Expression of Cytosolic Phospholipase A2 Alpha in Glioblastoma Is Associated With Resistance to Chemotherapy

Biosynthesis and Significance of Fatty Acids, Glycerophospholipids, and Triacylglycerol in the Processes of Glioblastoma Tumorigenesis

One area of glioblastoma research is the metabolism of tumor cells and detecting differences between tumor and healthy brain tissue metabolism. Here, we review differences in fatty acid metabolism, with a particular focus on the biosynthesis of saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA) by fatty acid synthase (FASN), elongases, and desaturases. We also describe the significance of individual fatty acids in glioblastoma tumorigenesis, as well as the importance of glycerophospholipid and triacylglycerol synthesis in this process. Specifically, we show the significance and function of various isoforms of glycerol-3-phosphate acyltransferases (GPAT), 1-acylglycerol-3-phosphate O-acyltransferases (AGPAT), lipins, as well as enzymes involved in the synthesis of phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylinositol (PI), and cardiolipin (CL). This review also highlights the involvement of diacylglycerol O-acyltransferase (DGAT) in triacylglycerol biosynthesis. Due to significant gaps in knowledge, the GEPIA database was utilized to demonstrate the significance of individual enzymes in glioblastoma tumorigenesis. Finally, we also describe the significance of lipid droplets in glioblastoma and the impact of fatty acid synthesis, particularly docosahexaenoic acid (DHA), on cell membrane fluidity and signal transduction from the epidermal growth factor receptor (EGFR).

Synthesis and Significance of Arachidonic Acid, a Substrate for Cyclooxygenases, Lipoxygenases, and Cytochrome P450 Pathways in the Tumorigenesis of Glioblastoma Multiforme, Including a Pan-Cancer Comparative Analysis

Glioblastoma multiforme (GBM) is one of the most aggressive gliomas. New and more effective therapeutic approaches are being sought based on studies of the various mechanisms of GBM tumorigenesis, including the synthesis and metabolism of arachidonic acid (ARA), an omega-6 polyunsaturated fatty acid (PUFA). PubMed, GEPIA, and the transcriptomics analysis carried out by Seifert et al. were used in writing this paper. In this paper, we discuss in detail the biosynthesis of this acid in GBM tumors, with a special focus on certain enzymes: fatty acid desaturase (FADS)1, FADS2, and elongation of long-chain fatty acids family member 5 (ELOVL5). We also discuss ARA metabolism, particularly its release from cell membrane phospholipids by phospholipase A₂ (cPLA₂, iPLA₂, and sPLA₂) and its processing by cyclooxygenases (COX-1 and COX-2), lipoxygenases (5-LOX, 12-LOX, 15-LOX-1, and 15-LOX-2), and cytochrome P450. Next, we discuss the significance of lipid mediators synthesized from ARA in GBM cancer processes, including prostaglandins (PGE₂, PGD₂, and 15-deoxy-Δ^12,14-PGJ₂ (15d-PGJ₂)), thromboxane A₂ (TxA₂), oxo-eicosatetraenoic acids, leukotrienes (LTB₄, LTC₄, LTD₄, and LTE₄), lipoxins, and many others. These lipid mediators can increase the proliferation of GBM cancer cells, cause angiogenesis, inhibit the anti-tumor response of the immune system, and be responsible for resistance to treatment.

Polyunsaturated Fatty Acid-Enriched Lipid Fingerprint of Glioblastoma Proliferative Regions Is Differentially Regulated According to Glioblastoma Molecular Subtype

Glioblastoma (GBM) represents one of the deadliest tumors owing to a lack of effective treatments. The adverse outcomes are worsened by high rates of treatment discontinuation, caused by the severe side effects of temozolomide (TMZ), the reference treatment. Therefore, understanding TMZ’s effects on GBM and healthy brain tissue could reveal new approaches to address chemotherapy side effects. In this context, we have previously demonstrated the membrane lipidome is highly cell type-specific and very sensitive to pathophysiological states. However, little remains known as to how membrane lipids participate in GBM onset and progression. Hence, we employed an ex vivo model to assess the impact of TMZ treatment on healthy and GBM lipidome, which was established through imaging mass spectrometry techniques. This approach revealed that bioactive lipid metabolic hubs (phosphatidylinositol and phosphatidylethanolamine plasmalogen species) were altered in healthy brain tissue treated with TMZ. To better understand these changes, we interrogated RNA expression and DNA methylation datasets of the Cancer Genome Atlas database. The results enabled GBM subtypes and patient survival to be linked with the expression of enzymes accounting for the observed lipidome, thus proving that exploring the lipid changes could reveal promising therapeutic approaches for GBM, and ways to ameliorate TMZ side effects.

