Immune Infiltration-Related Signature Predicts Risk Stratification and Immunotherapy Efficacy in Grade II and III Gliomas

Upregulation of HAS2 promotes glioma cell proliferation and chemoresistance via c-myc

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant human brain tumor. Although comprehensive therapies, including chemotherapy and radiotherapy following surgery, have shown promise in prolonging survival, the prognosis for GBM patients remains poor, with an overall survival rate of only 14.6 months. Chemoresistance is a major obstacle to successful treatment and contributes to relapse and poor survival rates in glioma patients. Therefore, there is an urgent need for novel strategies to overcome chemoresistance and improve treatment outcomes for human glioma patients. Recent studies have shown that the tumor microenvironment plays a key role in chemoresistance. Our study demonstrates that upregulation of HAS2 and subsequent hyaluronan secretion promotes glioma cell proliferation, invasion, and chemoresistance in vitro and in vivo through the c-myc pathway. Targeting HAS2 sensitizes glioma cells to chemotherapeutic agents. Additionally, we found that hypoxia-inducible factor HIF1α regulates HAS2 expression. Together, our findings provide insights into the dysregulation of HAS2 and its role in chemoresistance and suggest potential therapeutic strategies for GBM.

Integrated pan-cancer and scRNA-seq analyses identify a prognostic coagulation-related gene signature associated with tumor microenvironment in lower-grade glioma

Cancer-associated thrombosis is a significant complication in cancer patients, leading to increased morbidity and mortality. The expression of coagulation/fibrinolysis genes, termed the "coagulome", plays a critical role in this process. Using the single-sample gene set enrichment analysis (ssGSEA), we identified seven cancer types with significantly activated coagulation pathways, focusing on lower-grade glioma (LGG) and stomach adenocarcinoma due to their predictive value for overall survival. Through 1000 iterations of the Least Absolute Shrinkage and Selection Operator (LASSO), we selected prognostic genes and constructed effective Cox regression models, particularly for LGG. Incorporating clinical characteristics, we constructed a nomogram for LGG, achieving an impressive area under the curve (AUCs) of 0.79, 0.82, and 0.81 at 1, 3, and 5 years in the test dataset, indicating strong potential for clinical application. Functional enrichment analysis between high-risk and low-risk LGG groups revealed significant enrichment of genes involved in the inflammatory response, interferon-gamma response, and epithelial-mesenchymal transition pathways. Combined with CIBERSORT and single-cell RNA sequencing analysis of LGG, our results demonstrated that the interplay between coagulation and the tumor microenvironment, particularly involving gliomas and myeloid cells, significantly influences tumor progression and patient outcomes.

A novel ARHGAP family gene signature for survival prediction in glioma patients

ARHGAP family genes are often used as glioma oncogenic factors, and their mechanism of action remains unexplained. Our research entailed a thorough examination of the immune microenvironment and enrichment pathways across various glioma subtypes. A distinctive 6-gene signature was developed employing the CGGA cohort, leading to insights into the disparities in clinical characteristics, mutation patterns, and immune cell infiltration among distinct risk categories. Additionally, a unique nomogram was established, grounded on ARHGAPs, with DCA curves illustrating the model's prospective clinical utility in guiding therapeutic strategies. Emphasizing the role of ARHGAP30, integral to our model, its impact on glioma severity and the credibility of our risk assessment model were substantiated through RT-qPCR, Western blot analysis, and cellular functional assays. We identified 6 ARHGAP family genes associated with glioma prognosis. Analysis using the Kaplan-Meier method indicated a correlation between elevated risk levels and adverse outcomes in glioma patients. The risk score, linked with tumour staging and IDH mutation status, emerged as an independent factor predicting prognosis. Patients in the high-risk category exhibited increased immune cell infiltration, enhanced tumour mutational burden, more pronounced expression of immune checkpoint genes, and a better response to ICB therapy. A nomogram, integrating the risk score with the pathological features of glioma patients, was developed. DCA analysis and cellular studies confirmed the model's potential to improve clinical treatment outcomes for patients. A novel ARHGAP family gene signature reveals the prognosis of glioma.

Construction and validation of a risk model based on the key SNARE proteins to predict the prognosis and immune microenvironment of gliomas

Background

Synaptic transmission between neurons and glioma cells can promote glioma progression. The soluble N-ethylmaleimide-sensitive fusion factor attachment protein receptors (SNARE) play a key role in synaptic functions. We aimed to construct and validate a novel model based on the SNARE proteins to predict the prognosis and immune microenvironment of glioma.

