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Wang, J.Z.; Paulus, P.; Niu, Y.; Zhu, L.; Morisseau, C.; Rawling, T.; Murray, M.; Hammock, B.D.; Zhou, F. Autophagy in Uveal Melanoma. Encyclopedia. Available online: https://encyclopedia.pub/entry/55592 (accessed on 20 April 2024).
Wang JZ, Paulus P, Niu Y, Zhu L, Morisseau C, Rawling T, et al. Autophagy in Uveal Melanoma. Encyclopedia. Available at: https://encyclopedia.pub/entry/55592. Accessed April 20, 2024.
Wang, Janney Z., Paus Paulus, Yihe Niu, Ling Zhu, Christophe Morisseau, Tristan Rawling, Michael Murray, Bruce D. Hammock, Fanfan Zhou. "Autophagy in Uveal Melanoma" Encyclopedia, https://encyclopedia.pub/entry/55592 (accessed April 20, 2024).
Wang, J.Z., Paulus, P., Niu, Y., Zhu, L., Morisseau, C., Rawling, T., Murray, M., Hammock, B.D., & Zhou, F. (2024, February 28). Autophagy in Uveal Melanoma. In Encyclopedia. https://encyclopedia.pub/entry/55592
Wang, Janney Z., et al. "Autophagy in Uveal Melanoma." Encyclopedia. Web. 28 February, 2024.
Autophagy in Uveal Melanoma
Edit

Autophagy is a form of programmed cell degradation that enables the maintenance of homeostasis in response to extracellular stress stimuli. Autophagy is primarily activated by starvation and mediates the degradation, removal, or recycling of cell cytoplasm, organelles, and intracellular components in eukaryotic cells. Autophagy is also involved in the pathogenesis of human diseases, including several cancers. Autophagy mechanisms and mediators have emerged as promising therapeutic targets that could be used to develop new treatment options for uveal melanoma (UM).

