Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Predicted factors of surgical site infection in glioblastoma patients: A meta-analysis.

Surgical site infection (SSI) is one of the common postoperative complications after craniotomy for glioblastoma patients. Previous studies have investigated the risk factors for SSI in patients with glioblastoma. Whereas big differences in research results exist, and the correlation coefficients of different research results are quite different. A meta-analysis was conducted to examine the risk factors related to surgical site infection in patients with glioblastoma. We searched English databases to collect case-control studies or cohort studies published before 15 October 2023 including PubMed, Web of Science, Embase. The risk of bias of the included studies was assessed via Newcastle-Ottawa Scale. The analysis was performed using RevMan 5.4.1 tool. A total of 4 articles (n = 2222) were selected in this meta-analysis. The following risk factors were presented to be correlated with SSI in glioblastoma: irradiation (OR = 1.88, 95% CI [0.46, 7.60]), more than 3 surgeries (OR = 2.99, 95% CI [1.47, 6.08]). Occurrence of SSI is influenced by a variety of factors. Thus, we should pay close attention to high-risk subjects and take crucial targeted interventions to lower the SSI risk following craniotomy. Owing to the limited quality and quantity of the included studies, more rigorous studies with adequate sample sizes are needed to verify the conclusion.

Immunological classification of glioblastoma and its prognostic implications.

OBJECTIVES: The progress of immunotherapy for glioblastoma (GBM) is currently slow. To improve immunotherapy, we need a deeper understanding of the immune microenvironment of GBM. Here, we aimed to establish a classification system based on immune expression profile in GBM. METHODS: Immune gene expression profiles of 152 patients with GBM from The Cancer Genome Atlas (TCGA) were used to identify subtypes by consensus clustering, and the classification system was reproduced in the two validation datasets (CGGA and GSE16011). Clinical information, molecular characteristics, immune infiltration, and genomic variation were integrated to characterize the subtypes. RESULTS: Two distinct immune subtypes in GBM were successfully identified and validated. The Im2 subtype was closely related to IDH-wildtype and combined +7/-10, while the Im1 subtype was associated with IDH mutation. Survival curve analysis showed that the Im2 subtype was associated with significantly shorter survival than the Im1 subtype. Im2 showed a high immune score and stromal score, low tumor purity, enrichment of macrophages, and high immune checkpoint and HLA gene expression. Im1 was characterized by low immune score and stromal score, high tumor purity, enrichment of lymphocytes, and low immune checkpoint and HLA gene expression. Finally, we developed an immune-related signature in GBM with better prognosis prediction. CONCLUSIONS: Our study confirmed the immune heterogeneity of GBM and might provide valuable classification for immunotherapy.

WES data from 286 diffuse gliomas under the 2021 WHO Classification of Tumors of the Central Nervous System.

Diffuse gliomas (DGs) are the most common and lethal primary neoplasms in the central nervous system. The latest 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) was published in 2021, immensely changing the approach to diagnosis and decision making. As a part of the Chinese Glioma Genome Atlas (CGGA) project, our aim was to provide genomic profiling of gliomas in a Chinese cohort. Two hundred eighty six gliomas with different grades were collected over the last decade. Using the Illumina HiSeq platform, over 75.8 million high-quality 150 bp paired-end reads were generated per sample, yielding a total of 43.4 billion reads. We also collected each patient's clinical and pathological information and used it to annotate their genetic data. All patients were diagnosed and classified by neuro-pathologist under the 2021 WHO classification. This dataset provides an important reference for researchers and will significantly advance our understanding of gliomas.

Identification of a 6-RBP gene signature for a comprehensive analysis of glioma and ischemic stroke: Cognitive impairment and aging-related hypoxic stress.

There is mounting evidence that ischemic cerebral infarction contributes to vascular cognitive impairment and dementia in elderly. Ischemic stroke and glioma are two majorly fatal diseases worldwide, which promote each other's development based on some common underlying mechanisms. As a post-transcriptional regulatory protein, RNA-binding protein is important in the development of a tumor and ischemic stroke (IS). The purpose of this study was to search for a group of RNA-binding protein (RBP) gene markers related to the prognosis of glioma and the occurrence of IS, and elucidate their underlying mechanisms in glioma and IS. First, a 6-RBP (POLR2F, DYNC1H1, SMAD9, TRIM21, BRCA1, and ERI1) gene signature (RBPS) showing an independent overall survival prognostic prediction was identified using the transcriptome data from TCGA-glioma cohort (n = 677); following which, it was independently verified in the CGGA-glioma cohort (n = 970). A nomogram, including RBPS, 1p19q codeletion, radiotherapy, chemotherapy, grade, and age, was established to predict the overall survival of patients with glioma, convenient for further clinical transformation. In addition, an automatic machine learning classification model based on radiomics features from MRI was developed to stratify according to the RBPS risk. The RBPS was associated with immunosuppression, energy metabolism, and tumor growth of gliomas. Subsequently, the six RBP genes from blood samples showed good classification performance for IS diagnosis (AUC = 0.95, 95% CI: 0.902-0.997). The RBPS was associated with hypoxic responses, angiogenesis, and increased coagulation in IS. Upregulation of SMAD9 was associated with dementia, while downregulation of POLR2F was associated with aging-related hypoxic stress. Irf5/Trim21 in microglia and Taf7/Trim21 in pericytes from the mouse cerebral cortex were identified as RBPS-related molecules in each cell type under hypoxic conditions. The RBPS is expected to serve as a novel biomarker for studying the common mechanisms underlying glioma and IS.

Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks.

PURPOSE: This study aimed to evaluate the diagnostic performance of convolutional neural network (CNN) models in Chiari malformation type I (CMI) and to verify whether CNNs can identify the morphological features of the craniocervical junction region between patients with CMI and healthy controls (HCs). To date, numerous indicators based on manual measurements are used for the diagnosis of CMI. However, the corresponding postoperative efficacy and prognostic evaluations have remained inconsistent. From a diagnostic perspective, CNN models may be used to explore the relationship between the clinical features and image morphological parameters. METHODS: This study included a total of 148 patients diagnosed with CMI at our institution and 205 HCs were included. T1-weighted sagittal magnetic resonance imaging (MRI) images were used for the analysis. A total of 220 and 355 slices were acquired from 98 patients with CMI and 155 HCs, respectively, to train and validate the CNN models. In addition, median sagittal images obtained from 50 patients with CMI and 50 HCs were selected to test the models. We applied original cervical MRI images (CI) and images of posterior cranial fossa and craniocervical junction area (CVI) to train the CI- and CVI-based CNN models. Transfer learning and data augmentation were used for model construction and each model was retrained 10 times. RESULTS: Both the CI- and CVI-based CNN models achieved high diagnostic accuracy. In the validation dataset, the models had diagnostic accuracy of 100% and 97% (p = 0.005), sensitivity of 100% and 98% (p = 0.016), and specificity of 100% (p = 0.929), respectively. In the test dataset, the accuracy was 97% and 96% (p = 0.25), sensitivity was 97% and 92% (p = 0.109), and specificity was 100% (p = 0.123), respectively. For patients with cerebellar subungual herniation less than 5 mm, three out of the 10 CVI-based retrained models reached 100% sensitivity. CONCLUSIONS: Our results revealed that the CNN models demonstrated excellent diagnostic performance for CMI. The models had higher sensitivity than the application of cerebellar tonsillar herniation alone and could identify features in the posterior cranial fossa and craniocervical junction area of patients. Our preliminary experiments provided a feasible method for the diagnosis and study of CMI using CNN models. However, further studies are needed to identify the morphologic characteristics of patients with different clinical outcomes, as well as patients who may benefit from surgery.

Comprehensive analysis of a TNF family based-signature in diffuse gliomas with regard to prognosis and immune significance.

