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Introduction: The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG. Methods: Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model. Results: Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability. Discussion: Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment.

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Low-grade glioma (LGG) is a grade II-III glioma accompanied by distinct clinical and molecular characteristics and the studies related to its prognosis are still unclear. The objective of this study is to explore the involvement of mitochondrial-related genes SLBP, COMMD7, LSM4, TOMM34, RPP40, FKBP1A, ARPC1A, and TBCA for the prognosis of LGG. We detected differences in the expression of some of the genes by analyzing the bioinformatics dataset and combining it with RT-PCR experiments. Subsequently, a nomogram was constructed and validated for the clinical relevance of risk factors such as age, WHO grade, IDH mutation status, Ch.1p19q co-deletion status, and high and low expression of ARPC1A to predict the 1-, 3-, 5-year overall survival and prognostic relevance of ARPC1A. Gene set enrichment analysis was performed for the relevant datasets pertinent to the expression of ARPC1A to elucidate the cancer-promoting pathways involved in the LGG through KEGG and GO analysis. Transfection assays, CCK-8 assays, and flow cytometry were used to determine the proliferation rate, and apoptosis rate of the HS683 and SW1783 cell lines respectively. Western blotting was used to examine the involvement of the cancer-promoting activity of ARPC1A through MAPK signaling. In this study, the prognostic value of ARPC1A in LGG was found by bioinformatics analysis combined with experimental approach analysis and may be a significant independent risk factor. ARPC1A fosters a higher LGG proliferation rate that may control the MAP kinase signaling and could be a prominent biomarker for LGG. Future studies are warranted to explore its clinical implications.

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Background: Glioma is the most frequent type of malignancy that may damage the brain with high morbidity and mortality rates and patients' prognoses are still dismal. Ferroptosis, a newly uncovered mode of programmed cell death, may be triggered to destroy glioma cells. Nevertheless, the significance of ferroptosis-related genes (FRGs) in predicting prognosis in glioma individuals is still a mystery. Methods: The CGGA (The Chinese Glioma Atlas), GEO (Gene Expression Omnibus), and TCGA (The Cancer Genome Atlas) databases were all searched to obtain the glioma expression dataset. First, TCGA was searched to identify differentially expressed genes (DEGs). This was followed by a machine learning algorithm-based screening of the glioma's most relevant genes. Additionally, these genes were subjected to Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) functional enrichment analyses. The chosen biological markers were then submitted to single-cell, immune function, and gene set enrichment analysis (GSEA). In addition, we performed functional enrichment and Mfuzz expression profile clustering on the most promising biological markers to delve deeper into their regulatory mechanisms and assess their clinical diagnostic capacities. Results: We identified 4444 DEGs via differential analysis and 564 FRGs from the FerrDb database. The two were subjected to intersection analysis, which led to the discovery of 143 overlapping genes. After that, glioma biological markers were identified in fourteen genes by the use of machine learning methods. In terms of its use for clinical diagnosis, SMG9 stands out as the most significant among these biomarkers. Conclusion: In light of these findings, the identification of SMG9 as a new biological marker has the potential to provide information on the mechanism of action and the effect of the immune milieu in glioma. The promise of SMG9 in glioma prognosis prediction warrants more study.

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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.

