Multiomics and machine learning-based analysis of pancancer pseudouridine modifications.

Pseudouridine widely affects the stability and function of RNA. However, our knowledge of pseudouridine properties in tumors is incomplete. We systematically analyzed pseudouridine synthases (PUSs) expression, genomic aberrations, and prognostic features in 10907 samples from 33 tumors. We found that the pseudouridine-associated pathway was abnormal in tumors and affected patient prognosis. Dysregulation of the PUSs expression pattern may arise from copy number variation (CNV) mutations and aberrant DNA methylation. Functional enrichment analyses determined that the PUSs expression was closely associated with the MYC, E2F, and MTORC1 signaling pathways. In addition, PUSs are involved in the remodeling of the tumor microenvironment (TME) in solid tumors, such as kidney and lung cancers. Particularly in lung cancer, increased expression of PUSs is accompanied by increased immune checkpoint expression and Treg infiltration. The best signature model based on more than 112 machine learning combinations had good prognostic ability in ACC, DLBC, GBM, KICH, MESO, THYM, TGCT, and PRAD tumors, and is expected to guide immunotherapy for 19 tumor types. The model was also effective in identifying patients with tumors amenable to etoposide, camptothecin, cisplatin, or bexarotene treatment. In conclusion, our work highlights the dysregulated features of PUSs and their role in the TME and patient prognosis, providing an initial molecular basis for future exploration of pseudouridine. Studies targeting pseudouridine are expected to lead to the development of potential diagnostic strategies and the evaluation and improvement of antitumor therapies.

Integration of Single-Cell and Bulk RNA-seq Data to Identify the Cancer-Associated Fibroblast Subtypes and Risk Model in Glioma.

Cancer-associated fibroblasts (CAFs) are an important component of the stroma. Studies showed that CAFs were pivotally in glioma progression which have long been considered a promising therapeutic target. Therefore, the identification of prognostic CAF markers might facilitate the development of novel diagnostic and therapeutic approaches. A total of 1333 glioma samples were obtained from the TCGA and CGGA datasets. The EPIC, MCP-counter, and xCell algorithms were used to evaluate the relative proportion of CAFs in glioma. CAF markers were identified by the single-cell RNA-seq datasets (GSE141383) from the Tumor Immune Single-Cell Hub database. Unsupervised consensus clustering was used to divide the glioma patients into different distinct subgroups. The least absolute shrinkage and selection operator regression model was utilized to establish a CAF-related signature (CRS). Finally, the prognostic CAF markers were further validated in clinical specimens by RT‒qPCR. Combined single-cell RNA-seq analysis and differential expression analysis of samples with high and low proportions of CAFs revealed 23 prognostic CAF markers. By using unsupervised consensus clustering, glioma patients were divided into two distinct subtypes. Subsequently, based on 18 differentially expressed prognostic CAF markers between the two CAF subtypes, we developed and validated a new CRS model (including PCOLCE, TIMP1, and CLIC1). The nomogram and calibration curves indicated that the CRS was an accurate prognostic marker for glioma. In addition, patients in the high-CRS score group had higher immune infiltration and tumor mutation burden levels. Moreover, the CRS score had the potential to predict the response to immune checkpoint blockade (ICB) therapy and chemotherapy. Finally, the expression profiles of three CAF markers were verified by RT‒qPCR. In general, our study classified glioma patients into distinct subgroups based on CAF markers, which will facilitate the development of individualized therapy. We also provided insights into the role of the CRS in predicting the response to ICB and chemotherapy in glioma patients.

Neutrophil extracellular traps mediated by platelet microvesicles promote thrombosis and brain injury in acute ischemic stroke.

AIMS: Neutrophil extracellular traps (NETs) have been implicated in thrombotic diseases. There is no definitive explanation for how NETs form during acute ischemic strokes (AIS). The purpose of our study was to investigate the potential mechanism and role of NETs formation in the AIS process. METHODS: As well as 45 healthy subjects, 45 patients with AIS had ELISA tests performed to detect NET markers. Expression of high-mobility group box 1 (HMGB1) on platelet microvesicles (PMVs) was analyzed by flow cytometry in healthy subjects and AIS patients' blood samples. We established middle cerebral artery occlusion (MCAO) mice model to elucidate the interaction between PMPs and NETs. RESULTS: A significant elevation in NET markers was found in patient plasma in AIS patients, and neutrophils generated more NETs from patients' neutrophils. HMGB1 expression was upregulated on PMVs from AIS patients and induced NET formation. NETs enhanced Procoagulant activity (PCA) through tissue factor and via platelet activation. Targeting lactadherin in genetical and in pharmacology could regulate the formation of NETs in MCAO model. CONCLUSIONS: NETs mediated by PMVs derived HMGB1 exacerbate thrombosis and brain injury in AIS. Video Abstract.

