Frequent p53 mutation in brain (fetal)-type glycogen phosphorylase positive foci adjacent to human 'de novo' colorectal carcinomas

Ubiquitin-related gene markers predict immunotherapy response and prognosis in patients with epithelial ovarian carcinoma

Epithelial ovarian carcinoma (EOC) is the most fatal among female reproductive system tumors. The immune tumor microenvironment and ubiquitin-proteasome pathway are closely related to the proliferation, invasion, and response to chemotherapy in EOC. However, their specific roles in EOC have not been fully elucidated. Therefore, we aimed to recognize potential prognostic markers and novel therapeutic targets for EOC. We constructed the ubiquitin-related signature risk model comprising HSP90AB1, FBXO9, SIGMAR1, STAT1, SH3KBP1, EPB41L2, DNAJB6, VPS18, PPM1G, AKAP12, FRK, and PYGB, specifically for patients with EOC. The high-risk model presented a worse prognosis, primarily associated with the B-cell receptor signaling pathway, ECM receptor interaction, focal adhesion, and actin cytoskeleton regulations. Analysis of the immune landscape revealed a higher abundance of B cells, M2 macrophages, neutrophil CD4 T cells, cancer-associated fibroblasts, macrophage neutrophils, and fibroblasts in the high-risk group. It also exhibited lower tumor mutation burden, mRNAsi, and EREG-mRNAsi and reduced sensitivity to other chemotherapy drugs, except dasatinib. These findings serve as a valuable indicator for personalized treatment strategies and clinical stratification in managing patients with EOC. Additionally, our study will serve as a foundation for future mechanistic research to explore the association between the ubiquitin-proteasome pathway and EOC.

Brain-Type Glycogen Phosphorylase (PYGB) in the Pathologies of Diseases: A Systematic Review

Glycogen metabolism is a form of crucial metabolic reprogramming in cells. PYGB, the brain-type glycogen phosphorylase (GP), serves as the rate-limiting enzyme of glycogen catabolism. Evidence is mounting for the association of PYGB with diverse human diseases. This review covers the advancements in PYGB research across a range of diseases, including cancer, cardiovascular diseases, metabolic diseases, nervous system diseases, and other diseases, providing a succinct overview of how PYGB functions as a critical factor in both physiological and pathological processes. We present the latest progress in PYGB in the diagnosis and treatment of various diseases and discuss the current limitations and future prospects of this novel and promising target.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

BACKGROUND

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

METHODS

The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

RESULTS

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3- and 5-year survival, respectively. Similar results were found in the test sets, and the AUCs of 3-, 5-year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

CONCLUSION

Our study established a nine-GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

PYGB Promoted Tumor Progression by Regulating Wnt/β-Catenin Pathway in Gastric Cancer

Gastric cancer is one of the most common gastrointestinal malignancy with high mortality in East Asia. Investigation of pathogenic mechanisms of gastric cancer is crucial to develop novel therapeutic strategies and identify new therapeutic candidates. Brain-type glycogen phosphorylase is a glycogen phosphorylase involved in glycogen metabolism, which participates in multiple physiological and pathological processes. Overexpression of brain-type glycogen phosphorylase has been reported in various types of cancer, such as colorectal cancer and non-small cell lung cancer, however, the potential role of brain-type glycogen phosphorylase in gastric cancer remains unclear. Herein, we observed brain-type glycogen phosphorylase expression was significantly elevated in human gastric cancer tissues and positively correlated with the clinical-pathological features including tumor size, lymph node involvement, and tumor, node, metastasis stage of patients with gastric cancer. We further reported brain-type glycogen phosphorylase depletion suppressed the growth of gastric cancer, weakened the epithelial–mesenchymal transformation, and reduced the migration and invasion ability in cell models. We further confirmed brain-type glycogen phosphorylase depletion inhibited tumor growth and lung metastasis in mice. Importantly, we found brain-type glycogen phosphorylase regulated the progression of gastric cancer via Wnt/β-catenin pathway, shedding lights on brain-type glycogen phosphorylase as a promising therapeutic target for drug design and development targeting gastric cancer.

The Potentials and Pitfalls of Microarrays in Neglected Tropical Diseases: A Focus on Human Filarial Infections

Data obtained from expression microarrays enables deeper understanding of the molecular signatures of infectious diseases. It provides rapid and accurate information on how infections affect the clustering of gene expression profiles, pathways and networks that are transcriptionally active during various infection states compared to conventional diagnostic methods, which primarily focus on single genes or proteins. Thus, microarray technologies offer advantages in understanding host-parasite interactions associated with filarial infections. More importantly, the use of these technologies can aid diagnostics and helps translate current genomic research into effective treatment and interventions for filarial infections. Studying immune responses via microarray following infection can yield insight into genetic pathways and networks that can have a profound influence on the development of anti-parasitic vaccines.