Predictive indicators of immune therapy efficacy in hepatocellular carcinoma based on neutrophil-to-lymphocyte ratio

Abstract

Hepatocellular carcinoma (HCC) exhibits high incidence and mortality rates in China. Most cases are often diagnosed at late stages and require multi-strategy therapies. In recent years, immune checkpoint inhibitors (ICIs), particularly programmed cell death protein 1 (PD-1) antibodies, have demonstrated effectiveness in comprehensive HCC treatment. However, the efficacy and prognosis vary greatly among patients. Screening suitable patients and predicting outcomes are crucial for improving the efficacy of ICIs. Although PD-L1 expression levels in tumor cells have been used as predictors of PD-1/PD-L1 antibody therapy, they may not consistently correlate with clinical response in some studies; thus, exploring new biomarkers is necessary. The neutrophil-to-lymphocyte ratio (NLR) emerged as a new predictor of ICI immunotherapy efficacy, and its application in HCC is worth exploring. This study utilizes the Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) project in the Genomic Data Commons (GDC) database for methylation and transcriptome data analysis. The correlation between NLR and ICI immunotherapy efficacy for HCC was evaluated, identifying differentially expressed genes. Analysis revealed 74 up-regulated and 445 down-regulated genes in the high-NLR group compared to the low-NLR group. NLR-related differential methylation analysis identified 68 hypermethylated and 65 hypomethylated probes in the NLR high group. Furthermore, a machine learning model using 27 intersecting genes predicted PD-1 antibody therapy efficacy, achieving an AUC value of 0.813. In summary, we established a predictive model for HCC immunotherapy based on 27 genes related to differential expressions and NLR-associated methylation, showing significant potential for clinical research potential in this field.