Hepatocellular carcinomas: evolution to sorafenib resistance through hepatic leukaemia factor

Musso, Orlando; Beraza, Naiara

doi:10.1136/gutjnl-2019-318999

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…[ 16 ] Furthermore, HLF could act as an oncofetal protein to drive hepatocellular carcinoma onset and progression, regulate chemotherapy resistance to sorafenib, and predict patient prognosis. [ 15 , 22 ] In a previous study, Chen et al have confirmed that HLF overexpression could decrease cell viability, proliferation, migration, invasion, and radiosensitivity in gliomas via miR-132/TTK axis, exerting an anti-oncogenic effect, [ 17 ] which was in accordance with our findings that HLF was downregulated in glioma tissues than the normal brain, and decreased with advancing tumor grade. These results demonstrate that the role of HLF may be tissue or tumor type-specific.…”

Section: Discussionsupporting

confidence: 91%

“…[ 14 ] Recently, HLF was reported to take roles in liver fibrosis and chemotherapy resistance in hepatic carcinomas. [ 15 , 16 ] In 2016, Chen et al reported that HLF overexpression could inhibit proliferation, invasion, and colony formation in U87 glioma cell lines through the miR-132/TTK pathway. [ 17 ] However, HLF expression profiles in glioma species and its clinical significance have not been reported.…”

Section: Introductionmentioning

confidence: 99%

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High Hepatic leukemia factor expression indicates a favorable survival in glioma patients

Liu¹,

Ge²,

Liu³

et al. 2021

Medicine

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Hepatic leukemia factor (HLF) is an oncogenic transcript factor, but its role in gliomas is unclear. With the open-access data from the Cancer Genome Atlatls (TCGA), HLF expression was compared between normal and glioma tissues and its correlation to patient survival, age, gender, race, and tumor grade was analyzed. Multivariate Cox regression was adopted to explore the independent risk factors for patient survival. Survivals between high and low HLF expression, and high and low model predicted risk subgroups were compared. 1, 2, 3, and 5-year patient survival were predicted with the Cox regression model. Gene set enrichment analysis (GSEA) was performed to predict the potential function of HLF. Expression and clinical data of 5 normal brain samples and 655 glioma samples were obtained from TCGA. HLF expression was downregulated in gliomas than normal brain tissue ( P = .007), and negatively related to patient age and advancing tumor grade ( P < .001). HLF was a protective factor for patient survival (OR = 0.81, 95%CI 0.67–0.99, P = .035). Patients’ survivals were poorer in low HLF expression subgroups and the Cox regression model predicted high-risk subgroups ( P < .001). The accuracy of the model in predicting 1, 2, 3, and 5-year patient survival was 0.864, 0.895, 0.907, and 0.893, respectively. GSEA revealed HLF mainly took part in regulating tumor cell metabolism and cell cycle. HLF was downregulated in gliomas than normal tissue, negatively related to patient age and tumor grade, and was an independent protective factor for glioma patients.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

High Hepatic leukemia factor expression indicates a favorable survival in glioma patients

Liu¹,

Ge²,

Liu³

et al. 2021

Medicine

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“…HLF expression was also decreased in hematological malignancy and was found to be a novel leukemic stem cell regulator [ 45 ]. On the contrary, HLF overexpression was found to promote the evolution of sorafenib resistance in patients with hepatocellular carcinomas via upregulation of OCT4 and SOX2 [ 46 ]. These findings indicated the paradoxical roles of HLF in tumors, which are tumor type-dependent.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Four-Gene Signature Associated with the Prognosis Prediction of Lung Adenocarcinoma Based on Integrated Bioinformatics Analysis

Yang

Zhang

et al. 2022

Genes

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Lung adenocarcinoma (LUAD) is often diagnosed at an advanced stage, so it is necessary to identify potential biomarkers for the early diagnosis and prognosis of LUAD. In our study, a gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) in order to obtain the key modules and genes correlated with LUAD prognosis. Four hub genes (HLF, CHRDL1, SELENBP1, and TMEM163) were screened out using least absolute shrinkage and selection operator (LASSO)–Cox regression analysis; then, a prognostic model was established for predicting overall survival (OS) based on these four hub genes..Furthermore, the prognostic values of this four-gene signature were verified in four validation sets (GSE26939, GSE31210, GSE72094, and TCGA-LUAD) as well as in the GEPIA database. To assess the prognostic values of hub genes, receiver operating characteristic (ROC) curves were constructed and a nomogram was created. We found that a higher expression of four hub genes was associated with a lower risk of patient death. In a training set, it was demonstrated that this four-gene signature was a better prognostic factor than clinical factors such as age and stage of disease. Moreover, our results revealed that these four genes were suppressor factors of LUAD and that their high expression was associated with a lower risk of death. In summary, we demonstrated that this four-gene signature could be a potential prognostic factor for LUAD patients. These findings provide a theoretical basis for exploring potential biomarkers for LUAD prognosis prediction in the future.

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“…Systemic treatments with sorafenib or lenvatinib followed by regorafenib, nivolumab, or ramucirumab have been demonstrated to improve survival (Forner et al, 2018). Among them, sorafenib [against c-rapidly accelerated fibrosarcoma (c-RAF), b-RAF, vascular endothelial growth factor receptor (VEGFR), c-KIT, and platelet-derived growth factor receptor] (Wilhelm et al, 2006;Giordano and Columbano, 2014) was the first drug for first-line treatment of advanced HCC (Marisi et al, 2019;Musso and Beraza, 2019), until the appearance of lenvatinib [against VEGFR/rearranged during transfection proto-oncogene (RET)/fibroblast growth factor receptor (FGFR)] (Kudo et al, 2018). Second-line treatments have also been designed over the past few years, including regorafenib (antiangiogenesis) (Bruix et al, 2017), nivolumab [against the programed cell death-1 (PD1) immune checkpoint] (El- Khoueiry et al, 2017), and ramucirumab (against VEGFR2) (Zhu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Function of the HGF/c-Met Axis in Hepatocellular Carcinoma

Rao

Lou

et al. 2020

Front. Cell Dev. Biol.

121

100

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Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, leading to a large global cancer burden. Hepatocyte growth factor (HGF) and its highaffinity receptor, mesenchymal epithelial transition factor (c-Met), are closely related to the onset, progression, and metastasis of multiple tumors. The HGF/c-Met axis is involved in cell proliferation, movement, differentiation, invasion, angiogenesis, and apoptosis by activating multiple downstream signaling pathways. In this review, we focus on the function of the HGF/c-Met axis in HCC. The HGF/c-Met axis promotes the onset, proliferation, invasion, and metastasis of HCC. Moreover, it can serve as a biomarker for diagnosis and prognosis, as well as a therapeutic target for HCC. In addition, it is closely related to drug resistance during HCC treatment.

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Hepatocellular carcinomas: evolution to sorafenib resistance through hepatic leukaemia factor

Cited by 6 publications

References 11 publications

High Hepatic leukemia factor expression indicates a favorable survival in glioma patients

High Hepatic leukemia factor expression indicates a favorable survival in glioma patients

Identification of a Four-Gene Signature Associated with the Prognosis Prediction of Lung Adenocarcinoma Based on Integrated Bioinformatics Analysis

The Function of the HGF/c-Met Axis in Hepatocellular Carcinoma

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