Open AccessDevelopment and validation of an immune gene set-based prognostic signature in cutaneous melanoma
Author(s) -
Qiang Tian,
Huan Gao,
Wen Zhao,
Yan Zhou,
Jin Yang
Publication year - 2021
Publication title -
future oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.857
H-Index - 72
eISSN - 1744-8301
pISSN - 1479-6694
DOI - 10.2217/fon-2021-0104
Subject(s) - medicine , gene signature , signature (topology) , melanoma , immune system , gene , computational biology , oncology , set (abstract data type) , cancer research , immunology , gene expression , genetics , biology , computer science , geometry , mathematics , programming language
We aimed to fully understand the landscape of the skin cutaneous melanoma (SKCM) microenvironment and develop an immune prognostic signature that can predict the prognosis for SKCM patients. RNA sequencing data and clinical information were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases. The immune-prognostic signature was constructed by LASSO Cox regression analysis. We calculated the relative abundance of 29 immune-related gene sets based on the mRNA expression profiles of 314 SKCM patients in the Cancer Genome Atlas training set. Hierarchical clustering was performed to classify SKCM patients into three clusters: immunity-high, -medium and -low. The values of our prognostic model in predicting disease progression, metastasis and immunotherapeutic responses were also validated. In conclusion, the prognostic model demonstrated a powerful ability to distinguish and predict SKCM patients’ prognosis.