NBN, NCF2, CAPG, FES, BID, and PXN) have better prognosis in DH patients. These findings provide potential biomarkers to differentiate the disease subtype of IgA deposition bullous disease.

A novel model based on ubiquitination-related gene to predict prognosis and immunotherapy response in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a common cancer that is increasingly becoming a global health problem and a major public health concern. In order to improve patient outcomes, additional biomarkers and targets must be explored. Ubiquitination-related genes (URGs), as tumor regulators, exhibit multiple functions in tumor development. Our objective was to examine the influence of URGs on the prognosis of patients with HCC.

Methods

By utilizing unsupervised cluster analysis, we were able to identify URGs in the database and create a risk score profile for predicting the prognosis of patients with HCC. The model's clinical application was explored using subject operating characteristic curves, survival analysis, and correlation analysis. We additionally examined the variances in clinical traits, immune infiltration, somatic genetic alterations, and responsiveness to treatment among high- and low-risk populations identified by the prognostic model. Scores for immune cell infiltration and immune-related pathway activity were determined by performing ssGSEA enrichment analysis. Additionally, to investigate potential mechanisms, we utilized GO, KEGG and GSVA analyses.

Results

We developed a risk scoring model that relies on genes associated with ubiquitination. As the risk score increased, the malignancy and prognosis of the tumor worsened. The high-risk and low-risk groups exhibited notable disparities in relation to the immune microenvironment, genes associated with immune checkpoints, sensitivity to drugs, and response to immunotherapy.

Conclusion

The utilization of a risk model that relies on genes associated with ubiquitination can serve as a biomarker to assess the prognosis of patients with HCC, and aid in the selection of suitable therapeutic agents.

Single-Cell Sequencing Combined with Transcriptome Sequencing Constructs a Predictive Model of Key Genes in Multiple Sclerosis and Explores Molecular Mechanisms Related to Cellular Communication

Background

Multiple sclerosis (MS) causes chronic inflammation and demyelination of the central nervous system and comprises a class of neurodegenerative diseases in which interactions between multiple immune cell types mediate the involvement of MS development. However, the early diagnosis and treatment of MS remain challenging.

Methods

Gene expression profiles of MS patients were obtained from the Gene Expression Omnibus (GEO) database. Single-cell and intercellular communication analyses were performed to identify candidate gene sets. Predictive models were constructed using LASSO regression. Relationships between genes and immune cells were analyzed by single sample gene set enrichment analysis (ssGSEA). The molecular mechanisms of key genes were explored using gene enrichment analysis. An miRNA network was constructed to search for target miRNAs related to key genes, and related transcription factors were searched by transcriptional regulation analysis. We utilized the GeneCard database to detect the correlations between disease-regulated genes and key genes. We verified the mRNA expression of 4 key genes by reverse transcription-quantitative PCR (RT‒qPCR).

Results

Monocyte marker genes were selected as candidate gene sets. CD3D, IL2RG, MS4A6A, and NCF2 were found to be the key genes by LASSO regression. We constructed a prediction model with AUC values of 0.7569 and 0.719. The key genes were closely related to immune factors and immune cells. We explored the signaling pathways and molecular mechanisms involving the key genes by gene enrichment analysis. We obtained and visualized the miRNAs associated with the key genes using the miRcode database. We also predicted the transcription factors involved. We used validated key genes in MS patients, several of which were confirmed by RT‒qPCR.

Conclusion

The prediction model constructed with the CD3D, IL2RG, MS4A6A, and NCF2 genes has good diagnostic efficacy and provides new ideas for the diagnosis and treatment of MS.

M6A-mediated-upregulation of lncRNA BLACAT3 promotes bladder cancer angiogenesis and hematogenous metastasis through YBX3 nuclear shuttling and enhancing NCF2 transcription

Lymphatic metastasis is recognized as the leading manner of metastasis in bladder cancer (BLCa), but hematogenous metastasis accounts for a majority of cancer-associated deaths. The past two decades have witnessed tremendous attention in long non-coding RNAs (lncRNAs), which are a new hope for the development of targeted drug therapy for metastatic cancers; however, the underlying mechanism of lncRNAs involved in BLCa hematogenous metastasis remains to be elucidated. Here, we identified BLCa-associated transcript 3 (BLACAT3), a lncRNA, which was aberrantly upregulated in BLCa and corelated with poor prognosis of patients with muscle-invasive bladder cancer. Methodologically, m6A epitranscriptomic microarray, RNA sequencing and mass spectrometry (MS) were used to screen the key molecules of the regulatory axis. Functional assays, animal models and clinical samples were used to explore the roles of BLACAT3 in BLCa in vitro and in vivo. Mechanistically, m6A modification contributes to BLACAT3 upregulation by stabilizing RNA structure. BLACAT3 recruits YBX3 to shuttle into the nucleus, synergistically enhances NCF2 transcription, and promotes BLCa angiogenesis and hematogenous metastasis by activating downstream NF-κB signaling. Our findings will develop prognosis prediction tools for BLCa patients and discover novel therapeutic biological targets for metastatic BLCa.