Self-Assembled nanoparticles of natural bioactive molecules enhance the delivery and efficacy of paclitaxel in glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common primary malignant tumor in the central nervous system. Paclitaxel (PTX) is a well-established and highly effective anti-cancer drug for peripheral solid tumors. However, the application of PTX in GBM is hindered by several limitations, including poor water solubility, restricted entry across the blood-brain barrier (BBB), and enhanced excretion by efflux transporters. P-glycoprotein (P-gp) is a crucial efflux transporter that is abundantly present in cerebral vascular endothelial cells and GBM cells. It plays a significant role in the exocytosis of PTX within tumor tissues. METHODS: Recently, we have developed a novel technique for creating self-assembled nanoparticles utilizing a range of natural bioactive molecules. These nanoparticles can encapsulate insoluble drugs and effectively cross the BBB. In additional, we revealed that certain nanoparticles have the potential to act as P-gp inhibitors, thereby reducing the excretion of PTX. In this study, we conducted a screening of bioactive molecular nanoparticles to identify those that effectively inhibit the function of P-gp transporters. RESULTS: Among the candidates, we identified ursolic acid nanoparticles (UA NPs) as the P-gp inhibitors. Furthermore, we prepared co-assembled UA NPs embedded with paclitaxel, referred to as UA-PTX NPs. Our results demonstrate that UA-PTX NPs can enhance the blood concentration of PTX, facilitate its entry into the BBB, and inhibit the function of P-gp, resulting in a decrease in the excretion of PTX. This discovery effectively addressed the above three issues associated with the use of PTX in glioma treatment. CONCLUSIONS: UA-PTX NPs demonstrate strong anti-tumor effects and show great potential for treating GBM.

TMEM2 induces epithelial-mesenchymal transition and promotes resistance to temozolomide in GBM cells.

Glioblastoma multiforme (GBM) is the most common intracranial malignant tumor and is notorious for its poor prognosis. An important element in the short overall survival of GBM patients is the lack of understanding the pathogenesis and progression of tumor and deficiency biomarkers that can be used for early diagnosis and therapeutic sensitivity monitoring. Studies have shown that transmembrane protein 2 (TMEM2) is participated in tumorigenesis of various human tumors, including rectal and breast cancers. Although Qiuyi Jiang et al. have reported that TMEM2 combined with IDH1/2 and 1p19q can predict the survival time of glioma patients based on bioinformatics, its expression and biological role of glioma remain unclear. In our study, we investigated the effect of TMEM2 expression level on glioma malignancy in public datasets and an independent internal dataset. We revealed TEMM2 expression was higher in GBM tissues than in non-tumor brain tissues (NBT). Moreover, the increase in TMEM2 expression level was closely related to tumor malignancy. The survival analysis showed that TMEM2 high expression reduces survival time in all glioma patients, including GBM and LGG patients. Subsequent experiments demonstrated that knockdown TMEM2 inhibited proliferation of GBM cells. In addition, we analyzed TMEM2 mRNA levels in different GBM subtypes, and demonstrated that TMEM2 expression was upregulated in mesenchymal subtype. Meanwhile, bioinformatics analysis and transwell assay indicated that knockdown TMEM2 suppressed epithelial-mesenchymal transition (EMT) in GBM. Importantly, Kaplan-Meier analysis demonstrated that TMEM2 high expression reduced the treatment response to TMZ in GBM patients. Knockdown of TMEM2 alone did not reduce apoptosis GBM cells, but significant apoptotic cells were observed in the group treated with additional TMZ. These studies may contribute to improving the accuracy of early diagnosis and evaluating the effectiveness of TMZ treatment in GBM patients.

Identification and validation of a novel prognostic signature based on mitochondria and oxidative stress related genes for glioblastoma.

BACKGROUND: Mitochondria represent a major source of reactive oxygen species (ROS) in cells, and the direct increase in ROS content is the primary cause of oxidative stress, which plays an important role in tumor proliferation, invasion, angiogenesis, and treatment. However, the relationship between mitochondrial oxidative stress-related genes and glioblastoma (GBM) remains unclear. This study aimed to investigate the value of mitochondria and oxidative stress-related genes in the prognosis and therapeutic targets of GBM. METHODS: We retrieved mitochondria and oxidative stress-related genes from several public databases. The LASSO regression and Cox analyses were utilized to build a risk model and the ROC curve was used to assess its performance. Then, we analyzed the correlation between the model and immunity and mutation. Furthermore, CCK8 and EdU assays were utilized to verify the proliferative capacity of GBM cells and flow cytometry was used to analyze apoptosis rates. Finally, the JC-1 assay and ATP levels were utilized to detect mitochondrial function, and the intracellular ROS levels were determined using MitoSOX and BODIPY 581/591 C11. RESULTS: 5 mitochondrial oxidative stress-related genes (CTSL, TXNRD2, NUDT1, STOX1, CYP2E1) were screened by differential expression analysis and Cox analysis and incorporated in a risk model which yielded a strong prediction accuracy (AUC value = 0.967). Furthermore, this model was strongly related to immune cell infiltration and mutation status and could identify potential targeted therapeutic drugs for GBM. Finally, we selected NUDT1 for further validation in vitro. The results showed that NUDT1 was elevated in GBM, and knockdown of NUDT1 inhibited the proliferation and induced apoptosis of GBM cells, while knockdown of NUDT1 damaged mitochondrial homeostasis and induced oxidative stress in GBM cells. CONCLUSION: Our study was the first to propose a prognostic model of mitochondria and oxidative stress-related genes, which provided potential therapeutic strategies for GBM patients.

TFR2 regulates ferroptosis and enhances temozolomide chemo-sensitization in gliomas.

Glioma is a common type of brain tumor with high incidence and mortality rates. Iron plays an important role in various physiological and pathological processes. Iron entry into the cell is promoted by binding the transferrin receptor 2 (TFR2) to the iron-transferrin complex. This study was designed to assess the association between TFR2 and ferroptosis in glioma. Lipid peroxidation levels in glioma cells were assessed by determination of lipid reactive oxygen species (ROS), glutathione content, and mitochondrial membrane potential. The effect of TFR2 on TMZ sensitivity was examined by cell viability assays, flow cytometry, and colony formation assays. We found that Low TFR2 expression predicted a better prognosis for glioma patients. And overexpression of TFR2 promoted the production of reactive oxygen species and lipid peroxidation in glioma cells, thereby further promoting ferroptosis. This could be reversed by the ferroptosis inhibitors Fer-1 and DFO (both inhibitors of ferroptosis). Moreover, TFR2 potentiated the cytotoxic effect of TMZ (temozolomide) via activating ferroptosis. In conclusion, we found that TFR2 induced ferroptosis and enhanced TMZ sensitivity in gliomas. Our findings might provide a new treatment strategy for glioma patients and improve their prognosis.

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas.

Gliomas are one of the most frequent types of nervous system tumours and have significant morbidity and mortality rates. As a result, it is critical to fully comprehend the molecular mechanism of glioma to predict prognosis and target gene therapy. The goal of this research was to discover the hub genes of glioma and investigate their prognostic and diagnostic usefulness. In this study, we collected mRNA expression profiles and clinical information from glioma patients in the TCGA, GTEx, GSE68848, and GSE4920 databases. WGCNA and differential expression analysis identified 170 DEGs in the collected datasets. GO and KEGG pathway analyses revealed that DEGs were mainly enriched in gliogenesis and extracellular matrix. LASSO was performed to construct prognostic signatures in the TCGA cohort, and 17 genes were used to build risk models and were validated in the CGGA database. The ROC curve confirmed the accuracy of the prognostic signature. Univariate and multivariate Cox regression analyses showed that all independent risk factors for glioma except gender. Next, we performed ssGSEA to demonstrate a high correlation between risk score and immunity. Subsequently, 7 hub genes were identified by the PPI network and found to have great drug targeting potential. Finally, RPL39, as one of the hub genes, was found to be closely related to the prognosis of glioma patients. Knockdown of RPL39 in vitro significantly inhibited the proliferation and migration of glioma cells, whereas overexpression of RPL39 had the opposite effect. And we found that knockdown of RPL39 inhibited the polarization and infiltration of M2 phenotype macrophages. In conclusion, our new prognosis-related model provides more potential therapeutic strategies for glioma patients.

5-Hydroxymethylcytosines in circulating cell-free DNA reveal a diagnostic biomarker for glioma.

Gliomas typically have unfavorable prognosis, due to late detection and interventions. However, effective biomarkers for early glioma diagnosis based on 5-hydroxymethylcytosines (5 hm C) in circulating cell-free DNA (cfDNA) are not currently available. 5 hm C profiles in GSE132118 set were subjected for establishment of diagnostic model using the LASSO (least absolute shrinkage and selection operator) algorithm. The 5 hm C-based models demonstrated great potency in differentiating healthy subjects from gliomas, with area under the curves (AUCs) > 0.91 in the training and validation sets. Moreover, the indicator performed well in combination with clinicopathological characteristics to differentiate glioblastomas (GBMs) from lower grade glioma (LGGs). Enrichment analysis on 5 hm C profiles displayed great correlation with glioma pathophysiology. The 5 hm C-derived biomarker might act as an effective and non-invasive measure in glioma screening.

IRF2-ferroptosis related gene is associated with prognosis and EMT in gliomas.

Ferroptosis is a new type of programmed cell death that has excellent anti-tumor potential in different tumors. However, the research on ferroptosis in glioma is still incomplete. In this study, we aimed to revealed the relationship between ferroptosis-related genes (FRGs) and glioma. We collected gene expression profiles of glioma patients from the TCGA and CGGA databases. All glioma samples were classified into five subtypes using the R software ConsensusClusterPlus. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between different subtypes and immune status and ferroptosis. Then co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA). A Gene Ontology (GO) analysis was conducted to analyze the potential biological functions of the genes in the modules. Finally, we identified 10 hub genes using the PPI network. The in vitro experiments were used to validate our predictions. We found that the expression level of IRF2 is positively correlated with the grade of glioma. The overexpression of IRF2 could protect glioma cells from ferroptosis and enhance the invasive and migratory abilities. Silence of IRF2 had the opposite effect. In conclusion, we demonstrated a novel ferroptosis-related signature for predicting prognosis, and IRF2 could be a potential biomarker for diagnosis and treatment in glioma.

MS4A6A is a new prognostic biomarker produced by macrophages in glioma patients.

MS4A6A has been recognized as being associated with aging and the onset of neurodegenerative disease. However, the mechanisms of MS4A6A in glioma biology and prognosis are ill-defined. Here, we show that MS4A6A is upregulated in glioma tissues, resulting in unfavorable clinical outcomes and poor responses to adjuvant chemotherapy. Multivariate Cox regression analysis suggested that MS4A6A expression can act as a strong and independent predictor for glioma outcomes (CGGA1: HR: 1.765, p < 0.001; CGGA2: HR: 2.626, p < 0.001; TCGA: HR: 1.415, p < 0.001; Rembrandt: HR: 1.809, p < 0.001; Gravendeel: HR: 1.613, p < 0.001). A protein-protein interaction (PPI) network revealed that MS4A6A might be coexpressed with CD68, CD163, and macrophage-specific signatures. Enrichment analysis showed the innate immune response and inflammatory response to be markedly enriched in the high MS4A6A expression group. Additionally, single-cell RNA sequencing (scRNA-seq) analysis revealed distinctive expression features for MS4A6A in macrophages in the glioma immune microenvironment (GIME). Immunofluorescence staining confirmed colocalization of CD68/MS4A6A and CD163/MS4A6A in macrophages. Correlation analysis revealed that MS4A6A expression is positively related to the tumor mutation burden (TMB) of glioma, displaying the high potential of applying MS4A6A to evaluate responsiveness to immunotherapy. Altogether, our research indicates that MS4A6A upregulation may be used as a promising and effective indicator for adjuvant therapy and prognosis assessment.

TGF-ß induces GBM mesenchymal transition through upregulation of CLDN4 and nuclear translocation to activate TNF-α/NF-κB signal pathway.

Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor. The unregulated expression of Claudin-4 (CLDN4) plays an important role in tumor progression. However, the biological role of CLDN4 in GBM is still unknown. This study aimed to determine whether CLDN4 mediates glioma malignant progression, if so, it would further explore the molecular mechanisms of carcinogenesis. Our results revealed that CLDN4 was significantly upregulated in glioma specimens and cells. The inhibition of CLND4 expression could inhibit mesenchymal transformation, cell invasion, cell migration and tumor growth in vitro and in vivo. Moreover, combined with in vitro analysis, we found that CLDN4 can modulate tumor necrosis factor-α (TNF-α) signal pathway. Meanwhile, we also validated that the transforming growth factor-ß (TGF-ß) signal pathway can upregulate the expression of CLDN4, and promote the invasion ability of GBM cells. Conversely, TGF-ß signal pathway inhibitor ITD-1 can downregulate the expression of CLDN4, and inhibit the invasion ability of GBM cells. Furthermore, we found that TGF-ß can promote the nuclear translocation of CLDN4. In summary, our findings indicated that the TGF-ß/CLDN4/TNF-α/NF-κB signal axis plays a key role in the biological progression of glioma. Disrupting the function of this signal axis may represent a new treatment strategy for patients with GBM.

Betulinic acid self-assembled nanoparticles for effective treatment of glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common and fatal primary tumor in the central nervous system (CNS). Due to the existence of blood-brain barrier (BBB), most therapeutics cannot efficiently reach tumors in the brain, and as a result, they are unable to be used for effective GBM treatment. Accumulating evidence shows that delivery of therapeutics in form of nanoparticles (NPs) may allow crossing the BBB for effective GBM treatment. METHODS: Betulinic acid NPs (BA NPs) were synthesized by the standard emulsion approach and characterized by electron microscopy and dynamic light scattering analysis. The resulting NPs were characterized for their anti-tumor effects by cell viability assay, EdU-DNA synthesis assay, cell cycle assay, mitochondrial membrane potential, and PI-FITC apoptosis assay. Further mechanistic studies were carried out through Western Blot and immunostaining analyses. Finally, we evaluated BA NPs in vivo for their pharmacokinetics and antitumor effects in intracranial xenograft GBM mouse models. RESULTS: BA NPs were successfully prepared and formed into rod shape. BA NPs could significantly suppress glioma cell proliferation, induce apoptosis, and arrest the cell cycle in the G0/G1 phase in vitro. Furthermore, BA NPs downregulated the Akt/NFκB-p65 signaling pathway in a concentration dependent manner. We found that the observed anti-tumor effect of BA NPs was dependent on the function of CB1/CB2 receptors. Moreover, in the intracranial GBM xenograft mouse models, BA NPs could effectively cross the BBB and greatly prolong the survival time of the mice. CONCLUSIONS: We successfully synthesized BA NPs, which could cross the BBB and demonstrated a strong anti-tumor effect. Therefore, BA NPs may potentially be used for effective treatment of GBM.

Development and Verification of Glutamatergic Synapse-Associated Prognosis Signature for Lower-Grade Gliomas.

Background: Lower-grade glioma (LGG) is the most common histology identified in gliomas, a heterogeneous tumor that may develop into high-grade malignant glioma that seriously shortens patient survival time. Recent studies reported that glutamatergic synapses might play an essential role in the progress of gliomas. However, the role of glutamatergic synapse-related biomarkers in LGG has not been systemically researched yet. Methods: The mRNA expression data of glioma and normal brain tissue were obtained from The Cancer Genome Atlas database and Genotype-Tissue Expression, respectively, and the Chinese Glioma Genome Atlas database was used as a validation set. Difference analysis was performed to evaluate the expression pattern of glutamatergic synapse-related genes (GSRGs) in LGG. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct the glutamatergic synapse-related risk signature (GSRS), and the risk score of each LGG sample was calculated based on the coefficients and expression value of selected GSRGs. Univariate and multivariate Cox regression analyses were used to investigate the prognostic value of risk score. Immunity profile and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the association between risk score and the characters of tumor microenvironment in LGG. Gene set variation analysis (GSVA) was performed to investigate the potential pathways related to GSRS. The HPA database and real-time PCR were used to identify the expression of hub genes identified in GSRS. Results: A total of 22 genes of 39 GSRGs were found differentially expressed among normal and LGG samples. Through the LASSO algorithm, 14-genes GSRS constructed were associated with the prognosis and clinicopathological features of patients with LGG. Furthermore, the risk score level was significantly positively correlated with the infiltrating level of immunosuppressive cells, including M2 macrophages and regulatory T cells. GSVA identified a series of cancer-related pathways related to GSRS, such as P13K-AKT and P53 pathways. Moreover, ATAD1, NLGN2, OXTR, and TNR, hub genes identified in GSRS, were considered as potential prognostic biomarkers in LGG. Conclusion: A 14-genes GSRS was constructed and verified in this study. We provided a novel insight into the role of GSRS in LGG through a series of bioinformatics methods.

Tumor Immune Microenvironment Landscape in Glioma Identifies a Prognostic and Immunotherapeutic Signature.

The tumor immune microenvironment (TIME) has been recognized to be associated with sensitivity to immunotherapy and patient prognosis. Recent research demonstrates that assessing the TIME patterns on large-scale samples will expand insights into TIME and will provide guidance to formulate immunotherapy strategies for tumors. However, until now, thorough research has not yet been reported on the immune infiltration landscape of glioma. Herein, the CIBERSORT algorithm was used to unveil the TIME landscape of 1,975 glioma observations. Three TIME subtypes were established, and the TIMEscore was calculated by least absolute shrinkage and selection operator (LASSO)-Cox analysis. The high TIMEscore was distinguished by an elevated tumor mutation burden (TMB) and activation of immune-related biological process, such as IL6-JAK-STAT3 signaling and interferon gamma (IFN-γ) response, which may demonstrate that the patients with high TIMEscore were more sensitive to immunotherapy. Multivariate analysis revealed that the TIMEscore could strongly and independently predict the prognosis of gliomas [Chinese Glioma Genome Atlas (CGGA) cohort: hazard ratio (HR): 2.134, p < 0.001; Gravendeel cohort: HR: 1.872, p < 0.001; Kamoun cohort: HR: 1.705, p < 0.001; The Cancer Genome Atlas (TCGA) cohort: HR: 2.033, p < 0.001; the combined cohort: HR: 1.626, p < 0.001], and survival advantage was evident among those who received chemotherapy. Finally, we validated the performance of the signature in human tissues from Wuhan University (WHU) dataset (HR: 15.090, p = 0.008). Our research suggested that the TIMEscore could be applied as an effective predictor for adjuvant therapy and prognosis assessment.

Downregulation of CYP2E1 is associated with poor prognosis and tumor progression of gliomas.

OBJECTIVE: To explore the role and possible regulatory mechanisms of CYP2E1 in gliomas. METHODS: RNA-seq data and corresponding clinical information of glioma patients were collected from The Cancer Genome Atlas and Chinese Glioma Genome Atlas, and mRNA data of normal brain tissues were obtained by the Genotype-Tissue Expression project. The Wilcoxon test was performed to analyze the correlation between CYP2E1 expression and glioma subtypes. Univariate and multivariate Cox proportional hazards regression, receiver operating characteristic curves, and Kaplan-Meier plots were used to evaluate the prognostic value of CYP2E1 in glioma. Functional enrichment analyses and immune infiltration analyses were performed to investigate the potential function of CYP2E1 in gliomas. Moreover, we investigated the miRNA and epigenetic mechanisms that regulate CYP2E1 expression. Finally, network pharmacology and molecular docking experiments were used to predict drugs that target CYP2E1. RESULTS: The downregulation of CYP2E1 expression may predict a poor prognosis for glioma patients. CYP2E1 expression decreased with increasing WHO grade (II-IV), and its level was correlated with clinical features, including age, 1p19q codeletion status, and IDH state in glioma tissues. Furthermore, CYP2E1 was involved in lipid metabolism and ferroptosis and related to the tumor immune microenvironment due to its strong correlation with the levels of infiltrating monocytes and Tregs. Moreover, variation in the total methylation level and copy number of CYP2E1 was moderately correlated with its mRNA expression (p < 0.05). CYP2E1 was predicted to be targeted by hsa-miR-527, whose expression was negatively related to CYP2E1 mRNA expression (p < 0.05). In addition, effective compounds that target CYP2E1, including 18beta-glycyrrhetinic acid, styrene, toluene, nicotine, m-xylene, p-xylene, and colchicine, were identified. CONCLUSION: The downregulation of CYP2E1, which affects lipid metabolism and the ferroptosis signaling pathway, promotes the progression of gliomas.

WAC, a novel GBM tumor suppressor, induces GBM cell apoptosis and promotes autophagy.

