Combined targeting of KRT23 and NCCRP1 as a potential novel therapeutic approach for the treatment of triple-negative breast cancer

Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment

JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.

Immune checkpoint gene signature assesses immune infiltration profiles in bladder cancer and identifies KRT23 as an immunotherapeutic target

BACKGROUND

In the past few decades, researchers have made promising progress, including the development of immune checkpoint inhibitors (ICIs) in the therapy of bladder cancer (BLCA). Existing studies mainly focus on single immune checkpoint inhibitors but lack relevant studies on the gene expression profiles of multiple immune checkpoints.

METHODS

RNA-sequencing profiling data and clinical information of BLCA patients and normal human bladder samples were acquired from the Cancer Genome Atlas and Gene Expression Omnibus databases and analyzed to identify different expression profiles of immune checkpoint genes (ICGs) after consensus clustering analysis. Based on the 526 intersecting differentially expressed genes, the LASSO Cox regression analysis was utilized to construct the ICG signature.

RESULTS

According to the expression of ICGs, BLCA patients were divided into three subtypes with different phenotypic and mechanistic characteristics. Furthermore, the developed ICG signature were independent predictors of outcome in BLCA patients, and was correlated with the immune infiltration, the expression of ICGs and chemotherapeutic effect.

CONCLUSIONS

This study systematically and comprehensively analyzed the expression profile of immune checkpoint genes, and established the ICG signature to investigate the differences in ICGs expression and tumor immune microenvironment, which will help risk stratification and accelerate precision medicine. Finally, we identified KRT23 as the most critical model gene, and highlighted KRT23 as a potential target to enhance immunotherapy against BLCA.

A promising anoikis-related prognostic signature predicts prognosis of skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is a highly aggressive disease with a poor prognosis for advanced tumors. Anoikis is a caspase-dependent cell death process triggered by extracellular matrix (ECM) detachment, rectifies detachment-induced metabolic defects that compromise cell survival, recent study revealed the crucial role of anoikis for cancer cells to survive during metastasis. However, limited research focused on the role of anoikis in SKCM.

METHODS

Our study utilized the 27 anoikis-related genes (ARGs) to divide SKCM patients into two clusters, and obtain differentially expressed genes (DEGs) for each cluster. These DEGs were used in stepwise Cox regression analysis to develop a prediction model for SKCM patients consisting of nine ARGs, called the anoikis-related signature (ARS). Subsequently, we used the risk scores calculated from the ARS to divide SKCM patients into two groups and explored differences in immune microenvironment, immune checkpoint reactivity, and drug sensitivity between the groups.

RESULTS

Nine ARGs were identified to stratify SKCM patients into two risk groups, patients in the high-risk group had a poor prognosis and suppressed immune cell infiltration. Moreover, higher expression of immune checkpoint molecules and a greater sensitivity to immunotherapy and chemotherapy drugs were observed in the low-risk group. Finally, all of the ARS hub genes were found to be upregulated in SKCM tissues and cell lines.

CONCLUSION

A novel ARGs signature was identified for predicting the prognosis of SKCM. Based on the immune landscape associated with ARS discovered in our study, targeting ARS hub genes may be a promising treatment for SKCM.

A risk signature based on endoplasmic reticulum stress-associated genes predicts prognosis and immunity in pancreatic cancer

Introduction: The involvement of endoplasmic reticulum (ER) stress in cancer biology is increasingly recognized, yet its role in pancreatic cancer (PC) remains unclear. This study aims to elucidate the impact of ER stress on prognosis and biological characteristics in PC patients. Methods: A bioinformatic analysis was conducted using RNA-seq data and clinicopathological information from PC patients in the TCGA and ICGC databases. The ER stress-associated gene sets were extracted from MSigDB. ER stress-associated genes closely linked with overall survival (OS) of PC patients were identified via log-rank test and univariate Cox analysis, and further narrowed by LASSO method. A risk signature associated with ER stress was formulated using multivariate Cox regression and assessed through Kaplan-Meier curves, receiver operating characteristic (ROC) analyses, and Harrell's concordance index. External validation was performed with the ICGC cohort. The single-sample gene-set enrichment analysis (ssGSEA) algorithm appraised the immune cell infiltration landscape. Results: Worse OS in PC patients with high-risk signature score was observed. Multivariate analysis underscored our ER stress-associated signature as a valuable and independent predictor of prognosis. Importantly, these results based on TCGA were further validated in ICGC dataset. In addition, our risk signature was closely associated with homeostasis, protein secretion, and immune regulation in PC patients. In particular, PC microenvironment in the high-risk cluster exhibited a more immunosuppressive status. At last, we established a nomogram model by incorporating the risk signature and clinicopathological parameters, which behaves better in predicting prognosis of PC patients. Discussion: This comprehensive molecular analysis presents a new predictive model for the prognosis of PC patients, highlighting ER stress as a potential therapeutic target. Besides, the findings indicate that ER stress can have effect modulating PC immune responses.