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INTRODUCTION: Gliomas, a type of brain neoplasm, are prevalent and often fatal. Molecular diagnostics have improved understanding, but treatment options are limited. This study investigates the role of INTS9 in processing small nuclear RNA (snRNA), which is crucial to generating mature messenger RNA (mRNA). We aim to employ advanced bioinformatics analyses with large-scale databases and conduct functional experiments to elucidate its potential role in glioma therapeutics. MATERIALS AND METHODS: We collected genomic, proteomic, and Whole-Exon-Sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) for bioinformatic analyses. Then, we validated INTS9 protein expression through immunohistochemistry and assessed its correlation with P53 and KI67 protein expression. Gene Set Enrichment Analysis (GSEA) was performed to identify altered signaling pathways, and functional experiments were conducted on three cell lines treated with siINTS9. Then, we also investigate the impacts of tumor heterogeneity on INTS9 expression by integrating single-cell sequencing, 12-cell state prediction, and CIBERSORT analyses. Finally, we also observed longitudinal changes in INTS9 using the Glioma Longitudinal Analysis (GLASS) dataset. RESULTS: Our findings showed increased INTS9 levels in tumor tissue compared to non-neoplastic components, correlating with high tumor grading and proliferation index. TP53 mutation was the most notable factor associated with upregulated INTS9, along with other potential contributors, such as combined chromosome 7 gain/10 loss, TERT promoter mutation, and increased Tumor Mutational Burden (TMB). In GSEA analyses, we also linked INTS9 with enhanced cell proliferation and inflammation signaling. Downregulating INTS9 impacted cellular proliferation and cell cycle regulation during the function validation. In the context of the 12 cell states, INTS9 correlated with tumor-stem and tumor-proliferative-stem cells. CIBERSORT analyses revealed increased INTS9 associated with increased macrophage M0 and M2 but depletion of monocytes. Longitudinally, we also noticed that the INTS9 expression declined during recurrence in IDH wildtype. CONCLUSION: This study assessed the role of INTS9 protein in glioma development and its potential as a therapeutic target. Results indicated elevated INTS9 levels were linked to increased proliferation capacity, higher tumor grading, and poorer prognosis, potentially resulting from TP53 mutations. This research highlights the potential of INTS9 as a promising target for glioma treatment.

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A paradigm shift in preclinical evaluations of new anticancer GBM drugs should occur in favour of 3D cultures. This study leveraged the vast genomic data banks to investigate the suitability of 3D cultures as cell-based models for GBM. We hypothesised that correlating genes that are highly upregulated in 3D GBM models will have an impact in GBM patients, which will support 3D cultures as more reliable preclinical models for GBM. Using clinical samples of brain tissue from healthy individuals and GBM patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA), and Genotype-Tissue Expression (GTEx) databases, several genes related to pathways such as epithelial-to-mesenchymal transition (EMT)-related genes (CD44, TWIST1, SNAI1, CDH2, FN1, VIM), angiogenesis/migration-related genes (MMP1, MMP2, MMP9, VEGFA), hypoxia-related genes (HIF1A, PLAT), stemness-related genes (SOX2, PROM1, NES, FOS), and genes involved in the Wnt signalling pathway (DKK1, FZD7) were found to be upregulated in brain samples from GBM patients, and the expression of these genes were also enhanced in 3D GBM cells. Additionally, EMT-related genes were upregulated in GBM archetypes (wild-type IDH1R132 ) that historically have poorer treatment responses, with said genes being significant predictors of poorer survival in the TCGA cohort. These findings reinforced the hypothesis that 3D GBM cultures can be used as reliable models to study increased epithelial-to-mesenchymal transitions in clinical GBM samples.

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INTRODUCTION: Glioblastoma (GBM) is the most common and lethal brain tumor. The current treatment is surgical removal combined with radiotherapy and chemotherapy, Temozolomide (TMZ). However, tumors tend to develop TMZ resistance which leads to therapeutic failure. Ancient ubiquitous protein 1 (AUP1) is a protein associated with lipid metabolism, which is widely expressed on the surface of ER and Lipid droplets, involved in the degradation of misfolded proteins through autophagy. It has recently been described as a prognostic marker in renal tumors. Here, we aim to use sophisticated bioinformatics and experimental validation to characterize the AUP1's role in glioma. MATERIAL AND METHODS: We collected the mRNA, proteomics, and Whole-Exon-Sequencing from The Cancer Genome Atlas (TCGA) for bioinformatics analyses. The analyses included the expression difference, Kaplan-Meier-survival, COX-survival, and correlation to the clinical factors (tumor mutation burden, microsatellite instability, and driven mutant genes). Next, we validated the AUP1 protein expression using immunohistochemical staining on the 78 clinical cases and correlated them with P53 and KI67. Then, we applied GSEA analyses to identify the altered signalings and set functional experiments (including Western Blot, qPCR, BrdU, migration, cell-cycle, and RNAseq) on cell lines when supplemented with small interfering RNA targeting the AUP1 gene (siAUP1) for further validation. We integrated the single-cell sequencing and CIBERSORT analyses at the Chinese Glioma Genome Atlas (CGGA) and Glioma Longitudinal AnalySiS (GLASS) dataset to rationale the role of AUP1 in glioma. RESULTS: Firstly, the AUP1 is a prognostic marker, increased in the tumor component, and correlated with tumor grade in both transcriptomes and protein levels. Secondly, we found higher AUP1 associated with TP53 status, Tumor mutation burden, and increased proliferation. In the function validation, downregulated AUP1 expression merely impacted the U87MG cells' proliferation instead of altering the lipophagy activity. From the single-cell sequencing and CIBERSORT analyses at CGGA and GLASS data, we understood the AUP1 expression was affected by the tumor proliferation, stromal, and inflammation compositions, particularly the myeloid and T cells. In the longitudinal data, the AUP1 significantly dropped in the recurrent IDH wildtype astrocytoma, which might result from increased AUP1-cold components, including oligodendrocytes, endothelial cells, and pericytes. CONCLUSION: According to the literature, AUP1 regulates lipophagy by stabilizing the ubiquitination of lipid droplets. However, we found no direct link between AUP1 suppression and altered autophagy activity in the functional validation. Instead, we noticed AUP1 expression associated with tumor proliferation and inflammatory status, contributed by myeloid cells and T cells. In addition, the TP53 mutations seem to play an important role here and initiate inflamed microenvironments. At the same time, EGFR amplification and Chromosome 7 gain combined 10 loss are associated with increased tumor growth related to AUP1 levels. This study taught us that AUP1 is a poorer predictive biomarker associated with tumor proliferation and could report inflamed status, potentially impacting the clinical application.

