CGGA Databases Research Articles

Dysregulation of phosphoenolpyruvate carboxykinase in cancers: A comprehensive analysis

BackgroundPhosphoenolpyruvate carboxykinase (PEPCK) plays a crucial role in gluconeogenesis, glycolysis, and the tricarboxylic acid cycle by converting oxaloacetate into phosphoenolpyruvate. Two distinct isoforms of PEPCK, specifically cytosolic PCK1 and mitochondrial PCK2, have been identified. Nevertheless, the comprehensive understanding of their dysregulation in pan-cancer and their potential mechanism contributing to signaling transduction pathways remains elusive. MethodsWe conducted comprehensive analyses of PEPCK gene expression across 33 diverse cancer types using data from The Cancer Genome Atlas (TCGA). Multiple public databases such as HPA, TIMER 2.0, GEPIA2, cBioPortal, UALCAN, CancerSEA, and String were used to investigate protein levels, prognostic significance, clinical associations, genetic mutations, immune cell infiltration, single-cell sequencing, and functional enrichment analysis in patients with pan-cancer. PEPCK expression was analyzed about different clinical and genetic factors of patients using data from TCGA, GEO, and CGGA databases. Furthermore, the role of PCK2 in Glioma was examined using both in vitro and in vivo experiments. ResultsThe analysis we conducted revealed that the expression of PEPCK is involved in both clinical outcomes and immune cell infiltration. Initially, we verified the high expression of PCK2 in GBM cells and its role in metabolic reprogramming and proliferation in GBM. ConclusionOur study showed a correlation between PEPCK (PCK1 and PCK2) expression with clinical prognosis, gene mutation, and immune infiltrates. These findings identified two possible predictive biomarkers across different cancer types, as well as a comprehensive analysis of PCK2 expression in various tumors, with a focus on GBM.

Identification and classification of glioma subtypes based on RNA-binding proteins

BackgroundGlioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy. MethodsRNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers. ResultsSix tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways. ConclusionsThe study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: A bioinformatics approach

BackgroundIn the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination. MethodsDownload glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes. ResultsThe expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model. ConclusionsOur study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.

Pathway-based stratification of gliomas uncovers four subtypes with different TME characteristics and prognosis.

Increasing evidences have found that the interactions between hypoxia, immune response and metabolism status in tumour microenvironment (TME) have clinical importance of predicting clinical outcomes and therapeutic efficacy. This study aimed to develop a reliable molecular stratification based on these key components of TME. The TCGA data set (training cohort) and two independent cohorts from CGGA database (validation cohort) were enrolled in this study. First, the enrichment score of 277 TME-related signalling pathways was calculated by gene set variation analysis (GSVA). Then, consensus clustering identified four stable and reproducible subtypes (AFM, CSS, HIS and GLU) based on TME-related signalling pathways, which were characterized by differences in hypoxia and immune responses, metabolism status, somatic alterations and clinical outcomes. Among the four subtypes, HIS subtype had features of immunosuppression, oxygen deprivation and active energy metabolism, resulting in a worst prognosis. Thus, for better clinical application of this acquired stratification, we constructed a risk signature by using the LASSO regression model to identify patients in HIS subtype accurately. We found that the risk signature could accurately screen out the patients in HIS subtype and had important reference value for individualized treatment of glioma patients. In brief, the definition of the TME-related subtypes was a valuable tool for risk stratification in gliomas. It might serve as a reliable prognostic classifier and provide rational design of individualized treatment, and follow-up scheduling for patients with gliomas.