autophagy uveal melanoma biomarker drug development

1. Current Treatments Affecting Autophagy in Uveal Melanoma

Autophagy has been extensively studied in various cancers. However, being a rare cancer, the concept of autophagy largely remains unexplored in UM. UM is characterised by genetic mutations to the paralogous guanine nucleotide-binding protein Gq subunits alpha and alpha-11 (GNAQ and GNA11, respectively) which are observed in 80–90% of tumours [1][2]. However, despite the high incidence of GNAQ/GNA11 mutations, overall UM has a relatively low mutational burden so that targeted treatments based on genetic driver mutations have been difficult to identify [3]. Recent studies have suggested that GNAQ/GNA11 and autophagic pathways may be linked. Ambrosini et al. demonstrated that signalling by mutant GNAQ/GNA11 was impaired by the MEK inhibitor selumetinib and the AKT inhibitor MK2206. AKT and MEK are different signalling pathways that converge at GNAQ/GNA11. Therefore, when inhibitors of these two pathways are used together, there is a synergistic increase in autophagic cell death through activation of AMPK [4]. Furthermore, the combination inhibited tumour growth in xenograft mouse models [4]. These effects were genotype dependent since the autophagic markers beclin1 and LC3 were induced in GNAQ-mutant cells, whereas apoptotic cell death was activated in BRAF-mutant cells, and cells without either mutation underwent cell-cycle arrest [4]. Similar findings were noted with the MAPK inhibitor trametinib in combination with the autophagy and lysosomal inhibitor, chloroquine [5]. These apparent links between GNAQ/GNA11-driver mutations and autophagy now warrant further research to identify potential new treatments in UM.
In PDX isolates of UM, neratinib caused the internalization and degradation of GNAQ and GNA11 that was enhanced by the histone deacetylase inhibitor entinostat [6]. Down-regulation of GNAQ and GNA11 required Beclin1 and ATG5 [6]. The combination of neratinib and entinostat engaged multiple pathways to mediate killing, including ROS-dependent activation of the ATM kinase via the AMPK-ULK1-ATG13-Beclin1/ATG5 axis [6]. The knockdown of ATM, AMPK or ULK-1 prevented ATG13 phosphorylation and the degradation of RAS and Galpha subunits [6]. Over-expression of activated mTOR prevented ATG13 phosphorylation and suppressed killing [6]. Thus, neratinib and entinostat down-regulates oncogenic RAS and the oncogenic drivers present in most UM tumours and promotes autophagic cell death [6]. Indeed, targeting dysregulated AMPK-linked cascades may represent a new strategy in UM treatment. Thus, metformin—an adenosine monophosphate-activated kinase (AMPK) activator—inhibited the proliferation and migration of ocular melanoma cells both in vitro and in vivo and attenuated autophagic influx [7]. It would be of potential interest to pursue these observations and assess whether they may be new treatment modalities in UM.
Natural compounds are also under the spotlight as potential treatment options for UM. The compound (−)-4-O-(4-O-β-D-glucopyranosylcaffeoyl) quinic acid (QA) derived from the endophytic fungus Penicillium sp.FJ-1 of Avicennia marina has been investigated in UM. Treatment with QA demonstrated potent anti-proliferative effect in a concentration dependent manner. Cell autophagy was induced through upregulating mRNA expression of Beclin-1 and down regulation in LC-3, P62and PI3K signalling. This effect was further assessed in in vivo xenograft mouse model, where QA not only decreased tumour volume but also increased pro-apoptotic protein expression causing overall cell death [8].
Nonetheless, the conflicting nature of autophagy on tumour progression, survival, and suppression is also present in UM. The protective effect of autophagy was elucidated in UM through Annexin A2 receptor (AXIIR). Zhang et al. validated the dual effect of AXIIR in UM. Although overexpression of AXIIR through overexpression resulted in overall decrease in cell viability through apoptosis, this effect was reduced with the activation of autophagy. The use of autophagy inhibitor on AXIIR overexpressed cells resulted in a greater apoptotic death suggesting autophagy to resume a cellular protective role [9]. Similarly, novel drug therapy study using the autophagy inhibitor elaiophylin resulted in induced cell death in UM cell lines (i.e., C918, OCM-1A and Mel270) but not healthy retinal ARPE-19 cell line. Treatment with elaiophylin generated oxidative stress and mitochondrial dysfunction while causing the inhibition of mitophagy. The induced oxidative stress caused subsequent accumulation of defective mitochondria and ultimately cell death. The treatment result was further translated into in vivo xenograft mouse models where treatment with elaiophylin caused a reduction in tumour size as well as an increase in apoptosis [10]. Previous studies have linked autophagy with the resistance of tumours to chemotherapies [11]. The combination of selamectin and cisplatin showed a synergistic effect in inhibiting UM cell growth and in tumour-bearing nude mice [11]. Selamectin inhibited the expression of ATG9B, thus decreasing autophagy [11]. The cisplatin resistance-associated genes PDGFRB, DUSP1, MAST1 and IL11 were also downregulated in UM cells treated with selamectin [11]. These findings provide counter arguments for autophagy in UM. Admittingly, autophagy in UM remains disputed. Thus, new definitive markers for diagnosis and treatment are required to improve patient survival. 

