Mutational profiling of low-grade gliomas identifies prognosis and immunotherapy-related biomarkers and tumour immune microenvironment characteristics

Potential value of expression of receptor accessory protein 4 for evaluating the prognosis of lower-grade glioma patients

BACKGROUND

REEP4 is involved in the regulation of the biological process of mitosis. Lower grade glioma (LGG), as a malignant tumor, is accompanied by abnormalities in mitosis, but there have been no reports of REEP4 so far.

METHODS

We collected transcriptome data, DNA methylation data and the clinical characteristics of thousands of patients with LGG. Various big data analysis methods and molecular biology experiments were employed to reveal the impact of REEP4 on the pathological process of LGG.

RESULTS

It was found that the expression of REEP4 was significantly elevated and negatively regulated by its methylation site. Therefore, both the high expression of REEP4 and low methylation state of cg16311504 showed that the patients are correlated with lower patient survival rate. In addition, high REEP4 expression participates in the regulation of various cancer-related cellular signaling pathways, such as the cell cycle, MAPK signaling pathway, NOD-like receptor signaling pathway, etc. More importantly, the level of immune cell infiltration significantly increased in the high expression group of REEP4 in the LGG tumor microenvironment and REEP4 has a high positive correlation with PD-L1 and other immune checkpoints.

CONCLUSIONS

In brief, this study is the first to introduce REEP4 in malignant tumors, which can be used as an independent risk factor that participates in the malignant process of LGG. More importantly, REEP4 has the potential to become a new star in the field of anti-tumor treatment.

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics

Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.

Integrated analysis of inflammatory response subtype-related signature to predict clinical outcomes, immune status and drug targets in lower-grade glioma

Background: The inflammatory response in the tumor immune microenvironment has implications for the progression and prognosis in glioma. However, few inflammatory response-related biomarkers for lower-grade glioma (LGG) prognosis and immune infiltration have been identified. We aimed to construct and identify the prognostic value of an inflammatory response-related signature, immune infiltration, and drug targets for LGG.

Methods: The transcriptomic and clinical data of LGG samples and 200 inflammatory response genes were obtained from public databases. The LGG samples were separated into two inflammatory response-related subtypes based on differentially expressed inflammatory response genes between LGG and normal brain tissue. Next, inflammatory response-related genes (IRRGs) were determined through a difference analysis between the aforementioned two subtypes. An inflammatory response-related prognostic model was constructed using IRRGs by using univariate Cox regression and Lasso regression analyses and validated in an external database (CGGA database). ssGSEA and ESTIMATE algorithms were conducted to evaluate immune infiltration. Additionally, we performed integrated analyses to investigate the correlation between the prognostic signature and N 6-methyladenosine mRNA status, stemness index, and drug sensitivity. We finally selected MSR1 from the prognostic signature for further experimental validation.

Results: A total of nine IRRGs were identified to construct the prognostic signature for LGG. LGG patients in the high-risk group presented significantly reduced overall survival than those in the low-risk group. An ROC analysis confirmed the predictive power of the prognostic model. Multivariate analyses identified the risk score as an independent predictor for the overall survival. ssGSEA revealed that the immune status was definitely disparate between two risk subgroups, and immune checkpoints such as PD-1, PD-L1, and CTLA4 were significantly expressed higher in the high-risk group. The risk score was strongly correlated with tumor stemness and m6A. The expression levels of the genes in the signature were significantly associated with the sensitivity of tumor cells to anti-tumor drugs. Finally, the knockdown of MSR1 suppressed LGG cell migration, invasion, epithelial–mesenchymal transition, and proliferation.

Conclusion: The study constructed a novel signature composed of nine IRRGs to predict the prognosis, potential drug targets, and impact immune infiltration status in LGG, which hold promise for screening prognostic biomarkers and guiding immunotherapy for LGG.

Construction of a Risk Model to Predict the Prognosis and Immunotherapy of Low-Grade Glioma Ground on 7 Ferroptosis-Related Genes

Purpose

Ferroptosis is closely associated with tumors. The purpose of this study was to investigate the correlation between ferroptosis and prognosis of low grade glioma (LGG) via construction and verification of a risk model.

Patients and Methods

The data of LGG were downloaded from public databases. Through LASSO analysis of characteristic genes, a gene signature was constructed. Patients into were divided two groups based on risk score. Subsequently, survival, clinical phenotype, functional enrichment, immune cell infiltration and somatic mutation analysis were performed. In addition, whether ferroptosis-related genes (FRGs) signature can predict the patient's response to anti-PD-1/PD-L1 immunotherapy was also investigated.