Methods

Differential expression analysis and COX regression analysis were used to identify key SRGs in glioma datasets, and we constructed a prognostic risk model based on the key SRGs. The prognostic value and predictive performance of the model were assessed in The Cancer Genome Atlas (TCGA) and Chinese glioma Genome Atlas (CGGA) datasets. Functional enrichment analysis and immune-related evaluation were employed to reveal the association of risk scores with tumor progression and microenvironment. A prognostic nomogram containing the risk score was established and assessed by calibration curves and time-dependent receiver operating characteristic curves. We verified the changes of the key SRGs in glioma specimens and cells by real-time quantitative PCR and Western blot analyses.

Results

Vesicle-associated membrane protein 2 (VAMP2) and vesicle-associated membrane protein 5 (VAMP5) were identified as two SRGs affecting the prognoses of glioma patients. High-risk patients characterized by higher VAMP5 and lower VAMP2 expression had a worse prognosis. Higher risk scores were associated with older age, higher tumor grades, IDH wild-type, and 1p19q non-codeletion. The SRGs risk model showed an excellent predictive performance in predicting the prognosis in TCGA and CGGA datasets. Differentially expressed genes between low- and high-risk groups were mainly enriched in the pathways related to immune infiltration, tumor metastasis, and neuronal activity. Immune score, stromal score, estimate score, tumor mutational burden, and expression of checkpoint genes were positively correlated with risk scores. The nomogram containing the risk score showed good performance in predicting the prognosis of glioma. Low VAMP2 and high VAMP5 were found in different grades of glioma specimens and cell lines.

Conclusion

We constructed and validated a novel risk model based on the expression of VAMP2 and VAMP5 by bioinformatics analysis and experimental confirmation. This model might be helpful for clinically predicting the prognosis and response to immunotherapy of glioma patients.

Development of a Hallmark Pathway-Related Gene Signature Associated with Immune Response for Lower Grade Gliomas

Although some biomarkers have been used to predict prognosis of lower-grade gliomas (LGGs), a pathway-related signature associated with immune response has not been developed. A key signaling pathway was determined according to the lowest adjusted p value among 50 hallmark pathways. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox analyses were performed to construct a pathway-related gene signature. Somatic mutation, drug sensitivity and prediction of immunotherapy analyses were conducted to reveal the value of this signature in targeted therapies. In this study, an allograft rejection (AR) pathway was considered as a crucial signaling pathway, and we constructed an AR-related five-gene signature, which can independently predict the prognosis of LGGs. High-AR LGG patients had higher tumor mutation burden (TMB), Immunophenscore (IPS), IMmuno-PREdictive Score (IMPRES), T cell-inflamed gene expression profile (GEP) score and MHC I association immunoscore (MIAS) than low-AR patients. Most importantly, our signature can be validated in four immunotherapy cohorts. Furthermore, IC50 values of the six classic chemotherapeutic drugs were significantly elevated in the low-AR group compared with the high-AR group. This signature might be regarded as an underlying biomarker in predicting prognosis for LGGs, possibly providing more therapeutic strategies for future clinical research.

Basement-Membrane-Related Gene Signature Predicts Prognosis in WHO Grade II/III Gliomas

Gliomas that are classified as grade II or grade III lesions by the World Health Organization (WHO) are highly aggressive, and some may develop into glioblastomas within a short period, thus portending the conferral of a poor prognosis for patients. Previous studies have implicated basement membrane (BM)-related genes in glioma development. In this study, we constructed a prognostic model for WHO grade II/III gliomas in accordance with the risk scores of BM-related genes. Differentially expressed genes (DEGs) in the glioma samples relative to normal samples were screened from the GEO database, and five prognostically relevant BM-related genes, including NELL2, UNC5A, TNC, CSPG4, and SMOC1, were selected using Cox regression analyses for the risk score model. The median risk score was calculated, based on which high- and low-risk groups of patients were generated. The clinical information, pathological information, and risk group were combined to establish a prognostic nomogram. Both the nomogram and risk score model performed well in the independent CGGA cohort. Gene set enrichment analysis (GSEA) and immune profile, drug sensitivity, and tumor mutation burden (TMB) analyses were performed in the two risk groups. A significant enrichment of 'Autophagy-other', 'Collecting duct acid secretion', 'Glycosphingolipid biosynthesis-lacto and neolacto series', 'Valine, leucine, and isoleucine degradation', 'Vibrio cholerae infection', and other pathways were observed for patients with high risk. In addition, higher proportions of monocytes and resting CD4 memory T cells were observed in the low- and high-risk groups, respectively. In conclusion, the BM-related gene risk score model can guide the clinical management of WHO grade II and III gliomas.

