Survival In Lower-grade Gliomas Research Articles

Pan-cancer analysis of ADAR1 with its prognostic relevance in low-grade glioma

ADAR1, known as the primary enzyme for adenosine-to-inosine RNA editing, has recently been implicated in cancer development through both RNA editing-dependent and −independent pathways. These discoveries suggest that ADAR1′s functions may extend beyond our current understanding. A pan-cancer analysis offers a unique opportunity to identify both common and distinct mechanisms across various cancers, thereby advancing personalized medicine. Low-grade glioma (LGG), characterized by a diverse group of tumor cells, presents a challenge in risk stratification, leading to significant variations in treatment approaches. Recently discovered molecular alterations in LGG have helped to refine the stratification of of these tumors and offered novel targets for predicting likely outcomes. This study aims to provide a detailed analysis of ADAR mRNA across multiple cancers, emphasizing its prognostic significance in LGG. We observed inconsistent mRNA and consistent protein expression patterns of ADAR1/ADAR in pan-cancer analyses that across tumor types. ADAR mRNA expression did not always correlate with ADAR1 protein expression. Nevertheless, the transcript levels correlated significantly with genetic alterations, tumor mutation burden, microsatellite instability, overall survival, recurrence-free survival, immune marker presence, immune infiltration, and the survival of patients undergoing immunotherapy in select cancers. Furthermore, ADAR and its top 50 associated genes were primarily involved in mRNA-related events, as identified through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Utilizing the Cox proportional hazards model, we developed a 3-gene signature (ADAR, HNRNPK, and SMG7), which effectively stratified patients into high- and low-risk groups, with high-risk patients exhibiting poorer overall survival, higher tumor grades, and a greater number of non-codeletions. Overall, this signature was inversely related to immune infiltration across cancers. Transcription factor SPI1 and miR-206, potential upstream regulators of the signature genes, were closely linked to patient survival in LGG. The promoter regions of these genes were hypermethylated, further associating them with patient outcomes. Additionally, these genes displayed consistent drug susceptibility patterns. In conclusion, our findings reveal multiple aspects of ADAR1′s role in cancer and underscore its prognostic value in LGG, offering insights into potential therapeutic targets and strategies.

CD47 and IFT57 Are Colinear Genes That Are Highly Coexpressed in Most Cancers and Exhibit Parallel Cancer-Specific Correlations with Survival.

An association between high CD47 expression and poor cancer survival has been attributed to its function on malignant cells to inhibit phagocytic clearance. However, CD47 mRNA expression in some cancers lacks correlation or correlates with improved survival. IFT57 encodes an essential primary cilium component and is colinear with CD47 across amniote genomes, suggesting coregulation of these genes. Analysis of The Cancer Genome Atlas datasets identified IFT57 as a top coexpressed gene with CD47 among 1156 human cancer cell lines and in most tumor types. The primary cilium also regulates cancer pathogenesis, and correlations between IFT57 mRNA and survival paralleled those for CD47 in thyroid and lung carcinomas, melanoma, and glioma. CD47 ranked first for coexpression with IFT57 mRNA in papillary thyroid carcinomas, and higher expression of both genes correlated with significantly improved overall survival. CD47 and IFT57 mRNAs were coordinately regulated in thyroid carcinoma cell lines. Transcriptome analysis following knockdown of CD47 or IFT57 in thyroid carcinoma cells identified the cytoskeletal regulator CRACD as a specific target of IFT57. CRACD mRNA expression inversely correlated with IFT57 mRNA and with survival in low-grade gliomas, lung adenocarcinomas, and papillary thyroid carcinomas, suggesting that IFT57 rather than CD47 regulates survival in these cancers.

Higher YAP1 Levels Are Associated With Shorter Survival of Patients With Low Grade Astrocytoma.

