A Multiplex CRISPR-Screen Identifies PLA2G4A as Prognostic Marker and Druggable Target for HOXA9 and MEIS1 Dependent AML

Hassan, Jacob Jalil; Lieske, Anna; Dörpmund, Nicole; Klatt, Denise; Hoffmann, Dirk; Kleppa, Marc-Jens; Kustikova, Olga; Stahlhut, Maike; Schwarzer, Adrian; Schambach, Axel; Maetzig, Tobias

doi:10.3390/ijms22179411

Cited by 14 publications

(16 citation statements)

References 70 publications

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“…Previous study found that the common PLA2G4A upregulation in cancer cells contributes to their migration and invasion and is significantly correlated with unfavorable prognosis (14,15). PLA2G4A overexpression contributed to the malignant phenotype of AML cells together with its partners and is correlated to a poor prognosis in the patients with non-M3/NPM1 wildtype or Hoxa9-and Meis1-dependent AML (16,17). DGKA is the family member of conserved membrane lipid kinases and has been involved in human cancers (18)(19)(20).…”

Section: Discussionmentioning

confidence: 99%

A distinct lipid metabolism signature of acute myeloid leukemia with prognostic value

et al. 2022

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BackgroundAcute myeloid leukemia (AML) is a highly aggressive hematological malignancy characterized by extensive genetic abnormalities that might affect the prognosis and provide potential drug targets for treatment. Reprogramming of lipid metabolism plays important roles in tumorigenesis and progression and has been newly recognized a new hallmark of malignancy, and some related molecules in the signal pathways could be prognostic biomarkers and potential therapeutic targets for cancer treatment. However, the clinical value of lipid metabolism reprogramming in AML has not been systematically explored. In this study, we aim to explore the clinical value of lipid metabolism reprogramming and develop a prognostic risk signature for AML.MethodsWe implemented univariate Cox regression analysis to identify the prognosis-related lipid metabolism genes, and then performed LASSO analysis to develop the risk signature with six lipid metabolism-related genes (LDLRAP1, PNPLA6, DGKA, PLA2G4A, CBR1, and EBP). The risk scores of samples were calculated and divided into low- and high-risk groups by the median risk score.ResultsSurvival analysis showed the high-risk group hold the significantly poorer outcomes than the low-risk group. The signature was validated in the GEO datasets and displayed a robust prognostic value in the stratification analysis. Multivariate analysis revealed the signature was an independent prognostic factor for AML patients and could serve as a potential prognostic biomarker in clinical evaluation. Furthermore, the risk signature was also found to be closely related to immune landscape and immunotherapy response in AML.ConclusionsOverall, we conducted a comprehensive analysis of lipid metabolism in AML and constructed a risk signature with six genes related to lipid metabolism for the malignancy, prognosis, and immune landscape of AML, and our study might contribute to better understanding in the use of metabolites and metabolic pathways as the potential prognostic biomarkers and therapeutic targets for AML.

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Section: Discussionmentioning

confidence: 99%

A distinct lipid metabolism signature of acute myeloid leukemia with prognostic value

et al. 2022

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“…The cytoplasmic phospholipase A2-α (cPLA2α) encoded by the PLA2G4A gene acts as an upstream regulator of the eicosanoid signaling pathway by providing intracellular AA ( Bazhan and Khaniani, 2018 ). Studies have shown that the PLA2G4A gene can be used as a biomarker in various diseases such as gastric cancer ( Bazhan and Khaniani, 2018 ), acute myeloid leukemia ( Hassan et al, 2021 ; Lai et al, 2021 ), cholangiocarcinoma ( Sun et al, 2019 ), and colorectal cancer ( Zhan et al, 2021a ). PLA2G4A activates the colorectal cancer microenvironment to produce pro-cytokines IL-17A and adenosine, thereby establishing an effective immunosuppressive microenvironment and promoting immune evasion and tumor metastasis ( Zhan et al, 2021b ).…”

Section: Discussionmentioning

confidence: 99%

“…1, 524-533. doi:10.1002/ jcb.29258 Yan, H., Xu, J., Xu, Z., Yang, B., Luo, P., and He, Q. (2021). Defining therapeutic targets for renal fibrosis: Exploiting the biology of pathogenesis.…”

Section: Data Availability Statementmentioning

confidence: 99%

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Feng

Wang²,

Liu³

et al. 2022

Front. Pharmacol.

