Identification of PTK2 as an adverse prognostic biomarker in breast cancer by integrated bioinformatics and experimental analyses

A novel glycolysis-related gene signature for predicting prognosis and immunotherapy efficacy in breast cancer

Background

Previous studies have shown that glycolysis-related genes (GRGs) are associated with the development of breast cancer (BC), and the prognostic significance of GRGs in BC has been reported. Considering the heterogeneity of BC patients, which makes prognosis difficult to predict, and the fact that glycolysis is regulated by multiple genes, it is important to establish and evaluate new glycolysis-related prediction models in BC.

Methods

In total, 170 GRGs were selected from the GeneCards database. We analyzed data from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) database as a training set and data from the Gene Expression Omnibus (GEO) database as a validation cohort. Based on the overall survival data and the expression levels of GRGs, Cox regression analyses were applied to develop a glycolysis-related prognostic gene (GRPGs)-based prediction model. Kaplan (KM) survival and ROC analyses were performed to assess the performance of this model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to identify the potential biological functions of GRPGs. cBioPortal database was used to explore the tumor mutation burden (TMB). The tumor immune dysfunction and exclusion indicator (TIDE) was used to estimate the patient response to immune checkpoint blockade (ICB). The levels of tumor-infiltrating immune cells (TICs) and stromal cells were quantitatively analyzed based on gene expression profiles.

Results

We constructed a prediction model of 10 GRPGs (ADPGK, HNRNPA1, PGAM1, PIM2, YWHAZ, PTK2, VDAC1, CS, PGK1, and GAPDHS) to predict the survival outcomes of patients with BC. Patients were divided into low- and high-risk groups based on the gene signature. The AUC values of the ROC curves were 0.700 (1-year OS), 0.714 (3-year OS), 0.681 (5-year OS). TMB and TIDE analyses showed that patients in the high-risk group might respond better to ICB. Additionally, by combining the GRPGs signature and clinical characteristics of patients, a novel nomogram was constructed. The AUC values for this combined prediction model were 0.827 (1-year OS), 0.792 (3-year OS), and 0.783 (5-year OS), indicating an outstanding predictive performance.

Conclusion

A new GRPGs based prediction model was built to predict the OS and immunotherapeutic response of patients with BC.

Potential roles of exosomes in the initiation and metastatic progression of lung cancer

Lung cancer (LC) incidence and mortality continue to increase annually worldwide. LC is insidious and readily metastasizes and relapses. Except for its early diagnosis and surgical resection, there is no effective cure for advanced metastatic LC, and the prognosis remains dismal. Exosomes, a class of nano-sized extracellular vesicles produced by healthy or diseased cells, are coated with a bilayer lipid membrane and contain various functional molecules such as proteins, lipids, and nucleic acids. They can be used for intracellular or intercellular signaling or the transportation of biological substances. A growing body of evidence supports that exosomes play multiple crucial roles in the occurrence and metastatic progression of many malignancies, including LC. The elucidation of the potential roles of exosomes in the initiation, invasion, and metastasis of LC and their underlying molecular mechanisms may contribute to improved early diagnosis and treatment.