| 摘要: | Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. Therefore, this thesis aims to construct risk models based on genomic and clinical data for GBM using two approaches, data–driven and hypothesis–driven.
For the data–driven approach, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes’ biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.
For the hypothesis–driven approach, we explored how the cancer stem cell hypothesis could be incorporated into our workflow. Subpopulations of cancer stem cells (CSCs) are postulated to drive disease progression and recurrence in GBM. Identifying genetic markers of CSCs is crucial to target these residents in the tumor microenvironment and enhance treatment effectiveness. We screened a list of 166 stemness-related genes for association with poor survival in GBM in the TCGA PanCancer dataset. Next, a stemness-related signature was constructed based on each gene's hazard ratio and expression level to investigate their collective prognostic significance. The stemness-related signature consisted of ten genes, namely CD44, FBXO27, GATA3, GPX2, IDH1, LATS2, NKX2-5, RUNX1, THY1, VEGFA, and patients with GBM in the high risk group had significantly worse survival rate than those in the low risk group (p = 0.009). Differential expression analysis found CD44, GATA3, IDH1, LATS2, RUNX1, and VEGFA to be upregulated, and FBXO27 and THY1 to be downregulated in GBM. A total of 24.3% patients carried genetic alteration in at least one of the ten genes, with the majority belonging to IDH1 and GATA3. Our study proposed a stemness-related signature in GBM that can provide prognostic significance and guide immunotherapy options for patients with GBM.
Taken together, these results indicate that GBM is driven by the interplay between multiple genetic alterations. The two approaches presented in this thesis, while both demonstrated differential clinical outcome among people with GBM, encompassed distinct sets of prognostic–related genes. This opens another avenue to explore whether a consensus exists within the vast realm of bioinformatic–based studies, with the potential for fine–tuned risk models and clinically proven markers to diagnose and treat GBM.
Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. Therefore, this thesis aims to construct risk models based on genomic and clinical data for GBM using two approaches, data–driven and hypothesis–driven.
For the data–driven approach, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes’ biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.
For the hypothesis–driven approach, we explored how the cancer stem cell hypothesis could be incorporated into our workflow. Subpopulations of cancer stem cells (CSCs) are postulated to drive disease progression and recurrence in GBM. Identifying genetic markers of CSCs is crucial to target these residents in the tumor microenvironment and enhance treatment effectiveness. We screened a list of 166 stemness-related genes for association with poor survival in GBM in the TCGA PanCancer dataset. Next, a stemness-related signature was constructed based on each gene's hazard ratio and expression level to investigate their collective prognostic significance. The stemness-related signature consisted of ten genes, namely CD44, FBXO27, GATA3, GPX2, IDH1, LATS2, NKX2-5, RUNX1, THY1, VEGFA, and patients with GBM in the high risk group had significantly worse survival rate than those in the low risk group (p = 0.009). Differential expression analysis found CD44, GATA3, IDH1, LATS2, RUNX1, and VEGFA to be upregulated, and FBXO27 and THY1 to be downregulated in GBM. A total of 24.3% patients carried genetic alteration in at least one of the ten genes, with the majority belonging to IDH1 and GATA3. Our study proposed a stemness-related signature in GBM that can provide prognostic significance and guide immunotherapy options for patients with GBM.
Taken together, these results indicate that GBM is driven by the interplay between multiple genetic alterations. The two approaches presented in this thesis, while both demonstrated differential clinical outcome among people with GBM, encompassed distinct sets of prognostic–related genes. This opens another avenue to explore whether a consensus exists within the vast realm of bioinformatic–based studies, with the potential for fine–tuned risk models and clinically proven markers to diagnose and treat GBM. |