BACKGROUND: Several studies have shown that members of the tumor necrosis factor (TNF) family play an important role in cancer immunoregulation, and trials targeting these molecules are already underway. Our study aimed to integrate and analyze the expression patterns and clinical significance of TNF family-related genes in gliomas. METHODS: A total of 1749 gliomas from 4 datasets were enrolled in our study, including the Cancer Genome Atlas (TCGA) dataset as the training cohort and the other three datasets (CGGA, GSE16011, and Rembrandt) as validation cohorts. Clinical information, RNA expression data, and genomic profile were collected for analysis. We screened the signature gene set by Cox proportional hazards modelling. We evaluated the prognostic value of the signature by Kaplan-Meier analysis and timeROC curve. Gene Ontology (GO) and Gene set enrichment analysis (GSEA) analysis were performed for functional annotation. CIBERSORT algorithm and inflammatory metagenes were used to reveal immune characteristics. RESULTS: In gliomas, the expression of most TNF family members was positively correlated. Univariate analysis showed that most TNF family members were related to the overall survival of patients. Then through the LASSO regression model, we developed a TNF family-based signature, which was related to clinical, molecular, and genetic characteristics of patients with glioma. Moreover, the signature was found to be an independent prognostic marker through survival curve analysis and Cox regression analysis. Furthermore, a nomogram prognostic model was constructed to predict individual survival rates at 1, 3 and 5 years. Functional annotation analysis revealed that the immune and inflammatory response pathways were enriched in the high-risk group. Immunological analysis showed the immunosuppressive status in the high-risk group. CONCLUSIONS: We developed a TNF family-based signature to predict the prognosis of patients with glioma. Video abstract.

Tumor microenvironment is associated with clinical and genetic properties of diffuse gliomas and predicts overall survival.

Tumor microenvironment (TME) is a complex and dynamic evolving environment which facilitates tumor proliferation and progression. We aimed at investigating the characteristics of tumor microenvironment and its prognostic value in gliomas. Transcriptome data of 702 glioma samples from The Cancer Genome Atlas were included as training dataset, while 325 samples from Chinese Glioma Genome Atlas database and 268 samples from GSE16011 database were used to validate. We found that the infiltration of stromal and immune cell varied in gliomas of different grades and pathological types, and was associated with poor prognosis. Based on the gene expression profile, we constructed a TME-related signature (TMERS), which was closely related to clinical features and genomic variation of gliomas. In TMERS-high group, specific gene mutations and increased copy number alternations were observed. Kaplan-Meier survival and Cox regression analysis showed that TMERS was an independent prognostic indicator. Then we developed a nomogram prognostic model to predict 1-year, 3-year and 5-year survival of patients. Functional analysis confirmed that TMERS could reflect the status of glioma microenvironment, and immunological analysis showed that macrophages were significantly enriched in the TMERS-high group. We established a novel TME-related signature for predicting prognosis and provided new insights into immunotherapy.

MET overexpression contributes to STAT4-PD-L1 signaling activation associated with tumor-associated, macrophages-mediated immunosuppression in primary glioblastomas.

BACKGROUND: Dysregulated receptor tyrosine kinases, such as the mesenchymal-epidermal transition factor (MET), have pivotal role in gliomas. MET and its interaction with the tumor microenvironment have been previously implicated in secondary gliomas. However, the contribution of MET gene to tumor cells' ability to escape immunosurveillance checkpoints in primary gliomas, especially in glioblastoma (GBM), which is a WHO grade 4 glioma with the worst overall survival, is still poorly understood. METHODS: We investigated the relationship between MET expression and glioma microenvironment by using multiomics data and aimed to understand the potential implications of MET in clinical practice through survival analysis. RNA expression data from a total of 1243 primary glioma samples (WHO grades 2-4) were assembled, incorporating The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and GSE16011 data sets. RESULTS: Pearson's correlation test from the three data sets indicated that MET showed a robust correlation with programmed death-ligand 1 (PD-L1) and STAT pathways. Western blot analysis revealed that in GBM cell lines (N33 and LN229), PD-L1 and phosphorylated STAT4 were upregulated by MET activation treatment with hepatocyte growth factor and were downregulated on MET suppression by PLB-1001. Tumor tissue microarray analysis indicated a positive correlation between MET and PD-L1 and macrophage-associated markers. Chromatin immunoprecipitation-PCR assay showed enrichment of STAT4 in the PD-L1 DNA. Transwell co-culture and chemotaxis assays revealed that knockdown of MET in GBM cells inhibited macrophage chemotaxis. Moreover, we performed CIBERSORTx and single-cell RNA sequencing data analysis which revealed an elevated number of macrophages in glioma samples with MET overexpression. Kaplan-Meier survival analysis indicated that activation of the MET/STAT4/PD-L1 pathway and upregulation of macrophages were associated with shorter survival time in patients with primary GBM. CONCLUSIONS: These data indicated that the MET-STAT4-PD-L1 axis and tumor-associated macrophages might enforce glioma immune evasion and were associated with poor prognosis in GBM samples, suggesting potential clinical strategies for targeted therapy combined with immunotherapy in patients with primary GBM.

