Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

作者全名:"Chi, Hao; Peng, Gaoge; Yang, Jinyan; Zhang, Jinhao; Song, Guobin; Xie, Xixi; Strohmer, Dorothee Franziska; Lai, Guichuan; Zhao, Songyun; Wang, Rui; Yang, Fang; Tian, Gang"

作者地址:"[Chi, Hao; Peng, Gaoge; Wang, Rui] Southwest Med Univ, Clin Med Coll, Luzhou, Peoples R China; [Yang, Jinyan; Zhang, Jinhao; Song, Guobin; Xie, Xixi] Southwest Med Univ, Sch Stomatol, Luzhou, Peoples R China; [Strohmer, Dorothee Franziska] Ludwig Maximilians Univ Munchen, Dept Gen Visceral & Transplant Surg, Munich, Germany; [Lai, Guichuan] Chongqing Med Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, Chongqing, Peoples R China; [Zhao, Songyun] Nanjing Med Univ, Wuxi Peoples Hosp, Dept Neurosurg, Wuxi, Peoples R China; [Yang, Fang] Charite Univ Med Berlin, Dept Ophthalmol, Campus Virchow Klinikum, Berlin, Germany; [Tian, Gang] Southwest Med Univ, Affiliated Hosp, Dept Lab Med, Luzhou, Peoples R China"

通信作者:"Yang, F (通讯作者),Charite Univ Med Berlin, Dept Ophthalmol, Campus Virchow Klinikum, Berlin, Germany.; Tian, G (通讯作者),Southwest Med Univ, Affiliated Hosp, Dept Lab Med, Luzhou, Peoples R China."












关键词:sphingolipid metabolism; UVM; tumor microenvironment; immunotherapy; predictive signature

摘要:"BackgroundUveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM. MethodsIn this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals. Results4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy. Conclusions4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies."