GABARAPL1 Promotes AR+ Prostate Cancer Growth by Increasing FL-AR/AR-V Transcription Activity and Nuclear Translocation

A new perspective on the autophagic and non-autophagic functions of the GABARAP protein family: a potential therapeutic target for human diseases

Mammalian autophagy-related protein Atg8, including the LC3 subfamily and GABARAP subfamily. Atg8 proteins play a vital role in autophagy initiation, autophagosome formation and transport, and autophagy-lysosome fusion. GABARAP subfamily proteins (GABARAPs) share a high degree of homology with LC3 family proteins, and their unique roles are often overlooked. GABARAPs are as indispensable as LC3 in autophagy. Deletion of GABARAPs fails autophagy flux induction and autophagy lysosomal fusion, which leads to the failure of autophagy. GABARAPs are also involved in the transport of selective autophagy receptors. They are engaged in various particular autophagy processes, including mitochondrial autophagy, endoplasmic reticulum autophagy, Golgi autophagy, centrosome autophagy, and dorphagy. Furthermore, GABARAPs are closely related to the transport and delivery of the inhibitory neurotransmitter γ-GABAA and the angiotensin II AT1 receptor (AT1R), tumor growth, metastasis, and prognosis. GABARAPs also have been confirmed to be involved in various diseases, such as cancer, cardiovascular disease, and neurodegenerative diseases. In order to better understand the role and therapeutic potential of GABARAPs, this article comprehensively reviews the autophagic and non-autophagic functions of GABARAPs, as well as the research progress of the role and mechanism of GABARAPs in cancer, cardiovascular diseases and neurodegenerative diseases. It emphasizes the significance of GABARAPs in the clinical prevention and treatment of diseases, and may provide new therapeutic ideas and targets for human diseases. GABARAP and GABARAPL1 in the serum of cancer patients are positively correlated with the prognosis of patients, which can be used as a clinical biomarker, predictor and potential therapeutic target.

Revealing the impact of autophagy-related genes in rheumatoid arthritis: Insights from bioinformatics

Background

Rheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis.

Methods

In this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results.

Results

The results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated.

Conclusion

This study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

GABARAPL1 is essential in extracellular vesicle cargo loading and metastasis development

BACKGROUND AND PURPOSE

Hypoxia is a common feature of tumours, associated with poor prognosis due to increased resistance to radio- and chemotherapy and enhanced metastasis development. Previously we demonstrated that GABARAPL1 is required for the secretion of extracellular vesicles (EV) with pro-angiogenic properties during hypoxia. Here, we explored the role of GABARAPL1+ EV in the metastatic cascade.

MATERIALS AND METHODS

GABARAPL1 deficient or control MDA-MB-231 cells were injected in murine mammary fat pads. Lungs were dissected and analysed for human cytokeratin 18. EV from control and GABARAPL1 deficient cells exposed to normoxia (21% O2) or hypoxia (O2 < 0.02%) were isolated and analysed by immunoblot, nanoparticle tracking analysis, high resolution flow cytometry, mass spectrometry and next-generation sequencing. Cellular migration and invasion were analysed using scratch assays and transwell-invasion assays, respectively.

RESULTS

The number of pulmonary metastases derived from GABARAPL1 deficient tumours decreased by 84%. GABARAPL1 deficient cells migrate slower but display a comparable invasive capacity. Both normoxic and hypoxic EV contain proteins and miRNAs associated with metastasis development and, in line, increase cancer cell invasiveness. Although GABARAPL1 deficiency alters EV content, it does not alter the EV-induced increase in cancer cell invasiveness.

CONCLUSION

GABARAPL1 is essential for metastasis development. This is unrelated to changes in migration and invasion and suggests that GABARAPL1 or GABARAPL1+ EV are essential in other processes related to the metastatic cascade.

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer

Introduction

Autophagy can be triggered by oxidative stress and is a double-edged sword involved in the progression of multiple malignancies. However, the precise roles of autophagy on immune response in gastric cancer (GC) remain clarified.