PSMA2 promotes glioma proliferation and migration via EMT

BackgroundGliomas advance rapidly and are associated with a poor prognosis. Epithelial-mesenchymal transition (EMT) accelerates the progression of gliomas, exerting a pivotal role in glioma development. Proteasome subunit alpha type-2 (PSMA2) exhibits high expression levels in gliomas. however, its specific involvement in glioma progression and its correlation with EMT remain elusive. This study aims to elucidate the role of PSMA2 in glioma progression and its potential association with EMT. MethodsOnline tools were employed to analyze the expression patterns and survival curves of PSMA2 in gliomas. The relationship between PSMA2 and various characteristics of glioma patients was investigated using data from the TCGA and CGGA databases. In vitro, cell proliferation and migration were assessed through CCK-8, colony formation, and transwell assays. Furthermore, a tumor xenograft model in nude mice was established to evaluate in vivo tumorigenesis. Protein binding to PSMA2 was scrutinized using co-immunoprecipitation MS (co-IP MS). The potential biological functions and molecular pathways associated with PSMA2 were explored through GO analysis and KEGG analysis, and the correlation between PSMA2 and EMT was validated through correlation analysis and Western blot experiments. ResultsBioinformatics analysis revealed a significant upregulation of PSMA2 across various cancers, with particularly heightened expression in gliomas. Moreover, elevated PSMA2 levels were correlated with advanced tumor stages and diminished survival rates among glioma patients. Inhibition of PSMA2 demonstrated a pronounced suppressive effect on glioma cell proliferation, both in vitro and in vivo. Knockdown of PSMA2 also impeded the migratory capacity of glioma cells. GO and KEGG enrichment analyses indicated that PSMA2-binding proteins (identified through Co-IP-MS) were associated with cell adhesion molecule binding and cadherin binding. Western blot results further confirmed the role of PSMA2 in promoting epithelial-mesenchymal transition (EMT) in glioma cells. ConclusionOur study provides evidence supporting the role of PSMA2 as a regulatory factor in EMT and suggests its potential as a prognostic biomarker for glioma progression.

CD13 expression affects glioma patient survival and influences key functions of human glioblastoma cell lines in vitro

CD13 (APN) is an Alanyl-Aminopeptidase with diverse functions. The role of CD13 for gliomas is still unknown. In this study, data of glioma patients obtained by TCGA and CGGA databases were used to evaluate the survival rate and prognostic value of CD13 expression level. Protein expression of CD13 was confirmed by immunofluorescence staining of fresh patient tissues. Eight human glioblastoma cell lines were studied by RT-PCR, Western Blot, immunofluorescence staining and flow cytometry to define CD13 expression. Cell lines with different CD13 expression status were treated with a CD13 inhibitor, bestatin, and examined by MTT, scratch and colony formation assaysas well as by apoptosis assay and Western Blots. Bioinformatics analysis indicated that patients with high expression of CD13 had poor survival and prognosis. Additionally, CD13 protein expression was positively associated with clinical malignant characteristics. Investigated glioblastoma cell lines showed distinct expression levels and subcellular localization of CD13 with intracellular enrichment. Bestatin treatment reduced proliferation, migration and colony formation of glioma cells in a CD13-dependent manner while apoptosis was increased. In summary, CD13 has an impact on glioma patient survival and is important for the main function of specific glioma cells.

Long non-coding RNA lung cancer-associated transcript-1 promotes glioblastoma progression by enhancing Hypoxia-inducible factor 1 alpha activity.

Hypoxia is associated with poor prognosis in many cancers including glioblastoma (GBM). Glioma stem-like cells (GSCs) often reside in hypoxic regions and serve as reservoirs for disease progression. Long non-coding RNAs (lncRNAs) have been implicated in GBM. However, the lncRNAs that modulate GSC adaptations to hypoxia are poorly understood. Identification of these lncRNAs may provide new therapeutic strategies to target GSCs under hypoxia. lncRNAs induced by hypoxia in GSCs were identified by RNA-seq. Lung cancer-associated transcript-1 (LUCAT1) expression was assessed by qPCR, RNA-seq, Northern blot, single molecule FISH in GSCs, and interrogated in IvyGAP, The Cancer Genome Atlas, and CGGA databases. LUCAT1 was depleted by shRNA, CRISPR/Cas9, and CRISPR/Cas13d. RNA-seq, Western blot, immunohistochemistry, co-IP, ChIP, ChIP-seq, RNA immunoprecipitation, and proximity ligation assay were performed to investigate mechanisms of action of LUCAT1. GSC viability, limiting dilution assay, and tumorigenic potential in orthotopic GBM xenograft models were performed to assess the functional consequences of depleting LUCAT1. A new isoform of Lucat1 is induced by Hypoxia inducible factor 1 alpha (HIF1α) and Nuclear factor erythroid 2-related factor 2 (NRF2) in GSCs under hypoxia. LUCAT1 is highly expressed in hypoxic regions in GBM. Mechanistically, LUCAT1 formed a complex with HIF1α and its co-activator CBP to regulate HIF1α target gene expression and GSC adaptation to hypoxia. Depletion of LUCAT1 impaired GSC self-renewal. Silencing LUCAT1 decreased tumor growth and prolonged mouse survival in GBM xenograft models. A HIF1α-LUCAT1 axis forms a positive feedback loop to amplify HIF1α signaling in GSCs under hypoxia. LUCAT1 promotes GSC self-renewal and GBM tumor growth. LUCAT1 is a potential therapeutic target in GBM.