Comprehensive analysis of multi-omics data of recurrent gliomas identifies a recurrence-related signature as a novel prognostic marker.

Tumor recurrence is a common clinical dilemma in diffuse gliomas. We aimed to identify a recurrence-related signature to predict the prognosis for glioma patients. In the public Chinese Glioma Genome Atlas dataset, we enrolled multi-omics data including genome, epigenome and transcriptome across primary and recurrent gliomas. We included RNA sequencing data from the batch 1 patients (325 patients) as the training set, while RNA sequencing data from the batch 2 patients (693 patients) were selected as the validation set. The R language was used for subsequent analysis. Compared with primary gliomas, more somatic mutations and copy number alterations were revealed in recurrent gliomas. In recurrent gliomas, we identified 113 genes whose methylation levels were significantly different from those of the primary glioma. Through differential expression analysis between primary and recurrent gliomas, we screened 121 recurrence-related genes. Based on these 121 gene expression profiles, consensus clustering of 325 patients yielded two robust groups with different molecular and prognostic features. We developed a recurrence-related risk signature with the lasso regression algorithm. High-risk group had shorter survival and earlier tumor recurrence than the low-risk group. Compared with traditional indicators, the signature showed better prognostic value. In addition, we constructed a nomogram model to predict glioma survival. Functional characteristics analysis found that the signature was associated with cell division and cell cycle. Immune analysis suggested that immunosuppressive status and macrophages might promote glioma recurrence. We demonstrated a novel 18-gene signature that could effectively predict recurrence and prognosis for glioma patients.

Intratumor heterogeneity, microenvironment, and mechanisms of drug resistance in glioma recurrence and evolution.

Glioma is the most common lethal tumor of the human brain. The median survival of patients with primary World Health Organization grade IV glioma is only 14.6 months. The World Health Organization classification of tumors of the central nervous system categorized gliomas into lower-grade gliomas and glioblastomas. Unlike primary glioblastoma that usually develop de novo in the elderly, secondary glioblastoma enriched with an isocitrate dehydrogenase mutant typically progresses from lower-grade glioma within 5-10 years from the time of diagnosis. Based on various evolutional trajectories brought on by clonal and subclonal alterations, the evolution patterns of glioma vary according to different theories. Some important features distinguish the normal brain from other tissues, e.g., the composition of the microenvironment around the tumor cells, the presence of the blood-brain barrier, and others. The underlying mechanism of glioma recurrence and evolution patterns of glioma are different from those of other types of cancer. Several studies correlated tumor recurrence with tumor heterogeneity and the immune microenvironment. However, the detailed reasons for the progression and recurrence of glioma remain controversial. In this review, we introduce the different mechanisms involved in glioma progression, including tumor heterogeneity, the tumor microenvironment and drug resistance, and their pre-clinical implements in clinical trials. This review aimed to provide new insights into further clinical strategies for the treatment of patients with recurrent and secondary glioma.

Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients.

Gliomas are the most common and malignant intracranial tumors in adults. Recent studies have revealed the significance of functional genomics for glioma pathophysiological studies and treatments. However, access to comprehensive genomic data and analytical platforms is often limited. Here, we developed the Chinese Glioma Genome Atlas (CGGA), a user-friendly data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples from Chinese cohort. Currently, open access is provided to whole-exome sequencing data (286 samples), mRNA sequencing (1018 samples) and microarray data (301 samples), DNA methylation microarray data (159 samples), and microRNA microarray data (198 samples), and to detailed clinical information (age, gender, chemoradiotherapy status, WHO grade, histological type, critical molecular pathological information, and survival data). In addition, we have developed several tools for users to analyze the mutation profiles, mRNA/microRNA expression, and DNA methylation profiles, and to perform survival and gene correlation analyses of specific glioma subtypes. This database removes the barriers for researchers, providing rapid and convenient access to high-quality functional genomic data resources for biological studies and clinical applications. CGGA is available at http://www.cgga.org.cn.

