Effects of the inhibition of cytosolic phospholipase A2α in non-small cell lung cancer cells

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.

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.

Phospholipase A2 superfamily in cancer

Phospholipase A2 enzymes (PLA₂s) comprise a superfamily that is generally divided into six subfamilies known as cytosolic PLA₂s (cPLA₂s), calcium-independent PLA₂s (iPLA₂s), secreted PLA₂s (sPLA₂s), lysosomal PLA₂s, platelet-activating factor (PAF) acetylhydrolases, and adipose specific PLA₂s. Each subfamily consists of several isozymes that possess PLA₂ activity. The first three PLA₂ subfamilies play important roles in inflammation-related diseases and cancer. In this review, the roles of well-studied enzymes sPLA₂-IIA, cPLA₂α and iPLA₂β in carcinogenesis and cancer development were discussed. sPLA₂-IIA seems to play conflicting roles and can act as a tumor suppressor or a tumor promoter according to the cancer type, but cPLA₂α and iPLA₂β play protumorigenic role in most cancers. The mechanisms of PLA₂-mediated signal transduction and crosstalk between cancer cells and endothelial cells in the tumor microenvironment are described. Moreover, the mechanisms by which PLA₂s mediate lipid reprogramming and glycerophospholipid remodeling in cancer cells are illustrated. PLA₂s as the upstream regulators of the arachidonic acid cascade are generally high expressed and activated in various cancers. Therefore, they can be considered as potential pharmacological targets and biomarkers in cancer. The detailed information summarized in this review may aid in understanding the roles of PLA₂s in cancer, and provide new clues for the development of novel agents and strategies for tumor prevention and treatment.

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.

Modeling Membrane Curvature Generation due to Membrane⁻Protein Interactions

To alter and adjust the shape of the plasma membrane, cells harness various mechanisms of curvature generation. Many of these curvature generation mechanisms rely on the interactions between peripheral membrane proteins, integral membrane proteins, and lipids in the bilayer membrane. Mathematical and computational modeling of membrane curvature generation has provided great insights into the physics underlying these processes. However, one of the challenges in modeling these processes is identifying the suitable constitutive relationships that describe the membrane free energy including protein distribution and curvature generation capability. Here, we review some of the commonly used continuum elastic membrane models that have been developed for this purpose and discuss their applications. Finally, we address some fundamental challenges that future theoretical methods need to overcome to push the boundaries of current model applications.

cPLA2α activates PI3K/AKT and inhibits Smad2/3 during epithelial-mesenchymal transition of hepatocellular carcinoma cells

Cytosolic phospholipase A2α (cPLA2α), a key phospholipase that regulates lipid metabolism, plays an important role in tumor progression. In the present study of hepatocellular carcinoma (HCC), cPLA2α was overexpressed in highly metastatic HCC cell lines. Immunohistochemical staining showed increased levels of cPLA2α at the invasive edges of HCC, and a clinicopathological analysis of samples from 111 patients revealed that its expression level was linked with micro-vascular invasion and cirrhosis. Knockdown of cPLA2α inhibited migration, probably due to its role in actin polymerization. Overexpression of cPLA2α promoted cell migration and invasion. Based on the mechanistic analysis, our data suggested that cPLA2α mediate epidermal growth factor (EGF) induced epithelial-mesenchymal transition (EMT) through PI3K/AKT/ERK pathway. cPLA2α activity was required for the transforming growth factor-(TGF)-β-induced EMT. However, cPLA2α inhibited Smad2/3 activation and promoted the activation of the PI3K/AKT/ERK pathway. A xenograft tumor transplant model confirmed the role of cPLA2α in HCC invasion and metastasis. Based on the mechanistic analysis, cPLA2α mediated both EGF- and TGF-β-induced EMT, which are essential for HCC metastasis. cPLA2α is a potentially target for novel therapies of HCC.