MMBERT: a unified framework for biomedical named entity recognition.

Named entity recognition (NER) is an important task in natural language processing (NLP). In recent years, NER has attracted much attention in the biomedical field. However, due to the lack of biomedical named entity identification datasets, the complexity and rarity of biomedical named entities and so on, biomedical NER is more difficult than general domain NER. So in this paper, we propose a framework (MMBERT) based on Transformer to solve the problems above. To address the scarcity of biomedical named entity recognition datasets, we introduce ERNIE-Health, a new Chinese language representation model pre-trained on large-scale biomedical text corpora. Because of the complexity and rarity of biomedical named entities, we use the Bert and CW-LSTM structures to get the joint feature vector of word pairs relations. In addition, we design multi-granularity 2D convolution to refine the relationship and representation between word pairs. Finally, we design a convolutional neural network (CNN) structure and a co-predictor to improve the model's generalization capability and prediction accuracy. We have conducted extensive experiments on three benchmark datasets, and the experimental results show that our model achieves the best results compared with several baseline models in the experiment.

Using mixed reality technique combines multimodal imaging signatures to adjuvant glioma photodynamic therapy.

Background: Photodynamic therapy (PDT) promotes significant tumor regression and extends the lifetime of patients. The actual operation of PDT often relies on the subjective judgment of experienced neurosurgeons. Patients can benefit more from precisely targeting PDT's key operating zones. Methods: We used magnetic resonance imaging scans and created 3D digital models of patient anatomy. Multiple images are aligned and merged in STL format. Neurosurgeons use HoloLens to import reconstructions and assist in PDT execution. Also, immunohistochemistry was used to explore the association of hyperperfusion sites in PDT of glioma with patient survival. Results: We constructed satisfactory 3D visualization of glioma models and accurately localized the hyperperfused areas of the tumor. Tumor tissue taken in these areas was rich in CD31, VEGFA and EGFR that were associated with poor prognosis in glioma patients. We report the first study using MR technology combined with PDT in the treatment of glioma. Based on this model, neurosurgeons can focus PDT on the hyperperfused area of the glioma. A direct benefit was expected for the patients in this treatment. Conclusion: Using the Mixed Reality technique combines multimodal imaging signatures to adjuvant glioma PDT can better exploit the vascular sealing effect of PDT on glioma.

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing.

BACKGROUND: Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM. METHODS: We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes. RESULTS: We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors. CONCLUSIONS: By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

Expression of intra-tumoral necrosis-associated cytokine pattern correlated with prognosis and immune status in glioma.

Intra-tumoral necrosis (ITN) is reported to be an independent prognostic factor in glioma. However, knowledge of ITN is mainly limited to pseudopalisadwe, while its other aspects were neglected. Therefore, a deeper understanding of ITN could be valuable for understanding its exact role in glioma. The only reliable ITN model was time-dependently achieved with the GL261 syngeneic mouse model. The ITN-associated expression pattern was enriched from RNA sequencing. TCGA glioma samples were clustered into a high-expression group (HEG) and a low-expression group (LEG) based on their pattern and their association with prognosis, clinical status, immune status, and therapeutic responsiveness were compared. Mouse glioma with ITN demonstrated invasive histology. Cytokine signaling was significantly enriched in necrotic mouse glioma compared with non-necrotic glioma tissues. Nine pro-inflammatory (IL6, PPBP, IL1A, TNFSF11, CXCL11, CXCL9, CXCL10, CXCL3, and CCL8) and two anti-inflammatory cytokine (IL1RN and IL10) genes were found to be related to ITN-associated cytokine patterns. Comparative analysis showed that HEG had a significantly shorter survival time, five differentially distributed clinical statuses, more infiltrated immune cells, greater expression of immune checkpoints, and better therapeutic responsiveness than LEG. In conclusion, the ITN-associated cytokine pattern is characteristically expressed in glioma with ITN and might indicate necrosis missed in histology diagnosis. Its expression pattern could predict the prognosis, tumor grade, immune status, and therapeutic responsiveness of glioma patients.

