Reduced expression of DNA repair genes and chemosensitivity in 1p19q codeleted lower-grade gliomas

Spermine Synthase : A Potential Prognostic Marker for Lower-Grade Gliomas

OBJECTIVE

The objective of this study was to assess the relationship between spermine synthase (SMS) expression, tumor occurrence, and prognosis in lower-grade gliomas (LGGs).

METHODS

A total of 523 LGG patients and 1152 normal brain tissues were included as controls. Mann-Whitney U test was performed to evaluate SMS expression in the LGG group. Functional annotation analysis was conducted to explore the biological processes associated with high SMS expression. Immune cell infiltration analysis was performed to examine the correlation between SMS expression and immune cell types. The association between SMS expression and clinical and pathological features was assessed using Spearman correlation analysis. In vitro experiments were conducted to investigate the effects of overexpressing or downregulating SMS on cell proliferation, apoptosis, migration, invasion, and key proteins in the protein kinase B (AKT)/epithelialmesenchymal transition signaling pathway.

RESULTS

The study revealed a significant upregulation of SMS expression in LGGs compared to normal brain tissues. High SMS expression was associated with certain clinical and pathological features, including older age, astrocytoma, higher World Health Organization grade, poor disease-specific survival, disease progression, non-1p/19q codeletion, and wild-type isocitrate dehydrogenase. Cox regression analysis identified SMS as a risk factor for overall survival. Bioinformatics analysis showed enrichment of eosinophils, T cells, and macrophages in LGG samples, while proportions of dendritic (DC) cells, plasmacytoid DC (pDC) cells, and CD8+ T cells were decreased.

CONCLUSION

High SMS expression in LGGs may promote tumor occurrence through cellular proliferation and modulation of immune cell infiltration. These findings suggest the prognostic value of SMS in predicting clinical outcomes for LGG patients.

Pro-apoptotic gene BAX is a pan-cancer predictive biomarker for prognosis and immunotherapy efficacy

BACKGROUND

Apoptosis Regulator BCL2 Associated X (BAX) is a pro-apoptotic gene. Apoptosis is one of the important components of immune response and immune regulation. However, there is no systematic pan-cancer analysis of BAX.

METHODS

Original data of this study were downloaded from TCGA databases and GTEX databases. We conducted the gene expression analysis and survival analysis of BAX in 33 types of cancer via Gene Expression Profiling Interactive Analysis (GEPIA) database. Real-time PCR and immunohistochemistry (IHC) were further performed to examine the BAX expression in cancer cells and tissues. Moreover, the relationship between BAX and immune infiltration and gene alteration was studied by the Tumor Immune Estimation Resource (TIMER) and cBioPortal tools. Protein-protein interaction analysis was performed in the STRING database. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to evaluate the enrichment analysis.

RESULTS

BAX was highly expressed in most cancers and was associated with poor prognosis in nine cancer types. In addition, BAX showed significant clinical relevance, and the mRNA expression of BAX was also strongly associated with drug sensitivity of many drugs. Furthermore, BAX may participate in proliferation and metastasis of many cancers and was associated with methylation. Importantly, BAX expression was positively correlated with most immune infiltrating cells.

CONCLUSION

Our findings suggested that BAX can function as an oncogene and may be used as a potential predictive biomarker for prognosis and immunotherapy efficacy of human cancer, which could provide a new approach for cancer therapy.

The 2021 World Health Organization Central Nervous System Tumor Classification: The Spectrum of Diffuse Gliomas

The 2021 edition of the World Health Organization (WHO) classification of central nervous system tumors introduces significant revisions across various tumor types. These updates, encompassing changes in diagnostic techniques, genomic integration, terminology, and grading, are crucial for radiologists, who play a critical role in interpreting brain tumor imaging. Such changes impact the diagnosis and management of nearly all central nervous system tumor categories, including the reclassification, addition, and removal of specific tumor entities. Given their pivotal role in patient care, radiologists must remain conversant with these revisions to effectively contribute to multidisciplinary tumor boards and collaborate with peers in neuro-oncology, neurosurgery, radiation oncology, and neuropathology. This knowledge is essential not only for accurate diagnosis and staging, but also for understanding the molecular and genetic underpinnings of tumors, which can influence treatment decisions and prognostication. This review, therefore, focuses on the most pertinent updates concerning the classification of adult diffuse gliomas, highlighting the aspects most relevant to radiological practice. Emphasis is placed on the implications of new genetic information on tumor behavior and imaging findings, providing necessary tools to stay abreast of advancements in the field. This comprehensive overview aims to enhance the radiologist's ability to integrate new WHO classification criteria into everyday practice, ultimately improving patient outcomes through informed and precise imaging assessments.

