Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma

Rujia Qin; Wen Peng; Xuemin Wang; Chunyan Li; Yan Xi; Zhaoming Zhong; Chuanzheng Sun

doi:10.3389/fonc.2021.615963

Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma

Front Oncol. 2021 May 28:11:615963. doi: 10.3389/fonc.2021.615963. eCollection 2021.

Authors

Rujia Qin¹, Wen Peng¹, Xuemin Wang¹, Chunyan Li¹, Yan Xi¹, Zhaoming Zhong^{1

2}, Chuanzheng Sun¹

Affiliations

¹ Department of Head and Neck Surgery Section II, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China.
² Department of Medical Oncology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.

Abstract

Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein-protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify "real" hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.

Keywords: CIBERSORT; ESTIMATE; cutaneous melanoma; prognosis; protein–protein interaction; tumor microenvironment; tumor- infiltrating immune cells; weighted gene co-expression network analysis.