Survival Risk Prediction Models of Gliomas Based on IDH and 1p/19q

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci

Backgrounds

Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population.

Methods

A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes.

Results

Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable.

Conclusion

We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

Prognostic nomogram models for predicting survival probability in elderly glioblastoma patients

PURPOSE

To investigate the prognostic factors of survival and develop a predictive nomogram model for elderly GBM patients.

METHODS

Elderly patients (> = 65 years) with histologically diagnosed GBM were extracted from the SEER database. Survival analysis of overall survival (OS) was performed by Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses were used to determine independent prognostic factors and these factors were used to further construct the nomogram model.

RESULTS

A total of 9068 elderly GBM patients (5122 males and 3946 females) were included, with a median age of 72 years (65-96 years). All patients were divided randomly into the training group (n = 6044) and the validation group (n = 3024) by a ratio of 2:1. Cox regression analyses on OS showed eight independent prognostic factors (race, age, tumor side, tumor size, metastasis, surgery, radiotherapy, and chemotherapy) in the training cohort. Also, seven variables (except for race) were identified on CSS in the training group. By comprising these variables, the nomogram models on OS and CSS for predicting the 6-month, 1-year, and 2-year survival probability were constructed and exhibited moderate consistency, respectively. Then, they could be validated well in the validation cohort and by C-index, time-dependent ROC curve, calibration plot, and DCA curve.

CONCLUSIONS

Nomogram models on OS and CSS could provide an applicable tool to predict the survival probability and provide clinical references regarding treatment strategies and prognosis.

Clinical, pharmacological, and formulation evaluation of disulfiram in the treatment of glioblastoma - a systematic literature review

INTRODUCTION

Glioblastoma (GB) is one of the most challenging central nervous system (CNS) tumors in treatment options and response, urging the development of novel management strategies. The anti-alcoholism drug, disulfiram (DS), has a potential anticancer activity, and its complex mechanism of action is assumed to be well exploited against the heterogeneous GB.

AREA COVERED

Through a systematic literature review about repositioning DS to GB treatment, an evaluation of the clinical, pharmacological, and formulation strategies is provided to specify the challenges of drug delivery and thus to advance its clinical translation. From six databases, 35 articles were selected, including case report (1); clinical trials (3); original articles mainly representing in vitro and preclinical pharmacological data, and 10 dealing with technological approaches.

EXPERT OPINION

The repositioning of DS in GB treatment is facing drug and tumor-associated limitations due to the oral drug's low bioavailability, unwanted metabolism, and inefficient delivery to brain-tumor tissue. Development strategies using molecular encapsulation of DS and the parenteral dosage forms improve the anticancer pharmacology of the drug. The development of optimized drug delivery systems (DDS) shows promise for the clinical translation of DS into GB adjuvant therapy.

Bioinformatics analysis of LMAN1 expression, clinical characteristics, and its effects on cell proliferation and invasion in glioma

Glioma is the most common primary central nervous system malignant tumor with high heterogeneity and poor prognosis. So far, the complex pathological process of glioma has not been fully elucidated, and there is a lack of effective biomarkers for the diagnosis and molecular targeted therapy of glioma. Using bioinformatics methods, 77 upregulated and 89 downregulated differentially expressed genes (DEGs) were detected by intersection analysis in different gene expression datasets of glioma cases from public databases. Then, GO and KEGG pathway analysis revealed that the biological functions of these upregulated DEGs were mainly focused on immune response, and the signaling pathways were mainly enriched in integrin family cell surface interactions. The overexpression of the LMAN1 gene of interest was then confirmed using the TCGA dataset and further verified by qRT-PCR in 29 clinical samples and 5 glioma cell lines. Furthermore, high expression of LMAN1 was found to be associated with higher WHO grade, IDH status, and 1p/19q co-deletion. Survival analysis showed that high expression of LMAN1 was associated with poor prognosis in glioma. Gene set enrichment analysis (GSEA) indicated that many cancer-related pathways were associated with LMAN1-high phenotype. Protein-protein interaction (PPI) analysis revealed significant interaction between LMAN1 and MCFD2, F8, and TMED10. Finally, cell experiments showed that LMAN1 knockdown significantly inhibited the proliferation, migration and invasion and promoted apoptosis in glioma cells. This study highlighted the malignant role of LMAN1 in gliomas and provided a potentially valuable biomarker for prognosis evaluation and molecular targeted therapy of glioma.

PLEK2, RRM2, GCSH: A Novel WWOX-Dependent Biomarker Triad of Glioblastoma at the Crossroads of Cytoskeleton Reorganization and Metabolism Alterations

Glioblastoma is one of the deadliest human cancers. Its malignancy depends on cytoskeleton reorganization, which is related to, e.g., epithelial-to-mesenchymal transition and metastasis. The malignant phenotype of glioblastoma is also affected by the WWOX gene, which is lost in nearly a quarter of gliomas. Although the role of WWOX in the cytoskeleton rearrangement has been found in neural progenitor cells, its function as a modulator of cytoskeleton in gliomas was not investigated. Therefore, this study aimed to investigate the role of WWOX and its collaborators in cytoskeleton dynamics of glioblastoma. Methodology on RNA-seq data integrated the use of databases, bioinformatics tools, web-based platforms, and machine learning algorithm, and the obtained results were validated through microarray data. PLEK2, RRM2, and GCSH were the most relevant WWOX-dependent genes that could serve as novel biomarkers. Other genes important in the context of cytoskeleton (BMP4, CCL11, CUX2, DUSP7, FAM92B, GRIN2B, HOXA1, HOXA10, KIF20A, NF2, SPOCK1, TTR, UHRF1, and WT1), metabolism (MTHFD2), or correlation with WWOX (COL3A1, KIF20A, RNF141, and RXRG) were also discovered. For the first time, we propose that changes in WWOX expression dictate a myriad of alterations that affect both glioblastoma cytoskeleton and metabolism, rendering new therapeutic possibilities.