Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

Biomarkers in Cerebrospinal Fluid for the Diagnosis and Monitoring of Gliomas

Gliomas are the most common type of malignant brain tumor and are characterized by a plethora of heterogeneous molecular alterations. Current treatments require the emergence of reliable biomarkers that will aid personalized treatment decisions and increase life expectancy. Glioma tissues are not as easily accessible as other solid tumors; therefore, detecting prominent biomarkers in biological fluids is necessary. Cerebrospinal fluid (CSF) circulates adjacent to the cerebral parenchyma and holds promise for discovering useful prognostic, diagnostic, and predictive biomarkers. In this review, we summarize extensive research regarding the role of circulating DNA, tumor cells, proteins, microRNAs, metabolites, and extracellular vesicles as potential CSF biomarkers for glioma diagnosis, prognosis, and monitoring. Future studies should address discrepancies and issues of specificity regarding CSF biomarkers, as well as the validation of candidate biomarkers.

Identification of established and novel extracellular matrix components in glioblastoma as targets for angiogenesis and prognosis

Glioblastomas (GBM) are aggressive tumors known for their heterogeneity, rapid proliferation, treatment resistance, and extensive vasculature. Angiogenesis, the formation of new vessels, involves endothelial cell (EC) migration and proliferation. Various extracellular matrix (ECM) molecules regulate EC survival, migration, and proliferation. Culturing human brain EC (HBMEC) on GBM-derived ECM revealed a decrease in EC numbers compared to controls. Through in silico analysis, we explored ECM gene expression differences between GBM and brain normal glia cells and the impact of GBM microenvironment on EC ECM transcripts. ECM molecules such as collagen alpha chains (COL4A1, COL4A2, p < 0.0001); laminin alpha (LAMA4), beta (LAMB2), and gamma (LAMC1) chains (p < 0.0005); neurocan (NCAN), brevican (BCAN) and versican (VCAN) (p < 0.0005); hyaluronan synthase (HAS) 2 and metalloprotease (MMP) 2 (p < 0.005); MMP inhibitors (TIMP1-4, p < 0.0005), transforming growth factor beta-1 (TGFB1) and integrin alpha (ITGA3/5) (p < 0.05) and beta (ITGB1, p < 0.0005) chains showed increased expression in GBM. Additionally, GBM-influenced EC exhibited elevated expression of COL5A3, COL6A1, COL22A1 and COL27A1 (p < 0.01); LAMA1, LAMB1 (p < 0.001); fibulins (FBLN1/2, p < 0.01); MMP9, HAS1, ITGA3, TGFB1, and wingless-related integration site 9B (WNT9B) (p < 0.01) compared to normal EC. Some of these molecules: COL5A1/3, COL6A1, COL22/27A1, FBLN1/2, ITGA3/5, ITGB1 and LAMA1/B1 (p < 0.01); NCAN, HAS1, MMP2/9, TIMP1/2 and TGFB1 (p < 0.05) correlated with GBM patient survival. In conclusion, this study identified both established and novel ECM molecules regulating GBM angiogenesis, suggesting NCAN and COL27A1 are new potential prognostic biomarkers for GBM.

FBXL19 promotes malignant behaviours by activating MAPK signalling and negatively correlates with prognosis in hepatocellular carcinoma

FBXL19 is a member of the Skp1-Cullin-F-box family of E3 ubiquitin ligases and is linked to a variety of vital biological processes, such as cell proliferation, migration, and differentiation. Previous studies have identified it as an oncogene in breast cancer and glioma. However, its role in hepatocellular carcinoma (HCC) remains unclear. To comprehensively elucidate its role in tumour biology and its underlying mechanisms, a variety of sophisticated methods, including bioinformatics analysis, RNA-sequencing technique, and in vitro cell biology experiments, were used. Here, we found that FBXL19 was upregulated in patients with HCC and correlated with poor prognosis. In in vitro experiments, the specific targeting of short hairpin RNAs via lentiviruses successfully induced the knockdown of FBXL19, resulting in notable inhibition of the proliferation, migration, and invasion of HCC cells. Furthermore, FBXL19 downregulation resulted in significant induction of G0/G1 phase cell cycle arrest. Importantly, FBXL19 knockdown inhibited tumour malignant behaviour primarily by inactivating extracellular signal-regulated protein kinase 1/2 and p38 mitogen-activated protein kinases. In conclusion, this study revealed that FBXL19 was upregulated in patients with HCC, and that its expression was negatively correlated with prognosis. Thus, FBXL19 displays oncogenic properties in HCC by activating mitogen-activated protein kinase signalling.

