iUMRG: multi-layered network-guided propagation modeling for the inference of susceptibility genes and potential drugs against uveal melanoma

Recent Advances in Molecular and Genetic Research on Uveal Melanoma

Uveal melanoma (UM), a distinct subtype of melanoma, presents unique challenges in its clinical management due to its complex molecular landscape and tendency for liver metastasis. This review highlights recent advancements in understanding the molecular pathogenesis, genetic alterations, and immune microenvironment of UM, with a focus on pivotal genes, such as GNAQ/11, BAP1, and CYSLTR2, and delves into the distinctive genetic and chromosomal classifications of UM, emphasizing the role of mutations and chromosomal rearrangements in disease progression and metastatic risk. Novel diagnostic biomarkers, including circulating tumor cells, DNA and extracellular vesicles, are discussed, offering potential non-invasive approaches for early detection and monitoring. It also explores emerging prognostic markers and their implications for patient stratification and personalized treatment strategies. Therapeutic approaches, including histone deacetylase inhibitors, MAPK pathway inhibitors, and emerging trends and concepts like CAR T-cell therapy, are evaluated for their efficacy in UM treatment. This review identifies challenges in UM research, such as the limited treatment options for metastatic UM and the need for improved prognostic tools, and suggests future directions, including the discovery of novel therapeutic targets, immunotherapeutic strategies, and advanced drug delivery systems. The review concludes by emphasizing the importance of continued research and innovation in addressing the unique challenges of UM to improve patient outcomes and develop more effective treatment strategies.

The landscape of the methodology in drug repurposing using human genomic data: a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.

Single-cell characterization of macrophages in uveal melanoma uncovers transcriptionally heterogeneous subsets conferring poor prognosis and aggressive behavior

Uveal melanoma (UM) is the most frequent primary intraocular malignancy with high metastatic potential and poor prognosis. Macrophages represent one of the most abundant infiltrating immune cells with diverse functions in cancers. However, the cellular heterogeneity and functional diversity of macrophages in UM remain largely unexplored. In this study, we analyzed 63,264 single-cell transcriptomes from 11 UM patients and identified four transcriptionally distinct macrophage subsets (termed MΦ-C1 to MΦ-C4). Among them, we found that MΦ-C4 exhibited relatively low expression of both M1 and M2 signature genes, loss of inflammatory pathways and antigen presentation, instead demonstrating enhanced signaling for proliferation, mitochondrial functions and metabolism. We quantified the infiltration abundance of MΦ-C4 from single-cell and bulk transcriptomes across five cohorts and found that increased MΦ-C4 infiltration was relevant to aggressive behaviors and may serve as an independent prognostic indicator for poor outcomes. We propose a novel subtyping scheme based on macrophages by integrating the transcriptional signatures of MΦ-C4 and machine learning to stratify patients into MΦ-C4-enriched or MΦ-C4-depleted subtypes. These two subtypes showed significantly different clinical outcomes and were validated through bulk RNA sequencing and immunofluorescence assays in both public multicenter cohorts and our in-house cohort. Following further translational investigation, our findings highlight a potential therapeutic strategy of targeting macrophage subsets to control metastatic disease and consistently improve the outcome of patients with UM.

Monoacylglycerol lipase regulates macrophage polarization and cancer progression in uveal melanoma and pan-cancer

Background

Although lipid metabolism has been proven to play a key role in the development of cancer, its significance in uveal melanoma (UM) has not yet been elucidated in the available literature.

Methods

To identify the expression patterns of lipid metabolism in 80 UM patients from the TCGA database, 47 genes involved in lipid metabolism were analyzed. Consensus clustering revealed two distinct molecular groups. ESTIMATE, TIMER, and ssGSEA analyses were done to identify the differences between the two subgroups in tumor microenvironment (TME) and immune state. Using Cox regression and Lasso regression analysis, a risk model based on differentially expressed genes (DEGs) was developed. To validate the expression of monoacylglycerol lipase (MGLL) and immune infiltration in diverse malignancies, a pan-cancer cohort from the UCSC database was utilized. Next, a single-cell sequencing analysis on UM patients from the GEO data was used to characterize the lipid metabolism in TME and the role of MGLL in UM. Finally, in vitro investigations were utilized to study the involvement of MGLL in UM.

Results

Two molecular subgroups of UM patients have considerably varied survival rates. The majority of DEGs between the two subgroups were associated with immune-related pathways. Low immune scores, high tumor purity, a low number of immune infiltrating cells, and a comparatively low immunological state were associated with a more favorable prognosis. An examination of GO and KEGG data demonstrated that the risk model based on genes involved with lipid metabolism can accurately predict survival in patients with UM. It has been demonstrated that MGLL, a crucial gene in this paradigm, promotes the proliferation, invasion, and migration of UM cells. In addition, we discovered that MGLL is strongly expressed in macrophages, specifically M2 macrophages, which may play a function in the M2 polarization of macrophages and M2 macrophage activation in cancer cells.

Conclusion

This study demonstrates that the risk model based on lipid metabolism may be useful for predicting the prognosis of patients with UM. By promoting macrophage M2 polarization, MGLL contributes to the evolution of malignancy in UM, suggesting that it may be a therapeutic target for UM.