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.
Highlights
Glioblastoma (GBM) is the most aggressive diffuse and lethal malignancy in malignant gliomas [1]
Gene expression levels of 159 GBM samples contained in the Cancer Genome Atlas (TCGA) were filtered and a total of 14626 genes were obtained with the median expression levels >1(Figure 2(a))
The detailed information of 393 Differently expressed genes (DEGs) was listed in Supplementary Table 1
Summary
Glioblastoma (GBM) is the most aggressive diffuse and lethal malignancy in malignant gliomas [1]. Surgical resection followed by radiation therapy and chemotherapy is the frequently therapeutic intervention for GBM [2]. The therapy and prognosis of GBM remain dismal due to its invasive and aggressive behavior [3, 4]. GBM has a poor prognosis with relatively low survival and the five-year survival ratio is lower than 5% [5]. It is important to further reveal novel therapeutic methods and underlying risk factors to improve the treatment and prognosis of GBM. Poor prognosis with low relative survival rate is a major challenge for the treatment of GBM, and many risk factors have been identified to be associated with this outcome, such as age, gender, gene mutation, usage of drugs, and environment exposure [6]. The prognostic model of GBM is still rarely reported
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