The prognostic and immunological role of MCM3 in pan-cancer and validation of prognosis in a clinical lower-grade glioma cohort.

Background: Previous studies have shown that MCM3 plays a key role in initiating DNA replication. However, the mechanism of MCM3 function in most cancers is still unknown. The aim of our study was to explore the expression, prognostic role, and immunological characteristics of MCM3 across cancers. Methods: We explored the expression pattern of MCM3 across cancers. We subsequently explored the prognostic value of MCM3 expression by using univariate Cox regression analysis. Spearman correlation analysis was performed to determine the correlations between MCM3 and immune-related characteristics, mismatching repair (MMR) signatures, RNA modulator genes, cancer stemness, programmed cell death (PCD) gene expression, tumour mutation burden (TMB), microsatellite instability (MSI), and neoantigen levels. The role of MCM3 in predicting the response to immune checkpoint blockade (ICB) therapy was further evaluated in four immunotherapy cohorts. Single-cell data from CancerSEA were analysed to assess the biological functions associated with MCM3 in 14 cancers. The clinical correlation and independent prognostic significance of MCM3 were further analysed in the TCGA and CGGA lower-grade glioma (LGG) cohorts, and a prognostic nomogram was constructed. Immunohistochemistry in a clinical cohort was utilized to validate the prognostic utility of MCM3 expression in LGG. Results: MCM3 expression was upregulated in most tumours and strongly associated with patient outcomes in many cancers. Correlation analyses demonstrated that MCM3 expression was closely linked to immune cell infiltration, immune checkpoints, MMR genes, RNA modulator genes, cancer stemness, PCD genes and the TMB in most tumours. There was an obvious difference in outcomes between patients with high MCM3 expression and those with low MCM3 expression in the 4 ICB treatment cohorts. Single-cell analysis indicated that MCM3 was mainly linked to the cell cycle, DNA damage and DNA repair. The expression of MCM3 was associated with the clinical features of LGG patients and was an independent prognostic indicator. Finally, the prognostic significance of MCM3 in LGG was validated in a clinical cohort. Conclusion: Our study suggested that MCM3 can be used as a potential prognostic marker for cancers and may be associated with tumour immunity. In addition, MCM3 is a promising predictor of immunotherapy responses.

Effectiveness and Safety of Ultra-low-dose Fluorescein Sodium-Guided Resection of Malignant Glioma.

BACKGROUND: This study analyzed the effectiveness and safety of ultra-low dose fluorescein sodium (FL)-guided malignant glioma resection and its potential to predict the pathological characteristics of glioma. METHODS: Sixty patients who underwent FL-guided glioma resection were randomly divided into test (1 mg/kg) and control (5 mg/kg) groups. A retrospective analysis included 30 patients with gliomas who did not undergo FL-guided surgery; these patients were included as a blank control group. Surgical outcomes, Karnofsky performance scores (KPS), and progression-free survival (PFS) at 6 months postoperatively were compared between the 3 groups. The sensitivity and specificity of FL and the relationship between the intensity of FL and Glial fibrillary acidic protein (GFAP) or Ki-67 expression were compared. RESULTS: The total tumor resection rates in the test, control, and blank control groups were 90% (27/30), 86.7% (26/30), and 60% (18/30), respectively. There were significant differences (P < 0.05) in the extent of resection, KPS, and PFS at 6 months after surgery between the test and control groups and the blank control group; however, no significant differences (P > 0.05) were observed between the test and control groups. The intensity of FL and the Ki67 positivity rate (P < 0.05) were directly proportional, but this relationship was not observed with GFAP. CONCLUSIONS: Ultra-low-dose FL-guided resection of malignant gliomas is safe and effective. The Ki67 positivity rate was directly proportional to the intensity of FL, indicating its potential to predict gliomas during pathological examination.

Juglone induces ferroptosis in glioblastoma cells by inhibiting the Nrf2-GPX4 axis through the phosphorylation of p38MAPK.