Systematic analysis of the necroptosis index in pan-cancer and classification in discriminating the prognosis and immunotherapy responses of 1716 glioma patients.

Necroptosis is a programmed form of necrotic cell death that serves as a host gatekeeper for defense against invasion by certain pathogens. Previous studies have uncovered the essential role of necroptosis in tumor progression and implied the potential for novel therapies targeting necroptosis. However, no comprehensive analysis of multi-omics data has been conducted to better understand the relationship between necroptosis and tumor. We developed the necroptosis index (NI) to uncover the effect of necroptosis in most cancers. NI not only correlated with clinical characteristics of multiple tumors, but also could influence drug sensitivity in glioma. Based on necroptosis-related differentially expressed genes, the consensus clustering was used to classify glioma patients into two NI subgroups. Then, we revealed NI subgroup I were more sensitive to immunotherapy, particularly anti-PD1 therapy. This new NI-based classification may have prospective predictive factors for prognosis and guide physicians in prioritizing immunotherapy for potential responders.

5-ALA-PDT induced ferroptosis in keloid fibroblasts via ROS, accompanied by downregulation of xCT, GPX4.

Keloids display many cancerous properties, including uncontrolled and invasive growth, high rates of recurrence as well as similar bioenergetics. 5-aminolevulinic acid-based photodynamic therapy (5-ALA-PDT) is an effective treatment that performs cytotoxic effects by producing reactive oxygen species (ROS), which is linked to lipid peroxidation and ferroptosis. Herein, we explored underlying mechanisms of 5-ALA-PDT against keloids. We identified that 5-ALA-PDT led to elevated levels of ROS and lipid peroxidation in keloid fibroblasts, accompanied by downregulation of xCT and GPX4, which are associated with anti-oxidation effects and ferroptosis inhibition. These results may indicate that 5-ALA-PDT treatment increases ROS while inhibiting xCT and GPX4, thus promoting lipid peroxidation to induce ferroptosis in keloid fibroblasts.

Rapid progression of subcutaneous glioblastoma: A case report and literature review.

Extra-neural spread of glioblastoma (GBM) is extremely rare. We report a case of postoperative intracranial GBM spreading to the subcutaneous tissue via the channel of craniotomy defect in a 73-year-old woman. Radiological images and histopathology indicate that the tumor microenvironment of the subcutaneous tumor is clearly different from the intracranial tumor. We also model the invasion of GBM cells through the dura-skull defect in mouse. The retrospective analysis of GBM with scalp metastases suggests that craniectomy is a direct cause of subcutaneous metastasis in patients with GBM. Imaging examinations of other sites for systemic screening is also recommended to look for metastases outside the brain when GBM invades the scalp or metastasizes to it.

Integrated genomic, transcriptomic, and epigenetic analyses identify a leukotriene synthesis-related M2 macrophage gene signature that predicts prognosis and treatment vulnerability in gliomas.

The pathological implications of tumor-associated macrophages in the glioma microenvironment have been highlighted, while there lacks a gene signature to characterize the functional status and clinical implications of these cells. Comprehensive bioinformatics approaches were employed to develop an M2 macrophage-associated gene signature at bulk-tumor and single-cell levels and explore immunological and metabolic features. Consequently, the PI3K pathway and fatty acid metabolism were correlated with the M2 fraction. Further distilling the pathway members resulted in a leukotriene synthesis-related gene signature (Macro index), including PIK3R5, PIK3R6, ALOX5, ALOX5AP, and ALOX15B, that was primarily expressed by monocytes/macrophages. Increased Macro index predicted IL13-induced macrophages, and was associated with T-cell dysfunction at both transcriptional and epigenetic levels and predicted an unfavorable outcome. Besides, the Macro index was proportional with PAI1 at the protein level, with high levels of the latter suggesting a decreased progression-free interval of glioblastoma. Notably, the monocytes/macrophages in the glioma environment contribute to the expression of immune checkpoints and the Macro index predicts glioma responsiveness to anti-PD1 treatment. Together, our study proposed a leukotriene synthesis-related M2 macrophage gene signature, which may provide insights into the role of these cells in the glioma microenvironment and facilitate individually tailored therapeutic strategies for the disease.

