Multi-omics landscape and clinical significance of Siglec family genes: Implications for risk stratification and front line therapies in cutaneous melanoma

Background: Melanoma is widely recognized as the most aggressive and fatal type of skin cancer; however, effective prognostic markers are lacking. The sialic acid-binding immunoglobulin-type lectin (Siglec) gene family plays an important role in the development of tumors and immune escape, but its prognostic role in melanoma remains unknown. Method We downloaded simple nucleotide variations, transcriptome profiling, copy number variation data of cutaneous melanoma in TCGA, and transcriptome data of GSE91061 in GEO. 14 Siglecs genes, which were extracted for comprehensive analysis. We typed samples using consistent clustering and constructed risk score using LASSO to predict patient outcomes. Results Siglec genes have a high mutation frequency, with up to 8% in SIGLEC7. High expression levels of Siglecs in tumor bulk suggests a better prognosis. Siglecs also show a high degree of synergistic expression. We used unsupervised clustering to create a high expression of Siglec (HES) cluster and a low expression of Siglec (LES) cluster. The HES cluster correlated with high overall survival and increased expression levels of Siglec genes. The HES cluster also showed significant immune cell infiltration and activation of immune signaling pathways. We used least absolute shrinkage and selection operator (LASSO) regression analysis to reduce the dimensionality of Siglec cluster-related genes and constructed a prognostic model composed of SRGN and GBP4, which can risk-stratify patients in both the training and test datasets. Conclusion We conducted a multi-omics analysis of the Siglec family genes in melanoma and found that Siglecs play an important role in the occurrence and development of melanoma. Typing constructed using Siglecs can show risk stratification and derived prognostic models can predict a patient's risk score. In summary, Siglec family genes are potential targets for melanoma treatment as well as prognostic markers that can direct individualized treatments and improve overall survival.