Silencing of TRAF5 enhances necroptosis in hepatocellular carcinoma by inhibiting LTBR-mediated NF-κB signaling

Wu, Gang; Wu, Fenfang; Zhou, Yuhong; Lu, Wenwen; Hu, Fang; Fan, Xiaofen

doi:10.7717/peerj.15551

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…Verification of potential transcriptional targets is helpful in exploring the mechanism of EZH1. Further exploration of the molecular regulatory axis is crucial for elucidating the mechanism of tumorigenesis and development and developing therapeutic targets [28,29]. The transcriptional regulatory relationship between EZH1 and ATG7 was verified by ChIP-seq data downloaded from Cistrome DB datasets.…”

Section: Discussionmentioning

confidence: 99%

Clinical Significance of Upregulation of EZH1 Expression in Hepatocellular Carcinoma Tissues

Chen,

Li,

Huang

et al. 2024

JGLD

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Background and Aims: The incidence and mortality of hepatocellular carcinoma (HCC) are increasing. It is urgent to develop more effective HCC biomarkers for diagnosis and treatment. This project intends to verify the expression of enhancer of zeste 1 polycomb repressive complex 2 subunit (EZH1) and its mechanism in HCC. Methods: This study integrates global microarray and high-throughput sequencing datasets, combined with internal immunohistochemistry, to analyze the expression and prognostic value of EZH1 in HCC. Functional enrichment analysis was conducted to investigate transcriptional targets, which were achieved by intersecting HCC over-expressed genes, EZH1 co-expressed genes and putative transcriptional targets. The relationship between EZH1 and anticancer drugs was detected by drug sensitivity analysis. Results: In this study, 84 datasets from 40 platforms (3,926 HCC samples and 3,428 non-cancerous liver tissues) were included to show the high expression of EZH1 in HCC. Immunohistochemistry with 159 HCC samples and 62 non-HCC samples confirmed the high expression level. HCC patients with high EZH1 expression had worse survival prognoses. Gene ontology and Reactome analysis revealed that metabolism-related pathways, including autophagy, are critical for HCC. Interestingly, as one of the EZH1 potential transcriptional targets, autophagy-related 7 (ATG7) appeared in the above pathways. ATG7 was positively correlated with EZH1, upregulated in HCC, and mediated poor prognosis. Upregulation of EZH1 was found to be in contact with HCC anti-tumor drug resistance. Conclusions: The upregulation of EZH1 expression can promote the occurrence of HCC and lead to poor clinical progression and drug resistance; these effects may be mediated by regulating ATG7.

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Section: Discussionmentioning

confidence: 99%

Clinical Significance of Upregulation of EZH1 Expression in Hepatocellular Carcinoma Tissues

Chen,

Li,

Huang

et al. 2024

JGLD

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show abstract

“…Veri cation of potential transcriptional targets is helpful in exploring the mechanism of EZH1. Further exploration of the molecular regulatory axis is crucial for elucidating the mechanism of tumorigenesis and development and developing therapeutic targets (Wu et al 2023, Hao et al 2023). Among the multiple transcriptional targets in the autophagy pathway, EZH1 was found to have obvious binding peaks at the transcription start sites of multiple genes.…”

Section: Discussionmentioning

confidence: 99%

Clinical Significance of Upregulation of EZH1 Expression in Hepatocellular Carcinoma Tissues

Chen

Huang

et al. 2023

Preprint

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Purpose The incidence and mortality of hepatocellular carcinoma (HCC) are increasing. It is urgent to develop more effective HCC biomarkers for diagnosis and treatment. This project intends to explore the expression of the enhancer of zeste 1 polycomb repressive complex 2 subunit (EZH1) and its mechanism in HCC. Methods This study integrates global microarray and high-throughput sequencing datasets, combined with internal immunohistochemistry, to analyze the expression and prognostic value of EZH1 in HCC. Functional enrichment analysis was conducted to investigate transcriptional targets, which were achieved by intersecting HCC over-expressed genes and EZH1 co-expressed genes, putative transcriptional targets. The relationship between EZH1 and anticancer drugs was detected by drug sensitivity analysis. Results In this study, 84 datasets from 40 platforms (3926 HCC samples and 3428 non-cancerous liver tissues) were included to show the high expression of EZH1 in HCC. Immunohistochemistry with 159 HCC samples and 62 non-HCC samples confirmed a high expression level. HCC patients with high EZH1 expression had worse survival prognoses. GO and Reactome analysis revealed that metabolism-related pathways, including autophagy, are critical for HCC. Interestingly, as one of the EZH1 potential transcriptional targets, autophagy-related 7 (ATG7) appeared in the above pathways. ATG7 was positively correlated with EZH1, upregulated in HCC, and mediated poor prognosis. Upregulation of EZH1 was found to be in contact with HCC anti-tumor drug resistance. Conclusion The upregulation of EZH1 expression can promote the occurrence of HCC and lead to poor clinical progression and drug resistance, these effects may be mediated by regulating ATG7.

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“…TRAF5 enhanced the ability of HCC in proliferation and invasion-metastasis. Reduction of TRAF5 could induce necroptosis, thereby impeding HCC progression ( 61 , 62 ). SLC2A1 expression was upregulated in numerous solid tumors, including HCC.…”

Section: Discussionmentioning

confidence: 99%

Identifying the programmed cell death index of hepatocellular carcinoma for prognosis and therapy response improvement by machine learning: a bioinformatics analysis and experimental validation

Shi,

Feng,

Qiu

et al. 2023

Front. Immunol.

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BackgroundDespite advancements in hepatocellular carcinoma (HCC) treatments, the prognosis for patients remains suboptimal. Cumulative evidence suggests that programmed cell death (PCD) exerts crucial functions in HCC. PCD-related genes are potential predictors for prognosis and therapeutic responses.MethodsA systematic analysis of 14 PCD modes was conducted to determine the correlation between PCD and HCC. A novel machine learning-based integrative framework was utilized to construct the PCD Index (PCDI) for prognosis and therapeutic response prediction. A comprehensive analysis of PCDI genes was performed, leveraging data including single-cell sequencing and proteomics. GBA was selected, and its functions were investigated in HCC cell lines by in vitro experiments.ResultsTwo PCD clusters with different clinical and biological characteristics were identified in HCC. With the computational framework, the PCDI was constructed, demonstrating superior prognostic predictive efficacy and surpassing previously published prognostic models. An efficient clinical nomogram based on PCDI and clinicopathological factors was then developed. PCDI was intimately associated with immunological attributes, and PCDI could efficaciously predict immunotherapy response. Additionally, the PCDI could predict the chemotherapy sensitivity of HCC patients. A multilevel panorama of PCDI genes confirmed its stability and credibility. Finally, the knockdown of GBA could suppress both the proliferative and invasive capacities of HCC cells.ConclusionThis study systematically elucidated the association between PCD and HCC. A robust PCDI was constructed for prognosis and therapy response prediction, which would facilitate clinical management and personalized therapy for HCC.

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Silencing of TRAF5 enhances necroptosis in hepatocellular carcinoma by inhibiting LTBR-mediated NF-κB signaling

Cited by 6 publications

References 47 publications

Clinical Significance of Upregulation of EZH1 Expression in Hepatocellular Carcinoma Tissues

Clinical Significance of Upregulation of EZH1 Expression in Hepatocellular Carcinoma Tissues

Clinical Significance of Upregulation of EZH1 Expression in Hepatocellular Carcinoma Tissues

Identifying the programmed cell death index of hepatocellular carcinoma for prognosis and therapy response improvement by machine learning: a bioinformatics analysis and experimental validation

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