Results

FRGs signature had strong prognostic assessment ability, and high risk score was associated with poor overall survival (OS) of LGG. The high risk score group had higher degree of immune cell infiltration, stronger stromal activity, higher immune score, and high expression of immune checkpoint. In low risk score group anti-PD-1/PD-L1 immunotherapy has significant therapeutic advantages and clinical response. Genes and frequency of somatic mutations and clinical phenotypes in the high and low risk score groups were significantly different.

Conclusion

A prognostic model based on 7 FRGs can be used to predict the prognosis and immunotherapeutic response of LGG.

Identification and Validation of a Gene Signature for Lower-Grade Gliomas Based on Pyroptosis-Related Genes to Predict Survival and Response to Immune Checkpoint Inhibitors

Pyroptosis plays a critical role in the immune response to immune checkpoint inhibitors (ICIs) by mediating the tumor immune microenvironment. However, the impact of pyroptosis-related biomarkers on the prognosis and efficacy of ICIs in patients with lower-grade gliomas (LGGs) is unclear. An unsupervised clustering analysis identified pyroptosis-related subtypes (PRSs) based on the expression profile of 47 pyroptosis-related genes in The Cancer Genome Atlas-LGG cohort. A PRS gene signature was established using univariate Cox regression, random survival forest, least absolute shrinkage and selection operator, and stepwise multivariable Cox regression analyses. The predictive power of this signature was validated in the Chinese Glioma Genome Atlas database. We also investigated the differences between high- and low-risk groups in terms of the tumor immune microenvironment, tumor mutation, and response to target therapy and ICIs. The PRS gene signature comprised eight PRS genes, which independently predicted the prognosis of LGG patients. High-risk patients had a worse overall survival than did the low-risk patients. The high-risk group also displayed a higher proportion of M1 macrophages and CD8⁺ T cells and higher immune scores, tumor mutational burden, immunophenoscore, IMmuno-PREdictive Score, MHC I association immune score, and T cell-inflamed gene expression profile scores, but lower suppressor cells scores, and were more suitable candidates for ICI treatment. Higher risk scores were more frequent in patients who responded to ICIs using data from the ImmuCellAI website. The presently established PRS gene signature can be validated in melanoma patients treated with real ICI treatment. This signature is valuable in predicting prognosis and ICI treatment of LGG patients, pending further prospective verification.

Artificial Intelligence Algorithm-Based Intraoperative Magnetic Resonance Navigation for Glioma Resection

The study aimed to analyze the application value of artificial intelligence algorithm-based intraoperative magnetic resonance imaging (iMRI) in neurosurgical glioma resection. 108 patients with glioma in a hospital were selected and divided into the experimental group (intraoperative magnetic resonance assisted glioma resection) and the control group (conventional surgical experience resection), with 54 patients in each group. After the resection, the tumor resection rate, NIHSS (National Institute of Health Stroke Scale) score, Karnofsky score, and postoperative intracranial infection were calculated in the two groups. The results revealed that the average tumor resection rate in the experimental group was significantly higher than that in the control group (P < 0.05). There was no significant difference in Karnofsky score before and after the operation in the experimental group (P > 0.05). There was no significant difference in NIHSS score between the experimental group and the control group after resection (P > 0.05). The number of patients with postoperative neurological deficits in the experimental group was smaller than that in the control group. In addition, there was no significant difference in infection rates between the two groups after glioma resection (P > 0.05). In summary, intraoperative magnetic resonance navigation on the basis of a segmentation dictionary learning algorithm has great clinical value in neurosurgical glioma resection. It can maximize the removal of tumors and ensure the integrity of neurological function while avoiding an increased risk of postoperative infection, which is of great significance for the treatment of glioma.

Mutational profiling of low-grade gliomas identifies prognosis and immunotherapy-related biomarkers and tumour immune microenvironment characteristics

Low-grade glioma (LGG) is a heterogeneous tumour with the median survival rate less than 10 years. Therefore, it is urgent to develop efficient immunotherapy strategies of LGG. In this study, we analysed mutation profiles based on the data of 510 LGG patients from the Cancer Genome Atlas (TCGA) database and investigated the prognostic value of mutated genes and evaluate their immune infiltration. Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to indicate the characteristics of gliomas that respond to immune checkpoint blockade (ICB) therapy. Univariate and multivariate cox regression analysis was performed to identify indicators to construct the nomogram model. 485 (95.47%) of 508 LGG samples showed gene mutation, and 9 mutated genes were significantly related to overall survival (OS), among which 6 mutated genes were significantly correlated with OS between mutation and wildtypes. Immune infiltration and immune score analyses revealed that these six mutated genes were significantly associated with tumour immune microenvironment in LGG. The response of LGG with different characteristics to ICB was evaluated by TIDE algorithm. Finally, CIC gene was screened through both univariate and multivariate Cox regression analyses, and the nomogram model was established to determine the potential prognostic value of CIC in LGG. Our study provides comprehensive analysis of mutated genes in LGG, supporting modulation of mutated genes in the management of LGG.