Glioblastoma multiforme (GBM) is one of the most lethal types of brain cancer with a median survival of only 12 months due to its aggressiveness and lack of effective treatment options. Astrocytomas and oligodendrogliomas are classified as low-grade gliomas (LGG) and have the potential to progress into secondary GBM. YAP1 and TAZ are transcriptional co-activators of the hippo pathway and play an important role in tumorigenesis by controlling cell proliferation and differentiation. The aim of this study was to analyze whether YAP1 and TAZ influence the survival in patients with astrocytoma and oligodendroglioma. A total of 22 patient samples of astrocytoma and 11 samples of oligodendroglioma were analyzed using real-time PCR. We utilized open-access data from The Cancer Genome Atlas (TCGA) focusing on "brain lower grade glioma". mRNA expression rates were used to validate our findings on survival analysis. Expression of YAP1 was twice as high in astrocytoma than in oligodendroglioma, whereas there was no difference in TAZ. In oligodendrogliomas, the expression of TAZ was higher in relapsed than in primary tumors. Patients with astrocytoma having a high YAP1 expression had a significantly shorter overall survival than patients with lower expression (median survival 161 vs. 86 months, p=0.0248). These findings were validated with survival analysis of TCGA data. High YAP1 expression shows a high correlation with poorer overall survival in LGG. YAP1 has higher levels of expression in astrocytomas than in oligodendrogliomas.

Single center experience of IDH inhibitors in high-grade gliomas.

e14036 Background: IDH inhibitors have shown to improve progression free survival in low grade gliomas. Antitumor effect in high-grade gliomas is not well known, with conflicting views regarding the role of IDH mutations in high-grade gliomas and the potential for resistance to DNA-damage-inducing therapies. We present the outcomes of high-grade gliomas that were treated in our institution with IDH inhibitors. Methods: We performed a retrospective review of patients with gliomas treated with FDA approved IDH inhibitors (ivosidenib and enasidenib) in the past 5 years at our institution (January 1st 2019- January 1st 2024). Our search identified 32 patients of which 11 were identified as high-grade gliomas (both oligodendrogliomas and astrocytomas). Institutional Review Board PA17-0222 was used for this study. Results: Eleven high grade, IDH-mutant glioma patients treated with IDH inhibitors were identified with a median age at diagnosis of 31 years; 63.6% of these patients were men. Over 63% of patients were categorized as WHO grade 3 and 36.4% as grade 4 tumors. Over 63% of patients were astrocytoma (4 out of 7 patients with WHO grade 4 astrocytoma) while the rest were oligodendrogliomas. All patients underwent initial resection and had received radiation plus at least one prior line of systemic therapy prior to starting an IDH inhibitor. Over 72% were started on IDH-inhibitor therapy after 3 or more recurrences; previous lines of systemic therapy included temozolomide, bevacizumab, and lomustine. Five patients were treated with more than 1 cycle of IDH inhibitor, 2 patients with 8 cycles, and 1 patient with 18 cycles. Of the 11 treated patients, four had progression while on an IDH inhibitor, of which one patient died. IDH inhibitors were discontinued in two patients (due to headaches and abnormal liver enzymes). Median survival and progression free survival after IDH inhibitors were not reached. Conclusions: IDH inhibitors were started on patients with high-grade astrocytoma and oligodendroglioma after multiple recurrences and after receiving DNA-damage-inducing therapies. Prolonged responses were observed in 3 patients (one oligodendroglioma and two WHO grade 3 astrocytomas). Further investigation is needed to establish the role for IDH inhibitors in the treatment of high-grade gliomas.

A cuproptosis-based prognostic model for predicting survival in low-grade glioma.

It is unknown what variables contribute to the formation and multiplication of low-grade gliomas (LGG). An emerging process of cell death is called cuproptosis. Our research aims to increase therapeutic options and gain a better understanding of the role that cuproptosis-related genes play in the physical characteristics of low-grade gliomas. The TCGA database was utilized to find cuproptosis genes that may be used to develop LGG risk model. Cox analysis in three different formats: univariate, multivariate, and LASSO. The gene signature's independent predictive ability was assessed using ROC curves and Cox regression analysis based on overall survival. Use of CGGA data and nomogram model for external validation Immunohistochemistry, gene mutation, and functional enrichment analysis are also employed to clarify risk models' involvement. Next, we analyzed changes in the immunological microenvironment in the risk model and forecasted possible chemotherapeutic drugs to target each group. Finally, we validated the protein expression levels of cuproptosis-related genes using LGG and adjacent normal tissues in a small self-case-control study. This study developed a glioma predictive model based on five cuproptosis-associated genes. Compared to the high-risk group, the low-risk group's OS was significantly longer. The ROC curves showed high genetic signature performance in both groups. The signature-based categorisation was also linked to clinical characteristics and molecular subgroups. The prognosis of individuals with grade 2 or 3 glioma is also influenced by our risk model. Immunological testing revealed that the high-risk group had more immune cells and immunological function. The risk model also predicted immunotherapy and chemotherapy medication results. Also, this study confirmed that the expression of cuproptosis-related genes by Western blot. We developed a prediction model for LGG patients using genes associated with cuproptosis. With acceptable prediction performance, this risk model may effectively stratify the prognosis of glioma patients.

