Excel spreadsheets showing: (S1a) the differential expression analysis between mesenchymal vs proneural tumors; (S1b) the differential expression analysis between classical vs mesenchymal tumors; (S1c) the differential expression analysis between classical vs proneural tumors; (S1d) classification of tumor samples based on the RNA-Seq results into the three TCGA molecular subtypes (according to the GlioVis algorithms KNN, SVM, and ssGSEA) and classification according to the IGS clusters; (S1e) immunohistochemical results of samples classified in the three TCGA molecular subtypes (according to the GlioVis algorithms KNN, SVM, and ssGSEA) and IGS clusters; and (S1f) results of the gene fusion analysis in the tumor samples, the molecular subtype of the tumor (according to the GlioVis algorithms KNN, SVM, and ssGSEA), and the tumor IGS clusters

BackgroundFerroptosis is closely associated with cancer and is of great importance in the immune evasion of cancer. However, the relationship between ferroptosis and glioma is unclear.MethodsWe downloaded the expression profiles and clinical data of glioma from the GlioVis database and obtained the expression profiles of ferroptosis genes. A ferroptosis-related gene signature was developed for the prognosis of gliomas.ResultsWe screened out prognostic ferroptosis genes, named ferroptosis-related genes, by the Cox regression method. Based on these genes, we used unsupervised clustering to obtain two different clusters; the principal component analysis algorithm was applied to determine the gene score of each patient, and then all the patients were classified into two subgroups. Results showed that there exist obvious differences in survival between different clusters and different gene score subgroups. The prognostic model constructed by the 25 ferroptosis-related genes was then evaluated to predict the clinicopathological features of immune activity in gliomas.ConclusionThe ferroptosis-related genes play an important role in the malignant process of gliomas, potentially contributing to the development of prognostic stratification and treatment strategies.

Glioma is regarded as a prevalent form of cancer that affects the Central Nervous System (CNS), with an aggressive growth pattern and a low clinical cure rate. Despite the advancement of the treatment strategy of surgical resection, chemoradiotherapy and immunotherapy in the last decade, the clinical outcome is still grim, which is ascribed to the low immunogenicity and tumor microenvironment (TME) of glioma. The multifunctional molecule, called ceruloplasmin (CP) is involved in iron metabolism. Its expression pattern, prognostic significance, and association with the immune cells in gliomas have not been thoroughly investigated. Studies using a variety of databases, including Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and Gliovis, showed that the mRNA and protein expression levels of CP in patients suffering from glioma increased significantly with an increasing glioma grade. Kaplan-Meier (KM) curves and statistical tests highlighted a significant reduction in survival time of patients with elevated CP expression levels. According to Cox regression analysis, CP can be utilized as a stand-alone predictive biomarker in patients suffering from glioma. A significant association between CP expression and numerous immune-related pathways was found after analyzing the data using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Tumor Immune Estimation Resource (TIMER) and CIBERSORT analyses indicated a substantial correlation between the CP expression and infiltration of immunocytes in the TME. Additionally, immune checkpoints and CP expression in gliomas showed a favorable correlation. According to these results, patients with glioma have better prognoses and levels of tumor immune cell infiltration when their CP expression is low. As a result, CP could be used as a probable therapeutic target for gliomas and potentially anticipate the effectiveness of immunotherapy.

SPR. Supplemental material for Fig. 4. Primary data in GlioVis used to generate the survival curves demonstrating that elevated SPR mRNA expression correlates with poor glioma patient survival. (CSV 26 kb)

The most prominent RNA modification – N6-methyladenosine (m6A) – affects gene regulation and cancer progression. The extent and effect of m6A on long non-coding RNAs (lncRNAs) is, however, still not clear. The most established method for m6A detection is methylated RNA immunoprecipitation and sequencing (MeRIP-seq). However, Oxford Nanopore Technologies recently developed direct RNA-seq (dRNA-seq) method, allowing m6A identification at higher resolution and in its native form. We performed whole transcriptome sequencing of the glioblastoma cell line U87-MG with both MeRIP-seq and dRNA-seq. For MeRIP-seq, m6A peaks were identified using nf-core/chipseq, and for dRNA-seq – EpiNano pipeline. MeRIP-seq analysis revealed 5086 lncRNAs transcripts, while dRNA-seq identified 336 lncRNAs transcripts from which 556 and 198 were found to be m6A modified, respectively. While 24 lncRNAs with m6A overlapped between two methods. Gliovis database analysis revealed that the expression of the major part of identified overlapping lncRNAs was associated with glioma grade or patient survival prognosis. We found that the frequency of m6A occurrence in lncRNAs varied more than 9-fold throughout the provided list of 24 modified lncRNAs. The highest m6A frequency was detected in MIR1915HG, THAP9-AS1, MALAT1, NORAD1, and NEAT1 (49–88nt), while MIR99AHG, SNHG3, LOXL1-AS1, ILF3-DT showed the lowest m6A frequency (445–261nt). Taken together, (1) we provide a high accuracy list of 24 m6A modified lncRNAs of U87-MG cells; (2) we conclude that MeRIP-seq is more suitable for an initial m6A screening study, due to its higher lncRNA coverage, whereas dRNA-seq is most useful when more in-depth analysis of m6A quantity and precise location is of interest. Abbreviations: (dRNA-seq) direct RNA-seq, (GBM) glioblastoma, (LGG) low-grade glioma, (lncRNAs) long non-coding RNAs, (m6A) N6-methyladenosine, (MeRIP-seq) methylated RNA immunoprecipitation and sequencing, (ncRNA) non-coding RNA, (ONT) Oxford Nanopore Technologi; Lietuvos Mokslo Taryba