Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Dec 17:10:590352.
doi: 10.3389/fonc.2020.590352. eCollection 2020.

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Xuegang Niu  1 , Jiangnan Sun  2 , Lingyin Meng  3 , Tao Fang  4 , Tongshuo Zhang  5 , Jipeng Jiang  6   7 , Huanming Li  4
Affiliations

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Xuegang Niu et al. Front Oncol. .

Abstract

Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package "cluster profile" and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

Keywords: Chinese Glioma Genome Atlas; The Cancer Genome Atlas; glioblastoma; lncRNA; prognosis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
lncRNA screening in the TCGA and CGGA data sets. (A, B) The overlapping genes with HR>1 or HR<1 between TCGA and CGGA data sets. (C) Univariate cox regression analysis of 7 lncRNAs in the overall survival assessment.
Figure 2
Figure 2
The selection of 5 candidate lncRNAs and prognostic capacity assessment. (A) 5 lncRNAs were selected via the multivariate cox regression analysis. (B) K-M OS curve plotting with the 5-lncRNA signature. (C) The prognostic capacity evaluation of the model built by 5-lncRNA signature.
Figure 3
Figure 3
The evaluation of the predictive model in validation cohorts. (A) AUCs of time-dependent ROC curves for CGGA cohort. (B) The survival analysis of the CGGA validation cohort.
Figure 4
Figure 4
The building of a nomogram and its performance on the OS prediction. (A) A nomogram was built with four prognostic factors. (B–D) The OS-predicting performance of the nomogram was evaluated by calibration plots.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Scott JG, Berglund A, Schell MJ, Mihaylov I, Fulp WJ, Yue B, et al. A genome-based model for adjusting radiotherapy dose (GARD): a retrospective, cohort-based study. Lancet Oncol (2017) 18:202–11. 10.1016/S1470-2045(16)30648-9 - DOI - PMC - PubMed
    1. Wang C, Li J, Sinha S, Peterson A, Grant GA, Yang F. Mimicking brain tumor-vasculature microanatomical architecture via co-culture of brain tumor and endothelial cells in 3D hydrogels. Biomaterials (2019) 202:35–44. 10.1016/j.biomaterials.2019.02.024 - DOI - PMC - PubMed
    1. Ostrom QT, Gittleman H, Fulop J, Liu M, Blanda R, Kromer C, et al. CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2008-2012. Neuro Oncol (2015) 17(Suppl 4):iv1–1iv62. 10.1093/neuonc/nov189 - DOI - PMC - PubMed
    1. Stupp R, Taillibert S, Kanner AA, Kesari S, Steinberg DM, Toms SA, et al. Maintenance Therapy With Tumor-Treating Fields Plus Temozolomide vs Temozolomide Alone for Glioblastoma: A Randomized Clinical Trial. JAMA (2015) 314:2535–43. 10.1001/jama.2015.16669 - DOI - PubMed
    1. Tan AC, Ashley DM, López GY, Malinzak M, Friedman HS, Khasraw M. Management of glioblastoma: State of the art and future directions. CA Cancer J Clin (2020) 70:299–312. 10.3322/caac.21613 - DOI - PubMed