Global analysis of chromosome 1 genes among patients with lung adenocarcinoma, squamous carcinoma, large-cell carcinoma, small-cell carcinoma, or non-cancer

The present study aimed at investigating genetic variations, specific signal pathways, or biological processes of chromosome 1 genes between subtypes and stages of lung cancer and prediction of selected targeting genes for patient survival rate. About 537 patients with lung adenocarcinoma (ADC), 140 with lung squamous carcinoma (SCC), 9 with lung large-cell carcinoma (LCC), 56 with small-cell lung cancer (SCLC), and 590 without caner were integrated from 16 databases and analyzed in the present study. Three (ASPM, CDC20, KIAA1799) or 28 genes significantly up- or down-expressed in four subtypes of lung cancer. The activated cell division and down-regulated immune responses were identified in patients with lung cancer. Keratinocyte development associated genes S100 and SPRR families dominantly up-expressed in SCC and AKT3 and NRAS in SCLC. Subtype-specific genes of ADC, SCC, LCC, or SCLC were also identified. C1orf106, CAPN8, CDC20, COL11A1, CRABP2, and NBPF9 up-expressed at four stages of ADC. Fifty six related with keratinocytes or potassium channels up-expressed in three stages of SCC. CDC20, IL10, ECM1, GABPB2, CRABP2, and COL11A1 significantly predicted the poor overall survival of ADC patients and S100A2 and TIMM17A in SCC patients. Our data indicate that a number of altered chromosome 1 genes have the subtype and stage specificities of lung cancer and can be considered as diagnostic and prognosis biomarkers.

Group IVA Cytosolic Phospholipase A2 Regulates the G2-to-M Transition by Modulating the Activity of Tumor Suppressor SIRT2

The G2-to-M transition (or prophase) checkpoint of the cell cycle is a critical regulator of mitotic entry. SIRT2, a tumor suppressor gene, contributes to the control of this checkpoint by blocking mitotic entry under cellular stress. However, the mechanism underlying both SIRT2 activation and regulation of the G2-to-M transition remains largely unknown. Here, we report the formation of a multiprotein complex at the G2-to-M transition in vitro and in vivo. Group IVA cytosolic phospholipase A2 (cPLA2α) acts as a bridge in this complex to promote binding of SIRT2 to cyclin A-Cdk2. Cyclin A-Cdk2 then phosphorylates SIRT2 at Ser331. This phosphorylation reduces SIRT2 catalytic activity and its binding affinity to centrosomes and mitotic spindles, promoting G2-to-M transition. We show that the inhibitory effect of cPLA2α on SIRT2 activity impacts various cellular processes, including cellular levels of histone H4 acetylated at K16 (Ac-H4K16) and Ac-α-tubulin. This regulatory effect of cPLA2α on SIRT2 defines a novel function of cPLA2α independent of its phospholipase activity and may have implications for the impact of SIRT2-related effects on tumorigenesis and age-related diseases.

Ligand-conjugated mesoporous silica nanorattles based on enzyme targeted prodrug delivery system for effective lung cancer therapy

Epidermal growth factor receptor antibody (EGFRAb) conjugated silica nanorattles (SNs) were synthesized and used to develop receptor mediated endocytosis for targeted drug delivery strategies for cancer therapy. The present study determined that the rate of internalization of silica nanorattles was found to be high in lung cancer cells when compared with the normal lung cells. EGFRAb can specifically bind to EGFR, a receptor that is highly expressed in lung cancer cells, but is expressed at low levels in other normal cells. Furthermore, in vitro studies clearly substantiated that the cPLA2α activity, arachidonic acid release and cell proliferation were considerably reduced by pyrrolidine-2 loaded EGFRAb-SN in H460 cells. The cytotoxicity, cell cycle arrest and apoptosis were significantly induced by the treatment of pyrrolidine-2 loaded EGFRAb-SN when compared with free pyrrolidine-2 and pyrrolidine-2 loaded SNs in human non-small cell lung cancer cells. An in vivo toxicity assessment showed that silica nanorattles and EGFRAb-SN-pyrrolidine-2 exhibited low systemic toxicity in healthy Balb/c mice. The EGFRAb-SN-pyrrolidine-2 showed a much better antitumor activity (38%) with enhanced tumor inhibition rate than the pyrrolidine-2 on the non-small cell lung carcinoma subcutaneous model. Thus, the present findings validated the low toxicity and high therapeutic potentials of EGFRAb-SN-pyrrolidine-2, which may provide a convincing evidence of the silica nanorattles as new potential carriers for targeted drug delivery systems.

