The Spectrum of E2F in Liver Disease-Mediated Regulation in Biology and Cancer

Huntington, Justin T.; Tang, Xing; Kent, Lindsey N.; Schmidt, Carl; Leone, Gustavo

doi:10.1002/jcp.25242

Cited by 10 publications

(10 citation statements)

References 97 publications

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“…However, our GSVA result and PPI network failed to discern a direct pretherapy overactivated hypoxia-related biological process in TACE nonresponders. Instead, we found that pathways related to an aberrant cell cycle and proliferation, including G2/M checkpoint, E2F, MYC, and mTORC1 were significantly enriched in TACE nonresponders [ 27 – 29 ]. However, some previous studies have suggested that there is a positive correlation between hypoxia and activated mTORC1 and E2F pathways [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

A 10-Gene Signature Identified by Machine Learning for Predicting the Response to Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma

Tang

Xue

et al. 2022

Journal of Oncology

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Background. Transarterial chemoembolization (TACE) is recommended for intermediate-stage HCC patients. Owing to substantial variation in its efficacy, indicators of patient responses to TACE need to be determined. Methods. A Gene Expression Omnibus (GEO) dataset consisting of patients of different TACE-response status was retrieved. Differentially expressed genes (DEGs) were calculated and variable gene ontology analyses were conducted. Potential drugs and response to immunotherapy were predicted using multiple bioinformatic algorithms. We built and compared 5 machine-learning models with finite genes to predict patients’ response to TACE. The model was also externally validated to discern different survival outcomes after TACE. Tumor-infiltrating lymphocytes (TILs) and tumor stemness index were evaluated to explore potential mechanism of our model. Results. The gene set variation analysis revealed enhanced pathways related to G2/M checkpoint, E2F, mTORC1, and myc in TACE nonresponders. TACE responders had better immunotherapy response too. 373 DEGs were detected and the upregulated DEGs in nonresponders were enriched in IL-17 signal pathway. 5 machine-learning models were constructed and evaluated, and a linear support vector machine (SVM)-based model with 10 genes was selected (AQP1, FABP4, HERC6, LOX, PEG10, S100A8, SPARCL1, TIAM1, TSPAN8, and TYRO3). The model achieved an AUC and accuracy of 0.944 and 0.844, respectively, in the development cohort. In the external validation cohort comprised of patients receiving adjuvant TACE and postrecurrence TACE treatment, the predicted response group significantly outlived the predicted nonresponse counterparts. TACE nonresponders tend to have more macrophage M0 cells and lower resting mast cells in the tumor tissue and the stemness index is also higher than responders. Those characteristics were successfully captured by our model. Conclusion. The model based on expression data of 10 genes could potentially predict HCC patients’ response and prognosis after TACE treatment. The discriminating power was TACE-specific.

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Section: Discussionmentioning

confidence: 99%

A 10-Gene Signature Identified by Machine Learning for Predicting the Response to Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma

Tang

Xue

et al. 2022

Journal of Oncology

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“…E2F is an important family of transcription factors that regulate the cell cycle, and both the E2F and MYC pathways can promote the progression of HCC. 40 , 41 Additionally, high NLRP6 expression has been associated with more abundant neutrophils, NK cells, DC cells, and other tumor‐antagonizing immune cells. These results showed that NLRP6 can be used as a prognostic biomarker for determining the prognosis and immune infiltration levels in HCC.…”

Section: Discussionmentioning

confidence: 99%

A novel pyroptosis risk model composed of NLRP6 effectively predicts the prognosis of hepatocellular carcinoma patients

2022

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Background Pyroptosis is a unique inflammatory‐related cell death process, and inflammation is considered to be a key factor in hepatocellular carcinoma (HCC). However, the pyroptosis landscape in HCC has not been thoroughly studied. Methods Clinical data and RNA sequencing data of HCC patients were collected from The Cancer Genome Atlas database, and differentially expressed genes (DEGs) associated with pyroptosis were discovered. The relationship between DEGs and prognosis was studied. Using The Cancer Genome Atlas cohort, a least absolute shrinkage and selection operator regression model was built on the basis of pyroptosis‐related DEGs, which was then verified by the Gene Expression Omnibus (GEO) cohort. Functional enrichment analysis and immunological states were also studied between distinct risk subgroups. Finally, the potential tumor suppressive function of NLRP6 in HCC was analyzed. Results In total, 26 pyroptosis‐related DEGs were identified. Consensus clustering results showed patients with high levels of pyroptosis were associated with higher tumor stage, grade, and poor prognosis. The least absolute shrinkage and selection operator risk model was built using six genes linked with prognosis ( GSDMC, GSDME, NOD2, NLRP6, CASP8, and SCAF11 ). Risk score was an independent risk factor that suggested shortened overall survival in both the training cohort (HR: 3.52, 95% CI: 1.351–9.193) and validation cohort (HR: 3.31, 95% CI: 1.435–7.617). Meanwhile, the low‐risk population had higher immunological activity. We also found a novel potential tumor suppressor gene NLRP6, which may negatively regulate the E2F and MYC pathways and be associated with higher immune cell infiltration levels, which lead to better prognosis. Conclusions This study revealed the pyroptosis landscape of HCC and provided a promising clinical prognosis evaluation model. Additionally, some new targets related to prognosis such as NLRP6 are proposed for further study.

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“…E2F1 is a proto-oncogenic gene that could induce both cell proliferation and tumor suppression by promoting apoptosis in the liver. 40 Rho GTPases are involved in regulating cell proliferation, differentiation, and apoptosis, and are closely related to the infiltration and distant metastasis of liver cancer. 41–46 KRAS participates in the RAS/RAF/MEK/ERK signaling pathway and is an important regulator of cell growth.…”

Section: Discussionmentioning

confidence: 99%

HSPB11 is a Prognostic Biomarker Associated with Immune Infiltrates in Hepatocellular Carcinoma

Liu¹,

Yang²,

Dong³

2022

IJGM

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Purpose Heat shock proteins (HSPs) play important roles in oncogenesis and malignant progression. HSPB11 is highly expressed in many malignant tumors, but research on its role in hepatocellular carcinoma (HCC) is insufficient. Patients and Methods A comprehensive analysis of HSPB11 in HCC was performed based on data of patients with HCC and those from online public databases. Results HSPB11 was overexpressed in HCC, with a high discrimination ability between tumor and normal tissues (area under the curve =0.923). HSPB11 overexpression correlated with advanced tumor stage, poorer tumor differentiation, and worse prognosis and was an independent risk factor for HCC prognosis. The nomogram and calibration models composed of HSPB11, T stage, and M stage had good abilities to predict the 1-, 3-, and 5-year survival rates of patients. HSPB11 was determined to be involved in multiple oncogenic processes, including cell cycle checkpoints, the G2M checkpoint, E2F targets, Rho GTPases, and KRAS signaling. HSPB11 expression was related to immune cell infiltration, especially that of Th2 cells and dendritic cells. Conclusion HSPB11 is involved in oncogenesis and immune regulation in HCC and is a potential prognostic biomarker and therapeutic target.

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The Spectrum of E2F in Liver Disease-Mediated Regulation in Biology and Cancer

Cited by 10 publications

References 97 publications

A 10-Gene Signature Identified by Machine Learning for Predicting the Response to Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma

A 10-Gene Signature Identified by Machine Learning for Predicting the Response to Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma

A novel pyroptosis risk model composed of NLRP6 effectively predicts the prognosis of hepatocellular carcinoma patients

HSPB11 is a Prognostic Biomarker Associated with Immune Infiltrates in Hepatocellular Carcinoma

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