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Background: Glioma is the most common primary tumor of the central nervous system (CNS). Centromere protein A (CENPA) plays an essential role in ensuring that mitosis proceeds normally. The effect of CENPA on glioma is rarely reported. However, the current study aims to explore whether aberrant CENPA expression promotes glioma progression and the potential mechanisms involved. Methods: The GEPIA website, The Cancer Genome Atlas, and the Gene Expression Omnibus (GEO) were used to assess the expression of CENPA in glioma. The results were validated by real-time quantitative polymerase chain reaction and immunohistochemical staining of clinical samples. The relationship between the expression and prognostic value of the CENPA gene in glioma was investigated by Kaplan-Meier (KM) survival analysis with RNA-seq and clinical profiles downloaded from the Chinese Glioma Genome Atlas (CGGA) and UCSC Xena. The association between CENPA and clinical characteristics was also evaluated. Cell Counting Kit-8 (CCK8) assay, wound healing assay using two glioma cell lines, gene set enrichment analysis (GSEA), KEGG and gene ontology (GO) enrichment analysis, immune infiltration analysis, temozolomide (TMZ) sensitivity analysis, and single-cell sequence analysis were performed to explore the underlying mechanisms of high CENPA expression and its effect on glioma development. Finally, we performed a Cox analysis based on the expression of CENPA to predict patient prognosis. Results: CENPA was significantly upregulated in glioma tissue samples and correlated with patient prognosis. Moreover, the downregulation of CENPA inhibited the migration and proliferation of glioma cells. In addition, the expression level of CENPA was significantly correlated with the grade, primary-recurrent-secondary (PRS) type, IDH mutation status, and 1p19q codeletion status. Furthermore, CENPA could serve as an independent prognostic factor for glioma that mainly interferes with the normal progression of mitosis and regulates the tumor immune microenvironment favoring glioma development. Conclusion: CENPA may act as a prognostic factor in patients with glioma and provide a novel target for the treatment of gliomas.

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Background: TP53I13 is a protein coding tumor suppression gene encoded by the tumor protein p53. Overexpression of TP53I13 impedes tumor cell proliferation. Nevertheless, TP53I13 role and expression in the emergence and progression of glioma (low-grade glioma and glioblastoma) are yet to be identified. Thus, we aim to use comprehensive bioinformatics analyses to investigate TP53I13 and its prognostic value in gliomas. Methods: Multiple databases were consulted to evaluate and assess the expression of TP53I13, such as the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GeneMANIA, and Gene Expression Profiling Interactive. TP53I13 expression was further explored using immunohistochemistry (IHC) and multiplex immunohistochemistry (mIHC). Through Gene Set Enrichment Analysis (GSEA), the biological functions of TP53I13 and metastatic processes associated with it were studied. Results: The expression of TP53I13 was higher in tumor samples compared to normal samples. In samples retrieved from the TCGA and CGGA databases, high TP53I13 expression was associated with poor survival outcomes. The analysis of multivariate Cox showed that TP53I13 might be an independent prognostic marker of glioma. It was also found that increased expression of TP53I13 was significantly correlated with PRS type, status, 1p/19q codeletion status, IDH mutation status, chemotherapy, age, and tumor grade. According to CIBERSORT (Cell-type Identification by Estimating Relative Subsets of RNA Transcript), the expression of TP53I13 correlates with macrophages, neutrophils, and dendritic cells. GSEA shows a close correlation between TP53I13 and p53 signaling pathways, DNA replication, and the pentose phosphate pathway. Conclusion: Our results reveal a close correlation between TP53I13 and gliomas. Further, TP53I13 expression could affect the survival outcomes in glioma patients. In addition, TP53I13 was an independent marker that was crucial in regulating the infiltration of immune cells into tumors. As a result of these findings, TP53I13 might represent a new biomarker of immune infiltration and prognosis in patients with gliomas.

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Background: Recent discoveries have revealed that fibronectin type III domain containing 3B (FNDC3B) acts as an oncogene in various cancers; however, its role in glioma remains unclear. Methods: In this study, we comprehensively investigated the expression, prognostic value, and immune significance of FNDC3B in glioma using several databases and a variety of machine learning algorithms. RNA expression data and clinical information of 529 patients from the Cancer Genome Atlas (TCGA) and 1319 patients from Chinese Glioma Genome Atlas (CGGA) databases were downloaded for further investigation. To evaluate whether FNDC3B expression can predict clinical prognosis of glioma, we constructed a clinical nomogram to estimate long-term survival probabilities. The predicted nomogram was validated by CGGA cohorts. Differentially expressed genes (DEGs) were detected by the Wilcoxon test based on the TCGA-LGG dataset and the weighted gene co-expression network analysis (WGCNA) was implemented to identify the significant module associated with the expression level of FNDC3B. Furthermore, we investigated the correlation between FNDC3B with cancer immune infiltrates using TISIDB, ESTIMATE, and CIBERSORTx. Results: Higher FNDC3B expression displayed a remarkably worse overall survival and the expression level of FNDC3B was an independent prognostic indicator for patients with glioma. Based on TCGA LGG dataset, a co-expression network was established and the hub genes were identified. FNDC3B expression was positively correlated to the tumor-infiltrating lymphocytes and immune infiltration score, and high FNDC3B expression was accompanied by the increased expression of B7-H3, PD-L1, TIM-3, PD-1, and CTLA-4. Moreover, expression of FNDC3B was significantly associated with infiltrating levels of several types of immune cells and most of their gene markers in glioma. Conclusion: This study demonstrated that FNDC3B may be involved in the occurrence and development of glioma and can be regarded as a promising prognostic and immunotherapeutic biomarker for the treatment of glioma.

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[This corrects the article DOI: 10.3389/fonc.2021.657029.].

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Because of the heterogeneity of lower-grade gliomas (LGGs), patients show various survival outcomes that are not reliably predicted by histological classification. The tumour microenvironment (TME) contributes to the initiation and progression of brain LGGs. Identifying potential prognostic markers based on the immune and stromal components in the TME will provide new insights into the dynamic modulation of these two components of the TME in LGGs. We applied ESTIMATE to calculate the ratio of immune and stromal components from The Cancer Genome Atlas database. After combined differential gene expression analysis, protein-protein interaction network construction and survival analysis, CD44 was screened as an independent prognostic factor and subsequently validated utilizing data from the Chinese Glioma Genome Atlas database. To decipher the association of glioma cell CD44 expression with stromal cells in the TME and tumour progression, RT-qPCR, cell viability and wound healing assays were employed to determine whether astrocytes enhance glioma cell viability and migration by upregulating CD44 expression. Surprisingly, M1 macrophages were identified as positively correlated with CD44 expression by CIBERSORT analysis. CD44+ glioma cells were further suggested to interact with microglia-derived macrophages (M1 phenotype) via osteopontin signalling on the basis of single-cell sequencing data. Overall, we found that astrocytes could elevate the CD44 expression level of glioma cells, enhancing the recruitment of M1 macrophages that may promote glioma stemness via osteopontin-CD44 signalling. Thus, glioma CD44 expression might coordinate with glial activities in the TME and serve as a potential therapeutic target and prognostic marker for LGGs.

