TRAF2 is a Valuable Prognostic Biomarker in Patients with Prostate Cancer

Wei, Bingbing; Liang, Jian; Hu, Jimeng; Mi, Yuanyuan; Ruan, Jian; Zhang, Jian; Wang, Zhirong; Hu, Qiang; Jiang, Haowen; Ding, Qiang

doi:10.12659/msm.903500

Cited by 17 publications

(10 citation statements)

References 36 publications

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“…In recent years, many investigations concentrated on finding variant biomarkers in the prostate carcinogenesis progression. For example, matrix metalloproteinase‐26 and tumour necrosis factor receptor‐associated factor 2 have been identified (Cheng et al, ; Wei et al, ). Despite great progress has been made, the molecular mechanism of prostate cancer is far from being completely explained.…”

Section: Introductionmentioning

confidence: 99%

Identification of key genes in prostate cancer gene expression profile by bioinformatics

Lü

Ding

2018

Andrologia

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The aim of this study was to identify key candidate genes in prostate cancer. The gene expression profiles of GSE32448, GSE45016, GSE46602 and GSE104749 were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between prostate cancer and normal samples were identified by R language. The gene ontology functional and pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery software followed by the construction of protein-protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in Cytoscape software. The 217 DEGs were significantly enriched in biological processes including epithelial cell differentiation, response to estradiol and several pathways, mainly associated with protein digestion and absorption pathway in prostate cancer. Epithelial cell adhesion molecule, twist family basic helix-loop-helix transcription factor 1, CD38 molecule and vascular endothelial growth factor A were identified as hub genes. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas for prostate adenocarcinoma. These hub genes may be used as potential targets for prostate cancer diagnosis and treatment.

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Section: Introductionmentioning

confidence: 99%

Identification of key genes in prostate cancer gene expression profile by bioinformatics

Lü

Ding

2018

Andrologia

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“…A prostate cancer study showed that up regulation of TRAF2 associated with poor recurrence of metastatic cancer. (16) TNF-α which contained in CM could induce NF-κB through canonical pathway. Lately, breast cancer study proved that higher NF-κB expression lead to proliferation of MDA-MB-468.…”

Section: Discussionmentioning

confidence: 98%

TNFR, TRAF2, NF-κB mRNA Levels of Glioblastoma Multiforme Cells Treated by Conditioned Medium of Umbilical Cord-derived Mesenchymal Stem Cells

2019

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BACKGROUND: Glioblastoma multiforme (GBM) is a human malignant brain tumor which is arise from glial cells. Our previous study proved that GBM cells proliferation increased after treating by conditioned medium of umbilical cord-derived mesenchymal stem cells (CM-UCSCs). Cells proliferation is probably mediated by tumor necrosis factor (TNF)-α which could bind to membrane receptor and induce signaling pathway. Therefore, this research was intended to analyze the mRNA expression of TNF-α signaling pathway molecules on CM-treated GBM cells by measuring TNF receptor 1 and 2 (TNFR1 and TNFR2), TNFR associated factor 2 (TRAF2), nuclear factor kappa B (NF-κB) mRNA level, and TNFR2 protein level.METHODS: UCSCs and human glioblastoma T98G cells were cultured and harvested after 80% confluence. CM was prepared by growing UCSCs in serum alpha Minimum Essential Media (α-MEM) for 24 hours. Fifty percent concentration of CM-UCSCs was used to treat T98G cells for 24 hours. TNF-α level in CM-UCSC was detected using enzyme linked-immunosorbent assay (ELISA), while the expression of TNFR1, TNFR2, TRAF2 and NF-κB were detected using quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR), and TNFR2 protein level was detected using sandwich ELISA.RESULTS: TNF-α level was detected in CM-UCSCs 4.4 pg/mL. Moreover, the expression of TNFR1, TNFR2, TRAF2 and NF-κB were significantly 1.4-fold, 4.9-fold, 5.6-fold, 1.8-fold respectively higher in T98G treated cells than control. TNFR2 protein level in T98G treated cells was 11.57 pg/mg protein higher than control.CONCLUSION: The expression of molecules involved in TNF-α signaling pathway were up regulated in T98G cells treated by CM-UCSCs.KEYWORDS: CM-UCSCs, TNFR1, TNFR2, TRAF2, NF-κB, T98G cells

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“…This shows that there is a strong correlation between TRAF2 and TRAF6 and immune infiltration. In addition, TRAF2 is upregulated in breast cancer, pancreatic cancer, and other malignant tumors [ 41 , 42 ] and is significantly related to the prognosis of patients with prostate cancer [ 43 ]. Zhang et al found that IL12A showed significantly high expression in differentiated thyroid cancer, was associated with disease invasiveness, and was an independent predictor of the prognosis of differentiated thyroid cancer [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

Han

et al. 2020

Journal of Oncology

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Purpose. To construct a survival model for predicting the prognosis of patients with kidney renal clear cell carcinoma (KIRC) based on gene expression related to immune response regulation. Materials and Methods. KIRC mRNA sequencing data and patient clinical data were downloaded from the TCGA database. The pathways and genes involved in the regulation of the immune response were identified from the GSEA database. A single factor Cox analysis was used to determine the association of mRNA in relation to patient prognosis P < 0.05 . The prognostic risk model was further established using the LASSO regression curve. The survival prognosis model was constructed, and the sensitivity and specificity of the model were evaluated using the ROC curve. Results. Compared with normal kidney tissues, there were 28 dysregulated mRNA expressions in KIRC tissues P < 0.05 . Univariate Cox regression analysis revealed that 12 mRNAs were related to the prognosis of patients with renal cell carcinoma. The LASSO regression curve drew a risk signature consisting of six genes: TRAF6, FYN, IKBKG, LAT2, C2, IL4, EREG, TRAF2, and IL12A. The five-year ROC area analysis (AUC) showed that the model has good sensitivity and specificity (AUC >0.712). Conclusion. We constructed a risk prediction model based on the regulated immune response-related genes, which can effectively predict the survival of patients with KIRC.

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TRAF2 is a Valuable Prognostic Biomarker in Patients with Prostate Cancer

Cited by 17 publications

References 36 publications

Identification of key genes in prostate cancer gene expression profile by bioinformatics

Identification of key genes in prostate cancer gene expression profile by bioinformatics

TNFR, TRAF2, NF-κB mRNA Levels of Glioblastoma Multiforme Cells Treated by Conditioned Medium of Umbilical Cord-derived Mesenchymal Stem Cells

Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

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