Survival prediction in acute myeloid leukemia using gene expression profiling

BACKGROUND

Acute myeloid leukemia (AML) is a genetically heterogeneous blood disorder. AML patients are associated with a relatively poor overall survival. The objective of this study was to establish a machine learning model to accurately perform the prognosis prediction in AML patients.

METHODS

We first screened for prognosis-related genes using Kaplan-Meier survival analysis in The Cancer Genome Atlas dataset and validated the results in the Oregon Health & Science University dataset. With a random forest model, we built a prognostic risk score using patient's age, TP53 mutation, ELN classification and normalized 197 gene expression as predictor variable. Gene set enrichment analysis was implemented to determine the dysregulated gene sets between the high-risk and low-risk groups. Similarity Network Fusion (SNF)-based integrative clustering was performed to identify subgroups of AML patients with different clinical features.

RESULTS

The random forest model was deemed the best model (area under curve value, 0.75). The random forest-derived risk score exhibited significant association with shorter overall survival in AML patients. The gene sets of pantothenate and coa biosynthesis, glycerolipid metabolism, biosynthesis of unsaturated fatty acids were significantly enriched in phenotype high risk score. SNF-based integrative clustering indicated three distinct subsets of AML patients in the TCGA cohort. The cluster3 AML patients were characterized by older age, higher risk score, more frequent TP53 mutations, higher cytogenetics risk, shorter overall survival.

CONCLUSIONS

The random forest-based risk score offers an effective method to perform prognosis prediction for AML patients.

MicroRNA-221 promotes tumor progression by targeting HHIP in human glioblastoma

Background

MicroRNAs are found to be aberrantly expressed in multiple cancers, including glioblastoma (GBM), and microRNA-221 (miR-221) has been verified as an oncogene in various human cancers. Nevertheless, the role of miR-221 in GBM is unclear. This study aimed to investigate the miR-221 expression level in GBM and to evaluate its function and underlying mechanisms.

Methods

Western blotting and qPCR were used to determine the expression of human hedgehog-interacting protein (HHIP) and miR-221 levels. MiR-221-inhibited cell models were constructed, and siRNA was used for HHIP silencing. Cell proliferation was analyzed by MTT and colony formation assays and a subcutaneous xenograft model. Cell migration and invasion was analyzed by wound healing and Transwell invasion assays. A dual luciferase reporter assay system was used to clarify the relationship between miR-221 and HHIP.

Results

The results of this study revealed that miR-221 expression was upregulated in GBM tissues and A172, U251, as well as T98G cells, as detected by real-time PCR analysis. MTT, Transwell, and colony formation assays revealed that miR-221 knockdown could suppress GBM cells from proliferating, migrating, and invading in vitro. Moreover, animal experiments showed that tumor growth in vivo was inhibited when miR-221 expression decreased. Furthermore, HHIP was predicted and verified to be a target of miR-221 by bioinformatics analysis, and luciferase and western blot assays. In addition, HHIP silencing rescued the suppressive effect of a miR-221 inhibitor on the proliferation, migration, and invasion of GBM cells.

Conclusions

Our results indicated that miR-221 is upregulated in GBM and enhances tumor progression by targeting HHIP, which suggests this may be a potential therapeutic target for GBM.

Targeting cPLA2α inhibits gastric cancer and augments chemotherapy efficacy via suppressing Ras/MEK/ERK and Akt/β-catenin pathways

BACKGROUND

Cytosolic phospholipase A2alpha (cPLA2α), an enzyme that is responsible for the hydrolysis of membrane phospholipids, is a key mediator of tumor transformation, progression and metastasis. The role of cPLA2α in gastric cancer has not been revealed.

METHODS

cPLA2α expression was analyzed using RT-PCR and immunohistochemistry approaches in gastric cancer patient samples (n = 26) and multiple cell lines (n = 7). cPLA2α function was studied using plasmid overexpression and siRNA knockdown approaches in SNU-1, MKN-74 and MKN-45 cell lines. The downstream effectors of cPLA2α were determined using biochemical assays.

RESULTS

cPLA2α upregulation is a common feature in gastric cancer patients, particularly those with metastasis. cPLA2α overexpression is sufficient to promote gastric cancer cell growth and migration, and confer chemo-resistance. cPLA2α depletion is active against gastric cancer via inhibiting growth and migration, and inducing apoptosis in gastric cancer cells. Of note, cPLA2α depletion augments efficacy of chemotherapy. Mechanistic studies confirm that cPLA2α regulates gastric cancer biological activities via mainly regulating Ras/MEK/ERK and possibly Akt/β-catenin pathways. Pearson correlation coefficient analysis also suggests a moderate positive correlation between cPLA2α and RAS in gastric cancer.

CONCLUSIONS

Our work demonstrates cPLA2α inhibition as a therapeutic strategy to overcome chemo-resistance and highlights the association of cPLA2α and Ras in gastric cancer.

A Novel 85-Gene Expression Signature Predicts Unfavorable Prognosis in Acute Myeloid Leukemia

Aim:

Acute myeloid leukemia (AML) is a heterogeneous disorder with complex genetic basis and adverse prognosis. Cytogenetics risk, somatic mutations and gene expression profiles are important prognostic factors for AML patients. However, accurate stratification of patient prognosis remains an unsolved problem in AML. This study was to to develop a novel gene profile to accurately classify AML patients into subgroups with different survival probabilities.

Methods:

Survival-related genes were determined by Kaplan–Meier survival analysis and multivariate analysis using the expression and clinical data of 405 AML patients from Oregon Health & Science University (OHSU) dataset and validated in The Cancer Genome Atlas (TCGA) database. Feature selection was performed by using the Least Absolute Shrinkage and Selection Operator (LASSO) method. With the LASSO model, a prognostic 85-gene score was established and compared with 2 known gene-expression risk scores. The stratification of AML patients was performed by unsupervised hierarchical clustering of 85 gene expression levels to identify clusters of AML patients with different survival probabilities.

Results:

The LASSO model comprising 85 genes was considered as the optimal model based on relatively high area under curve value (0.83) and the minimum mean squared error. The 85-gene score was associated with increased mortality in AML patients. Hierarchical clustering analysis of the 85 genes revealed 3 subgroups of AML patients in the OHSU dataset. The cluster1 AML patients were associated with more female cases, higher percent of bone marrow blast cells, 85-gene score, cytogenetics risk, more frequent FLT3-ITD, DNMT3A, NP1 mutations, less frequent TP53, RUNX1 mutations, poorer overall survival than cluster2 tumors. The 85-gene score had higher AUC (0.75) than the 5-gene risk score and LSC17 score (0.74 and 0.65).

Conclusions:

The 85-gene score is superior to the 2 established prognostic gene signatures in the prediction of prognosis of AML patients.

Novel prognostic genes and subclasses of acute myeloid leukemia revealed by survival analysis of gene expression data

Background

Acute myeloid leukemia (AML) is biologically heterogeneous diseases with adverse prognosis. This study was conducted to find prognostic biomarkers that could effectively classify AML patients and provide guidance for treatment decision making.

Methods

Weighted gene co-expression network analysis was applied to detect co-expression modules and analyze their relationship with clinicopathologic characteristics using RNA sequencing data from The Cancer Genome Atlas database. The associations of gene expression with patients’ mortality were investigated by a variety of statistical methods and validated in an independent dataset of 405 AML patients. A risk score formula was created based on a linear combination of five gene expression levels.

Results

The weighted gene co-expression network analysis detected 63 co-expression modules. The pink and darkred modules were negatively significantly correlated with overall survival of AML patients. High expression of FNDC3B, VSTM1 and CALR was associated with favourable overall survival, while high expression of PLA2G4A was associated with adverse overall survival. Hierarchical clustering analysis of FNDC3B, VSTM1, PLA2G4A, GOLGA3 and CALR uncovered four subgroups of AML patients. The cluster1 AML patients showed younger age, lower cytogenetics risk, higher frequency of NPM1 mutations and more favourable overall survival than cluster3 patients. The risk score was demonstrated to be an indicator of adverse prognosis in AML patients

Conclusions

The FNDC3B, VSTM1, PLA2G4A, GOLGA3, CALR and risk score may serve as key prognostic biomarkers for the stratification and ultimately guide rational treatment of AML patients.

