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Prognostic power of a lipid metabolism gene panel for diffuse gliomas.

ABSTRACT: Lipid metabolism reprogramming plays important role in cell growth, proliferation, angiogenesis and invasion in cancers. However, the diverse lipid metabolism programmes and prognostic value during glioma progression remain unclear. Here, the lipid metabolism-related genes were profiled using RNA sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database. Gene ontology (GO) and gene set enrichment analysis (GSEA) found that glioblastoma (GBM) mainly exhibited enrichment of glycosphingolipid metabolic progress, whereas lower grade gliomas (LGGs) showed enrichment of phosphatidylinositol metabolic progress. According to the differential genes of lipid metabolism between LGG and GBM, we developed a nine-gene set using Cox proportional hazards model with elastic net penalty, and the CGGA cohort was used for validation data set. Survival analysis revealed that the obtained gene set could differentiate the outcome of low- and high-risk patients in both cohorts. Meanwhile, multivariate Cox regression analysis indicated that this signature was a significantly independent prognostic factor in diffuse gliomas. Gene ontology and GSEA showed that high-risk cases were associated with phenotypes of cell division and immune response. Collectively, our findings provided a new sight on lipid metabolism in diffuse gliomas.

SUBMITTER: Wu F

PROVIDER: S-EPMC6815778 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Prognostic power of a lipid metabolism gene panel for diffuse gliomas.

Wu Fan F Zhao Zheng Z Chai Rui-Chao RC Liu Yu-Qing YQ Li Guan-Zhang GZ Jiang Hao-Yu HY Jiang Tao T

Journal of cellular and molecular medicine 20190901 11

Lipid metabolism reprogramming plays important role in cell growth, proliferation, angiogenesis and invasion in cancers. However, the diverse lipid metabolism programmes and prognostic value during glioma progression remain unclear. Here, the lipid metabolism-related genes were profiled using RNA sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database. Gene ontology (GO) and gene set enrichment analysis (GSEA) found that glioblastoma (GBM) mainly exhib ...[more]

PMID: 31475440

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Project description:Background: Diffuse gliomas are a group of diseases that contain different degrees of malignancy and complex heterogeneity. Previous studies proposed biomarkers for certain grades of gliomas, but few of them have conducted a systematic analysis of different grades to search for molecular markers. Methods: WGCNA was used to find significant genes associated with malignant progression of diffuse glioma in TCGA glioma sequencing expression data and the GEO expression profile-merge meta dataset. Lasso regression was used for potential model building and the best model was selected by CPE, IDI, and C_index. Risk score model was used to evaluate the gene signature prognostic power. Multi-omics data, including CNV, methylation, clinical traits, and mutation, were used for model evaluation. Results: We found out 67 genes significantly associated with malignant progression of diffuse glioma by WGCNA. Next, we established a new 4 gene molecular marker (KDELR2, EMP3, TIMP1, and TAGLN2). Multivariate cox analysis identified the risk score of the 4 genes as an independent predictor of prognosis in patients with diffuse gliomas, and its predictive power was independent of the histopathological grades of glioma. Further, we had confirmed in five independent test datasets and the risk score remained good predictive power. The combination of the prognosis model with specific molecular characteristics possessed a better predictive power. Furthermore, we divided the low-risk group into three subtypes: LowRisk_IDH1wt, LowRisk_IDH1mut/ATRXmut, and LowRisk_IDH1mut/ATRXwt by combining IDH1 mutation with ATRX mutation, which possessed obvious survival difference. In further analysis, we found that the 4 gene prognosis model possessed multi-omics features. Conclusion: We established a malignant-related 4-gene molecular marker by glioma expression profile data from multiple microarrays and sequencing data. The four markers had good predictive power on the overall survival of glioma patients and were associated with gliomas' clinical and genetic backgrounds, including clinical features, gene mutation, methylation, CNV, signal pathways.