Machine learning-based screening of an epithelial-mesenchymal transition-related long non-coding RNA signature reveals lower-grade glioma prognosis and the tumor microenvironment and predicts antitumor therapy response.

Epithelial-mesenchymal transition (EMT) confers high invasive and migratory capacity to cancer cells, which limits the effectiveness of tumor therapy. Long non-coding RNAs (lncRNAs) can regulate the dynamic process of EMT at different levels through various complex regulatory networks. We aimed to comprehensively analyze and screen EMT-related lncRNAs to characterize lower-grade glioma (LGG) tumor biology and provide new ideas for current therapeutic approaches. We retrieved 1065 LGG samples from the Cancer Genome Atlas and Chinese Glioma Genome Atlas by machine learning algorithms, identified three hub lncRNAs including CRNDE, LINC00665, and NEAT1, and established an EMT-related lncRNA signature (EMTrLS). This novel signature had strong prognostic value and potential clinical significance. EMTrLS described LGG genomic alterations and clinical features including gene mutations, tumor mutational burden, World Health Organization (WHO) grade, IDH status, and 1p/19q status. Notably, stratified analysis revealed activation of malignancy-related and metabolic pathways in the EMTrLS-high cohort. Moreover, the population with increased EMTrLS scores had increased cells with immune killing function. However, this antitumor immune function may be suppressed by increased Tregs and macrophages. Meanwhile, the relatively high expression of immune checkpoints explained the immunosuppressive state of patients with high EMTrLS scores. Importantly, we validated this result by quantifying the course of antitumor immunity. In particular, EMTrLS stratification enabled assessment of the responsiveness of LGG to chemotherapeutic drug efficacy and PD1 blockade. In conclusion, our findings complement the foundation of molecular studies of LGG, provide valuable insight into our understanding of EMT-related lncRNAs, and offer new strategies for LGG therapy.

Neutrophil extracellular traps: A novel therapeutic target for intracranial hemorrhage.

Neutrophil extracellular traps (NETs) are known to play a role in various diseases affecting coagulation. As of now, it is unclear whether NETs are present in hematoma samples collected from patients who have suffered an intracranial hemorrhage (ICH). The objective of this was to determine whether NETs are present in circulation and hematoma samples from ICH patients and to evaluate the procoagulant activity (PCA) of NETs during the ICH process. The expression of NET markers in samples from 78 ICH patients and 35 healthy donners was detected by ELISA and flow cytometry. Immunostaining for neutrophil markers (neutrophil CD66b) and NET markers (citrullinated histone H3 [H3Cit] and extracellular DNA) was performed on hematoma samples obtained from ICH patients undergoing intracranial hematoma evacuation. Our findings suggest that plasma and hematoma samples from patients with ICH showed high levels of NETs. Furthermore, using DNase I to target NETs enhanced ex vivo hematoma lysis. In conclusion, NETs play an important role in the ICH process and may be a novel therapeutic target for treatment of ICH patients.

A cuproptosis-related lncRNAs signature for prognosis, chemotherapy, and immune checkpoint blockade therapy of low-grade glioma.

Cuproptosis is a new type of cell death that is associated with mitochondrial respiration of the tricarboxylic acid cycle. Previous studies showed that long non-coding RNAs (lncRNAs) regulated low-grade glioma (LGG) progression. However, the potential applications of cuproptosis-related lncRNAs (CRLs) in LGG were not explored. A comprehensive analysis was performed in The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts. We first screened two distinct cuproptosis subtypes based on prognostic CRLs using consensus clustering. To facilitate individualized survival prediction in LGG, we constructed a prognostic signature (including CRNDE, HAR1A, and FAM181A-AS1) in the TCGA dataset. The prognostic signature exhibited excellent predictive ability and reliability, which was validated in the CGGA_325 and CGGA_693 datasets. Notably, patients in the high-risk group had increased immune cell infiltration and expression of immune checkpoints, which indicated that they may benefit more from immune checkpoint blockade (ICB) therapy. Finally, the prognostic signature screened the population with sensitivity to chemotherapy and ICB therapy. In summary, this study initially explored the mechanism of CRLs in LGG and provides some insights into chemotherapy and ICB therapy of LGG.

IGFBPs were associated with stemness, inflammation, extracellular matrix remodeling and poor prognosis of low-grade glioma.

