GPI-80 Augments NF-κB Activation in Tumor Cells

Investigation of epigenetics insights of hypertension: A bibliometric analysis

PURPOSE

Hypertension remains a major risk factor for myocardial infarction, heart failure, end-stage renal disease, and stroke. Multiple genes are involved in the process of hypertension with an additional dimension of interaction with the environment. This study conducted a bibliometric analysis of publications in the field of hypertension and epigenetics over the past 10 years to summarize the current status of the field and analyze the trends in the field.

METHODS

On February 5, 2023, we chose the web of science core collection database as the study data source. VOS viewer 1.6.18 and Cite Space 6.1.6 were used to examine publications of research on hypertension and epigenetics that were published between 2013 and 2022. We looked through the papers for journals, organizations, nations and regions, authors, and key terms.

RESULTS

This analysis covered a total of 1535 papers on studies into hypertension and epigenetics. There were 7279 authors, 83 nations, 1983 organizations, and 606 journals in all of the articles. In the USA, 540 publications were the most. The institution with the most publications was Harvard Medical School. The author with the most articles was Zhao Lubo.

CONCLUSION

This study summarizes the global research trends in hypertension and epigenetics. Publications in this field have increased year by year in the last decade and the field of research on hypertension and epigenetics has good prospects for growth.

Prognostic model for hepatocellular carcinoma based on anoikis-related genes: immune landscape analysis and prediction of drug sensitivity

Background

Hepatocellular carcinoma (HCC) represents a complex ailment characterized by an unfavorable prognosis in advanced stages. The involvement of immune cells in HCC progression is of significant importance. Moreover, metastasis poses a substantial impediment to enhanced prognostication for HCC patients, with anoikis playing an indispensable role in facilitating the distant metastasis of tumor cells. Nevertheless, limited investigations have been conducted regarding the utilization of anoikis factors for predicting HCC prognosis and assessing immune infiltration. This present study aims to identify hepatocellular carcinoma-associated anoikis-related genes (ANRGs), establish a robust prognostic model for HCC, and delineate distinct immune characteristics based on the anoikis signature. Cell migration and cytotoxicity experiments were performed to validate the accuracy of the ANRGs model.

Methods

Consensus clustering based on ANRGs was employed in this investigation to categorize HCC samples obtained from both TCGA and Gene Expression Omnibus (GEO) cohorts. To assess the differentially expressed genes, Cox regression analysis was conducted, and subsequently, prognostic gene signatures were constructed using LASSO-Cox methodology. External validation was performed at the International Cancer Genome Conference. The tumor microenvironment (TME) was characterized utilizing ESTIMATE and CIBERSORT algorithms, while machine learning techniques facilitated the identification of potential target drugs. The wound healing assay and CCK-8 assay were employed to evaluate the migratory capacity and drug sensitivity of HCC cell lines, respectively.

Results

Utilizing the TCGA-LIHC dataset, we devised a nomogram integrating a ten-gene signature with diverse clinicopathological features. Furthermore, the discriminative potential and clinical utility of the ten-gene signature and nomogram were substantiated through ROC analysis and DCA. Subsequently, we devised a prognostic framework leveraging gene expression data from distinct risk cohorts to predict the drug responsiveness of HCC subtypes.

Conclusion

In this study, we have established a promising HCC prognostic ANRGs model, which can serve as a valuable tool for clinicians in selecting targeted therapeutic drugs, thereby improving overall patient survival rates. Additionally, this model has also revealed a strong connection between anoikis and immune cells, providing a potential avenue for elucidating the mechanisms underlying immune cell infiltration regulated by anoikis.

(Patho)Physiology of Glycosylphosphatidylinositol-Anchored Proteins I: Localization at Plasma Membranes and Extracellular Compartments

Glycosylphosphatidylinositol (GPI)-anchored proteins (APs) are anchored at the outer leaflet of plasma membranes (PMs) of all eukaryotic organisms studied so far by covalent linkage to a highly conserved glycolipid rather than a transmembrane domain. Since their first description, experimental data have been accumulating for the capability of GPI-APs to be released from PMs into the surrounding milieu. It became evident that this release results in distinct arrangements of GPI-APs which are compatible with the aqueous milieu upon loss of their GPI anchor by (proteolytic or lipolytic) cleavage or in the course of shielding of the full-length GPI anchor by incorporation into extracellular vesicles, lipoprotein-like particles and (lyso)phospholipid- and cholesterol-harboring micelle-like complexes or by association with GPI-binding proteins or/and other full-length GPI-APs. In mammalian organisms, the (patho)physiological roles of the released GPI-APs in the extracellular environment, such as blood and tissue cells, depend on the molecular mechanisms of their release as well as the cell types and tissues involved, and are controlled by their removal from circulation. This is accomplished by endocytic uptake by liver cells and/or degradation by GPI-specific phospholipase D in order to bypass potential unwanted effects of the released GPI-APs or their transfer from the releasing donor to acceptor cells (which will be reviewed in a forthcoming manuscript).

Upregulated Vanins and their potential contribution to periodontitis

BACKGROUND

Although Vanins are closely related to neutrophil regulation and response to oxidative stress, and play essential roles in inflammatory diseases with clinical significance, their contribution to periodontitis remains to be determined. This research was designed to assess the expression of Vanins in human gingiva, and to define the relationship between Vanins and periodontitis.

