Conditional Deletion of Eaf1 Induces Murine Prostatic Intraepithelial Neoplasia in Mice

Pascal, Laura E; Su, Fei; Wang, Dan; Ai, Junkui; Song, Qiong; Wang, Yujuan; O’Malley, Katherine J.; Cross, Brian W.; Rigatti, Lora H.; Green, Anthony; Dhir, Rajiv; Wang, Zhou

doi:10.1016/j.neo.2019.05.005

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…It has previously been suggested that EAF2 serves as a tumor suppressor in prostate cancer [27,28]. EAF2 is frequently down-regulated in advanced prostate cancer and EAF2 deletion in the mouse induced the development of murine prostatic intraepithelial neoplasia [29,30]. Furthermore, one study demonstrated that EAF2 might inhibit the invasion, migration, and angiogenesis of colorectal cancer cells, and play a cancer suppressive and protective role in the occurrence and development of colorectal cancer [31].…”

Section: Discussionmentioning

confidence: 99%

WITHDRAWN: Screening and identification of the potential diagnostic genes for Parkinson's Disease based on Machine Learning algorithm

Peng

Chen

et al. 2023

Preprint

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Background: Parkinson’s disease (PD) is one of the common neurodegenerative diseases, which have an increasingly morbidity rate, and the genetic markers of PD are receiving increasing attention currently. Method: From the Gene Expression Omnibus (GEO) database, we downloaded five array datasets which from the brain tissue of patients with PD and healthy people. We screened the differentially expressed genes (DEGs) between PD and healthy control samples while performed intersection analysis with Weighted Gene Coexpression Network Analysis (WGCNA) genes. Then three machine learning algorithms were employed the least absolute shrinkage and selection operator (LASSO) regression, supported vector machine recursive feature elimination (SVM-RFE) and the Random Forest (RF) analyze to selected the hub gene of PD. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of hub gene. Subsequently, the reverse transcription quantitative polymerase chain reaction (RT-qPCR) experimentwas utilized to confirm the expression of hub gene based on the clinic samples. Finally, the correlations between the expression of hub gene and immune cell ratios were investigated using the CIBERSORT algorithm. Results: Based on the GEO database, 254 overlapping genes were screened from DEG and the blue modules of WGCNA. Functional enrichment analysis showed that these candidate disease genes were associated with PD and might participated in the progression of PD through the MAPK signaling pathway. Two hub genes (EAF2 and HTR1F) were subsequently selected through machine learning algorithms, and the ROC analysis showed that the area under curve (AUC) of both genes were all greater than 0.7 in the experimental datasets and validation datasets. Afterwards, the results of the RT-qPCR experiments demonstrated that the mRNA expression of EAF2 and HTR1F were lower in patients with PD, which consistent with the gene expression results in the GEO dataset. Finally, CIBERSORT analysis indicated the changes of the immune environment in PD patients, and revealed the types of immune cells associated with the expression of EAF2and HTR1F. Conclusion: Two hub genes of PD, EAF2 and HTR1F, were identified by machine learning algorithms, which might provide a significant target for potential diagnosis and personalized treatment of PD in the future.

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Section: Discussionmentioning

confidence: 99%

WITHDRAWN: Screening and identification of the potential diagnostic genes for Parkinson's Disease based on Machine Learning algorithm

Peng

Chen

et al. 2023

Preprint

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“…KIF13B interacts with the tubulin VEGFR2, and KIF13B depletion prevents VEGF-mediated neo-vascularization and endothelial migration( Yamada et al, 2014 ). EAF2 is a potential tumor suppressor that interacts with the eleven-nineteen lysine-rich leukemia (ELL) protein to inhibit cancer progression of B-cell lymphoma, prostatic cancer and hepatocellular carcinoma( Pascal et al, 2019 ; Xiao et al, 2008 ). MFSD9 encodes a transport protein, but its function in cancer has not been reported.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of peroxisome proliferator-activated receptors to predict the drug resistance, immune microenvironment, and prognosis in stomach adenocarcinomas

