Phytanoyl-CoA 2-Hydroxylase-Interacting Protein-Like Gene Is a Therapeutic Target Gene for Glioblastoma Multiforme

Fu, Haidong; Ge, Bin; Chen, Duankai; Wu, Yue-Qing; Luo, Qiong; Li, XueYu; Zheng, Chuanhua; Tang, Qianli

doi:10.12659/msm.913895

Cited by 12 publications

(13 citation statements)

References 35 publications

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“…Not much is known about the function of PHYHIPL now. Previous findings from TCGA database reported that the downregulation of PHYHIPL is associated with poor OS, demonstrating that this gene is involved in the development of Glioblastoma multiforme (GBM) (42). RBP1 (Retinol Binding Protein 1), is also named Cellular Retinol Binding Protein 1 (CRBP1) and is located in the cytogenetic region 3q23 (43).…”

Section: Discussionmentioning

confidence: 99%

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Huang

Zhang

et al. 2021

Front. Oncol.

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BackgroundAberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.MethodsMethylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).ResultsWe identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HRindirect = 1.473, P = 0.001, Proportion mediated, 69.10%).ConclusionsWe identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

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Section: Discussionmentioning

confidence: 99%

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Huang

Zhang

et al. 2021

Front. Oncol.

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“…The ‘Intestinal immune network for IgA production’ signaling pathway is associated with inflammatory immune diseases, including kidney diseases and osteoarthritis ( 40 , 41 ), and is emerging as a novel target of potential therapeutic interventions ( 42 ). As an important metabolic pathway, the activation of the ‘cAMP signaling pathway’ has also been reported to inhibit proliferation of glioma ( 43 , 44 ). For instance, oncogenic cAMP/Protein Kinase A Inhibitor signal transduction is implicated in the occurrence and development of various human cancer types ( 45 ).…”

Section: Discussionmentioning

confidence: 99%

Development of a prognostic model of glioma based on immune‑related genes

Wang¹,

Wang²,

Zhu³

et al. 2020

Oncol Lett

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Glioma is the most common type of primary brain cancer, and the prognosis of most patients with glioma, and particularly that of patients with glioblastoma, is poor. Tumor immunity serves an important role in the development of glioma. However, immunotherapy for glioma has not been completely successful, and thus, comprehensive examination of the immune-related genes (IRGs) of glioma is required. In the present study, differentially expressed genes (DEGs) and differentially expressed IRGs (DEIRGs) were identified using the edgeR package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used for functional enrichment analysis of DEIRGs. Survival-associated IRGs were selected via univariate Cox regression analysis. A The Cancer Genome Atlas prognostic model and GSE43378 validation model were established using lasso-penalized Cox regression analysis. Based on the median risk score value, patients were divided into high-risk and low-risk groups for clinical analysis. Receiver operating characteristic curve and nomogram analyses were used to assess the accuracy of the models. Reverse transcription-quantitative PCR was performed to measure the expression levels of relevant genes, such as cyclin-dependent kinase 4 (CDK4), interleukin 24 (IL24), NADPH oxidase 4 (NOX4), bone morphogenetic protein 2 (BMP2) and baculoviral IAP repeat containing 5 (BIRC5). A total of 3,238 DEGs, including 1,950 upregulated and 1,288 downregulated DEGs, and 97 DEIRGs, including 60 upregulated and 37 downregulated DEIRGs, were identified. ‘Neuroactive ligand-receptor interaction’ and ‘Cytokine-cytokine receptor interaction’ were the most significantly enriched pathways according to KEGG pathway analysis. A prognostic model and a validation prognostic model were created for glioma, including 15 survival-associated IRGs (FCER1G, NOX4, TRIM5, SOCS1, APOBEC3C, BIRC5, VIM, TNC, BMP2, CMTM3, IL24, JAG1, CALCRL, HNF4G and CDK4). Furthermore, multivariate Cox regression analysis results suggested that age, high WHO Grade by histopathology, wild type isocitrate dehydrogenase 1 and high risk score were independently associated with poor overall survival. The infiltration of B cells, CD8 + T cells, dendritic cells, macrophages and neutrophils was positively associated with the prognostic risk score. In the present study, several clinically significant survival-associated IRGs were identified, and a prognosis evaluation model of glioma was established.

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“…ure 2C, Table S4). One of the fraction-shifting proteins, phytanoyl-CoA hydroxylase-interacting protein-like (PHIPL_HUMAN), 76 was not identified as an early-, late-responding, or gradually-changing protein due to the correction for multiple testing when analyzing nonAD, p-preAD, and symAD as three separate groups.…”

Section: Identification Of Proteins Showing Level Changes During the Pathogenesis Of Admentioning

confidence: 99%

Sequence of proteome profiles in preclinical and symptomatic Alzheimer's disease

Tsolis

Koper

et al. 2021

Alzheimer's & Dementia

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Proteome profile changes in Alzheimer's disease (AD) brains have been reported. However, it is unclear whether they represent a continuous process, or whether there is a sequential involvement of distinct proteins. To address this question, we used mass spectrometry. We analyzed soluble, dispersible, sodium dodecyl sulfate, and formic acid fractions of neocortex homogenates (mainly Brodmann area 17-19) from 18 pathologically diagnosed preclinical AD, 17 symptomatic AD, and 18 cases without signs of neurodegeneration. By doing so, we identified four groups of ADrelated proteins being changed in levels in preclinical and symptomatic AD cases: earlyresponding, late-responding, gradually-changing, and fraction-shifting proteins. Gene ontology analysis of these proteins and all known AD-risk/causative genes identified vesicle endocytosis and the secretory pathway-related processes as an early-involved AD component. In conclusion, our findings suggest that subtle changes involving the secretory pathway and endocytosis precede severe proteome changes in symptomatic AD as part of the preclinical phase of AD. The respective early-responding proteins may also contribute to synaptic vesicle cycle alterations in symptomatic AD.

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Phytanoyl-CoA 2-Hydroxylase-Interacting Protein-Like Gene Is a Therapeutic Target Gene for Glioblastoma Multiforme

Cited by 12 publications

References 35 publications

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Development of a prognostic model of glioma based on immune‑related genes

Sequence of proteome profiles in preclinical and symptomatic Alzheimer's disease

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