Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

Abstract

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR=3.192, 95%CI=2.182‐4.670; CGGA: HR=1.922, 95%CI=1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

“…The lncRNAs play a crucial role in transcriptional regulation, epigenetic gene regulation, and disease, a kind of non-coding RNAs greater than 200 nucleotides, with no protein-coding function (10,11). With the deepening of research, the relationship between lncRNA and progression, metastasis, and prognosis of various tumors has been found, such as the digestive, nervous, bone, and urinary system (12)(13)(14)(15)(16)(17).…”

A Innovative Prognostic Symbol Based on Autophagy-Related Long Noncoding RNAs Signature Predicts in Low-Grade Glioma

“…Although maximum surgical resection, radiotherapy and chemotherapy are considered as the main strategies combating LGG, tumor metastasis, recurrence and patient death cannot be completely prevented [8]. Therefore, there is a need to further explore additional tools for the diagnosis treatment and prognosis of LGG and to develop new risk factors and molecular markers for early strati cation of low-and high-risk patients with preventive treatment [9]. In this context, identifying high-risk LGG individuals with unsatisfying outcome turns out an essential work that needs to be concerned.…”

DataSheet_1.pdf

“…The AUC of HRLscore in survival prediction was generally higher than that of each clinicopathologic feature, and addition of the HRLscore to these variables could improve the prognostic performance (Supplementary Table 2). Furthermore, we compared the predictive accuracy of HRLscore versus several multigene signatures in existing publications by Zhang ret al ( 27), Wang et al (28), and Zhao et al (29). The result demonstrated that the HRLscore was not inferior to other three prognostic models in the TCGA-LGG cohort (Supplementary Figure 5).…”

“…Also for LGG, increasing studies have focused on establishing prognostic gene models based on mRNA, lncRNA, or proteomic approaches. For example, Zhao et al (29) recently constructed a metabolism-related lncRNA-mRNA that could predict the clinical outcomes of LGG patients. In our study, we have compared the prognostic accuracy of HRLscore and other multigene models from previous publications (27)(28)(29).…”

“…For example, Zhao et al (29) recently constructed a metabolism-related lncRNA-mRNA that could predict the clinical outcomes of LGG patients. In our study, we have compared the prognostic accuracy of HRLscore and other multigene models from previous publications (27)(28)(29). We found the HRLscore showed the highest C-index in CGGA cohorts, suggesting that our HRL signature may have a relatively high accuracy in the prognostic assessment of LGG.…”