BACKGROUND: Ferroptosis, a non-apoptotic form of cell death induced by accumulation of free iron ions and lipid peroxidation, its importance for cancer treatment is gradually being recognized. Research on the anti-cancer mechanism of juglone is accumulating. However, the specific mechanism by which it directs glioblastoma (GBM) to death is unknown. METHODS: We used in vitro and in vivo experiments to explore the anti-GBM effect generated by juglone through the ferroptosis pathway. RESULTS: Juglone mainly causes cell death by inducing ferroptosis. Mechanistically, juglone can significantly activate the phosphorylation of p38MAPK. According to transcriptome sequencing and protein interaction analysis, the Nrf2-GPX4 signaling pathway is identified as the primary pathway through which juglone mediates ferroptosis. In vitro and in vivo experiments further verified that juglone induces the ferroptosis of GBM by activating the phosphorylation of p38MAPK and negatively regulating the Nrf2-GPX4 signaling pathway. CONCLUSION: Juglone induces ferroptosis and inhibits the growth of GBM by targeting the Nrf2/Gpx4 signaling pathway and thus holds promise as a novel ferroptosis inducer or anti-GBM drug.

Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma.

Background: Programmed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma. Methods: We first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining. Results: Most DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses. Conclusion: The DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.

Identification and validation of prognostic genes and immune landscape signatures based on a fatty acid oxidation­related risk score model in glioma.

Fatty acid oxidation (FAO) plays a crucial role in glioma metabolism and its interaction with the immune microenvironment. The aim of the present study was to investigate the relationship between FAO-related genes and glioma by constructing gene clusters using a glioma cohort. A total of 287 differentially expressed genes related to FAO were identified and the top 50 genes were selected based on their P-values. Subsequently, patients were classified into two distinct gene subtypes (A and B) based on these genes. Scores for each patient were calculated using the 50 genes and the patients were divided into the high and low-score groups accordingly. Patients in subtype B exhibited higher tumor grades and poor prognostic factors such as older age and worse survival rates. The high-score subgroup had unfavorable indicators, including isocitrate dehydrogenase 1 wild-type, high tumor grade and 1p19q non-codeleted, while immune checkpoint expression was generally higher in the high-score subgroup. The constructed scoring model was validated using an external dataset, and the tissue inhibitor of metalloproteinase 1 gene was identified through protein interaction analysis, suggesting its potential involvement in glioma malignancy and promotion of glioblastoma proliferation. In conclusion, FAO-related genes may contribute to tumor development through immune mechanisms and the present study may provide novel insights for potential therapeutic strategies in glioma treatment.

Identification of an m6A RNA Methylation Regulator Risk Score Model for Prediction of Clinical Prognosis in Astrocytoma.

Astrocytoma (AS) is the most ubiquitous primary malignancy of the central nervous system (CNS). The vital involvement of the N6-methyladenosine (m6A) RNA modification in the growth of multiple human tumors is known. This study entailed probing m6A regulators with AS prognosis to construct a risk prediction model (RS) for potential clinical use. A total of 579 AS patients' (of the Chinese Glioma Genome Atlas,CGGA) data and the expression of 12 published m6A-related genes were included in this study. Cox and selection operator (LASSO) regression analyses for independent prognostic factors and multifactor Cox analysis established an R.S. model to predict the AS patient prognosis. This was subject to verification employing 331 samples from the TCGA data set followed by gene ontology and pathway enrichment study with gene set enrichment analysis (GSEA). The R.S. constructed with three m6A genes inclusive of WTAP, RBM15, and YTHDF2 emerged as independent prognostic factors in AS patients with vital involvement in the advancement and development of the malignancy. In a nutshell, this work reported an m6A-related gene risk model to predict the prognosis of AS patients to pave the way for discerning diagnostic and prognostic biomarkers. Further corroboration employing relevant wet-lab assays of this model is warranted.

Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis.

Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics method was employed to identify potential biomarkers and LGG molecular mechanisms. Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development.