Glioblastoma multiforme (GBM) remains the top challenge to radiotherapy with only 25% one-year survival after diagnosis. Here, we reveal that co-enhancement of mitochondrial fatty acid oxidation ...(FAO) enzymes (CPT1A, CPT2 and ACAD9) and immune checkpoint CD47 is dominant in recurrent GBM patients with poor prognosis. A glycolysis-to-FAO metabolic rewiring is associated with CD47 anti-phagocytosis in radioresistant GBM cells and regrown GBM after radiation in syngeneic mice. Inhibition of FAO by CPT1 inhibitor etomoxir or CRISPR-generated CPT1A
, CPT2
, ACAD9
cells demonstrate that FAO-derived acetyl-CoA upregulates CD47 transcription via NF-κB/RelA acetylation. Blocking FAO impairs tumor growth and reduces CD47 anti-phagocytosis. Etomoxir combined with anti-CD47 antibody synergizes radiation control of regrown tumors with boosted macrophage phagocytosis. These results demonstrate that enhanced fat acid metabolism promotes aggressive growth of GBM with CD47-mediated immune evasion. The FAO-CD47 axis may be targeted to improve GBM control by eliminating the radioresistant phagocytosis-proofing tumor cells in GBM radioimmunotherapy.
Glioma is the most common primary malignant tumor of the central nervous system in clinical practice. Most adult diffuse gliomas have poor efficacy after standard treatment, especially glioblastoma. ...With the in-depth understanding of brain immune microenvironment, immunotherapy as a new treatment has attracted much attention. In this study, through analyzing a large number of glioma cohorts, we reported that TSPAN7, a member of the tetraspanin family, decreased in high-grade gliomas and low expression was associated with poor prognosis in glioma patients. Meanwhile, the expression pattern of TSPAN7 was verified in glioma clinical samples and glioma cell lines by qPCR, Western Blotting and immunofluorescence. In addition, functional enrichment analysis showed that cell proliferation, EMT, angiogenesis, DNA repair and MAPK signaling pathways were activated in the TSPAN7 lower expression subgroup. Lentiviral plasmids were used to overexpress TSPAN7 in U87 and LN229 glioma cell lines to explore the anti-tumor role of TSPAN7 in glioma. Moreover, by analyzing the relationship between TSPAN7 expression and immune cell infiltration in multiple datasets, we found that TSPAN7 was significantly negatively correlated with the immune infiltration of tumor-related macrophages, especially M2-type macrophages. Further analysis of immune checkpoints showed that, the expression level of TSPAN7 was negatively correlated with the expression of PD-1, PD-L1 and CTLA-4. Using an independent anti-PD-1 immunotherapy cohorts of GBM, we demonstrated that TSPAN7 expression may had a synergistic effect with PD-L1 on the response to immunotherapy. Based on the above findings, we speculate that TSPAN7 can serve as a biomarker for prognosis and a potential immunotherapy target in glioma patients.
Petroclival meningiomas (PCMs) are regarded as one of the most formidable challenges in neurosurgery. We retrospectively assessed the surgical outcomes of PCMs based on a tumor classification to ...evaluate the long-term outcomes. A series of 168 patients with PCMs from July 1996 to January 2017. On the basis of the difference in the origin of dural attachment and patterns of growth, the PCMs were classified into 4 different types. The clinical characteristics, surgical record, and follow-up data of each type were reviewed. The study included 138 females (82.1%) with an average age of 49.9 ± 16.2 years. And 138 cases (82.1%) had developed neurological deficits preoperatively with the average tumor size of 44.0 ± 10.6 mm. Specific surgical approaches were applied depended on the tumor classification. Gross total resection (GTR) was achieved in 119 cases (70.8%) with the complications of 46 cases (27.7%). With a median follow-up of 86.5 months, there were 41 cases of recurrence/progress (25.7%) and 39 cases of morbidity (26.4%). Compared with the non-GTR group, the GTR significantly decreased the R/P rate (P = 0.001), prolonged the R/P-FS time (P = 0.032) and improved the follow-up neurological status (P = 0.026). Favorable outcomes and acceptable morbidity were achieved with the treatment strategy of the choice of specific approaches for each type. Meanwhile, the differences of each type in diverse clinical characteristic were verified. Individualized assessment and suitable approach choice should be based on the tumor classification to improved the GTR and quality of life for patients.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Despite a generally better prognosis than high-grade glioma (HGG), recurrence and malignant progression are the main causes for the poor prognosis and difficulties in the treatment of low-grade ...glioma (LGG). It is of great importance to learn about the risk factors and underlying mechanisms of LGG recurrence and progression. In this study, the transcriptome characteristics of four groups, namely, normal brain tissue and recurrent LGG (rLGG), normal brain tissue and secondary glioblastoma (sGBM), primary LGG (pLGG) and rLGG, and pLGG and sGBM, were compared using Chinese Glioma Genome Atlas (CGGA) and Genotype-Tissue Expression Project (GTEx) databases. In this study, 296 downregulated and 396 upregulated differentially expressed genes (DEGs) with high consensus were screened out. Univariate Cox regression analysis of data from The Cancer Genome Atlas (TCGA) yielded 86 prognostically relevant DEGs; a prognostic prediction model based on five key genes (HOXA1, KIF18A, FAM133A, HGF, and MN1) was established using the least absolute shrinkage and selection operator (LASSO) regression dimensionality reduction and multivariate Cox regression analysis. LGG was divided into high- and low-risk groups using this prediction model. Gene Set Enrichment Analysis (GSEA) revealed that signaling pathway differences in the high- and low-risk groups were mainly seen in tumor immune regulation and DNA damage-related cell cycle checkpoints. Furthermore, the infiltration of immune cells in the high- and low-risk groups was analyzed, which indicated a stronger infiltration of immune cells in the high-risk group than that in the low-risk group, suggesting that an immune microenvironment more conducive to tumor growth emerged due to the interaction between tumor and immune cells. The tumor mutational burden and tumor methylation burden in the high- and low-risk groups were also analyzed, which indicated higher gene mutation burden and lower DNA methylation level in the high-risk group, suggesting that with the accumulation of genomic mutations and epigenetic changes, tumor cells continued to evolve and led to the progression of LGG to HGG. Finally, the value of potential therapeutic targets for the five key genes was analyzed, and findings demonstrated that KIF18A was the gene most likely to be a potential therapeutic target. In conclusion, the prediction model based on these five key genes can better identify the high- and low-risk groups of LGG and lay a solid foundation for evaluating the risk of LGG recurrence and malignant progression.
Intracranial hemangiopericytoma/solitary fibrous tumor (SFT/HPC) is a rare type of neoplasm containing malignancies of infiltration, peritumoral edema, bleeding, or bone destruction. However, SFT/HPC ...has similar radiological characteristics as meningioma, which had different clinical managements and outcomes. This study aims to discriminate SFT/HPC and meningioma
deep learning approaches based on routine preoperative MRI.
We enrolled 236 patients with histopathological diagnosis of SFT/HPC (n = 144) and meningioma (n = 122) from 2010 to 2020 in Xiangya Hospital. Radiological features were extracted manually, and a radiological diagnostic model was applied for classification. And a deep learning pretrained model ResNet-50 was adapted to train T1-contrast images for predicting tumor class. Deep learning model attention mechanism was visualized by class activation maps.
Our study reports that SFT/HPC was found to have more invasion to venous sinus (
= 0.001), more cystic components (
< 0.001), and more heterogeneous enhancement patterns (
< 0.001). Deep learning model achieved a high classification accuracy of 0.889 with receiver-operating characteristic curve area under the curve (AUC) of 0.91 in the validation set. Feature maps showed distinct clustering of SFT/HPC and meningioma in the training and test cohorts, respectively. And the attention of the deep learning model mainly focused on the tumor bulks that represented the solid texture features of both tumors for discrimination.
Abstract
Background
Mood swings have been observed in patients with intracranial aneurysm (IA), but it is still unknown whether mood swings can affect IA.
Aim
To explore the causal association ...between mood swings or experiencing mood swings and IA through a two‐sample Mendelian randomization (MR) study.
Methods
S
ummary‐level statistics of mood swings, experiencing mood swings, IA, aneurysm‐associated subarachnoid hemorrhage (aSAH), and non‐ruptured IA (uIA) were collected from the genome‐wide association study. Two‐sample MR and various sensitivity analyses were employed to explore the causal association between mood swings or experiencing mood swings and IA, or aSAH, or uIA. The inverse‐variance weighted method was used as the primary method.
Results
Genetically determined mood swings (odds ratio OR = 5.23, 95% confidence interval (95%CI): 1.65–16.64,
p
= .005) and experiencing mood swings (OR = 2.50, 95%CI: 1.37–4.57,
p
= .003) were causally associated with an increased risk of IA. Mood swings (OR = 5.67, 95%CI: 1.40–23.04,
p
= .015) and experiencing mood swings were causally associated with the risk of aSAH (OR = 2.91, 95%CI: 1.47–5.75,
p
= .002). Neither mood swings (OR = 1.95, 95%CI: .31–12.29,
p
= .478) nor experiencing mood swings (OR = 1.20, 95%CI: .48–3.03,
p
= .693) were associated with uIA.
Conclusions
Mood swings and experiencing mood swings increased the risk of IA and aSAH incidence. These results suggest that alleviating mood swings may reduce IA rupture incidence and aSAH incidence.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory ...networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next introduce LPRI (Lung Cancer Prognostic Regulon Index), a precision oncology framework to identify new biomarkers associated with prognosis by leveraging the single cell regulon atlas and bulk RNA sequencing or microarray datasets. We confirmed that LPRI could be a robust biomarker to guide prognosis stratification across lung adenocarcinoma cohorts. Finally, a multi-omics data analysis to characterize molecular alterations associated with LPRI was performed from The Cancer Genome Atlas (TCGA) dataset. Our study provides a comprehensive chart of regulons in LUAD. Additionally, LPRI will be used to help prognostic prediction and developing personalized treatment for future studies.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The presence of tumor-associated stroma and tumor-infiltrated immune cells have been largely reported across glioblastomas. Tumor purity, defined as the proportion of tumor cells in the tumor, was ...associated with the genomic and clinicopathologic features of the tumor and may alter the interpretation of glioblastoma biology.