ELAVL1 Role in Cell Fusion and Tunneling Membrane Nanotube Formations with Implication to Treat Glioma Heterogeneity

Homotypic and heterotypic cell fusions via permanent membrane fusions and temporal tunneling nanotube formations in the glioma microenvironment were recently documented in vitro and in vivo and mediate glioma survival, plasticity, and recurrence. Chronic inflammation, a hypoxic environment, aberrant mitochondrial function, and ER stress due to unfolded protein accumulation upregulate cell fusion events, which leads to tumor heterogeneity and represents an adaptive mechanism to promote tumor cell survival and plasticity in cytotoxic, nutrient-deprived, mechanically stressed, and inflammatory microenvironments. Cell fusion is a multistep process, which consists of the activation of the cellular stress response, autophagy formation, rearrangement of cytoskeletal architecture in the areas of cell-to-cell contacts, and the expression of proinflammatory cytokines and fusogenic proteins. The mRNA-binding protein of ELAV-family HuR is a critical node, which orchestrates the stress response, autophagy formation, cytoskeletal architecture, and the expression of proinflammatory cytokines and fusogenic proteins. HuR is overexpressed in gliomas and is associated with poor prognosis and treatment resistance. Our review provides a link between the HuR role in the regulation of cell fusion and tunneling nanotube formations in the glioma microenvironment and the potential suppression of these processes by different classes of HuR inhibitors.

Identifying a novel 5-gene signature predicting clinical outcomes in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is the most common type of acute leukemia and biologically heterogeneous diseases with poor prognosis. Thus, we aimed to identify prognostic markers to effectively predict the prognosis of AML patients and eventually guide treatment.

METHODS

Prognosis-associated genes were determined by Kaplan-Meier and multivariate analyses using the expression and clinical data of 173 AML patients from The Cancer Genome Atlas database and validated in an independent Oregon Health and Science University dataset. A prognostic risk score was computed based on a linear combination of 5-gene expression levels using the regression coefficients derived from the multivariate logistic regression model. The classification of AML was established by unsupervised hierarchical clustering of CALCRL, DOCK1, PLA2G4A, FCHO2 and LRCH4 expression levels.

RESULTS

High FCHO2 and LRCH4 expression was related to decreased mortality. While high CALCRL, DOCK1, PLA2G4A expression was associated with increased mortality. The risk score was predictive of increased mortality rate in AML patients. Hierarchical clustering analysis of the five genes discovered three clusters of AML patients. The cluster1 AML patients were associated with lower cytogenetics risk than cluster2 or 3 patients, and better prognosis than cluster3 patients (P values < 0.05 for all cases, fisher exact test or log-rank test).

CONCLUSION

The gene panel comprising CALCRL, DOCK1, PLA2G4A, FCHO2 and LRCH4 as well as the risk score may offer novel prognostic biomarkers and classification of AML patients to significantly improve outcome prediction.

A Patient-Derived Cell Atlas Informs Precision Targeting of Glioblastoma

Glioblastoma (GBM) is a malignant brain tumor with few therapeutic options. The disease presents with a complex spectrum of genomic aberrations, but the pharmacological consequences of these aberrations are partly unknown. Here, we report an integrated pharmacogenomic analysis of 100 patient-derived GBM cell cultures from the human glioma cell culture (HGCC) cohort. Exploring 1,544 drugs, we find that GBM has two main pharmacological subgroups, marked by differential response to proteasome inhibitors and mutually exclusive aberrations in TP53 and CDKN2A/B. We confirm this trend in cell and in xenotransplantation models, and identify both Bcl-2 family inhibitors and p53 activators as potentiators of proteasome inhibitors in GBM cells. We can further predict the responses of individual cell cultures to several existing drug classes, presenting opportunities for drug repurposing and design of stratified trials. Our functionally profiled biobank provides a valuable resource for the discovery of new treatments for GBM.

Inhibition of cytosolic phospholipase A2 alpha increases chemosensitivity in cervical carcinoma through suppressing β-catenin signaling

Cytosolic phospholipase A2alpha (cPLA2α) is a key mediator of tumorigenesis. In this study, by using a combination of pharmacological and genetic approaches in cell models and patient samples, we identify cPLA2α as a selective target to increase chemosensitivity in cervical cancer. We found that transcript and protein levels of cPLA2α but not other forms of cPLA2 (e.g., cPLA2β and cPLA2αδ) were consistently increased in all tested malignant cervical cancer cells and tissues compared to normal counterparts, suggesting that cPLA2α upregulation is a common feature in cervical cancer. We further found that promoting growth and survival rather than invasion were the predominant roles of cPLA2α on cervical cancer. In addition, chemotherapeutic agents achieved ~100% inhibition efficacy in cPLA2α-depleted cervical cancer cells, demonstrating the important role of cPLA2α in chemoresistance. Importantly, we identify that β-catenin is critically involved in the molecular mechanism of cPLA2α's action in cervical cancer. In summary, our work demonstrates the multiple essential roles of cPLA2α in cervical cancer, particularly in chemoresistance, via a β-catenin-dependent manner. Our work also suggests that targeting cPLA2α has a therapeutic value in overcoming chemoresistance in cervical cancer or other cPLA2α-regulated cancers.