Development and Validation of a Novel DNA Methylation-Driven Gene Based Molecular Classification and Predictive Model for Overall Survival and Immunotherapy Response in Patients With Glioblastoma: A Multiomic Analysis

Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives

Glioblastoma (GBM), the predominant and primary malignant intracranial tumor, poses a formidable challenge due to its immunosuppressive microenvironment, thereby confounding conventional therapeutic interventions. Despite the established treatment regimen comprising surgical intervention, radiotherapy, temozolomide administration, and the exploration of emerging modalities such as immunotherapy and integration of medicine and engineering technology therapy, the efficacy of these approaches remains constrained, resulting in suboptimal prognostic outcomes. In recent years, intensive scrutiny of the inhibitory and immunosuppressive milieu within GBM has underscored the significance of cellular constituents of the GBM microenvironment and their interactions with malignant cells and neurons. Novel immune and targeted therapy strategies have emerged, offering promising avenues for advancing GBM treatment. One pivotal mechanism orchestrating immunosuppression in GBM involves the aggregation of myeloid-derived suppressor cells (MDSCs), glioma-associated macrophage/microglia (GAM), and regulatory T cells (Tregs). Among these, MDSCs, though constituting a minority (4-8%) of CD45+ cells in GBM, play a central component in fostering immune evasion and propelling tumor progression, angiogenesis, invasion, and metastasis. MDSCs deploy intricate immunosuppressive mechanisms that adapt to the dynamic tumor microenvironment (TME). Understanding the interplay between GBM and MDSCs provides a compelling basis for therapeutic interventions. This review seeks to elucidate the immune regulatory mechanisms inherent in the GBM microenvironment, explore existing therapeutic targets, and consolidate recent insights into MDSC induction and their contribution to GBM immunosuppression. Additionally, the review comprehensively surveys ongoing clinical trials and potential treatment strategies, envisioning a future where targeting MDSCs could reshape the immune landscape of GBM. Through the synergistic integration of immunotherapy with other therapeutic modalities, this approach can establish a multidisciplinary, multi-target paradigm, ultimately improving the prognosis and quality of life in patients with GBM.

Identification and validation of DNA methylation-driven gene PCDHB4 as a novel tumor suppressor for glioblastoma diagnosis and prognosis

Aberrant DNA methylation is a critical regulator of gene expression in the development and progression of glioblastoma (GBM). However, the impact of methylation-driven gene PCDHB4 changes on GBM occurrence and progression remains unclear. Therefore, this study aimed to identify the PCDHB4 gene for early diagnosis and prognostic evaluation and clarify its functional role in GBM. Methylation-driven gene PCDHB4 was selected for GBM using the multi-omics integration method based on publicly available data sets. The diagnostic capabilities of PCDHB4 methylation and 5-hydroxymethylcytosines were validated in tissue and blood cell-free DNA (cfDNA) samples, respectively. Combined survival analysis of PCDHB4 methylation and immune infiltration cells evaluated the prognostic predictive performance of GBM patients. We identified that the PCDHB4 gene achieved high discriminative capabilities for GBM and normal tissues with an area under the curve value of 0.941. PCDHB4 hypermethylation was observed in cfDNA blood samples from GBM patients. Compared with GBM patients with PCDHB4 hypermethylation level, patients with PCDHB4 hypomethylation level had significantly poorer overall survival (p = 0.035). In addition, GBM patients with PCDHB4 hypermethylation and high infiltration of CD4+ T cell activation level had a favorable survival (p = 0.026). Moreover, we demonstrated that mRNA expression of PCDHB4 was downregulated in GBM tissues and upregulated in GBM cell lines with PCDHB4 demethylation, and PCDHB4 overexpression inhibited GBM cell proliferation and migration. In summary, we discovered a novel methylation-driven gene PCDHB4 for the diagnosis and prognosis of GBM and demonstrated that PCDHB4 is a tumor suppressor in vitro experiments.

