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Large-Scale Transcriptomics-Driven Approach Revealed Overexpression of CRNDE as a Poor Survival Prognosis Biomarker in Glioblastoma.

ABSTRACT: Glioblastoma is the most common and malignant brain malignancy worldwide, with a 10-year survival of only 0.7%. Aggressive multimodal treatment is not enough to increase life expectancy and provide good quality of life for glioblastoma patients. In addition, despite decades of research, there are no established biomarkers for early disease diagnosis and monitoring of patient response to treatment. High throughput sequencing technologies allow for the identification of unique molecules from large clinically annotated datasets. Thus, the aim of our study was to identify significant molecular changes between short- and long-term glioblastoma survivors by transcriptome RNA sequencing profiling, followed by differential pathway-activation-level analysis. We used data from the publicly available repositories The Cancer Genome Atlas (TCGA; number of annotated cases = 135) and Chinese Glioma Genome Atlas (CGGA; number of annotated cases = 218), and experimental clinically annotated glioblastoma tissue samples from the Institute of Pathology, Faculty of Medicine in Ljubljana corresponding to 2-58 months overall survival (n = 16). We found one differential gene for long noncoding RNA CRNDE whose overexpression showed correlation to poor patient OS. Moreover, we identified overlapping sets of congruently regulated differential genes involved in cell growth, division, and migration, structure and dynamics of extracellular matrix, DNA methylation, and regulation through noncoding RNAs. Gene ontology analysis can provide additional information about the function of protein- and nonprotein-coding genes of interest and the processes in which they are involved. In the future, this can shape the design of more targeted therapeutic approaches.

SUBMITTER: Sorokin M

PROVIDER: S-EPMC8303503 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objectives: DNA damage inducible transcript 4 (DDIT4) plays a key role in different cancers, but the role of DDIT4 in lung adenocarcinoma (LUAD) is not completely understood. The aim of this study was to evaluate the utility of DDIT4 as a prognostic biomarker for LUAD. Methods: First, DDIT4 mRNA expression in LUAD cell lines (A549, H1299 and HBE) and tissues (89 cases) was assessed by RT-PCR. Next, DDIT4 protein expression in LUAD tissues and normal tissues was assessed by immunohistochemistry (75 cases). Then, the correlation between DDIT4 expression and overall survival was analyzed using the Kaplan-Meier method. After that, we verified the utility of the DDIT4 gene as a prognostic marker of lung cancer in the TCGA database (1133 cases). Finally, the possible mechanism of the DDIT4 gene as a prognostic marker of LUAD was preliminarily explored. Results: mRNA levels of DDIT4 in HBE cells were significantly lower than in A549 and H1299 cells (P<0.05), and expression of the DDIT4 gene in cancer tissues was significantly higher than in adjacent tissues (P<0.0001). Immunohistochemical staining results showed that high expression of DDIT4 accounted for approximately 68.0% of LUAD tissues. DDIT4 expression was significantly correlated with differentiation (P < 0.05). However, it was not correlated with sex, age, smoking, tumor size, lymph node metastasis, or TNM stage (P>0.05). The survival analysis demonstrated that high DDIT4 expression was correlated with shorter overall survival (P < 0.05). Univariate and multivariate analyses indicated that DDIT4 was an independent predictor of overall survival for LUAD, which was confirmed by data from the TCGA database. Finally, we found that DDIT4 gene expression was significantly increased in the hypoxic environment compared to the normal oxygen environment, indicating that the DDIT4 gene may play an important role in the hypoxic microenvironment of tumor tissue. Conclusion: High expression levels of DDIT4 correlated with poor overall survival in patients with LUAD, and DDIT4 was an independent predictor of overall survival. These findings provide new insight for understanding the development of LUAD.

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Large-Scale Transcriptomics-Driven Approach Revealed Overexpression of CRNDE as a Poor Survival Prognosis Biomarker in Glioblastoma.

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