Arf6-driven endocytic recycling of CD147 determines HCC malignant phenotypes

Qi, Shanshan; Su, Linjia; Li, Jing; Zhang, Chuanshan; Ma, Zhe; Liu, Guiqiu; Zhang, Qing; Jia, Guhe; Piao, Yongjun; Zhang, Sihe

doi:10.1186/s13046-019-1464-9

Cited by 29 publications

(38 citation statements)

References 54 publications

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“…Alox5ap has been identified as specifically upregulated in leukemia stem cells and unchanged in hematopoietic stem cells, indicating specificity in cancer cell self-renewal and differentiation [ 68 , 69 ]. There is relatively little oncological literature on Acap3 , however it has been identified as highly down-regulated in late-stage liver cancer patients [ 70 ]. Bcl3 up-regulation has been associated with poorer prognostic outcomes [ 71 , 72 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of ferroptosis genes in immune infiltration and prognosis in thyroid papillary carcinoma using network analysis

Lin

Fogarty

et al. 2021

BMC Genomics

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Background Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation. Methods The thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated. Results We identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer. Conclusions Our study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.

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Section: Discussionmentioning

confidence: 99%

Identification of ferroptosis genes in immune infiltration and prognosis in thyroid papillary carcinoma using network analysis

Lin

Fogarty

et al. 2021

BMC Genomics

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“…There is relatively little oncological literature on Acap3, however it has been identi ed as highly down-regulated in late-stage liver cancer patients [69]. Bcl3 up-regulation has been associated with poorer prognostic outcomes [70,71]. It is classi ed as an oncogene and has been implicated in breast cancer cell migration [72,73].…”

Section: Discussionmentioning

confidence: 99%

Identification of Ferroptosis Genes In Immune Infiltration and Prognosis In Thyroid Papillary Carcinoma Using Network Analysis

Lin

Fogarty

et al. 2021

Preprint

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Background: Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analysed the gene expression profiles of the disease and established a new model for the correlation. Methods: The thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 kinds of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated.Results: We identified two subtypes in PTC, using Braf as the major mutation of subtype C1 and NRAS as the major mutation of subtype C2. The proportion of cluster 1 was significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. Subsequent analyses of TME immune cells infiltration indicated cluster 1 was remarkably richer than cluster 2. The risk score of THCA in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data showed that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer.Conclusions: Our study found seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we constructed the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.

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“…Also, ARF6 was linked to liver cancer through the regulation of the endocytic recycling of CD147, a tumor-related adhesive protein that promotes invasion of liver cancer cells (Qi et al, 2019). Moreover, the increased expression of ARF6-CD147 signaling components, like Cytohesin 2/ARNO, an ARF6 GEF and Rac1 were associated with poor overall survival of hepatocellular carcinoma patients (Qi et al, 2019).…”

Section: Arf6mentioning

confidence: 99%

“…Expression of ARF GEFs in human neoplastic tissues and cancer cells. LiverXu et al, 2013;Pan et al, 2014;Qi et al, 2019 …”

mentioning

confidence: 99%

The Role of ARF Family Proteins and Their Regulators and Effectors in Cancer Progression: A Therapeutic Perspective

Casalou

Ferreira

Barral

2020

Front. Cell Dev. Biol.

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The Adenosine diphosphate-Ribosylation Factor (ARF) family belongs to the RAS superfamily of small GTPases and is involved in a wide variety of physiological processes, such as cell proliferation, motility and differentiation by regulating membrane traffic and associating with the cytoskeleton. Like other members of the RAS superfamily, ARF family proteins are activated by Guanine nucleotide Exchange Factors (GEFs) and inactivated by GTPase-Activating Proteins (GAPs). When active, they bind effectors, which mediate downstream functions. Several studies have reported that cancer cells are able to subvert membrane traffic regulators to enhance migration and invasion. Indeed, members of the ARF family, including ARF-Like (ARL) proteins have been implicated in tumorigenesis and progression of several types of cancer. Here, we review the role of ARF family members, their GEFs/GAPs and effectors in tumorigenesis and cancer progression, highlighting the ones that can have a pro-oncogenic behavior or function as tumor suppressors. Moreover, we propose possible mechanisms and approaches to target these proteins, toward the development of novel therapeutic strategies to impair tumor progression.

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Arf6-driven endocytic recycling of CD147 determines HCC malignant phenotypes

Cited by 29 publications

References 54 publications

Identification of ferroptosis genes in immune infiltration and prognosis in thyroid papillary carcinoma using network analysis

Identification of ferroptosis genes in immune infiltration and prognosis in thyroid papillary carcinoma using network analysis

Identification of Ferroptosis Genes In Immune Infiltration and Prognosis In Thyroid Papillary Carcinoma Using Network Analysis

The Role of ARF Family Proteins and Their Regulators and Effectors in Cancer Progression: A Therapeutic Perspective

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