Methods

We endeavor to explore the novel autophagy-related clusters and develop a multi-gene signature for predicting the prognosis and the response to immunotherapy in GC. A total of 1505 patients from eight GC cohorts were categorized into two subtypes using consensus clustering. We compare the differences between clusters by the multi-omics approach. Cox and LASSO regression models were used to construct the prognostic signature.

Results

Two distinct clusters were identified. Compared with cluster 2, the patients in cluster 1 have favorable survival outcomes and lower scores for epithelial-mesenchymal transition (EMT). The two subtypes are further characterized by high heterogeneity concerning immune cell infiltration, somatic mutation pattern, and pathway activity by gene set enrichment analysis (GSEA). We obtained 21 autophagy-related differential expression genes (DEGs), in which PTK6 amplification and BCL2/CDKN2A deletion were highly prevalent. The four-gene (PEA15, HSPB8, BNIP3, and GABARAPL1) risk signature was further constructed with good predictive performance and validated in 3 independent datasets including our local Tianjin cohort. The risk score was proved to be independent prognostic factor. A prognostic nomogram showed robust validity of GC survival. The risk score was significantly associated with immune cell infiltration status, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint molecules. Furthermore, the model was efficient for predicting the response to tumor-targeted agent and immunotherapy and verified by the IMvigor210 cohort. This model is also capable of discriminating between low and high-risk patients receiving chemotherapy.

Conclusion

Altogether, our exploratory research on the landscape of autophagy-related patterns may shed light on individualized therapies and prognosis in GC.

Prognostic Autophagy-Related Genes of Gastric Cancer Patients on Chemotherapy

Background: Chemotherapy resistance based on fluorouracil and cisplatin is one of the most encountered postoperative clinical problems in patients diagnosed with gastric cancer (GC), resulting in poor prognosis. Aim of the Study: This study aimed to combine autophagy-related genes (ARGs) to investigate the susceptibility patients with GC to postoperative chemotherapy. Methods: Based on The Cancer Genome Atlas (TCGA) database, gene expression data for GC patients undergoing chemotherapy were integrated and analyzed. Prognostic genes were screened based on univariate and multivariate analysis regression analysis. Subjects were divided into high-risk and low-risk groups according to the median risk score. Kaplan-Meier method was used to evaluate OS and DFS. The accuracy of the prediction was determined by the subject operating characteristic curve analysis. In addition, stratified analyses based on different clinical variables was performed to assess the correlation between risk scores and clinical variables. Quantitative real-time (qRT) PCR was used to verify the expression of CXCR4 in GC tissues and cell lines. Results: A total of nine ARGs related to the prognosis of chemotherapy patients were screened out. Compared with normal gastric mucosa cell, CXCR4 showed elevated expression in GC and was significantly associated with survival. Based on GEO and TCGA databases, the model accurately predicted DFS and OS after chemotherapy. Conclusion: This study established prognostic markers based on nine genes, predicting that ARGs are related to chemotherapy susceptibility of GC patients, which can provide better individualized treatment regimens for clinical practice.