The Abundance of Tumor-Infiltrating CD8+ Tissue Resident Memory T Lymphocytes Correlates with Patient Survival in Glioblastoma

Glial tumors alone account for 40% of all CNS tumors and present a low survival rate. The tumor microenvironment is a critical regulator of tumor progression and therapeutic effectiveness in glioma. Growing evidence from numerous studies of human solid tumor-infiltrating CD8⁺ T cells indicates that tissue-resident memory T cells (TRM) represent a substantial subpopulation of tumor-infiltrating lymphocytes (TILs). Although it is reported that some types of cancer patients with high immune infiltration tend to have better outcomes than patients with low immune infiltration, it seems this does not happen in gliomas. This study aimed to characterize TRMs cells in the glioma tumor microenvironment to identify their potential predictive and prognostic role and the possible therapeutic applications. Fluorescence activated cell sorting (FACS) analysis and immunofluorescence staining highlighted a statistically significant increase in CD8⁺ TRM cells (CD103⁺ and CD69⁺ CD8⁺ T cells) in gliomas compared to control samples (meningioma). In-silico analysis of a dataset of n = 153 stage IV glioma patients confirmed our data. Moreover, the gene expression analysis showed an increase in the expression of TRM-related genes in tumor tissues compared to normal tissues. This analysis also highlighted the positive correlation between genes associated with CD8⁺ TRM and TILs, indicating that CD8⁺ TRMs cells are present among the infiltrating T cells. Finally, high expression of Integrin subunit alpha E (ITGAE), the gene coding for the integrin CD103, and high CD8⁺ TILs abundance were associated with more prolonged survival, whereas high ITGAE expression but low CD8⁺ TILs abundance were associated with lower survival.

Development and validation of a prognostic gene expression signature for lower-grade glioma following surgery and adjuvant radiotherapy

BACKGROUND AND PURPOSE

Standard of care for lower-grade glioma (LGG) is maximal safe resection and risk-adaptive adjuvant therapy. While patients who benefit the most from adjuvant chemotherapy have been elucidated in prospective randomized studies, comparable insights for adjuvant radiotherapy (RT) are lacking. We sought to identify and validate patterns of gene expression that are associated with differential outcomes among LGG patients treated by RT from two large genomics databases.

MATERIALS AND METHODS

Patients from The Cancer Genome Atlas (TCGA) with LGG (WHO grade II-III glioma) treated by surgery and adjuvant RT were randomized 1:1 to a discovery cohort or an internal validation cohort. Using the discovery cohort only, associations between tumor RNA-seq expression and progression-free survival (PFS) as well as overall survival (OS) were evaluated with adjustment for clinicopathologic covariates. A Genomic Risk Score (GRS) was then constructed from the expression levels of top genes also screened for involvement in glioma carcinogenesis. The prognostic value of GRS was further assessed in the internal validation cohort of TCGA and a second distinct database, compiled by the Chinese Glioma Genome Association (CGGA).

RESULTS

From TCGA, 289 patients with LGG received adjuvant RT alone (38 grade II, 30 grade III) or chemoradiotherapy (CRT) (51 grade II, 170 grade III) between 2009 and 2015. From CGGA, 178 patients with LGG received adjuvant RT alone (40 grade II, 13 grade III) or CRT (41 grade II, 84 grade III) between 2004 and 2016. The genes comprising GRS are involved in MAP kinase activity, T cell chemotaxis, and cell cycle transition: MAP3K15, MAPK10, CCL3, CCL4, and ADAMTS1. High GRS, defined as having a GRS in the top third, was significantly associated with poorer outcomes independent of age, sex, glioma histology, WHO grade, IDH mutation, 1p/19q co-deletion, and chemotherapy status in the discovery cohort (PFS HR 1.61, 95% CI 1.10-2.36, P=0.014; OS HR 2.74, 95% CI 1.68-4.47, P<0.001). These findings were replicated in the internal validation cohort (PFS HR 1.58, 95% CI 1.05-2.37, P=0.027; OS HR 1.84, 95% CI 1.13-3.00, P=0.015) and the CGGA external validation cohort (OS HR 1.72, 95% CI 1.27-2.34, P<0.001). Association between GRS and outcomes was observed only among patients who underwent RT, in both TCGA and CGGA.

CONCLUSION

This study successfully identified an expression signature of five genes that stratified outcomes among LGG patients who received adjuvant RT, with two rounds of validation leveraging independent genomics databases. Expression levels of the highlighted genes were associated with PFS and OS only among patients whose treatment included RT, but not among those with omission of RT, suggesting that expression of these genes may be predictive of radiation treatment response. While additional prospective studies are warranted, interrogation of these genes may be considered in the multidisciplinary management of LGG.

Identification of Prognostic Genes in Gliomas Based on Increased Microenvironment Stiffness

With a median survival time of 15 months, glioblastoma multiforme is one of the most aggressive primary brain cancers. The crucial roles played by the extracellular matrix (ECM) stiffness in glioma progression and treatment resistance have been reported in numerous studies. However, the association between ECM-stiffness-regulated genes and the prognosis of glioma patients remains to be explored. Thus, using bioinformatics analysis, we first identified 180 stiffness-dependent genes from an RNA-Seq dataset, and then evaluated their prognosis in The Cancer Genome Atlas (TCGA) glioma dataset. Our results showed that 11 stiffness-dependent genes common between low- and high-grade gliomas were prognostic. After validation using the Chinese Glioma Genome Atlas (CGGA) database, we further identified four stiffness-dependent prognostic genes: FN1, ITGA5, OSMR, and NGFR. In addition to high-grade glioma, overexpression of the four-gene signature also showed poor prognosis in low-grade glioma patients. Moreover, our analysis confirmed that the expression levels of stiffness-dependent prognostic genes in high-grade glioma were significantly higher than in low-grade glioma, suggesting that these genes were associated with glioma progression. Based on a pathophysiology-inspired approach, our findings illuminate the link between ECM stiffness and the prognosis of glioma patients and suggest a signature of four stiffness-dependent genes as potential therapeutic targets.

