Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer

• Published in: Journal of translational medicine 2024-12, Vol.22 (1), p.1141-18, Article 1141
• Main Authors: Ren, Shuai, Lu, Yongjian, Zhang, Guangping, Xie, Ke, Chen, Danni, Cai, Xiangna, Ye, Maodong
• Format: Article
• Language: English
• Subjects: Analysis; Apoptosis - genetics; Bioinformatics; Bladder cancer; Care and treatment; Cell Line, Tumor; Cell Movement - genetics; Cell Proliferation - genetics; Complications and side effects; Computational Biology; Gene Expression Regulation, Neoplastic; Graph Neural Networks; Humans; Immunotherapy; Immunotherapy response; Multiomics; Neural networks; Neural Networks, Computer; Patient outcomes; Prognosis; Proteasome Endopeptidase Complex - genetics; Proteasome Endopeptidase Complex - metabolism; Reproducibility of Results; Single-Cell Analysis; Treatment Outcome; Tumor microenvironment; Tumor Microenvironment - genetics; Tumor Microenvironment - immunology; Urinary Bladder Neoplasms - genetics; Urinary Bladder Neoplasms - immunology; Urinary Bladder Neoplasms - pathology; Urinary Bladder Neoplasms - therapy
• ISSN: 1479-5876; 1479-5876
• 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.