2. Protein Based UM Autophagy Biomarkers

There is an urgent need for new prognostic approaches in UM for potential diagnostic and prognostic tools that also serve as treatment markers. Protein mutations in Table 1 are apparent in many cancers including UM and can serve as both diagnostic tool as well as target for novel drug therapies. Ealy intervention could prohibit the development of metastatic disease and improve survival rates.
A promising autophagy-related biomarker is Beclin-1, which is encoded by the BECN1 gene on chromosome 17q21 (Table 1). BECN1 is essential for autophagosome formation since it facilitates the recruitment of other ATG proteins. Deletion of BECN1 has been reported in human breast, ovarian and prostatic cancer cell lines and BECN1+/− mutant mice exhibit a high incidence of spontaneous tumours, which implies a tumour suppressor function for autophagy [12]. In UM BECN expression is associated with a lower risk of metastasis and an increase in disease-free survival [12]. Giatromanolaki et al. explored the potential role of BECN1 as a prognostic marker in cohort of 99 UM tumours following enucleation. Survival analysis showed that both under- and over-expression of BECN1 was associated with metastasis and poor disease survival. However, under-expression of BECN1 was related to a slower rate of initial metastasis rate than with overexpression of BECN1 [13]. At present the role of beclin-1 in cancer is not completely clear but it remains a potential biomarker in UM.
The BCL2 19 kD protein-interacting protein 3 (BNIP3) has been assessed as a potential prognostic biomarker in UM (Table 1). BNIP3 is a BH3 containing protein from the BCL-1 family that modulates cell death, autophagy and cytoprotection. BNIP3 possess the BH3 domain (Bcl-2 homology) common to both pro- and anti-apoptotic bcl-2 family proteins. However, unlike other Bcl-2 proteins, BNIP3 interacts directly with other BCL-2 family members via its C-terminal transmembrane domain rather than BH3, which underlies its dual effect on cell survival [14]. Apart from activation of apoptotic pathways involved with Bcl-2, upregulation of BNIP3 also induces mitochondrial depolarisation and autophagy [15]. Studies in breast cancer and malignant glioma have reported that increased BNIP3 decreased metastasis and improved treatment outcomes [16][17]. However, conflicting conclusions have also noticed in salivary adenoid cystic carcinoma and non-small cell lung cancer [18][19]. In these tumours, increased BNIP3 led to poorer prognosis and decreased metastatic free survival. Similar findings were made in the cohort study of Jiang et al., which found that the high expression of BNIP3 was associated with hyper-pigmentation, deeper scleral invasion and a poor prognosis in UM [20]. BNIP3 detection could help stratify high-risk patients and identify new therapies targeting BNIP3 as a promising approach to treat UM [20].
Autophagy is also modulated by the PI3K/AKT/mTOR, p53, MAPK and NFκB signalling pathways (Table 1) [21][22][23]. AMPK and mTOR are involved in the autophagic process as the activator and inhibitor, respectively. Notably, mTOR is the key integrator of nutrient signalling and cellular growth factor. It is responsible for autophagy inhibition under nutrient sufficient conditions via preventing the activation of Ulk1 [24]. Protein tyrosine kinase 6 (PTK6) is an mTOR regulator and promotes breast, colorectal and lung tumorigenesis by activating multiple signalling pathways [25][26][27]. Although PTK6 has been explored in other cancers, its involvement in UM is yet to be fully elucidated. However, Liu et al. reported that increased PTK6 expression in UM cells was associated with a poor prognosis [28]. Increased PTK6 activates mTOR and in turn, increases tumorigenesis by promoting the proliferation, migration, and invasion of UM cells and inhibiting autophagy [28]. PTK6 binds to SOCS3 in UM cells, so that targeting the SOCS3-PTK6 signalling axis might be a novel and promising therapeutic strategy for patients with UM [28]. The trial of neratinib and entinostat also demonstrated the therapeutic role of mTOR as well as other crucial signallings such as AMPK-ULK1-ATG13-Beclin1/ATG5 axis, which study indicated that the regulation of mTOR activity and its control of autophagosomes played a key role in treatment response to neratinib/entinostat combination [6]. The drug combination increases the phosphorylation of ULK-1 S317 and reduces phosphorylation of mTOR. Furthermore, this study demonstrated that the activation of mutant form of mTOR supressed the drug combination-induced activation of ATM, AMPK and ULK-1 resulting in decreased killing effect, which suggested that mTOR activation plays a vital role in cellular autophagic processes [6].
Mutations in the BRAF gene are common in cutaneous melanoma but not in UM [29]. However, from gene profiling studies, this mutation was detected in some cell lines [30][31][32]. Indeed, in BRAF V600E mutant UM cells vemurafenib produced cell death by inhibiting BRAF and mTOR [33]. Thus, the mTOR-linked signalling pathway is implicated in UM survival.
Table 1. Summary of key genes/proteins as potential late biomarkers for autophagy.

