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

Immunogenic profiling of metastatic uveal melanoma discerns a potential signature related to prognosis

BACKGROUND

Uveal melanoma (UM) is an aggressive intraocular malignant tumor. The present study aimed to identify the key genes associated with UM metastasis and established a gene signature to analyze the relationship between the signature and prognosis and immune cell infiltration. Later, a predictive model combined with clinical variables was developed and validated.

METHODS

Two UM gene expression profile chip datasets were downloaded from TCGA and GEO databases. Immune-related genes (IRGs) were obtained from IMPORT database. First, these mRNAs were intersected with IRGs, and weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate Cox regression analysis screened the genes related to prognosis. LASSO-Cox established a risk score to distinguish high-risk group and low-risk group. Then the GSEA enrichment pathway and immune cell infiltration of the two groups were compared. And combined with clinical variables, a predictive model was constructed. The time-dependent receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to verify the stability and accuracy of the final predictive model, and a nomogram was then drawn.

RESULTS

The MEblack, MEpurple, and MEblue modules were significantly associated with the metastasis of UM patients (P value < 0.001, = 0.001, = 0.022, respectively). Four genes (UBXN2B, OTUD3, KAT8, LAMTOR2) were obtained by Pearson correlation analysis, weighted gene correlation network analysis (WGCNA), univariate Cox, and LASSO-Cox. And a novel prognostic risk score was established. Immune-related prognostic signature can well classify UM patients into high-risk and low-risk groups. Kaplan-Meier curve showed that the OS of high-risk patients was worse than that of low-risk patients. In addition, the risk score played an important role in evaluating the signaling pathway and immune cell infiltration of UM patients in high-risk and low-risk groups. Both the training set and validation set of the model showed good predictive accuracy in the degree of differentiation and calibration (e.g., 1-year overall survival: AUC = 0.930 (0.857-1.003)). Finally, a nomogram was established to serve in clinical practice.

SIGNIFICANCE

UM key gene signature and prognosis predictive model might provide insights for further investigation of the pathogenesis and development of UM at the molecular level, and provide theoretical basis for determining new prognostic markers of UM and immunotherapy.

Extraction and Prioritization of a Gene-Cancer-By-Survival Network Involved in Homeostasis of Intracellular Calcium Concentrations Using TCGA PANCAN Data

Regulation of intracellular calcium concentrations, [Ca++]i is important in maintaining the viability of normal as well as cancer cells and can be mediated by tumor microenvironment. Calcium release-activated calcium channel protein (ORAI) calcium channels on the plasma membrane (PM) become physically connected by stromal interaction molecules (STIMs) to the endoplasmic reticulum (ER), on which paralogous receptors of inositol phosphate and of ryanodine are also present along with ATP2A/SERCA (sarco/endoplasmic reticulum calcium ATPases) subunits (also known as PM-ER geneset). Proper expression of this functionally and physically interconnected geneset is essential for the maintenance of [Ca++]i , yet has not been interrogated as a whole for its role in cancer prognosis using multivariable Cox regression. In the present study, we examined whether the expression profile of the PM-ER geneset exhibited prognostic significance across different cancers found in The Cancer Genome Atlas (TCGA) by generating gene-cancer-by-survival networks, in which the nodes represented either genes or cancers and the edges, the logarithmically transformed hazard ratios for overall survival (OS). We then applied network clustering to identify the gene-cancer subnetworks with high connectivity, among which uveal melanoma (UVM) emerged exhibiting the highest degree of genes (k = 10). BAP1, a well-known [Ca++]i regulator and a tumor suppressor, was not found to be significant in predicting OS by PM-ER geneset for UVM, yet it was for several others, including mesothelioma (MESO). Moreover, the best subset of the PM-ER geneset obtained by lasso predicted OS in the TCGA UVM cohort with an area under the receiver operating characteristics (AUC) of 91.4%, comparable to or better than previous prognostic signatures in the literature. Our findings indicate that homeostasis of [Ca++]i is an essential determinant of prognosis in multiple cancers and particularly in UVM. The proposed gene-cancer-by-survival network approach can be extended with other gene sets as well as different survival types.

Integrative analysis identifies key genes related to metastasis and a robust gene-based prognostic signature in uveal melanoma

PURPOSE

Uveal melanoma (UM) is an aggressive intraocular malignancy, leading to systemic metastasis in half of the patients. However, the mechanism of the high metastatic rate remains unclear. This study aimed to identify key genes related to metastasis and construct a gene-based signature for better prognosis prediction of UM patients.

METHODS

Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate, Lasso-penalized and multivariate Cox regression analyses were performed to establish a prognostic signature for UM patients.

RESULTS

The tan and greenyellow modules were significantly associated with the metastasis of UM patients. Significant genes related to the overall survival (OS) in these two modules were then identified. Additionally, an OS-predicting signature was established. The UM patients were divided into a low- or high-risk group. The Kaplan-Meier curve indicated that high-risk patients had poorer OS than low-risk patients. The receiver operating curve (ROC) was used to validate the stability and accuracy of the final five-gene signature. Based on the signature and clinical traits of UM patients, a nomogram was established to serve in clinical practice.

CONCLUSIONS

We identified key genes involved in the metastasis of UM. A robust five-gene-based prognostic signature was constructed and validated. In addition, the gene signature-based nomogram was created that can optimize the prognosis prediction and identify possible factors causing the poor prognosis of high-risk UM patients.

The Interplay between Tumour Microenvironment Components in Malignant Melanoma

Malignant melanoma has shown an increasing incidence during the last two decades, exhibiting a large spectrum of locations and clinicopathological characteristics. Although current histopathological, biochemical, immunohistochemical, and molecular methods provide a deep insight into its biological behaviour and outcome, melanoma is still an unpredictable disease, with poor outcome. This review of the literature is aimed at updating the knowledge regarding melanoma’s clinicopathological and molecular hallmarks, including its heterogeneity and plasticity, involving cancer stem cells population. A special focus is given on the interplay between different cellular components and their secretion products in melanoma, considering its contribution to tumour progression, invasion, metastasis, recurrences, and resistance to classical therapy. Furthermore, the influences of the specific tumour microenvironment or “inflammasome”, its association with adipose tissue products, including the release of “extracellular vesicles”, and distinct microbiota are currently studied, considering their influences on diagnosis and prognosis. An insight into melanoma’s particular features may reveal new molecular pathways which may be exploited in order to develop innovative therapeutic approaches or tailored therapy.