IGSF8 is a potential target for the treatment of gliomas

BackgroundImmunoglobulin superfamily member 8, or IGSF8, is a member of the recently identified immunoglobulin family of proteins. It is mostly produced on cell membranes and has a unique transmembrane structure. It has recently been demonstrated that there is a strong correlation between the expression variation of IGSF8 and the growth of gliomas. Therefore, we used data from the TCGA and CGGA databases to evaluate the function of IGSF8. MethodsThe TCGA and GTEx data sets' RNA-seq data were utilized to examine IGSF8 expression. The Gene Cards database was utilized to get IGSF8 protein data. The Cluster Profiler data package was used to carry out the IGSF8 enrichment study. The GO and KEGG databases were used to examine the relationship between IGSF8 and cellular physiological and biochemical processes. The TCGA immune cell infiltration scores were obtained from online databases and published studies. Clinical survival data from TCGA and CGGA were used to investigate the predictive significance of IGSF8. ResultsTGGA revealed that the majority of cancers had differential expression of IGSF8. IGSF8 was discovered to be enriched in numerous significant pathways in tumor cells by GO and KEGG. Moreover, a strong correlation was seen between the expression of IGSF8 and the immunomodulatory interactions that occur between non-lymphocytes and lymphocytes. T-cell infiltration, immunological checkpoints, immune-activating and immune-suppressive genes, chemokines, and chemokine receptors were all strongly correlated with IGSF8 expression. Lastly, the TCGA and CGGA databases showed a strong correlation between IGSF8 and the grade and prognosis of gliomas. ConclusionAccording to our findings, IGSF8 may be a glioma marker. In order to control the immunological microenvironment, IGSF8 may cooperate with a number of immune checkpoints. This information may be utilized to create novel targeted immunotherapy medications.

Integrated multiomic analysis reveals disulfidptosis subtypes in glioblastoma: implications for immunotherapy, targeted therapy, and chemotherapy.

Glioblastoma (GBM) presents significant challenges due to its malignancy and limited treatment options. Precision treatment requires subtyping patients based on prognosis. Disulfidptosis, a novel cell death mechanism, is linked to aberrant glucose metabolism and disulfide stress, particularly in tumors expressing high levels of SLC7A11. The exploration of disulfidptosis may provide a new perspective for precise diagnosis and treatment of glioblastoma. Transcriptome sequencing was conducted on samples from GBM patients treated at Tiantan Hospital (January 2022 - December 2023). Data from CGGA and TCGA databases were collected. Consensus clustering based on disulfidptosis features categorized GBM patients into two subtypes (DRGclusters). Tumor immune microenvironment, response to immunotherapy, and drug sensitivity were analyzed. An 8-gene disulfidptosis-based subtype predictor was developed using LASSO machine learning algorithm and validated on CGGA dataset. Patients in DRGcluster A exhibited improved overall survival (OS) compared to DRGcluster B. DRGcluster subtypes showed differences in tumor immune microenvironment and response to immunotherapy. The predictor effectively stratified patients into high and low-risk groups. Significant differences in IC50 values for chemotherapy and targeted therapy were observed between risk groups. Disulfidptosis-based classification offers promise as a prognostic predictor for GBM. It provides insights into tumor immune microenvironment and response to therapy. The predictor aids in patient stratification and personalized treatment selection, potentially improving outcomes for GBM patients.