Characterization and expression of the microtubule associated protein tau gene isoforms and their impact on clinical outcomes in glioma patients

Gliomas are the most common tumors found in the central nervous system. They are classified based on their histopathological malignancy. Low-grade gliomas (LGG) can turn into glioblastomas (GBM), the most aggressive form of brain cancer with the worst prognosis. Previous studies have shown that microtubule-associated protein tau (MAPT) is increased in GBM and that reducing it can improve survival in animal models. On the other hand, elevated MAPT expression has been linked to increased survival in LGG, indicating its potential as a survival biomarker. However, previous studies have primarily focused on the canonical protein-coding transcripts and gene-level expression of MAPT, disregarding the diversity of its many isoforms. Many diseases in almost every tissue have been associated with a dysregulation of alternative splicing, either through an altered ratio of the expressed isoforms or by expressing aberrant transcripts with different alternative transcription start and termination sites. This study investigates the association of specific MAPT transcripts with survival in LGG and GBM using publicly available genomic datasets and performing statistical analyses. The results of the study indicated coding and several non-coding isoforms associated with overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). After including the relevant predictors as covariates, it was observed that among the protein-coding isoforms, MAPT-205 and MAPT-201 were significantly associated with clinical survival endpoints, but with opposite trends. These findings highlight the importance of studying the role of MAPT isoforms in glioma cellular phenotypes, aggressiveness, and patient survival.

Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma.

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma. A total of 1306 patients have been included in this study, and the results obtained from the training set can be verified by four independent validation sets. The low-risk subgroup had longer overall survival in five datasets, and its AUC values all reached above 0.7. The risk groups divided by the NTGs signature exhibited a distinct difference in targeted therapies from the copy-number variation, somatic mutation, LGG's surrounding microenvironment, and drug response. MR corroborated that TGIF1 was a potential causal target for increasing the risk of glioma. Our study identified a five-NTGs signature that presented an excellent survival prediction and potential biological function, providing new insight for the selection of LGGs therapy.

A pyroptosis gene-based prognostic model for predicting survival in low-grade glioma.

Pyroptosis, a lytic form of programmed cell death initiated by inflammasomes, has been reported to be closely associated with tumor proliferation, invasion and metastasis. However, the roles of pyroptosis genes (PGs) in low-grade glioma (LGG) remain unclear. We obtained information for 1,681 samples, including the mRNA expression profiles of LGGs and normal brain tissues and the relevant corresponding clinical information from two public datasets, TCGA and GTEx, and identified 45 differentially expressed pyroptosis genes (DEPGs). Among these DEPGs, nine hub pyroptosis genes (HPGs) were identified and used to construct a genetic risk scoring model. A total of 476 patients, selected as the training group, were divided into low-risk and high-risk groups according to the risk score. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves verified the accuracy of the model, and a nomogram combining the risk score and clinicopathological characteristics was used to predict the overall survival (OS) of LGG patients. In addition, a cohort from the Gene Expression Omnibus (GEO) database was selected as a validation group to verify the stability of the model. qRT-PCR was used to analyze the gene expression levels of nine HPGs in paracancerous and tumor tissues from 10 LGG patients. Survival analysis showed that, compared with patients in the low-risk group, patients in the high-risk group had a poorer prognosis. A risk score model combining PG expression levels with clinical features was considered an independent risk factor. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched among the DEPGs and that immune activity was increased in the high-risk group. In summary, we successfully constructed a model to predict the prognosis of LGG patients, which will help to promote individualized treatment and provide potential new targets for immunotherapy.

The effect Of vascular related CeRNA genes and corresponding imaging biomarkers on survival in lower grade glioma.

