Identification of Immune Function-Related Subtypes in Cutaneous Melanoma.

Lin Liu,Bo Jiang,Li Xu,Fangrong Yan,Mengjia Huang,Junkai Zhu,Yi Chen,Tong Jin,Wenxuan Chen

doi:10.3390/life11090925

Abstract

Tumour immunotherapy combined with molecular typing is a new therapy to help select patients. However, molecular typing algorithms related to tumour immune function have not been thoroughly explored. We herein proposed a single sample immune signature network (SING) method to identify new immune function-related subtypes of cutaneous melanoma of the skin. A sample-specific network and tumour microenvironment were constructed based on the immune annotation of cutaneous melanoma samples. Then, the differences and heterogeneity of immune function among different subtypes were analysed and verified. A total of 327 cases of cutaneous melanoma were divided into normal and immune classes; the immune class had more immune enrichment characteristics. After further subdividing the 327 cases into three immune-related subtypes, the degree of immune enrichment in the “high immune subtype” was greater than that in other subtypes. Similar results were validated in both tumour samples and cell lines. Sample-specific networks and the tumour microenvironment based on immune annotation contribute to the mining of cutaneous melanoma immune function-related subtypes. Mutations in B2M and PTEN are considered potential therapeutic targets that can improve the immune response. Patients with a high immune subtype can generally obtain a better immune prognosis effect, and the prognosis may be improved when combined with TGF-β inhibitors.

Highlights

Cancer is not a single disease but a collection of multiple biological entities, and each has its own molecular characteristics and clinical significance [1]
146 samples fell into the first category, called the C1 class and 181 samples fell into the second category, called the C2 class
To verify the validity of the clustering results, the two clustered categories were compared with the The Cancer Genome Atlas (TCGA) classification results

Summary

Introduction

Cancer is not a single disease but a collection of multiple biological entities, and each has its own molecular characteristics and clinical significance [1]. Cancer classification was based on histopathology and clinical characteristics, most of which depend on clinicians’ judgement. Studies show that prognosis and treatment responses vary between cancer subtypes and within subtypes. Some progress has been made in the field, there are still unexplained intertumoural heterogeneities leading to varying survival outcomes and differences in treatment responses [4]. Genes have been successfully used as biomarkers for cancer diagnosis [8], it is not clear whether gene biomarkers are the most suitable method for treatment indicator classification. It may be more meaningful to describe diseases by using system-specific dysfunction rather than the dysfunction [9,10] of individual molecules, which is based on the gene regulatory network

Methods

Results

Conclusion