Guangdong Province Key Laboratory for Biotechnology Drug Candidates, School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510006, China.
Department of Radiation Oncology, Dongguan People's Hospital, Affiliated Dongguan Hospital of Southern Medical University, Dongguan, Guangdong 523059, China.
Math Biosci Eng. 2022 Jan;19(2):1448-1470. doi: 10.3934/mbe.2022067. Epub 2021 Dec 8.
Most of the malignant melanomas are already in the middle and advanced stages when they are diagnosed, which is often accompanied by the metastasis and spread of other organs. Besides, the prognosis of patients is bleak. The characteristics of the local immune microenvironment in metastatic melanoma have important implications for both tumor progression and tumor treatment. In this study, data on patients with metastatic melanoma from the TCGA and GEO datasets were selected for immune, stromal, and estimate scores, and overlapping differentially expressed genes were screened. A nine-IRGs prognostic model (ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) was established by univariate COX regression, LASSO and multivariate COX regression. Receiver operating characteristic curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT and Xcell in high-risk and low-risk groups. The immune infiltration of the high-risk group was significantly lower than that of the low-risk group. Immune checkpoint analysis revealed that the expression of PDCD1, CTLA4, TIGIT, CD274, HAVR2 and LAG3 demonstrated the visible difference in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes from the nine-IRG prognostic model, and the yellow-green module had the highest correlation with risk scores. The results of GO and KEGG suggested that the genes in the yellow-green module were mainly enriched in immune-related biological processes. Finally, the expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 were analyzed between metastatic melanoma and normal samples. Overall, a prognostic model for metastatic melanoma based on the tumor immune microenvironment characteristics was established, which left plenty of space for further studies. It could function well in helping people to understand characteristics of the immune microenvironment in metastatic melanoma.
大多数恶性黑色素瘤在诊断时已处于中晚期，常伴有其他器官的转移和扩散。此外，患者的预后较差。转移性黑色素瘤局部免疫微环境的特征对肿瘤的进展和肿瘤的治疗都有重要的意义。本研究从 TCGA 和 GEO 数据集选择转移性黑色素瘤患者的数据，进行免疫、基质和估计评分，并筛选重叠的差异表达基因。通过单因素 COX 回归、LASSO 和多因素 COX 回归建立了一个由 9 个免疫相关基因（ALOX5AP、ARHGAP15、CCL8、FCER1G、GBP4、HCK、MMP9、RARRES2 和 TRIM22）组成的预后模型。采用受试者工作特征曲线（ROC）来检验模型的预测准确性。通过 CIBERSORT 和 Xcell 在高低风险组中分析免疫浸润情况。高风险组的免疫浸润明显低于低风险组。免疫检查点分析显示，不同风险评分组之间 PDCD1、CTLA4、TIGIT、CD274、HAVR2 和 LAG3 的表达存在明显差异。WGCNA 分析发现，黄色-绿色模块包含 9 个免疫相关基因预后模型中的 7 个基因，黄色-绿色模块与风险评分的相关性最高。GO 和 KEGG 的结果表明，黄色-绿色模块中的基因主要富集在免疫相关的生物过程中。最后，分析了转移性黑色素瘤和正常样本中 ALOX5AP、ARHGAP15、CCL8、FCER1G、GBP4、HCK、MMP9、RARRES2 和 TRIM22 的表达特征。总的来说，建立了一个基于肿瘤免疫微环境特征的转移性黑色素瘤预后模型，为进一步研究提供了充分的空间。它可以很好地帮助人们了解转移性黑色素瘤免疫微环境的特征。
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