Dataset Information

Identification of genes from ten oncogenic pathways associated with mortality and disease progression in glioblastoma.

ABSTRACT: Glioblastoma multiforme (GBM) is the most malignant brain tumor with an extremely poor prognosis. The Cancer Genome Atlas (TCGA) database has been used to confirm the roles played by 10 canonical oncogenic signaling pathways in various cancers. The purpose of this study was to evaluate the expression of genes in these 10 canonical oncogenic signaling pathways, which are significantly related to mortality and disease progression in GBM patients. Clinicopathological information and mRNA expression data of 525 patients with GBM were obtained from TCGA database. Gene sets related to the 10 oncogenic signaling pathways were investigated via Gene Set Enrichment Analysis. Multivariate Cox regression analysis was performed for all the genes significantly associated with mortality and disease progression for each oncogenic signaling pathway in GBM patients. We found 12 independent genes from the 10 oncogenic signaling pathways that were significantly related to mortality and disease progression in GBM patients. Considering the roles of these 12 significant genes in cancer, we suggest possible mechanisms affecting the prognosis of GBM. We also observed that the expression of 6 of the genes significantly associated with a poor prognosis of GBM, showed negative correlations with CD8+ T-cells in GBM tissue. Using a large-scale open database, we identified 12 genes belonging to 10 well-known oncogenic canonical pathways, which were significantly associated with mortality and disease progression in patients with GBM. We believe that our findings will contribute to a better understanding of the mechanisms underlying the pathophysiology of GBM in the future.

SUBMITTER: Han MH

PROVIDER: S-EPMC9399757 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Identification of genes from ten oncogenic pathways associated with mortality and disease progression in glioblastoma.

Han Myung-Hoon MH Min Kyueng-Whan KW Noh Yung-Kyun YK Kim Jae Min JM Cheong Jin Hwan JH Ryu Je Il JI Won Yu Deok YD Koh Seong-Ho SH Park Young Mi YM

Frontiers in oncology 20220810

Glioblastoma multiforme (GBM) is the most malignant brain tumor with an extremely poor prognosis. The Cancer Genome Atlas (TCGA) database has been used to confirm the roles played by 10 canonical oncogenic signaling pathways in various cancers. The purpose of this study was to evaluate the expression of genes in these 10 canonical oncogenic signaling pathways, which are significantly related to mortality and disease progression in GBM patients. Clinicopathological information and mRNA expression ...[more]

PMID: 36033456

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Project description:BackgroundEndocrine therapy is the backbone therapy in estrogen receptor α (ER)-positive breast cancer, and tamoxifen resistance is a great challenge for endocrine therapy. Tamoxifen-resistant and sensitive samples from the international public repository, the Gene Expression Omnibus (GEO) database, were used to identify therapeutic biomarkers associated with tamoxifen resistance.Materials and methodsIn this study, integrated analysis was used to identify tamoxifen resistance-associated genes. Differentially expressed genes (DEGs) were identified. Gene ontology and pathway analysis were then analyzed. Weighted correlation network analysis (WGCNA) was performed to find modules correlated with tamoxifen resistance. Protein-protein interaction (PPI) network was used to find hub genes. Genes of prognostic significance were further validated in another GEO dataset and cohort from Shanghai Ruijin Hospital using RT-PCR.ResultsA total of 441 genes were down-regulated and 123 genes were up-regulated in tamoxifen-resistant samples. Those up-regulated genes were mostly enriched in the cell cycle pathway. Then, WGCNA was performed, and the brown module was correlated with tamoxifen resistance. An overlap of 81 genes was identified between differentially expressed genes (DEGs) and genes in the brown module. These genes were also enriched in the cell cycle. Twelve hub genes were identified using PPI network, which were involved in the mitosis phase of the cell cycle. Finally, 10 of these 12 genes were validated to be up-regulated in tamoxifen-resistant patients and were associated with poor prognosis in ER-positive patients.ConclusionOur study suggested mitosis-related genes are mainly involved in tamoxifen resistance, and high expression of these genes could predict poor prognosis of patients receiving tamoxifen. These genes may be potential targets to improve efficacy of endocrine therapy in breast cancer, and inhibitors targeted these genes could be used in endocrine-resistant patients.

Project description:BackgroundGlioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM. Disruptions in various oncogenic signaling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signaling, Phosphoinositide 3- Kinases (PI3Ks), tumor protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumor types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients.MethodsWe collected post-operative MRI scans (T1w, T1c, FLAIR, T2w) from the BRATS-19 dataset, including scans from patients with both GBM and LGG, linked to genetic and clinical data via TCGA and CPTAC. Signaling pathway data was manually extracted from cBioPortal. Radiomic features were extracted from four MRI modalities using PyRadiomics. Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. Five ML models were trained to predict signaling pathways, with Grid Search optimizing hyperparameters and 5-fold cross-validation ensuring unbiased performance. Each model's performance was evaluated using various metrics on test data.ResultsOur results showed a positive association between most signaling pathways and the radiomic features derived from MRI scans. The best models achieved high AUC scores, namely 0.7 for RTK-RAS, 0.8 for PI3K, 0.75 for TP53, and 0.4 for NOTCH, and therefore, demonstrated the potential of ML models in accurately predicting oncogenic signaling pathways from radiomic features, thereby informing personalized therapeutic approaches and improving patient outcomes.ConclusionWe present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.

Dataset Information

Identification of genes from ten oncogenic pathways associated with mortality and disease progression in glioblastoma.

Publications

Identification of genes from ten oncogenic pathways associated with mortality and disease progression in glioblastoma.

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