Research Paper Volume 12, Issue 8 pp 6966—6980

A novel immune-related genes prognosis biomarker for melanoma: associated with tumor microenvironment

Rongzhi Huang1, *, , Min Mao1, *, , Yunxin Lu1, *, , Qingliang Yu1, *, , Liang Liao1,2, ,

  • 1 The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, The Guangxi Zhuang Autonomous Region, China
  • 2 Department of Traumatic Orthopedics and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, The Guangxi Zhuang Autonomous Region, China
* Equal contribution

Received: August 7, 2019       Accepted: March 29, 2020       Published: April 20, 2020
How to Cite

Copyright © 2020 Huang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: Melanoma is a cancer of the skin with potential to spread to other organs and is responsible for most deaths due to skin cancer. It is imperative to identify immune biomarkers for early melanoma diagnosis and treatment.

Results: 63 immune-related genes of the total 1039 unique IRGs retrieved were associated with overall survival of melanoma. A multi-IRGs classifier constructed using eight IRGs showed a powerful predictive ability. The classifier had better predictive power compared with the current clinical data. GSEA analysis showed multiple signaling differences between high and low risk score group. Furthermore, biomarker was associated with multiple immune cells and immune infiltration in tumor microenvironment.

Conclusions: The immune-related genes prognosis biomarker is an effective potential prognostic classifier in the immunotherapies and surveillance of melanoma.

Methods: Melanoma samples of genes were retrieved from TCGA and GEO databases while the immune-related genes (IRGs) were retrieved from the ImmPort database. WGCNA, Cox regression analysis and LASSO analysis were used to classify melanoma prognosis. ESTIMATE and CIBERSORT algorithms were used to explore the relationship between risk score and tumor immune microenvironment. GSEA analysis was performed to explore the biological signaling pathway.


IRGs: Immune-related genes; TME: Tumor microenvironment; GEO: Gene Expression Omnibus; TCGA: The cancer genome atlas project; LASSO: Least Absolute Shrinkage and Selection Operator; MEs: Module eigengenes; GS: Gene significance; ROC: Receiver operating characteristic curve; AUC: Area under the curve; RS: Risk score; OS: Overall survival.