Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma

Abstract

Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs associated with them were identified. We performed immune microenvironment and functional enrichment analyses. The analysis was followed by applying univariate/multivariate Cox regression algorithms to finally identify three lncRNAs associated with the immune pathway for the construction of prognostic prediction models (CXCL10, RXRG, and SCG2). This stepwise downscaling method, which finally screens out prognostic factors and key genes and then uses them to build a risk model, has excellent predictive power. According to analyses of the model’s reliability, it was able to differentiate the prognostic value and continued existence of Skin cutaneous melanoma patient populations more effectively. This study is an analysis of the immune pathway that leads lncRNAs in Skin cutaneous melanoma in an effort to open up new treatment avenues for Skin cutaneous melanoma.

A seven-immune-genes risk model predicts the survival and suitable treatments for patients with skin cutaneous melanoma

“…Before our research, various prognostic immune-related gene signatures for skin melanoma had been identified. These signatures have different numbers of marker genes, ranging from 2 to 33 [ 35 , 36 , [49] , [50] , [51] , [52] ]. While a larger number of marker genes is usually considered to enhance the specificity and predictive power of prognostic models, an excessive number of marker genes can also complicate the data, introduce noise, and increase the cost of gene testing, thus impeding clinical translation.…”