Predictive potentials of glycosylation-related genes in glioma prognosis and their correlation with immune infiltration

Glycosylation is currently considered to be an important hallmark of cancer. However, the characterization of glycosylation-related gene sets has not been comprehensively analyzed in glioma, and the relationship between glycosylation-related genes and glioma prognosis has not been elucidated. Here, we firstly found that the glycosylation-related differentially expressed genes in glioma patients were engaged in biological functions related to glioma progression revealed by enrichment analysis. Then seven glycosylation genes (BGN, C1GALT1C1L, GALNT13, SDC1, SERPINA1, SPTBN5 and TUBA1C) associated with glioma prognosis were screened out by consensus clustering, principal component analysis, Lasso regression, and univariate and multivariate Cox regression analysis using the TCGA-GTEx database. A glycosylation-related prognostic signature was developed and validated using CGGA database data with significantly accurate prediction on glioma prognosis, which showed better capacity to predict the prognosis of glioma patients than clinicopathological factors do. GSEA enrichment analysis based on the risk score further revealed that patients in the high-risk group were involved in immune-related pathways such as cytokine signaling, inflammatory responses, and immune regulation, as well as glycan synthesis and metabolic function. Immuno-correlation analysis revealed that a variety of immune cell infiltrations, such as Macrophage, activated dendritic cell, Regulatory T cell (Treg), and Natural killer cell, were increased in the high-risk group. Moreover, functional experiments were performed to evaluate the roles of risk genes in the cell viability and cell number of glioma U87 and U251 cells, which demonstrated that silencing BGN, SDC1, SERPINA1, TUBA1C, C1GALT1C1L and SPTBN5 could inhibit the growth and viability of glioma cells. These findings strengthened the prognostic potentials of our predictive signature in glioma. In conclusion, this prognostic model composed of 7 glycosylation-related genes distinguishes well the high-risk glioma patients, which might potentially serve as caner biomarkers for disease diagnosis and treatment.

Expression of overall survival-EMT-immune cell infiltration genes predict the prognosis of glioma

This study investigates the crucial role of immune- and epithelial-mesenchymal transition (EMT)-associated genes and non-coding RNAs in glioma development and diagnosis, given the challenging 5-year survival rates associated with this prevalent CNS malignant tumor. Clinical and RNA data from glioma patients were meticulously gathered from CGGA databases, and EMT-related genes were sourced from dbEMT2.0, while immune-related genes were obtained from MSigDB. Employing consensus clustering, novel molecular subgroups were identified. Subsequent analyses, including ESTIMATE, TIMER, and MCP counter, provided insights into the tumor microenvironment (TIME) and immune status. Functional studies, embracing GO, KEGG, GSVA, and GSEA analyses, unraveled the underlying mechanisms governing these molecular subgroups. Utilizing the LASSO algorithm and multivariate Cox regression, a prognostic risk model was crafted. The study unveiled two distinct molecular subgroups with significantly disparate survival outcomes. A more favorable prognosis was linked to low immune scores, high tumor purity, and an abundance of immune infiltrating cells with differential expression of non-coding RNAs, including miRNAs. Functional analyses illuminated enrichment of immune- and EMT-associated pathways in differentially expressed genes and non-coding RNAs between these subgroups. GSVA and GSEA analyses hinted at abnormal EMT status potentially contributing to glioma-associated immune disorders. The risk model, centered on OS-EMT-ICI genes, exhibited promise in accurately predicting survival in glioma. Additionally, a nomogram integrating the risk model with clinical characteristics demonstrated notable accuracy in prognostic predictions for glioma patients. In conclusion, OS-EMT-ICI gene and non-coding RNA expression emerges as a valuable indicator intricately linked to immune microenvironment dysregulation, offering a robust tool for precise prognosis prediction in glioma patients within the OBMRC framework.