Prognostic analysis and nomogram construction for older patients with IDH-wild-type glioblastoma

As many countries face an ageing population, the number of older patients with glioblastoma (GB) is increasing. Thus, there is an urgent need for prognostic models to aid in treatment decision-making and life planning. A total of 98 patients with isocitrate dehydrogenase (IDH)-wild-type GB aged ≥65 years were analysed from January 2012 to January 2020. Independent prognostic factors were identified by prognostic analysis. Using the independent prognostic factors for overall survival (OS), a nomogram was constructed by R software to predict the prognosis of older patients with IDH-wild-type GB. The concordance index (C-index) and receiver operating characteristic (ROC) curve were used to assess model discrimination, and the calibration curve was used to assess model calibration. Prognostic analysis showed that the extent of resection (EOR), adjusted Charlson comorbidity index (ACCI), O6-methylguanine-DNA methyltransferase (MGMT) methylation status, postoperative radiotherapy, and postoperative temozolomide (TMZ) chemotherapy were independent prognostic factors for OS. MGMT methylation status and subventricular zone (SVZ) involvement were independent prognostic factors for progression-free survival (PFS). A nomogram was constructed based on EOR, ACCI, MGMT methylation status, postoperative radiotherapy and postoperative TMZ chemotherapy to predict the 6-month, 12-month and 18-month OS of older patients with IDH-wild-type GB. The C-index of the nomogram was 0.72, and the ROC curves showed that the areas under the curve (AUCs) at 6, 12 and 18 months were 0.874, 0.739 and 0.779, respectively. The calibration plots showed that the nomogram was in good agreement with the actual observations in predicting the OS of older patients with IDH-wild-type GB. Older patients with IDH-wild-type GB can benefit from gross total resection (GTR), postoperative radiotherapy and postoperative TMZ chemotherapy. A high ACCI score and MGMT nonmethylation are poor prognostic factors. We constructed a nomogram including the ACCI to facilitate clinical decision-making and follow-up interval selection.

LASSO-based screening for potential prognostic biomarkers associated with glioblastoma

Background

Glioblastoma is the most common malignancy of the neuroepithelium, yet existing research on this tumor is limited. LASSO is an algorithm of selected feature coefficients by which genes associated with glioblastoma prognosis can be obtained.

Methods

Glioblastoma-related data were selected from the Cancer Genome Atlas (TCGA) database, and information was obtained for 158 samples, including 153 cancer samples and five samples of paracancerous tissue. In addition, 2,642 normal samples were selected from the Genotype-Tissue Expression (GTEx) database. Whole-gene bulk survival analysis and differential expression analysis were performed on glioblastoma genes, and their intersections were taken. Finally, we determined which genes are associated with glioma prognosis. The STRING database was used to analyze the interaction network between genes, and the MCODE plugin under Cytoscape was used to identify the highest-scoring clusters. LASSO prognostic analysis was performed to identify the key genes. Gene expression validation allowed us to obtain genes with significant expression differences in glioblastoma cancer samples and paracancer samples, and glioblastoma independent prognostic factors could be derived by univariate and multivariate Cox analyses. GO functional enrichment analysis was performed, and the expression of the screened genes was detected using qRT-PCR.

Results

Whole-gene bulk survival analysis of glioblastoma genes yielded 607 genes associated with glioblastoma prognosis, differential expression analysis yielded 8,801 genes, and the intersection of prognostic genes with differentially expressed genes (DEG) yielded 323 intersecting genes. PPI analysis of the intersecting genes revealed that the genes were significantly enriched in functions such as the formation of a pool of free 40S subunits and placenta development, and the highest-scoring clusters were obtained using the MCODE plug-in. Eight genes associated with glioblastoma prognosis were identified based on LASSO analysis: RPS10, RPS11, RPS19, RSL24D1, RPL39L, EIF3E, NUDT5, and RPF1. All eight genes were found to be highly expressed in the tumor by gene expression verification, and univariate and multivariate Cox analyses were performed on these eight genes to identify RPL39L and NUDT5 as two independent prognostic factors associated with glioblastoma. Both RPL39L and NUDT5 were highly expressed in glioblastoma cells.

Conclusion

Two independent prognostic factors in glioblastoma, RPL39L and NUDT5, were identified.

A novel autophagy-related long non-coding RNAs prognostic risk score for clear cell renal cell carcinoma

BACKGROUND

As the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated.

METHODS

The RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores.