A Bioinformatics-Based Analysis of an Anoikis-Related Gene Signature Predicts the Prognosis of Patients with Low-Grade Gliomas

Low-grade glioma (LGG) is a highly aggressive disease in the skull. On the other hand, anoikis, a specific form of cell death induced by the loss of cell contact with the extracellular matrix, plays a key role in cancer metastasis. In this study, anoikis-related genes (ANRGs) were used to identify LGG subtypes and to construct a prognostic model for LGG patients. In addition, we explored the immune microenvironment and enrichment pathways between different subtypes. We constructed an anoikis-related gene signature using the TCGA (The Cancer Genome Atlas) cohort and investigated the differences between different risk groups in clinical features, mutational landscape, immune cell infiltration (ICI), etc. Kaplan-Meier analysis showed that the characteristics of ANRGs in the high-risk group were associated with poor prognosis in LGG patients. The risk score was identified as an independent prognostic factor. The high-risk group had higher ICI, tumor mutation load (TMB), immune checkpoint gene expression, and therapeutic response to immune checkpoint blockers (ICB). Functional analysis showed that these high-risk and low-risk groups had different immune statuses and drug sensitivity. Risk scores were used together with LGG clinicopathological features to construct a nomogram, and Decision Curve Analysis (DCA) showed that the model could enable patients to benefit from clinical treatment strategies.

P-Rex1 Signaling Hub in Lower Grade Glioma Patients, Found by In Silico Data Mining, Correlates With Reduced Survival and Augmented Immune Tumor Microenvironment

Systematic analysis of tumor transcriptomes, combined with deep genome sequencing and detailed clinical assessment of hundreds of patients, constitutes a powerful strategy aimed to identify potential biomarkers and therapeutic targets to guide personalized treatments. Oncogenic signaling cascades are integrated by multidomain effector proteins such as P-Rex1, a guanine nucleotide exchange factor for the Rac GTPase (RacGEF), known to promote metastatic dissemination of cancer cells. We hypothesized that patients with high P-Rex1 expression and reduced survival might be characterized by a particular set of signaling proteins co-expressed with this effector of cell migration as a central component of a putative signaling hub indicative of poor prognosis. High P-Rex1 expression correlated with reduced survival of TCGA Lower Grade Glioma (LGG) patients. Thus, guided by PREX1 expression, we searched for signaling partners of this RacGEF by applying a systematic unbiased in silico data mining strategy. We identified 30 putative signaling partners that also correlated with reduced patient survival. These included GPCRs such as CXCR3, GPR82, FZD6, as well as MAP3K1, MAP2K3, NEK8, DYRK3 and RPS6KA3 kinases, and PTPN2 and PTPN22 phosphatases, among other transcripts of signaling proteins and phospho-substrates. This PREX1 signaling hub signature correlated with increased risk of shorter survival of LGG patients from independent datasets and coincided with immune and endothelial transcriptomic signatures, indicating that myeloid infiltration and tumor angiogenesis might contribute to worsen brain tumor pathology. In conclusion, P-Rex1 and its putative signaling partners in LGG are indicative of a signaling landscape of the tumor microenvironment that correlates with poor prognosis and might guide the characterization of signaling targets leading the eventual development of immunotherapeutic strategies.

A Novel CRISPR/Cas9 Screening Potential Index for Prognostic and Immunological Prediction in Low-Grade Glioma

Glioma is a malignancy with the highest mortality in central nervous system disorders. Here, we implemented the computational tools based on CRISPR/Cas9 to predict the clinical outcomes and biological characteristics of low-grade glioma (LGG). The transcriptional expression profiles and clinical phenotypes of LGG patients were retrieved from The Cancer Genome Atlas and Chinese Glioma Genome Atlas. The CERES algorithm was used to screen for LGG-lethal genes. Cox regression and random survival forest were adopted for survival-related gene selection. Nonnegative matrix factorization distinguished patients into different clusters. Single-sample gene set enrichment analysis was employed to create a novel CRISPR/Cas9 screening potential index (CCSPI), and patients were stratified into low- and high-CCSPI groups. Survival analysis, area under the curve values (AUCs), nomogram, and tumor microenvironment exploration were included for the model validation. A total of 20 essential genes in LGG were used to classify patients into two clusters and construct the CCSPI system. High-CCSPI patients were associated with a worse prognosis of both training and validation set (p < 0.0001) and higher immune fractions than low-CCSPI individuals. The CCSPI system had a promising performance with 1-, 3-, and 5-year AUCs of 0.816, 0.779, 0.724, respectively, and the C-index of the nomogram model reached 0.743 (95% CI = 0.725–0.760). Immune-infiltrating cells and immune checkpoints such as PD-1/PD-L1 and POLD3 were positively associated with CCSPI. In conclusion, the CCSPI had prognostic value in LGG, and the model will deepen our cognition of the interaction between the CNS and immune system in different LGG subtypes.