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

The Atg8-family proteins are subdivided into two subfamilies: the GABARAP and LC3 subfamilies. These proteins, which are major players of the autophagy pathway, present a conserved glycine in their C-terminus necessary for their association to the autophagosome membrane. This family of proteins present multiple roles from autophagy induction to autophagosome-lysosome fusion and have been described to play a role during cancer progression. Indeed, GABARAPs are described to be downregulated in cancers, and high expression has been linked to a good prognosis. Regarding LC3 s, their expression does not correlate to a particular tumor type or stage. The involvement of Atg8-family proteins during cancer, therefore, remains unclear, and it appears that their anti-tumor role may be associated with their implication in selective protein degradation by autophagy but might also be independent, in some cases, of their conjugation to autophagosomes. In this review, we will then focus on the involvement of GABARAP and LC3 subfamilies during autophagy and cancer and highlight the similarities but also the differences of action of each subfamily member.Abbreviations: AIM: Atg8-interacting motif; AMPK: adenosine monophosphate-associated protein kinase; ATG: autophagy-related; BECN1: beclin 1; BIRC6/BRUCE: baculoviral IAP repeat containing 6; BNIP3L/NIX: BCL2 interacting protein 3 like; GABARAP: GABA type A receptor-associated protein; GABARAPL1/2: GABA type A receptor associated protein like 1/2; GABRA/GABAA: gamma-aminobutyric acid type A receptor subunit; LAP: LC3-associated phagocytosis; LMNB1: lamin B1; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MTOR: mechanistic target of rapamycin kinase; PI4K2A/PI4KIIα: phosphatidylinositol 4-kinase type 2 alpha; PLEKHM1: plecktrin homology and RUN domain containing M1; PtdIns3K-C1: class III phosphatidylinositol 3-kinase complex 1; SQSTM1: sequestosome 1; ULK1: unc51-like autophagy activating kinase 1.

The expression characteristics and prognostic roles of autophagy-related genes in gastric cancer

Background

Autophagy is an evolutionally highly conserved process, accompanied by the dynamic changes of various molecules, which is necessary for the orderly degradation and recycling of cellular components. The aim of the study was to identify the role of autophagy-related (ATG) genes in the occurrence and development of gastric cancer (GC).

Methods

Data from Oncomine dataset was used for the differential expression analysis between cancer and normal tissues. The association of ATG genes expression with clinicopathologic indicators was evaluated by The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Moreover, using the TCGA datasets, the prognostic role of ATG genes was assessed. A nomogram was further built to assess the independent prognostic factors.

Results

The expression of autophagy-related genes AMBRA1, ATG4B, ATG7, ATG10, ATG12, ATG16L2, GABARAPL2, GABARAPL1, ULK4 and WIPI2 showed differences between cancer and normal tissues. After verification, ATG14 and ATG4D were significantly associated with TNM stage. ATG9A, ATG2A, and ATG4D were associated with T stage. VMP1 and ATG4A were low-expressed in patients without lymph node metastasis. No gene in autophagy pathway was associated with M stage. Further multivariate analysis suggested that ATG4D and MAP1LC3C were independent prognostic factors for GC. The C-index of nomogram was 0.676 and the 95% CI was 0.628 to 0.724.

Conclusion

Our study provided a comprehensive illustration of ATG genes expression characteristics in GC. Abnormal expressions of the ubiquitin-like conjugated system in ATG genes plays a key role in the occurrence of GC. ATG8/LC3 sub-system may play an important role in development and clinical outcome of GC. In the future, it is necessary to further elucidate the alterations of specific ATG8/LC3 forms in order to provide insights for the discovery, diagnosis, or targeting for GC.

Knockout of Akt1/2 suppresses the metastasis of human prostate cancer cells CWR22rv1 in vitro and in vivo

Although primary androgen deprivation therapy resulted in tumour regression, unfortunately, majority of prostate cancer progress to a lethal castration‐resistant prostate cancer, finally die to metastasis. The mutual feedback between AKT and AR pathways plays a vital role in the progression and metastasis of prostate cancer. Therefore, the treatment of a single factor will eventually inevitably lead to failure. Therefore, better understanding of the molecular mechanisms underlying metastasis is critical to the development of new and more effective therapeutic agents. In this study, we created prostate cancer CWR22rv1 cells with the double knockout of Akt1 and Akt2 genes through CRISPR/Cas9 method to investigate the effect of Akt in metastasis of prostate cancer. It was found that knockout of Akt1/2 resulted in markedly reduced metastasis in vitro and in vivo, and appeared to interfere AR nuclear translocation through regulating downstream regulatory factor, FOXO proteins. It suggests that some downstream regulatory factors in the AKT and AR interaction network play a vital role in prostate cancer metastasis and are potential targeting molecules for prostate cancer metastasis treatment.

