Cell senescence-associated genes predict the malignant characteristics of glioblastoma

Abstract

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.

A senescence-associated signature refines the classification of different modification patterns and characterization of tumor immune microenvironment infiltration in triple-negative breast cancer

“…Wang et al (Wang et al, 2022) used the senescence-related gene set to define a cellular senescence score in pancancer and demonstrated that this score can represent the degree of immune activation in the tumor microenvironment and can identify groups with better prognosis. In specific cancers, researchers have also constructed classification models and prognostic prediction models based on senescence gene sets, including clear cell renal cell carcinoma (Lu et al, 2022), hepatocellular carcinoma (Luo et al, 2022), glioblastoma (Tan et al, 2022), etc., and explored the relationship between senescencerelated genes and clinical characteristics, tumor microenvironment, immunotherapy, etc. These studies demonstrate the importance of senescence genes in cancer therapy.…”

Development and validation of an inflammatory response-related prognostic model and immune infiltration analysis in glioblastoma

“…Many prognostic models have previously been reported in GBM. For example, a study reported a prognostic model constructed by 11 cell senescence-associated genes in GBM ( 16 ). In addition, it was also found that the model constructed by m6A-Related lncRNA could predict the prognosis of GBM patients ( 17 ).…”