To investigate the differential expression of vascular related ceRNA regulatory genes in LGG with different mutations of IDH1 and MGMT, and to verify imaging gene markers that can be closely associated with vascular related ceRNA regulatory genes. Five hundred fifteen patients with LGG were collected from TCGA database. CeRNA network analysis, GO analysis and Cox risk regression were used to find vascular ceRNA regulatory genes and their genetic markers related to survival. The preoperative MRI image data and postoperative tumor tissues of 14 patients with WHO grade III glioma were collected for full transcriptome analysis. The correlation between image characteristics of LGG and survival related vascular ceRNA regulatory genes was compared using nonparametric U test and Pearson correlation coefficient analysis. Vascular related genes ranked first in the functional enrichment analysis of differentially expressed genes in LGG. EPHA2, ETS1, YAP1 and MEIS1 could significantly affect the survival of patients in each group of LGG. The volume of enhanced region was negatively correlated with IDH1 (r = -0.622, P = 0.009) mutation and TMEM100 (r = -0.535, P = 0.024), and positively correlated with MEIS1 (r = 0.551, P = 0.021), rCBFmax value was negatively correlated with TMEM100 (r = -0.492, P = 0.037). Under different IDH1 mutations, lncRNA-dominated vascular-related ceRNA regulatory genes were the first differentially expressed subset of each group, and could be used as an effective risk factor affecting the survival of LGG. The image characteristics of LGG was an ideal image gene marker. It was a reliable imaging biological marker which can truly reflect the pathophysiological characteristics of glioma.

A pan-cancer analysis of RGR opsin expression and its downregulation associated with poor prognosis in glioma.

Retinal G protein-coupled receptor (RGR) serves a retinal photoisomerase function to mediate retinoid metabolism and visual chromophore regeneration in the human eyes. Retinoids display critical functions in cell proliferation, differentiation, and apoptosis. Abnormal retinoid metabolism may contribute to tumor development. However, in human tumor tissues, the expression of RGR remains uncharacterized. Herein, we performed the analysis of RGR expression in 620 samples from 24 types of tumors by immunohistochemistry (IHC) and 33 cancer types from the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases by bioinformatic analyses. Furthermore, the biological role of RGR in glioma cells was investigated using molecular biology approaches in vitro. Notably, we found that brain lower grade glioma (LGG), in contrast to other tumor types, had the highest median score of IHC and RNA level of RGR expression. Survival analysis showed that low RGR expression was associated with worse overall survival in LGG (p<0.0001). RGR expression levels in glioma were also associated with pathological subtypes, grades, and isocitrate dehydrogenase (IDH) mutations. Moreover, its molecular function was closely associated with cadherin-related family member 1 (CDHR1), a tumor suppressive protein in glioma, suggesting that RGR might negatively regulate the tumorigenesis and progression of LGG through interacting with CDHR1. Our findings provide new insight into the role of RGR in human cancer, especially in glioma.

SLC10A3 Is a Prognostic Biomarker and Involved in Immune Infiltration and Programmed Cell Death in Lower Grade Glioma

The association between SLC10A3 (solute carrier family 10 member 3) and lower grade glioma (LGG) remains unclear. We used public databases and bioinformatics analysis to analyze SLC10A3. These included The Cancer Genome Atlas, Genotype-Tissue Expansion, Chinese Glioma Genome Atlas, Human Protein Atlas, GeneCards, cBioPortal, Search Tool for the Retrieval of Interacting Genes/Proteins, Gene Expression Profiling Interactive Analysis, Tumor Immune Estimation Resource, Tumor-Immune System Interaction Database, receiver operating characteristic curve analysis, Kaplan-Meier analysis, Cox analysis, nomograms, calibration plots, gene ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis, gene set enrichment analysis, single-sample gene set enrichment analysis, and Spearman's correlation analysis. SLC10A3 was upregulated in adrenocortical carcinoma, glioblastoma, and LGG and was associated with good overall survival (OS) in adrenocortical carcinoma and poor OS in LGG and glioblastoma. SLC10A3 was increased with increased World Health Organization grade, upregulated in isocitrate dehydrogenase-wild type, 1p/19q (chromosome arms 1p and 19q) non-co-deleted, and higher in astrocytoma. Patients with LGG were grouped by the occurrence of the clinical outcome endpoints (i.e., OS, disease-specific survival [DSS], and progression-free interval events). Genetic alterations in SLC10A3 were associated with poor progression-free survival in LGG. Most of clinical characteristics were associated with the SLC10A3 expression level. SLC10A3 with diagnostic and prognostic value (OS, DSS, and progression-free interval) was an independent prognostic factor in LGG. Moreover, Nomograms (WHO grade, 1p/19q codeletion, age and SLC10A3) had moderately accurate predictive for OS and DSS. Functional analysis showed that SLC10A3 might participate in the transport of multiple substances, neurogenic signaling, immune response, and programmed cell death in LGG. SLC10A3 correlated with immune infiltration in LGG and moderately correlated with the gene signature of pyroptosis, lysosome-dependent cell death, necroptosis, apoptosis, ferroptosis, alkaliptosis, and autophagy-dependent cell death. SLC10A3 is a potential diagnostic and prognostic biomarker for LGG and might be associated with substance transport, neurogenic signaling, immune infiltration, and programmed cell death in LGG.