We use an integrative approach to infer tumor purity based on multi-omic data and comprehensively evaluate the impact of tumor purity on glioblastoma (GBM) prognosis, genomic profiling, and the immune microenvironment in the Cancer Genome Atlas Consortium (TCGA) cohort.
We found that low tumor purity was significantly associated with reduced survival time. Additionally, we established a purity-relevant 5-gene signature that was an independent prognostic biomarker and validated it in the TCGA, CGGA and GSE4412 cohort. Moreover, we correlated tumor purity with genomic characteristics and tumor microenvironment. We identified that gamma delta T cells in glioblastoma microenvironment were positively correlated with purity and served as a marker for favorable prognosis, which was validated in both TCGA and CGGA dataset.
We observe the potential confounding effects of tumor purity on GBM clinical and molecular information interpretation. GBM microenvironment could be purity-dependent, which provides new insights into the clinical implications of glioblastoma.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Aim
This study aimed to explore the expression pattern of
MLLT11
under different pathological features, evaluate its prognostic value for glioma patients, reveal the relationship between
MLLT11
mRNA ...expression and immune cell infiltration in the tumor microenvironment (TME), and provide more evidence for the molecular diagnosis of glioma and immunotherapy.
Methods
Using large-scale bioinformatic approach and RNA sequencing (RNA-seq) data from public databases The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and The Gene Expression Omnibus (GEO)), we investigated the relationship between
MLLT11
mRNA levels and pathologic characteristics. The distribution in the different subtypes was observed based on Verhaak bulk and Neftel single-cell classification. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used for bioinformatic analysis. Kaplan–Meier survival analysis and Cox regression analysis were used for survival analysis. Correlation analyses were performed between
MLLT11
expression and 22 immune cells and immune checkpoints in the TME.
Results
We found that
MLLT11
expression is decreased in high-grade glioma tissues; we further verified this result by RTPCR, Western blotting, and immunohistochemistry using our clinical samples. According to the Verhaak classification, high
MLLT11
expression is mostly clustered in pro-neutral (PN) and neutral (NE) subtypes, while in the Neftel classification,
MLLT11
mainly clustered in neural progenitor-like (NPC-like) neoplastic cells. Survival analysis revealed that low levels of
MLLT11
expression are associated with a poorer prognosis;
MLLT11
was identified as an independent prognostic factor in multivariate Cox regression analyses. Functional enrichment analyses of
MLLT11
with correlated expression indicated that low
MLLT11
expression is associated with the biological process related to the extracellular matrix, and the high expression group is related to the synaptic structure. Correlation analyses suggest that declined
MLLT11
expression is associated with increased macrophage infiltration in glioma, especially M2 macrophage, and verified by RTPCR, Western blotting, and immunohistochemistry using our clinical glioma samples.
MLLT11
had a highly negative correlation with immune checkpoint inhibitor (ICI) genes including
PDCD1
,
PD-L1
,
TIM3(HAVCR2)
, and
PD‐L2 (PDCD1LG2)
.
Conclusion
MLLT11
plays a crucial role in the progression of glioma and has the potential to be a new prognostic marker for glioma.
ZBTB42 is a transcription factor that belongs to the ZBTB transcript factor family and plays an important role in skeletal muscle development. Dysregulation of ZBTB42 expression can lead to a variety ...of diseases. However, the function of ZBTB42 in glioma development has not been studied by now.
We analyzed the expression of ZBTB42 in LGG and GBM
the The Cancer Genome Atlas CGA and Chinese Glioma Genome Atlas database. Gene Ontology, KEGG, and GSVA analyses were performed to illustrate ZBTB42-related pathways. ESTIMATE and CIBERSORT were applied to calculate the immune score and immune cell proportion in glioma. One-class logistic regression OCLR algorithm was used to study the stemness of glioma. Multivariate Cox analysis was employed to detect the prognostic value of five ZBTB42-related genes.
Our results show that ZBTB42 is highly expressed in glioma and may be a promising prognostic factor for Low Grade Glioma and GBM. In addition, ZBTB42 is related to immune cell infiltration and may play a role in the immune suppression microenvironment. What's more, ZBTB42 is correlated with stem cell markers and positively associated with glioma stemness. Finally, a five genes nomogram based on ZBTB42 was constructed and has an effective prognosis prediction ability.
We identify that ZBTB42 is a prognostic biomarker for Low Grade Glioma and GBM and its function is related to the suppressive tumor microenvironment and stemness of glioma.
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