The functions of Atg8-family proteins in autophagy and cancer: linked or unrelated?

Jacquet, Marine; Guittaut, Michaël; Fraîchard, Annick; Despouy, Gilles

doi:10.1080/15548627.2020.1749367

Cited by 44 publications

(35 citation statements)

References 124 publications

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“…ATG8 gene expression has been poorly studied in cancers and during EMT [20]. In our study, we found that ATG8 expression is correlated with EMT markers, and that GABARAPL1 is the gene most correlated with the EMT markers in lung adenocarcinoma tumors.…”

Section: Discussionsupporting

confidence: 54%

“…This recruitment requires an autophagic adaptor containing the conserved AIM sequence (ATG8-interacting motif) found in these proteins [18,19]. These ATG8 proteins are also involved in non-selective autophagy (macroautophagy) through the regulation of key steps of the autophagic pathway, such as initiation, elongation, autophagosome transport, and autophagosome/lysosome fusion [20].…”

Section: Introductionmentioning

confidence: 99%

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GABARAPL1 Inhibits EMT Signaling through SMAD-Tageted Negative Feedback

Jacquet

Hervouet

Baudu

et al. 2021

Biology

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The pathway of selective autophagy, leading to a targeted elimination of specific intracellular components, is mediated by the ATG8 proteins, and has been previously suggested to be involved in the regulation of the Epithelial–mesenchymal transition (EMT) during cancer’s etiology. However, the molecular factors and steps of selective autophagy occurring during EMT remain unclear. We therefore analyzed a cohort of lung adenocarcinoma tumors using transcriptome analysis and immunohistochemistry, and found that the expression of ATG8 genes is correlated with that of EMT-related genes, and that GABARAPL1 protein levels are increased in EMT+ tumors compared to EMT- ones. Similarly, the induction of EMT in the A549 lung adenocarcinoma cell line using TGF-β/TNF-α led to a high increase in GABARAPL1 expression mediated by the EMT-related transcription factors of the SMAD family, whereas the other ATG8 genes were less modified. To determine the role of GABARAPL1 during EMT, we used the CRISPR/Cas9 technology in A549 and ACHN kidney adenocarcinoma cell lines to deplete GABARAPL1. We then observed that GABARAPL1 knockout induced EMT linked to a defect of GABARAPL1-mediated degradation of the SMAD proteins. These findings suggest that, during EMT, GABARAPL1 might intervene in an EMT-regulatory loop. Indeed, induction of EMT led to an increase in GABARAPL1 levels through the activation of the SMAD signaling pathway, and then GABARAPL1 induced the autophagy-selective degradation of SMAD proteins, leading to EMT inhibition.

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Section: Discussionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

GABARAPL1 Inhibits EMT Signaling through SMAD-Tageted Negative Feedback

Jacquet

Hervouet

Baudu

et al. 2021

Biology

Self Cite

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“…The members of the GABARAP (gamma-aminobutyric acid hand-related protein) family (GABARAP, GABARAPL1/GEC1 and GABARAPL2/GATE-16) are one of the subfamilies of the ATG8 family of proteins, and are closely related to the intracellular transport of receptors and the autophagy pathway [33]. GABARAP is described as being down-regulated in cancer, and high expression is associated with a good prognosis [34]. The study of Y et al Showed that GABARAP is overexpressed in colorectal cancer, which is related to the shortened survival time of patients, which shows the prognostic significance of GABARAP [35].…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients

et al. 2020

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Background: The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer. Methods: GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability. Results: GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagyrelated genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients. Conclusions: This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.

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“…The members of the GABARAP (gamma-aminobutyric acid hand-related protein) family (GABARAP, GABARAPL1 / GEC1 and GABARAPL2 / GATE-16) are one of the subfamilies of the ATG8 family of proteins, and are closely related to the intracellular transport of receptors and the autophagy pathway [34]. GABARAP is described as being down-regulated in cancer, and high expression is associated with a good prognosis [35]. The study of Y et al Showed that GABARAP is overexpressed in colorectal cancer, which is related to the shortened survival time of patients, which shows the prognostic signi cance of GABARAP [36].…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

Qiu

Sun

Wang

et al. 2020

Preprint

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Background : The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer. Methods : GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability. Results : GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients. Conclusions: This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.

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The functions of Atg8-family proteins in autophagy and cancer: linked or unrelated?

Cited by 44 publications

References 124 publications

GABARAPL1 Inhibits EMT Signaling through SMAD-Tageted Negative Feedback

GABARAPL1 Inhibits EMT Signaling through SMAD-Tageted Negative Feedback

Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients

Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

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