Transcription factor E2F4 is an indicator of poor prognosis and is related to immune infiltration in hepatocellular carcinoma

Zheng, Qiuxian; Fu, Qiang; Xu, Jia; Gu, Xinyu; Zhou, Haibo; Chen, Zhi

doi:10.7150/jca.51616

Cited by 16 publications

(25 citation statements)

References 40 publications

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“…The second large component includes the E2F4 transcription factor and its targets. E2F4 is widely studied in the context of cancer as a crucial regulator in liver cancer [32], breast cancer [33, 34], hepatocellular carcinoma [32], prostate cancer [35], skin cancer [36] and other [37]. Genes from the induced subnetwork showed significant enrichment for transcriptional dysregulation in the cancer KEGG pathway (adjusted p-value 0.000488).…”

Section: Resultsmentioning

confidence: 99%

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

Lazareva

Louadi

Kersting

et al. 2022

Preprint

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Gene regulation is frequently altered in diseases in unique and often patient-specific ways. Hence, personalized strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not focus on disease-specific dysregulation or lack assessments of statistical significance. Moreover, they do not account for clinically important confounders such as age, sex or treatment history.To overcome these shortcomings, we present DysRegNet, a novel method for inferring patient-specific regulatory alterations (dysregulations) from gene expression profiles. We compared DysRegNet to state-of-the-art methods and demonstrated that DysRegNet produces more interpretable and biologically meaningful networks. Independent information on promoter methylation and single nucleotide variants further corroborate our results. We apply DysRegNet to eleven TCGA cancer types and illustrate how the inferred networks can be used for down-stream analysis. We show that unique as well as cancer-type-specific dysregulation patterns exist and highlight immune-related mechanisms that are not obvious when focusing on individual genes rather than their interactions.DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package) and analysis results for eleven TCGA cancer types are further available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).

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Section: Resultsmentioning

confidence: 99%

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

Lazareva

Louadi

Kersting

et al. 2022

Preprint

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“…In both clusters, TFs exhibit relatively high PageRank scores and show active activity across almost all stages. Example TFs include constitutively active genes identified earlier, such as Sp1 [52], Zfx [53], and E2f4 [54], which are essential for immune system development and maintenance of normal functions of various immune cells.…”

Section: Resultsmentioning

confidence: 99%

Systems-level identification of key transcription factors in immune cell specification

Liu

Omilusik

Toma

et al. 2022

Preprint

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SummaryTranscription factors (TFs) are crucial for regulating cell differentiation during the development of the immune system. However, the key TFs for orchestrating the specification of distinct immune cells are not fully understood. Here, we integrated the transcriptomic and epigenomic measurements in 73 mouse and 61 human primary cell types, respectively, that span the immune cell differentiation pathways. We constructed the cell-type-specific transcriptional regulatory network and assessed the global importance of TFs based on the Taiji framework, which is a method we have previously developed that can infer the global impact of TFs using integrated transcriptomic and epigenetic data. Integrative analysis across cell types revealed putative driver TFs in cell lineage-specific differentiation in both mouse and human systems. We have also identified TF combinations that play important roles in specific developmental stages. Furthermore, we validated the functions of predicted novel TFs in murine CD8+ T cell differentiation and showed the importance of Elf1 and Prdm9 in the effector versus memory T cell fate specification and Kdm2b and Tet3 in promoting differentiation of CD8+ tissue resident memory (Trm) cells, validating the approach. Thus, we have developed a bioinformatic approach that provides a global picture of the regulatory mechanisms that govern cellular differentiation in the immune system and aids the discovery of novel mechanisms in cell fate decisions.

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“…The recurrence rate after 5 years is as high as 60%-70%. 11 Therefore, new biomarkers and effective therapeutic targets must be found as soon as possible. There have been a variety of studies in recent years.…”

Section: Discussionmentioning

confidence: 99%

Phosphoribosyl Pyrophosphate Amido Transferase: A New Prognostic Biomarker for Hepatocellular Carcinoma

Chu¹,

Gu²,

Zheng³

et al. 2022

IJGM

Self Cite

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Background PPAT (phosphoribosyl pyrophosphate amido transferase) catalyzes the first committed step of de novo purine biosynthesis and is a key regulatory point in the biosynthesis of nascent purine nucleotides. However, the clinical significance and biologic role of PPAT in hepatocellular carcinoma (HCC) remain unknown. Methods We compared the expression of PPAT in carcinomatous and precancerous hepatocellular carcinoma tissues by immunohistochemistry in 90 cases of HCC. Correlation analysis was also made on clinical data, survival, classification, and staging. Results The expression of PPAT in HCC tumor tissues is significantly higher than that in adjacent normal tissues. The results of the Kaplan–Meier analysis showed that HCC patients with high PPAT expression survived shorter than those with low PPAT expression. Moreover, the expression of PPAT was significantly associated with the tumor grade (P=0.014), PD-L1 (P<0.001), and CTLA4 (P=0.003). The later grade of the tumor, the higher the expression of PPAT. In the PD-L1 high expression group, PPAT is also highly expressed. Conclusion Our study demonstrated that PPAT expression might be included in the process of carcinogenesis and prognosis. Hence, PPAT could be served as a new prognostic biomarker for patients of HCC.

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Transcription factor E2F4 is an indicator of poor prognosis and is related to immune infiltration in hepatocellular carcinoma

Cited by 16 publications

References 40 publications

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

DysRegNet: Patient-specific and confounder-aware dysregulated network inference

Systems-level identification of key transcription factors in immune cell specification

Phosphoribosyl Pyrophosphate Amido Transferase: A New Prognostic Biomarker for Hepatocellular Carcinoma

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