Background: The IGFBP family of insulin-like growth factor binding proteins has important biological functions in the organism. However, the role of the IGFBP family in low-grade glioma (LGG) has not been fully explored. Methods: We validated the clinical value of the IGFBP family using RNA-seq and clinical data of LGG in the TCGA and constructed an IGFBPScore using LASSO-regression analysis for prognosis prediction, subtype determination, and treatment sensitivity determination. Subsequently, we explored the role of the IGFBP family in the development of LGG using PanCanAtlas data. Results: Our results suggest that most IGFBP family members were aberrantly expressed and were strongly associated with poor prognosis in LGG. By constructing an IGFBPScore representing the IGFBP family, we found that tumor samples with a high IGFBPScore had a glioblastoma-like mutation pattern characterized by IDH1wt, EGFRmut, PTENmut, and NF1mut with hypo-methylation and glioma stem cell (GSC) diversity. In contrast, the low IGFBPScore group was characterized by IDH1mut accompanied by TP53mut, CICmut, and ATRXmut, and had hyper-methylation status as well as the GSC restriction. Additionally, the high-IGFBPScore group had a high inflammation phenotype with increased immune antigenicity and increased infiltration of immune molecules and cells, as well as a high extracellular matrix phenotype and enhanced multiple metabolic pathways compared with the immune-quiet phenotype of the low-IGFBPScore group, which was strongly associated with poor prognosis. Conclusion: Our study provides a summary analysis and a theoretical basis for the biological role and clinical value of the IGFBP family in LGG, providing an important therapeutic target for LGG.

Gut microbiota: A new target for T2DM prevention and treatment.

Type 2 diabetes mellitus (T2DM), one of the fastest growing metabolic diseases, has been characterized by metabolic disorders including hyperglycemia, hyperlipidemia and insulin resistance (IR). In recent years, T2DM has become the fastest growing metabolic disease in the world. Studies have indicated that patients with T2DM are often associated with intestinal flora disorders and dysfunction involving multiple organs. Metabolites of the intestinal flora, such as bile acids (BAs), short-chain fatty acids (SCFAs) and amino acids (AAs)may influence to some extent the decreased insulin sensitivity associated with T2DM dysfunction and regulate metabolic as well as immune homeostasis. In this paper, we review the changes in the gut flora in T2DM and the mechanisms by which the gut microbiota modulates metabolites affecting T2DM, which may provide a basis for the early identification of T2DM-susceptible individuals and guide targeted interventions. Finally, we also highlight gut microecological therapeutic strategies focused on shaping the gut flora to inform the improvement of T2DM progression.

Neutrophil extracellular traps promote thrombogenicity in cerebral venous sinus thrombosis.

BACKGROUND: Neutrophil extracellular traps (NETs) contribute to the creation of a coagulation state in various diseases. Currently, it is not clear whether NETs are present in the thrombi and plasma of patients with cerebral venous sinus thrombosis (CVST). This study aimed to investigate the presence of NETs in thrombi and blood samples from CVST patients and the procoagulant activity (PCA) of NETs during the progression of CVST. RESULTS: Thrombi obtained from CVST patients undergoing thrombectomy were examined by immunochemistry using neutrophil elastase (NE), CD66b and citrullinated histone H3(citH3). The presence of NET markers in samples from 37 CVST patients and 32 healthy people was evaluated by ELISA. NET-producing neutrophils and neutrophil-platelet (PLT) aggregates were examined in samples obtained from CVST patients and healthy people by flow cytometry. The TAT complex in plasma sample from each group was detected by ELISA to evaluate the procoagulant activity of NETs in CVST patients. Neutrophils from healthy subjects were treated with PLT-rich plasma in the presence of anti-PF4 antibodies or an autophagy inhibitor and analyzed by flow cytometry and confocal microscopy. After treatment with NETs, the expression of von Willebrand factor (VWF), tissue factor (TF) and CD31 in human brain microvascular endothelial cells (HBMECs) was measured by confocal microscopy and western blotting. Our results showed that NETs were abundant in the plasma and thrombi from CVST patients. Platelet factor 4 (PF4) from CVST PLTs induced NET generation through autophagy. NETs could induce PCA by modulating TF and phosphatidylserine (PS) in CVST. NETs also disrupted the endothelial barrier and transformed ECs into a procoagulant phenotype to exacerbate thrombogenicity. CONCLUSIONS: NET generation was mediated by PF4 from PLTs through autophagy and contribute to thrombosis in CVST patients.

