Ye Maodong, Ren Shuai, Luo Huanjuan, Wu Xiumin, Lian Hongwei, Cai Xiangna, Ji Yingchang
Medical Cosmetic Center, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, P.R. China.
Shantou University Medical College, Shantou, Guangdong, 515041, P.R. China.
BMC Cancer. 2025 Apr 9;25(1):648. doi: 10.1186/s12885-025-13611-4.
The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise in advancing cancer diagnosis and treatment strategies.
GNNs models were developed to predict the response to immunotherapy and to pinpoint key pathways. Utilizing the genes from these key pathways, multi-omics bioinformatics methods were employed to refine the construction of a gene signature, termed responseScore, aimed at enhancing the precision of immunotherapy response predictions. Subsequently, responseScore was explored from the perspectives of prognosis, genetic variation, pathway enrichment, and the tumor microenvironment. Concurrently, the association among 13 genes contributing to responseScore and factors such as immunotherapy response, prognosis, and the tumor microenvironment was investigated. Among these genes, PSMB6 was subjected to an in-depth analysis of its biological effect through experimental approaches like transfection and co-culture.
In the finalized model utilizing GNNs, it has revealed an AUC of 0.854 within the training dataset and 0.824 within the testing set, pinpointing key pathways such as R-HSA-70,268. The indicator named as responseScore excelled in its predictive accuracy regarding immunotherapy response and patient prognosis. Investigations into genetic variation, pathway enrichment, tumor microenvironment disclosed a profound association between responseScore and the enhancement of immune cell infiltration and anti-tumor immunity. A negative correlation was observed between the expression of PSMB6 and immune genes, with elevated PSMB6 expression correlating with poor prognosis. ELISA detection after co-cultivation experiments revealed significant reductions in the levels of cytokines IL-6 and IL-1β in specimens from the PCDH-PSMB6 group.
The GNNs prediction model and the responseScore developed in this research effectively indicate the immunotherapy response and prognosis for patients with skin melanoma. Additionally, responseScore provides insights into the tumor microenvironment and the characteristics of tumor immunity of melanoma. Thirteen genes identified in this study show promise as potential tumor markers or therapeutic targets. Notably, PSMB6 emerges as a potential therapeutic target for skin melanoma, where its elevated expression exhibits an inhibitory effect on the tumor immunity.
免疫疗法评估在皮肤黑色素瘤的临床管理中起着关键作用。图神经网络（GNN）与其他深度学习算法和生物信息学方法一起，在推进癌症诊断和治疗策略方面已展现出巨大潜力。
开发GNN模型以预测免疫疗法反应并确定关键途径。利用这些关键途径中的基因，采用多组学生物信息学方法优化构建一个名为responseScore的基因特征，旨在提高免疫疗法反应预测的准确性。随后，从预后、基因变异、途径富集和肿瘤微环境等角度对responseScore进行探索。同时，研究了对responseScore有贡献的13个基因与免疫疗法反应、预后和肿瘤微环境等因素之间的关联。在这些基因中，通过转染和共培养等实验方法对PSMB6的生物学效应进行了深入分析。
在最终使用GNN的模型中，训练数据集内的AUC为0.854，测试集内为0.824，确定了关键途径如R-HSA-70,268。名为responseScore的指标在免疫疗法反应和患者预后的预测准确性方面表现出色。对基因变异、途径富集、肿瘤微环境的研究揭示了responseScore与免疫细胞浸润增强和抗肿瘤免疫之间存在密切关联。观察到PSMB6的表达与免疫基因呈负相关，PSMB6表达升高与预后不良相关。共培养实验后的ELISA检测显示，PCDH-PSMB6组标本中细胞因子IL-6和IL-1β水平显著降低。
本研究中开发的GNN预测模型和responseScore有效地表明了皮肤黑色素瘤患者的免疫疗法反应和预后。此外，responseScore为黑色素瘤的肿瘤微环境和肿瘤免疫特征提供了见解。本研究中鉴定的13个基因有望成为潜在的肿瘤标志物或治疗靶点。值得注意的是，PSMB6成为皮肤黑色素瘤的潜在治疗靶点，其表达升高对肿瘤免疫具有抑制作用。