Clinical and Molecular Characterization of Incidentally Discovered Lower-Grade Gliomas with Enrichment of Aerobic Respiration.

PURPOSE: Incidentally discovered diffusely infiltrating lower-grade gliomas (incidental LGGs, iLGGs) are defined as gliomas occasionally found in patients without tumor-related symptoms. At present, very few in-depth research studies on incidental LGGs were reported. We aimed to find out the inherent difference between iLGGs and LGGs with tumor-related symptoms. PATIENTS AND METHODS: We enrolled 2486 all-grade gliomas and screened 1594 lower-grade gliomas for further analysis. Medical records were retrospectively reviewed for iLGGs. Clinical and mRNA sequencing data were collected for in-depth analysis. RESULTS: We found that with increasing grade, the proportion of incidental glioma patients decreased obviously. In 1594 patients who underwent craniotomy for LGG, 80 (5%) patients were discovered incidentally. Grade II patients (88%) and patients bearing 1p/19q co-deletion in their tumors (23%) were more likely to be diagnosed as iLGGs. Regular radiological screening (48%) and trauma (24%) were the main complaint for brain imaging for iLGGs. Kaplan-Meier survival analysis indicated that iLGGs patients lived a significantly longer survival and Cox regression analysis revealed that iLGGs were an independent indicator of better prognosis. Subsequent gene set enrichment analysis and differential expression analysis based on the gene expression profile revealed that mitochondrial aerobic respiration process was enriched in iLGGs. Moreover, we found that iLGGs tended to generate energy by unique mitochondrial aerobic respiration. CONCLUSION: These results provided a primitive exploration of iLGGs, which may potentially assist clinical neurosurgeons with personalized management of iLGGs.

LINC00174 is a favorable prognostic biomarker in glioblastoma via promoting proliferative phenotype.

OBJECTIVE: To investigate the expression pattern, prognostic value and biological functional of LINC00174 in glioma. METHODS: In total, 140 glioma samples were collected as discovery cohort. TCGA RNA sequence dataset was obtained as validation set. Kaplan-Meier survival and multivariate Cox analysis were performed to evaluate survival difference. Furthermore, the biological function of LINC00174 was analyzed by clonogenic and intracranial tumor model assays. RESULTS: Overexpressed LINC00174 was significantly correlated with tumor grade as well as the higher mortality in survival analysis both in the discovery and the validation GBM cohorts. Besides, LINC00174 served as an independent prognostic indicator in glioblastoma patients. Additionally, knock down of LINC00174 expression significantly suppressed GBM cells' proliferation both in vitro and vivo. CONCLUSION: LINC00174 acts as an oncogene in glioma and may be a new potential therapeutic target.

Pre-treatment neutrophils count as a prognostic marker to predict chemotherapeutic response and survival outcomes in glioma: a single-center analysis of 288 cases.

BACKGROUND: Glioma is the most common and deadliest malignant primary intracranial brain tumor in adults. It remains unclear whether the pre-treatment peripheral blood test parameters might serve as biomarkers for treatment outcome. The purpose of the current study was to investigate the predictive and prognostic value of pre-treatment peripheral blood test parameters in glioma. METHODS: In total, 288 glioma patients with complete results of pre-operation peripheral blood test, clinical information and tumor transcriptome data from Chinese Glioma Genome Atlas (CGGA project) were enrolled in our study. Receiver operating characteristic (ROC) curve, Kaplan-Meier analysis and Cox proportional hazards models were performed to evaluate the diagnostic and prognostic value of pre-treatment peripheral blood test parameters in glioma patients. RESULTS: The white blood cells (WBC) and neutrophils (NEU) counts and neutrophil to lymphocyte ratio (NLR) were positively correlated with tumor grade. IDH mutation and 1p/19q codeletion occurred frequently in patients with higher NEU counts and NLR. We also found that glioma patients with higher NEU or NLR were more likely to have a significantly decreased overall survival. Meanwhile, NEU count was a prognostic marker for TMZ standard treatment GBM patients or IDH wild-type GBM patients. Further biological and functional analysis revealed that NEU count was positively associated with cell cycle and DNA duplication. CONCLUSION: Our study was first to highlight the clinical significance of NEU count in GBM clinical treatment, which should be fully valued for clinical prediction and precise management.

