Author:
Shen Kangjie,Wang Qiangcheng,Wang Lu,Yang Yang,Ren Min,Li Yanlin,Gao Zixu,Zheng Shaoluan,Ding Yiteng,Ji Jiani,Wei Chenlu,Zhang Tianyi,Zhu Yu,Feng Jia,Qin Feng,Yang Yanwen,Wei Chuanyuan,Gu Jianying
Abstract
Abstract
Background
Melanoma is the deadliest form of skin tumor, and G protein-coupled receptors (GPCRs) play crucial roles in its carcinogenesis. Furthermore, the tumor microenvironment (TME) affects the overall survival (OS) and the response to immunotherapy. The combination of GPCRs and TME from a multi-omics perspective may help to predict the survival of the melanoma patients and their response to immunotherapy.
Methods
Bulk-seq, single-cell RNA sequencing (scRNA-seq), gene mutations, immunotherapy responses, and clinicopathologic feature data were downloaded from public databases, and prognostic GPCRs and immune cells were screened using multiple machine learning algorithms. The expression levels of GPCRs were detected using real-time quantitative polymerase chain reaction (qPCR) in A375 and HaCaT cell lines. The GPCR–TME classifier was constructed and verified using different cohorts and multi-omics. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and tracking tumor immunophenotype (TIP) were used to identify the key biological pathways among the GPCR–TME subgroups. Then, tumor mutational burden (TMB), vital mutant genes, antigen presentation genes, and immune checkpoints were compared among the subgroups. Finally, the differences in immunotherapy response rates among the GPCR–TME subgroups were investigated.
Results
A total of 12 GPCRs and five immune cell types were screened to establish the GPCR–TME classifier. No significant differences in the expression levels of the 12 GPCRs were found in the two cell lines. Patients with high GPCR score or low TME score had a poor OS; thus, the GPCRlow/TMEhigh subgroup had the most favorable OS. The scRNA-seq result revealed that immune cells had a higher GPCR score than tumor and stromal cells. The GPCR–TME classifier acted as an independent prognostic factor for melanoma. GSEA, WGCNA, and TIP demonstrated that the GPCRlow/TMEhigh subgroup was related to the activation and recruitment of anti-tumor immune cells and the positive regulation of the immune response. From a genomic perspective, the GPCRlow/TMEhigh subgroup had higher TMB, and different mutant genes. Ultimately, higher expression levels of antigen presentation genes and immune checkpoints were observed in the GPCRlow/TMEhigh subgroup, and the melanoma immunotherapy cohorts confirmed that the response rate was highest in the GPCRlow/TMEhigh cohort.
Conclusions
We have developed a GPCR–TME classifier that could predict the OS and immunotherapy response of patients with melanoma highly effectively based on multi-omics analysis.
Funder
Xiamen Municipal Bureau of Science and Technology
China Postdoctoral Science Foundation
Shanghai Sailing Program
National Natural Science Foundation of China
Shanghai ShenKang Hospital Development Centre Project
Publisher
Springer Science and Business Media LLC
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