Immune Score Indicator for the Survival of Melanoma Patients Based on Tumor Microenvironment

Abstract

Xuchao Ning,1 Renzhi Li,2 Bin Zhang,3 Yue Wang,4 Ziyi Zhou,1 Zanzan Ji,5 Xiajie Lyu,6 Zhenyu Chen1 1Department of Cosmetic and Plastic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266072, People’s Republic of China; 2Dalian Medical University, Dalian, Liaoning, 116000, People’s Republic of China; 3Office of Family Planning, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266072, People’s Republic of China; 4Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266072, People’s Republic of China; 5The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266072, People’s Republic of China; 6Weifang Medical University, Weifang, Shandong, 261000, People’s Republic of ChinaCorrespondence: Zhenyu ChenDepartment of Cosmetic and Plastic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266072, People’s Republic of ChinaEmail [email protected]: Tumor microenvironment (TME) refers to the cellular environment where tumors exist, including immune cells, fibroblasts, stromal cells, chemokines, etc. TME is closely related to the prognosis of various tumors; nevertheless, limited studies have established predictive prognosis models based on TME. This work aims to construct a survival prediction model for melanoma patients based on TME.Methods: Data of 482 melanoma patients were extracted from The Cancer Genome Atlas (TCGA) database. Based on the infiltration of immune cells (Immune score), stromal cells (Stromal score), and tumor purity (Estimate score), the “Estimate” algorithm was used to construct 3 scores for each patient. To identify the differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using DAVID database and visualized using the R software. The STRING database was used to construct the protein-protein interaction (PPI) network and functional modules. FGD2 expression was confirmed via Western Blotting and quantitative reverse transcription PCR (RT-qPCR) analyses.Results: Patients with higher immune scores estimate scores showed better OS than those with lower scores. All three scores were related to age and primary tumor stage. Further, DEGs between patients with high immune/stromal scores and low immune/stromal scores were screened. Eventually, 10 down-regulated DEGs and 201 up-regulated DEGs were identified as TME associated genes. Out of these, the FGD2 gene demonstrated close association with survival and was confirmed in the included melanoma patients.Conclusion: In summary, TME is closely associated with the prognosis of melanoma patients. Besides, genes including FGD2 promote the TME-mediated regulation of melanoma.Keywords: tumor microenvironment, the cancer genome atlas, melanoma, FGD2

Keywords

• tumor microenvironment
• the cancer genome atlas
• melanoma
• fgd2