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Background: Glioma as the most frequently discovered tumor affecting the brain shows significant morbidity and fatality rates with unfavorable prognosis. There is an urgent need to find novel therapeutic targets to overcome the low chemotherapeutic efficacy of glioma. This research examined whether the copper-metabolism-domain protein, COMMD4, had predictive and therapeutic significance in glioma. Methods: Using the freely accessible CGGA (The Chinese Glioma Atlas) and TCGA (The Cancer Genome Atlas) databases, we examined the function of COMMD4 in GBM and LGG. CIBERSORT and TIMER were utilized to assess the associations between COMMD4 and immune cells. The Gene Set Enrichment Analysis (GSEA) was employed to examine the functional data. Furthermore, the link between COMMD4 expression and predicted treatment response was evaluated via CellMiner Cross-Database. Meanwhile, qRT-PCR was conducted to examine COMMD4 expression in human glioma. Finally, Migration and invasion of glioma cells (U-87, U-251) were assessed using transwell assays. R was used to analyze the statistical data. Results: According to our findings, COMMD4 expression level was higher in patients having grade-dependent glioma who also showed an unfavorable prognosis. Furthermore, qRT-PCR confirmed the high expression of COMMD4 in glioma tissues and cells. Additionally, using integrated correlation analysis, we acquired significant prognostic findings between isocitrate dehydrogenase 1(IDH1) and COMMD4. Meanwhile, a link between COMMD4 and many tumor-infiltrating immune cells was observed. GSEA and drug response analysis revealed the potential mechanism of COMMD4 in drug resistance of glioma. Conclusion: The current findings validated COMMD4 as a novel biological marker, which might offer insights into the possible drug resistance mechanisms and the impact of the immune microenvironment on glioma. COMMD4 might be used to predict glioma prognosis.

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Background: Accumulating evidence shows that m6A regulates oncogene and tumor suppressor gene expression, thus playing a dual role in cancer. Likewise, there is a close relationship between the immune system and tumor development and progression. However, for glioblastoma, m6A-associated immunological markers remain to be identified. Methods: We obtained gene expression, mutation, and clinical data on glioblastoma from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Next, we performed univariate COX-least absolute shrinkage and selection operator (LASSO)-multivariate COX regression analyses to establish a prognostic gene signature and develop a corresponding dynamic nomogram application. We then carried out a clustering analysis twice to categorize all samples according to their m6A-regulating and m6A-associated immune gene expression levels (high, medium, and low) and calculated their m6A score. Finally, we performed quantitative reverse transcription-polymerase chain reaction, cell counting kit-8, cell stemness detection, cell migration, and apoptosis detection in vitro assays to determine the biological role of CD81 in glioblastoma cells. Results: Our glioblastoma risk score model had extremely high prediction efficacy, with the area under the receiver operating characteristic curve reaching 0.9. The web version of the dynamic nomogram application allows rapid and accurate calculation of patients' survival odds. Survival curves and Sankey diagrams indicated that the high-m6A score group corresponded to the groups expressing medium and low m6A-regulating gene levels and high m6A-associated prognostic immune gene levels. Moreover, these groups displayed lower survival rates and higher immune infiltration. Based on the gene set enrichment analysis, the pathophysiological mechanism may be related to the activation of the immunosuppressive function and related signaling pathways. Moreover, the risk score model allowed us to perform immunotherapy benefit assessment. Finally, silencing CD81 in vitro significantly suppressed proliferation, stemness, and migration and facilitated apoptosis in glioblastoma cells. Conclusion: We developed an accurate and efficient prognostic model. Furthermore, the correlation analysis of different stratification methods with tumor microenvironment provided a basis for further pathophysiological mechanism exploration. Finally, CD81 may serve as a diagnostic and prognostic biomarker in glioblastoma.

