Ren Shuai, Lu Yongjian, Zhang Guangping, Xie Ke, Chen Danni, Cai Xiangna, Ye Maodong
Medical Cosmetic Center, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, People's Republic of China.
Shantou University Medical College, Shantou, Guangdong, 515041, People's Republic of China.
J Transl Med. 2024 Dec 23;22(1):1141. doi: 10.1186/s12967-024-05976-0.
The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagnosis and treatment.
A GNNs model was constructed to predict the immunotherapy response and identify key pathways. Based on the genes of key pathways, bioinformatic methods were used to generate a simple linear scoring model, namely responseScore. The intrinsic mechanism of responseScore was explored from the perspectives of multi-omics analysis. The relationship between each gene involved in responseScore and prognosis was also explored. Transfection experiments with human bladder cancer cells were used to investigate the biological effects of PSMB9 gene.
The final GNNs model had an AUC of 0.785 on the training set and an AUC of 0.839 on the validation set. R-HSA-69620 and others were identified as key pathways. ResponseScore had a good performance in predicted the immunotherapy response and prognosis. Analysis results from genetic variation, pathways and tumor microenvironment, showed that responseScore was significantly associated with immune cell infiltration and anti-tumor immunity. The results of single-cell analysis showed that responseScore was closely related to the functional state of natural killer cells. Compared with the PCDH-NC group, cell migration and proliferation were significantly inhibited while cell apoptosis increased in the PCDH-PSMB9 group.
The GNNs predictive model and responseScore constructed in this study can reflect the immunotherapy response and prognosis of bladder cancer patients. ResponseScore can also reflect features such as tumor microenvironment, antitumor immunity, and natural killer cell function status in bladder cancer. PSMB9 was identified as a significant gene for prognosis. High expression of PSMB9 can inhibit bladder cancer cell migration and proliferation while increasing cell apoptosis.
评估免疫疗法的疗效对膀胱癌的临床治疗具有重要价值。图神经网络（GNNs）、通路分析和多组学分析在癌症诊断和治疗领域已显示出巨大潜力。
构建GNNs模型以预测免疫治疗反应并识别关键通路。基于关键通路的基因，采用生物信息学方法生成一个简单的线性评分模型，即responseScore。从多组学分析的角度探讨responseScore的内在机制。还探讨了responseScore中涉及的每个基因与预后的关系。用人膀胱癌细胞进行转染实验，以研究PSMB9基因的生物学效应。
最终的GNNs模型在训练集上的AUC为0.785，在验证集上的AUC为0.839。R-HSA-69620等被确定为关键通路。ResponseScore在预测免疫治疗反应和预后方面表现良好。基因变异、通路和肿瘤微环境的分析结果表明，responseScore与免疫细胞浸润和抗肿瘤免疫显著相关。单细胞分析结果表明，responseScore与自然杀伤细胞的功能状态密切相关。与PCDH-NC组相比，PCDH-PSMB9组细胞迁移和增殖明显受到抑制，而细胞凋亡增加。
本研究构建的GNNs预测模型和responseScore能够反映膀胱癌患者的免疫治疗反应和预后。ResponseScore还能反映膀胱癌的肿瘤微环境、抗肿瘤免疫和自然杀伤细胞功能状态等特征。PSMB9被确定为一个重要的预后基因。PSMB9的高表达可抑制膀胱癌细胞的迁移和增殖，同时增加细胞凋亡。
