A robust two‐gene signature for glioblastoma survival prediction

Abstract

Glioblastoma multiforme (GBM) is a highly malignant brain tumor. We explored the prognostic gene signature in 443 GBM samples by systematic bioinformatics analysis, using GSE16011 with microarray expression and corresponding clinical data from Gene Expression Omnibus as the training set. Meanwhile, patients from The Chinese Glioma Genome Atlas database (CGGA) were used as the test set and The Cancer Genome Atlas database (TCGA) as the validation set. Through Cox regression analysis, Kaplan-Meier analysis, t-distributed Stochastic Neighbor Embedding algorithm, clustering, and receiver operating characteristic analysis, a two-gene signature (GRIA2 and RYR3) associated with survival was selected in the GSE16011 dataset. The GRIA2-RYR3 signature divided patients into two risk groups with significantly different survival in the GSE16011 dataset (median: 0.72, 95% confidence interval [CI]: 0.64-0.98, vs median: 0.98, 95% CI: 0.65-1.61 years, logrank test P < .001), the CGGA dataset (median: 0.84, 95% CI: 0.70-1.18, vs median: 1.21, 95% CI: 0.95-2.94 years, logrank test P = .0017), and the TCGA dataset (median:1.03, 95% CI: 0.86-1.24, vs median: 1.23, 95% CI: 1.04-1.85 years, logrank test P = .0064), validating the predictive value of the signature. And the survival predictive potency of the signature was independent from clinicopathological prognostic features in multivariable Cox analysis. We found that after transfection of U87 cells with small interfering RNA, GRIA2 and RYR3 influenced the biological behaviors of proliferation, migration, and invasion of glioblastoma cells. In conclusion, the two-gene signature was a robust prognostic model to predict GBM survival. K E Y W O R D Sglioblastoma multiforme, prognostic, protein-coding genes, signature

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

“…Subsequently, 255 studies were enrolled, the characteristics of each study were shown in Supplementary Table 2 . Among these, 161 studies were conducted in China [ 16 , 18 , 20 – 21 , 23 – 29 , 31 , 34 , 36 – 37 , 40 – 44 , 47 – 48 , 50 , 53 , 55 , 64 , 66 – 69 , 71 – 74 , 80 , 82 – 87 , 89 , 92 – 95 , 97 – 98 , 102 – 105 , 107 , 109 – 113 , 115 – 118 , 120 – 124 , 126 – 127 , 135 – 137 , 139 – 144 , 146 – 152 , 154 – 155 , 157 , 160 – 164 , 166 , 168 – 170 , 172 – 173 , 177 – 179 , 184 – 190 , 193 – 195 , 197 – 197 , 206 – ...…”

Comprehensive analysis of transcriptomics and metabolomics to understand tail-suspension-induced myocardial injury in rat

“…Calpain activation participates microgravity-induced myocardial abnormalities in mice, which was through p38 and EERK1/2 MAPK ( 26 ). Ccnb2 (Cyclin B2) is a cell cycle related gene, which is associated with cancer progression and prognosis ( 39 – 41 ). Polo-like kinase 1 encoded by Plk1 is a negative regulator of FOXO3 ( 42 ).…”

A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma

“…As mentioned at first, lots of genes have been found related to overall survival and could be used to predict the prognosis of patients with GBM. We compared the prognostic capability of our model with signatures reported in other studies, including the five-gene signature ( PTPRN , RGS14 , G6PC3 , IGFBP2 and TIMP4 ) screened by Yin et al [ 19 ], the two-gene signature ( GRIA2 and RYR3 ) screened by Pan et al [ 22 ], the five-gene signature ( DES , RANBP17 , CLEC5A , HOXC11 and POSTN ) screened by Wang et al [ 25 ], the four-gene signature ( LHX2 , MEOX2 , SNAI2 and ZNF22 ) derived by Cheng et al [ 31 ].…”

“…Cao et al built a four-gene signature-derived ( OSMR , HOXC10 , SCARA3 and SLC39A10 ) risk score model to predict the survival and treatment response of GBM patients [ 21 ]. Pan et al identified two genes ( GRIA2 and RYR3 ) strongly associated with survival of GBM, and the two-gene signature was a robust prognostic model to predict GBM survival [ 22 ].…”