Mast cells in the microenvironment of uveal melanoma

Saakyan, S.V.; Zakharova, G. P.; Myakoshina, E. B.

doi:10.17116/patol20198106163

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Mast cells have many effects on tumors, including mast cell-mediated cytotoxicity, mast cell-directed angiogenesis, tissue remodeling in the adjacent environment, and immune cell recruitment ( 75 ). Some studies have suggested that mast cells have a great influence on tumor angiogenesis in UVM ( 76 ). However, the actual role of mast cells in UVM and the specific mechanism still need a lot of experiments to explain.…”

Section: Discussionmentioning

confidence: 99%

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

Chi

Peng²,

Yang³

et al. 2022

Front. Endocrinol.

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BackgroundUveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM.MethodsIn this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals.Results4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy.Conclusions4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies.

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Section: Discussionmentioning

confidence: 99%

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

Chi

Peng²,

Yang³

et al. 2022

Front. Endocrinol.

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“…Mast cells act as pro-tumor in the TME (52)(53)(54). Targeting mast cells infiltrated in TME combined with immune checkpoint inhibitors or turning tumorpromoting mast cells into tumor-inhibiting mast cells might be an effective approach for the treatment of melanoma (55). Here, we calculated the relative expression level of immune cells in each sample using CIBERSORT.…”

Section: Discussionmentioning

confidence: 99%

Characterization of Immune Infiltration and Construction of a Prediction Model for Overall Survival in Melanoma Patients

Zhu

Liu

2021

Front. Oncol.

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Reports indicate that the use of anti-programmed cell death-1 (PD-1) and death ligand-1 (PD-L1) monoclonal antibodies for the treatment of patients diagnosed with melanoma has demonstrated promising efficacy. Nonetheless, this therapy is limited by the resistance induced by the tumor microenvironment (TME). As such, understanding the complexity of the TME is vital in enhancing the efficiency of immunotherapy. This study used four different methods to estimate the infiltrating level of immune cells. Besides, we analyzed their infiltration pattern in primary and metastatic melanoma obtained from The Cancer Genome Atlas (TCGA) database. As a consequence, we discovered a significantly higher infiltration of immune cells in metastatic melanoma compared to primary tumor. Consensus clustering identified four clusters in melanoma with different immune infiltration and clusters with higher immune infiltration demonstrated a better overall survival. To elucidate the underlying mechanisms of immune cell infiltration, the four clusters were subdivided into two subtypes denoted as hot and cold tumors based on immune infiltration and predicted immune response. Enrichment analysis of differentially expressed genes (DEGs) revealed different transcriptome alterations in two types of tumors. Additionally, we found tyrosinase-related protein1 (TYRP1) was negatively correlated with CD8A expression. In vitro experiments showed that knockdown TYRP1 promoted the expression of HLA-A, B, and C. Eventually, we constructed a prediction model which was validated in our external cohort. Notably, this model also performed effectively in predicting the survival of patients under immunotherapy. In summary, this work provides a deeper understanding of the state of immune infiltration in melanoma and a prediction model that might guide the clinical treatment of patients with melanoma.

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