Effect of Thrombin on the Metabolism and Function of Murine Macrophages

Macrophages are plastic and heterogeneous immune cells that adapt pro- or anti-inflammatory phenotypes upon exposure to different stimuli. Even though there has been evidence supporting a crosstalk between coagulation and innate immunity, the way in which protein components of the hemostasis pathway influence macrophages remains unclear. We investigated the effect of thrombin on macrophage polarization. On the basis of gene expression and cytokine secretion, our results suggest that polarization with thrombin induces an anti-inflammatory, M2-like phenotype. In functional studies, thrombin polarization promoted oxLDL phagocytosis by macrophages, and conditioned medium from the same cells increased endothelial cell proliferation. There were, however, clear differences between the classical M2a polarization and the effects of thrombin on gene expression. Finally, the deletion and inactivation of secreted modular Ca²⁺-binding protein 1 (SMOC1) attenuated phagocytosis by thrombin-stimulated macrophages, a phenomenon revered by the addition of recombinant SMOC1. Manipulation of SMOC1 levels also had a pronounced impact on the expression of TGF-β-signaling-related genes. Taken together, our results show that thrombin induces an anti-inflammatory macrophage phenotype with similarities as well as differences to the classical alternatively activated M2 polarization states, highlighting the importance of tissue levels of SMOC1 in modifying thrombin-induced macrophage polarization.

A Novel Defined Pyroptosis-Related Gene Signature for Predicting Prognosis and Treatment of Glioma

Pyroptosis, a form of programmed cell death, that plays a significant role in the occurrence and progression of tumors, has been frequently investigated recently. However, the prognostic significance and therapeutic value of pyroptosis in glioma remain undetermined. In this research, we revealed the relationship of pyroptosis-related genes to glioma by analyzing whole transcriptome data from The Cancer Genome Atlas (TCGA) dataset serving as the training set and the Chinese Glioma Genome Atlas (CGGA) dataset serving as the validation set. We identified two subgroups of glioma patients with disparate prognostic and clinical features by performing consensus clustering analysis on nineteen pyroptosis-related genes that were differentially expressed between glioma and normal brain tissues. We further derived a risk signature, using eleven pyroptosis-related genes, that was demonstrated to be an independent prognostic factor for glioma. Furthermore, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to implement functional analysis of our gene set, and the results were closely related to immune and inflammatory responses in accordance with the characteristics of pyroptosis. Moreover, Gene Set Enrichment Analysis (GSEA) results showed that that the high-risk group exhibited enriched characteristics of malignant tumors in accordance with its poor prognosis. Next, we analyzed different immune cell infiltration between the two risk groups using ssGSEA. Finally, CASP1 was identified as a core gene, so we subsequently selected an inhibitor targeting CASP1 and simulated molecular docking. In addition, the inhibitory effect of belnacasan on glioma was verified at the cellular level. In conclusion, pyroptosis-related genes are of great significance for performing prognostic stratification and developing treatment strategies for glioma.

PODNL1 Methylation Serves as a Prognostic Biomarker and Associates with Immune Cell Infiltration and Immune Checkpoint Blockade Response in Lower-Grade Glioma

Lower-grade glioma (LGG) is a diffuse infiltrative tumor of the central nervous system, which lacks targeted therapy. We investigated the role of Podocan-like 1 (PODNL1) methylation in LGG clinical outcomes using the TCGA-LGG transcriptomics dataset. We identified four PODNL1 CpG sites, cg07425555, cg26969888, cg18547299, and cg24354933, which were associated with unfavorable overall survival (OS) and disease-free survival (DFS) in univariate and multivariate analysis after adjusting for age, gender, tumor-grade, and IDH1-mutation. In multivariate analysis, the OS and DFS hazard ratios ranged from 0.44 to 0.58 (p < 0.001) and 0.62 to 0.72 (p < 0.001), respectively, for the four PODNL1 CpGs. Enrichment analysis of differential gene and protein expression and analysis of 24 infiltrating immune cell types showed significantly increased infiltration in LGGs and its histological subtypes with low-methylation levels of the PODNL1 CpGs. High PODNL1 expression and low-methylation subgroups of the PODNL1 CpG sites were associated with significantly increased PD-L1, PD-1, and CTLA4 expressions. PODNL1 methylation may thus be a potential indicator of immune checkpoint blockade response, and serve as a biomarker for determining prognosis and immune subtypes in LGG.

Research Progress of Gliomas in Machine Learning

In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.