Integrated Analysis Revealing the Senescence-Mediated Immune Heterogeneity of HCC and Construction of a Prognostic Model Based on Senescence-Related Non-Coding RNA Network - Jiang, Yanan; Luo, Kunpeng; Xu, Jincheng; Shen, Xiuyun; Gao, Yang; Fu, Wenqi; Zhang, Xuesong; Wang, Hongguang; Liu, Bing (Frontiers in Oncology, 2022)
Background: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. Noncoding RNAs play important role in HCC. This study aims to identify a senescence-related noncoding RNA network-based prognostic model for individualized therapies for HCC. Methods: HCC subtypes with senescence status were identified based on the senescence-related genes. Immune status of the subtypes was analyzed by CIBERSORT and ESTIMATE algorithm. The differentially expressed mRNAs, microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) were identified between the two HCC subtypes. A senescence-based competing endogenous RNA (ceRNA) co-expression network in HCC was constructed. Based on the ceRNA network, LASSO Cox regression was used to construct the senescence-related prognostic model (S score). The prognosis potential of the S score was evaluated in the training dataset and four external validation datasets. Finally, the potential of the prognostic model in predicting immune features and response to immunotherapy of HCC was evaluated. Results: The HCC samples were classified into senescence active and inactivate subtypes. The senescence active group showed an immune suppressive microenvironment compared to the senescence inactive group. A total of 2902 mRNAs, 19 miRNAs, and 308 lncRNAs were identified between the two subtypes. A ceRNA network was constructed using these differentially expressed genes. Based on the ceRNA network, S score was constructed to predict prognosis of HCC patients. The S score was correlated with immune features and can predict response to immunotherapy of cancer. Conclusion: The present study analyzed the biological heterogeneity across senescence-related subtypes and constructed a senescence-related ceRNA-network-based prognostic model for predicting prognosis and immunotherapy responsiveness of HCC.