T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas

AIM

To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs).

MATERIALS AND METHODS

One hundred and forty-six patients, who had pathologically confirmed isocitrate dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 7:3. The T2-FLAIR mismatch sign and conventional MRI features were evaluated. Radiomics features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coefficient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displayed the best performance of each sequence were selected, and their predicted values as well as the T2-FLAIR mismatch sign data were collected to establish a final stacking model. Receiver operating characteristic curve (ROC) analyses and area under the curve (AUC) values were applied to evaluate and compare the performance of the models.

RESULTS

The T2-FLAIR mismatch sign was more common in the IDH mutant 1p/19q non-co-deleted group (p<0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and accuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort.

CONCLUSION

The stacking model based on multiparametric MRI can serve as a supplementary tool for pathological diagnosis, offering valuable guidance for clinical practice.

The evaluation of six genes combined value in glioma diagnosis and prognosis

PURPOSE

Glioma is the most common and fatal type of brain tumour. Owing to its aggressiveness and lethality, early diagnosis and prediction of patient survival are very important. This study aimed to identify key genes and biomarkers for glioma that can guide clinicians in making rapid diagnosis and prognostication.

METHODS

Data mining of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data, and Genotype-Tissue Expression Project brain expression data revealed significantly differentially expressed genes (DEGs), and the risk scores of individual patients were calculated. WGCNA was utilized to screen for genes most related to clinical diagnosis. Prognostic genes associated with glioma were selected via combining the LASSO regression with univariate and multivariate Cox regression and protein-protein interaction network analyses. Then, a nomogram was constructed. And CGGA dataset was utilized to validated. The protein expression levels of the signature were detected using the human protein atlas. Drug response prediction was carried out using the package "pRRophetic".

RESULTS

A six-gene signature (KLF6, CHI3L1, SERPINE1, ANGPT2, TGFBR1, and PTX3) was identified and used to stratify patients into low- and high-risk groups. Survival, ROC curve, and Cox analyses clarified that the six hub genes were a favourable independent prognostic factor for patients with glioma. A nomogram was set up by integrating clinical parameters with risk signatures, showing high precision for predicting 2-, 3-, 4-, 5-years survival. In addition, the expression of most genes was consistent with protein expression. Furthermore, the sensitivity to the top ten drugs in the GDSC database of the high-risk group was significantly higher than the low-risk group.

CONCLUSION

Based on genetic profiles and clinicopathological features, including age, grade, isocitrate dehydrogenase mutation status, we constructed a comprehensive prognostic model for patients with glioma. These signatures can be regarded as biomarkers to predict the prognosis of gliomas, possibly providing more therapeutic strategies for future clinical research.

Aquaporin-4 as a New Potential Molecular Biomarker for Prognosis of Low-Grade Glioma: Comprehensive Analysis Based on Online Platforms

OBJECTIVE

Aquaporin-4 (AQP4) is a significant factor in transcellular and transepithelial water movement, and abnormal expression of AQP4 has been detected in many types of tumors. The purpose of this study was to explore its role in low-grade gliomas (LGG) using freely available online bioinformatics tools.

METHODS

OncoLnc database was used to analyze Cox coefficients and compare AQP4 expression between various types of tumors; Tumor Immune Estimation Resource database and Gene Expression Profiling Interactive Analysis were used to compare gene expression between LGG and normal tissues; University of California Santa Cruz Xena browser generated Kaplan-Meier survival curves in the LGG cohort in The Cancer Genome Atlas and subgroups; LinkedOmics database screened the most relevant genes based on Pearson correlation coefficient; Gene Ontology Biological Process and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed through gene set enrichment analysis to explore possible molecular mechanisms.