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Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

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“…Then, the synergism between Tan I and EADM was tested in HCC cell lines. The Bliss synergy score (SynergyFinder), 29,30 positive excess over bliss additivism (EOBA), 29,31,32 and the drug combination index (CI) 33 were calculated to assess whether Tan I and EADM were synergistic, additive, or antagonistic. These assays allowed us to observe the level of synergy between Tan I and EADM at different dose combinations, while allowing us to visualize the pattern of synergy and predict the dose combination that exhibited the greatest inhibition of proliferation.…”

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confidence: 99%

“…Furthermore, based on the results of the Bliss synergy score (SynergyFinder), EOBA, and CI, we selected the most reasonable and effective combination concentration as the combined concentration of Tan I and EADM to treat HCC cancer cell lines. [29][30][31][32][33] Synergism between Tan I and EADM was defined as Bliss synergy score (SynergyFinder), 29,30 EOBA, 29,31,32 and CI. 33 The Bliss synergy score was calculated by SynergyFinder, in which 0 means additive, < 0 antagonistic, and > 0 synergistic.…”

mentioning

confidence: 99%

Combined Treatment of Tanshinone I and Epirubicin Revealed Enhanced Inhibition of Hepatocellular Carcinoma by Targeting PI3K/AKT/HIF-1α

Zhao¹,

Lin²,

Cai³

et al. 2022

DDDT

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Background: Epirubicin (EADM) is a common chemotherapeutic agent in hepatocellular carcinoma (HCC). The accumulation of hypoxia-inducible factor-1α (HIF-1α) is an important cause of drug resistance to EADM in HCC. Tanshinone I (Tan I) is an agent with promising anti-cancer effects alone or with other drugs. Some tanshinones mediate HIF-1α regulation via PI3K/AKT. However, the role of Tan I combined with EADM to reduce the resistance of HCC to EADM has not been investigated. Therefore, this study aimed to investigate the combined use of Tan I and EADM in HCC and the underlying mechanism of PI3K/AKT/HIF-1α. Methods: HCC cells were treated with Tan I, EADM, or the combined treatment for 48 hrs. Cell transfection was used to construct HIF-1α overexpression HCC stable cells. Cell viability, colony formation, and flow cytometric assays were used to detect the viability, proliferation, and apoptosis in HCC cells. Synergism between Tan I and EADM were tested by calculating the Bliss synergy score, positive excess over bliss additivism (EOBA), and the combination index (CI). Western blotting analyses were used to detect the levels of β-actin, HIF-1α, PI3K p110α, p-Akt Thr308, Cleaved Caspase-3, and Cleaved Caspase-9. Toxicity parameters were used to evaluate the safety of the combination in mice. The xenograft model of mice was built by HCC stable cell lines, which was administrated with Tan I, EADM, or a combination of them for 8 weeks. Immunohistochemistry staining (IHC) was used to assess tumor apoptosis in mouse models. Results: Hypoxia could upregulate HIF-1α to induce drug resistance in HCC cancer cells. The combination of Tan I and EADM was synergistic. Although Tan I or EADM alone could inhibit HCC cancer cells, the combination of them could further enhance the cytotoxicity and growth inhibition by targeting the PI3K/AKT/HIF-1α signaling pathway. Furthermore, Tan I and EADM synergistically reversed HIF-1α-mediated drug resistance to inhibit HCC. The results of toxicity parameters showed that the combination was safe in mice. Meanwhile, animal models showed that Tan I not only improved the anti-tumor effect of EADM, but also reduced the drug reactions of EADM-induced weight loss. Conclusion: Our results suggested that Tan I could effectively improve the anti-tumor effect of EADM, and synergize EADM to reverse HIF-1α mediated resistance via targeting PI3K/AKT/HIF-1α signaling pathway.

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A Multiplex CRISPR-Screen Identifies PLA2G4A as Prognostic Marker and Druggable Target for HOXA9 and MEIS1 Dependent AML

Cited by 14 publications

References 70 publications

A distinct lipid metabolism signature of acute myeloid leukemia with prognostic value

A distinct lipid metabolism signature of acute myeloid leukemia with prognostic value

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Combined Treatment of Tanshinone I and Epirubicin Revealed Enhanced Inhibition of Hepatocellular Carcinoma by Targeting PI3K/AKT/HIF-1α

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