The prognostic landscape of adaptive immune resistance signatures and infiltrating immune cells in the tumor microenvironment of uveal melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults and has a high mortality rate. Tumor microenvironment (TME) is crucial in controlling and influencing the behavior of malignant tumors. Thus, illustrating the prognostic values of adaptive immune resistance signatures and infiltrating immune cells in the TME of UM may provide scientific rationales for immunotherapy. In this study, the gene expression data of 80 primary UM and 103 primary skin cutaneous melanoma (SKCM) samples with relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The TME was analyzed by the xCell, EPIC, ESTIMATE and TIMER algorithms. The relationships and prognostic values of immune infiltrates and mutated genes were further investigated. We found that primary UM and primary SKCM exhibited distinct TMEs. Higher levels of infiltrating stromal and immune cells in UM were related to more aggressive biology and poor prognosis. Increased CD8+ T cell level, as well as several adaptive immune resistance markers, was a predictive factor of poor prognosis in UM. Furthermore, some common mutations of UM were associated with its TME. This study analyzed the immune landscape of adaptive immune resistance signatures and infiltrating immune cells in the TME of UM. Identification of these immune-related biomarkers may thus enable the prediction of prognosis and the selection of optimal immunotherapy strategies in UM.

Wang Y ，Xu Y ，Dai X ，Lin X ，Shan Y ，Ye J ... -  《-》

Identification of survival-related genes and a novel gene-based prognostic signature involving the tumor microenvironment of uveal melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults and almost fifty percent of patients subsequently develop systemic metastases usually involving the liver. The tumor microenvironment (TME) is crucial to the initiation and progression of tumors. In the present study, we comprehensively evaluated the TME of primary UM samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database by using several bioinformatic algorithms. The prognostic value of immune score and infiltrating immune cells in the TME were evaluated. Differentially expressed genes between the low- and high-immune score groups were also identified. The majority of tumor-infiltrating lymphocytes in UM have been determined to be activated CD8 + T cells. Therefore, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules and genes significantly associated with the level of infiltrating CD8 + T cells in UM. Survival-related genes involved in the TME were identified by univariate Cox regression analysis. Furthermore, an eight-gene-based prognostic signature was established in the training dataset TCGA-UM via Lasso-penalized and multivariate Cox regression analyses. The predictive value of this signature was validated in two testing datasets. Besides, a nomogram was established to serve clinical practice. Moreover, hub genes involved in the infiltrating CD8 + T cells were identified and a potential targeted therapy for preventing metastasis of UM was proposed based on the results. In summary, our results provided a robust gene-based prognostic signature for predicting prognosis of UM patients and proposed a potential targeted therapy for preventing UM metastasis.

Lei S ，Zhang Y 《-》

Identification of prognostic genes in uveal melanoma microenvironment.

Luo H ，Ma C 《PLoS One》

Additive Role of Immune System Infiltration and Angiogenesis in Uveal Melanoma Progression.

García-Mulero S ，Alonso MH ，Del Carpio LP ，Sanz-Pamplona R ，Piulats JM ... -  《-》

Development of an IFNγ response-related signature for predicting the survival of cutaneous melanoma.

The tumor microenvironment (TME) plays a critical role in tumorigenesis, development, and therapeutic efficacy. Major advances have been achieved in the treatment of various cancers through immunotherapy. Nevertheless, only a minority of patients have positive responses to immunotherapy, which is partly due to conditions of the immunosuppressive microenvironment. Therefore, it is essential to identify prognostic biomarkers that reflect heterogeneous landscapes of the TME. Based upon the ESTIMATE algorithm, we evaluated the infiltrating levels of immune and stromal components derived from patients afflicted by various types of cancer from The Cancer Genome Atlas database (TCGA). According to respective patient immune and stromal scores, we categorized cases into high- and low-scoring subgroups for each cancer type to explore associations between TME and patient prognosis. Gene Set Enrichment Analyses (GSEA) were conducted and genes enriched in IFNγ response signaling pathway were selected to facilitate establishment of a risk model for predicting overall survival (OS). Furthermore, we investigated the associations between the prognostic signature and tumor immune infiltration landscape by using CIBERSORT algorithm and TIMER database. Among the cancers assessed, the immune scores for skin cutaneous melanoma (SKCM) were the most significantly correlated with patients' survival time (P < .0001). We identified and validated a five-IFNγ response-related gene signature (UBE2L6, PARP14, IFIH1, IRF2, and GBP4), which was closely correlated with the prognosis for SKCM afflicted patients. Multivariate Cox regression analysis indicated that this risk model was an independent prognostic factor for SKCM. Tumor-infiltrating lymphocytes and specific immune checkpoint molecules had notably differential levels of expression in high- compared to low-risk samples. In this study, we established a novel five-IFNγ response-related gene signature that provided a better and increasingly comprehensive understanding of tumor immune landscape, and which demonstrated good performance in predicting outcomes for patients afflicted by SKCM.

Hu B ，Wei Q ，Li X ，Ju M ，Wang L ，Zhou C ，Chen L ，Li Z ，Wei M ，He M ，Zhao L ... -  《Cancer Medicine》