WAC is closely related to the occurrence and development of tumors. However, its role in human glioblastoma (GBM) and its potential regulatory mechanisms have not been investigated. This study demonstrated that WAC is downregulated in GBM, and its low expression predicts a poor prognosis. We investigated the effect of WAC on the proliferation of glioma cells through a CCK-8 assay, EdU incorporation, and cell formation. The effects of WAC on apoptosis and autophagy in glioma were determined by flow cytometry, TUNEL detection, immunofluorescence, q-PCR, WB, and scanning electron microscopy. We found that overexpression of WAC inhibited the proliferation of glioma cells, promoted apoptosis, and induced autophagy. Therefore, WAC is likely to play a role as a new regulatory molecule in glioma.

Identification of Tumor Antigens and Immune Landscape in Glioblastoma for mRNA Vaccine Development.

Background: Clinical benefits from standard therapies against glioblastoma (GBM) are limited in part due to the intrinsic radio- and chemo-resistance. As an essential part of tumor immunotherapy for adjunct, therapeutic tumor vaccines have been effective against multiple solid cancers, while their efficacy against GBM remains undefined. Therefore, this study aims to find the possible tumor antigens of GBM and identify the suitable population for cancer vaccination through immunophenotyping. Method: The genomic and responding clinical data of 169 GBM samples and five normal brain samples were obtained from The Cancer Genome Atlas (TCGA). The mRNA_seq data of 940 normal brain tissue were downloaded from Genotype-Tissue Expression (GTEx). Potential GBM mRNA antigens were screened out by differential expression, copy number variant (CNV), and mutation analysis. K-M survival and Cox analysis were carried out to investigate the prognostic association of potential tumor antigens. Tumor Immune Estimation Resource (TIMER) was used to explore the association between the antigens and tumor immune infiltrating cells (TIICs). Immunophenotyping of 169 samples was performed through consensus clustering based on the abundance of 22 kinds of immune cells. The characteristics of the tumor immune microenvironment (TIME) in each cluster were explored through single-sample gene set enrichment analysis based on 29 kinds of immune-related hallmarks and pathways. Weighted gene co-expression network analysis (WGCNA) was performed to cluster the genes related to immune subtypes. Finally, pathway enrichment analyses were performed to annotate the potential function of modules screened through WGCNA. Results: Two potential tumor antigens selected were significantly positively associated with the antigen-presenting immune cells (APCs) in GBM. Furthermore, the expression of antigens was verified at the protein level by Immunohistochemistry. Two robust immune subtypes, immune subtype 1 (IS1) and immune subtype 2 (IS2), representing immune status "immune inhibition" and "immune inflamed", respectively, had distinct clinical outcomes in GBM. Conclusion: ARPC1B and HK3 were potential mRNA antigens for developing GBM mRNA vaccination, and the patients in IS2 were considered the most suitable population for vaccination in GBM.

Identification of tumor antigens and immune subtypes in lower grade gliomas for mRNA vaccine development.

BACKGROUND: As an important part of tumor immunotherapy for adjunct, therapeutic tumor vaccines have been effective against multiple solid cancers, while their efficacy against lower grade glioma (LGG) remains undefined. Immunophenotyping of tumors is an essential tool to evaluate the immune function of patients with immunodeficiency or autoimmunity. Therefore, this study aims to find the potential tumor antigen of LGG and identify the suitable population for cancer vaccination based on the immune landscape. METHOD: The genomic and clinical data of 529 patients with LGG were obtained from TCGA, the mRNA_seq data of normal brain tissue were downloaded from GTEx. Differential expression gene and mutation analysis were performed to screen out potential antigens, K-M curves were carried out to investigate the correlation between the level of potential antigens and OS and DFS of patients. TIMER dataset was used to explore the correlation between genes and immune infiltrating cells. Immunophenotyping of 529 tumor samples was based on the single-sample gene sets enrichment analysis. Cibersort and Estimate algorithm were used to explore the tumor immune microenvironment characteristics in each immune subtype. Weighted gene co-expression network analysis (WGCNA) clustered immune-related genes and screened the hub genes, and pathway enrichment analyses were performed on the hub modules related to immune subtype in the WGCNA. RESULTS: Selecting for the mutated, up-regulated, prognosis- and immune-related genes, four potential tumor antigens were identified in LGG. They were also significantly positively associated with the antigen-presenting immune cells (APCs). Three robust immune subtypes, IS1, IS2 and IS3, represented immune status "desert", "immune inhibition", and "inflamed" respectively, which might serve as a predictive parameter. Subsequently, clinicopathological features, including the codeletion status of 1p19q, IDH mutation status, tumor mutation burden, tumor stemness, etc., were significantly different among subtypes. CONCLUSION: FCGBP, FLNC, TLR7, and CSF2RA were potential antigens for developing cancer vaccination, and the patients in IS3 were considered the most suitable for vaccination in LGG.