RESULTS

A total of 9 SARlncRs were established as a risk score model. The Kaplan-Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC.

CONCLUSIONS

We established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.

An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment

Glioblastoma multiforme (GBM) is the most aggressive and common malignant primary brain tumor. Patients with GBM often have poor prognoses, with a median survival of ∼15 months. Enhanced understanding of the molecular biology of central nervous system tumors has led to modifications in their classifications, the most recent of which classified these tumors into new categories and made some changes in their nomenclature and grading system. This review aims to give a panoramic view of the last 3 years' findings in glioblastoma characterization, its heterogeneity, and current advances in its treatment. Several molecular parameters have been used to achieve an accurate and personalized characterization of glioblastoma in patients, including epigenetic, genetic, transcriptomic and metabolic features, as well as age- and sex-related patterns and the involvement of several noncoding RNAs in glioblastoma progression. Astrocyte-like neural stem cells and outer radial glial-like cells from the subventricular zone have been proposed as agents involved in GBM of IDH-wildtype origin, but this remains controversial. Glioblastoma metabolism is characterized by upregulation of the PI3K/Akt/mTOR signaling pathway, promotion of the glycolytic flux, maintenance of lipid storage, and other features. This metabolism also contributes to glioblastoma's resistance to conventional therapies. Tumor heterogeneity, a hallmark of GBM, has been shown to affect the genetic expression, modulation of metabolic pathways, and immune system evasion. GBM's aggressive invasion potential is modulated by cell-to-cell crosstalk within the tumor microenvironment and altered expressions of specific genes, such as ANXA2, GBP2, FN1, PHIP, and GLUT3. Nevertheless, the rising number of active clinical trials illustrates the efforts to identify new targets and drugs to treat this malignancy. Immunotherapy is still relevant for research purposes, given the amount of ongoing clinical trials based on this strategy to treat GBM, and neoantigen and nucleic acid-based vaccines are gaining importance due to their antitumoral activity by inducing the immune response. Furthermore, there are clinical trials focused on the PI3K/Akt/mTOR axis, angiogenesis, and tumor heterogeneity for developing molecular-targeted therapies against GBM. Other strategies, such as nanodelivery and computational models, may improve the drug pharmacokinetics and the prognosis of patients with GBM.

A DNA-Methylation-Driven Genes Based Prognostic Signature Reveals Immune Microenvironment in Pancreatic Cancer

Pancreatic cancer (PACA), which is characterized by an immunosuppressive nature, remains one of the deadliest malignancies worldwide. Aberrant DNA methylation (DNAm) reportedly influences tumor immune microenvironment. Here, we evaluated the role of DNA methylation driven genes (MDGs) in PACA through integrative analyses of epigenomic, transcriptomic, genomic and clinicopathological data obtained from TCGA, ICGC, ArrayExpress and GEO databases. Thereafter, we established a four-MDG signature, comprising GPRC5A, SOWAHC, S100A14, and ARNTL2. High signature risk-scores were associated with poor histologic grades and late TNM stages. Survival analyses showed the signature had a significant predictive effect on OS. WGCNA revealed that the signature may be associated with immune system, while high risk-scores might reflect immune dysregulation. Furthermore, GSEA and GSVA revealed significant enrichment of p53 pathway and mismatch repair pathways in high risk-score subgroups. Immune infiltration analysis showed that CD8+ T cells were more abundant in low score subgroups, while M0 macrophages exhibited an opposite trend. Moreover, negative regulatory genes of cancer-immunity cycle (CIC) illustrated that immunosuppressors TGFB1, VEGFA, and CD274 (PDL1) were all positively correlated with risk-scores. Furthermore, the four signature genes were negatively correlated with CD8+ lymphocytes, but positively associated with myeloid derived suppressor cells (MDSC). Conversely, specimens with high risk-scores exhibited heavier tumor mutation burdens (TMB) and might show better responses to some chemotherapy and targeted drugs, which would benefit stratification of PACA patients. On the other hand, we investigated the corresponding proteins of the four MDGs using paraffin-embedded PACA samples collected from patients who underwent radical surgery in our center and found that all these four proteins were elevated in cancerous tissues and might serve as prognostic markers for PACA patients, high expression levels indicated poor prognosis. In conclusion, we successfully established a four-MDG-based prognostic signature for PACA patients. We envisage that this signature will help in evaluation of intratumoral immune texture and enable identification of novel stratification biomarkers for precision therapies.