METHODS

Forty-eight patients with periodontitis and forty-two periodontal healthy individuals were enrolled for gingival tissue sample collection. Expression levels of VNN1, VNN2 and VNN3 were evaluated by RT-qPCR and validated in datasets GSE10334 and GSE16134. Western blot and immunohistochemistry identified specific proteins within gingiva. The histopathological changes in gingival sections were investigated using HE staining. Correlations between Vanins and clinical parameters, PD and CAL; between Vanins and inflammation, IL1B; and between Vanins and MPO in periodontitis were investigated by Spearman's correlation analysis respectively. Associations between VNN2 and indicators of neutrophil adherence and migration were further validated in two datasets.

RESULTS

Vanins were at higher concentrations in diseased gingival tissues in both RT-qPCR and dataset analysis (p < 0.01). Assessment using western blot and immunohistochemistry presented significant upregulations of VNN1 and VNN2 in periodontitis (p < 0.05). The higher expression levels of Vanins, the larger the observed periodontal parameters PD and CAL (p < 0.05), and IL1B (p < 0.001). Moreover, positive correlations existed between VNN2 and MPO, and between VNN2 and neutrophil-related indicators.

CONCLUSION

Our study demonstrated upregulation of Vanins in periodontitis and the potential contribution of VNN2 to periodontitis through neutrophils-related pathological processes.

Genomic and Transcriptomic Predictors of Response to Immune Checkpoint Inhibitors in Melanoma Patients: A Machine Learning Approach

Immune checkpoint inhibitors (ICIs) became one of the most revolutionary cancer treatments, especially in melanoma. While they have been proven to prolong survival with lesser side effects compared to chemotherapy, the accurate prediction of response remains to be an unmet gap. Thus, we aim to identify accurate clinical and transcriptomic biomarkers for ICI response in melanoma. We also provide mechanistic insight into how high-performing markers impose their effect on the tumor microenvironment (TME). Clinical and transcriptomic data were retrieved from melanoma studies administering ICIs from cBioportal and GEO databases. Four machine learning models were developed using random-forest classification (RFC) entailing clinical and genomic features (RFC7), differentially expressed genes (DEGs, RFC-Seq), survival-related DEGs (RFC-Surv) and a combination model. The xCELL algorithm was used to investigate the TME. A total of 212 ICI-treated melanoma patients were identified. All models achieved a high area under the curve (AUC) and bootstrap estimate (RFC7: 0.71, 0.74; RFC-Seq: 0.87, 0.75; RFC-Surv: 0.76, 0.76, respectively). Tumor mutation burden, GSTA3, and VNN2 were the highest contributing features. Tumor infiltration analyses revealed a direct correlation between upregulated genes and CD8+, CD4+ T cells, and B cells and inversely correlated with myeloid-derived suppressor cells. Our findings confirmed the accuracy of several genomic, clinical, and transcriptomic-based RFC models, that could further support the use of TMB in predicting response to ICIs. Novel genes (GSTA3 and VNN2) were identified through RFC-seq and RFC-surv models that could serve as genomic biomarkers after robust validation.

Comprehensive analysis of pyroptosis regulation patterns and their influence on tumor immune microenvironment and patient prognosis in glioma

BACKGROUND

Glioma is the most common intracranial malignancy with a poor prognosis. Although remarkable advances have been made in the study of diagnostic and prognostic biomarkers, the efficacy of current treatment strategies is still unsatisfactory. Therefore, developing novel and reliable targets is desperately needed for glioma patients. Pyroptosis reshapes tumor immune microenvironment (TME) and promotes the destruction of the tumor by the immune system. Moreover, pyroptosis levels correlate with prognosis and immunotherapy response in many cancer patients. This study performed a comprehensive analysis of pyroptosis in the glioma, unveiling its potential value in glioma prognosis prediction and therapy efficacy.

METHODS

Firstly, the pyroptosis regulation patterns were comprehensively evaluated on 33 pyroptosis-related genes in 1716 glioma samples. The correlations were analyzed between pyroptosis regulation patterns and TME immune cell infiltration properties. Next, pyroptosis regulation patterns were measured by the PSscore model based on principal component analysis algorithms. The correlations were analyzed between PSscore and tumor mutational burden (TMB), immune checkpoint blockade (ICB) therapeutic advantages. Last, the findings were validated in an independently collected external clinical cohort.

RESULTS

We determined two distinct pyroptosis regulation patterns. The cluster-A was high immune cell infiltration with a poor prognosis (p < 0.001), whereas the cluster-B was low immune cell infiltration with a better prognosis (p < 0.001). We developed the PSscore as a measure for pyroptosis regulation patterns. The high PSscore with an inflamed TME phenotype, a high TMB (p < 0.0001), increased innate immune response, and a poor prognosis (p < 0.001). It was in stark contrast to the low PSscore (p < 0.001). Analysis of PSscore with checkpoint therapy indicated high PSscore were correlated with enhanced response to anti-PD-1 immunotherapy (p = 0.0046). For validation, we utilized in vitro experiments on an external clinical cohort. The results demonstrated that GSDMD expression level in the high PSscore group was significantly upregulated compared to the low PSscore group (p < 0.001); the CD3+ T cells and the CD3+PD-1+ cells significantly increased in the high PSscore group compared to the low PSscore group (p < 0.01).

CONCLUSIONS

The PSscore of pyroptosis regulation pattern is a reliable biomarker, and it is valuable to predict prognosis, TME, and ICB therapeutic efficiency in glioma patients.