IGFBP3 induces PD-L1 expression to promote glioblastoma immune evasion

BackgroundGlioblastoma (GBM) characterized by immune escape is the most malignant primary brain tumors, which has strong immunosuppressive effect. Programmed death ligand-1 (PD-L1) is a recognized immunosuppressive member on the surface of tumor cells, and plays a crucial role in immune evasion of tumors. Actually, little is known about the regulation of PD-L1 expression in GBM. Insulin-like growth factor binding protein 3 (IGFBP3) is upregulated in GBM and is related to poor patient prognosis. However, it remains unclear whether IGFBP3 plays a role in the regulation of PD-L1 expression in GBM.MethodsThe role of IGFBP3 in the glioma immune microenvironment was investigated using the CIBERSORT algorithm. The correlation between IGFBP3 and PD-L1 expression was analyzed using TCGA and CGGA databases. QRT-PCR, immunoblotting and RNA-seq were used to examine the regulatory effect of IGFBP3 on PD-L1 expression. Co-culture assay, cell counting kit (CCK-8), qRT-PCR, ELISA and flow cytometry were performed to explore the function of IGFBP3 in inducing immunosuppression. The biological role of IGFBP3 was verified using immunohistochemical, immunofluorescence and mice orthotopic tumor model.ResultsIn this study, we analyzed immune cells infiltration in gliomas and found that IGFBP3 may be associated with an immunosuppressive microenvironment. Then, by analyzing TCGA and CGGA databases, our results showed that IGFBP3 and PD-L1 expression were positively correlated in GBM patients, but not in LGG patients. In vitro experiments conducted on different GBM cell lines revealed that the overexpression of IGFBP3 led to an increase in PD-L1 expression, which was reversible upon knockdown IGFBP3. Mechanistically, IGFBP3 activated the JAK2/STAT3 signaling pathway, leading to an increase in PD-L1 expression. Additionally, co-culture experiments results showed IGFBP3 overexpression induced upregulation of PD-L1 expression promoted apoptosis in Jurkat cells, and this effect was blocked by IGFBP3 antibody and PDL-1 inhibitors. Importantly, in vivo experiments targeting IGFBP3 suppressed tumor growth and significantly prolonged the survival of mice.ConclusionsThis research demonstrated IGFBP3 is a novel regulator for PD-L1 expression in GBM, and identified a new mechanism by which IGFBP3 regulates immune evasion through PD-L1, suggesting that IGFBP3 may be a potential novel target for GBM therapy.

Identification of PANoptosis-related predictors for prognosis and tumor microenvironment by multiomics analysis in glioma.

PANoptosis is a newly described inflammatory programmed cell death, that highlights coordination between pyroptosis, apoptosis and necroptosis. However, the functions of PANoptosis-related genes in glioma progression still remain to be explored. This study aims to identify PANoptosis-related predictors that may be utilized for prognosis prediction and development of new therapeutic targets. Firstly, bulk and single-cell RNA-seq (scRNA-seq) data of glioma patients were extracted from TCGA, CGGA and GEO database. Genetic analysis indicates a considerably high mutation frequency of PANoptosis-related genes (PANRGs) in glioma. Consensus clustering was applied to reveal different subtypes of glioma based on PANRGs. Two PANoptosis subtypes with distinct prognostic and TME characteristics were identified. Then, with LASSO-Cox regression analysis, four PANoptosis-related predictors (MYBL2, TUBA1C, C21orf62 and KCNIP2) were determined from bulk and scRNA-seq analysis. Predictive PANRG score model was established with these predictors and its correlation with tumor microenvironment (TME) was investigated. The results showed that patients with low PANRG score, had higher infiltration of anti-tumor immune cells, higher MSI score and lower TIDE score, which are more likely to benefit from immunotherapy. Further analysis identified 16 potential drugs associated with PANoptosis-related predictors. Moreover, the expression levels of four PANoptosis-related predictors were examined in clinical samples and the results were consistent with those analyzed in the database. Besides, we also confirmed the biological functions of two oncogenic predictors (MYBL2 and TUBA1C) by cell experiments, which revealed that knockdown of MYBL2 or TUBA1C could significantly inhibit the proliferation and migration of glioma cells. These findings highlight the prognostic value and biological functions of PANRGs in glioma, which may provide valuable insights for individualized treatment.

Hypoxia-responsive gene F3 Promotes GBM Cell Proliferation and Migration through Activating NF-κB/p65 Signaling Pathway.