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The B7-CD28 gene family plays a crucial role in modulating immune functions and has served as potential targets for immunotherapeutic strategies. Therefore, we systematically analyzed B7-CD28 family gene expression profiles and constructed a B7-CD28 family-based prognostic signature to predict survival and immune host status in diffuse gliomas. The TCGA dataset was used as a training cohort, and three CGGA datasets (mRNAseq_325, mRNAseq_693 and mRNA-array) were employed as validation cohorts to intensify the findings that we have revealed in TCGA dataset. Ultimately, we developed a B7-CD28 family-based signature that consisted of CD276, CD274, PDCD1LG2 and CD80 using LASSO Cox analysis. This gene signature was validated to have significant prognostic value, and could be used as a biomarker to distinguish pathological grade and IDH mutation status in diffuse glioma. Additionally, we found that the gene signature was significantly related to intensity of immune response and immune cell population, as well as several other important immune checkpoint genes, holding a great potential to be a predictive immune marker for immunotherapy and tumor microenvironment. Finally, a B7-CD28 family-based nomogram was established to predict patient life expectancy contributing to facilitate personalizing therapy for tumor sufferers. In summary, this is the first mathematical model based on this gene family with the aim of providing novel insights into immunotherapy for diffuse glioma.

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BACKGROUND: Trophinin-associated protein (TROAP) mediates embryonic transfer, regulates microtubules, and is associated with the biological behavior of various cancers. However, there is limited information on the role of TROAP in glioma. METHODS AND RESULTS: We obtained clinical information on 1948 patients with glioma from The Cancer Genome Atlas, Gene Expression Omnibus and the Chinese Glioma Genome Atlas. Basal assays were used to measure changes in TROAP expression levels in high-grade glioma cell lines and in normal human astrocytes. Quantitative reverse transcription polymerase chain reaction assays showed that TROAP expression was higher in glioma cell lines than in normal astrocytes. The expression level of TROAP in 749 glioma was significantly higher than that in 228 normal brain tissues using Student's t test. The expression of TROAP has a positive relationship with the clinical characteristics of poor prognosis, such as WHO grade, age and has negatively correlated with the indicators of beneficial prognosis, such as IDH mutation and 1p19q co-deletion. Kaplan-Meier survival curves, single multifactor analysis were used to analyze correlations between TROAP and clinical features and prognosis of gliomas. In addition, TROAP overexpression was an independent risk factor for glioma and was associated with reduced overall survival of patients with glioma particularly in patients with WHO grade III and grade IV glioma. Gene set enrichment analysis showed that homologous recombination, cell cycle, and p53 signaling pathways were enriched in samples overexpressing TROAP. CONCLUSION: TROAP is a potential risk factor associated with poor prognosis in patients with glioma and may act as a highly specific biomarker, offering the possibility of individualized glioma treatment.

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Gliomas are the most common and aggressive malignancies of the central nervous system. Histone deacetylases (HDACs) are important targets in cancer treatment. They regulate complex cellular mechanisms that influence tumor biology and immunogenicity. However, little is known about the function of HDACs in glioma. The Oncomine, Human Protein Atlas, Gene Expression Profiling Interactive Analysis, Broad Institute Cancer Cell Line Encyclopedia, Chinese Glioma Genome Atlas, OmicShare, cBioPortal, GeneMANIA, STRING, and TIMER databases were utilized to analyze the differential expression, prognostic value, and genetic alteration of HDAC and immune cell infiltration in patients with glioma. HDAC1/2 were considerable upregulated whereas HDAC11 was significantly downregulated in cancer tissues. HDAC1/2/3/4/5/7/8/11 were significantly correlated with the clinical glioma stage. HDAC1/2/3/10 were strongly upregulated in 11 glioma cell lines. High HDCA1/3/7 and low HDAC4/5/11 mRNA levels were significantly associated with overall survival and disease-free survival in glioma. HDAC1/2/3/4/5/7/9/10/11 are potential useful biomarkers for predicting the survival of patients with glioma. The functions of HDACs and 50 neighboring genes were primarily related to transcriptional dysregulation in cancers and the Notch, cGMP-PKG, and thyroid hormone signaling pathways. HDAC expression was significantly correlated with the infiltration of B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells in glioma. Our study indicated that HDACs are putative precision therapy targets and prognostic biomarkers of survival in glioma patients.