ADPRH is a prognosis-related biomarker and correlates with immune infiltrates in low grade glioma.

Background: ADPRH is a modulator of CD8+ T cell functions, and dysregulation of ADPRH has been identified to involve in carcinogenesis of cancers. However, the association of ADPRH with low grade glioma (LGG) remains unclear. Methods: The expression of ADPRH in LGG was first analyzed in GLIOVIS and GEPIA databases and then validated by real-time PCR (rt-PCR), immunochemistry and human protein atlas (HPA). Univariate and multivariate Cox analysis and Kaplan-Meier plots were designed to assess the prognostic value of ADPRH in LGG. The correlation of ADPRH and immune infiltration was evaluated by data in TIMER and ESTIMATE databases. Gene set enrichment analysis was conducted to detect biological processes associated with ADPRH. Results: ADPRH was significantly upregulated in LGG in comparison to non-tumor brain samples in transcriptomic and proteomic levels. The high ADPRH expression indicated unfavorable overall survival (OS) and progression-free survival (PFS) in patients with LGG using Kaplan-Meier plots. And multivariate Cox analysis demonstrated the expression level of ADPRH was an independent prognosis-predicting index for OS and PFS of LGG patients in all cohorts separately. Gene Set Enrichment Analysis (GSEA) indicated that high expression of ADPRH was involved in the upregulation of P53 signaling pathway, KRAS signaling pathway, IL6/JAK-STAT3 signaling and TNF-beta signaling pathways. By TIMER and ESTIMATE databases, we identified ADPRH expression had strong correlation with tumor immune infiltrating cells (TIICs). Conclusions: In summary, our findings demonstrated that ADPRH might be a potential prognostic biomarker and correlated with TIICs in LGG.

Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis.

BACKGROUND: To accurately predict the prognosis of glioma patients. METHODS: A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model. RESULTS: By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. CONCLUSION: We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

FCGBP Is a Prognostic Biomarker and Associated With Immune Infiltration in Glioma.

The Fc Fragment of IgG Binding Protein (FCGBP) has been proven to participate in intestinal tumor immunity. However, the biological role of FCGBP has remained unclear in glioma. The differential expression of FCGBP was explored by Oncomine and GEPIA databases. The effect of FCGBP on prognosis was analyzed via Kaplan-Meier plotter and GEPIA. The Tumor Immune Estimation Resource (TIMER) tool was used to determine the correlations of FCGBP expression with tumor immune infiltration. Firstly, FCGBP was highly expressed in glioma and correlated with a worse prognosis. Gene Ontology (GO) and KEGG pathway enrichment analyses revealed that the differentially expressed genes (DEGs) and co-expression genes of FCGBP were mainly involved in the immune response. Furthermore, FCGBP expression was positively associated with multiple immune cells infiltrates as well as the expression levels of multiple immune markers in glioma. FCGBP co-expression networks mostly participated in the regulation of immune response. Finally, immunohistochemistry (IHC) assays were conducted to explore the expression of FCGBP, PD-L1, CCL2 and CD8 in glioma and correlations between them. We found that PDL1 and FCGBP were synchronously upregulated in glioma tissues. These findings revealed a new mechanism by which FCGBP participates in the immune tolerance of glioma, and implied the potential of FCGBP as a therapeutic target or predictive marker for patients.

Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme.

BACKGROUND: Gliomas are the most common intracranial tumors and are classified as I-IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research revealed four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unraveled new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients. MATERIALS AND METHODS: To identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. RESULTS: After differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly enriched subclasses of the GO classification of these genes included mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and gene co-expression networks were constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazards regression model using the top 20 genes significantly correlated with survival time. We identified a four-protein prognostic signature that could divide patients into high-risk and low-risk groups. Increased expression of SLC12A5, CCL2, IGFBP2, and PDPN was associated with increased risk scores. Finally, the K-M curves confirmed that these genes could be used as independent predictors of survival in patients with glioblastoma. CONCLUSION: Our analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.