Background: Glioblastoma multiforme (GBM) is the most common malignant form of glioma, but the molecular mechanisms underlying the progression of GBM in hypoxic microenvironment remain elusive. This study aims to explore the pathological functions of hypoxia-responsive genes on GBM progression and its downstream signaling pathways. Methods: RNA-seq was performed in normoxic and hypoxic U87 cells to identify the differentially expressed genes (DEGs) under hypoxia. The mRNA expression levels of hypoxia-responsive gene F3 in glioma clinical samples were analyzed according to the transcriptional information from CGGA, TCGA and Rembrandt databases. EdU, transwell and wound-healing assays were conducted to evaluate the pathological functions of F3 on GBM proliferation and migration under hypoxia. RNA-seq and gene set enrichment analysis were conducted to analyze the enriched pathways in LN229 cells overexpressed F3 compared to controls. GBM cells were treated with NF-κB inhibitor PDTC, and cell experiments were performed to evaluate the effects of PDTC on OE-F3-LN229 and OE-F3-U87 cells. Western blot was performed to validate the downstream pathways. Results: F3 was identified as a hypoxia responsive gene in GBM cells. The mRNA expression level of F3 was negatively correlated with the overall survival of glioma patients, and significantly increased in grade IV and GBM than lower grade or other histology of glioma. Overexpression of F3 enhanced the proliferation and migration of hypoxic U87 and LN229 cells, while knockdown inhibited them. In OE-F3-LN229 cells, the NF-κB pathway was activated, with an increased level of phosphorylated p65. PDTC treatment effectively rescued the enhanced proliferation and migration of OE-F3-LN229 cells under hypoxia, indicating that the effect of F3 on GBM progression is probably dependent on the NF-κB pathway. Conclusion: Hypoxia-induced F3 activates NF-κB pathway through upregulation of the phosphorylated p65, thus promoting the proliferation and migration of GBM cells under hypoxia, which might be a potential therapeutic target for GBM treatment.

Identification and validation of a novel Parkinson-Glioma feature gene signature in glioma and Parkinson's disease.

The prognosis for glioma is generally poor, and the 5-year survival rate for patients with this disease has not shown significant improvement over the past few decades. Parkinson's disease (PD) is a prevalent movement disorder, ranking as the second most common neurodegenerative disease after Alzheimer's disease. Although Parkinson's disease and glioma are distinct diseases, they may share certain underlying biological pathways that contribute to their development. This study aims to investigate the involvement of genes associated with Parkinson's disease in the development and prognosis of glioma. We obtained datasets from the TCGA, CGGA, and GEO databases, which included RNA sequencing data and clinical information of glioma and Parkinson's patients. Eight machine learning algorithms were used to identify Parkinson-Glioma feature genes (PGFGs). PGFGs associated with glioma prognosis were identified through univariate Cox analysis. A risk signature was constructed based on PGFGs using Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) method. We subsequently validated its predictive ability using various methods, including ROC curves, calibration curves, KM survival analysis, C-index, DCA, independent prognostic analysis, and stratified analysis. To validate the reproducibility of the results, similar work was performed on three external test datasets. Additionally, a meta-analysis was employed to observe the heterogeneity and consistency of the signature across different datasets. We also compared the differences in genomic variations, functional enrichment, immune infiltration, and drug sensitivity analysis based on risk scores. This exploration aimed to uncover potential mechanisms of glioma occurrence and prognosis. We identified 30 PGFGs, of which 25 were found to be significantly associated with glioma survival. The prognostic signature, consisting of 19 genes, demonstrated excellent predictive performance for 1-, 2-, and 3-year overall survival (OS) of glioma. The signature emerged as an independent prognostic factor for glioma overall survival (OS), surpassing the predictive performance of traditional clinical variables. Notably, we observed differences in the tumor microenvironment (TME), levels of immune cell infiltration, immune gene expression, and drug resistance analysis among distinct risk groups. These findings may have significant implications for the clinical treatment of glioma patients. The expression of genes related to Parkinson's disease is closely associated with the immune status and prognosis of glioma patients, potentially regulating glioma pathogenesis through multiple mechanisms. The interaction between genes associated with Parkinson's disease and the immune system during glioma development provides novel insights into the molecular mechanisms and targeted therapies for glioma.

FBLIM1 mRNA is a novel prognostic biomarker and is associated with immune infiltrates in glioma.