Jia,

Li,

Wang

et al. 2024

PeerJ

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Background Peroxisome proliferator-activated receptors (PPARs) exert multiple functions in the initiation and progression of stomach adenocarcinomas (STAD). This study analyzed the relationship between PPARs and the immune status, molecular mutations, and drug therapy in STAD. Methods The expression profiles of three PPAR genes (PPARA, PPARD and PPARG) were downloaded from The Cancer Genome Atlas (TCGA) dataset to analyze their expression patterns across pan-cancer. The associations between PPARs and clinicopathologic features, prognosis, tumor microenvironment, genome mutation and drug sensitivity were also explored. Co-expression between two PPAR genes was calculated using Pearson analysis. Regulatory pathways of PPARs were scored using gene set variation analysis (GSVA) package. Quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, Cell Counting Kit-8 (CCK-8) assay and transwell assay were conducted to analyze the expression and function of the PPAR genes in STAD cell lines (AGS and SGC7901 cells). Results PPARA, PPARD and PPARG were more abnormally expressed in STAD samples and cell lines when compared to most of 32 type cancers in TCGA. In STAD, the expression of PPARD was higher in Grade 3+4 and male patients, while that of PPARG was higher in patient with Grade 3+4 and age > 60. Patients in high-PPARA expression group tended to have longer survival time. Co-expression analysis revealed 6 genes significantly correlated with the three PPAR genes in STAD. Single-sample GSEA (ssGSEA) showed that the three PPAR genes were enriched in 23 pathways, including MITOTIC_SPINDLE, MYC_TARGETS_V1, E2F_TARGETS and were closely correlated with immune cells, including NK_cells_resting, T_cells_CD4_memory_resting, and macrophages_M0. Immune checkpoint genes (CD274, SIGLEC15) were abnormally expressed between high-PPAR expression and low-PPAR expression groups. TTN, MUC16, FAT2 and ANK3 genes had a high mutation frequency in both high-PPARA/PPARG and low-PPARA/PPARG expression group. Fourteen and two PPARA/PPARD drugs were identified to be able to effectively treat patients in high-PPARA/PPARG and low-PPARA/PPARG expression groups, respectively. We also found that the chemotherapy drug Vinorelbine was positively correlated with the three PPAR genes, showing the potential of Vinorelbine to serve as a treatment drug for STAD. Furthermore, cell experiments demonstrated that PPARG had higher expression in AGS and SGC7901 cells, and that inhibiting PPARG suppressed the viability, migration and invasion of AGS and SGC7901 cells. Conclusions The current results confirmed that the three PPAR genes (PPARA, PPARD and PPARG) affected STAD development through mediating immune microenvironment and genome mutation.

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“…Recently, studies have identified RORA as a novel candidate gene for ASD, which may be conducive to the known pathophysiology, behaviors, and sex bias of ASD ( 34 ). ELL Associated Factor 1 (EAF1) is one of the EAF family members, which plays an essential role in tumor suppression and embryogenesis ( 35 ). EAF1 and EAF2 can suppress Wnt/β-catenin signaling to affect neuroectodermal and mesodermal patterning ( 36 ).…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel Diagnostic Model for Childhood Autism Spectrum Disorder Based on Ferroptosis-Related Genes

Qin

et al. 2022

Front. Psychiatry

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Autism spectrum disorder (ASD) is a highly genetic heterogeneous neurodevelopmental disorder, which is usually considered a heritable and heterogeneous neurodevelopmental disorder and has caused a great burden to society and families. Emerging roles of ferroptosis have been observed in neurological disorders. This study aimed to construct a diagnostic model based on ferroptosis-related genes (FRGs) to contribute to the early and precise diagnosis of childhood ASD. In the candidate FRGs, we identified 27 differentially expressed genes (DEGs) between ASD patients and typically developing (TD) controls. Four key FRGs were identified using the random forest analysis for further analysis. Utilization of the four gene expression, we constructed a diagnostic model and the AUC value in the training dataset (GSE18123) is 0.7002. We deem that a patient with a score less than 0.9904 is likely to have ASD. Three validation datasets (GSE111176, GSE113834, and GSE28521) were collected and the AUC value is 0.7442, 0.7444, and 0.6474, respectively. A multi-factor regulatory network based on four FRGs indicated that RORA, EAF1, NFYB, miR-4703-3p, and miR-6073 may play a role in the development of ASD. In addition, we found piperaquine may have the potential to be a promising drug for the treatment of ASD. Overall, we constructed a diagnostic model of childhood ASD, which could contribute to the precision diagnosis and timely treatment of childhood ASD.

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Conditional Deletion of Eaf1 Induces Murine Prostatic Intraepithelial Neoplasia in Mice

Cited by 9 publications

References 28 publications

WITHDRAWN: Screening and identification of the potential diagnostic genes for Parkinson's Disease based on Machine Learning algorithm

WITHDRAWN: Screening and identification of the potential diagnostic genes for Parkinson's Disease based on Machine Learning algorithm

Comprehensive analysis of peroxisome proliferator-activated receptors to predict the drug resistance, immune microenvironment, and prognosis in stomach adenocarcinomas

Development and Validation of a Novel Diagnostic Model for Childhood Autism Spectrum Disorder Based on Ferroptosis-Related Genes

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