Development and validation of a novel survival model for head and neck squamous cell carcinoma based on autophagy-related genes

BACKGROUND

In view of the critical role of autophagy-related genes (ARGs) in the pathogenesis of various diseases including cancer, this study aims to identify and evaluate the potential value of ARGs in head and neck squamous cell carcinoma (HNSCC).

METHODS

RNA sequencing and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by univariate Cox regression analysis and Lasso Cox regression analysis model established a novel 13- autophagy related prognostic genes, which were used to build a prognostic risk model. A multivariate Cox proportional regression model and the survival analysis were used to evaluate the prognostic risk model. Moreover, the efficiency of prognostic risk model was tested by receiver operating characteristic (ROC) curve analysis based on data from TCGA database and Gene Expression Omnibus (GEO). Besides, the other independent datasets from Human Protein Atlas dataset (HPA) also applied.

RESULTS

13 ARGs (GABARAPL1, ITGA3, USP10, ST13, MAPK9, PRKN, FADD, IKBKB, ITPR1, TP73, MAP2K7, CDKN2A, and EEF2K) with prognostic value were identified in HNSCC patients. Subsequently, a prognostic risk model was established based on 13 ARGs, and significantly stratified HNSCC patients into high- and low-risk groups in terms of overall survival (OS) (HR = 0.379，95% CI: 0.289-0.495, p < 0.0001). The multivariate Cox analysis revealed that this model was an independent prognostic factor (HR = 1.506, 95% CI = 1.330-1.706, P < 0.001). The areas under the ROC curves (AUC) were significant for both the TCGA and GEO, with AUC of 0.685 and 0.928 respectively. Functional annotation revealed that model significantly enriched in many critical pathways correlated with tumorigenesis, including the p53 pathway, IL2 STAT5 signaling, TGF beta signaling, PI3K Ak mTOR signaling by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). In addition, we developed a nomogram shown some clinical net could be used as a reference for clinical decision-making.

CONCLUSIONS

Collectively, we developed and validated a novel robust 13-gene signatures for HNSCC prognosis prediction. The 13 ARGs could serve as an independent and reliable prognostic biomarkers and therapeutic targets for the HNSCC patients.

Cytoprotective autophagy induction by withaferin A in prostate cancer cells involves GABARAPL1

Withaferin A (WA) is a naturally occurring steroidal lactone with proven cancer chemopreventive activity in preclinical models of different cancers including prostate adenocarcinoma. Previously we compared the RNA-seq data from control and WA-treated 22Rv1 human prostate cancer cells to identify mechanistic targets of this phytochemical. The Gene Ontology pathway analysis of the RNA-seq data revealed significant upregulation of genes associated with autophagy upon WA treatment in 22Rv1 cells. In this study, we extended these findings to investigate the mechanism underlying WA-induced autophagy. Initially, we confirmed autophagy induction by WA treatment by transmission electron microscopy using three prostate cancer cell lines (LNCaP, 22Rv1, and PC-3). Fourteen common genes altered by 8- and 16-hour exposure to WA were identified from human autophagy PCR array and these results were consistent with the RNA-seq data. Two key autophagy markers (LC3BII and SQSTM1) were robustly increased in WA-exposed LNCaP, 22Rv1, and PC-3 cells as determined by immunoblotting, and this effect was elevated in the presence of autophagy inhibitor bafilomycin A1 (BafA1). BafA1 treatment augmented WA's cytotoxicity and subsequently its proapoptotic potential. WA treatment induced GABARAPL1 (ATG8L) protein expression in all three cell lines and its knockdown by RNA interference attenuated WA-mediated apoptosis. WA-induced autophagy was not affected in the presence of an antioxidant (EUK134). Taken together, the present study reveals that WA-mediated autophagy is cytoprotective and mediated by GABARAPL1.