Yu JunJie, Mao WeiPu, Sun Si, Hu Qiang, Wang Can, Xu ZhiPeng, Liu RuiJi, Chen SaiSai, Xu Bin, Chen Ming
Medical College, Southeast University, Nanjing, 210009, People's Republic of China.
Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, 210009, People's Republic of China.
Cancer Manag Res. 2022 Jan 5;14:67-88. doi: 10.2147/CMAR.S346240. eCollection 2022.
The study aimed to identify an autophagy-related molecular subtype and characterize a novel defined autophagy-immune related genes score (AI-score) signature and prognosis model in bladder cancer (BLCA) patients using public databases.
The transcriptome cohorts downloaded from TCGA and GEO database were carried out with genomic analysis and unsupervised methods to obtain autophagy-related molecular subtypes. The single-sample gene-set enrichment analysis (ssGSEA) was utilized to perform immune subtype clustering. We defined a novel autophagy subtype and evaluated the role in TME cell infiltration. Then, the principal-component analysis (PCA) was applied to construct an AI-score signature. Subsequently, two immunotherapeutic cohorts were used to evaluate the predictive value in immunotherapeutic benefits and immune response. Finally, univariate, Lasso and multivariate Cox regression algorithm were used to construct and evaluate an autophagy-immune-related genes prognosis model. Also, qRT-PCR and IHC was applied to validate the expression of the 6 genes in the model.
Three distinct autophagy clusters and immune-related clusters were identified, and a novel autophagy-related molecular subtypes were defined. Furthermore, the roles in TME cell infiltration and clinical traits for the autophagy subtypes were characterized. Meanwhile, we constructed an AI-score signature and demonstrated it could predict genetic mutation, clinicopathological traits, prognosis, and TME stromal activity. We found that it could accurately predict the clinicopathological characteristics and immune response of individual BLCA patients and provide guidance for selecting immunotherapy. Ultimately, we constructed and verified an autophagy-immune-related prognostic model of BLCA patients and established a prognostic nomogram with a good prediction accuracy.
We constructed AI-score signatures and prognosis risk model to characterize their role in clinical features and TME immune cell infiltration. It revealed that the AI-score signature and prognosis model could be a valid predictive tool, which could accurately predict the prognosis of BLCA patients and contribute to choosing effective personalized immunotherapy strategies.
本研究旨在利用公共数据库识别膀胱癌（BLCA）患者的自噬相关分子亚型，表征一种新定义的自噬免疫相关基因评分（AI评分）特征及预后模型。
对从TCGA和GEO数据库下载的转录组队列进行基因组分析和无监督方法，以获得自噬相关分子亚型。利用单样本基因集富集分析（ssGSEA）进行免疫亚型聚类。我们定义了一种新的自噬亚型，并评估其在肿瘤微环境（TME）细胞浸润中的作用。然后，应用主成分分析（PCA）构建AI评分特征。随后，使用两个免疫治疗队列评估其在免疫治疗益处和免疫反应中的预测价值。最后，采用单变量、Lasso和多变量Cox回归算法构建并评估自噬免疫相关基因预后模型。此外，应用qRT-PCR和免疫组化（IHC）验证模型中6个基因的表达。
识别出三种不同的自噬簇和免疫相关簇，并定义了一种新的自噬相关分子亚型。此外，还表征了自噬亚型在TME细胞浸润和临床特征中的作用。同时，我们构建了AI评分特征，并证明其可预测基因突变、临床病理特征、预后和TME基质活性。我们发现它可以准确预测个体BLCA患者的临床病理特征和免疫反应，并为选择免疫治疗提供指导。最终，我们构建并验证了BLCA患者的自噬免疫相关预后模型，并建立了具有良好预测准确性的预后列线图。
我们构建了AI评分特征和预后风险模型，以表征它们在临床特征和TME免疫细胞浸润中的作用。结果表明，AI评分特征和预后模型可能是一种有效的预测工具，能够准确预测BLCA患者的预后，并有助于选择有效的个性化免疫治疗策略。