3. Gene Based UM Biomarkers

With advancements in technology, patient-based tumour gene sequencing and related therapies become predominant. Early identification of genetic mutations and variations can serve as prognostic and treatment tools to aid in patient recovery and minimise metastasis.
ARGs have been evaluated increasingly as potential cancer biomarkers [34][35][36][37]. In recent years, several ATG genes have been identified as potential biomarkers for use in cancers such as breast cancer, colon cancer and glioblastoma [38][39][40]. ATG genes and other ARGs could also be developed as UM biomarkers. Recently, Zheng et al. identified a robust 9-ARG signature that was prognostic of survival in a cohort of 230 patients with UM [41]. The Cancer Genome Atlas (TCGA) UM cohort was used as a training set to identify the signature that was then validated using four other cohorts of 150 UM patients [41]. The 9-ARG signature was distinctively enriched in high-risk UM patients and was associated with several cancer hallmarks, including angiogenesis, IL6-JAK-STAT3 signalling, reactive oxygen species production and oxidative phosphorylation, as well as immune-related functional pathways and immune cell infiltration [41]. Moreover, the ARG signature seemed to distinguish between low- and high-risk UMs [41]. Although the small sample size in the investigation highlights the need for caution, ARG analysis could well be a promising new approach for further evaluation in UM.
Autophagy-associated long non-coding RNA (lncRNA) may also be potential prognostic indicators in UM (Table 2). lncRNA are RNAs of 200 nucleotides or longer that do not encode protein and that were previously considered to be transcriptional noise [42]. However, lncRNA may be important in cellular development, including epigenetics, chromatin remodelling and genetic imprinting [43][44]. Dysregulation of lncRNA is associated with tumorigenesis [45][46][47]. Multiple lncRNA are linked to epigenetic and genomic modifications, and are both tumour suppressors and oncogenes [48]. Detailed analyses, such as that performed by Li et al., have demonstrated that induction of the lncRNA ZNNT1 by rapamycin upregulated ATG12 expression in UM cells and inhibited tumorigenesis by activating autophagic cell death [49]. Specific lncRNA might be developed as biomarkers and treatment targets of UM [50][51]. Another novel discovery of LnRNA is LINC01278. This gene is closely related to autophagic genes in UM and acts as a double-edged sword where it can promote tumour metastasis. On the other hand, LINC01278 inhibited the progression cancer progression through MiRNA activation. Liu et al. discovered the inhibitory effects of LINC01278 on UM. resulting data proposed LINC01278 to have inhibitory effects on UM where overexpression caused a decrease in proliferation, migration, and invasion levels of UM cell line OCM-1 and MUM-2B. This was carried out through activating the autophagic pathway with P62 up regulation and decreasing LC3 II/LC3 ratio along with mTOR signalling pathway. This finding was also translated onto xenograft mouse models where upregulation of LINC01278 reduced the volume and weight of tumour compared to control. With these as the basis, more research into the insight of the function and use of LnRNA is encouraged to uncover potential diagnostics and treatment options for UM [52].
As with the lncRNA, micro-RNA (miRNA) may be potential UM biomarkers and anti-cancer drug targets (Table 2). miRNAs are single non-coding strands of 21–23 nucleotides that modulate gene expression by regulating mRNA decay or translation inhibition/activation [53]. An individual miRNA can target multiple genes—a property that may be advantageous in the development of new anti-cancer therapies [54]. Targeting miRNA has been evaluated in gastric cancer, hepatocellular carcinoma, lung cancer, and oesophageal cancer [55][56][57][58]. Multiple miRNAs appear to be dysregulated in UM [59][60][61]. Certain miRNAs are potential prognostic markers for UM and clustering tumours according to miRNA expression appears to correlate with metastatic risk [62][63]. Wu et al. found using cell and mouse models that overexpression of miRNA miR-608 down-regulated the tumour promoting gene HOXC4, that was attenuated by overexpression of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), which is implicated in cancer as a pivotal regulator of pro-tumorigenic signalling [64]. Knockdown of HOXC4 suppressed UM cell migration, proliferation, invasion, and cell cycle progression (81). Targeting MALAT1 may be a viable approach in the development of new UM treatments. MALAT1 was upregulated in UM tissues and its knockdown has been found to suppress UM cell proliferation, colony information, invasion, and migration [65].
