A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients

Abstract

Objective. This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model. Methods. Based on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA. Results. We identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10−9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets. Conclusion. The prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.

Genomic profiling of thymoma using a targeted high-throughput approach

“…The most recent studies on thymoma and other pathologies like glioblastomas and lung adenocarcinoma are related to transriptomic profiling [24][25][26][27][28]. Nevertheless, there amplicon-based technology that we used for thymoma analysis provides reliable and usable data for optimal treatment options, due to its high coverage detection of low-frequency somatic variants.…”

A SEMA3 Signaling Pathway-Based Multi-Biomarker for Prediction of Glioma Patient Survival

“…For example, six protein-coding genes (PCG) and five lncRNAs were screened out by a risk score model and a PCG-lncRNA signature was formed that which predicted survival and TMZ-chemoradiation response in GBM patients [ 30 ]. Other research groups identified novel biomarkers that have a potential in the outcome prediction of GBM [ 31 , 32 , 33 ] and which enable to classify patients into high- and low-risk groups based on expression level analysis of the survival relevant genes [ 34 ]. Moreover, the novel signature composed of five genes ( DES, RANBP17, CLEC5A, HOXC11, and POSTN ) was significantly associated with the 1-, 3-, and 5-year survival of GBM patients, IDH status, MGMT methylation status, and radio-chemotherapy [ 35 ].…”

Characterization of the Survival Influential Genes in Carcinogenesis

“…Next, to assess the association between the identified SIGs and cancer, we complied a cancer-associated gene list from three datasets: (1) 688 cancer genes from the COSMIC release (v85) [ 51 ]; (2) 550 cancer essential genes screened from the CRISPR system in human cancer cell lines [ 3 ]; and (3) 769 cancer genes from the cancer dependency map [ 4 ]. Additionally, we collected a small but highly confident harmful gene set from the literature [ 52 , 53 , 54 , 55 , 56 , 57 ]. We then performed enrichment analysis using Fisher’s exact test to evaluate the association between SIGs and cancer.…”