RESULTS

LGG had higher AQP4 expression compared with normal tissues and ranked first among 21 different types of cancer (P < 0.05). The oligodendroglioma group had the lowest AQP4 expression and the longest overall survival (OS) (P < 0.05). LGG with astrocytoma, isocitrate dehydrogenase mutation, or 1p/19q codeletion had lower AQP4 expression and longer OS (P < 0.001). LGG with lower AQP4 expression, without 1p/19q codeletion, without chemotherapy, and with or without radiation therapy had longer OS (P < 0.05). AQP4 and coexpressed genes were involved in complex biological processes in LGG, including regulation of neurotransmitter level, peroxisome proliferator-activated receptor signaling pathway, cell adhesion molecules, and others.

CONCLUSIONS

AQP4 is a prognostic marker in LGG and its subgroups. Patients with lower AQP4 expression may have longer OS.

Prospects of POLD1 in Human Cancers: A Review

Cancer is the second leading cause of death globally, exceeded only by cardiovascular disease. Despite the introduction of several survival-prolonging treatment modalities, including targeted therapy and immunotherapy, the overall prognosis for the metastatic disease remains challenging. Therefore, the identification of new molecular biomarkers and therapeutic targets related to cancer diagnosis and prognosis is of paramount importance. DNA polymerase delta 1 (POLD1), a catalytic and proofreading subunit of the DNA polymerase δ complex, performs a crucial role in DNA replication and repair processes. Recently, germline and somatic mutations of the POLD1 gene have been acknowledged in several malignancies. Moreover, diversified POLD1 expression profiles have been reported in association with clinicopathological features in a variety of tumor types. With this review, we aim to summarize the current knowledge on the role of POLD1 in cancers. In addition, we discuss the future prospects and clinical applications of the assessment of POLD1 mutation and expression patterns in tumors.

Hypermutation as a potential predictive biomarker of immunotherapy efficacy in high-grade gliomas: a broken dream?

A high tumor mutational burden and mismatch repair deficiency are observed in 'hypermutated' high-grade gliomas (HGGs); however, the molecular characterization of this distinct subtype and whether it predicts the response to immune checkpoint inhibitors (ICIs) are largely unknown. Pembrolizumab is a valid therapeutic option for the treatment of hypermutated cancers of diverse origin, but only a few clinical trials have explored the activity of ICIs in hypermutated HGGs. HGGs appear to differ from other cancers, likely due to the prevalence of subclonal versus clonal neoantigens, which are unable to elicit an immune response with ICIs. The main aim of this review is to summarize the current knowledge on hypermutation in HGGs, focusing on the broken promises of tumor mutational burden and mismatch repair deficiency as potential biomarkers of response to ICIs.

The Value of H2BC12 for Predicting Poor Survival Outcomes in Patients With WHO Grade II and III Gliomas

Purpose: Glioma is a common primary malignant brain tumor. Grade II (GII) gliomas are prone to develop into anaplastic grade III (GIII) gliomas, which indicate a higher malignancy and poorer survival outcome. This study aimed to satisfy the increasing demand for novel sensitive biomarkers and potential therapeutic targets in the treatment of GII and GIII gliomas.

Methods: A TCGA dataset was used to investigate the expression of H2BC12 mRNA in GII and GIII gliomas and its relation to clinical pathologic characteristics. Glioma tissues were collected to verify results from the TCGA dataset, and H2BC12 mRNA was detected by RT-qPCR. ROC analysis was employed to evaluate the classification power for GII and GIII. The significance of H2BC12 mRNA GII and GIII gliomas was also investigated. In addition, H2BC12 expression-related pathways were enriched by gene set enrichment analysis (GSEA). DNA methylation level and mutation of H2BC12 were analyzed by the UALCAN and CBioPortal databases, respectively.

Results: Based on the sample data from multiple databases and RT-qPCR, higher expression of H2BC12 mRNA was found in GII and GIII glioma tissue compared to normal tissue, which was consistent with a trend with our clinical specimen. H2BC12 mRNA had a better power in distinguishing between GII and GIII and yielded an AUC of 0.706 with a sensitivity of 76.9% and specificity of 81.8%. Meanwhile, high H2BC12 levels were associated with IDH status, 1p/19q codeletion, primary therapy outcome, and the histological type of gliomas. Moreover, the overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI) of GII glioma patients with higher levels of H2BC12 were shorter than those of patients with lower levels as well as GIII patients. In the multivariate analysis, a high H2BC12 level was an independent predictor for poor survival outcomes of gliomas. The Wnt or PI3K-AKT signaling pathways, DNA repair, cellular senescence, and DNA double-strand break repair were differentially activated in phenotypes that were positively associated with H2BC12. H2BC12 DNA methylation was high in TP53 nonmutant patients, and no H2BC12 mutation was observed in gliomas patients.

