Cell senescence-associated genes predict the malignant characteristics of glioblastoma

Background: Glioblastoma (GBM) is the most malignant, aggressive and recurrent primary brain tumor. Cell senescence can cause irreversible cessation of cell division in normally proliferating cells. According to studies, senescence is a primary anti-tumor mechanism that may be seen in a variety of tumor types. It halts the growth and spread of tumors. Tumor suppressive functions held by cellular senescence provide new directions and pathways to promote cancer therapy.

Methods: We comprehensively analyzed the cell senescence-associated genes expression patterns. The potential molecular subtypes were acquired based on unsupervised cluster analysis. The tumor immune microenvironment (TME) variations, immune cell infiltration, and stemness index between 3 subtypes were analyzed. To identify genes linked with GBM prognosis and build a risk score model, we used weighted gene co-expression network analysis (WGCNA), univariate Cox regression, Least absolute shrinkage and selection operator regression (LASSO), and multivariate Cox regression analysis. And the correlation between risk scores and clinical traits, TME, GBM subtypes, as well as immunotherapy responses were estimated. Immunohistochemistry (IHC) and cellular experiments were performed to evaluate the expression and function of representative genes. Then the 2 risk scoring models were constructed based on the same method of calculation whose samples were acquired from the CGGA dataset and TCGA datasets to verify the rationality and the reliability of the risk scoring model. Finally, we conducted a pan-cancer analysis of the risk score, assessed drug sensitivity based on risk scores, and analyzed the pathways of sensitive drug action.

Results: The 3 potential molecular subtypes were acquired based on cell senescence-associated genes expression. The Log-rank test showed the difference in GBM patient survival between 3 potential molecular subtypes (P = 0.0027). Then, 11 cell senescence-associated genes were obtained to construct a risk-scoring model, which was systematically randomized to distinguish the train set (n = 293) and the test set (n = 292). The Kaplan-Meier (K-M) analyses indicated that the high-risk score in the train set (P < 0.0001), as well as the test set (P = 0.0053), corresponded with poorer survival. In addition, the high-risk score group showed a poor response to immunotherapy. The reliability and credibility of the risk scoring model were confirmed according to the CGGA dataset, TCGA datasets, and Pan-cancer analysis. According to drug sensitivity analysis, it was discovered that LJI308, a potent selective inhibitor of RSK pathways, has the highest drug sensitivity. Moreover, the GBM patients with higher risk scores may potentially be more beneficial from drugs that target cell cycle, mitosis, microtubule, DNA replication and apoptosis regulation signaling.

Conclusion: We identified potential associations between clinical characteristics, TME, stemness, subtypes, and immunotherapy, and we clarified the therapeutic usefulness of cell senescence-associated genes.

 

Comments:

The study you provided presents a comprehensive analysis of glioblastoma (GBM) focusing on cell senescence-associated genes. Here's a breakdown of the key findings and conclusions of the research:

### **Background:**
- **GBM Characteristics:** GBM is a highly malignant, aggressive, and recurrent brain tumor.
- **Cell Senescence:** Senescence, a process leading to the irreversible cessation of cell division, can act as an anti-tumor mechanism in various cancers, including GBM. It halts tumor growth and spread.

### **Methods:**
- **Data Analysis:** The study analyzed gene expression patterns related to cell senescence. Molecular subtypes of GBM were identified through cluster analysis.
- **TME and Immune Cell Infiltration:** Variations in the tumor immune microenvironment and immune cell infiltration were studied across different GBM subtypes.
- **Risk Score Model:** A risk score model was developed using various analytical methods, including WGCNA, univariate Cox regression, LASSO, and multivariate Cox regression analysis. This model was associated with GBM prognosis and validated across different datasets.
- **Drug Sensitivity Analysis:** Drug sensitivity analysis was performed, revealing potential therapeutic options for patients with different risk scores.

### **Results:**
- **Molecular Subtypes:** Three molecular subtypes of GBM were identified based on cell senescence-associated gene expression patterns.
- **Survival Analysis:** Patients with high-risk scores, based on the developed model, had poorer survival outcomes.
- **Immunotherapy Response:** Patients with high-risk scores exhibited poor responses to immunotherapy.
- **Drug Sensitivity:** Analysis suggested specific drugs, such as LJI308, as potential treatments, especially for patients with high-risk scores.

### **Conclusion:**
- **Clinical Implications:** The study highlighted associations between clinical characteristics, tumor microenvironment, stemness, molecular subtypes, and immunotherapy response in GBM.
- **Therapeutic Implications:** Cell senescence-associated genes were identified as potential therapeutic targets. Patients with high-risk scores might benefit from drugs targeting cell cycle, mitosis, microtubule, DNA replication, and apoptosis regulation pathways.
 
In summary, the research provides valuable insights into the molecular and clinical aspects of GBM. It not only identifies specific molecular subtypes but also suggests potential therapeutic strategies and emphasizes the importance of understanding cell senescence in developing targeted therapies for GBM patients. Additionally, the study underscores the significance of personalized medicine by tailoring treatments based on individual risk profiles, paving the way for more effective and precise GBM therapies in the future.