<i>PLA2G4A</i> Is a Potential Biomarker Predicting Shorter Overall Survival in Patients with Non-M3/<i>NPM1</i> Wildtype Acute Myeloid Leukemia

Bai, Hansong; Zhou, Miaofang; Zeng, Ming; Han, Li

doi:10.1089/dna.2019.5187

Cited by 20 publications

(21 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Phospholipase can hydrolyze membrane phospholipids to arachidonic acid, which is further involved in many Pathological and physiological processes, including inducing in ammation, transmitting signals, and promoting cell growth, etc. Although one study has shown that down expression of PLA2G4A can promote the migration and invasion of esophageal squamous cell carcinoma [24], most researchers considered that PLA2G4A was an oncogene [25][26][27], which was consistent with our results. The data showed that the expression level of PLA2G4A notably decrease after HULC silencing.…”

Section: Discussionsupporting

confidence: 91%

The Quantitative Proteomics Analysis in Glioblastoma After HULC Silencing

Yin

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Glioblastoma multiforme (GBM) is a malignant intracranial tumor threatening patients' survival. The study aimed to find the menchanisms of lncRNA highly up-regulated in liver cancer (HULC) affecting GBM or involved signaling pathways, which may providing theoretical support for targeted therapy.Methods: Two cell lines were constructed: HULC-small interfering RNA (siRNA) and negative control. Then qRT-PCR was operated to detect the transfection efficiency and quantitative proteomics based on mass spectrometry (MS) were conducted. Finally, the differentially expressed proteins were analyzed in gene ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment.Results: The relative expression level of HULC in HULC-siRNA was significantly lower than that in NC detected by qRT-PCR. A total of 136 differentially expressed proteins were identified, including 24 down-regulated and 112 up-regulated. GO classification results illustrated that they were centralized in cellular or single-organism process and biological regulation in biological process, cell and organelle in cellular component, binding and catalytic activity in molecular function. KEGG pathway enrichment analysis indicated that some were sinificantly enriched, including tight junction, metabolic pathways and arachidonic acid metabolism. It was further discovered that PLA2G4A was down regulated obviously after HULC silencing.Conclusion: HULC changed the proteomics characteristics of GBM, including regulating the expression of PLA2G4A. This study provided a new perspective on the menchanisms and potential drug targets of GBM.

show abstract

Section: Discussionsupporting

confidence: 91%

The Quantitative Proteomics Analysis in Glioblastoma After HULC Silencing

Yin

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Perhaps from the side, our model genes do not depend on structural and functional changes, that is, qualitative changes, to affect the prognosis of AML; they play high-risk roles in the prognosis of AML by the quantitative change. This point can be inferred from our previous discussion on model genes (41)(42)(43)(44).…”

Section: Discussionmentioning

confidence: 85%

“…Multiple validations can basically determine that the metabolic model genes we have found are high-risk genes and independent prognostic factors. A recent paper shows that bioinformatic analysis identifies that PLA2G4A has physical interactions with several oncogenic proteins (such as RUVBL2, CAP1, STAT3, and MYCBP) in AML, resulting in multiple effects on the malignant phenotype of AML cells (41). Also, another recent paper reports that AK1 is an independent adverse prognostic factor for AML patients receiving chemotherapy, and patients with high AK1 expression may be recommended for early Allo-HSCT (42).…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of Prognostic Markers for Acute Myeloid Leukemia Based on Four Metabolic Genes

Zhang

Wang

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

Background: Metabolic reprogramming is the core characteristic of tumors during the development of tumors, and cancer cells can rely on metabolic changes to support their rapid growth. Nevertheless, an overall analysis of metabolic markers in acute myeloid leukemia (AML) is absent and urgently needed. Methods: Within this work, genetic expression, mutation data and clinical data of AML were queried from Genotype-Tissue Expression (GTEx) database, The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The tumor samples of TCGA were randomly divided into a training group (64 samples) and an internal validation group (64 samples) at one time, and the tumor samples of GEO served as two external validation groups (99 samples, 374 samples). According to the expression levels of survival-associated metabolic genes, we divided all TCGA tumor samples into high, medium and low metabolism groups, and evaluated the immune cell activity in the tumor microenvironment of the three metabolism groups by single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, we examined the mutations and prognostic effects of each model gene. Results: Four metabolism-related genes were screened and applied to construct a prognostic model for AML, giving excellent results. As for the area under the curve (AUC) value of receiver operating characteristic (ROC) curve, the training group was up to 0.902 (1-year), 0.81 (3-year), and 0.877 (5-year); and the internal and external validation groups also met the expected standards, showing high potency in predicting patient outcome. Univariate and multivariate prognostic analyses indicated that the riskScore obtained from our prognostic model was an independent prognostic factor. ssGSEA analysis revealed the high metabolism group had higher immune activity. Single and multiple gene survival analysis validated that each model gene had significant effects on the overall survival of AML patients. Conclusions: In our study, a high-efficiency prognostic prediction model was built and validated for AML patients. The results showed that metabolism-related genes could become potential prognostic biomarkers for AML.

show abstract

“…Interestingly there is only partial overlap between the top features identified in BRCA ER vs. subtypes tasks. Considering AML-OS task, it is noteworthy to mention that the top feature identified has been recently reported as a potential biomarker predicting overall survival in a subset of AML patients ( 37 ).…”

Section: Resultsmentioning

confidence: 99%