Identification of EMT-Related Genes and Prognostic Signature With Significant Implications on Biological Properties and Oncology Treatment of Lower Grade Gliomas.

The epithelial-mesenchymal transition (EMT) is an important process that drives progression, metastasis, and oncology treatment resistance in cancers. Also, the adjacent non-tumor tissue may affect the biological properties of cancers and have potential prognostic implications. Our study aimed to identify EMT-related genes in LGG samples, explore their impact on the biological properties of lower grade gliomas (LGG) through the multi-omics analysis, and reveal the potential mechanism by which adjacent non-tumor tissue participated in the malignant progression of LGG. Based on the 121 differentially expressed EMT-related genes between normal samples from the GTEx database and LGG samples in the TCGA cohort, we identified two subtypes and constructed EMTsig. Because of the genetic, epigenetic, and transcriptomic heterogeneity, malignant features including clinical traits, molecular traits, metabolism, anti-tumor immunity, and stemness features were different between samples with C1 and C2. In addition, EMTsig could also quantify the EMT levels, variation in prognosis, and oncology treatment sensitivity of LGG patients. Therefore, EMTsig could assist us in developing objective diagnostic tools and in optimizing therapeutic strategies for LGG patients. Notably, with the GSVA, we found that adjacent non-tumor tissue might participate in the progression, metastasis, and formation of the tumor microenvironment in LGG. Therefore, the potential prognostic implications of adjacent non-tumor tissue should be considered when performing clinical interventions for LGG patients. Overall, our study investigated and validated the effects of EMT-related genes on the biological properties from multiple perspectives, and provided new insights into the function of adjacent non-tumor tissue in the malignant progression of LGG.

Characterization of the Ferroptosis-Related Genes for Prognosis and Immune Infiltration in Low-Grade Glioma.

Background: Although ferroptosis has been validated to play a crucial role in some types of tumors, the influence of ferroptosis-related genes (FRGs) on the immune microenvironment in low-grade glioma (LGG) remains unclear. In this research, we screen the FRGs to assess the prognosis value and immune microenvironment in LGG, to provide reliable diagnosis and treatment evidence for the clinic. Methods: A total of 1,239 patients of LGG samples were selected for subsequent analyses from The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and the Repository of Molecular Brain Neoplasia Data datasets. Univariate Cox regression analysis was used to screen for prognostic FRGs. Consensus clustering was utilized to determine ferroptosis subtypes of LGG patients. Next, the prognostic model was constructed based on differentially expressed FRGs and validation in the validating datasets. The immune microenvironment, biological pathway, and hypoxia score were explored by single-sample gene set enrichment analysis. The potential response of chemotherapy and immune checkpoint blockade therapy was also estimated. In addition, the correlation between the risk score and autophagy-related genes was examined by the Pearson correlation coefficient. Results: A total of three ferroptosis subtypes were identified by consensus clustering for prognostic FRGs which exhibited different outcomes, clinicopathological characteristics, and immune microenvironment. Afterward, a prognostic model that performed great predictive ability based on nine prognostic FRGs has been constructed and validated. Moreover, the prognostic model had the potential to screen the sensitivity to chemotherapy and immunotherapy in LGG patients. Finally, we also found that the prognostic model has a great connection to autophagy and hypoxia. Conclusion: We developed a ferroptosis-related prognostic model which strongly linked to diagnosis, treatment, prognosis, and recurrence of LGG. This study also reveals the connection between ferroptosis and tumor immune microenvironment.

Ferroptosis-Related Gene Contributes to Immunity, Stemness and Predicts Prognosis in Glioblastoma Multiforme.

Ferroptosis, a recently discovered regulated programmed cell death, is associated with tumorigenesis and progression in glioblastoma. Based on widely recognized ferroptosis-related genes (FRGs), the regulation of ferroptosis patterns and corresponding characteristics of immune infiltration of 516 GBM samples with GSE13041, TCGA-GBM, and CGGA-325 were comprehensively analyzed. Here, we revealed the expression, mutations, and CNV of FRGs in GBM. We identified three distinct regulation patterns of ferroptosis and found the hub genes of immunity and stemness among DEGs in three patterns. A prognostic model was constructed based on five FRGs and verified at the mRNA and protein level. The risk score can not only predict the prognosis but also the degree of immune infiltration and ICB responsiveness by functional annotation. The overall assessment of FRGs in GBM patients will guide the direction of improved research and develop new prognostic prediction tools.