Identification of a prognostic six-immune-gene signature and a nomogram model for uveal melanoma

Abstract

Background To identify an immune-related prognostic signature and find potential therapeutic targets for uveal melanoma. Methods The RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic six-immune-gene signature was constructed through least absolute shrinkage and selection operator and multi-variate Cox regression analyses. Functional enrichment analysis and single sample GSEA were carried out. In addition, a nomogram model established by integrating clinical variables and this signature risk score was also constructed and evaluated. Results We obtained 130 prognostic immune genes, and six of them were selected to construct a prognostic signature in the TCGA uveal melanoma dataset. Patients were classified into high-risk and low-risk groups according to a median risk score of this signature. High-risk group patients had poorer overall survival in comparison to the patients in the low-risk group (p < 0.001). These findings were further validated in two external GEO datasets. A nomogram model proved to be a good classifier for uveal melanoma by combining this signature. Both functional enrichment analysis and single sample GSEA analysis verified that this signature was truly correlated with immune system. In addition, in vitro cell experiments results demonstrated the consistent trend of our computational findings. Conclusion Our newly identified six-immune-gene signature and a nomogram model could be used as meaningful prognostic biomarkers, which might provide uveal melanoma patients with individualized clinical prognosis prediction and potential novel treatment targets.

Machine Learning Methods for Gene Selection in Uveal Melanoma

“…CARD11 was detected as a prognostic marker, with high expression associated to poor OS in the TCGA UVM dataset; in particular, metastatic patients had higher expression of this gene [31]; however, the MGS based on a larger dataset [17] assigned a protective effect to this gene, probably due to a set of patients with limited survival, metastatic disease and low CARD11 expression (Figures S2 and S3). HTR2B, TNFRSF19 and PTGER4 were previously found to be overexpressed in class 2 tumors (metastatic) [32]; in particular, TCGA UVM patients with high PTGER4 expression had worse survival [33]. Gene set enrichment analysis of MGS elements (Table 2) shows that these genes are involved in inflammatory (CARD11, PDE4B, TNFRSF19, HTR2B) and cell-motility-related biological processes (MTUS1, ROBO1, PTGER4, CHL1, HTR2B, PDE4B, ROPN1, Figure 6, Table S2).…”

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy

“…Following the rapid advancement of bioinformatics ( Song et al, 2022a ; Zhao et al, 2022a ; Jin et al, 2022 ), a considerable amount of research has been conducted to establish models for predicting the prognosis of UVM through machine learning. For example, Zheng et al established an autophagy-related gene (ARG) risk model and validated it with TCGA and four external independent UVM cohorts, revealing that UVM patients with higher risk scores exhibited higher levels of immune cell infiltration and enrichment of tumor markers ( Zheng et al, 2021 ); Lv et al constructed a UVM prognostic model based on the Epithelial-mesenchymal transition (EMT) signature, which found that patients with high EMT scores potentially had higher response rates to immunotherapy ( Lv et al, 2022 ); Yang et al utilized immune markers systematically to develop a prognostic six-immune-gene signature via RNA sequencing data from TCGA and GEO databases for predicting the overall survival outcome of UVM patients ( Yang et al, 2023 ). Meanwhile, several studies have reported that BMRG signatures could predict the prognosis of tumor survivors and provide a potential target for immunotherapy ( Cai et al, 2022 ; Shen et al, 2023 ).…”

Investigation of the function of the novel tumor marker BEND5 in lung adenocarcinoma based on data mining and in vitro analysis

“…Public databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) are ideal for accessing transcriptomic information, which promotes effective ways of identifying gene signatures (18)(19)(20). Many studies have attempted to build risk models for biological features or prognostic assessment of malignant tumors that might have a clinical influence (21)(22)(23). In the current study, we used the TCGA-LUAD database to assess the expression and prognostic value of BEND5 in LUAD and performed gene set enrichment analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Ontology (GO) enrichment analysis, and immune infiltration analysis.…”