Drug treatments that modulate miRNA expression have also been explored to some extent and exhibit promising activity. Sun et al. evaluated the flavonoid genistein in UM. Inhibition of UM cell growth by genistein was time- and dose-related [66]. In in vivo studies genistein inhibited xenograft growth [66]. Genistein modulated miR-27a expression and, in turn, the Zinc Finger And BTB Domain Containing 10 gene that regulates RNA polymerase related DNA transcription [66].
Genetic profiling with the ever-expanding patient database is becoming the frontier of cancer research. Correlation between genetic mutations and patient prognosis has been utilised in many cancers, but this area of study is still generally lacking in UM (Table 2). With increased UM patient data, this field of research will provide imperative information to the diagnostics and treatment paradigm of UM. By using the TCGA-UVM gene database then validating it with gene expression database (GSE84976, GSE 22138) Liu et al. was able to successfully correlate autophagy and immune related genes to reveal four novel genes (PRKCD, MPL, EREG, and JAG2) in UM that has been correlated with prognosis in other malignancies [67]. The risk scores generated for the four genes are closely related to chromosome 3 status and was an accurate interpretation of patient prognosis when used in the three patient databases. Further correlation identified immune changes such as fraction of CD4, CD8, regulatory T cell and NK cell activation, monocytes, macrophages M1, and mast cells in the high-risk score group to significantly differ from UM patients in the low-risk score group. Additionally, association between drug resistance of two common drug among the three database and risk score determined significant differences in drug sensitivity where high risk group could be more sensitive to chemotherapy [67].
In the recent report, Jin et al. applied multi-omics approaches to analyse UM patients’ clinical and molecular features, which study discovered six novel prognostic biomarkers in relation to autophagy in UM (i.e., SPHK1, HTR2B, FEZ1, EEF1A2, HAP1, and GRID1) [68]. The high expression of these genes has been found to be associated with poor patient prognosis and tumour progression. Thus, experimental evidence is highly desired to validate the clinical relevance of these newly identified marker genes in UM.
Overall, multiple potential prognostic biomarkers that regulate autophagy in UM have been found. Greater understanding of the molecular roles of autophagy and its regulation in UM could clarify whether these proteins may be useful in understanding and diagnosis of UM progression and whether they may be potential drug targets.
Table 2. Summary of key genes/RNAs as potential biomarkers for autophagy.
Gene/RNA Physiological Role(s) Clinical Advantages/Disadvantages References
Autophagy related genes (ARGs)
-
Set of genes required for autophagy.
-
Mutations of ARGs may result in issues with the autophagy process.
-
Multiple targetable genes allow for combination therapy.
-
Ubiquitous, difficult to differentiate tumour from healthy cells.
[34][35][36][37][41][68]
Long non-coding RNA
-
Non protein coding RNA.
-
Effect on cellular regulatory functions.
-
Multiple targetable genes allow for combination therapy.
-
Mutations in the genes allow for targeted therapy.
-
Involved in cellular development, such as epigenetics, chromatin remodelling and genetic imprinting.
[42][43][44][48][50][51]
Micro RNA (miRNA)
-
Highly conserved non-coding RNA molecules.
-
Involved in the regulation of gene expression.
-
Multiple targetable genes may allow for combination therapy.
-
Difficult to be considered as a target for pharmacotherapies due to its versatility in the body.
[53][54][56][59][62][63]
Genetic profiling
-
Mutation identification through high output methods.
-
Genetic mutation can result in cancer formation.
-
Can pinpoint UM based mutation. On different cell lines
-
Can easily verify findings on different databases.
-
May not translate directly into clinical situation.
[67]

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