Prediction value of unmeasurable MR enhancement at early stage after gross-total resection on the survival state of patients with high-grade glioma

Treatment Response and Prognosis Evaluation in High-Grade Glioma: An Imaging Review Based on MRI

In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 4.

Expression of CD44 and the survival in glioma: a meta-analysis

Background: Higher tumor expression of CD44, a marker of cancer stem cells (CSCs), is associated with poor overall survival (OS) in various cancers. However, the association between CD44 and poor OS remains inconsistent in glioma. We aimed to evaluate the potential predictive role of CD44 for prognosis of glioma patients in a meta-analysis.

Methods: Observational studies comparing OS of glioma patients according to the level of CD44 were identified through searching PubMed, Embase, and Cochrane’s Library databases. Meta-analyses were performed with a random- or fixed-effect model according to the heterogeneity. Subgroup analyses were performed to evaluate the influences of study characteristics.

Results: Eleven retrospective cohort studies were included. Results showed that increased CD44 expression in tumor predicted poor OS in glioma patients (hazard ratio [HR]: 1.42, 95% confidence interval [CI]: 1.02–1.97, P=0.04). Subgroup analyses showed that higher tumor CD44 expression significantly predicted poor OS in patients with World Health Organization (WHO) stages II–III glioma (HR: 2.99, 95% CI: 1.53–5.89, P=0.002), but not in patients with glioblastoma (HR: 1.26, 95% CI: 0.76–2.08, P=0.47; P for subgroup difference = 0.03). Results were not statistically different between subgroups according to patient ethnicity, sample size, CD44 detection method, CD44 cutoff, HR estimation, univariate or multivariate analysis, or median follow-up durations (P-values for subgroup difference all >0.10).

Conclusion: Higher tumor expression of CD44 may predict poor survival in patients with glioma, particularly in those with WHO stage II–III glioma.

A Novel Four-Gene Signature Associated With Immune Checkpoint for Predicting Prognosis in Lower-Grade Glioma

The overall survival of patients with lower grade glioma (LGG) varies greatly, but the current histopathological classification has limitations in predicting patients’ prognosis. Therefore, this study aims to find potential therapeutic target genes and establish a gene signature for predicting the prognosis of LGG. CD44 is a marker of tumor stem cells and has prognostic value in various tumors, but its role in LGG is unclear. By analyzing three glioma datasets from Gene Expression Omnibus (GEO) database, CD44 was upregulated in LGG. We screened 10 CD44-related genes via protein–protein interaction (PPI) network; function enrichment analysis demonstrated that these genes were associated with biological processes and signaling pathways of the tumor; survival analysis showed that four genes (CD44, HYAL2, SPP1, MMP2) were associated with the overall survival (OS) and disease-free survival (DFS)of LGG; a novel four-gene signature was constructed. The prediction model showed good predictive value over 2-, 5-, 8-, and 10-year survival probability in both the development and validation sets. The risk score effectively divided patients into high- and low- risk groups with a distinct outcome. Multivariate analysis confirmed that the risk score and status of IDH were independent prognostic predictors of LGG. Among three LGG subgroups based on the presence of molecular parameters, IDH-mutant gliomas have a favorable OS, especially if combined with 1p/19q codeletion, which further confirmed the distinct biological pattern between three LGG subgroups, and the gene signature is able to divide LGG patients with the same IDH status into high- and low- risk groups. The high-risk group possessed a higher expression of immune checkpoints and was related to the activation of immunosuppressive pathways. Finally, this study provided a convenient tool for predicting patient survival. In summary, the four prognostic genes may be therapeutic targets and prognostic predictors for LGG; this four-gene signature has good prognostic prediction ability and can effectively distinguish high- and low-risk patients. High-risk patients are associated with higher immune checkpoint expression and activation of the immunosuppressive pathway, providing help for screening immunotherapy-sensitive patients.

Expression of microRNA-210 and the prognosis in glioma patients: a meta-analysis

Aim: To evaluate the predictive power of tumor microRNA-210 (miR-210) on overall survival (OS) in glioma patients. Materials & methods: Studies were identified through searching PubMed, Embase and China National Knowledge Internet electronic databases. Meta-analyses were performed with a random- or fixed-effect model according to the heterogeneity. Results: Six studies were included. Results showed that increased miR-210 expression in tumor independently predicted poor OS in glioma patients (hazard ratio [HR]: 1.38; p = 0.001). Subgroup analyses showed that the prognostic efficacy of tumor miR-210 levels for OS was stronger in overall patients with glioma (HR: 2.22; p < 0.001) than in those with glioblastoma (HR: 1.13; p = 0.01). Conclusion: Expression of miR-210 may predict poor survival in patients with glioma.