Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma

Abstract

Background Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments. Methods The RNA-seq data for LGG was downloaded from UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender and pathologic grade. The prognostic signature was validated in the CGGA dataset. Finally, weighted gene co-expression network analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic curves of our model and other models are constructed. Results In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio (HR) = 0.198 (95% CI [0.120–0.325])) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore, the model was validated in the CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3- and 5-year survival of LGG patients compared to other models and may be a promising independent biomarker of LGG.

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

“…It is highly expressed in normal human lung tissues (Ballard et al, 2010) and poorly expressed in LUAD tissues. Patients with LUAD, BLCA and LGG with low expression of CRTAC1 have poor prognosis, which is consistent with that found in glioma (Xiao et al, 2020). Cytochrome P450 4B1 (CYP4B1) is a tissue-specific detoxification-related monooxygenase and is down-expressed in LUAD, but its role in tumorigenesis is still unclear (Lim et al, 2020;Ni and Sun, 2019).…”

“…Gene expression profile array could be used to identify differentially expressed genes (DEGs) between tumor samples and normal samples (Dong et al, 2020), and some key genes can be identified by bioinformatics mining (Xiao et al, 2020). However, integrating the contributions of several key genes to improve the accuracy and the sensitivity of tumor prognosis judgment has always been a difficult problem in its clinical application.…”

Development of a Prognostic Five-Gene Signature with Radiotherapy Guidance Significance for Diffuse Lower-Grade Glioma Patients Based on Large-Scale Sequencing Data

“…Expression of WEE1 has been found to be associated with poor prognosis in a variety of tumor types including gliomas [25]. Two other gene signatures constructed to predict the prognosis of LGG are also consistent with the results of this article, and found that WEE1 is a prognostic risk factor [26,27]. H2B clustered histone 12(H2BC12 or HIST1H2BK) is a replication-dependent histone and belongs to the histone H2B family.…”

Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer

“…This observation is supported by other studies who have found a clear relation between mitochondrial genomic alterations and BCR (Ellinger et al, 2008;Kalsbeek et al, 2016;Xu et al, 2020). This is not the first time that predictive three-genes signatures have been identified in various diseases (Sun et al, 2015;Thakkar et al, 2015;De Palma et al, 2016;Ibrahim et al, 2016;Wang et al, 2016;Li et al, 2017;Chen et al, 2018;Yang et al, 2018;Bao et al, 2019;Ding et al, 2019;Saidak et al, 2019;Xiao et al, 2020), hence showing that extensive research is ongoing The omics model is based on three genes and the clinic model is a model based on the grade, stage and PSA. The omics + clinic model integrates all the selected features together.…”