Prognostic Correlation of Autophagy-Related Gene Expression-Based Risk Signature in Patients with Glioblastoma.

PURPOSE: Autophagy plays a vital role in cancer initiation, malignant progression, and resistance to treatment; however, autophagy-related gene sets have rarely been analyzed in glioblastoma. The purpose of this study was to evaluate the prognostic significance of autophagy-related genes in patients with glioblastoma. PATIENTS AND METHODS: Here, we collected whole transcriptome expression data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets to explore the relationship between autophagy-related gene expression and glioblastoma prognosis. R language was the primary analysis and drawing tool. RESULTS: We screened 531 autophagy-related genes and identified 14 associated with overall survival in data from 986 patients with glioblastoma. Patients could be clustered into two groups (high and low risk) using expression data from the 14 associated genes, based on significant differences in clinicopathology and prognosis. Next, we constructed a signature based on the 14 genes and found that most patients designated high risk using our gene signature were IDH wild-type, MGMT promoter non-methylated, and likely to have more malignant tumor subtypes (including classical and mesenchymal subtypes). Survival analysis indicated that patients in the high-risk group had dramatically shorter overall survival compared with their low-risk counterparts. Cox regression analysis further confirmed the independent prognostic value of our 14 gene signature. Moreover, functional and ESTIMATE analyses revealed enrichment of immune and inflammatory responses in the high-risk group. CONCLUSION: In this study, we identified a novel autophagy-related signature for the prediction of prognosis in patients with glioblastoma.

PABPC1 relevant bioinformatic profiling and prognostic value in gliomas.

Aim: We aimed at investigating molecular features and potential clinical value of PABPC1 in gliomas. Materials & methods: We assembled totally 1000 glioma samples with mRNA expression data from Chinese Glioma Genome Atlas and The Cancer Genome Atlas. We utilized R language as the main analysis tool. Gene Ontology was performed for functional analysis. Results: PABPC1 was downregulated in gliomas with higher malignance and PABPC1 may contribute as potential predictor of proneural subtype in gliomas. Higher expression of PABPC1 was significantly related to better prognosis and related to biological process of translation. Conclusion: Our finding improves the understanding of PABPC1 as a novel biomarker with potential therapeutic connotations.

A Novel DNA Methylation-Based Signature Can Predict the Responses of MGMT Promoter Unmethylated Glioblastomas to Temozolomide.

Glioblastoma (GBM) is the most malignant glioma, with a median overall survival (OS) of 14-16 months. Temozolomide (TMZ) is the first-line chemotherapy drug for glioma, but whether TMZ should be withheld from patients with GBMs that lack O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is still under debate. DNA methylation profiling holds great promise for further stratifying the responses of MGMT promoter unmethylated GBMs to TMZ. In this study, we studied 147 TMZ-treated MGMT promoter unmethylated GBM, whose methylation information was obtained from the HumanMethylation27 (HM-27K) BeadChips (n = 107) and the HumanMethylation450 (HM-450K) BeadChips (n = 40) for training and validation, respectively. In the training set, we performed univariate Cox regression and identified that 3,565 CpGs were significantly associated with the OS of the TMZ-treated MGMT promoter unmethylated GBMs. Functional analysis indicated that the genes corresponding to these CpGs were enriched in the biological processes or pathways of mitochondrial translation, cell cycle, and DNA repair. Based on these CpGs, we developed a 31-CpGs methylation signature utilizing the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. In both training and validation datasets, the signature identified the TMZ-sensitive GBMs in the MGMT promoter unmethylated GBMs, and only the patients in the low-risk group appear to benefit from the TMZ treatment. Furthermore, these identified TMZ-sensitive MGMT promoter unmethylated GBMs have a similar OS when compared with the MGMT promoter methylated GBMs after TMZ treatment in both two datasets. Multivariate Cox regression demonstrated the independent prognostic value of the signature in TMZ-treated MGMT promoter unmethylated GBMs. Moreover, we also noticed that the hallmark of epithelial-mesenchymal transition, ECM related biological processes and pathways were highly enriched in the MGMT unmethylated GBMs with the high-risk score, indicating that enhanced ECM activities could be involved in the TMZ-resistance of GBM. In conclusion, our findings promote our understanding of the roles of DNA methylation in MGMT umethylated GBMs and offer a very promising TMZ-sensitivity predictive signature for these GBMs that could be tested prospectively.

