Identification of glioblastoma-specific prognostic biomarkers via an integrative analysis of DNA methylation and gene expression

Key genes altered in glioblastoma based on bioinformatics (Review)

Glioblastoma multiforme (GBM) is an aggressive brain tumor with poor prognosis. Recent advancements in bioinformatics have contributed to uncovering the genetic alterations that underlie the development and progression of GBM. Analysis of extensive genomic data led to the identification of significant pathways involved in GBM, such as the PI3K/AKT/mTOR and Ras/Raf/MEK/ERK signaling pathways, alongside key genes such as EGFR, TP53 and TERT. These findings have enhanced our understanding of GBM biology and led to the identification of new therapeutic targets. Bioinformatics has become an indispensable tool in pinpointing the genetic modifications that drive GBM, paving the way for innovative treatment strategies. This approach not only aids in comprehending the complexities of GBM but also holds promise for improving outcomes in patients suffering from this devastating disease. The ongoing integration of bioinformatics in GBM research continues to be vital for advancing therapeutic options.

Size matters: Biomolecular compositions of small and large extracellular vesicles in the urine of glioblastoma patients

The promise of urinary extracellular vesicles (uEVs) in biomarker discovery is emerging. However, the characteristics and compositions of different uEV subpopulations across normal physiological and pathological states require rigorous explication. We recently reported proteomic signatures of small (s)-uEVs (<200 nm membranous nanoparticles) and described putative biomarkers corresponding to the diagnosis, tumour burden and recurrence of the lethal adult primary brain tumour, glioblastoma. Here, we comprehensively characterise uEV populations with significantly different mean and mode particle sizes obtained by differential centrifugation at 100,000 × g (100K-uEVs; smaller) and 17,000 × g (17K-uEVs; larger) using Fourier-transform infrared spectroscopy and quantitative data-independent acquisition mass spectrometry. We show distinct differences in protein and lipid content, prominent protein secondary structures, and proteome distributions between uEV populations that can distinguish glioblastoma patients from healthy controls and correspond to clinically relevant tumour changes (i.e., recurrence and treatment resistance). Among the key findings is a putative seven-protein biomarker panel associated with 17K-uEVs that could distinguish all glioblastoma patients from healthy controls and accurately classify 98.2% of glioblastoma samples. These novel, significant findings demonstrate that both uEV populations offer individual and combined biomarker potential. Further research is warranted to elucidate the complete diagnostic, prognostic, and predictive capabilities of often-neglected 17K-uEV populations.

A Genomic Instability-Associated Prognostic Signature for Glioblastoma Patients

BACKGROUND

Genomic instability and aberrant tumor mutation burden are widely accepted hallmarks of cancer. Glioblastoma (GBM) is a common brain tumor in adults, and survival of patients with GBM is poor. This study aimed to investigate the prognostic value of genomic instability-derived genes in GBM.

METHODS

GBM data were downloaded from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Differential expression analysis of all samples with different tumor mutation burden was performed. Univariate Cox and LASSO Cox regression analyses were integrated to determine the optimal genes for constructing a risk score model. Multivariate Cox regression analysis and survival analysis determined independent prognostic indicators. Immune cell infiltration was analyzed by CIBERSORT algorithm.

RESULTS

In GMB patients with high and low tumor mutation burden, we identified 154 differentially expressed genes, which were significantly enriched in 47 Gene Ontology terms and 6 Kyoto Encyclopedia of Genes and Genomes pathways. To establish a risk score, 9 genes were further screened, including SDC1, CXCL1, CXCL6, RGS4, PCDHGB2, CA9, ZAR1, CHRM3, and SLN. High-risk patients had worse prognosis in two databases. The performance of a nomogram including prognostic factors (risk score and age) was good. Moreover, mast cells resting was significantly differentially infiltrated between high- and low-risk GBM samples.

CONCLUSIONS

The risk score constructed by 9 genomic instability-derived genes could reliably predict prognosis of GBM patients. The nomogram based on age and risk score also had a good prognostic predictive value.

Integrative analysis of somatic mutations and differential expression profiles in glioblastoma based on aging acceleration

BACKGROUND

Glioblastoma (GBM) is an aggressive brain tumor and the mechanisms of progression are very complex. Accelerated aging is a driving factor of GBM. However, there has not been thorough research about the mechanisms of GBM progression based on aging acceleration.

METHODS

The aging predictor was modeled based on normal brain samples. Then an aging acceleration background network was constructed to explore GBM mechanisms.

RESULTS

The accelerated aging-related mechanisms provided an innovative way to study GBM, wherein integrative analysis of somatic mutations and differential expression revealed key pathologic characteristics. Moreover, the influence of the immune system, the nervous system and other critical factors on GBM were identified. The survival analysis also disclosed crucial GBM markers.

CONCLUSION

An integrative analysis of multi-omics data based on aging acceleration identified new driving factors for GBM.