An alternative extension of telomeres related prognostic model to predict survival in lower grade glioma.

The alternative extension of the telomeres (ALT) mechanism is activated in lower grade glioma (LGG), but the role of the ALT mechanism has not been well discussed. The primary purpose was to demonstrate the significance of the ALT mechanism in prognosis estimation for LGG patients. Gene expression and clinical data of LGG patients were collected from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) cohort, respectively. ALT-related genes obtained from the TelNet database and potential prognostic genes related to ALT were selected by LASSO regression to calculate an ALT-related risk score. Multivariate Cox regression analysis was performed to construct a prognosis signature, and a nomogram was used to represent this signature. Possible pathways of the ALT-related risk score are explored by enrichment analysis. The ALT-related risk score was calculated based on the LASSO regression coefficients of 22 genes and then divided into high-risk and low-risk groups according to the median. The ALT-related risk score is an independent predictor of LGG (HR and 95% CI in CGGA cohort: 5.70 (3.79, 8.58); in TCGA cohort: 1.96 (1.09, 3.54)). ROC analysis indicated that the model contained ALT-related risk score was superior to conventional clinical features (AUC: 0.818 vs 0.729) in CGGA cohorts. The results in the TCGA cohort also shown a powerful ability of ALT-related risk score (AUC: 0.766 vs 0.691). The predicted probability and actual probability of the nomogram are consistent. Enrichment analysis demonstrated that the ALT mechanism was involved in the cell cycle, DNA repair, immune processes, and others. ALT-related risk score based on the 22-gene is an important factor in predicting the prognosis of LGG patients.

Increased KCNN4 Expression Is Correlated With Poor Survival in Lower Grade Glioma.

Networks of glioma cells are linked to small groups of pacemaker cells in which levels of calcium ions pulse periodically, driving a signal through the network that causes tumor growth. Using inhibitors, one study blocked the activity of the Ca2+activated potassium-channel protein KCa3.1 in in vitro models and mice, preventing the proliferation of glioma cells and tumor expansion. Marked reduction of tumor cell viability occurred within the entire network, as well as reduced tumor growth in mice and extended animal survival. KCa3.1 is encoded by the gene potassium calcium-activated channel subfamily N member 4 (KCNN4) on the chromosomal location 19q13.31. We used the Cancer Genome Atlas (TCGA) to evaluate the effect of KCNN4 on human glioma survival in the TCGA Lower Grade Glioma (LGG) dataset. In humans, KCNN4 is prognostic in glioma; high expression is unfavorable. In addition, KCNN4 copy number variations are prognostic. Increased masked copy number segments are unfavorable in lower grade glioma. KCNN4 is lost in gliomas with the 1p 19q co-deletion, which may explain in part the comparatively favorable prognosis of 1p 19q co-deletion tumors. Our finding of increased KCNN4 expression related to poor survival in human lower grade glioma suggests that developing novel therapies, such as KCa3.1-inhibiting drugs, might be worthwhile.

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79–0.86) for the training cohort over 10 years and 0.74 (Cl 0.64–0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73–0.82) for training and 0.67 (Cl 0.57–0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

ABCC8 is correlated with immune cell infiltration and overall survival in lower grade glioma

ATP binding cassette subfamily C member 8 (ABCC8) encodes a protein regulating the ATP-sensitive potassium channel. Whether the level of ABCC8 mRNA in lower grade glioma (LGG) correlates with immune cell infiltration and patient outcomes has not been evaluated until now. Comparisons of ABCC8 expression between different tumors and normal tissues were evaluated by exploring publicly available datasets. The association between ABCC8 and tumor immune cell infiltration, diverse gene mutation characteristics, tumor mutation burden (TMB), and survival in LGG was also investigated in several independent datasets. Pathway enrichment analysis was conducted to search for ABCC8-associated signaling pathways. Through an online database, we found that ABCC8 expression in LGG was lower than in normal tissues. Then, the association of ABCC8 expression and immune cell infiltration in LGG was discussed. As we expected, the ABCC8 mRNA levels were negatively associated with non-T immune cell infiltration levels in all datasets. Consistently, TCGA_LGG RNA-seq data revealed that ABCC8 downregulated several non-T immune cell-associated signaling pathways in gene set enrichment analysis. Different ABCC8 expression groups showed diverse gene mutation characteristics and TMB. The high expression of ABCC8 was linked to improved survival of LGG patients. A pathway enrichment analysis of ABCC8-associated genes indicated that the GABAergic synapse signaling pathway might be involved in regulating immunity in LGG. Our findings show that ABCC8 reflects LGG tumor immunity and is an ideal prognostic biomarker for LGG.