Tumor Heterogeneity in Glioblastomas: From Light Microscopy to Molecular Pathology

One of the main reasons for the aggressive behavior of glioblastoma (GBM) is its intrinsic intra-tumor heterogeneity, characterized by the presence of clonal and subclonal differentiated tumor cell populations, glioma stem cells, and components of the tumor microenvironment, which affect multiple hallmark cellular functions in cancer. "Tumor Heterogeneity" usually encompasses both inter-tumor heterogeneity (population-level differences); and intra-tumor heterogeneity (differences within individual tumors). Tumor heterogeneity may be assessed in a single time point (spatial heterogeneity) or along the clinical evolution of GBM (longitudinal heterogeneity). Molecular methods may detect clonal and subclonal alterations to describe tumor evolution, even when samples from multiple areas are collected in the same time point (spatial-temporal heterogeneity). In GBM, although the inter-tumor mutational landscape is relatively homogeneous, intra-tumor heterogeneity is a striking feature of this tumor. In this review, we will address briefly the inter-tumor heterogeneity of the CNS tumors that yielded the current glioma classification. Next, we will take a deeper dive in the intra-tumor heterogeneity of GBMs, which directly affects prognosis and response to treatment. Our approach aims to follow technological developments, allowing for characterization of intra-tumor heterogeneity, beginning with differences on histomorphology of GBM and ending with molecular alterations observed at single-cell level.

Randomized Controlled Immunotherapy Clinical Trials for GBM Challenged

Simple Summary

Although multiple meta-analyses on active specific immunotherapy treatment for glioblastoma multiforme (GBM) have demonstrated a significant prolongation of overall survival, no single research group has succeeded in demonstrating the efficacy of this type of treatment in a prospective, double-blind, placebo-controlled, randomized clinical trial. In this paper, we explain how the complexity of the tumor biology and tumor–host interactions make proper stratification of a control group impossible. The individualized characteristics of advanced therapy medicinal products for immunotherapy contribute to heterogeneity within an experimental group. The dynamics of each tumor and in each patient aggravate comparative stable patient groups. Finally, combinations of immunotherapy strategies should be integrated with first-line treatment. We illustrate the complexity of a combined first-line treatment with individualized multimodal immunotherapy in a group of 70 adults with GBM and demonstrate that the integration of immunogenic cell death treatment within maintenance chemotherapy followed by dendritic cell vaccines and maintenance immunotherapy might provide a step towards improving the overall survival rate of GBM patients.

Abstract

Immunotherapies represent a promising strategy for glioblastoma multiforme (GBM) treatment. Different immunotherapies include the use of checkpoint inhibitors, adoptive cell therapies such as chimeric antigen receptor (CAR) T cells, and vaccines such as dendritic cell vaccines. Antibodies have also been used as toxin or radioactive particle delivery vehicles to eliminate target cells in the treatment of GBM. Oncolytic viral therapy and other immunogenic cell death-inducing treatments bridge the antitumor strategy with immunization and installation of immune control over the disease. These strategies should be included in the standard treatment protocol for GBM. Some immunotherapies are individualized in terms of the medicinal product, the immune target, and the immune tumor–host contact. Current individualized immunotherapy strategies focus on combinations of approaches. Standardization appears to be impossible in the face of complex controlled trial designs. To define appropriate control groups, stratification according to the Recursive Partitioning Analysis classification, MGMT promotor methylation, epigenetic GBM sub-typing, tumor microenvironment, systemic immune functioning before and after radiochemotherapy, and the need for/type of symptom-relieving drugs is required. Moreover, maintenance of a fixed treatment protocol for a dynamic, deadly cancer disease in a permanently changing tumor–host immune context might be inappropriate. This complexity is illustrated using our own data on individualized multimodal immunotherapies for GBM. Individualized medicines, including multimodal immunotherapies, are a rational and optimal yet also flexible approach to induce long-term tumor control. However, innovative methods are needed to assess the efficacy of complex individualized treatments and implement them more quickly into the general health system.