Conclusion: H2BC12 is a promising biomarker for the diagnosis and prognosis of patients with WHO grade II and III gliomas.

Transcriptomic Profiling of DNA Damage Response in Patient-Derived Glioblastoma Cells before and after Radiation and Temozolomide Treatment

Glioblastoma is a highly aggressive, invasive and treatment-resistant tumour. The DNA damage response (DDR) provides tumour cells with enhanced ability to activate cell cycle arrest and repair treatment-induced DNA damage. We studied the expression of DDR, its relationship with standard treatment response and patient survival, and its activation after treatment. The transcriptomic profile of DDR pathways was characterised within a cohort of isocitrate dehydrogenase (IDH) wild-type glioblastoma from The Cancer Genome Atlas (TCGA) and 12 patient-derived glioblastoma cell lines. The relationship between DDR expression and patient survival and cell line response to temozolomide (TMZ) or radiation therapy (RT) was assessed. Finally, the expression of 84 DDR genes was examined in glioblastoma cells treated with TMZ and/or RT. Although distinct DDR cluster groups were apparent in the TCGA cohort and cell lines, no significant differences in OS and treatment response were observed. At the gene level, the high expression of ATP23, RAD51C and RPA3 independently associated with poor prognosis in glioblastoma patients. Finally, we observed a substantial upregulation of DDR genes after treatment with TMZ and/or RT, particularly in RT-treated glioblastoma cells, peaking within 24 h after treatment. Our results confirm the potential influence of DDR genes in patient outcome. The observation of DDR genes in response to TMZ and RT gives insight into the global response of DDR pathways after adjuvant treatment in glioblastoma, which may have utility in determining DDR targets for inhibition.

Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma

Background

Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors.

Methods

A total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built.

Results

The 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively.

Conclusion

The combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.

A Novel DNA Damage Repair-Related Gene Signature for Predicting Glioma Prognosis

Background

Glioma is one of the most prevalent tumors in the central nervous system of adults and shows a poor prognosis. This study aimed to develop a DNA damage repair (DDR)-related gene signature to evaluate the prognosis of glioma patients.

Methods

Differentially expressed genes (DEGs) were extracted based on 276 DDR genes. Then, a gene signature was developed for the survival prediction in glioma patients by means of univariate, multivariate Cox, and least absolute shrinkage and selector operation (Lasso) analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. A total of 693 gliomas from the Chinese Glioma Genome Atlas (CGGA) were used for external validation. In addition, we used glioma tumor tissues for qPCR experiment to verify.

Results

A 12-DDR-related gene signature was identified from the 75 DEGs to stratify the survival risk of glioma patients. The overall survival of high-risk group was significantly shorter than that of low-risk group (P < 0.001). Besides, according to the risk score assessment, patients in high- or low-risk group also had significant correlations with clinicopathological parameters, including age (P < 0.01), grade (P < 0.001), IDH status (P < 0.001) and 1p19q codeletion status (P < 0.001). The nomogram provided favorable C-index and calibration plots. The C-index of training set and verification set was 0.761 and 0.746, respectively, and the calibration curve also showed that both training set and verification set were close to the standard curve. The qPCR results showed that there were significant differences in the expression of some typical DDR-related genes in tumor tissues and paracancer tissues (P_(WEE1)=0.0002, P_(RECQL)=0.0117, P_(RPA1)=0.021, P_(RRM1)=0.0035, P_(PARP4)=0.0006, P_(ELOA)=0.0023).

Conclusion

Our study developed a novel 12 DDR-related gene signature as a practical prognostic predictor for glioma patients. A nomogram combining the signature and clinical parameters was established as an individual clinical prediction tool.

