Elevated Expression of RIOK1 Is Correlated with Breast Cancer Hormone Receptor Status and Promotes Cancer Progression

The RioK1 network determines p53 activity at multiple levels

By responding to a host of adverse conditions, ranging from DNA damage to viral infection, transcription factor p53 supports genomic stability, cellular health, and survival. Not surprisingly, tumours across the cancer spectrum carry mutations in p53, misexpress the protein, or dysregulate its activity. Several signalling pathways, many of which comprise oncogenic proteins, converge upon p53 to control its stability and activity. We here present the conserved kinase/ATPase RioK1 as an upstream factor that determines p53 activity at the DNA, RNA, and protein levels. It achieves this task by integrating the regulatory events that act on p53 into a coherent response circuit. We will also discuss how RIOK1 overexpression represents an alternative mechanism for cancers to inactivate p53, and how targeting RioK1 could eradicate malignancies that are driven by a dysregulated RioK1-p53 network.

Establishment of a 5-gene risk model related to regulatory T cells for predicting gastric cancer prognosis

Background

Gastric cancer (GC) is one of the high-risk cancers that lacks effective methods for prognosis prediction. Therefore, we searched for immune cells related to the prognosis of GC and studied the role of related genes in GC prognosis.

Methods

In this study, we collected the mRNA data of GC from The Cancer Genome Atlas (TCGA) database and studied the immune cells that were closely related to the prognosis of GC. Spearman correlation analysis was performed to show the association between immune cell-related genes and the differentially expressed genes (DEGs) of GC. Univariate and multivariate Cox regression analyses were conducted on the immune cell-related genes with a high correlation with GC. A prognostic risk score model was constructed and the most significant feature genes were identified. Kaplan–Meier method was then used to compare the overall survival (OS) of patients with high-risk and low-risk, and receiver operating characteristic (ROC) analysis was used to assess the accuracy of the risk model. In addition, GC patients were grouped according to the median expression of the features genes, and survival analysis was further carried out.

Results

It was noted that regulatory T cells (Tregs) were significantly correlated with the prognosis of GC, and 172 genes related to Tregs were found to be closely associated with GC. An optimal prognostic risk model was constructed, and a 5-gene (including LRFN4, ADAMTS12, MCEMP1, HP and MUC15) signature-based risk score was established. Survival analysis showed significant difference in OS between low-risk and high-risk samples. ROC analysis results indicated that the risk model had a high accuracy for the prognosis prediction of samples (AUC = 0.717). The results of survival analysis on each feature gene based on expression levels were consistent with the results of multivariate Cox analysis for predicting the risk rate of the 5 genes.

Conclusion

These results proved that the 5-gene signature-based risk score could be used to predict the survival of GC patients, and these 5 genes were closely related to Tregs. These findings are of great significance for studying the role of immune cells and related immune factors in regulating the prognosis of GC.