Glioma is the most common primary brain tumor. Filamin-binding LIM protein 1 (FBLIM1) has been identified in multiple cancers and is suspected of playing a part in the development of tumors. However, the potential function of FBLIM1 mRNA in glioma has not been investigated. In this study, the clinical information and transcriptome data of glioma patients were, respectively, retrieved from the TCGA and CGGA databases. The expression level of FBLIM1 mRNA was shown to be aberrant in a wide variety of malignancies. Significantly, when glioma samples were compared to normal brain samples, FBLIM1 expression was shown to be significantly elevated in the former. A poor prognosis was related to high FBLIM1 expression, which was linked to more advanced clinical stages. Notably, multivariate analyses demonstrated that FBLIM1 expression was an independent predictor for the overall survival of glioma patients. Immune infiltration analysis disclosed that FBLIM1 expression had relevance with many immune cells. The results of RT-PCR suggested that FBLIM1 expression was markedly elevated in glioma specimens. Functional experiments unveiled that the knockdown of FBLIM1 mRNA suppressed glioma cell proliferation. In general, we initially discovered that FBLIM1 mRNA might be a possible prognostic marker in glioma.

Leveraging a gene signature associated with disulfidptosis identified by machine learning to forecast clinical outcomes, immunological heterogeneities, and potential therapeutic targets within lower-grade glioma.

Disulfidptosis, a newly defined type of programmed cell death, has emerged as a significant regulatory process in the development and advancement of malignant tumors, such as lower-grade glioma (LGG). Nevertheless, the precise biological mechanisms behind disulfidptosis in LGG are yet to be revealed, considering the limited research conducted in this field. We obtained LGG data from the TCGA and CGGA databases and performed comprehensive weighted co-expression network analysis, single-sample gene set enrichment analysis, and transcriptome differential expression analyses. We discovered nine genes associated with disulfidptosis by employing machine learning methods like Cox regression, LASSO regression, and SVM-RFE. These were later used to build a predictive model for patients with LGG. To confirm the expression level, functional role, and impact on disulfidptosis of ABI3, the pivotal gene of the model, validation experiments were carried out in vitro. The developed prognostic model successfully categorized LGG patients into two distinct risk groups: high and low. There was a noticeable difference in the time the groups survived, which was statistically significant. The model's predictive accuracy was substantiated through two independent external validation cohorts. Additional evaluations of the immune microenvironment and the potential for immunotherapy indicated that this risk classification could function as a practical roadmap for LGG treatment using immune-based therapies. Cellular experiments demonstrated that suppressing the crucial ABI3 gene in the predictive model significantly reduced the migratory and invasive abilities of both SHG44 and U251 cell lines while also triggering cytoskeletal retraction and increased cell pseudopodia. The research suggests that the prognostic pattern relying on genes linked to disulfidptosis can provide valuable insights into the clinical outcomes, tumor characteristics, and immune alterations in patients with LGG. This could pave the way for early interventions and suggests that ABI3 might be a potential therapeutic target for disulfidptosis.

Identification and validation of chromatin regulator-related signatures as a novel prognostic model for low-grade gliomas using translational bioinformatics

AimsThe purpose of this study is to explore the potential biological role and prognostic significance of chromatin regulators (CRs) in low-grade gliomas (LGGs). Main methodsCRs were obtained from the FACER database. Transcription profiles of LGG patients were collected from the TCGA and CGGA databases. Differentially expressed CRs (DECRs) between LGGs and normal controls were identified using DESeq2. The consensus clustering algorithm was employed to distinguish subtypes of LGGs based on prognosis-related DECRs. The differences in clinical and molecular characteristics between different subtypes were explored. R packages, GSVA, ssGSEA, and ESTIMATE were utilized to elucidate the tumor microenvironment and activated pathways in different subtypes. Subsequently, a CRs-related signature was developed using LASSO Cox regression. Its performance was evaluated by Kaplan–Meier curve and ROC curve analyses. In vitro experiments were performed to explore the function of JADE3 in LGGs, which predominantly expressed in glioma cells. Key findingsWe identified 43 DECRs and two CRs-related subtypes of LGGs. The subtype characterized by shorter survival displayed significant enrichment for pathways associated with DNA damage response and repair, along with heightened immune cell infiltration. Furthermore, the CRs-based signature exhibited excellent prognostic performance in both the TCGA and CGGA databases. Knockdown of JADE3 significantly increased the invasion, migration, and proliferation abilities of Hs683. SignificanceOur study reveals the aberrant expression and prognostic value of CRs in LGGs. It emphasizes the potential regulatory role of CRs in the microenvironment and DNA damage repair in LGGs. JADE3 could be a possible therapeutic target for LGGs.•Two chromatin modification patterns which were closely related to the clinical and molecular characteristic of low-grade gliomas were identified.•Meanwhile, different modification patterns were also closely associated with the tumor microenvironment of low-grade gliomas.•A precise prognostic model based on seven chromatin regulators was successfully constructed and verified.•In vitro experiments had demonstrated that JADE3 could serve as an anti-oncogenic role in gliomas.