From Laboratory Studies to Clinical Trials: Temozolomide Use in IDH-Mutant Gliomas

In this review, we discuss the use of the alkylating agent temozolomide (TMZ) in the treatment of IDH-mutant gliomas. We describe the challenges associated with TMZ in clinical (drug resistance and tumor recurrence) and preclinical settings (variabilities associated with in vitro models) in treating IDH-mutant glioma. Lastly, we summarize the emerging therapeutic targets that can potentially be used in combination with TMZ.

TEAD4 is a novel independent predictor of prognosis in LGG patients with IDH mutation

TEA domain family members (TEADs) play important roles in tumor progression. Till now, the genomic status of TEADs in patients with glioma has not been well investigated. To confirm whether the genomic status of TEADs could affect the prognosis of patients with glioma, the copy number variation (CNV), mutation and expression data of glioma cohorts in The Cancer Genome Atlas, Gene Expression Omnibus and Chinese Glioma Genome Atlas were comprehensively analyzed. Results showed that TEAD CNV frequency in lower grade gliomas (LGGs) was higher than in glioblastoma multiforme (GBM). Multivariate cox regression analysis showed that TEAD4 CNV increase was significantly associated with overall survival (OS) and disease-free survival (DFS) in LGGs (OS p = 0.022, HR = 1.444, 95% CI: 1.054–1.978; DFS p = 0.005, HR = 1.485, 95% CI: 1.124–1.962), while not in GBM. Patients with TEAD4 CNV increase showed higher expression level of TEAD4 gene. In LGG patients with IDH mutation, those with higher TEAD4 expression levels had shorter OS and DFS. Integrating TEAD4 CNV increase, IDH mutations, TP53 mutation, ATRX mutation and 1p19q co-deletion would separate patients with LGG into four groups with significant differences in prognosis. These study results suggested that TEAD4 variations were independent predictive biomarkers for the prognosis in patients with LGG with IDH mutation.

Developing a lncRNA Signature to Predict the Radiotherapy Response of Lower-Grade Gliomas Using Co-expression and ceRNA Network Analysis

BACKGROUND

Lower-grade glioma (LGG) is a type of central nervous system tumor that includes WHO grade II and grade III gliomas. Despite developments in medical science and technology and the availability of several treatment options, the management of LGG warrants further research. Surgical treatment for LGG treatment poses a challenge owing to its often inaccessible locations in the brain. Although radiation therapy (RT) is the most important approach in this condition and offers more advantages compared to surgery and chemotherapy, it is associated with certain limitations. Responses can vary from individual to individual based on genetic differences. The relationship between non-coding RNA and the response to radiation therapy, especially at the molecular level, is still undefined.

METHODS

In this study, using The Cancer Genome Atlas dataset and bioinformatics, the gene co-expression network that is involved in the response to radiation therapy in lower-grade gliomas was determined, and the ceRNA network of radiotherapy response was constructed based on three databases of RNA interaction. Next, survival analysis was performed for hub genes in the co-expression network, and the high-efficiency biomarkers that could predict the prognosis of patients with LGG undergoing radiotherapy was identified.

RESULTS

We found that some modules in the co-expression network were related to the radiotherapy responses in patients with LGG. Based on the genes in those modules and the three databases, we constructed a ceRNA network for the regulation of radiotherapy responses in LGG. We identified the hub genes and found that the long non-coding RNA, DRAIC, is a potential molecular biomarker to predict the prognosis of radiotherapy in LGG.

Prognostic Role of EYA4 in Lower Grade Glioma with IDH1 Mutation and 1p19q Co-Deletion

BACKGROUND

Eyes absent 4 (EYA4) participates in an important role in various cancers. Patients with low EYA4 expression have significantly favorable prognosis compared with those with high EYA4 expression. However, the expression and role of EYA4 in lower grade glioma (LGG) has not been fully elucidated.

METHODS

The R2 and UCSC Xena browser based on data from 284 cases in GSE16011 from Gene Expression Omnibus datasets and 530 cases of patients with LGG in The Cancer Genome Atlas database were extracted for bioinformatic analyses. The EYA4 expression in different subtypes of LGG was detected. Kaplan-Meier survival curves were generated to explore the association between EYA4 expression and overall survival (OS) in both datasets.

