Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients - Huang, Shenglan; Zhang, Jian; Lai, Xiaolan; Zhuang, Lingling; Wu, Jianbing (Frontiers in Molecular Biosciences, 2021)
Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research designed to analyze the correlation between TME and HCC patient prognosis and construct a TME-related long noncoding RNAs (lncRNAs) signature to determine the prognosis and response to immunotherapy of HCC patients. Methods: We assessed the Stromal/Immune/Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on The Cancer Genome Atlas database, and its associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to Immune/Stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high and low risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC. Results: Stromal/Immune /Estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. The Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognosis model. Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with the high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways was activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses towards cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict treatment response of HCC patients. Conclusions: We analyzed the influence of Stromal/Immune /Estimate scores on the prognosis of HCC patients. A novel TME-related lncRNAs risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients