A four-gene signature-derived risk score for glioblastoma: prospects for prognostic and response predictive analyses

Abstract

Objective: Glioblastoma (GBM) is the most common primary malignant brain tumor regulated by numerous genes, with poor survival outcomes and unsatisfactory response to therapy. Therefore, a robust, multi-gene signature-derived model is required to predict the prognosis and treatment response in GBM. Methods: Gene expression data of GBM from TCGA and GEO datasets were used to identify differentially expressed genes (DEGs) through DESeq2 or LIMMA methods. The DEGs were then overlapped and used for survival analysis by univariate and multivariate COX regression. Based on the gene signature of multiple survival-associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan–Meier analysis and log-rank test. Gene set enrichment analysis (GSEA) was conducted to explore high-risk score-associated pathways. Western blot was used for protein detection. Results: Four survival-associated DEGs of GBM were identified: OSMR, HOXC10, SCARA3, and SLC39A10. The four-gene signature-derived risk score was higher in GBM than in normal brain tissues. GBM patients with a high-risk score had poor survival outcomes. The high-risk group treated with temozolomide chemotherapy or radiotherapy survived for a shorter duration than the low-risk group. GSEA showed that the high-risk score was enriched with pathways such as vasculature development and cell adhesion. Western blot confirmed that the proteins of these four genes were differentially expressed in GBM cells. Conclusions: The four-gene signature-derived risk score functions well in predicting the prognosis and treatment response in GBM and will be useful for guiding therapeutic strategies for GBM patients.

The Role of Gender-Related Immune Genes in Childhood Acute Myeloid Leukemia

“…A model was then built. The “LASSO coefficients” ( β ) observes the following: Risk Score =∑ i =1 n Expi βi [ 29 – 31 ].…”

neoDL: A novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival

“…In this paper, we presented a prognostic prediction deep learning model based on neoantigen intrinsic features. Although several survival prediction models have been reported based on the expression of several genes [40][41][42] or medical images [43,44], they are not related to neoantigens and immune response. As neoantigens are associated with tumor-specific T-cell responses and anti-tumor immune responses, the method we provided can help predict the prognosis of IDH wild-type GBM patients who will likely benefit from neoantigen based personalized immunetherapy.…”

A Novel Prognostic Model Based on Seven Necroptosis-Related miRNAs for Predicting the Overall Survival of Patients with Lung Adenocarcinoma

“…The risk score of each patient is calculated as the sum of the remaining scores of each miRNA, and each miRNA score is calculated by multiplying the miRNA coefficient by the miRNA expression level. The specific risk score formula is as follows: risk score = ∑ n i=1 coefi × expi (n represents the number of miRNAs, coefi represents the regression coefficient of miRNAi, and expi represents the expression level of miRNAi) [18,19]. According to the median value of the risk score, TCGA LUAD patients were divided into high-and low-risk groups.…”