Immune Cytolytic Activity Is Associated With Genetic and Clinical Properties of Glioma.

Background: Immunotherapy provided unprecedented advances in the treatment of several previously untreated cancers. However, these immunomodulatory maneuvers showed limited response to patients with glioma in clinical trials. Our aim was to depict the immune characteristics of glioma with immune cytolytic activity at genetic and transcriptome levels. Methods: In total, 325 gliomas from CGGA dataset as training cohort and 699 gliomas from TCGA dataset as validation cohort were enrolled in our analysis. We calculated the immune cytolytic activity for 1,000 of gliomas. The characteristics of immune cytolytic activity in gliomas were interpreted by the corresponding clinical, molecular genetics and radiological information. Results: We found that immune cytolytic activity was highly associated with molecular, clinical, and edema extent. High cytolytic activity gliomas were more likely to be diagnosed as glioblastoma and might be a potential marker of mesenchymal subtype. Moreover, those gliomas exhibited significantly increased copy number alterations including recurrent focal amplifications of PDGFA and EGFR, as well as recurrent deletions of CDKN2A/B. Subsequent biological function analysis revealed that the immune response and immune checkpoints expression were significantly correlated with the cytolytic activity of gliomas. Immune cytolytic activity was significantly positively associated with the extent of peri-tumor edema and was independently correlated with reduced survival time. Conclusion: Our results highlighted the immunoregulatory mechanism heterogeneity of gliomas. Cytolytic activity, indirectly reflected by the extent of peri-tumor edema, may provide a potential index to evaluate the status of immune microenvironment and immune checkpoints in glioma, which should be fully valued for precision classification and immunotherapy.

PD-L2 expression is correlated with the molecular and clinical features of glioma, and acts as an unfavorable prognostic factor.

Background: Gliomas are aggressive tumors with various molecular and clinical characteristics and exhibit strongly resistance to radio-chemotherapy. Programmed cell death 1 ligand 2 (PD-L2) is a cell surface protein, which was reported in many cancers, modulating cancer-associated immune responses, while the role of PD-L2 in gliomas remained unclear. Herein, we aimed to investigate the biological behaviors and clinical prognostic values of PD-L2 in gliomas. Methods: Totally, we enrolled RNA sequencing data of 325 glioma samples from Chinese Glioma Genome Atlas (CGGA) as training cohort and RNA expression data of 1032 samples from The Cancer Genome Atlas (TCGA) dataset as validation cohort in this research. Then, the clinical and molecular characteristics, and the prognostic value of PD-L2 were analyzed. Results: We found that PD-L2 expression level was significantly upregulated in higher grade glioma and IDH wild-type glioma. Receiver Operating Characteristic (ROC) analysis revealed that PD-L2 was a potential indicator of mesenchymal subtype. PD-L2 exhibited tight relationship with immune response and immune-modulating process in glioma. Moreover, PD-L2 expression level could predict unfavorable prognoses of patients independent of age, grade, IDH status and 1p/19q status. Conclusions: Our study revealed that PD-L2 was closely related with inflammation and immune response. Patients with lower PD-L2 expression level tended to experience improved survival. Targeting PD-L2 may become a valuable approach for the treatment of gliomas in clinical practice.