An inflammation-associated ferroptosis signature can optimize the diagnosis, prognosis evaluation and immunotherapy options in hepatocellular carcinoma

Background: Inflammation and ferroptosis crosstalk complexly with immune microenvironment of hepatocellular carcinoma (HCC), thus affecting the efficacy of immunotherapy. Herein, our aim was to identify the inflammation associated ferroptosis (IAF)- biomarkers for contributing the immunotherapy of HCC.

Methods: The train cohort from The Cancer Genome Atlas (TCGA) was clustered into three subtypes (C1, C2, and C3) based on the genes related to inflammation and ferroptosis. Intersecting differentially expressed genes (DEGs) among the subtypes were set as IAF-genes and enrolled into construction of a risk model following by LASSO, univariate Cox and multivariate COX regression analysis. The effectiveness of risk model on prognosis evaluation, immunological signature recognition and immunotherapy options was analyzed by ESTIMATE, CIBERSORT, and TIDE algorithm. The practicability of risk model in HCC tissues (N = 40) from our research group were detected by performing qRT-PCR, immunohistochemical (IHC) analysis and reviewing of clinical characters. 

Results: A total of 224 intersecting DEGs identified from different inflammation- and ferroptosis-subtypes were set as IAF-genes. Seven of them including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 were used for construction of a risk model which classified HCC patients into two groups (high and low risk). HCC patients in high risk group exhibited shorter survival rate and higher immune score, and were predicted to have higher respond rate in immune checkpoint inhibition (ICI) therapy. qRT-PCR results demonstrated that levels of the seven genes were significantly changed in HCC tissues in comparison to adjacent tissues. After inserting the gene expression into the risk model, we found that the risk model exhibited the higher diagnostic value for distinguish HCC tissues compared each single gene. Furthermore, through performing reviewing clinical characters and IHC analysis, we found HCC tissues from our research group with high risk score exhibited more cases of microsatellite instability (MSI), heavier tumor mutational burden (TMB), higher expression level of PDL1 and cells with CD8.

Conclusions: The signature constructed by seven IAF-genes including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 can act as a biomarker for optimizing the diagnosis, prognosis evaluation and immunotherapy options in HCC patients.