The molecular basis of ceramide-1-phosphate recognition by C2 domains

Group IVA cytosolic phospholipase A₂ (cPLA₂α), which harbors an N-terminal lipid binding C2 domain and a C-terminal lipase domain, produces arachidonic acid from the sn-2 position of zwitterionic lipids such as phosphatidylcholine. The C2 domain has been shown to bind zwitterionic lipids, but more recently, the anionic phosphomonoester sphingolipid metabolite ceramide-1-phosphate (C1P) has emerged as a potent bioactive lipid with high affinity for a cationic patch in the C2 domain β-groove. To systematically analyze the role that C1P plays in promoting the binding of cPLA₂α-C2 to biological membranes, we employed biophysical measurements and cellular translocation studies along with mutagenesis. Biophysical and cellular translocation studies demonstrate that C1P specificity is mediated by Arg⁵⁹, Arg⁶¹, and His⁶² (an RxRH sequence) in the C2 domain. Computational studies using molecular dynamics simulations confirm the origin of C1P specificity, which results in a spatial shift of the C2 domain upon membrane docking to coordinate the small C1P headgroup. Additionally, the hydroxyl group on the sphingosine backbone plays an important role in the interaction with the C2 domain, further demonstrating the selectivity of the C2 domain for C1P over phosphatidic acid. Taken together, this is the first study demonstrating the molecular origin of C1P recognition.

C2 domain membrane penetration by group IVA cytosolic phospholipase A₂ induces membrane curvature changes

Group IVA cytosolic phospholipase A(2) (cPLA(2)α) is an 85 kDa enzyme that regulates the release of arachidonic acid (AA) from the sn-2 position of membrane phospholipids. It is well established that cPLA(2)α binds zwitterionic lipids such as phosphatidylcholine in a Ca(2+)-dependent manner through its N-terminal C2 domain, which regulates its translocation to cellular membranes. In addition to its role in AA synthesis, it has been shown that cPLA(2)α promotes tubulation and vesiculation of the Golgi and regulates trafficking of endosomes. Additionally, the isolated C2 domain of cPLA(2)α is able to reconstitute Fc receptor-mediated phagocytosis, suggesting that C2 domain membrane binding is sufficient for phagosome formation. These reported activities of cPLA(2)α and its C2 domain require changes in membrane structure, but the ability of the C2 domain to promote changes in membrane shape has not been reported. Here we demonstrate that the C2 domain of cPLA(2)α is able to induce membrane curvature changes to lipid vesicles, giant unilamellar vesicles, and membrane sheets. Biophysical assays combined with mutagenesis of C2 domain residues involved in membrane penetration demonstrate that membrane insertion by the C2 domain is required for membrane deformation, suggesting that C2 domain-induced membrane structural changes may be an important step in signaling pathways mediated by cPLA(2)α.

Curtailing overexpression of E2F3 in breast cancer using siRNA (E2F3)-based gene silencing

BACKGROUND AND AIMS

The E2F3 transcription factor claims its role in controlling cell cycle progression. As reported earlier, nuclear E2F3 overexpression leads to development of bladder and prostate cancer in humans. Accordingly, the present investigation has been designed to assess to what extent E2F3 would be overexpressed in breast cancer. The aim of this study was to emphasize that the levels of E2F3 are increased in breast cancer and highlights the efficacy of siRNA targeted to E2F3.

METHODS

To investigate the expression level of E2F3 and the progression of breast tumors, quantitative real-time PCR analysis was carried out. Western blotting analysis was performed to measure its counterparts, namely, E2F3a and E2F3b.

RESULTS

In the novel axis of E2F3, a large set of 11 breast cancer cell lines were identified to have the property of overexpression. Furthermore, the small interfering RNA (siRNA) developed against E2F3 significantly blocked the expression of the E2F3 in the selected breast cancer cell lines. Thus, the present findings authenticate the efficiency of siRNA (E2F3) to fight against breast cancer; hence, the siRNA mediated E2F3 gene silencing knockdown the E2F3.

CONCLUSIONS

This in vitro study demonstrates that E2F3 is a newly identified diagnostic and potential therapeutic target in breast cancer. Outcomes of this study affirm that siRNA for E2F3 facilitates the silencing of E2F3 overexpression and fights against breast cancer. Therefore, it plays a vital role as an alternative for diagnosis and clinical outcome for the treatment of breast cancer.