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BACKGROUND: Glioblastoma (GBM) is considered the most malignant and devastating intracranial tumor without effective treatment. Autophagy, apoptosis, and necrosis, three classically known cell death pathways, can provide novel clinical and immunological insights, which may assist in designing personalized therapeutics. In this study, we developed and validated an effective signature based on autophagy-, apoptosis- and necrosis-related genes for prognostic implications in GBM patients. METHODS: Variations in the expression of genes involved in autophagy, apoptosis and necrosis were explored in 518 GBM patients from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were performed to construct a combined prognostic signature. Kaplan-Meier survival, receiver-operating characteristic (ROC) curves and Cox regression analyses based on overall survival (OS) and progression-free survival (PFS) were conducted to estimate the independent prognostic performance of the gene signature. The Chinese Glioma Genome Atlas (CGGA) dataset was used for external validation. Finally, we investigated the differences in the immune microenvironment between different prognostic groups and predicted potential compounds targeting each group. RESULTS: A 16-gene cell death index (CDI) was established. Patients were clustered into either the high risk or the low risk groups according to the CDI score, and those in the low risk group presented significantly longer OS and PFS than the high CDI group. ROC curves demonstrated outstanding performance of the gene signature in both the training and validation groups. Furthermore, immune cell analysis identified higher infiltration of neutrophils, macrophages, Treg, T helper cells, and aDCs, and lower infiltration of B cells in the high CDI group. Interestingly, this group also showed lower expression levels of immune checkpoint molecules PDCD1 and CD200, and higher expression levels of PDCD1LG2, CD86, CD48 and IDO1. CONCLUSION: Our study proposes that the CDI signature can be utilized as a prognostic predictor and may guide patients' selection for preferential use of immunotherapy in GBM.

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Background: Glioma is one of the most typical tumors in the central nervous system with a poor prognosis, and the optimal management strategy remains controversial. Lactate in the tumor microenvironment is known to promote cancer progression, but its impact on clinical outcomes of glioma is largely unknown. Methods: Glioma RNA-seq data were obtained from TCGA and GCGA databases. Lactate metabolism genes (LMGs) were then evaluated to construct an LMG model in glioma using Cox and LASSO regression. Immune cell infiltration, immune checkpoint gene expression, enriched pathways, genetic alteration, and drug sensitivity were compared within the risk subgroups. Based on the risk score and clinicopathological features, a nomogram was developed to predict prognosis in patients with glioma. Results: Five genes (LDHA, LDHB, MRS2, SL16A1, and SL25A12) showed a good prognostic value and were used to construct an LMG-based risk score. This risk score was shown as an independent prognostic factor with good predictive power in both training and validation cohorts (p < 0.001). The LMG signature was found to be correlated with the expression of immune checkpoint genes and immune infiltration and could shape the tumor microenvironment. Genetic alteration, dysregulated metabolism, and tumorigenesis pathways could be the underlying contributing factors that affect LMG risk stratification. The patients with glioma in the LMG high-risk group showed high sensitivity to EGFR inhibitors. In addition, our nomogram model could effectively predict overall survival with an area under the curve value of 0.894. Conclusion: We explored the characteristics of LMGs in glioma and proposed an LMG-based signature. This prognostic model could predict the survival of patients with glioma and help clinical oncologists plan more individualized and effective therapeutic regimens.

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BACKGROUND: Gliomas are prevalent primary intracerebral malignant tumors. Increasing evidence indicates an association between the immune signature and Grade II/III glioma prognosis. Thus, we aimed to develop an immune-related gene pair (IRGP) signature that can be used as a prognostic tool in Grade II/III glioma. METHODS: The gene expression levels and clinical information of Grade II/III glioma patients were collected from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The TCGA data were randomly divided into a training cohort (n = 249) and a validation cohort (n = 162), and a CGGA dataset served as an external validation group (n = 605). IRGPs significantly associated with prognosis were selected by Cox regression. Gene set enrichment analysis and filtration were performed with the IRGPs. RESULTS: Within a set of 1991 immune genes, 8 IRGPs including 15 unique genes that significantly affect survival constituted a gene signature. In the validation datasets, the IRGP signature significantly stratified patients with Grade II/III glioma into low- and high-risk groups (P < 0.001), and the IRGP index was found to be an independent prognostic factor through univariate and multivariate analyses (P < 0.05). Additionally, 26 functional pathways were identified through the intersection of Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) enrichment analysis. CONCLUSION: The IRGP signature demonstrated good prognostic value for Grade II/III gliomas, which may provide new insights into individual treatment for glioma patients. The IRGPs might function through the identified 26 functional pathways.