Identification and Validation of a Novel Tumor Microenvironment-Related Prognostic Signature of Patients With Hepatocellular Carcinoma - Li, Rui; Zhao, Weiheng; Liang, Rui; Jin, Chen; Xiong, Huihua (Frontiers in Molecular Biosciences, 2022)
Background: In recent years, immunotherapy has changed the therapeutic landscape of hepatocellular carcinoma (HCC). Since the efficacy of immunotherapy is closely related to the tumor microenvironment (TME), in this study, we constructed a prognostic model based on TME to predict the prognosis and immunotherapy effect of HCC patients. Methods: Transcriptome and follow-up data of 374 HCC patients were acquired from the TCGA Cancer Genome Atlas (TCGA) database. The immune/stromal/estimate scores (TME scores) and tumor purity were calculated using the ESTIMATE algorithm and the module most associated with TME scores were screened by weighted gene co-expression network analysis (WGCNA). A TME score related prognostic model was constructed and patients were divided into high-risk group and low-risk group. Kaplan-Meier survival curves and receiver operator characteristic curve (ROC) were used to evaluate the performance of the TME risk prognostic model and validated with external database International Cancer Genome Consortium (ICGC) cohort. Combined with clinicopathologic factors, a prognostic nomogram was established. The nomogram's ability to predict prognosis was assessed by ROC, calibration curve and the decision curve analysis (DCA). Gene Set Enrichment Analyses (GSEA) was conducted to explore the underlying biological functions and pathways of this risk signature. Besides, the possible correlation of risk signature with TME immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, single-nucleotide polymorphisms (SNPs) and drug sensitivity were assessed. Finally, Real-time PCR was used to verify the gene expression levels in normal liver cell and cancer cells. Results: KM survival analysis results indicated that high immune/stromal/estimate score groups were closely associated with a better prognosis, while the tumor purity showed a reverse trend (p < 0.01). WGCNA demonstrated that the yellow module was significantly correlated with the TME score. 5-genes TME risk signature was built to predict the prognosis of patients with HCC included DAB2, IL18RAP, RAMP3, FCER1G, LHFPL2. Patients with a low risk score have higher levels of tumor-infiltrating and higher expression of immune checkpoints, which may be more sensitive to immunotherapy. Conclusion: It provided a theoretical basis for predicting the prognosis of liver cancer patients and personalized treatment of HCC patients.