Establishment of a cell senescence related prognostic model for predicting prognosis in glioblastoma

Abstract

Background: Glioblastoma (GBM) is highly malignant and has a worse prognosis with age, and next-generation sequencing (NGS) provides us with a huge amount of information about GBM.Materials and Methods: Through the enrichment scores of cell senescence-related pathways, we constructed a consensus matrix and mined molecular subtypes and explored the differences in pathological, immune/pathway and prognostic. Also we identified key genes related to cell senescence characteristics using least absolute shrinkage and selection operator (Lasso) regression and univariate COX regression analysis models. The use of risk factor formats to construct clinical prognostic models also explored the differences in immunotherapy/chemotherapy within the senescence-related signatures score (SRS.score) subgroups. Decision trees built with machine learning to identify the main factors affecting prognosis have further improved the prognosis model and survival prediction.Results: We obtained seven prognostic-related pathways related to cell senescence. We constructed four different molecular subtypes and found patients with subtype C1 had the worst prognosis. C4 had the highest proportion of patients with IDH mutations. 1005 differentially expressed genes (DEGs) were analyzed, and finally 194 Risk genes and 38 Protective genes were obtained. Eight key genes responsible for cell senescence were finally identified. The clinical prognosis model was established based on SRS.score, and the prognosis of patients with high SRS.score was worse. SRS.score and age were the vital risk factors for GBM patients through decision tree model mining.Conclusion: We constructed a clinical prognosis model that could provide high prediction accuracy and survival prediction ability for adjuvant treatment of patients with GBM.

Construction of an Excellent 7 mRNAsi-Related Gene Model Based on Cancer Stem Cells for Predicting Survival Outcome of Cervical Cancer

“…The high-throughput RNAseq data (TCGA-CESC) and clinical information of CC were from TCGA database. In this study, we followed the methods of Li et al [21] to process RNA data. Samples without survival time, status, or clinical follow-up information were removed.…”

Machine learning recognises senescence in glioblastoma and discovers senescence-inducing compounds

“…However, even with treatment, cancer reoccurs. Both radiotherapy and chemotherapy have been found to induce senescence in GBM cells [19, 20], and although there is mounting evidence that senescence burden leads to poorer outcomes for GBM patients [21, 22], we currently do not understand the role of senescence in treatment. Furthermore, primary GBM tumours show a mutational spectrum consistent with senescence escape, with frequent mutations in the TERT promoter and CDKN2A, indicating that escape from senescence likely plays a role in the etiology of GBM [23].…”

Unusual presentation of glioblastoma in the brainstem: a case report of a diffuse pontine glioblastoma multiforme and surgical management

“…These subclasses were proposed to exist along a spectrum with proneural and proliferative subclasses progressing to mesenchymal subclass ( 11 ). The prognostic capabilities of these tumor markers were found to be good ( 12 ). Since this initial subclassification, a fourth subclass was added as the classical group which was associated with EGFR mutations and a lack of TP53 mutations as well as mutations of the RB pathway.…”