Dataset Information

Identification of Versican as an Independent Prognostic Factor in Uveal Melanoma.

ABSTRACT:

Objective

To assess the role of versican (VCAN) in uveal melanoma (UVM) from its expression, prognostic value and biological function.

Methods

The general profile of VCAN mRNA and protein expression levels were obtained using bioinformatic approaches. Then, UALCAN database was adopted to examine the association of VCAN mRNA expression and clinical factors in UVM. The prognostic value of VCAN was assessed by UALCAN, GEPIA and TISIDB databases. Besides, Cox regression analysis was performed to predict the independent prognostic factors for UVM. Further, functional enrichment analysis was conducted to reveal the biological functions of VCAN involved in UVM through DAVID, Cytoscape and GSEA analyses.

Results

VCAN showed a relative low expression level in normal eye but was highly expressed in UVM cell lines. Tumor histology and stage in UVM were significantly related to VCAN mRNA expression (all P <0.05). Besides, high VCAN mRNA expression led to unfavorable prognosis of UVM patients, especially in female patients and those aged <60 years (all P <0.05). Cox regression analysis indicated that VCAN mRNA expression was an independent prognostic factor for overall survival in UVM. Enrichment analysis suggested that VCAN was mainly involved in cytokine-cytokine receptor interaction, chemokine signaling pathway and T cell receptor signaling pathway (all P <0.05). Meanwhile, hyaluronic acid was revealed to be a potential drug for the UVM treatment.

Conclusion

VCAN served as an independent prognostic factor for UVM. Further analysis found that VCAN was positively correlated with metastasis-related pathway, which might imply the metastasis risk of UVM. Our study initially revealed the vital role of VCAN in the process of UVM and provided a therapeutic target for UVM treatment.

SUBMITTER: Xu J

PROVIDER: S-EPMC8380807 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundUveal melanoma (UM) is the most common primary intraocular malignancy in adults. Many previous studies have demonstrated that the infiltrating of immune and stromal cells in the tumor microenvironment contributes significantly to prognosis.MethodsDataset TCGA-UVM, download from TCGA portal, was taken as the training cohort, and GSE22138, obtained from GEO database, was set as the validation cohort. ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to initial screen for potential prognostic genes. Then these genes entered into the validation cohort for validation using the same methods as that in the training cohort. Moreover, we conducted correlation analyses between the genes obtained in the validation cohort and the status of chromosome 3, chromosome 8q, and tumor metastasis to get prognosis genes. At last, the immune infiltration analysis was performed between the prognostic genes and 6 main kinds of tumor-infiltrating immune cells (TICs) for understanding the role of the genes in the tumor microenvironment.Results959 intersection DEGs were found in the UM microenvironment. Kaplan-Meier and Cox analysis was then performed in the training and validation cohorts on these DEGs, and 52 genes were identified with potential prognostic value. After comparing the 52 genes to chromosome 3, chromosome 8q, and metastasis, we obtained 21 genes as the prognostic genes. The immune infiltration analysis showed that Neutrophil had the potential prognostic ability, and almost every prognostic gene we had identified was correlated with abundances of Neutrophil and CD8+ T Cell.ConclusionsIdentifying 21 prognosis genes (SERPINB9, EDNRB, RAPGEF3, HFE, RNF43, ZNF415, IL12RB2, MTUS1, NEDD9, ZNF667, AZGP1, WARS, GEM, RAB31, CALHM2, CA12, MYEOV, CELF2, SLCO5A1, ISM1, and PAPSS2) could accurately identify patients' prognosis and had close interactions with Neutrophil in the tumor environment, which may provide UM patients with personalized prognosis prediction and new treatment insights.