RESULTS

Patients with LGG with lower EYA4 expression had significantly longer 5- and 10-year OS in 2 datasets (P < 0.001). By matching histological subtypes and gene expression profiles of patients with LGG, oligoastrocytoma and oligodendroglioma groups had lower EYA4 expression and longer OS compared with the astrocytoma group (P < 0.05). Patients with IDH1 mutations and 1p19q co-deletion had longer 5- and 10-year OS (P < 0.001), and EYA4 expression was significantly downregulated in these patients (P < 0.001).

CONCLUSIONS

This study suggests that EYA4 can be used as a prognostic marker and provide a potential therapeutic target in patients with LGG with IDH1 mutation and 1p19q co-deletion.

Low expression of FOSL1 is associated with favorable prognosis and sensitivity to radiation/pharmaceutical therapy in lower grade glioma

Objectives: FOSL1 is overexpressed in multiple cancers including malignant glioma and contributes to different cellular processes. However, little attention has been paid to the lower grade glioma (LGG).Methods: Cox coefficients were examined to compare FOSL1 expression among different tumors types using OncoLnc. The UCSC Xena browser was used to generate Kaplan-Meier survival curves and explore the association between FOSL1 expression and overall survival (OS) in TCGA-LGG and subgroups.Results: FOSL1 expression in LGG was ranked first among 21 different cancers. LGG with lower FOSL1 expression had longer OS (P < 0.001). The astrocytoma group had the highest FOSL1 expression and shortest OS, followed by oligoastrocytoma and oligodendroglioma (P < 0.05). The 1p19q co-deletion or IDH mutation subgroups had lower FOSL1 expression and longer OS (P < 0.001). Compared with the corresponding groups, LGG with lower FOSL1 expression had longer OS than the following groups: astrocytoma, oligodendroglioma, with/without 1p19q co-deletion, with IDH mutation, with radiation, and with pharmaceutical therapy (P < 0.05).Discussion: FOSL1 is a prognostic marker in LGG and subgroups.

MPC1 deletion is associated with poor prognosis and temozolomide resistance in glioblastoma

OBJECT

Mitochondrial pyruvate carrier (MPC) proteins MPC1 and MPC2 form a transporter complex to control rate-limiting pyruvate transportation through the inner mitochondrial membrane. MPC1 plays a crucial role in the tumor metabolite and biosynthesis process. However, the role of MPC1 in glioblastoma (GBM) is unknown.

METHODS

The Cancer Genome Atlas (TCGA) data set, which included 631 cases of GBM with genomic and clinical data, was obtained from the UCSC Xena browser. The clinical data set contained demographic, survival rate, and histological and pathological information. The association between MPC1 gene copy number segments and GBM patient overall survival was analyzed by Kaplan-Meier survival analysis, which was performed using the R2 web-based platform to identify the best cut-off. GraphPad Prism 7 was used to compare the differences in MPC1 gene copy number segments between various groups and subtypes.

RESULTS

A total of 631 patients with glioblastoma (mean age 57.78 ± 14.36 years, 59% of males) were examined in this study, including 438 cases with MPC1 intact (MPC1 copy number segments > - 0.1, 69.4%) and 157 cases with MPC1 deletion (24.9%) tumors. Among the four GBM subtypes, the proneural group had the highest MPC1 copy number segments and GBM patients diagnosed with proneural subtype showed the best outcome. The expression of MPC1 transcripts was different in the TCGA-GBM dataset compared with the GTEx dataset. MPC1 copy number segments showed a significant correlation with MGMT copy number segments (r = 0.1322, p = 0.0012). MGMT gene expression level in MPC1 intact tumors was significantly lower than that in MPC1 deletion tumors (p = 0.0003). Significant relevancy was observed between better OS and the MPC1 intact group compared with the MPC1 deletion group (p = 0.020). Moreover, patients with MPC1 deletion tumors treated with temozolomide (TMZ) had worse survival than patients with MPC1 intact tumors (p = 0.027).

CONCLUSIONS

Our results suggest a role of decreased MPC1 copy number segments in reducing overall survival in glioblastoma. MPC1 deletion is associated with poor response to TMZ chemotherapy in GBM.

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas

PURPOSE

Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions.

METHODS

Preoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive.

RESULTS

Our model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198).

CONCLUSION

Using machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.