IDH1R132H mutation increases radiotherapy efficacy and a 4-gene radiotherapy-related signature of WHO grade 4 gliomas

The prognosis for the WHO grade 4 IDH-mutant astrocytoma is better than IDH-wildtype glioblastoma (GBM) patients. The purpose of this study is to explore the potential mechanism of how IDH1 mutation can increase the efficacy of radiotherapy and to establish a risk-score model to predict the efficacy of radiotherapy in WHO grade 4 gliomas. First, we conducted experimental study on the effect of IDH1R132H mutation on glioma cells in vitro. Radiosensitivity of glioma cells was detected by γ-H2AX after 5 Gy radiation. Cell proliferation, migration and invasion were determined respectively by CCK-8, EDU, monolayer cell migration scratch assay and Transwell assay. Then we analyzed IDH1 gene status and the survival of WHO grade 4 glioma patients received radiotherapy in our center and verified our results by analyzing CGGA and TCGA database. For the risk-score model, we use CGGA data to find genetic differences between WHO grade 4 IDH-mutant astrocytoma and IDH-wildtype GBM patients, and determined a 4-gene radiotherapy-related signature through survival analysis by R software. Evaluation and verification through different glioma validation sets and different statistical methods. For in vitro experiments, we established glioma cells stably overexpressing IDH1 wild-type and IDH1-mutant proteins. γ-H2AX assay showed that IDH1-mutant glioma cells had higher radiosensitivity than wild-type. CCK-8 and EDU assay showed that proliferation capacity of IDH1-mutant glioma cells declined. Transwell assay and monolayer cell migration scratch assay also showed that IDH1-mutant glioma cells reduced migration and invasion capabilities. Among the 83 WHO grade 4 glioma patients who received radiotherapy in our center, WHO grade 4 IDH-mutant astrocytoma patients had longer OS and PFS versus IDH-wildtype GBM (P = 0.0336, P = 0.0324, respectively). TCGA and CGGA database analysis had the similar results. Through complex analysis of CGGA and TCGA databases, we established a risk-model that can predict the efficacy of radiotherapy for WHO grade 4 glioma patients. The 4-gene radiotherapy-related signature including ADD3, GRHPR, RHBDL1 and SLC9A9. Patients in the high-risk group had worse OS compared to low-risk group (P = 0.0001). High- and low-risk groups of patients receiving radiotherapy have significant survival differences, while patients who did not receive radiotherapy have no survival difference both in CGGA and TCGA databases. WHO grade 4 IDH-mutant astrocytoma is more radiosensitive than IDH-wildtype GBM patients. Our 4-gene radiotherapy-related signature can predict the radiation efficacy of WHO grade 4 glioma patients, and it may provide some reference for clinical treatment options.

A TGF-β signaling-related lncRNA signature for prediction of glioma prognosis, immune microenvironment, and immunotherapy response.

The dysregulation of TGF-β signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF-β signaling in glioma is currently unclear. Data on glioma's RNA-seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF-methylation-gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. Invitro and invivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA. In glioma, a TGF-β signaling-related 15-lncRNA signature was constructed, including AC010173.1, HOXA-AS2, AC074286.1, AL592424.1, DRAIC, HOXC13-AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC-AS1, AGAP2-AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP-1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2-AS1 knockdown in invitro and invivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF-κB and ERK 1/2 in the TGF-β signaling pathway. A prognostic lncRNA signature of TGF-β signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2-AS1 plays an important role in glioma progression.