Project description:Mutations in the telomerase reverse transcriptase gene promoter (TERTp) are common in glioblastomas (GBMs) and oligodendrogliomas (ODGs), and therefore, have a key role in tumorigenesis and may be of prognostic value. However, the extent of their prognostic importance in various gliomas is controversial. We studied 168 patients separated into five groups: Group 1: 65 patients with ODG carrying an IDH1 or IDH2 mutation (IDH-mutant) and 1p/19q-codeletion, Group 2: 23 patients with anaplastic astrocytoma (AA), IDH-mutant, Group 3: 13 patients with GBM, IDH-mutant, Group 4: 15 patients with AA, IDH-wildtype (WT), and Group 5: 52 patients with GBM, IDH-WT. TERTp mutations were found in 96.9%, 4.4%, 76.9%, 20.0%, and 84.6% of patients in Groups 1, 2, 3, 4, and 5, respectively. The R132H mutation in IDH1 was found in 60.5% (23/38) of patients in the AA cohort (Groups 2 and 4) and 20.0% (13/65) of patients from our GBM cohort (Groups 3 and 5), whereas all patients with ODG (Group 1) had a mutation either in IDH1 (n = 62) or IDH2 (n = 3). Using Kaplan Meier survival analysis, we found that the TERTp mutation was correlated with poor overall survival (OS) in Groups 2 and 4 combined (P = 0.001) and in Group 4 (P = 0.113), and in multivariate analysis, the TERTp mutant group was associated with significantly poor survival in Group 5 (P = 0.045). However, IDH mutation, MGMT methylation, and younger patient age (<55 years old) were significantly correlated with favorable OS (all P < 0.05) in our cohort of astrocytic and ODGs. In patients with ODG (Group 1), mutant IDH and TERTp did not have prognostic value because these mutations were universally present. Based on the revised 2016 WHO classification of gliomas, we found that TERTp mutation was frequently present in patients with GBM or ODG and because it was strongly correlated with poor survival outcome in patients with IDH-WT GBM in multivariate analysis, it may be of prognostic value in this subgroup of patients with gliomas.

Project description:BackgroundGlioma is the most common primary brain tumor in adults and accounts for more than 70% of brain malignancies. Lipids are crucial components of biological membranes and other structures in cells. Accumulating evidence has supported the role of lipid metabolism in reshaping the tumor immune microenvironment (TME). However, the relationship between the immune TME of glioma and lipid metabolism remain poorly described.Materials and methodsThe RNA-seq data and clinicopathological information of primary glioma patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). An independent RNA-seq dataset from the West China Hospital (WCH) also included in the study. Univariate Cox regression and LASSO Cox regression model was first to determine the prognostic gene signature from lipid metabolism-related genes (LMRGs). Then a risk score named LMRGs-related risk score (LRS) was established and patients were stratified into high and low risk groups according to LRS. The prognostic value of the LRS was further demonstrated by construction of a glioma risk nomogram. ESTIMATE and CIBERSORTx were used to depicted the TME immune landscape. Tumor Immune Dysfunction and Exclusion (TIDE) was utilized to predict the therapeutic response of immune checkpoint blockades (ICB) among glioma patients.ResultsA total of 144 LMRGs were differentially expressed between gliomas and brain tissue. Finally, 11 prognostic LMRGs were included in the construction of LRS. The LRS was demonstrated to be an independent prognostic predictor for glioma patients, and a nomogram consisting of the LRS, IDH mutational status, WHO grade, and radiotherapy showed a C-index of 0.852. LRS values were significantly associated with stromal score, immune score, and ESTIMATE score. CIBERSORTx indicated remarkable differences in the abundance of TME immune cells between patients with high and low LRS risk levels. Based on the results of TIDE algorithm, we speculated that the high-risk group had a greater chance of benefiting from immunotherapy.ConclusionThe risk model based upon LMRGs could effectively predict prognosis in patients with glioma. Risk score also divided glioma patients into different groups with distinct TME immune characteristics. Immunotherapy is potentially beneficial to glioma patients with certain lipid metabolism profiles.

Dataset Information

Prognostic power of a lipid metabolism gene panel for diffuse gliomas.

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Prognostic power of a lipid metabolism gene panel for diffuse gliomas.

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