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. Non-coding RNAs play an important role in HCC. This study aims to identify a senescence-related non-coding RNA network-based prognostic model for individualized therapies for HCC. Methods HCC subtypes with senescence status were identified on the basis of the senescence-related genes. Immune status of the subtypes was analyzed by CIBERSORT and ESTIMATE algorithm. The differentially expressed mRNAs, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) were identified between the two HCC subtypes. A senescence-based competing endogenous RNA (ceRNA) co-expression network in HCC was constructed. On the basis of 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 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 2,902 mRNAs, 19 miRNAs, and 308 lncRNAs were identified between the two subtypes. A ceRNA network was constructed using these differentially expressed genes. On the basis of the ceRNA network, S score was constructed to predict the prognosis of patients with HCC. 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.