Exploring the Correlation of Abnormal CXCL9 Expression with Immune Infiltration in Glioma and Patient Prognosis Based on TCGA and GTEx Databases.

In this study, we intend to identify key immune-related genes (IRGs) in gliomas using the TCGA and GTEx databases. Following collection of the RNA-seq data of lower-grade glioma (LGG) and glioblastoma (GBM) patients from the TCGA and GTEx databases, the differentially expressed IRGs (DE-IRGs) were screened. The ESTIMATE algorithm was utilized to evaluate StromalScore and ImmuneScore of LGG and GBM samples and a multifactorial Cox risk model was constructed to identify the related risk genes. The core IRGs of LGG and GBM were screened through a PPI network, followed by exploration of their correlation with glioma prognosis. The relationship between IRGs and immune cells in LGG and GBM was detected. In vitro assays were performed to detect the effect of CXCL9 on glioma cell development. We screened 403 and 492 DE-IRGs in LGG and GBM. StromalScore and ImmuneScore were related to overall survival in LGG, but not in GBM. CXCL9 was identified as a core gene in LGG and GBM and shared association with the prognosis of gliomas. Furthermore, a correlation was found between CXCL9 and immune infiltration of LGG and GBM. Glioma cell proliferation and invasion could be inhibited by silencing of CXCL9. Overall, CXCL9 is correlated to the prognosis of glioma patients and may accelerate glioma development via immune regulation.

A Risk Model Developed Based on Homologous Recombination Deficiency Predicts Overall Survival in Patients With Lower Grade Glioma

The role of homologous recombination deficiency (HRD) in lower grade glioma (LGG) has not been elucidated, and accurate prognostic prediction is also important for the treatment and management of LGG. The aim of this study was to construct an HRD-based risk model and to explore the immunological and molecular characteristics of this risk model. The HRD score threshold = 10 was determined from 506 LGG samples in The Cancer Genome Atlas cohort using the best cut-off value, and patients with high HRD scores had worse overall survival. A total of 251 HRD-related genes were identified by analyzing differentially expressed genes, 182 of which were associated with survival. A risk score model based on HRD-related genes was constructed using univariate Cox regression, least absolute shrinkage and selection operator regression, and stepwise regression, and patients were divided into high- and low-risk groups using the median risk score. High-risk patients had significantly worse overall survival than low-risk patients. The risk model had excellent predictive performance for overall survival in LGG and was found to be an independent risk factor. The prognostic value of the risk model was validated using an independent cohort. In addition, the risk score was associated with tumor mutation burden and immune cell infiltration in LGG. High-risk patients had higher HRD scores and “hot” tumor immune microenvironment, which could benefit from poly-ADP-ribose polymerase inhibitors and immune checkpoint inhibitors. Overall, this big data study determined the threshold of HRD score in LGG, identified HRD-related genes, developed a risk model based on HRD-related genes, and determined the molecular and immunological characteristics of the risk model. This provides potential new targets for future targeted therapies and facilitates the development of individualized immunotherapy to improve prognosis.

FCGR3A Is a Prognostic Biomarker and Correlated with Immune Infiltrates in Lower-Grade Glioma.

Low-grade gliomas (LGGs) are primary invasive brain tumors that grow slowly but are incurable and eventually develop into high malignant glioma. Fc fragment of IgG receptor IIIa (FCGR3A) gene polymorphism may correlate with some cancers' treatment responses. However, the expression and prognosis value of FCGR3A and correlation with tumor-immune infiltrate in LGG remain unclear. FCGR3A mRNA expression in gastric cancer (GC) was examined using TIMER and GEPIA databases. Correlations between FCGR3A expression and clinicopathological parameters were analyzed using ULACAN and CGGA databases. GEPIA, OncoLnc, and ULACAN databases were used to examine the clinical prognostic significance of FCGR3A in LGG. TIMER was used to analyze the correlations among FCGR3A and tumor-infiltrating immune cells. Signaling pathways related to FCGR3A expression were identified by LinkedOmics. We found that FCGR3A expression was higher in LGG than in normal tissue and was correlated with various clinical parameters. In addition, high FCGR3A expression predicted poor overall survival in LGG. More importantly, FCGR3A expression positively correlated with immune checkpoint molecules, including PD1, PD-L1, PD-L2, CTLA4, LAG-3 and TIM-3, and tumor-associated macrophage (TAM) gene markers in LGG. GO and KEGG pathway analyses indicated that TUBA1C may potentially regulate the pathogenesis of LGG through immune-related pathways. These findings indicated that FCGR3A plays a vital role in the infiltration of immune cells and could constitute a promising prognostic biomarker in LGG patients.

PTPRN Serves as a Prognostic Biomarker and Correlated with Immune Infiltrates in Low Grade Glioma.

Background: Glioma is one of the most common malignant tumors of the central nervous system. Immune infiltration of tumor microenvironment was associated with overall survival in low grade glioma (LGG). However, effects of Tyrosine phosphatase receptor type N (PTPRN) on the progress of LGG and its correlation with tumor infiltration are unclear. Methods: Here, datasets of LGG were from The Cancer Genome Atlas (TCGA) and normal samples were from GTEx dataset. Gepia website and Human Protein Atlas (HPA) Database were used to analyze the mRNA and protein expression of PTPRN. We evaluated the influence of PTPRN on survival of LGG patients. MethSurv was used to explore the expression and prognostic patterns of single CpG methylation of PTPRN gene in LGG. The correlations between the clinical information and PTPRN expression were analyzed using logistic regression and Multivariate Cox regression. We also explored the correlation between PTPRN expression and cancer immune infiltration by TIMER. Gene set enrichment analysis (GSEA) was formed using TCGA RNA-seq datasets. Results: PTPRN mRNA and protein expression decreased in LGG compared to normal brain tissue in TCGA and HPA database. Kaplan-Meier analysis showed that the high expression level of PTPRN correlated with a good overall survival (OS) of patients with LGG. The Multivariate Cox analysis demonstrated that PTPRN expression and other clinical-pathological factors (age, WHO grade, IDH status, and primary therapy outcome) significantly correlated with OS of LGG patients. The DNA methylation pattern of PTPRN with significant prognostic value were confirmed, including cg00672332, cg06971096, cg01382864, cg03970036, cg10140638, cg16166796, cg03545227, and cg25569248. Interestingly, PTPRN expression level significantly negatively correlated with infiltrating level of B cell, CD4+ T cells, Macrophages, Neutrophils, and DCs in LGG. Finally, GSEA showed that signaling pathways, mainly associated with tumor microenvironment and immune cells, were significantly enriched in PTPRN high expression. Conclusion: PTPRN is a potential biomarker and correlates with tumor immune infiltration in LGG.

Construction and Verification of a Glycolysis-Associated Gene Signature for the Prediction of Overall Survival in Low Grade Glioma.

The overall survival of patients with lower grade glioma (LGG) that might develop into high-grade malignant glioma shows marked heterogeneity. The currently used clinical evaluation index is not sufficient to predict precise prognostic outcomes accurately. To optimize survival risk stratification and the personalized management of patients with LGG, there is an urgent need to develop an accurate risk prediction model. The TCGA-LGG dataset, downloaded from The Cancer Genome Atlas (TCGA) portal, was used as a training cohort, and the Chinese Glioma Genome Atlas (CGGA) dataset and Rembrandt dataset were used as validation cohorts. The levels of various cancer hallmarks were quantified, which identified glycolysis as the primary overall survival-related risk factor in LGGs. Furthermore, using various bioinformatic and statistical methods, we developed a strong glycolysis-related gene signature to predict prognosis. Gene set enrichment analysis showed that in our model, high-risk glioma correlated with the chemoradiotherapy resistance and poor survival. Moreover, based on established risk model and other clinical features, a decision tree and a nomogram were built, which could serve as useful tools in the diagnosis and treatment of LGGs. This study indicates that the glycolysis-related gene signature could distinguish high-risk and low‐risk patients precisely, and thus can be used as an independent clinical feature.